andarge yitbarek baye - Université de Poitiers

University of Poitiers
Université de Poitiers
Faculty of Basic and Applied Sciences
Faculté des Sciences Fondamentales et Appliquées
PhD THESIS
THESE
For obtaining the rank of Doctor of Philosophy
Pour l’obtention du Grade de DOCTEUR
Specialization: Hydrogeology
Spécialité: Hydrogéologie
Submitted by
Présentée par
ANDARGE YITBAREK BAYE
Hydrogeological and hydrochemical framework of complex volcanic system in the
Upper Awash River basin, Central Ethiopia : with special emphasis on inter-basins
groundwater transfer between Blue Nile and Awash rivers
Structure hydrogéologique et hydrochimique du système volcanique complexe du
bassin supérieur du fleuve Awash, Ethiopie centrale : attention particulière sur les
transferts d'eau souterraine inter-bassins entre le Nil Bleu et le fleuve Awash
Defended on 10 December 2009
Soutenue publiquement
Before the Board of Examiners
Le 10 Décembre 2009
Devant la Commission d’examen
JURY
Alain MEUNIER
Professeur, Directeur de FRE 3114, Université de Poitiers
Président
Jacques MUDRY
Professeur, Université de Franche- Comté
Rapporteur
Yves TRAVI
Professeur, Université d'Avignon
Rapporteur
Mohamed JALLUDIN
Docteur, Directeur du CERD, Djibouti
Examinateur
Tenalem AYENEW Professeur, Université d'Addis Ababa, Ethiopie
Examinateur
(Co-directeur de thèse)
Moumtaz RAZACK
Professeur, Université de Poitiers
Examinateur
(Directeur de thèse)
This dissertation is dedicated to my wife Fikirte Birhanu and my
daughters Hiwot and Eden
Résume
Une approche utilisant plusieurs méthodes convergentes a été mise en œuvre pour étudier le cadre
hydrogéologique du système aquifère volcanique fracturé et complexe du bassin supérieur du fleuve
Awash situé sur le bord du Rift éthiopien. L’écoulement des eaux souterraines et les mécanismes de
recharge des différents aquifères ont été étudiés à l’aide de méthodes conventionnelles de terrain, de
l’hydrochimie, de l’hydrologie isotopique et de la modélisation numérique des flux souterrains. Des
relations litho-hydrostratigraphiques ont été établies à partir des logs lithologiques de forages
exploratoires profonds. Les résultats montrent un modèle d'écoulement et des caractéristiques
hydrauliques
des
différents
aquifères
volcaniques
très
complexes.
La
corrélation
litho-
hydrostratigraphique indique que l’aquifère basaltique inférieur, constitué de scories poreuses et
perméables, est continu tout le long depuis le Nil Bleu jusqu’à la zone étudiée. L’analyse de la variation
temporelle et spatiale des échantillons d’eau provenant d’endroits différents a révélé des interactions
nettes entre l’eau souterraine et l’eau superficielle. De nouvelles évidences des transferts d'eau interbassins sont apparues. Deux aquifères basaltiques régionaux (l'aquifère supérieur et l'aquifère inférieur)
ont été identifiés, montrant des signatures hydrochimiques et isotopiques bien distinctes. Dans la partie
sud de la zone étudiée, l’aquifère supérieur et l’aquifère inférieur forment un système aquifère régional
non confiné. Dans les parties nord et centrale du bassin au contraire, il apparaît que les deux systèmes
sont séparés par un aquiclude régional, donnant lieu par endroits à des puits artésiens. Les eaux
souterrainex provenant des puits d’exploration profonds (plus de 250 m) pénétrant l’aquifère basaltique
inférieur et des puits situés au sud se sont révélées modérément mineralisées (TDS 400-650 mg/l), avec
une composition isotopique stable, relativement moins enrichie et avec presque pas de tritium. Par contre,
l’aquifère supérieur superficiel a une concentration ionique moins importante, davantage enrichie
isotopiquement. Les résultats des différentes méthodes montrent clairement qu’il existe un transfert d’eau
souterraine du nord du bassin adjacent du Nil Bleu vers le bassin supérieur du fleuve Awash. Les résultats
convergent également pour attester de l’origine commune de la recharge et de la continuité hydraulique
de l’aquifère basaltique inférieur exploité par des forages. Ceci peut avoir des implications pratiques
capitales car l'existence d'importantes ressources d’eau souterraine en profondeur peut résoudre les
problèmes d’approvisionnement de nombreuses villes, y compris la capitale, Addis Ababa. Ces résultats
pourront aussi contribuer à mettre à jour d’autres aquifères régionaux le long des limites du rift dans des
zones ayant une structure hydrogéologique similaire à celle du bassin supérieur du fleuve Awash.
Mots-clés : Ethiopie, Bassin Awash, aquifères, stratigraphie, recharge des nappes d’eau
souterraine, hydrochimie, isotopes, modélisation numérique.
I
ABSTRACT
Integrated approach has been used to investigate the hydrogeological framework of a complex
fractured volcanic aquifer system in the Upper Awash river basin located at the western shoulder of
the Ethiopian rift. The groundwater flow system and mechanism of recharge of different aquifers
have been studied using conventional hydrogeological field investigations, hydrochemistry, isotope
hydrology and numerical groundwater flow modeling techniques. Litho-hydrostratigraphic
relationships were constructed from lithologic logs obtained from exploratory drilling of deep
boreholes. The result indicates quite complex flow pattern and hydraulic characteristics of the
different volcanic aquifers. The litho-hydrostratigraphic correlation indicates that the permeable and
porous scoraceous lower basaltic aquifer is extended laterally all the way from the Blue Nile Plateau
to the study area. .The analysis of the temporal and spatial variation of water samples from different
places revealed clear groundwater-surface water interactions. New evidences have also emerged on
the inter-basin groundwater transfer. Two distinct regional basaltic aquifers (Upper and lower) are
identified showing distinct hydrochemical and isotopic signatures. In the southern part of the study
area the upper and lower aquifers form one unconfined regional aquifer system. In the northern and
central part of the basin, it appears that the two systems are separated by regional aquiclude forming
confined aquifers, in places with artesian wells. The groundwater from the deep exploratory wells
(>250m) tapping the lower basaltic aquifer and wells located in the south were found to be
moderately mineralized (TDS: 400-600 mg/l), with relatively depleted stable isotope composition
and with almost zero tritium. In contrast, the upper shallow aquifer has lesser ionic concentration,
more isotopically enriched. Evidences from the different methods clearly indicate inter-basin
groundwater transfer from the Blue Nile basin to the Upper Awash basin. The evidences also
converge to testify common origin of recharge, presence of hydraulic connectivity for systems
tapping the lower basaltic aquifer. This has enormous practical implication in finding large
groundwater reserve at a greater depth that can solve the current water supply problems of the
community including the capital Addis Ababa. It will also have important role in finding more
regional aquifers along the plateau-rift margins in many areas having similar hydrogeological setup
as the study area.
Key words: Ethiopia, Awash Basin, aquifers, stratigraphy, Groundwater recharge,
Hydrochemistry, Isotope, Groundwater flow modeling
II
ACKNOWLEDGMENTS
First of all, I would like to thank MAWARI (Sustainable Management of Water Resources in the
east African Rift System) project and Addis Ababa University, Departments of Geosciences for
the initiation of this research work and giving me the opportunity to pursue my PhD thesis. The
French Ministry of Foreign affairs for the finance, and the staffs from CIFEG (Centre
International pour la Formation et les Echanges en Géosciences) in facilitating the project; Mr.
François PINARD and Madame Sylvie ORLYK are highly appreciated for their commitments in
facilitating the administrative issues related to the field works in Ethiopia and my stay in France.
I have the greatest appreciation for my direct supervisor, Professor Moumtaz Razack, who
hosted me in his team and helped me to define the good development of the PhD work during the
field work in Ethiopia as well as during all my stays in the University of Poitiers, France. A
special thank is addressed to my co- advisor, Dr. Tenalem Ayenew, who invested his time,
appeal knowledge and energy both during the field work and in upgrading the manuscript of the
PhD thesis. I would like to express my warmest gratitude to Mr. Engida Zemedagegnehu, a
renowned hydrogeologist from Water Works Design and Supervision Enterprise, Ethiopian
Ministry of Water Resources, for his unprecedented generosity not only in acquiring the
necessary data but also for his fundamental ideas in outlining the research methodology, without
which this research would not be successful.
I am grateful to the Bahir Dar Water Resources Development Bureau and its staffs not only for
providing the study leave but also for the logistics offer during all my field work missions in
Ethiopia. The data used in this study were generously provided by Ministry of Water Resources,
National Meteorological Service, Addis Ababa University, Ethiopian Geological Surveys, Addis
Ababa Water supply and Sewerage Authority and other private firms and individuals. I would
like to express my special gratitude to Tilahun Azagegn and Worku Degu for their great
contributions in the tedious field operation during the sampling of the exploratory deep
boreholes.
III
I would like to stress my gratitude to professionals and friends who directly or indirectly
involved for the successful completion of this research work, in particular a big thank to: Dr.
Dereje Ayalew, Dr. Balmwal Atinafu, Dr. Dagnachew Legesse, Dr. Molla Demlie, Shiferaw
Lulu, Dr. Birhanu Gizaw, Zenaw Tessema, Tesfaye Tadesse, Solomon Waltenigus, Nehemiya
Solomon, Birhanu Tilahun, the Ethiopian community in Poitiers (special thanks to Hailu and
Aberash families), Wakgari Furi and Juma Chrispine not only for their contribution in editing
the manuscript of the thesis but also for sharing all ups and downs together during the last years
of my stay in France and all others, whose name is not included in my rather short list. Many
thanks to Ghyslaine and Christian who helped me to explore and appreciate the French culture
including going to the beach (bord la mer) where,
I saw the magical regressions and
transgressions of the Atlantic Ocean. The secretaries of the department of hydrogeology,
University of Poitiers deserve appreciations for their help in administrative matters, special
thanks to Marie-France HUBERT for her contribution in the French version of the abstract.
The biggest share of my deepest heart felt gratitude goes to my caring wife, Fikirte Birhanu, who
was the source of my strength and inspiration. Her continuous encouragement, support and love
gave me a moral boost for the day to day progress of my research work and played a key role in
my academic life. I greatly appreciate her patience in nursing our two daughters alone, my
daughters Hiowt and Eden, I love you. All the family and friend at home are truly acknowledged
for their support. Their moral, material and spiritual support for me and my family have
contributed a great deal to my success.
IV
Table of contents
Page
ABSTRACT.................................................................................................................................................ii
ACKNOWLEDGMENTS ....................................................................................................................... III
TABLE OF CONTENTS .......................................................................................................................... V
LIST OF FIGURES .............................................................................................................................. VIII
LIST OF TABLES ..................................................................................................................................... X
1. INTRODUCTION................................................................................................................................... 1
1.1
BACKGROUND ............................................................................................................................. 1
1.2
PREVIOUS STUDIES ..................................................................................................................... 2
1.3
OBJECTIVES OF THE RESEARCH ................................................................................................ 3
1.4
APPROACH AND METHODOLOGY .............................................................................................. 4
1.5
STRUCTURE OF THE THESIS ....................................................................................................... 6
2. PHYSICAL CHARACTERISTICS OF UPPER AWASH BASIN ................................................... 7
2.1 LOCATION .......................................................................................................................................... 7
2.2 CLIMATE ............................................................................................................................................ 8
2.3 PHYSIOGRAPHY ................................................................................................................................. 8
2.4 GEOLOGY ......................................................................................................................................... 11
2.4.1 Regional Geological Setting...................................................................................................... 11
2.4.2 Geology of the Upper Awash Basin .......................................................................................... 13
2.4.3 Geological Structures in the Upper Awash basin ..................................................................... 15
3. HYDROMETEOROLOGY OF UPPER AWASH BASIN ............................................................... 17
3.1 METEOROLOGICAL PARAMETERS.................................................................................................. 17
3.2 SURFACE WATER HYDROLOGY ....................................................................................................... 17
3.2.1 Rivers......................................................................................................................................... 18
3.2.2 Lakes and Reservoirs ................................................................................................................ 19
3.3 PRECIPITATION DATA ANALYSIS AND ESTIMATION OF AREAL DEPTH OF RAINFALL ................. 20
3.4 HYDROGRAPH ANALYSIS................................................................................................................. 26
3.5 GROUNDWATER LEVEL MONITORING ............................................................................................ 31
3.6 EVAPOTRANSPIRATION ................................................................................................................... 33
3.7 RECHARGE ESTIMATION ................................................................................................................. 34
V
3.7.1 General...................................................................................................................................... 34
3.7.2 Base flow evaluation in relation to groundwater recharge....................................................... 35
3.7.3 Groundwater recharge estimation using groundwater table fluctuation method ..................... 36
4. AQUIFER CONFIGURATIONS OF UPPER AWASH BASIN ...................................................... 38
4.1 LITHO-HYDROSTRATIGRAPHY ....................................................................................................... 38
4.2 HYDROSTRATIGRAPHIC RELATIONSHIPS, MECHANISM OF GROUNDWATER RECHARGE AND
CIRCULATION: EVIDENCES FROM EXPLORATORY DRILLING ............................................................. 41
5. HYDRODYNAMIC AND HYDRAULIC PROPERTIES OF THE VOLCANIC AQUIFERS OF
UPPER AWASH BASIN.......................................................................................................................... 47
5.1 INTRODUCTION ................................................................................................................................ 47
5.2 INTERPOLATION TECHNIQUES ........................................................................................................ 47
5.2.1 Geostatstical analysis................................................................................................................ 47
5.2.2 Variograms and Kriging ........................................................................................................... 48
5.3 PEZIOMETRIC HEAD DISTRIBUTION AND FLOW DIRECTION ......................................................... 50
5.4 TRANSMISSIVITY DISTRIBUTION .................................................................................................... 53
5.4.1 Introduction............................................................................................................................... 53
5.4.2 Determination of Transmissivity from pumping test ................................................................. 54
5.4.3 Estimation of transmissivity using empirical relation from specific capacity........................... 57
5.4.4 Upscaling of transmissivity using geostatstics.......................................................................... 61
5.5 HYDRAULIC CONDUCTIVITY .......................................................................................................... 68
5.5.1 Hydraulic Conductivity estimation............................................................................................ 68
5.5.2 Upscaling of hydraulic conductivity.......................................................................................... 70
5.6 STORAGE COEFFICIENT .................................................................................................................. 73
6. HYDROCHEMISTRY AND ISOTOPE HYDROLOGY ................................................................. 74
6.1 INTRODUCTIONS .............................................................................................................................. 74
6.2 SAMPLE COLLECTION AND ANALYTICAL TECHNIQUES ................................................................ 75
6.3 MAJOR ION HYDROCHEMISTRY AND ITS SPATIAL VARIATION ..................................................... 77
6.3.1 General...................................................................................................................................... 77
6.3.2 Graphical representations......................................................................................................... 79
6.3.3 Statistical analysis..................................................................................................................... 86
6.3.4 Geochemical modeling.............................................................................................................. 95
6.4 TRACE ELEMENTS ........................................................................................................................... 98
VI
6.4.1 General...................................................................................................................................... 98
6.4.2 Water Quality aspects ............................................................................................................... 99
6.4.3 Correlation of trace and major element compositions............................................................ 106
6.5 ISOTOPE HYDROLOGY ................................................................................................................... 115
6.5.1 General.................................................................................................................................... 115
6.5.2 δ2H and δ18O composition of precipitation in the study area................................................. 116
6.5.3 Spatial variation of 18O and 2H isotopes in the waters of Upper Awash basin ....................... 119
6 .5.4 Radioactive Isotopes .............................................................................................................. 124
6 .5.4.1 Tritium (3H)..................................................................................................................... 125
6.6 CHEMICAL STRATIFICATION IN WELLS ....................................................................................... 130
6.7 WATER CHEMISTRY MONITORING AND GROUNDWATER-SURFACE WATER INTERACTION...... 131
6.7.1 Groundwater and Lakes .......................................................................................................... 132
6.7.2 Groundwater and rivers .......................................................................................................... 134
7. HYDROGEOLOGICAL FRAMEWORK OF UPPER AWASH BASIN: GENERALIZED
FROM CONVERGING EVIDENCES OF RESULTS ....................................................................... 136
7.1 AQUIFER SYSTEM AND HYDRAULIC CHARACTERISTICS ............................................................. 136
7.2 WATER CHEMISTRY ...................................................................................................................... 138
7.3 ISOTOPIC SIGNATURES .................................................................................................................. 140
7.4 GROUNDWATER OCCURRENCE AND CIRCULATION CONCEPTUAL MODEL ................................ 141
8. NUMERICAL GROUNDWATER FLOW MODEL OF UPPER AWASH BASIN:
PRELIMINARY RESULTS .................................................................................................................. 144
8.1 INTRODUCTION AND PURPOSE ...................................................................................................... 144
8.2 CONCEPTS ABOUT GROUNDWATER MODELS ............................................................................... 145
8.3 MODEL DESIGN .............................................................................................................................. 150
8.4 BOUNDARY CONDITIONS ............................................................................................................... 151
8.5 MODEL INPUT PARAMETERS ......................................................................................................... 154
8.6 MODEL SIMULATIONS AND SENSITIVITY ANALYSIS .................................................................... 157
8.7 MODEL CALIBRATION AND RESULTS............................................................................................ 159
9. CONCLUSIONS AND RECOMMENDATIONS........................................................................... 167
9.1 CONCLUSIONS ................................................................................................................................ 167
9.2 RECOMMENDATIONS ..................................................................................................................... 171
APPENDICES ......................................................................................................................................... 186
VII
List of Figures
Figure 2.1 Location Map of Upper Awash Basin with major towns and volcanic ridges .............. 7
Figure 2.2 Digital elevation model of upper Awash basin (b)........................................................ 9
Figure 2.3 Drainage map of the study area with hydrometeorological stations ........................... 10
Figure 2.4 Geological map of Ethiopia (V.Kazmin, 1975)........................................................... 12
Figure 2.5 Simplified geological map of Upper Awash, (Modified after WWDSE, 2008) ......... 25
Figure 2.6 lineament map of Upper Awash basin......................................................................... 16
Figure 3.1 Awash River sub-basins and adjacent watersheds ...................................................... 18
Figure 3.1 Seasonal drifting of the ITCZ and its control on the rainfall regime .......................... 21
Figure 3.2 Meteorological stations in upper Awash basin with mean annual rainfall (mm)........ 22
Figure 3.3 Relation between mean annual precipitation and altitude (data from NMA) ............. 24
Figure 3.4 Thiessen polygon map constructed from selected meteorological stations................. 25
Figure 3.5 Mean monthly rainfall (mm) and river discharge (mcm) at selected stations............. 26
Figure 3.6 Mean monthly flow volumes (mcm) in the two gauging stations of Awash River..... 27
Figures 3.7 Separation of total flow of Awash river at Melkakunture ......................................... 29
Figures 3.8 Separation of total flow of Awash river at Hombole ............................................... 30
Figure 3.9 Location of groundwater monitoring sites .................................................................. 32
Figure 3.10 Data logger record of groundwater level monitored at the WWDSE well................ 33
Figure 4.1 Generalized Hydrostratigraphy map of upper Awash Basin ....................................... 39
Figure 4.2 N-S hole-to-hole lithologic sections............................................................................ 42
Figure 4.3 NNE-SSW hole-to-hole lithologic sections................................................................. 44
Figure 4.4 NW-SE hole-to-hole lithologic sections...................................................................... 45
Figure 5.1 Experimental variogram of the spatial correlation of the head data of 2008 .............. 51
Figure 5.2 kriging estimate of the distribution of depth to static water level ............................... 52
Figure 5.3 Spatial distributions of calculated data points ............................................................. 55
Figure 5.4 Lognormal distributions of transmissivity (a) and specific capacity (b) data ............. 57
Figure 5.5 Plot of Transmissivity vs. Specific capacity, T and Sc in m2/d................................... 58
Figure 5.6 Plot of log T vs., log (Sc), T and Sc in m2/d ............................................................... 59
Figure 5.7 Regression of log calculated transmissivity and log estimated transmissivity............ 60
VIII
Figure 5.8 Distribution of transmissivity (a) and specific capacity (b) data points in the Upper
Awash basin ............................................................................................................................ 62
Figure 5.9 Experimental variogram of LogT for calculated transmissivity data set and fitted with
spherical (a) and exponential (b) models. ............................................................................... 63
Figure 5.10 Estimated transmissivity and prediction standard error map of the study area........ 67
Fig 5.11 Spatial distribution of hydraulic conductivity data points.............................................. 69
Figure 5.12 Log-normal distributions of hydraulic conductivity data.......................................... 70
Figure 5.13 Experimental Variogram of log K fitted with linear variogram model..................... 71
Figure 5.14 predicted hydraulic conductivity and prediction standard error map of the area .... 72
Figure 6.1 Spatial distributions of hydrochemical data points ..................................................... 78
Figure 6.2 Piper plots of the hydrochemical data showing groundwater facies. .......................... 79
Figure 6.3 (a) Distribution of logEC (µS/cm) and Plot of elevation vs. TDS .............................. 85
Figure 6.4 Dendrogram of the hydrochemical data ...................................................................... 89
Figure 6.5 scatter plot of Na versus Ca for group II-1 (a) and group II-2 (b) waters ................... 91
Figure 6.5 projections of the variables on the factor plane (1x2) ................................................. 93
Figure 6.6 projections of the cases (samples) on the factor plane (1x2)....................................... 94
Figure 6.7 Arsenic concentrations versus pH plot for wells along the NNW-SSE .................... 102
Figure 6.8 Aluminum concentration and pH trends in the study area ........................................ 105
Figure 6.9 Concentration trends of trace elements in the study area .......................................... 108
Figure 6.10 TDS versus aluminum concentration of groundwater in the area ........................... 109
Figure 6.11 plots of Ca versus Sr (a) and Na versus Sr (b) ........................................................ 110
Figure 6.12 plot of TDS versus arsenic (a) and boron versus arsenic (b)................................... 111
Figure 6.13 plots of TDS versus some of the trace elements...................................................... 112
Figure 6.14 Dendrogram of the trace element data..................................................................... 114
Figure 6.15 Plot of δ18O‰ versus δ2H‰ of precipitation at Addis Ababa GNIP station .......... 118
Figure 6.16 Effects of evaporation on the isotopic composition of precipitation....................... 131
Figure 6.17 Spatial distributions of isotope data points of
Figure 6.18 plot of
²H versus
18
18
O and 2H.................................... 120
O‰ of waters in the Upper Awash basin ........................... 122
Figure 6.19 Tritium concentrations of deep wells in the study area ........................................... 127
Figure 6.20 Water quality monitoring points in the study area .................................................. 131
Figure 6.21 Conductivity of monitored Lakes of Debrezeit ....................................................... 132
IX
Figure 6.22 Conductivity of monitored Boreholes ..................................................................... 133
Figure 6.23 Electrical conductivity of Awash River monitored at Melkakunture and Hombole135
Figure 7.1 Northwest-Southeast schematic conceptual model of groundwater occurrence and
circulation in the upper Awash basin, central Ethiopia ........................................................ 143
Figure 8.1 finite-difference and finite- element meshs............................................................... 147
Figure 8.2 Finite-difference model grid...................................................................................... 151
Figure 8.3 Model boundary conditions....................................................................................... 155
Figure 8.4 Result of the auto sensitivity analysis ....................................................................... 159
Figure 8.5 Simulated groundwater level contours ...................................................................... 163
Figure 8.6 Scatter plot of observed vs. computed target values ................................................. 164
List of Tables
Table 3.1 Mean annual rainfall and temperature of selected meteorological stations.................. 28
Table 3.2 Mean annual discharge of Awash rive and its major tributaries................................... 19
Table 3.3 Salient features of Debrezeit area lakes (WWDSE, 2008) ........................................... 20
Table 3.4 Mean monthly rainfall distribution and seasonal category........................................... 34
Table 3.5 Thiessen polygon weighted mean rainfall of upper Awash.......................................... 25
Table 3.6 basic statistics of flows ( m3/s) at the two stations of the main Awash River .............. 30
Table 5.1 Basic statistics of measured hydraulic parameters ....................................................... 56
Table 5.2 Empirical relation between transmissivity and specific capacity in volcanic rocks..... 59
Table 5.3 Basic statistics of measured and estimated data points................................................ 60
Table 5.4 Summary statistics of cross validation results using spherical model (Group A),
exponential model (Group B) ................................................................................................. 65
Table 5.5 Summary of variogram parameters using spherical model (Group A), exponential
model (Group B). .................................................................................................................... 65
Table 5.6 Basic statistics of predicted transmissivity values........................................................ 66
Table 5.7 Basic statistics of estimated hydraulic conductivity values.......................................... 69
Table 6.1 Ca-Mg-HCO3 type waters............................................................................................. 80
Table 6.2 Ca-Na-HCO3 type waters.............................................................................................. 81
Table 6.3 Na-Ca-HCO3 type waters.............................................................................................. 83
X
Table 6.4 Na-HCO3 type waters ................................................................................................... 84
Table 6.4 Ca-HCO3 type waters.................................................................................................... 84
Table 6.5 Descriptive statistics of hydrochemical variables for the entire data set...................... 87
Table 6.6 Correlation matrix of variables of the hydrochemical data set ..................................... 87
Table 6.7 Mean hydrochemical composition of cluster groups and sub-groups .......................... 89
Table 6.8 Summary of interpretation for the HCA groups and sub-groups.................................. 90
Table 6.9 Proportion of a variable’s variance explained by a factor structure ............................. 93
Table 6.10 Factor loadings (Varimax normalized, marked loadings are >0.7) ............................ 94
Table 6.11 Mean hydrochemical parameters input of the initial and end members in the inverse
geochemical modeling ............................................................................................................ 96
Table 6.12 results of inverse geochemical modeling for the proposed paths ............................... 98
Table 6.13 descriptive statistics of trace element concentrations and their comparison with the
WHO guidelines.................................................................................................................... 100
Table 6.14 Arsenic concentrations, pH-Eh of some selected water wells .................................. 102
Table 6.15 correlation matrix of trace elements (µg/l), pH and some major ions (mg/.l) .......... 107
Table 6.16: Decay constants and half-lives of selected radioactive isotopes ............................. 125
Table 6.17 tritium content of deep wells in the study area ......................................................... 128
Table 6.18 some chemical parameters showing vertical chemical stratification in some wells. 130
Table 8.1 Summary of calibration errors .................................................................................... 162
Table 8.2 Simulation result of long-term annual water balance of Upper Awash basin
groundwater domain ............................................................................................................. 166
XI
List of Acronyms
AAWSA: Addis Ababa Water Supply and Sewerage Authority
ABGREP: Adaa-Becho Groundwater Resources Evaluation Project
Amsl: Above mean sea level
DEM: Digital Elevation Model
GNIP: Global Network of Isotopes in Precipitation
GSA: Geological Society of America
GV: Groundwater Vistas
IAEA: International Atomic Energy Agency
ITCZ: Inter-Tropical Convergence Zone
mcm: Million cubic meters
MER: .Main Ethiopian Rift
MoWR: Ministry of Water Resources
NMA: National Meteorological Agency
SIHD: Symposium International en Hydrogéologie Djibouti
SRTM: Shuttle Radar Topography Mission
USGS: United States Geological Surveys
WWDSE: Water Works Design and Supervision Enterprise
XII
1. Introduction
1.1 Background
Most of the Ethiopian landmass is covered with volcanic rocks with highly variable composition
and stratigraphic setup. These rocks provide one of the country’s best aquifers and fertile soils
for agriculture. The Ethiopian volcanics has been the centre of geological investigations in the
past (Di Paola, 1972; Kazmin, 1979; Zanettin et al., 1974; 1980; Woldegebriel et al., 1990).
Experience in groundwater exploration in different parts of the country revealed that the major
productive aquifers are confined within the volcanic sequences, particularly basalts and
associated volcano-clastic and alluvial deposits. The Upper Awash basin is characterized by one
of Ethiopia’s best aquifers providing water supply for different cities including the capital Addis
Ababa.
The aquifers of the study area are very complex. The different reciprocal stratigraphic
relationships, their changeable contacts with very old and recent rocks, their great compositional,
structural and textural variability, and different level of fracturing and weathering, contribute to
their complex hydraulic characteristics (Vernier, 1993). The different generations of faults
associated with the formation of the rift valley affected the region. Upper Awash basin is found
at the intersection of two major regional structures namely the NNE-SSW trending Ethiopian rift
and the East-West trending Addis Ababa-Nekemit (Yerer-Tuluwelel) volcanic lineament (Abebe
et al, 1998). The basin is located at the transition zone of the Main Ethiopian Rift (MER) and the
central highlands. The basin has all the three major physiographic zones: rift, escarpment and
highlands.
Several studies have been carried out on the stratigraphy (Girmay et al 1989; Tefera et al, 1996),
hydrogeology (Melaku, 1982;
Bekele, 1999) and hydrochemistry and isotope hydrology
(Ayenew, 2005; Kebede et al., 2005 and 2008) in the area at regional and sub-regional scale.
These studies provided limited conceptual hydrogeological model of the groundwater flow and
hydrostratigraphic features of shallow and intermediate aquifer systems. In recent years
relatively better hydrogeological studies have been carried out in the Upper Awash basin, but
1
most of these studies are limited to its sub catchment Akaki River and around Addis Ababa
(Gizaw 2002; Alemayehu et al., 2005; Demlie et al, 2006; 2007; and 2008). Despite reasonably
good works done in the study area, there is no clear conceptualization of the lateral and vertical
extent of the aquifers and the mechanism of groundwater flow. This is particularly the case in
deeper aquifers owing to lack of aquifer parameters on these aquifers from pumping test data and
well log, geophysics and hydrochemical and isotope data from systematically collected samples.
In this PhD research work, attempt is made to fill the gaps by collecting as much systematic
information as possible from the different aquifers and integrated hydrogeological data from
deeper aquifers obtained from recent deep well drilling made in many parts of the Upper Awash
and adjacent Blue Nile Basin.
1.2 Previous studies
Several geological and hydrogeological studies have been conducted in the Upper Awash basin
and its adjacent areas. Hydrogeological studies have been conducted in connection with the
water supply of
the towns of Debrezeit and Modjo under the Twelve Towns Water Supply
Project (Ministry of Water Resources (MoWR), 1980). Based on these studies, wells were drilled
in the vicinity of the two towns. The boreholes are shallow having a maximum depth of 90m.
The lithologic logs of these wells show that lacustrine sediments form good aquifers. The
lacustrine deposit has a thickness varying from 50 to 80 meters and is underlain by vesicular
basalt. These wells represent the lacustrine aquifer of the south eastern part of the Upper Awash
basin.
A number of geological and hydrogeological studies were also conducted in the north central
part of the Upper Awash basin within the Akaki river catchment in relation to the water supply
of the city of Addis Ababa. The Addis Ababa Water Supply and Sewerage Authority (AAWSA)
have undertaken three stages of investigation. Stages I and II were aimed mainly at the
completion of the Legedadi Reservoir and related works. Stage III is aimed at planning,
designing and contracting water supply facilities including groundwater investigation for the city
up to the year 2020. Previous groundwater investigations related to the water supply of the city
of Addis Ababa were chronologically documented in the work of AAWSA et al (2000), which
2
were hydrological and hydrogeological studies that lead to the development of regional
numerical groundwater flow model for the Akaki catchment and for the central Dukem plain up
to the Awash River. Several other geological, hydrogeological and environmental studies and
researches have been undertaken in this part of the Upper Awash, especially in Addis Ababa and
its environs. Among others Girmay and Asefa (1989), Morton (1974), Mohr and Zanetini
(1988), Gaspron et al (1993), Tsehayu and Hailemariam (1990), Abebe et al (1999) are to
mention a few who conducted studies on the geology of Addis Ababa and its vicinity.
Alemayehu (2001), Gizaw (2002), Nigussa (2003), Beyene (2005), Demlie (2007) contributed a
lot in the hydrogeology, water quality and pollution aspects of Addis Ababa area.
There are very few regional studies that cover the whole Upper Awash basin. The most
important ones are Melaku (1982), Bekele (1999) and Gemechu (2004). However, still the
knowledge of the complex volcanic geology and hydrogeology of the Upper Awash basin is
inadequate. The three dimensional geological and hydrogeological set up, the lateral and vertical
extent of aquifers, hydraulic connection of the different aquifer units, hydrochemistry, spatial
variations and degree of water-rock interaction are poorly understood. Therefore, detailed
investigations on the complex hydrogeological setup, groundwater dynamics and geochemical
evolution will have a great importance in the future groundwater resource development and
management in the region.
1.3 Objectives of the research
The main objective of this work is to characterize the hydrogeological system of the Upper
Awash basin. The major issues in the course of characterization of the basin are:-
• Litho-hydrostratigraphic relationships, the groundwater flow dynamics and inter-basin
water transfer
• Aquifer properties and the mechanism of recharge of different aquifers
• Depths of groundwater circulation and geochemical evolution
• Development of a numerical groundwater flow model of the basin.
Special emphasis is given to the following more specific issues:-
3
•
Investigating litho-hydrostratigraphic relationships of the different volcanic aquifer units
of the basin
• Investigating the mechanism, source and spatial variation of groundwater recharge
• Investigating hydrogeological setup of the volcanic aquifers of uppers Awash, their
lateral and vertical extent, the interconnection among each other
• Investigating the hydraulic properties of the volcanic aquifers of the area and establishing
empirical relationships among aquifer parameters
• Investigating groundwater flow dynamics, continuity along the Plateau-Rift transects and
the probable inter-basin water transfer along the Blue Nile and Awash basins
•
Investigating the geochemical evolution of the groundwater chemistry of the basin
following the regional groundwater flow direction
•
Investigating boundary conditions conceptualize the flow mechanism of the groundwater
and develop a numerical groundwater flow model.
1.4 Approach and methodology
For this study, great effort has been made to collect pertinent hydrogeological data from
concerned institutions and previous works. Aside from the collected secondary data, field
hydrogeological investigations were conducted in two seasons (summer 2007, winter 2008). The
objective of the field investigation was to collect original data through field testing and mapping
so as to validate relatively old secondary data. Converging evidence approach was followed
using
integrated
hydrogeological
investigation
techniques:
Litho-hydrostartigraphic
relationships, hydrochemistry, isotope hydrology and numerical groundwater flow modeling.
The main systematic database used is related to the newly drilled deep exploratory wells, which
are scattered in the Upper Awash basin and adjacent Blue Nile plateau. The wells were
constructed in connection with the Adaa-Becho Groundwater Resources Evaluation Project
(ABGREP), Ministry of Water Resources. A two hydrological year time series monthly data of
water quality monitoring of six boreholes, four lakes and the Awash River monitored at
Melkakunture and Hombole, a one year continuous groundwater level monitoring data from
automated loggers is used. In addition, representative water samples were collected and
analysed.
4
Data loggers of type Dipper-T3 purchased from SEBA Hydrometrie of Germany were installed
to generate continuous groundwater monitoring records. The data loggers automatically register
groundwater level and temperature at a given time interval. The loggers have a battery life of 10
years and an interface adapter/converter for downloading data. Groundwater monitoring records
were totally lacking in the country, and these loggers are the first of their kind to be installed for
the intended purpose. The loggers are installed in the newly drilled exploratory wells of the
ABGREP, Ministry of Water Resources. The site selection of the loggers for installation is based
on the regional groundwater flow direction to observe the groundwater dynamics in the plateau,
transitional escarpment and the rift valley part of the basin. Accordingly the loggers are installed
one at the plateau (Blue Nile basin), two at the transition zone (Becho and Addis Ababa areas)
and one in the rift valley (Modjo).
Water samples were collected systematically from different water points in all the three
physiographic regions, by giving emphasis on the deeper aquifer. Special sampling apparatus
was used to collect samples from the newly drilled exceptionally deep (up to 368m) exploratory
boreholes. The samples from these boreholes were taken at different depths based on the screen
casing arrangements. The sampling was done with KLL-S 4" Sampler purchased from SEBA
Hydrometrie of Germany, a mechanical sampler that can go up to a depth of 400m and with a
capacity of taking one liter sample at a time. Spring samples were collected from the emanation
point. Along with the sampling in situ water quality measurement was made. These include EC,
TDS, Temperature, pH, Eh, and carbonate/bicarbonate (alkalinity). Samples for major ion
chemistry were analyzed in the laboratory of the Ministry of Water Resources; samples for
tritium were analyzed in the hydrogeological laboratory of the Department of Earth Sciences,
Addis Ababa University, samples for stable isotopes of oxygen and hydrogen and for trace
elements were analyzed in the University of Avignon, France. Statistical and geostatstical
methods, Microsoft word, Excel, modeling techniques, GIS (ArcView 3.2, ArcGIS 9.2) and
other tools (such as Surfer, AquaChem, Aquifertest, Variowin, Statistica, Groundwater Vistas,
etc...) were applied in analyzing, quarrying and mapping spatial data.
This PhD research work is initiated in the framework of MAWARI (Sustainable Management of
Water Resources in the East African Rift System), a joint project between Ethiopia, Kenya,
5
Djibouti and France. The title of the project in the Ethiopian side is “Systematic groundwater
investigation in selected sites of the Ethiopian rift: Towards an understanding of the
hydrogeology of the rift and adjacent highlands”. The thesis is supervised by two professors: one
from the University of Poitiers, France and another from Addis Ababa University, Ethiopia.
1.5 Structure of the thesis
This thesis is organized in nine chapters. Chapter one deals with the general introduction,
objective and previous studies. Chapter two gives the physical characteristics of the Upper
Awash basin that includes the climate, physiography and geology. Chapter three gives an
overview on the hydro-meteorological characteristics of the study area. Chapter 4 presents the
aquifer configurations and litho-hydrostratigraphic relationships in the Upper Awash basin and
the adjacent Blue Nile plateau as observed from exploratory drilling, field mapping and
complemented by data collected from a number of sources. Chapter 5 deals with aquifer
hydraulic parameters. Chapter 6 presents the results of the hydrochemistry, environmental
isotope and trace element data analysis. Chapter 7 deals with the hydrogeological conceptual
model of the study area, Chapter 8 on the numerical groundwater modeling and chapter 9
presents the general conclusions, summary of the major results of the work and
recommendations. Communications were made presenting the major results of this study, among
others; part of the result of the research was presented during the International symposium of
Hydrogeology (SIHD), which was held 14-17 December 2008 in Djibouti, east Africa, the
extended abstract of the paper is published on the proceedings of the symposium and can be
referred to Hydrogeology of volcanic rocks, SIHD-2008 Djibouti, p31-36; a paper entitled
“Characterization of volcanic aquifers and assessment of the movement of groundwater in
the Upper Awash Basin, Central Ethiopia” , which summarizes the major findings of the
research was presented on the annual meeting and exhibition of the Geological Society of
America (GSA), which was held 18-21 October 2009 in Portland, Oregon, USA. The abstract
of this paper is published and can be obtained by referring to the Geological Society of America
Abstracts, Vol. 41, No. 7, p. 177 (Appendix 7).
6
2. Physical characteristics of Upper Awash basin
2.1 Location
The Upper Awash basin is located in central Ethiopia at the western margin of the Main
Ethiopian Rift (MER). The capital Addis Ababa is located at the northern end of the basin. The
study area is confined within the limits of 370000-540000E longitude and 910000-1030000N
latitude (Fig. 2.1). The area of the basin is 10,841 km2.
Figure 2.1 Location Map of Upper Awash Basin with major towns and volcanic ridges
(1=Mt.Intoto, 2= Mt.Wechecha, 3= Mt.Furi, 4= Mt.Guji, 5= Mt.Yerer, 6= Mt.Bedegebaba,
7=Mt.Ziquala, 8=Weliso Highlands, 9=Gurage Highlands) and Y-T on the inset represents the
inferred regional Yere-Tuluwelel Volcanic lineament and Ambo fault belt.
7
2.2 Climate
There exists a large topographic difference in the basin; as a result the climate is also variable. It
is humid at the highlands and arid to semi-arid in the escarpment and rift valley. The climatic
characteristics of the area are determined largely by the annual movements of air currents across
the country, Atlantic equatorial westerlies, and the southerly and easterly Indian Ocean currents
being the moisture sources for precipitations.
During October to February when northeasterly winds persist, long periods of dry weather are
experienced. Between February and the ends of April the weather becomes more unsettled and a
convergence of moist southeasterly air stream causes small rains, commonly referred to the
“Belg Rains”. The main rainfall is between June to September when moist winds from the
Atlantic and Indian oceans converge over the Ethiopian Highlands (Gemechu, 1977). Thus the
year is characterized by a major rainy season from June to September, during which about 6675% of the Annual rain falls in the study area, followed by a relatively dry season until the end
of January and the small rainy season from February to the end of April.
The mean annual rainfall in the basin ranges from more than 1200mm in the highlands and
around 1090mm in the escarpments and below 900mm in the rift valley part of the study area.
The mean annual temperature varies from 13 to 20.1oC. Based on the mean monthly temperature
distribution, December is the coldest and May the warmest months.
2.3 Physiography
Upper Awash basin is dominated by chain of volcanic mountains forming the watershed divide.
Isolated acidic volcanic ridges are also common features known by compartmentalizing the basin
acting as local divides. It is bounded in the north by the east-west trending rift escarpment
(Ambo fault belt) and the Intoto mountain range, in the west by Weliso and Guraghe highlands,
in the east by Kesem river basin and in the south by the Koka reservoir. The physiography of the
basin is shaped by volcano-tectonic and erosional processes. The basin is characterized by very
steep slope in the northern, eastern and western part, undulating topography in the central and
8
gentle to flat in the southern part. The elevation drops more than 1400m in about a 100 km
length from north to south. The major volcanic centres and ridges with in the basin are
Wechecha, Furi, Guji, Bedegebaba, Ziquala and Yerer (Fig. 2.2).
a
A
B
b
A
B
Figure 2.2 Digital elevation model (a) and north-south vertical profile of Upper Awash basin (b)
9
Awash River and its tributaries form dendritic drainage pattern. Awash flows in a NW – SE
general direction. Ginchi, Berga, Holeta, Bantu, Lemen Akaki and Modjo are the major
tributaries of Awash (fig. 2.3). There are artificial and crater lakes in Upper Awash. Out of the
artificial lakes, Dire, Gefersa and Legedadi were constructed for water supply of the city of
Addis Ababa; Abasamuel and Koka were constructed for hydropower production. The crater
lakes are concentrated in the southern sector of the study area around Debrezeit town (fig. 2.3).
Figure 2.3 Drainage map of the study area with hydrometeorological stations
Most of the land uses of the basin are agricultural land, urban, forest and reservoirs. The urban
land use pattern is farther divided into residential areas, gardens, parks, markets, industrial, petrol
stations, parking lots, garages, cemeteries and sporting grounds. The main industries are tanning,
textile, paint, food and beverages, plastics, chemicals, pharmaceuticals and paper. Most of the
mountains in the area are covered by forest. The agricultural land use covers wide and mostly
gentle slopping areas. Major crops grown in the area are Teff, wheat, barely, beans and oil seeds.
10
2.4 Geology
2.4.1 Regional Geological Setting
The formation of the Ethiopian Rift during the Miocene separated the eastern and western
highlands. The Upper Awash basin is exclusively confined within the north-central plateau and
the adjacent escarpment and rift. The adjacent central plateau is drained due west by the Blue
Nile River drainage system and due north-east by the Awash River drainage system.
The Precambrian basement upon which all the younger formations were deposited contains the
oldest rocks in the country, with ages of over 600 million years. The Precambrian contains a
wide variety of sedimentary, volcanic and intrusive rocks which have been metamorphosed to
varying degrees. At the end of Precambrian times uplift occurred, which was followed by a long
period of erosion. Any sediments which were deposited during the Paleozoic interval, which
lasted some 375 million years, have been largely removed by erosion, except for shales and
deposits partly of glacial origin laid down in northern Ethiopia towards the end of this period.
Subsidence occurred in the Mesozoic, which began some 225 million years ago, and a shallow
sea spread initially over the Ogaden and then extended farther north and west as the land
continued to subside. Sand, now sandstone, was deposited on the old land surface. Deposition of
mudstone and limestone followed as the depth of water increased. Extensive fracturing occurred
early in the Cenozoic, the earliest rocks of which are dated at 65 million years (V. kazmin,
1975).
Faulting was accompanied by widespread volcanic activity and the two processes, which are
partly related, have largely determined the form of the landscape in the western half of Ethiopia
and in the Afar Depression. The outpouring of vast quantities of basaltic lava over the western
half of the country was accompanied by, and alternated with, the eruption of large amounts of
ash and coarser fragmental material, forming the Trap Series. Several shield volcanoes, also
consisting of alkali basalts and fragmental material, then developed around the eastern edge of
the Lake Tana depression and Bale area. More recent volcanism is associated with the
11
development of the Rift Valley, activity being concentrated within this structure and along the
edge of the adjoining plateau. Volcanism has persisted into the present time in the Afar region
within small eruptive centers. The composition of the lavas produced ranges from basalt to
siliceous types.
The youngest sediments are of Quaternary age. These include conglomerate, sand clay and reef
limestone which accumulated in the Afar Depression and the northern end of the main Rift
Valley. Sediments which accumulated in the former lakes occur in the south end of the Afar, in
the main Rift Valley, and in the Omo valley. Undifferentiated Quaternary sediments and
superficial deposits occur intermittently along the Sudanese and Kenyan borders (V. Kazmin,
1975).
Figure 2.4 Geological map of Ethiopia (V.Kazmin, 1975)
12
Generally speaking, along the plateau-rift transect the geology changes from dominantly lower
Tertiary basaltic volcanic plateau to inter-layered Tertiary basaltic and acid volcanics in the
centre and Quaternary volcano- sedimentary sequences to the south towards the rift floor.
2.4.2 Geology of the Upper Awash Basin
The western and northwestern plateau area of Upper Awash, around the watershed divide of
the Awash and Blue Nile river basins is mainly covered with Tarmaber Basalts (Figure 2.5).
In the rift valley part of the study area, this unit is downthrown by the regional east-west
trending Ambo Fault and overlain by thick (282m) younger ignimbrite as revealed from deep
water well logs of Legedadi and Melkakunture localities (WWDSE, 2008). The unit is mainly
scoraceous lava flows and at places it is columnar basalt as pockets within the scoraceous
units. It is highly weathered, fractured and pinkish to grayish in color (Kazmin, 1972, Zanetin
& Justin-Visentin, 1974).
In the north and central part of the area around Addis Abba, Akaki and Dukem localities,
three major rock units outcrop; Addis Ababa and Akaki basalts and Addis Ababa Ignimbrites.
Addis Ababa basalt is characterized by fine to coarse grained texture and in most cases it is
relatively thin (20m) lava flow overlying the ignimbrite (Chernet et al., 1998; Morton et al.,
1979). The Addis Ababa Ignimbrite outcrops in most part of the plain area around Addis
Ababa and in the western part (Becho plain). It is composed of welded tuff (ignimbrite) and
non welded pyroclastics fall (ash and tuff). Around the Legedadi plain and in Melkakunture
area the thickness of this unit reaches up to 200m (WWDSE, 2008). In the Becho plain it is
covered with 5-7m thick residual soil developed from the same rock. The age of this unit is
5.11-3.26 Ma (Morton et al., 1979).
Akaki basalt outcrops in Akaki and Dukem areas. It is coarse grained, porphyritic, highly
vesicular basalt and in places the vesicles are filled by secondary minerals. It consists of scoria
and spatter cones with associated lava flows. The thickness of this unit around Akaki is 202m
13
(exploration drilling data). The age of the Akaki basalt is 2.9-2.0 Ma (Chernet et al., 1998;
Morton et al., 1979).
The eastern and south eastern parts of the area are covered with Chefedonsa volcanic rock
units that consist of fall deposits (ash, tuff and pumice) and poorly welded ignimbrites of
rhyolitic composition. At places in the Dukem and Modjo area they are covered with
lacustrine deposits. The age of this unit is 2.24 to 1.71 Ma (Morton et al., 1979).
Figure 2.5 Simplified geological map of Upper Awash, (Modified from WWDSE, 2008)
14
The southern part of the study area around Debrezeit and Modjo is covered with lacustrine
deposits. The scattered volcanic mountains such as Wechecha, Furi and Yerer are trachytic
with the exception of Intoto which is dominantly rhyolitic. The acidic volcanic ridges act as
watershed boundaries between the Omo-Gibe and Awash River, in the south and south
western part and with that of the Blue Nile in the north. The rhyolite picks in the Becho
plain and the trachytic domes like Ziquala and Bedegebaba form isolated cones forming local
groundwater divides with in the watershed. Figure 2.5 shows simplified geological map of
the Upper Awash basin.
2.4.3 Geological Structures in the Upper Awash basin
As Upper Awash is situated at the intersection of two major regional structures namely the NNESSW trending MER and the East-West trending Addis Ababa-Nekemit (Yerer-Tuluwelel)
volcanic lineament. The Addis Ababa-Nekemit fault has a down-throw to the south in the Addis
Ababa area and the Intoto silicic rocks are confined along this fault (Girmay and Assefa, 1989).
Another prominent fault trending NE-SW (Morton, 1974, Girmay and Assefa, 1989) is the
Filwuha fault which is down-thrown to the south and is located at central Addis Ababa. Many
hot springs and thermal wells exist along this fault.
The volcanic rocks of the region have undergone extensive faulting often having a general trend
of NE – SW, E-W and at places NW-SE. Most of the lineaments follow the trend of the rift (Fig.
2.6). The density of faults and lineaments increases to the southeast towards the rift valley. Some
of the basaltic lava and cinder cones seem to have erupted through these fractures as they are
concentrated along the major NE - SW trending fault systems.
15
Figure 2.6 Lineament map of Upper Awash basin (Modified from WWDSE, 2008)
16
3. Hydrometeorology of Upper Awash basin
3.1 Meteorological parameters
There are more than 30 meteorological stations in the Upper Awash basin. The majority of the
stations are 4th class, where there are only rainfall and temperature records. In addition to this,
the records are incomplete in most of the stations. The 1st class meteorological stations are
located at Addis Ababa, Holeta and Debrezeit, where most of the meteorological elements are
well documented.
Monthly meteorological data was obtained from the Ethiopian National
Meteorological Agency (NMA) at ten different station selected on data availability and location
with respect to the study area (Table 3.1).
Stations
Record
Class
UTM E
UTM N
period
Elevation
Mean annual
Mean annual
(m)
Rainfall (mm)
Temp. (ºC)
Addis Ababa
observatory
Akaki
1900-2007
1st
471983
997946
2405
1211.3
16.2
1980-2007
nd
477500
980200
2090
1017.0
19.5
th
514290
991475
1600
841.5
16.5
2
Chefedonsa
1980-2007
4
Debrezeit
1980-2007
1st
494500
964941
1850
851.0
19.9
Ginchi
1980-2007
4
th
403279
998222
2290
1194.4
17.6
Holeta
1980-2007
1st
442854
1002569
2380
1092.4
14.9
1980-2007
4
th
470880
1003826
2920
1248.3
14.7
4
th
502010
937436
1598
805.7
21.9
rd
516504
952784
1870
847.4
20.1
th
500000
1011372
2560
1137.9
18.4
414187
958396
2100
948.3
16.8
Intoto
Koka
Modjo
1980-2007
1980-2007
3
Sendafa
1980-2007
4
Tulubolo
1980-2007
2nd
Table 3.1 Mean annual rainfall and temperature of some selected meteorological stations (Data
from NMA)
3.2 Surface water hydrology
The Awash River covers a total catchment area of 113,700 km2 and serves as home to more than
10.5 million inhabitants. The river originates on the high plateau near Ginchi town west of Addis
Ababa in Ethiopia and flows along the rift valley into the Afar triangle, and terminates in salty
17
Lake Abbe on the border with Djibouti, being an endorheic basin. The total length of the main
course is some 1,200 km (Kinfe, 1999). The Awash River basin has been divided in to three
main sub-basins: Upper (upstream of Koka Dam station), Middle (between Koka and Awash
station), and Lower (comprising the deltaic alluvial plains in Tendaho, and the terminal lakes
area) (Fig. 3.1). Awash basin share borders with three Trans- boundary basins; Blue Nile, OmoGibe, Wabishebelle and also with the endorheic Ziway-Shalla basin (fig. 3.1 inset). The study
area is the upper part of Awash River basin, this part of the Awash basin is a junction for two
regional drainage basins; Blue Nile and Awash. The river has big perennial and small
intermittent tributaries. There are both artificial and natural lakes in the basin.
Figure 3.1 Awash River sub-basins and adjacent watersheds
3.2.1 Rivers
The Upper Awash river basin includes that part of the Awash River basin from the source of the
river at its northern watershed boundary down to Koka dam. Before entering the artificial lake
(reservoir), Koka at longitude of 507360E and latitude 931933N, the Awash River has twelve
main tributaries amongst which the Akaki River is the largest. The major tributaries in order of
18
decreasing discharge are, the Akaki, Modjo, Teji, Ginchi, Holeta, Berga, Lemen and Bantu
Rivers. There are also many intermittent tributary streams.
The main Awash River is gauged at three points upstream of Lake Koka. The first gauging
station is at Melkakunture, which is 45km south west of Addis Ababa with a total upstream
catchment area of 4456km2. The second station is at Hombole with a catchment area of 7656
km2 and the other station is near Koka before the river enters the lake, commanding the whole
catchment area of Upper Awash. But due to back flow effect of the lake to the staff gauges of
this station, river discharge measurement data from this station is found to be unreliable for
interpretation. According to the record obtained from the Ministry of Water Resources, MoWR
(Table 3.2), the total river discharge from the entire area is estimated to be more than 1550
million cubic meters (mcm).
Area_
Elev_
Mean Annual
2
Station
Record
(km )
Utm E Utm N (m)
discharge (mcm)
Melkakunture 1980-2007
4456.0 455998 961708
2014 867.5
Hombole
1980-2007
7656.0 475779 926314
1850 1367.1
Modjo
1980-2007
1264 508803 950063 1780.0 183.6
Akaki
1980-2007
1470 476790 981229
2070 357.1
Table 3.2 Mean annual discharge of Awash rive and its major tributaries (Data from MoWR)
3.2.2 Lakes and Reservoirs
One of the surface water reservoirs in the study area is lake. There are natural lakes of volcanic
origin, called crater lakes, in the area: Debrezeit lakes located at southern parts of the study area.
There is no clear surface water out let from the lakes. Table 3.3 displays the salient features of
the crater lakes of Debrezeit. There are also artificial lakes/reservoirs in Upper Awash. The
artificial lakes (Gefersa, Dire, and Legedadi) were constructed for water supply purposes and
hydropower production (Koka, Abasamuel).
19
Name
Kilole
Bishefitu
Guda
Kurifitu
Hora
Bishefitu
Hora Hoda
Ziquala
Abo
altitude
(m)
1880
Area
(km2)
0.85
1860
1860
1840
1870
1840
0.6
0.3
1.1
0.93
0.75
2840
0.56
Max.
depth
(m)
6.4
Average
depth (m)
2.6
65
-
38
-
38
87
32
-
Catchment
area (km2)
2.55
22.8
0.2
0.32
0.8
0.4
0.5
1682
1682
1691
1679
1691
0.85
1286
17.5
55
18.5
-
Volume
(mcm)
2.21
19.25
51.15
13.88
-
Mean annual
evaporation (mm)
1674
Table 3.3 Salient features of Debrezeit area lakes (WWDSE, 2008)
3.3 Precipitation data analysis and estimation of areal depth of rainfall
The seasonal variability of precipitation is an important aspect of hydrology since it determines
the river flow and groundwater recharge. The seasonal variation and distribution of rainfall in
Ethiopia is commonly explained with reference to the migration of the Inter-Tropical
Convergence Zone (ITCZ) (Gemechu, 1977). It is a low pressure area of convergence between
the tropical easterlies and equatorial westerlies along which equatorial wave disturbance takes
place. As a result, the amount of rainfall in Ethiopia is influenced by the location of the point
relative to the source of moisture, the direction of winds and the topographical relief (Gebeyehu,
2005).
In April, the ITCZ is located in southern Ethiopia. A low pressure cell develops on central Sudan
and a high pressure system develops over the Gulf of Aden and the Indian Ocean and generates
moist, easterly air currents over southern Ethiopia (Fig. 3.1). These moist air currents ascend
over the highlands in spring producing the main rainy season in southern Ethiopia bringing the
small “Belg” rains of spring to the study area. In May, the ITCZ starts to move northwards, in
June and July it is located in north of Ethiopia. During this time most of Ethiopia is under the
influence of the Atlantic Equatorial Westerlies, which ascends over the highlands from the
southwest and produce the main rainy season “Kiremt” throughout Ethiopia and the study area in
particular. The mean monthly distribution of rainfall and temperature for some stations within
Upper Awash basin are given in Fig. 3.2.
20
Figure 3.1 Seasonal drifting of the ITCZ and its control on the rainfall regime (Gemechu, 1977)
21
Figure 3.2 Meteorological stations in Upper Awash basin with mean annual rainfall (mm) and
temperature (ºC) for some stations (1980-2007, data from NMA). The irregular line at the
background marks the Upper Awash basin boundary.
22
Quantitative seasonal category based on the rainfall distribution can be explained by using the
rainfall coefficient (R.C.), which is the ratio between mean monthly rainfall and one twelfth of
the annual mean of the total rainfall (Gemechu, 1977).
R.C. = 12 Pm
(3.1)
Pa
Where, R.C. = rainfall coefficient (unit less), Pa = annual total rainfall of the area (mm)
Pm = Mean monthly rainfall (mm)
Based on R.C. values the following precipitation category can be made for Upper Awash using
the data at Addis Ababa Observatory station (Table 3.4):
• Dry months (R.C. < 0.6): October, November, December, January and February.
• . Rainy months (R.C.> 0.6): This can be further grouped into the following:
o small rain (0.6 _ 0.9): March, April and May
o big rain (> 1.0):
AAO Station
Mean monthly rainfall
RC
Seasonal category
Jan
16.7
0.2
Dry
Moderate concentration (1.0 - 1.9): June and September.
High concentrations (2.0 - 2.9): July and August
Very high concentrations ( > 3.0).
Feb
45.5
0.5
Dry
Mar
71.4
0.7
Rainy
Apr
90.8
0.9
Rainy
May
85.3
0.8
Rainy
Jun
133.2
1.3
Rainy
Jul
264.7
2.6
Rainy
Aug
280.2
2.8
Rainy
Sep
178.3
1.8
Rainy
Oct
25.6
0.3
Dry
Nov
11.1
0.1
Dry
Mean
annual
rainfall
1211.3
Dec
8.5
0.1
Dry
Table 3.4 Mean monthly rainfall distribution and rainfall coefficient (Data from NMA at Addis
Ababa Observatory station, 1980-2007)
The amount of rainfall and its spatial variability in Ethiopia is determined by the direction of
moisture bearing seasonal air currents and elevation (Gemechu, 1977; Gebeyehu, 2005).
Observation of the amount of rainfall at the ten meteorological stations considered in this study
show that there is a strong correlation (r2= 0.85) between altitude and the amount of rainfall. The
mean annual rainfall increases with an increase in altitude (Fig. 3.3).
.
23
Mean annual rainfall (mm)
1300
y = 0.38x + 207.67
R2 = 0.85
1200
Addis Ababa
Ginchi
Intoto
Sendafa
1100
Holeta
Akaki
1000
900
Tulu Boilo
Chefedonsa
800
700
1400
Modjo Debre Zeit
koka
1700
2000
2300
2600
2900
3200
Altitude (m)
Figure 3.3 Relation between mean annual precipitation and altitude (Data from NMA)
Since the precipitation of the area is strongly dependent on altitude, computation of the areal
depth of precipitation over the basin should consider altitude as one input variable. This means
that using simple arithmetic mean of point rainfall data of the stations may not represent the
actual aerial depth of precipitation over the catchment. Among the methods being used to
estimate aerial depth of precipitation, Thiessen polygon method is more reliable for non-uniform
gauge distribution (Shaw, 1988). Hence, in this study the areal precipitation computed through
the Thiessen polygon weighting method is used.
Thiessen polygon method is given by:
P = A1P1 +A2P2+- - - AnPn
(3.2)
∑ Ai
Where P ; Average aerial depth of rainfall of the basin
P1, P2 - - - Pn= mean annual rainfalls recorded at each rain gauge stations
A1, A2, An- - - Polygonal area around each station with in the basin
Using this method the result obtained for upper Awash is 974mm (Table 3.5 and Fig. 3.4), which
is not very much different from the arithmetic mean of 944mm.
24
Station
Mean Annual
rainfall (mm)
Area of influence (km2)
Addis Ababa Obs.
Akaki
Chefedonsa
Debrezeit
Ginchi
Holeta
Intoto
Koka
Modjo
Sendafa
Tulubolo
1211.3
1017
841.5
851
1194.4
1092.4
1248.3
805.7
847.4
1137.9
948.3
429.3
1319.5
845.4
1027.2
917.7
1253.5
190.9
1541.6
517.8
386.0
2268.4
Weighted
area
(%)
4
12
8
10
9
12
2
14
5
4
21
Total 100
Weighted
rainfall
(mm)
49
125
67
82
102
128
22
116
41
41
201
974
Table 3.5 Thiessen polygon weighted mean rainfall of upper Awash
Figure 3.4 Thiessen polygon map constructed from some selected meteorological stations within
the study area, the number in millimeter indicates mean annual rainfall of the station (1980-2007,
Data from NMA)
25
3.4 Hydrograph analysis
The main Awash River and most of its major tributaries are gauged at different locations. Major
tributaries: Akaki, Holeta, Ginchi, Modjo are gauged at their outlets before joining Awash. As it
is explained in section 3.2.1, the main Awash River is gauged at Melkakunture, Hombole and
near Koka reservoir. The discharge records exhibit similar trends, the highest flow corresponding
with the wettest months of July, August and September (Fig. 3.5). The data from the gauging
station near Koka, before the river enters the lake, best represents the whole river discharge from
the Upper Awash basin, but, as it is explained a bit earlier, due to back flow effect of the lake to
the staff gauges of this station the data is not found to be reliable for interpretation, hence flow
records at Melkakunture and Hombole were used in this study (Fig. 3.6).
DEC
NOV
OCT
SEP
AUG
JUL
JUN
MAY
APR
MAR
FEB
0
50
100
150
200
250
300
350
70
60
50
40
30
20
10
0
Rainfal_Addis Ababa
mean monthly
Discharge-MCM
Mean monthly Rainfallmm
JAN
Months
Discharge_Akaki river
100
150
200
250
300
Rainfall_Modjo station
DEC
NOV
OCT
SEP
AUG
160
140
120
100
80
60
40
20
0
mean monthly
Discharge-MCM
Mean Monthly
Rainfall-mm
0
50
JUL
JUN
MAY
APR
MAR
FEB
JAN
Months
Discharge_Modjo river
Figure 3.5 Mean monthly rainfall (mm) and river discharge (mcm) at selected stations
26
Melkakunture
mean monthly
discharge_mcm
400
300
200
100
AUG
SEP
OCT
NOV
DEC
AUG
SEP
OCT
NOV
DEC
JUL
JUN
MAY
APR
MAR
FEB
JAN
0
Months
JUL
JUN
MAY
APR
MAR
FEB
700
600
500
400
300
200
100
0
JAN
Mean monthly
discharge_mcm
Hombole
Months
Figure 3.6 Mean monthly flow volumes in million cubic meters (mcm) in the two gauging
stations of the main Awash River
Base flow is defined as water which enters a stream or river from persistent, slowly varying
sources, maintaining stream flow between water input (precipitation, snowmelt) events, which
contrasts with water that enters a stream or river promptly, called storm flow or event flow
(Dingman, 1994). When the water accumulated in the subsurface soil exceeds a critical value,
27
subsurface flow appears. Groundwater flow is produced directly from the groundwater layer.
Subsurface flow and groundwater flow generally constitute the base flow. In dry periods the
subsurface flow is minimal and therefore the base flow component of the stream flow is regarded
as being produced mainly from groundwater flow. The process of base flow separation, also
termed hydrograph analysis, is concerned with partitioning stream flow records into runoff and
base flow components, i.e. differentiate total flows into the high-frequency, low amplitude 'base
flow' component and the low-frequency, high-amplitude 'flood' flows.
The mean monthly flow volumes shown on the hydrograph of Figure 3.6 give no indication to
their origin (overland flow, inter flow, direct precipitation and base flow). Therefore, it is
important to separate the hydrograph into its components (direct runoff and base flow) so that the
sources and more importantly the contribution of groundwater to the river flow and groundwater
recharge to the contributing sub basins could be estimated. Manual base flow separation
techniques are highly subjective and less accurate compared to the automated digital filtering
methods. In this study, hydrograph separations for daily discharge measurements were done
digitally using ABSCAN (Parker, 2006).
The Eckhardt recursive digital filter (Eckhardt, 2005) was used to separate the river flow data
into their respective components of base flow and direct runoff. The separation of the base flow
component from the river flow hydrograph was done by taking the filter parameter of 0.995. A
base flow index (BFI) value of 0.25 was used as a predicted value in accordance with the
Eckhardt’s assignment for perennial streams with hard rock aquifers since the Upper Awash
basin is characterized mostly by fractured volcanic aquifers. The value of BFI is dependent on
the hydrological and hydrogeological characteristics of the different catchments as depicted by
Eckhardt, (2005).
As a result, the flow data (16 years, 1990-2005) of the main Awash River at Melkakunture and
Hombole representing a catchment area of 4456 and 7656 Km2 respectively were separated into
components of direct runoff and base flow. If base flow comparison is made between the years
1990 and 2005 for the 16 years average base flow, the maximum groundwater contribution to the
flow of Awash River at Melkakunture is 1537m3/s and at Hombole is 3111m3/s which were the
28
base flows for the year 1998. The minimum groundwater contribution at Melkakunture is
525m3/s which was the base flow of the year 1995 and at Hombole is 1041m3/s which was the
base flow of the year 2001 (Table 3.6 and Figures 3.7 and 3.8).
Direct Runoff
a
Baseflow
Flow (m3/s
200
150
100
50
0
1
25 49 73 97 121 145 169 193 217 241 265 289 313 337 361
Days
Awash at Melkakunture
b
Baseflow
1999
2000
2001
2002
2003
2004
2005
1999
2000
2001
2002
2003
2004
2005
1998
1997
1996
1995
1994
1993
1992
1991
14000
12000
10000
8000
6000
4000
2000
0
1990
Flow (m3/s)
Direct Runoff
Years
c
Baseflow
Flow (m3/s)
2000
1500
1000
500
1998
1997
1996
1995
1994
1993
1992
1991
1990
0
Years
Figures 3.7 Separation of total flow of Awash river at Melkakunture into direct runoff and base
flow (a) trends of annual runoff and base flow (b) and trend of annual (c) (note: different scales)
29
n= 16, (1990-2005)
Awash river at Melkakunture
Total flow
Direct Runoff
Base flow
% of the base flow
Awash river at Hombole
Total flow
Direct Runoff
Base flow
% of the base flow
Min
5521.875
4805.638
525.2037
9.5
Max
14346.49
12873.73
1537.006
10.7
Mean
9687.779
8736.831
950.9483
9.8
St.Dev
2502.971
2293.561
324.7091
8352.66
6890.432
1040.653
25518.24
22796.19
3111.204
15350.27
13348.31
2001.966
4884.38
4391.16
568.103
12.4
12.8
12.6
3
Table 3.6 Basic statistics of flows (in m /s) at the two stations of the main Awash River (Data
from MoWR)
a
Direct Runoff
Baseflow
Flow (m3/s)
500
400
300
200
100
0
1
25 49 73 97 121 145 169 193 217 241 265 289 313 337 361
Days
Awash river at Hombole
b
Direct Runoff
Baseflow
Flow (m3/s)
25000
20000
15000
10000
5000
1999
2000
2001
2002
2003
2004
2005
2000
2001
2002
2003
2004
2005
1998
1999
1997
1996
1995
1994
1993
1992
1991
1990
0
Years
Baseflow
1998
1997
1996
1995
1994
1993
1992
1991
3500
3000
2500
2000
1500
1000
500
0
1990
Flow (m3/s)
c
Years
Figures 3.8 Separation of total flow of Awash river at Hombole into direct runoff and base flow
(a), trends of annual runoff and base flow (b) and trends of annual base flow (c) (note: different
scales)
30
3.5 Groundwater level monitoring
Groundwater systems are dynamic and adjust continually to short-term and long-term changes in
climate, groundwater withdrawal, and landuse. Water level measurements from wells are the
principal source of information about the hydrologic stresses acting on aquifers and how these
stresses affect groundwater recharge, storage, and discharge. Long term, systematic
measurements of water levels provide essential data needed to evaluate changes in the resource
over time, to develop groundwater models and forecast trends, and to design, implement, and
monitor the effectiveness of groundwater management and protection programs.
But continuous groundwater level monitoring is given less attention in the country. It can be
said that the first attempt to systematically monitor groundwater level is realized in this study.
Hydrologic, geologic, and land and water use settings will all influence where and how many
locations are going to be established for groundwater level monitoring. Hydrologic factors such
as presence of surface water, geologic factors such as presence of fault lines that may influence
groundwater movement and changes in land and water use such as new residential, industrial,
agricultural, environmental uses, are the considerations that may influence where and how many
locations are established for groundwater level monitoring (Charles and William, 2001). In this
study, by taking into consideration the factors that can affect the distribution of groundwater
monitoring sites, four locations were selected for installation of data loggers for monitoring
groundwater levels. The data loggers were installed in the three physiographic regions of the
study area (one in the plateau, two in the transition and one in the rift part) along the regional
groundwater flow direction (Fig. 3.9).
31
Figure 3.9 Location of groundwater monitoring sites
Groundwater level monitoring data can be used to determine annual and long term changes of
groundwater storage, estimate recharge rates, determine direction and gradient of groundwater
flow, understand how aquifer systems work, gain insight for well construction and where to set
pump position for efficient extraction (Charles and William, 2001).
The loggers record water level and temperature by pressure transducers and measurements are
being made in a 12 hours interval, i.e. twice a day. A 10 months record (April, 2007-January
2008) of groundwater level was obtained from the two wells, Sululta (Blue Nile plateau) and
32
WWDSE (central Addis Ababa, Fig. 3.10) while the data from Becho and Dukem loggers was
limited to very short periods of time due to the failures of the instrument at Asgori well (Becho)
and problems faced in Dukem well related to request to use the well for the town water supply.
All the wells had not been under any pumping activity during the monitoring period and
fluctuation of groundwater levels was due to natural factors. The groundwater level data from the
two sites were used for estimating recharge by using the water table fluctuation method as
detailed in section 3.7.2.
-69.5
-70
1/12/2009
12/28/2008
12/13/2008
11/28/2008
11/13/2008
10/29/2008
10/14/2008
9/29/2008
9/14/2008
8/30/2008
8/15/2008
7/31/2008
7/16/2008
7/1/2008
6/16/2008
6/1/2008
5/17/2008
5/2/2008
4/17/2008
-70.5
4/2/2008
waterlevel (m, bgs)
-69
time
Figure 3.10 Data logger record of groundwater level monitored at the WWDSE well in central
Addis Ababa
3.6 Evapotranspiration
The combination of two separate processes whereby water is lost on the one hand from the land
surface and open water bodies by evaporation and on the other hand from plants by transpiration
is referred to as evapotranspiration (ET). It is difficult, and of little value, to separate evaporation
and transpiration for vegetated surfaces; thus they are often lumped together and termed
evapotranspiration. Other terms are sometimes used, such as consumptive use, or crop water
requirement. The rate of evapotranspiration is expressed in a volume of water evaporated per
unit area per unit time (mm3 mm-2 d-1 = mm d-1), the same units as rainfall. For evaporation to
take place there must be:
33
•
An input of energy (mainly solar energy). Depends on the amount of sunshine and its
intensity;
•
A vapor pressure gradient between the evaporating surface and the air. This depends on the
relative humidity and temperature;
•
Movement of air (wind speed), otherwise the layer immediately above the evaporating
surface would become saturated and no more evaporation would take place.
In other words, Evapotranspiration involves many physical processes which are affected by
many meteorological variables and is one of the most difficult parameter to quantify. Seleshi,
(2002) developed a relationship between the potential evapotranspiration and altitude for each
month for the region including the study area. The relationship is developed using regional
regression analysis and is constant from year to year. But studies undertaken by American
Society of Civil Engineers and a consortium of European research institute to evaluate the
performance of different evapotranspiration estimation techniques indicated the FAO PenmanMonteith method of reference evapotranspiration computation has superior accuracy and has
consistence performance in both arid and humid climates. It is also recommended as a standard
method in evapotranspiration estimation (FAO, 1998). Demlie, (2007) used the FAO PenmanMeonteith method and estimated the mean annual potential evapotranspiration of Akaki
catchment to be about 1215mm. Since Akaki catchment is located with in the study are and most
of meteorological stations that fulfill the parameters to be used in the Penman-Meonteith method
are in this part of the study area, this value is adopted in this study.
3.7 Recharge estimation
3.7.1 General
Recharge is defined as the process of downward movement of water through the saturated zone
under the force of gravity or in the direction determined by hydraulic conditions (Balek, 1988).
Groundwater recharge is a fundamental component in the water balance of any watershed,
however, because it is impossible to measure it directly, numerous methods, widely ranging in
complexity and cost, have been used to estimate recharge. It is a key component in any model of
34
groundwater flow or contaminant transport and its accurate quantification is crucial to proper
management and protection of groundwater resources.
Precipitation, surface waters and irrigation losses can be sources of groundwater recharge.
Recharge
mechanism
from
these
sources
can
be
diffuse
(direct)
or
preferential
(localized/indirect). Diffuse recharge mechanism refers to the water added to the groundwater
reservoir in excess of soil-moisture deficit and evapotranspiration. The preferential recharge also
called localized recharge mechanism refers to the concentrated percolation of water to the water
table following runoff and localization in joints, low lying areas, on lakes or through the beds of
surface water sources (Lerner et al, 1990). Preferential recharge takes place via pathways such as
macropores opposed to diffuse recharge which takes place through the entire vadose porous
medium (Zagana et al. , 2007).
The groundwater recharge in the study area has been estimated by different researchers using
different approaches. Using the water balance (WATBAL) model developed by David Yates
(Yates, 1996) the mean annual recharge over the study area is estimated to be about 47mm
(WWDSE, 2008). The mean annual recharge estimated using semi-distributed catchment
soil-water balance model (Thornthwaite and Mather, 1957: Alley 1985) and chloride mass
balance method (Sharma and Hughes, 1985) for the Akaki catchment part of the study area
resulted 105.4 and 265mm respectively (Demlie, 2007).
In this study, mainly not to duplicate efforts and owning the availability of the groundwater
level monitoring records from the data loggers, the recharge estimation will be made mainly
using groundwater table fluctuation method and base flow, and the result will be compared
with the results obtained by different methods from the previous works listed above.
3.7.2 Base flow evaluation in relation to groundwater recharge
In some cases base flow has been used as an approximation of recharge with an assumption that
it is probably less than the amount of recharging the groundwater system (Chen and Lee, 2003).
35
Base flow is that part of the stream flow usually attributed to groundwater discharge, Some
authors say that although base flow is not an absolute recharge, it sometimes could be used as an
approximation of recharge when underflow, evapotranspiration from riparian vegetation and
other loses of groundwater from watershed are thought to be minimal. When base flow is used as
a proxy for recharge, it is referred to as effective recharge, base recharge, or observable recharge
to acknowledge that it probably represents some amount less than which recharged the aquifer
(Riser et al, 2005). A common recommendation from different authors is that the recharge should
be estimated by the use of multiple methods like: unsaturated-zone drainage collected in gravity
hypsometers, daily water balance, and water fluctuation in wells. The result should always be
compared carefully (Riser, Gburek and Folmar, 2005).
The major assumption in using base flow for estimating recharge is that base flow equals
groundwater discharge from the aquifer storage and that groundwater discharge is approximately
equal to recharge, assuming that loses from gauged watersheds caused by underflow,
groundwater evapotranspiration and abstraction are minimal.
Similarly, based on the base flow obtained by using recursive digital filters in section 3.4 above,
the average annual groundwater recharge of Upper Awash (mean annual base flow divided by
the area of the upstream catchment) as measured at Hombole commanding a catchment area of
7656km2 is estimated to be 22.6mm, which amounts to only 2.3% of the mean annual areal
precipitation of the study area, similarly the annual groundwater recharge at Melkakunture with
upper catchment area of 4456km2 is about 18.4mm, which is only 1.9% of the mean annual areal
precipitation. This amount is equivalent to a total volume of about 173 million cubic meters
(mcm) per year or 2002m3/s, in the case of Hombole.
3.7.3 Groundwater recharge estimation using groundwater table fluctuation method
Recharge and lateral inflow of groundwater increases groundwater storage, whereas groundwater
evapotranspiration, exploitation, base flow to streams and lateral outflow reduce groundwater
storage. The water table fluctuation method provides an estimate of groundwater recharge from
changes in groundwater storage by analyzing groundwater level fluctuation in a given time with
36
out considering the lateral flow. The water table fluctuation method is based on the principle that
rises in groundwater levels in unconfined aquifers are due to recharge water arriving at the water
table (Healy and Cook, 2002) and recharge is calculated as:
R= Sy(∆h/∆t)
(3.3)
Where Sy is specific yield, h is water-table height, and t is time. In addition to monitoring of
water levels in one or more wells or piezometers, an estimate of specific yield is required. It is
the most widely used technique for groundwater recharge estimation due to its simplicity and
ease of use. Uncertainty in estimates generated by this method relate to the limited accuracy with
which specific yield can be determined. It was not possible to calculate storage coefficients of
the aquifers of the study are from the existing pumping test data given that almost all pumping
test were single well tests. Weathered, fractured and at places scoraceous basalt is the main
aquifer in the area, an average storage coefficient/specific yield of 0.1 was taken from litratures
for estimation of the recharge.
Groundwater recharge was estimated using the groundwater table fluctuation method from
groundwater level fluctuation data of one hydrologic year (April, 2007-January, 2008) for the
study area. Using Eq.3.3, ∆h is set equal to the difference between the peak of the rise and low
point of the extrapolated antecedent recession curve at the time of the peak. Based on the
groundwater level records in a well at central Addis Ababa (WWDSE well, Figure 3.10), the
maximum groundwater level recorded was obtained during the month of October which is
69.25m below the ground surface (bgs) and the minimum level in the month of August, 70.06m,
bgs. The groundwater level fluctuation in this well is 0.81m and similarly at Sululta well is
0.90m. Hence, an average annual recharge value of 81mm was estimated for the Addis Ababa
area, while 90mm was estimated from the record of Sululta well. This amounts to 6.7and 7.7% of
the total annual precipitation for the Addis Ababa and Sululta areas respectively. The amount of
recharge varies depending on the value and estimation accuracy of the storage coefficient of the
aquifer.
37
4. Aquifer Configurations of Upper Awash Basin
4.1 Litho-hydrostratigraphy
The aquifer properties in the basin are controlled by the litho-stratigraphy of the volcanic rocks
and the structures that affect them. Because of the complex nature of the lava flow, the volcanic
rocks have highly variable primary porosity. Later on through time, these volcanic rocks have
been subjected to weathering and fracturing related to tectonics giving rise to secondary
porosities. These volcanic aquifers can be considered as a double porosity medium due to the
fact that both the matrix and the fracture porosity contribute to the circulation and storage of
groundwater.
The aquifers in Upper Awash can be divided broadly into two categories; primary porosity
aquifers and double porosity aquifers. The first category comprises aquifers related to
Quaternary alluvial and lacustrine deposits and the second broad categories belongs to the
basaltic volcanics and again subdivided in to upper and lower basaltic aquifers separated by less
permeable, along fractured and weathered zones and/or impermeable otherwise, of acidic
volcanics. Figure 4.1 shows simplified aquifer configuration and hydrogeological set up of
upper Wash basin.
The alluvial and lacustrine aquifers are found dominantly in the southeast around Debrezeit
and Modjo towns, and locally in the northwestern part of the Becho plain and along the
main perennial river courses. The alluvial and lacustrine deposits around Debrezeit and
Modjo have thickness up to 80 meters and composed of coarse sediments (WWDSE, 2008).
Depth to static water level varies from 7 to 39 meters. The alluvial aquifers in the southern
part are with direct hydraulic connection with the underlying basaltic main aquifer.
38
Figure 4.1 Generalized Hydrostratigraphy map of upper Awash Basin (modified from WWDSE,
2008)
39
The volcanic units can be grouped in to three major zones based on the degree of fracturing
and their hydrostratigraphic position as follows:
1. Upper Basaltic Aquifer: - This unit is composed of Quaternary flows of Weliso-Ambo
basalts, Akaki basalts, scoria and spatter cones, and Tertiary-Neogene basalts of Addis Ababa
area. The upper basalt aquifer has wide distribution in the area: in the north central part
around Addis Ababa, Akaki and found overlain by ignimbrites and tuffs in Becho and
Legedadi areas. It forms confined and unconfined aquifer system, locally obliterated by
trachytic and rhyolitic volcanic centers and ridges. The thickness of this formation is highly
variable from more than 400 meters at Legetafo to less than 50 meters in the Becho plain.
The static water level in this aquifer is highly variable; at places artesian conditions to a
maximum of 150 meters below ground surface (WWDSE, 2008).
2. Lower Basaltic Aquifer: - This unit is composed of lower Tertiary Tarmaber and Amba
Aiba basalts, dominantly scoraceous. The recent exploratory wells drilled in Becho, Holeta,
Melkakunture, and Legedadi areas penetrated this aquifer under thick impermeable
ignimbrites (up to 225m). The water level significantly rises from its first striking depth
(220m rise at Asgori well) and the yield of the wells were progressively increasing when the
depth of penetration increase in this aquifer. So far none of the deep wells fully penetrate
this aquifer. Static water level varies from artesian condition to a depth of 67.5 meters below
ground surface.
3. Localized and regional aquicludes: - Quaternary Bedegebaba rhyolites, Ziquala trachytes
and Tertiary Intoto-Becho rhyolites, Central Volcanics of Wechecha, Furi and Yerer have
low permeability, except along weathered and fractured zones and act as local aquicludes by
compartmentalizing the upper basaltic aquifer. Chefedonsa Pyroclastics, Nazaret group
Welded ignimbrites and Addis Ababa ignimbrites of low productivity along the weathered
and fractured zones and/or impermeable otherwise, act as aquicludes by separating the upper
40
and lower basaltic aquifers in the northwestern, north-central and northeastern part of the
basin.
4.2 Hydrostratigraphic relationships, mechanism of groundwater recharge and circulation:
Evidences from exploratory drilling
From the exploratory drilling in Upper Awash and adjacent Blue Nile plateau the following
litho-hydrostratigraphic configurations are outlined. Lithologic logs of some selected boreholes
are presented in appendix 1. In the adjacent Blue Nile plateau, the formations encountered from
top to bottom (younger to older) are: the relatively dominant scoraceous and/or vesicular
Tarmaber basalt, underlain by Alaji Rhyolites consisting of rhyolites and ignimbrites followed
by the columnar jointed Amba Aiba and highly weathered Ashangi basalts respectively. The
oldest volcanic unit before the underlying Mesozoic sedimentary formation is the massive Blue
Nile basalt.
During drilling of the exploratory wells in the adjacent Blue Nile plateau, the Tarmaber basalt is
the dominant unit encountered starting nearly from the surface, except thin top soil developments
at places. This unit is also observed in exposures at the western and northern plateau parts of the
study area around watershed divide of the Awash and Blue Nile Rivers, being the relatively
younger lithologic unit in this part of the area. In Upper Awash part of the study area, this unit is
overlain by thick (about 282m) younger ignimbrite (Legedadi and Melkakunture drilling data).
In areas where the weathered, fractured and scoraceous basalt is exposed to the surface (in the
adjacent Blue Nile plateau, northeastern and northwestern extremes of the study area; Fig 4.3 and
4.4), recharge is possible directly from precipitation; due to the presence of both primary
(volcanic structures) and secondary (weathering and fracturing) porosity and permeability of the
basaltic rocks. In the north central part of the study area (around Addis Ababa (Hilton), Sululta,
and Legedadi; figures 4.2 and 4.3), acidic volcanics are overlain by thin soil cover and are near
to the surface. Acidic volcanics are characterized by low permeability and porosity, except along
weathered and fractured zones; in this case, direct recharge is limited and is restricted to the
zones of fracturing and weathering.
41
N
S
2850m
Sululta 2610
8
2500m Chancho 2468
38
324
56
AA Hilton 2373
21
67
278
157 Akaki 2070
304
4
2000m
400
202
328
Over Burden/ soil
Alluvium/Lacustrine
Paleaosol
Dukem 1924
26
132
178
180
206
242
254
264
282
1500m
10km
20km
30km
40km
Acidic Volcanics
(Rhyolite,trachyte...
Massive Basalt
Weathered &
Fractured Basalt
Scoraceous
Basalt
50km
Figure 4.2 N-S hole-to-hole lithologic sections ( Chancho – Dukem), numbers along with the well names represent elevations in
meters above mean sea level, numbers along with the lithologic logs represent depth in meters below ground surface and the red
irregular line correlates the highly productive scoraceous basalt aquifer (lower aquifer in the transition and rift part of the study area)
42
In the southern part of the study area, where there exists significantly thick alluvial/lacustrine
deposit, direct recharge is possible due to primary porosity and permeability of this unit. In areas
where this unit is directly underlain by weathered, fractured and/ or scoraceous basalt, the
recharge in the alluvium percolates to the underlying basaltic unit. In areas where the alluvium is
underlain by acidic volcanics (Fig. 4.2, at Dukem) and (figure 4.3 and 4.4, at Modjo), percolation
to the next weathered, fractured and/or scoraceous basaltic unit will be restricted to zones of the
fracturing and weathering of the acidic volcanics which overlies it.
Based on evidences from the exploratory drilling in the central, northern and western part of
Upper Awash, two distinct basaltic aquifers are encountered, i.e. upper and lower basaltic
aquifer. The lower aquifer is confined and the upper is confined at some places and
unconfined in others. How ever, drilling and water quality monitoring in the southern part of
Upper Awash showed that the upper and lower aquifer forms one unconfined regional
aquifer system south of Melkakunture and Dukem areas may be due to the intensive faulting
and fracturing in this part of the study area.
The different aquifers in the area at different places have different water levels. In the upper
basaltic aquifer, the static water level varies from place to place from artesian condition to
120 to 150 meters below ground surface. The lower aquifer water level varies from artesian
condition to a depth of 67.5 meters below ground surface. However, in some cases the water
levels in wells tapping the upper weathered and fractured ignimbrites and trachytes was
found to be the water level of the lower basalt aquifer which comes upward along fault and
fracture zones interconnecting the two zones. This is evidenced during drilling of
exploratory wells at different localities such as Asgori, Melkakunture and CMC (WWDSE,
2008).
43
NNE
3000m
Onado 2904
4
82
228
252
2500m
SSW
Bekie 2578
348
30 Legedadi 2468
8
Ayat-II 2373
73
115
116
170
197
282
300
354
200
2000m
Legend
Over Burden/Soil
Acidic Volcanics
(Rhyolite,trachyte…)
Borora 1879
Modjo Ude 1836
14
62
6
1500m
10km
20km
30km
40km
98
128
130
300
50km
194
278
60km
Paleaosol
Weathered &
Fractured Basalt
Scoraceous Basalt
Figure 1Figure 4.3 NNE-SSW hole-to-hole lithologic sections (Onado –Modjo), numbers along with the well names represent
elevations in meters above mean sea level, numbers along with the lithologic logs represent depth in meters below ground surface and
the red irregular line correlates the highly productive scoraceous basalt aquifer (lower aquifer in the transition and rift part of the study
area)
44
2700m
NW
SE
2500m Inchini 2457
76
92
96
146
Holeta 2525
6
60
72
132
150
236
300
2000m
Legend
Over Burden/ soil
Paleosol
Dimajalewa 2090
Asgori 2075 Melkakunture 2014
52
34
110
172
136
192
164
300
290
224
311
225
308
Acidic Volcanics
(Rhyolite,trachyte..
Massive Basalt
Adulala 1765
4
Modjo Muda 1697
22
12
60
128
225
20km
30km
40km
50km
60km
Scoraceous Basalt
320
330
1300m
10km
Weathered &
Fractured Basalt
70km
80km
Figure 4.4 NW-SE hole-to-hole lithologic sections (Inchini - Modjo), numbers along with the well names represent elevations in
meters above mean sea level, numbers along with the lithologic logs represent depth in meters below ground surface and the red
irregular line correlates the highly productive scoraceous basalt aquifer (lower aquifer in the transition and rift part of the study area)
45
Based on the stratigraphic relationship correlated from the drilling data of the exploratory
boreholes (Figures 4.2, 4.3, and 4.4) along with the respective geological structure setup,
groundwater movement could be connected lateraly through the permeable and porous
scoraceous basaltic unit. It is believed that in Upper Awash side and/or the transition and rift
valley part of the study area, this unit is downthrown by the regional east west running Ambo
fault (WWDSE, 2008). The scoraceous Tarmaber and Amba Aiba formation together with the
tectonic structures is therefore responsible in conveying the recharge from the adjacent Blue Nile
plateau to the Upper Awash groundwater system. This conclusion will be further constrained by
the results obtained from aquifer parameters, hydrochemistry and isotope hydrology signatures in
the subsequent chapters. The presence of acidic volcanic ridges and centers: like Wechecha and
Furi in the western part of the study area; Yerer in eastern part and Ziquala in the southern part
act as local barriers for the groundwater movement and circulation.
46
5. Hydrodynamic and Hydraulic properties of the volcanic aquifers of Upper
Awash basin
5.1 Introduction
The development and efficient management of groundwater resources requires a good
understanding of the hydrogeological properties of the rocks that form the major aquifers.
Hydrogeological parameters such as piezometric head, transmissivity, hydraulic conductivity,
storage coefficient, aquifer thickness, etc. are all functions of space. These variables are not
purely random (de Marsily, 1986), but there is some kind of correlation in the spatial distribution
of their magnitudes.
In this study attempt is made to investigate the spatial distribution and correlation of aquifer
parameters of the volcanic aquifers of the Upper Awash. Interpolation is made using different
methods from the available measured data to fairly estimate the values in unmeasured points.
The available data of the respective parameters were interpreted using: the statistical software
called STATISTICA (Stat Soft Inc. 2008), geostatstical softwares; VarioWin2.21 (Pannatier,
1996) and geostatistical analyst of ArcGIS 9.2 (Arcmap-Arcinfo) (ESRI, 2006) and Surfer 8
(Golden software, 2004). Based on the analysis different maps showing the spatial variability
were prepared.
5.2 Interpolation techniques
5.2.1 Geostatstical analysis
Classic statistics is generally devoted to the analysis and interpretation of uncertainties caused by
limited sampling of a property under study based on the basic assumption that all samples of a
population are normally distributed and independent. However, such an estimate by statistical
regression does not describe the spatial variability of the different parameters. Most earth science
data (e.g., hydraulic properties, groundwater chemistry, rock properties, contaminant
concentrations, etc.) do not satisfy these assumptions as they often tend to be highly skewed
and/or possess spatial correlation (i.e. data values from locations that are closer together tend to
47
be more similar than data values from locations that are far apart). To most geologists, the fact
that closely spaced samples tend to be similar is hardly surprising since such samples have been
influenced by similar physical and chemical processes. Compared to the classical approaches
which examine the statistical distribution of sample data, geostatistics incorporates both the
statistical distribution of sample data and the spatial correlation between the sample data, which
is fundamental to characterize aquifers. Therefore in this work, the hydraulic parameters
(hydraulic head, transmissivity, hydraulic conductivity, etc.) were regarded as regionalized
variables, i.e. each value depends on its geographical position, and treated using geostatstical
methods. The first objective of the geostatistical analysis is to describe the spatial correlation
between sample points and the second objective is to provide the best estimation of the variable
at unmeasured points. The basics of geostatistics and its application in hydrogeology are detailed
in Kitanidis (2000).
5.2.2 Variograms and Kriging
Kriging is a linear geostatistical estimation method which enables the estimation of a
regionalized variable (say Y) at any point in space based on its measured values at other
locations. Cokriging is a multivariate geostatistical estimation technique which enables the
estimation of a principal regionalized variable (Y) using several other regionalized variables
(accessory variables) with which Y is correlated. Commonly, these accessory variables are often
known at more locations than the principal Y to be estimated and can be measured more easily
either at the same points as Y, and/or at different places. The variogram is the key function in
geostatistics as it is used to fit a model of the spatial correlation of the data. The variogram is the
expected squared difference between two data values separated by a distance vector, i.e. by
considering a stationary random function Y with known mean m and variance δ2, the variogram
is given by the equation,
2γ(h) =
[
Y(u) - Y(u+h)
]2
(5.1)
Where the semivariogram γ(h) is half of the variogram 2γ(h), u being the location vector and h
the distance vector (Gringarten and Deutsch, 1999).
48
The variogram is a measure of variability; it increases as samples become more dissimilar. The
covariance is a statistical measure that is used to measure correlation (it is a measure of
similarity) given by:
C(h)= [Y(u)×Y(u + h)]-m2
(5.2 )
By definition, the covariance at h=0, C(0), is the variance δ2 . The covariance C(h) is 0.0 when
the values h-apart are not linearly correlated. Expanding the square in equation (4.1) leads to the
following relation between the semi-variogram and covariance:
γ (h)=C(0)-C(h) or C(h)=C(0)- γ (h)
(5.3)
This relation depends on the model decision that the mean and variance are constant and
independent of location. These relations are the foundation for variogram interpretation. That is,
•
The “sill” of the variogram is the variance, which is the variogram value that corresponds
to zero correlation,
•
The correlation between Y(u) and Y(u+h) is positive when the variogram value is less
than the sill, and
•
The correlation between Y(u) and Y(u+h) is negative when the variogram exceeds the
sill.
The nugget effect represents any small-scale data variability or possible sampling (measurement
and/or location) errors. A detailed presentation of these methods can be found in basic
geostatistics references (Isaak and Srivastava, 1989; Kitanidis, 2000).
The general equation of the theoretical variogram models fitted to the experimental
transmissivity variogram is given as:
Spherical model: γ (h) = C0 + C1
[
Exponential model: γ (h) = C0 + C1
3
2
[
(
h
a
)─
1
2
1─ exp ( ─3
h
a
(
h
a
) ]
)3 ]
(5.4)
(5.5)
Where C0 is the nugget effect, C1 is the partial sill, C = C0 + C1 is the sill, a is the range (distance
at which 95% of the sill has been reached), h is the distance between sampling points (lag size).
49
5.3 Peziometric head distribution and flow direction
A water table contour map is a very important tool in groundwater investigations as one can
derive from it the gradient and the direction of the groundwater flow. A water table contour map
or a contour map of the potentiometric surface of an aquifer is a graphic representation of the
hydraulic gradient of the water table or potentiometric surface. The hydraulic gradients, which
can be directly derived from these maps, are the basis for calculating the rate of groundwater
flow through cross-sections (De Ridder, 1980). The contour lines of a water table map or a
potentiometric surface map are in fact equipotential lines. Hence the direction of the groundwater
flow, being perpendicular to the equipotential lines, can be directly deduced from these maps.
Furthermore, an effluent (gaining) or influent (losing) from a source (upper lands or river) and
artesian effect can be determined using these maps.
A depth-to-water table map or isobath map, as these names imply, shows the spatial distribution
of the depth of the water table below the land surface. It can be prepared in two ways. The water
level data from all the observation wells for a certain date should first be converted to water
levels below land surface. One plots then the transformed data on the topographical base map
near each observation point and draws isobaths or lines of equal depth to groundwater. Another
way of preparing an isobath map is made by taking the difference between the topographic
elevation and the depth-to water level, which gives the groundwater elevation from means sea
level (datum).
Head observations of water wells having depth greater than 150m are considered in this study, on
the basic assumption that these wells could represent the regional aquifer. The experimental
variogram consisting of head measurements made in 2008 at more than 175 water points is fitted
with a linear variogram model (Fig 5.1). The resulting peziometric map (Fig 5.2b) indicates
steep hydraulic gradient in the northern and northeastern part of the basin and gentle in the
western and southern part.
50
Figure 5.1 Experimental variogram of the spatial correlation of the head data of 2008 and the
linear model fit
The interpolation of depth to static water level (Fig 5.2a) demonstrates complex situations, from
artesian conditions to deep levels. Generally, depth to static water level increases from north to
south except in some localities where geologic structures and local barriers dislocate aquifers and
consequently also depth to static water level and flow. A maximum water level depth is mapped
at the southern sector of the basin near the rift. This could be due to the intensive fracturing of
the area by regional faults. Although the regional groundwater flow direction (Fig 5.2b.) is from
north to south following the regional topographic gradient, local barriers, lithology and structures
are responsible for the complex flow pattern.
51
b
Flow direction
Figure 5.2 Kriging estimates of the distribution of depth to static water level referenced from the
ground surface (isobath map) (a) and peziometric head referenced to mean sea level (b) in Upper
Awash basin in the year 2008
52
5.4 Transmissivity distribution
5.4.1 Introduction
Transmissivity of an aquifer measures how much water can be transmitted horizontally. It is the
product of the hydraulic conductivity times the thickness of the aquifer (Driscoll, 1986).
Transmissivity (T) is a hydraulic parameter of an aquifer that is known employed in most
groundwater flow equations to understand the flow dynamics and is generally estimated from
pumping tests (Freeze & Cherry, 1979). Spatially variable aquifer parameters are often available,
yet appropriate data that covers wide area uniformly are lacking due to the fact that the cost of
performing a large number of aquifer tests is relatively expensive and time consuming. Thus,
simple and inexpensive parameter estimation methods that may cover extensive areas are often
preferred. In fact such approaches require quantification of one or more easily measurable
aquifer parameters. One such aquifer parameter that is easy to measure is the specific capacity
(Sc) of a well, which is the ratio of pumping rate (Q) to drawdown (s) in the well. The fact that Sc
is correlated with hydraulic-flow properties (Theis, 1963) can simplify parameter estimation
mainly because Sc values are more abundant in groundwater databases than values of T or
hydraulic conductivity (K), and offer another approach to estimate hydraulic parameters of
aquifers.
Various analytical, empirical and geostatistical techniques have been developed over the years to
relate T and Sc. In the analytical approach, theoretical equations derived by Theis (1963) and
others are used. The empirical approach is based on determining the empirical relations between
T and Sc, which are generally regarded as aquifer and/or area specific (e.g. Razack and Huntley
1991, Jalludin and Razack 2004, Razack and Lasm 2007). In the geostatistical approach, T is
estimated from Sc using geostatistical estimation methods (e.g. Ahmed and de Marsily 1987,
Ahmed et al. 1988). Recently, remote sensing based hydrogeomorphological approach is
proposed to upscale T from point or well scale to aquifer scale (Srivastav et al, 2007).
53
The interpolated head in Fig 5.2b suggest the existence of transmissivity contrast as reflected by
variable hydraulic gradient (non-uniform flow pattern). This is due to strong variability in
lithology and partly due to variation in fracture patterns. Thus the extreme variations of lithology
and fracture conditions have resulted not only in considerable variation of productivity of the
volcanic aquifers but also considerable hydraulic parameter variation.
In this study the
transmissivity of the volcanic aquifers of the study area have been examined using a data set of
214 transmissivity values calculated from pumping test and an additional 199 estimated values
from specific capacity data compiled from different sources. A simple linear regression was
performed on both the actual data and log transformed data to obtain an empirical relation.
Geostatistical analysis has been done to fairly estimate transmissivity in unmeasured points using
the spatial relation of the measured data. Figure 5.3 displays the spatial distribution of measured
data points in the study area.
5.4.2 Determination of Transmissivity from pumping test
Hydraulic properties of aquifers can be estimated through laboratory and field techniques. The
aquifer pumping test is a frequently employed in-situ method which is used to determine the
hydraulic properties of water bearing formations. The test involves putting an artificial stress on
the aquifer by continuously pumping water from the borehole and measuring water level changes
in the pumped well. The change in hydraulic head can then be used to estimate the hydraulic
properties of the aquifer (transmissivity, hydraulic conductivity and storativity). Different
analytical methods are available for interpretation of pumping test data. Raw pumping test data
of all the exploratory wells and some recently drilled wells in the study area were collected and
re-interpreted using appropriate analytical methods. For most of the boreholes the pumping test
lasted between 24 and 72 hours. All pumping test were single well constant rate tests and
pumping wells were simultaneously used for monitoring drawdown. Recovery tests were also
available for most of these wells where residual drawdowns were recorded.
54
Figure 5.3 Spatial distributions of calculated data points
From single well tests, the storage term of the aquifer can not be determined, hence existing
pumping test data are used only for estimation of transmissivity and hydraulic conductivity. In
the Absence of observation wells, estimating storage properties using analytical methods could
be difficult (Kruseman and De Ridder, 1990). The transmissivity of the aquifer has been
calculated from the time-drawdown and recovery data of the constant rate test. Aquifer Test pro
(Pumping Test & Slug Test Analysis) Software version 3.5 (Waterloo Hydrogeologic, Inc. 2002)
was used for the analysis. In addition to the hydrogeological information obtained during
drilling, the time-drawdown data is plotted on a semi-log graph so as to understand the type of
aquifers and afterwards use the appropriate pumping tests analysis method.
55
It is obvious that under field conditions an almost perfect match tends to be the exception rather
than the rule due to the fact that all the analysis methods are based on highly simplified
representations of the natural aquifer. Deviations between theoretical drawdown curves and field
data could very well stem from the fact that one or more of the general assumptions and
conditions mentioned for the method may not met in the field. In this study, attempt is made to
distinguish between these kinds of deviations and deviations which stem from the fact that the
selected method is not the correct one for the field data. Moreover, if the drawdown in an
unconfined aquifer is small compared to the initial saturated thickness of the aquifer, the
condition of horizontal flow towards the well is approximately satisfied, so that methods used for
confined aquifers can also be applied to determine the aquifer properties (Jacob 1947).
Accordingly, the majority of the pumping test data is analyzed with Cooper-Jacob timedrawdown method (Cooper and Jacob, 1946), except for few where Neuman method is
employed (Neuman, 1975). All the recovery data is analyzed by using Theis Recovery method
(Theis, 1935). Transmissivity estimates range from 0.33 to10000 m2/d (Table 5.1).
The specific capacity of a well can be defined as the ratio of the pumping rate (Q) to the total
drawdown (s) of the well (i.e., the specific capacity = Q/s). In groundwater flow equations, it is
assumed that T is linearly proportional to the Q/s of a well (Thesis 1963). Specific capacity was
determined by using steady-state drawdown and it was found to range from 0.28 to 6053 m2/d.
The wide range of transmissivity and specific capacity values which are explained with a very
high variance is attributed to the heterogeneity of the volcanic aquifers in the study area.
n=214
min
max
Arithmetic .mean
median
St.D
Variance
skewness
Kurtosis
2
T(m /d)
0.33
100000
438.68
27.28
1272.39
1618981.72
4.68
24.73
2
Sc (m /d)
0.28
6053.53
326.26
22.73
911.01
829935.42
4.34
20.42
logT
logSc
-0.48
4.00
1.61
1.44
0.97
0.94
0.38
-0.45
-0.56
3.78
1.51
1.36
0.95
0.90
0.39
-0.38
Table 5.1 Basic statistics of measured hydraulic parameters
56
5.4.3 Estimation of transmissivity using empirical relation from specific capacity
The frequency distribution of both specific capacity and transmissivity indicate that both
variables are log-normally distributed (Fig.5.4). A random variable X is said to be log-normally
distributed if log(X) is normally distributed. A probability distribution that plots all of its values
in a symmetrical fashion and most of the results are situated around the probability's mean is said
to be normally distributed; grouping takes place at values that are close to the mean and then tails
off symmetrically away from the mean.
a
b
Figure 5.4 Lognormal distributions of transmissivity (a) and specific capacity (b) data
57
Simple regression analysis has been applied to derive the empirical relationship between specific
capacity and transmissivity. A linear relation between the actual values of the transmissivity and
specific capacity was developed (Fig.5.5). The following relation is obtained with a correlation
coefficient R of 0.79 with linear regression line at 95% confidence interval:
T= 1.25 (Sc) + 32.55
(5.6)
Where, T is the estimated transmissivity (m2/day) and Sc is the specific capacity (m2/day).
Figure 5.5 Plot of Transmissivity vs. Specific capacity, T and Sc in m2/d
An empirical relation between the logarithm of T and Sc (for 214 pairs) has been also
developed using linear regression (Fig.5.6). The correlation coefficient (R) of the linear log–log
regression equation at 95% confidence interval is equal to 0.97, and the equation for the
regression line representing the best estimate of transmissivity is given by:
logT= 1.003log(Sc) + 0.096
(5.7)
or
58
T= 1.25(Sc)1.003
(5.8)
Figure 5.6 Plot of log T vs., log (Sc), T and Sc in m2/d
The correlation coefficient of the log-log relation (R= 0.97) is better than the linear relation
(R=0.79) and because the two parameters Sc and T, are log normally distributed (Fig.5.4), the
logarithmic relation is also better statistically justified than the linear relation.
Comparing the empirical relations obtained in this study with that of the previous studies (Table
5.2), for a given specific capacity the transmissivity of the volcanic aquifers of the Upper Awash
basin shows a magnitude less than the transmissivity of the neighboring Djibouti volcanic
aquifers and greater than the volcanic aquifers of Jeju Island, South Korea. The values are found
to be significantly greater than the values obtained for fractured batholiths of San Diego, USA
and the fractured metamorphic and crystalline rocks of western Ivory Coast, Africa. Differences
in the transmissivity of the volcanic aquifers of the three countries (Ethiopia, Djibouti and
Korea) could be due to the heterogeneity of the volcanic rocks (type, chemistry, etc...) and mode
of eruptions which leads to the formations of different volcanic structures there by resulted in
differences in permeability and porosity.
59
Aquifer
Empirical
relation
T= 0.12(Q/s)1.18
Correlation
coefficient (R)
0.89
Volcanic
(Republic of Djibouti, Africa)
T=3.64(Q/s)0.938
0.91
Jalludin and
Razack (2004)
Volcanic
(Jeju Island, South Korea)
T=0.99(Q/s)0.89
0.94
Hamm et al.
(2005)
Fractured metamorphic and
crystalline (Western Ivory Coast,
Africa)
T=0.33(Q/s)1.30
0.93
Razack and
Lasm (2007)
Fractured batholith
(San Diego, USA)
Source
Huntley et al.
(1992)
Volcanic
Present work
T= 1.25(Q/s)1.003 0.97
(Upper Awash basin, Ethiopia)
Table 5.2 Empirical relation between transmissivity and specific capacity in volcanic and/or
fractured hard rocks
In addition to this the nature and intensity of the post eruption process of weathering and
fracturing could also be responsible for the differences in the hydraulic properties of the volcanic
aquifers. However, similar studies (Huntely et al., 1992, Fetter, 2001, Jalludin and Razack, 2004)
showed that such relations of T and Sc under estimates T values due to lower (less than 100%)
efficiency of most wells. As a result, the specific capacity data is corrected under head losses due
to turbulent flow. The T data used in this study are estimated from the uncorrected specific
log(T c alc ulated) (m2/day )
capacity data, but the estimated and calculated values are in a very good agreement (Fig. 5.7).
5.0
4.0
y = 1.000x - 0.0 01
2
R = 0.9 65
3.0
2.0
1.0
0.0
-1.0
-1.0
0.0
1.0
2 .0
3.0
4.0
5.0
log(T estimate d) (m2/day)
Figure 5.7 Regression of log calculated and estimated transmissivity
60
5.4.4 Upscaling of transmissivity using geostatstics
For wells where proper pumping test is not done, the specific capacity is estimated from 199
available discharge and drawdown data. The transmissivity is again estimated from the specific
capacity using equation 5.8.
n=413 (214+199)
min
max
Arithmetic .mean
median
St.D
Variance
skewness
Kurtosis
2
All T (m /d)
0.33
488997.57
2751.66
46.20
25610.33
655889214.25
17.09
317.71
2
All Sc (m /d)
0.28
378345.60
2129.71
36.84
19825.65
393056407.15
17.06
317.06
Log (all T)
-0.48
5.69
1.76
1.66
1.01
1.02
0.73
0.94
Log (all Sc)
-0.56
5.58
1.66
1.57
1.00
0.99
0.76
1.02
Table 5.3 Basic statistics of measured (214 data points) and estimated (199 data points) hydraulic
parameters
Different inputs and methods were used to estimate the transmissivity of the study area: 1) the
calculated transmissivity data (214 data points) were used and estimation was made using
ordinary kriging (OK); 2) 413 transmissivity data (214 calculated + 199 estimated using
empirical relations of T and Sc) were used and estimation was made using OK; 3) Estimation is
performed using ordinary cokriging, by taking T as a principal variable with its calculated values
and Sc as an accessory variable with its values in the 413 data points. In all the three cases (1-3),
spherical and exponential theoretical variogram models were fitted to the experimental
variogram which is calculated from the values of the data points explained in each case.
Comparison of the accuracy of these different geostatistical estimates were performed using the
cross-validation procedure, the results of the cross-validation procedure, using different inputs
and estimation methods, are summarized in Table 5.4.
61
a
b
Figure 5.8 Distribution of transmissivity (a) and specific capacity (b) data points in the Upper
Awash basin, X and Y are in meters
62
a
b
Figure 5.9 Experimental variogram of LogT for calculated transmissivity data set and fitted with
spherical (a) and exponential (b) models (h in meters).
63
Before producing the final surface of estimate, cross validation helps to make an informed
decision as to which model provides the best prediction. The calculated statistics serve as
diagnostics that indicate whether the model and/or its associated parameter values are
reasonable. The summary statistics on the kriging prediction errors are used as diagnostics for
the following three basic ideas:
•
The predictions are preferred to be unbiased (centered on the measured values). If the
prediction errors are unbiased, the mean prediction error should be near zero. However,
this value depends on the scale of the data, therefore to standardize, the prediction errors
are divided by their prediction standard errors, which gives the standardized prediction
errors for each predicted value. For a good prediction the mean of the standardized
prediction errors should also be near zero.
•
The predictions have to be as close to the measured values as possible. The root-mean
prediction error computed as the square root of the average of the squared distance
between the predicted and the measured points helps us to evaluate this. The smaller the
root-mean squared prediction errors, the closer the predictions are to their true values.
This summary can be used to compare different models by seeing how closely they
predict the measured values. The smaller the root-mean square prediction error, the better
the prediction.
•
The assessments of uncertainty i.e. the prediction standard error have to be valid. Each of
the kriging methods gives the estimated prediction kriging standard errors. Besides
making predictions, variability of the predictions from the measured values is estimated.
If the average standard errors are close to the root-mean squared prediction errors then we
are correctly assessing the variability in prediction. If the average standard errors are
greater than the root-mean square prediction errors, then we are overestimating the
variability of our predictions. If the average standard errors are less than the root-mean
square prediction errors, then we are underestimating the variability in our predictions.
Another way to look at this is to divide each root-mean square prediction error by its
estimated prediction standard error, to get the root-mean square standardized error. The
root-mean square standardized errors should be close to one if the prediction standard
errors are valid.
64
Case
No.
1
2
Group
Input
Kriging
model
MPE
ASPE
SDMPE
RMSPE
SDRMSPE
A
A
OK
OK
Spherical
Spherical
0.010
-0.008
0.776
0.792
0.010
-0.005
0.789
0.756
1.008
0.966
3
A
Measured T
All T
(measured +
estimated)
Measured T
(Sc as
accessories)
Measured T
All T
(measured +
estimated)
Measured T
(Sc as
accessories)
OCK
Spherical
0.848
-0.003
0.744
0.882
1
2
B
B
3
B
0.005
OK
OK
Exponential
Exponential
-0.005
-0.008
0.783
0.791
-0.004
-0.006
0.781
0.749
0.99
0.955
OCK
Exponential
-0.007
0.819
-0.005
0.700
0.867
Table 5.4 Summary statistics of cross validation results using spherical model (Group A),
exponential model (Group B), different inputs (case 1-3) and kriging methods. Where: OK
stands ordinary kriging, OCK for ordinary cokriging, MPE for mean prediction error, ASPE for
average standard prediction error, SDMPE for standardized mean prediction error, RMSPE for
root-mean square prediction error and SDRMSPE for standardized root-mean square prediction
errors.
Case
No.
1
Group
Input
Kriging
model
Range
Nugget
42871
Partial
Sill
0.55188
0.5854
Lag
size
11542
A
Measured T
OK
Spherical
2
A
OK
3
A
1
B
All T (measured +
estimated)
Measured T (Sc as
accessories)
Measured T
Spherical
27853
0.49048
0.6527
5808
OCK
Spherical
31489
0.52765
0.5646
7312
OK
Exponential
44584
0.69568
0.4471
11542
2
B
3
B
All T (measured +
estimated)
Measured T (Sc as
accessories)
OK
Exponential
25622
0.60357
0.5324
5808
OCK
Exponential
28605
0.65911
0.4193
7312
Table 5.5 Summary of variogram parameters using spherical model (Group A), exponential
model (Group B), different inputs (cases 1-3) and kriging methods.
From the above stated three basic ideas of the summary statistics, cross-validation allows to
determine how good the model is. The goal of this work should therefore be to have standardized
mean prediction errors near 0, small root-mean square prediction errors, average standard error
near root-mean square prediction errors and standardized root-mean square prediction errors near
1. Different geostatstical models and spatial data statistics is detailed in many books of
geostatstics (Armstrong, 1998; Cressie, 1990).
65
logT
OK-spherical
Min
Max
mean
St.D
Variance
0.78
3.10
1.60
0.58
0.33
logT
OK-exponential
0.77
3.11
1.61
0.58
0.34
Log (allT)
OK-spherical
0.39
4.16
1.75
0.70
0.49
log(all T)
OK-exponential
0.38
4.17
1.75
0.70
0.49
logT
OCK-spherical
0.75
3.18
1.61
0.60
0.35
logT
OCKexponential
0.65
3.41
1.61
0.63
0.40
Table 5.6 Basic statistics of predicted transmissivity values
The prediction made by ordinary kriging using the measured transmissivity data and the
experimental variogram which is fitted with exponential model produces relatively low rootmean square error (Table 5.4, group B, case no. 1), this is optimal as far as the root-mean square
prediction is concerned. Moreover, when we compare the root – mean square prediction errors
and the average standard prediction errors of all the cases, the two values are much closer when
the prediction is made in this case, i.e. using the measured transmissivity by ordinary kriging and
the experimental variogram fitted to the exponential model. This is also more valid because
when we make prediction at a point without data, we only have the estimated standard prediction
errors to assess the uncertainty of that prediction. When the average estimated standard
prediction errors are close to the root - mean square prediction errors from cross validation, and
then we are confident that the prediction standard errors are appropriate. Hence using this
scenario, Fig.5.10a displays the best estimation of the transmissivity of the Upper Awash basin
volcanic aquifer that can be currently achieved on the basis of the available transmissivity data.
The prediction standard error map (Fig.5.10b) shows that the accuracy of estimates is greater in
the centre of the study area, where most data points cluster. Highest values of prediction errors
are found near the borders of the study area where data points are rare or lacking.
66
a
b
Figure 5.10 Estimated transmissivity (a) and prediction standard error (b) map of the study area
67
5.5 Hydraulic Conductivity
5.5.1 Hydraulic Conductivity estimation
Hydraulic conductivity (K) defines the rate of movement of water through a porous medium such
as a soil or aquifer. It is the constant of proportionality in Darcy’s Law and as such is defined as
the flow volume per unit cross-sectional area of porous medium under the influence of a unit
hydraulic gradient. This translates to SI units of m3/m2/day or m/d. As it is explained in section
5.3, transmissivity (T) is the volume of water flowing through a cross-sectional area of an
aquifer that is 1m X the aquifer thickness (b), under a hydraulic gradient of 1m / 1m in a given
amount of time (usually a day). If we think about our definition of hydraulic conductivity, we
can conclude that transmissivity (T) is actually equal to hydraulic conductivity (K) times aquifer
thickness (b), or otherwise denoted as;
T = Kb
5.9
We can also conclude that transmissivity is expressed as m2/day because if T = Kb, then T =
(m/day)(m/1).
Measurement of hydraulic conductivity is problematic, considering the parameter can differ over
several orders of magnitude across the spectrum of rock types. Hydraulic conductivity is also
scale dependent, so that measurements taken at the core sample level may not be directly
extrapolated to the aquifer scale. It is also direction dependent, so that hydraulic conductivity can
be markedly different in the vertical from the horizontal.
As shown in Eq. 5.9 hydraulic conductivity is commonly calculated by using the formula,
K=T/b, where, T is the transmissivity of the aquifer and b the aquifer thickness, or length of
screen in wells. But for many groundwater wells the length of the screened portion of the aquifer
is poorly documented, so like other hydraulic parameters the calculation and estimation of
hydraulic conductivity for the study area is very difficult. The volcanic aquifers of the Upper
Awash basin are characterized by multi-layers; most wells are screened at more than one aquifer
layers, so water is abstracted simultaneously from the multi-layer aquifer system.
68
In this study, an attempt is made to estimate the hydraulic conductivity using 81 wells having
transmissivity data calculated from pumping test analysis with known screen length. The
transmissivity of the aquifer has been calculated from the time-drawdown and recovery data of
the constant rate test explained in section 5.4. Hydraulic conductivity estimates range from
0.0.02 to1646 m/d (Table 5.7).
n=81
logK
K(m/d)
min
max
Arithmetic .mean
median
St.D
Variance
skewness
Kurtosis
0.02
1645.67
39.55
1.15
188.17
35408.2
8.03
68.48
-1.79
3.22
0.28
0.06
1.02
1.04
0.55
0.11
Table 5.7 Basic statistics of estimated hydraulic conductivity values
Fig 5.11 Spatial distribution of hydraulic conductivity data points in Upper Awash and its
vicinity
69
5.5.2 Upscaling of hydraulic conductivity
Regional K predictions are of practical importance for groundwater management, especially for
Upper Awash, where the aquifers are relatively among the most stressed in the region. Defining
aquifer K maps facilitates the development of a detailed numerical model for the area in the
absence of well documented hydraulic parameters data. The prediction of such maps requires
spatial-variability analysis using experimental variograms. As it is the case for the transmissivity
the frequency distribution of the estimated hydraulic conductivity data indicate that this variable
is log-normally distributed (Fig 5.12).
Figure 5.12 Log-normal distributions of hydraulic conductivity data
Ordinary kriging was used to interpolate the point estimates to obtain basin-wide K maps for the
study area. The omni-directional experimental variograms were fitted with a spherical model.
Figure 5.13 shows the variogram for K estimates for Upper Awash basin. The parameters for the
spherical model fit using Eq. (5.4) are C0=0.2, C1=1.3, a=33000 and h=3000.
70
Figure 5.13 Experimental Variogram of log K fitted with spherical variogram model
The prediction made by ordinary kriging using the estimated hydraulic conductivity data and the
experimental variogram fitted with spherical model produces mean prediction error of about 0.02
and standardized root-mean square prediction errors of about 1.1. Moreover, the root – mean
square prediction errors and the average standard prediction errors are also closer (about 0.017
and 0.02 respectively. As it is explained in section 5.3, when the average estimated standard
prediction errors are close to the root - mean square prediction errors from cross validation, and
then we are confident that the prediction standard errors are appropriate. Fig.5.14a displays the
best estimation of the hydraulic conductivity of the Upper Awash basin volcanic aquifer that can
be currently achieved on the basis of the available hydraulic conductivity data. The prediction
standard error map (Fig.5.14b) shows that the accuracy of estimates is greater in areas where
most data points cluster. Highest values of prediction errors are found where data points are rare
or lacking
71
a
b
Figure 5.14 predicted hydraulic conductivity (a) and prediction standard error (b) map of the
study area
72
5.6 Storage coefficient
Specific storage (Ss), storativity (S), specific yield (Sy) and specific capacity (Sc) are material
physical properties that characterize the capacity of an aquifer to release groundwater from
storage in response to a decline in hydraulic head. For that reason they are sometimes referred to
as storage properties. In the field of hydrogeology, these properties are often determined using
some combination of field hydraulic tests (e.g., pumping tests) and laboratory tests on aquifer
material samples (Ferris et al, 1962).
The specific storage of a confined aquifer is the amount of water that a portion of an aquifer
releases from storage, per unit volume of aquifer, per unit change in hydraulic head, while
remaining fully saturated (Freeze and Cherry, 1979). Storativity is the volume of water released
from storage per unit decline in hydraulic head in the aquifer, per unit area of the aquifer, in
other words, it is the vertically integrated specific storage value for a confined aquifer. Specific
yield of unconfined aquifer, also known as the drainable porosity, is the amount of water that a
given aquifer will yield when all the water is allowed to drain out of it under the forces of
gravity. Storage coefficient is a storage parameter which describes the volume of water that a
permeable unit will absorb or expel from storage per unit surface area per unit change in head.
The storage coefficient of an unconfined aquifer is approximately equal to the specific yield. In
confined aquifers the storage coefficient, which is the storativity equals specific storage times
layer thickness.
As it is explained a bit earlier, the calculation and estimation of storage coefficient is more
difficult due to the absence of observation wells during pumping test. Almost all pumping tests
in the study area were single well tests; hence, estimating storage properties using analytical
methods was not possible. However, storage coefficient for the exploratory wells around Becho
was estimated about 0.15 on average, those around Adaa 0.25 and values ranging from 0.03-7%
are obtained for other places (WWDSE, 2008). These data are very few and unevenly
distributed, therefore should only be taken as indicative values, and do not represent the vast
Upper Awash basin. Since the data is limited to a very small area of the basin, spatial analysis is
also not possible.
73
6. Hydrochemistry and isotope hydrology
6.1 Introductions
Geochemistry has contributed significantly to the understanding of groundwater systems over the
last decades. Historic advances include development of the hydrochemical facies concept,
application of equilibrium theory, investigation of redox processes, and radiocarbon dating.
Other hydrochemical concepts, tools, and techniques have helped elucidate mechanisms of flow
and transport in groundwater systems. Hydrochemical and isotopic information can be used to
interpret the origin and mode of groundwater recharge, refine estimates of time scales of
recharge and groundwater flow, decipher reactive processes, provide paleohydrological
information, and calibrate groundwater flow models (Plummer and Glynn, 2005).
Each groundwater system in an area is known to have a unique chemistry, which is acquired as a
result of chemical alteration of the meteoric water recharging the system (Back 1966). The
chemical alteration of meteoric water depends on several factors such (i) acid-base reactions, (ii)
precipitation and dissolution of minerals, (iii) sorption and ion exchange, (iv) oxidationreduction reactions, (v) biodegradation and (vi) dissolution and exsolution of gases (Stallard and
Edmond 1983; Subba Rao 2002). Hence, hydrochemistry can be interpreted to understand the
key processes that have occurred during the movement of water through aquifers and to obtain
information about the recharge, the rate and direction of movement, the nature of the aquifer
through which it has circulated and anthropogenic activities influencing it.
Isotope hydrology is a key to understanding fundamental physical, chemical, biological, and
climate forcing processes occurring in a watershed. Isotopes can help address, among other
hydrological aspects, areas of recharge and discharge as well as cross-boundary groundwater
flow and river-aquifer interactions Water molecules carry unique fingerprints, based in part on
differing proportions of the oxygen and hydrogen isotopes that constitute all water. Isotopes are
forms of the same element that have variable numbers of neutrons in their nuclei. Air, soil and
water contain mostly oxygen 16 (16O). Oxygen 18 (18O) occurs in one oxygen atom in every five
hundred (Faure, 1986) and is a bit heavier than oxygen 16, it has two extra neutrons. From a
74
simple energy standpoint this results in a preference for evaporating the lighter
water and leaving more of the
18
16
O containing
O water behind in the liquid state. Thus seawater tends to be
richer in 18O and rain and snow relatively depleted in 18O.
A number of researchers have reported about the hydrochemistry of some localities in Upper
Awash, the Akaki catchment, particularly Addis Ababa area has been frequently reported.
Demlie (2008), Alemayehu et al. (2005), Nigussa (2003), Gizaw (2002), Alemayehu (2001) and
Tale (2000) are some to mention. However, none of them gave details and exhaustive analysis
and interpretation on the hydrochemistry of the volcanic aquifers of the whole Upper Awash
basin. By making use of the existing hydrochemical data compiled in the ABGREP database, an
attempt is made by Azagegn (2008) to interpret the hydrochemical evolution following selected
flow lines. Still little detail is known about the hydrogeology of these volcanic aquifers, their
hydrochemistry, hydraulic connection between different aquifers, degree of water-rock
interaction and its spatial and temporal evolution.
In the present work, an attempt is made to systematically sample water in the Upper Awash basin
giving emphasis to the deep aquifer systems. The samples were analyzed for their major ions,
stable isotopes, tritium and trace elements. This chapter presents groundwater circulation,
recharge and hydrochemical evolution patterns from a detailed analysis of original
hydrochemical data complemented with existing published data and data compiled in the
AGREP database. Using multivariate statistics and inverse geochemical modeling, coupled with
environmental isotope and trace element data, sources and spatial patterns of groundwater
recharge, circulation and geochemical evolution along the regional flow paths are discussed.
6.2 Sample collection and analytical techniques
Water samples were collected systematically from different water points in all the three
physiographic regions; plateau, transition and rift part of the study area (Fig. 6.1) in a north-south
and east-west transect across the basin in two field missions (summer 2007 and winter 2008).
During sampling special emphasis was given for the deep aquifer penetrated by the exploratory
wells, on the fact that due to the limited penetration depth of previously constructed water wells,
75
hydrochemistry data of the deep aquifer is found to be very limited in databases and previous
works. Special sampling apparatus, KLL-S 4”, was used to collect samples from the newly
drilled deep (up to 368m) exploratory boreholes. The samples from these boreholes were taken at
different depths based on the screen casing arrangements. The KLL-S 4" Sampler was obtained
from SEBA Hydrometrie of Germany, a mechanical sampler that can go up to a depth of 400m
and with a capacity of taking one liter sample at a time. Spring samples were collected from the
emanation point. Along with the sampling, insitu water quality measurements; electrical
conductivity (EC), total dissolved solids (TDS), Temperature, pH, Eh, and alkalinity were also
made. Isotope samples were untreated and sealed in special bottles. All the hydrochemical
samples were filtered through a 0.45µm membrane filter. Trace element samples were acidified
with drops of HNO3 (nitric acid) immediately after sampling.
Samples for major ion chemistry were analyzed by ion chromatography in the laboratory of the
Ministry of Water Resources, Ethiopia. Tritium was measured by the standard liquid scintillation
method in the hydrogeological laboratory of the Department of Earth Sciences, Addis Ababa
University. Samples for stable isotopes of oxygen and hydrogen and trace elements were
analyzed in the University of Avignon, France. Oxygen and hydrogen stable isotopic ratios are
measured by isotope mass spectrometry. Oxygen and hydrogen isotope compositions are
reported relative to an agreed sample of ocean water, referred to as the Standard Mean Ocean
Water (SMOW), representing the largest and most equilibrated water body. Stable isotope ratios
of deuterium/hydrogen (2H/1H) and 18O/16O of water are expressed as units of parts per thousand
(per mil, ‰) deviation from SMOW. Trace elements were measured by graphite furnace Atomic
Absorption Spectrophotometry.
Present data was complemented by hydrochemical and environmental isotope data compiled in
the ABGREP database (WWDSE, 2008), published data from Demlie et al (2008), Kebede et al
(2008), and Gizaw (2002). Long term environmental isotope data of rainfall (from 1961-2005 for
δ18O and δ 2H, 1961-1997 for tritium with interruption of measurements in between) is available
at Addis Ababa GNIP (Global Network of Isotopes in Precipitation) station of the International
Atomic Energy Agency (IAEA) database.
76
Based on the principle that the sum of cations in meq/l should nearly be equal to the sum of
anions in meq/l for the chemical data to be reliable, an attempt is made to calculate the charge
balance of the chemical analysis results considered in this study. Analysis results with charge
balance error not exceeding 5% are selected for the interpretation of the hydrochemistry data.
After calculating the charge balance, the chemical data with charge balance error greater than 5%
and those missing any one of the four major cations were discarded. Hydrochemical, trace
element and isotope data are presented in appendices 2, 3, and 4 respectively.
6.3 Major ion hydrochemistry and its spatial variation
6.3.1 General
Water in its pure form is H2O. By reason of its good solvent attribute, this composition is
modified as it passes through the atmosphere and percolates to the ground dissolving soluble
materials of varying compositions along its flow path. Solutes that are present in groundwater are
derived from two main sources: (i) input from rainfall, which have their origin from both marine
salts and continental dust, and (ii) acquisition during weathering and water-rock interactions. The
composition of groundwater varies from bicarbonate at outcrops to sulphate water at
intermediate depths to chloride waters at greater depths of continuous flow (Chebotarev 1955).
These changes reflect the signatures of one or some of such factors as soil/rock composition,
prevailing climatic condition, pH, the resident time of water within the formation and topography
(Davis and De Wiest, 1966 and Todd 1980). Hydrochemistry can be interpreted to understand
the key processes that have occurred during the movement of water through aquifers. The overall
implication of this is that the hydrochemical facies of groundwater changes in response to its
flow path history.
In this study, the hydrochemical data points are plotted on the base map of the study area using
GIS (Fig.6.1). The study area is subdivided into three physiographic regions, plateau, transition
and rift based on the digital elevation model (DEM) and the geomorphology of the study area.
Samples in each physiographic region are grouped in to two categories, shallow and deep
systems. All water samples from springs, rivers, reservoirs, hand dug wells and wells with depth
77
less than 150m belong to the shallow systems. Wells with depth greater than 150m are
considered as deep systems. Accordingly, in the sample symbology, those preceded by “SH”
represent the shallow and “D” for deep systems in the respective regions. In the sample
presentation, the physiographic regions are denoted by PL, for plateau, T for transition and R for
rift part of the study area. Therefore a symbology denoted by SHPL, for example, represents a
shallow system in the plateau part of the area.
Figure 6.1 Spatial distributions of hydrochemical data points, the red irregular lines mark
physiographic regions used in the interpretation of the hydrochemical and isotope data
78
6.3.2 Graphical representations
The diagnostic chemical properties of water are presented by graphical methods, the most
common of which are the hydrochemical facies, e.g. the Piper (1944) trilinear diagram. This
diagram is useful in screening and sorting large numbers of chemical data, which makes
interpretation easier. Furthermore, a Piper diagram can define the patterns of spatial change in
the water chemistry among geological units, along a line of section or along a flow path (Raji &
Alagbe, 1997). In this study, the results of the chemical analyses of all the original and
complementary secondary data points, in the three defined physiographic zones, are plotted on
Piper diagrams (Fig 6.2) so as to visualize trends in the major ion composition of the water
systems in the study area.
Legend
40
40
I
20
Na+K
HCO3
SO4
80
60
40
B
40
20
40
60
80
Ca
20
A
A
I
A
Mg A
BA
A ACAJ
A
IA
CI
B
J
I
L
J
JB
J
I
J
B
II
J
A
J
J
L
I
L
I
L
C
J
J
J
B
J
C
LI
CL
B
JC
B
C
I
J
C
L
B
L
A
J
I
80
JJ
C
J
C
J
BI
I
B
C
CI
C
C
L
C
C
B
I
B
CCB
A
L
B CB I
B
C
B B
60
LC C
S
C
C SS
I
A
B
C
I
CCB
40
I
S
J
S
CL J
I
S
A
J IL
B IJ J B
S
L
C
L
I
I
J
B
I
J
B
J
B
C
S
L C
L
A AB
CL
J
LI
C
C
J
J
C
I
L
20 AIA
I
J
JIJJJC
BCC
IB C
BB
S
JL
I
A A
CCI
CC
C CI
CLB
B
B
B
B
C
J BB
SS
J AI
CIB
B
B
S
B
B CC
S
I
I
J
C
C
S
J
C
A
S
B
J
B
BCB
I
A
I
C
C
C
J
B
I
C
J
B
B
CS
A
J
S
S
C
BSS
I
B
C
J
C
J
L
JC
C
I
A
C
L
S
B
B
A
C
A
C
B
A
A
L
S
I
J
I
I
L
J
J
J
J
C
C
C
I
C
L
I
J
L
B
L
B
C
C
A
C
S A
S
20
80
20
60
+S
60
g
+M
Cl
60
Deep Rift
Shallow Rift
Deep transition
Shallow transition
Deep plateau
Shallow plateau
S Thermal systems
C
L
J
I
B
A
80
Ca
<=
O4
=>
80
Cl
Figure 6.2 Piper plots of the hydrochemical data showing groundwater facies.
79
Using the Piper plot (Fig. 6.2) and the facies calculator of Aquachem 5.1.33 (Waterloo2006), the
waters of the study area are classified into five major groups of chemical facies based on the
dominant cations and anions: Ca-Mg-HCO3, Ca–Na–HCO3, Na-Ca-Mg-HCO3, Na-HCO3 and
Ca–HCO3. Water groups represented by Ca–Mg-HCO3 (Table 6.1), are associated with the
shallow systems (springs, Rivers and wells of depth less than 150m) in all the three
physiographic regions of the study area. Waters in this group are characterized by dilute
chemistry (TDS<340mg/l) and known to circulate in the upper basaltic aquifer.
Sample ID
SHPL1
SHPL3
SHPL4
SHPL10
SHPL11
SHT49
SHT50
SHR89
SHR95
SHR96
DPL37
SHT39
DT57
DT74
DT76
DT70
DT71
X coordinate
445773
441584
431584
453522
454124
460464
460810
495561
500494
500766
466050
456988
451590
478580
464607
477330
478998
Y coordinate
1001323
1003445
998853
1001474
1005990
974637
981473
968574
974376
973335
993650
952135
954524
976051
973547
976793
977937
Elevation (m)
2389
2453
2313
2565
2629
2090
2224
1906
1914
1894
2371
2065
2117
2083
2134
2067
2098
Well Depth (m)
75
50
0
115
0
61
102
60
81
80
140
114
156
130
135
130
130
TDS (mg/l)
130
170
269
144
172
236
222
303
338
220
280
246
280
291
312
291
303
Table 6.1 Ca-Mg-HCO3 type waters
Samples having chemical facies of type Ca–Na–HCO3 (Table 6.2) are associated with
moderately mineralized waters (TDS< 500mg/l) and are mainly distributed to the transition and
rift part of the study area. This part of the area is characterized by intercalations of acidic
volcanics (rhyolites, ignimbrites, tuff, trachytes and Pyroclastics), where water systems are
tapping the weathered and fractured rocks rich in Ca- and Na- plagioclases which could be
responsible to have such water chemistry.
.
80
Sample ID
SHT40
SHT41
SHT43
SHT44
SHT48
SHT51
SHT53
SHR97
SHR92
SHR98
DPL15
DPL16
DPL31
DT58
DT59
DT60
DT61
DT65
DT69
DT72
DT77
DT80
DT68
DT73
DR102
DR103
DR115
DR121
DR122
DR125
DR127
DR131
DR113
DR116
DR130
Water Type
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-Mg-HCO3
Ca-Na-Mg-HCO3
Ca-Na-Mg-HCO3
Ca-Na-HCO3
Ca-Na-Mg-HCO3
Ca-Na-Mg-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
Ca-Na-HCO3
X coordinate
459470
459658
456740
430267
469142
474800
481600
500494
507714
500424
447549
447200
493518
462554
462875
456314
450359
433200
473576
466662
484475
481200
484152
489950
489485
481162
507950
515449
506765
512957
482579
510700
483243
507950
507861
Y coordinate
942670
946538
962388
987498
966835
984700
982900
974376
955875
974376
1007893
998889
1004421
944481
950361
962592
981037
959670
972821
970715
975622
980000
989566
976019
943724
935226
951364
979174
957179
947774
924575
967092
961360
951364
960487
Elevation (m)
2159
2126
2005
2071
1946
2146
2211
1902
1849
1903
2508
2294
2468
2112
2077
2014
2084
2111
2093
2060
2110
2161
2205
2072
1724
1808
1816
2108
1836
1771
1705
1908
1879
1819
1848
Well Depth (m)
120
103
39
38
0
71
120
81
33
82
203
126
354
127
187
290
280
194
150
148
220
173
182
220
140
200
150
230
278
134
131
384
170
150
324
TDS (mg/l)
278
258
350
380
371
354
380
350
476
397
110
146
242
280
250
360
312
404
305
276
408
385
240
315
310
232
482
478
414
376
240
372
330
439
438
Table 6.2 Ca-Na-HCO3 type waters
The other facies groups are the Na–Ca–HCO3 type waters and are mainly encountered in two
different physiographic regions, the plateau and the rift part of the study area (Table 6.3). In the
plateau part of the study area these waters are associated with deep wells having a diluted
chemistry (TDS<235mg/l). In many groundwater circulation systems strata of high permeability
may be separated by clay or shale layer of much less permeable rocks. These layers can act as
81
semi permeable membranes and thus give rise to anomalous effects on the concentration of
dissolved ions. Sodium is retained by adsorption on mineral surface, especially by minerals
having high cation-exchange capacities such as clays. Cation exchange process in fresh water
systems tend to extract divalent ions such as Ca2+ from solution and to replace them with
monovalent ions such as Na+ (Hem, 1992). The Na+ dominance in some of the borehole samples,
therefore, might indicate the exchange of Ca2+ by Na+, the presence of thin layer of
clay/paleaosols in some of the borehole logs of the plateau area supports this idea (Appendex 1,
Figures 4.2, 4.3 and4.4).
In the rift part of the area this type facies is associated with wells having a moderate
mineralization (TDS<600mg/l). The reason for this could be groundwater evolution along its
flow path. As it is indicated in section 5.3, the general groundwater flow direction is from north
to south, these wells are located at the southern part of the study area. along the flow path, as the
resident time of ground water increases both sodium and potassium could be derived from the
dissolution of silicate minerals, such as plagioclase feldspars, which make up the volcanic rocks
in the area by weathering reactions, as is shown below for the weathering of the Na-feldspar (
albite) to kaolinite (Langmuir 1997).
2NaAlSi3O8 + 2CO2 + 11H2O → Al2Si2O5 (OH)4 +2Na+ + 4H4SiO4 + 2HCO3-
(6.1)
In the process, some minerals, such as calcite CaCO3, can precipitate from solution to form a
solid phase, which could be the reason for the depletion of calcium in the water at it evolves.
82
Sample ID
DPL17
DPL18
DPL20
DPL22
DPL23
DPL25
DPL26
DPL28
DPL34
DT62
SHT42
SHT47
SHR84
SHR85
SHR94
DR104
DR105
DR106
DR108
DR112
DR118
DR126
DR123
DR129
Water Type
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
Na-Ca-HCO3
X coordinate
445987
421795
473900
465410
464031
468875
477515
455620
513157
427126
413137
466690
485970
497100
512011
478740
490444
491980
490336
527667
509020
518458
504878
503928
Y coordinate
1001623
1040108
1001050
1002944
1002909
993750
997474
1026514
1025381
971361
973900
976790
935007
968198
949196
933707
951336
965840
970789
941523
950736
946916
970766
965114
Elevation (m)
2378
2457
2568
2607
2583
2301
2410
2610
2904
2075
2090
2087
1708
1896
1766
1773
1765
1957
1924
1615
1796
1865
1879
1885
Well Depth (m)
330
146
200
124
200
130
216
273
348
308
60
93
98
92
90
155
225
250
282
152
150
214
300
303
TDS (m)
158
158
139
89
172
163
182
152
170
142
798
376
253
306
449
260
364
238
350
470
590
293
348
440
Table 6.3 Na-Ca-HCO3 type waters
Na- HCO3 type waters are found to be associated to three different regions (Table 6.4). Some
diluted chemistry deep wells in the plateau area have this type of water in which a similar reason
could be adopted as those plateau wells having Na-Ca-HCO3 discussed earlier in this section,
likely with intensive ion exchange that replaces the whole calcium in the water. Some wells in
the southern part (rift) of the study area (SHR82, DR107, DR109) are also found to have this
facies which could be an outcome of groundwater evolution along the regional flow path. The
other systems with this facies are the highly mineralized (TDS>1600mg/l) thermal wells which
are located along the Filwuha fault belt of central Addis Ababa (DPL36, SHPL14) and else
where in the study area (DT67, SHR101). From their association to acidic volcanics in place,
these waters might have evolved from Ca–Na–HCO3 and Ca–HCO3 type water-silicic rock
interaction at elevated temperatures where Ca is lost through CaCO3 precipitation. The other
process could be deep circulation and longer residence time which leads to further hydrolysis of
silicate minerals in the Ca–Mg–HCO3 type waters whereby the concentration of Na, K and
83
HCO3 increase. Similar conclusions were made by previous researchers (Kebede et al. 2005 and
Demlie et al 2008)
Sample ID
DPL19
DPL21
DPL27
DPL32
DPL33
DT63
SHPL14
DPL36
DT67
DT66
SHR101
SHR82
DR107
DR109
Water Type
Na-HCO3
Na-HCO3
Na-HCO3
Na-HCO3
Na-HCO3
Na-HCO3-Cl
Na-HCO3
Na-HCO3
Na-HCO3
Na-HCO3-Cl
Na-HCO3
Na-HCO3
Na-HCO3
Na-HCO3
X coordinate
440274
465578
473911
507086
489127
427395
474175
473276
484821
427126
483833
503210
478990
506464
Y coordinate
1006055
999808
1031930
1012954
999697
992768
996550
996535
994284
971361
904045
930527
955803
941989
Elevation (m)
2525
2481
2543
2578
2441
2109
2381
2350
2320
2075
1666
1600
1830
1697
Well Depth (m)
300
193
324
300
280
243
120
504
368
308
52
100
330
268
TDS (mg/l)
164
206
108
235
344
282
2049
2240
1601
572
1184
915
400
578
Table 6.4 Na-HCO3 type waters
Ca–HCO3 type waters are also encountered in the study area (Table 6.5). Based on the
groundwater chemical evolution models, this group represent groundwaters that are recently
recharged and/or contain waters at the early stages of geochemical evolution which have not
undergone significant water–rock interactions (Bartolino et al., 2003). This is further evidenced
by the association of these water types to the shallow systems of the plateau area.
Sample ID
SHPL2
SHPL5
SHPL6
SHPL9
SHPL12
SHT38
Water Type
Ca-HCO3
Ca-HCO3
Ca-HCO3
Ca-HCO3
Ca-HCO3
Ca-HCO3
X coordinate
448532
432432
404656
452124
459689
439632
Y coordinate
1008047
1024464
997733
1002590
998340
993521
Elevation (m)
2525
2603
2230
2629
2592
2186
Well Depth
50
70
81
0
48
39
TDS (mg/l)
174
312
434
148
132
300
Table 6.5 Ca-HCO3 type waters
As illustrated in the graphical representations of the hydrochemical data, the ionic concentration
of the waters of the study area show systematic variation pattern of the chemical facies. The
recharge zones in the plateau have a relatively dilute hydrochemistry which gradually evolves to
84
a relatively concentrated hydrochemical composition as groundwater moves to the transition and
rift zones of the study area along the flow path, which assumes a north-south general direction.
a
.
b
Figure 6.3 Distribution of logEC (µS/cm) (a), excluding the highly mineralized thermal systems
(experimental data fitted with linear variogram model), the green irregular line marks the
boundary of the study area. Plot of elevation vs. TDS of the hydrochemical data set considered in
this study (b).
85
Patterns of electrical conductivity (EC) and total dissolved solids (TDS) (Fig. 6.3) constraint
flow and recharge mechanisms in the study area. Both vary systematically with altitude and
depth. Excluding the thermal systems, the EC of the water systems in the study area (Fig. 6.3a),
generally increases from north to south constraining the flow direction and possible recharge
areas.
6.3.3 Statistical analysis
Early studies on the characterization of groundwater facies and chemical evolutionary history
utilized graphical representations of major ionic composition of groundwater (Piper, 1944; Stiff,
1951; Schoeller, 1962). These schemes were useful in visually describing differences in majorion chemistry in groundwater and classifying water compositions into identifiable groups, which
are usually of similar genetic history (Freeze and Cherry, 1979). Recently, multivariate statistical
analysis has been used with remarkable success as a tool in the study of groundwater chemistry
to support the conventional hydrochemical techniques. An investigation of statistical associations
among dissolved constituents can help in understanding the chemical composition of natural
waters. A statistical association does not establish any cause-and-effect relationship, but it
frequently presents data in a way that such relationships can be inferred (Drever 1997).The aim
of multivariate analysis is to interpret the governing processes through data reduction and
classification. The effectiveness of this method in groundwater chemistry discrimination over the
traditional piper and stiffs schemes stems from its ability to reveal hidden inter-variable
relationships and allows the use of virtually limitless numbers of variable, thus trace elements
and physical parameters can be part of the classification parameters.
In this study, STATISTICA data analysis software version 8.0 (Stat Soft Inc. 2008), commercial
software package was used for the descriptive statistics (Table 6.6), correlation matrix of
variables (Table 6.7) and multivariate statistical analysis (hierarchical cluster analysis (HCA),
factor and principal component analysis).
For the correlation matrix of the variables presented in Table 6.7, it holds the general truth that,
the higher the absolute value of the correlation coefficient, the closer the relation; if the value is
86
positive, the relation is “positive” (high values of one variable correspond to high values of the
other variable; likewise, low values of one variable correspond to low values of the other
variable). If the value is negative, the opposite is true (low values of one variable correspond to
high values of the other variable).
Variables
Minimum Maximum Mean Range Variance Std.Dev. Coef.Var. Standard - Error
*
EC
164.0
3380.0 638.1 3216.0 333036.0
577.1
90.4
61.2
@
TDS
108.0
2240.0 409.6 2132.0 136935.4
370.0
90.3
39.2
PH
6.0
9.0
7.6
3.0
0.5
0.7
9.1
0.1
@
Na
5.0
930.0
88.8
925.0
23264.8
152.5
171.8
16.2
K
1.0
49.0
9.1
48.0
61.2
7.8
86.0
0.8
Ca
3.0
118.0
42.4
115.0
709.5
26.6
62.9
2.8
Mg
0.0
40.3
11.1
40.3
72.4
8.5
76.6
0.9
Cl
0.0
122.0
15.9
122.0
386.4
19.7
123.3
2.1
NO3
0.0
101.0
6.6
101.0
142.5
11.9
181.4
1.3
F
0.0
28.0
2.2
28.0
24.6
5.0
225.7
0.5
@
HCO3
70.0
2198.0 365.3 2128.0 115773.2
340.3
93.2
36.1
CO3
0.0
102.0
7.4
102.0
553.4
23.5
316.3
2.5
SO4
0.0
113.0
11.8
113.0
510.1
22.6
191.2
2.4
@are characterized by higher standard deviations and standard errors because of big variations in the values
of these variables between thermal and cold groundwaters
@
Table 6.6 Descriptive statistics of hydrochemical variables for the entire data set (n=89)
considered in this study (EC in µS/cm and all others in mg/l)
Variables
Elev Well_Depth Cond TDS PH
Elev (m)
1
NA
K
CA
MG
CL
NO3
F
HCO3 CO3 SO4
Well_Depth (m) 0.11 1
EC (µS/cm)
-0.26 0.12
1
TDS
-0.26 0.13
0.99 1
PH
-0.04 0.33
0.23 0.21 1
Na
-0.14 0.18
0.96 0.95 0.32 1
K
-0.46 0.31
0.52 0.55 0.01 0.43 1
Ca
-0.22 -0.18
-0.08 -0.06 -0.37 -0.29 0.08 1
Mg
-0.29 -0.15
0.07 0.08 -0.32 -0.13 0.29 0.63 1
Cl
-0.29 -0.08
0.52 0.51 0.16 0.50 0.43 -0.13 -0.07 1
NO3
-0.10 -0.05
0.06 0.08 -0.30 -0.04 0.38 0.38 0.41 -0.01 1
F
-0.18 0.03
0.77 0.76 0.28 0.81 0.18 -0.33 -0.28 0.38 -0.13 1
HCO3
-0.23 0.12
0.97 0.97 0.15 0.92 0.54 0.02 0.16 0.44 0.12 0.69 1
CO3
-0.08 0.46
0.28 0.30 0.06 0.27 0.33 0.04 0.19 0.03 0.30 0.19 0.29
1
SO4
0.01 0.30
0.69 0.71 0.12 0.66 0.57 -0.02 0.15 0.40 0.24 0.35 0.65
0.31 1
Table 6.7 Correlation matrix of variables of the hydrochemical data set considered in the study,
significant correlations are marked (values in mg/l unless indicated)
87
6.3.3.1 Hierarchical Cluster Analysis (HCA)
The term cluster analysis encompasses a number of different algorithms and methods for
grouping objects of similar kind into respective categories (Blashfield, 1980). Clustering is the
assignment of a set of observations into subsets (called clusters) so that observations in the same
cluster are similar in some sense.
The clusters could be arrived at either from weeding out dissimilar observations (divisive method) or
joining together similar observations (agglomerative method). Most common statistical packages use
agglomerative method. Hierarchical algorithms find successive clusters using previously
established clusters. Since the input variables are combined via a distance function (Euclidean
distance) in HCA, the contribution of an input will depend heavily on its variability relative to
other inputs. If one input has a narrow range of 0 to 1, while another input has a wider range of 0
to 10, 000 or so, then the contribution of the first input to the distance will be swamped by the
second input. To avoid this, all hydrochemical data were standardized to their z-scores so that
each variable measured in different units were rescaled to a similar range of 0 to 1 to have equal
weight for the HCA. Using standard scores (z-scores), it is possible to compare scores from
different distributions where measurement was based on a different scale (Brown 1998).
The 9 hydrochemical variables (TDS, pH, Ca, Mg, Na, K, Cl, SO4 and HCO3) were used in the
multivariate statistical analysis. All available hydrochemical data (n=89) having complete
analysis of all these variables were classified by HCA in 9-dimensional space and the result is
presented as a dendrogram (Fig. 6.4). Two broad groups (with large linkage distance between
them) are selected by visual inspection of the dendrogram, representing cold (low TDS) and
thermal (high TDS) systems respectively. The second group (cold systems) is further classified
into four sub-groups representing different recharge, residence time and degree of rock-water
interactions. Mean chemical composition of each cluster group is presented in Table 6.8.
Descriptions of each subgroup interns of lithology, location in the basin and resident time and
degree of rock-water interaction is presented in Table 6.9.
88
Cluster group
I (n=13)
II-1 (n=14)
II-2 (n=21)
II-3 (n=14)
II-4 (n=27)
*EC
1677.3
320.1
502.9
285.0
590.9
TDS
1079.5
196.1
327.0
180.6
380.6
pH
7.8
8.5
8.0
6.9
7.3
Na
358.7
55.7
50.0
15.6
44.1
K
19.5
3.3
11.3
3.3
8.4
Ca
29.8
12.9
43.9
38.5
64.5
Mg
9.8
3.3
10.2
7.1
18.5
Cl
53.2
12.2
10.2
4.6
10.3
NO3
1.3
1.8
5.5
6.8
8.7
F
9.7
0.8
1.1
0.7
0.9
HCO3
917.7
128.4
302.8
175.4
369.1
CO3
8.4
8.6
1.7
0.0
0.0
SO4
49.9
11.2
3.0
2.0
5.8
Table 6.8 Mean hydrochemical composition of cluster groups and sub-groups (*(µS/cm) and all
others except pH in mg/l)
Figure 6.4 Dendrogram of the hydrochemical data (some samples /cases are not shown for ease
of presentation)
89
Cluster group
Group I
Interpretation
This group has high EC, TDS (800-2250mg/l) and HCO3. Group members are
thermal groundwaters and are exclusively Na-Hco3 type. In the north central part of
the study area, the wells are located in the Filwuha fault belt of central Addis
Ababa.
Group II-1
All the samples clustered in this sub-group are deep wells exclusively draining the
plateau part of the study area having a diluted chemistry (TDS<235mg/l). The
plateau part of the study area is dominated by basaltic rock out crops and thin
paleaosol intercalations during drilling. Most of the members are Na-Ca-HCO3 and
few samples are Na-Hco3 type waters.
Group II-2
Groundwater draining the transitional and rift part of the study area. They are
moderately mineralized (TDS<470>238mg/l) draining the transitional and rift part
of the study area. They area Ca-Na-HCO3 type in the transition and Na-Ca- HCO3
in the rift part of the study area constraining rift ward evolution along the flow path
Group II-3
This group of waters are mostly associated with shallow systems of the plateau area
and have diluted chemistry (TDS<200mg/l). Few samples of shallow groundwater
systems from the transitional and rift part of the study area are also found being
clustered in this group. They are Ca-HCO3 type waters. Their typical association
with the shallow systems and the water facies signifies their direct contact with the
recent recharge and/or early stage of chemical evolution
Group II-4
Moderately diluted groundwater systems (TDS<450>130mg/l) found scattered in all
the three physiographic regions of the study area. The majority of the samples are
Ca-Mg-HCO3, in the transition and rift part of the study area, wells near scoria
cones are found to have such water facies signifying the basic volcanic influence on
the water chemistry.
Table 6.9 Summary of interpretation for the HCA groups and sub-groups
+
2+
For the Na-Ca-CHO3 type group II-1 and group II-2 waters a scatter plot of Na versus Ca
+
is
2+
made (Fig. 6.5). In the case of group II-1 waters, Na show negative correlation with Ca
2+
(Fig.6.5 (a)), which suggests that the ion exchange of Ca
+
for Na is likely to be the main
90
+
process supplying Na for the deep well waters of the plateau area. For the Na-Ca-CHO3 type
+
2+
waters of group II-2, Na does not show strong negative correlation with Ca
2+
(Fig.6.5 (b)),
+
which suggests that not the ion exchange of Ca for Na but dissolution of Na- rich plagioclases
+
such as albite likely to be the main process supplying Na for the waters in this group.
a
30
Ca (mg/l)
25
20
15
10
5
0
0
50
b
100
200
Na (mg/l)
90
80
70
Ca (mg/l)
150
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
Na (mg/l)
Figure 6.5 Scatter plot of Na versus Ca for group II-1 (a) and group II-2 (b) waters
6.3.3.2 Principal component factor analysis
Factor analysis is a mathematical method used to form a subset of uncorrelated theoretical
variables (or factors) that adequately explains the variation in the original variable set (Brown
1998). Factor analysis assumes that observed variables are products of linear combinations of
some few underlying sources variables known as factors. It therefore attempts to find out these
91
factors, which can explain a large amount of the variance of the analytical data. In factor analysis
a form of principal component analysis is used, transforming the set of variables into factors
which successively extract a maximal part of the variance in the data set (Wackernagel, 1995). A
factor is a weighted combination of variables, defined in such a way that the variance attached to
it is maximized. The variance attached to a factor is described by the factor’s eigenvalue. An
eigenvalue gives a measure of the significance of the factor, the factors with the highest
eigenvalues are the most significant and Eigenvalues of 1.0 or greater are considered significant
(Kim and Mueller, 1987). Accordingly factor analysis was applied to the data to extract related
variables or factors. From the pattern of relationships among these variables, an interpretation of
the processes responsible for these relationships was made.
Factor analysis was performed on n=89 data sets so as to reduce the number of variables into
more important variable groups (factors). These factors may explain the underlying
hydrochemical processes that gave rise to the observed chemistry and pattern of variable
relationships. From the variance (eigenvalue) attached to each factors (Table 6.10), only two
factors have got values greater than 1, hence these two factors (factors 1 and 2) will be used to
explain the pattern of relationship of the variables and the process responsible to the relationship
will be discussed. The two factors explain 69.7% of the variance of the original data set: factor 1,
which is associated with the variables EC, TDS, K, Na, HCO3, SO4 and in some extent with Cl
explains 47% of the variance; factor 2, which is composed of the variables Ca and Mg and in
some extent with pH explains 22.6% of the variance (Fig. 6.5 and Table 6.11). Figure 6.6
presents the projection of the cases (samples) on the factor plane, the two principal components
of the hydrochemical data set (the two broad water groups) identified by HCA are well captured
again here.
92
Factors
Eigenvalue
% Total - variance
Cumulative - %
1
4.236251
47.06945
47.0695
2
2.034909
22.61010
69.6796
3
0.755157
8.39063
78.0702
4
0.683430
7.59367
85.6639
5
0.562385
6.24872
91.9126
6
0.370313
4.11459
96.0272
7
0.326440
3.62711
99.6543
8
0.020829
0.23143
99.8857
9
0.010287
0.11430
100.0000
Table 6.10 Proportion of a variable’s variance explained by a factor structure for the
hydrochemical data set
Figure 6.5 projections of the variables on the factor plane (1x2)
93
Factor - 1
Variables
TDS
PH
Na
K
CA
Mg
Cl
HCO3
SO4
0.960451
0.200837
0.903482
0.704113
-0.031044
0.161509
0.611085
0.935694
0.810741
Factor - 2
-0.073168
-0.635298
-0.320054
0.252136
0.852205
0.853158
-0.176016
0.018616
0.050815
Table 6.11 Factor loadings (Varimax normalized, marked loadings are >0.7)
The association EC, TDS, K, Na, HCO3, SO4 and Cl in factor 1 can be explained as the
contribution of those major ions to the groundwater concentration. The second factor has a
higher loading for Ca, Mg and in some extent to pH. The samples contributing to this factor are
waters tapping the basaltic aquifers in the plateau and elsewhere in the study area, which are
characterized by Ca and Mg rich minerals such as olivine and plagioclases. In addition to this,
pH is negatively correlated with both Ca and Mg (Table 6.6); hence these waters are associated
with relatively lower pH conditions.
Figure 6.6 projections of the cases (samples) on the factor plane (1x2)
94
6.3.4 Geochemical modeling
The geochemical evolution of groundwater begins when rain water infiltrates the soil. Carbon
dioxide present in the atmosphere dissolves in rain water and forms aqueous CO2, which
associates with water molecules to form carbonic acid, H2CO3. Production of CO2 occurs also in
the subsoil vadoze zone and shallow saturated zones by decay of organic matter, respiration of
plant root and microbiological communities (Keller, 1991; Bennet & Rogers, 2000). The
production of CO2 and H2CO3, and release of H+ ions to solution are important initial processes
for water – rock interaction (Paces, 1972). The rain water that infiltrates the soil and bedrock is
low in dissolved solids, slightly acidic and is undersaturated with most, if not all, common
minerals (Amorson, 1999). Water – rock interaction processes then release ions to solution.
Geochemical models are tools that aid in the interpretation of geochemical reactions.
Geochemical models can be used for a variety of purposes including determination of the
prevailing geochemical reactions, quantification of the extent to which these reactions occur,
predictions of the fate of contaminants and estimation of the direction and rates of groundwater
flow (Alley, 1993). Plummer et al (1983) divided geochemical modeling into two general
approaches: (1) inverse modeling, which uses observed groundwater composition to deduce
geochemical reactions; and (2) forward modeling, which uses hypothesized geochemical
reactions to predict groundwater composition. Inverse modeling produces quantitative
geochemical reactions that describe the chemical evolution in a groundwater system, whereas
forward modeling begins at some starting composition and simulates the chemical evolution of
groundwater in response to sets of specified reactions. The purpose of inverse modeling is to
determine net chemical reactions that quantitatively account for the chemical and isotopic
composition of water samples. The modeling is typically applied to water samples from two
sources that are assumed to be on the same flow path in order to determine the net chemical
reaction that has occurred between these two sources (Deutsch, 1997). In areas like Upper
Awash, where chemical and isotopic data are available, inverse modeling is the most efficient
approach because its result reproduces the chemistry of the available samples and accounts for
the chemical changes as water evolves along a flow path.
95
Using conventional and multivariate statistical techniques, as explained in the previous sections
of this chapter, it was possible to identify groundwater facies, the type of lithology through
which they have been circulating and the degree of water-rock interaction. To further constraint
these results and the chemical changes that occurred along the flow path, the plateau via
transition to the rift part of the basin in the general north to south direction, an inverse
geochemical modeling was made. Inverse geochemical modeling was carried out using
PHREEQC (Parkhurst and Appelo, 1999). The flow paths were selected based on the pattern of
variation of the hydrochemistry, the physiographic location of the sample in the basin and the
hydraulic head variation within the regional flow system. During the modeling it is assumed that
groundwater flows from north to south, aquifer units are hydraulically interconnected and
hydrochemical evolution is from north to south in the direction of the regional flow.
Through equilibrium mass balance and saturation indices calculations from known composition
of the aquifers, the process of water-rock interaction in Upper Awash basin is explained. Initial
(I) input of groundwater for all the four paths considered in the inverse geochemical model is
taken from HCA subgroup II-3, plateau area diluted chemistry Ca-CHO3 type waters. Path 1 (P1)
is diluted chemistry deep well of plateau area Na-Ca-CHO3 type waters; path 2 (P2), Ca-NaHCO3 type waters draining the transitional and rift part of the study area; path3 (P3), Na-CaHCO3 type waters draining the transitional and rift part of the study area (P4) from thermal
systems (Table 6.12). The results of the inverse geochemical modeling are presented in Table
6.13.
paths
I (n=8)
P1 (10)
P2 (8)
P3 (11)
P4 (9)
TDS
178.1
204.8
418.9
339.2
1342.7
pH
6.9
8.5
7.3
8.0
8.1
Na
9.3
57.9
50.0
62.5
460.0
K
2.4
4
10.9
15.1
20.7
Ca
43.1
13.7
72.9
37.4
34.1
Mg
8.3
3.6
16.0
9.7
12.5
Cl
4.0
11.5
8.5
10.7
54.0
NO3
9.9
1.6
5.4
5.0
1.1
F
0.6
0.8
1.0
1.2
12.3
HCO3
175.1
131
417.6
321.5
1174.2
CO3
0.0
8.7
0.0
1.6
12.6
SO4
1.4
13.2
4.0
3.4
59.1
Table 6.12 Mean hydrochemical parameters input of the initial and end members of groundwater
used in the inverse geochemical modeling, values in mg/l and the number of members of each
subgroup is indicated in parenthesis.
96
The mineral phase selection was done based on the geology of the study area and the prevailing
rock weathering reactions. In areas like study area, the Upper Awash basin,
which is
characterized by complex intercalations of volcanic lithology, rocks such as basalt contain
appreciable amounts of aluminosilicate minerals (Plagioclase, k-feldspar, olivine, pyroxene,
amphiboles), which are formed at temperatures far above those near land surfaces. These
minerals are therefore thermodynamically unstable and dissolve or weather to clays and oxides
when in contact with water (Freeze and Chery 1979; Deutsch, 1997). The susceptibility of
primary silicate minerals to weathering is related to their position in Bowen’s (1928) reaction
series (Goldich 1938). Secondary minerals such as clays (e.g. kaolinite, montmorillonite and
illite) and Al- and Fe- oxides (e.g. gibbsite, goethite and hematite) are formed during silicate
mineral weathering processes. These clays and oxides formed by incongruent dissolution of
aluminosilicate minerals, whereby the ratio of the elements that appear in solution is different to
that in the dissolving mineral (Appelo and Postma 1996). The removal of dissolved constituents
by the precipitation of secondary minerals ensures that the water remains undersaturated with
respect to the primary minerals; the primary minerals therefore continue to dissolve and
secondary minerals precipitate (Amorsson, 1999). Other mineral phases such as calcite found to
occur as cavity fillings in volcanic rocks as a result of precipitation during plagioclase and
pyroxene weathering. Similarly, anhydrite and gypsum could occur and fill amygdaloidal
cavities of volcanic rocks and/or on the surface of clay materials.
The inverse geochemical modeling shows that the dissolution of pyroxenes, olivine, plagioclases,
K-feldspars, K-micas and gaseous CO2, and precipitation of calcite, chalcedony and clay
minerals are the major chemical processes to derive the observed natural groundwater chemistry
in the study area.
97
Flow
path
I-P1
I-P2
Reactants
Mole
Transfer
Reactions
Anorthite (CaAl2Si2O8)
Forsterite (Mg2SiO4)
Na-Montmorillonite (Na0.5Al1.5
Mg0.5Si4O10OH)2
Calcite (CaCO3)
CO2(g)
K-feldspar (KAlSi3O8)
Anorthite (CaAl2Si2O8)
-6.878
-9.552
-3.203
(Ca-HCO3 water) + plagioclase + olivine +
Albite (NaAlSi3O8)
Diopside (CaMgSi2O6)
Calcite (CaCO3)
CO2(g)
K-feldspar (KAlSi3O8)
Kaolinite (Al2Si2O5(OH)4)
K-mica (KAl3Si3O10(OH)2)
Anorthite (CaAl2Si2O8)
I-P3
I-P4
Albite (NaAlSi3O8)
Chalcedony (SiO2)
Calcite (CaCO3)
CO2(g)
K-feldspar (KAlSi3O8)
Kaolinite (Al2Si2O5(OH)4)
K-mica (KAl3Si3O10(OH)2)
Anorthite (CaAl2Si2O8)
Diopside (CaMgSi2O6)
Albite (NaAlSi3O8)
Chalcedony (SiO2)
Calcite (CaCO3)
CO2(g)
K-feldspar (KAlSi3O8)
Kaolinite (Al2Si2O5(OH)4)
5.551
-5.551
-2.227
-5.77
-1.23
-6.167
5.67
-5.67
-3.80
6.48
-6.17
-2.39
-3.54
1.53
5.67
-5.67
-3.80
6.48
-6.17
-7.30
-7.30
-5.21
1.53
7.30
-7.30
-5.71
9.52
clay + CO2(g)+ K-feldspar
(Na-Ca-CHO3 water) + Calcite
(Ca-HCO3 water) + plagioclase +
pyroxene+ K-mica + CO2(g)+ K-feldspar
(Ca-Na-CHO3 water) + Calcite + clay
(Ca-HCO3 water) + plagioclase + K-mica +
CO2(g)+ K-feldspar
(Na-Ca-CHO3 water) + Calcite + clay
+ Chalcedony
(Ca-HCO3 water) + plagioclase + pyroxene
+ CO2(g)+ K-feldspar
(Na-CHO3 water) +
Chalcedony + Calcite + clay
Table 6.13 Results of inverse geochemical modeling for the proposed paths
6.4 Trace elements
6.4.1 General
There is no general acceptance of what level of concentration should define trace elements,
especially now that there are very sensitive instruments that can determine concentrations of
most elements to a few ppb levels. Basically trace elements are those of concentration range less
98
than 0.01 to 100 µg /l (Ward, 1995). In geochemistry, a trace element is a chemical element
whose concentration is less than 1000 ppm or 0.1% of a rock's composition (wikipedia.com).
Groundwater flow influences hydrochemical patterns because flow carries the chemical imprints
of biological and anthropogenic changes in the recharge area, and leaches the aquifer system.
Chemical weathering of crustal rocks is one of the principal processes controlling the
geochemical cycle of elements at the Earth surface. Chemical erosion consumes atmospheric
and/or endogenous carbon dioxide and extracts metals from the rocks, these latter being released
to groundwaters (Garrels, 1967). Usually groundwater is not treated but provided raw in its
original form. It is usually taken for granted that the water is clean because it is found deep
below the earth, since pathogenic bacteria and other visible disease causing organisms cannot
survive at very great depths below the earth and therefore the water is considered safe for
consumption. Even though, these pathogens may not be available at these depths, there are other
substances which may not depend on depths of groundwater but are equally dangerous when
taken in excess. These substances include inorganic elements which are either metals or
nonmetals and are derived from the rocks, soils and/or anthropogenic sources. It has been
established that various trace elements affect health of living organisms (WHO, 2008). But the
extent to which these elements affect health of living organisms depends on the chemical
characteristics and the concentration of the element in the water consumed.
From the trace element analyses result of this study, an attempt is made to: 1) investigate the
concentration pattern of trace metals and metalloids with respect to groundwater quality; 2)
investigate the possible geochemical processes that can influence the distribution and mobility of
the trace element composition in the groundwater flow system so as to better understand the
geochemical evolution of the groundwater in the studied aquifers
6.4.2 Water Quality aspects
In order to investigate the concentration of trace elements which can affect human health, the
first task was to make comparisons with the world health organization (WHO) guidelines (Table
6.14). Trace element analysis was made for deep groundwater sources and the result is presented
99
in appendix 3.The result indicate that the concentration of almost all trace elements except boron
(B), aluminum (Al) and arsenic (As) is either low or within the permissible limits of the world
health organization guideline values for drinking water (WHO, 2008). All the samples are found
to be extremely enriched with respect of Aluminum and boron. 79% of the samples analyzed
have arsenic concentrations above WHO guideline values for drinking water. Sample DT47
(Dimajelewa), is found exceptionally rich in manganese (Mn), zinc(Zn) and barium (Ba) having
values 6619, 29600 and 829.4 µg/l respectively and exceptionally low in its arsenic value(0.3
µg/l). Samples DPL29 (Shegole) and DR72 (Abusera) are also enriched with respect of zinc
having values 4139 and 1967 µg/l respectively.
Trace element
Minimum
Maximum
Mean
St.dev
WHO
guideline
0.9
204.2
25.3
47.5
Lithium (Li)
529.6
1208.0
832.9
202.4
Boron (B)
510
286.1
1711.0
1103.6
397.8
Aluminum (Al)
200
0.7
418.2
49.7
109.0
Titanium (Ti)
0.2
22.6
6.4
6.4
Vanadium (V)
2.2
22.1
6.6
4.8
Chromium (Cr)
50
10.7
179.6
53.5
41.9
Manganese (Mn)
400
0.2
22.7
1.8
5.2
Cobalt (Co)
2.5
42.9
10.9
10.9
Nickel (Ni)
70
3.2
52.5
17.2
15.7
Copper (Cu)
2000
41.4
829.4
274.8
250.2
Zinc (Zn)
0.3
39.9
17.9
11.3
Arsenic (As)
10
2.2
69.5
19.1
18.7
Rubidium (Rb)
13.4
632.9
220.6
164.1
Strontium (Sr)
29.6
5.4
7.2
Molybdenum (Mo) 0.2
70
0.0
0.7
0.1
0.2
Cadmium (Cd)
3
0.0
0.8
0.2
0.2
cesium (Cs)
95.5
395.1
158.2
174.2
700
Barium (Ba)
1.6
26.8
6.1
6.2
Lead (Pb)
10
0.1
4.6
1.3
1.6
Uranium (U)
15
Table 6.14 Descriptive statistics of trace element concentrations and their comparison with the
WHO guidelines (measurement units in µg/l unless otherwise indicated). Some outliers (sample
DT47 in case of Mn, Zn and Ba; DPL29 and DR72 in the case of Zn,) are excluded in the
calculation of the descriptive statistics but discussed in the text
100
Arsenic
Numerous aquifers worldwide carry soluble arsenic at concentrations greater than the World
Health Organization recommended drinking water standard of 10 µg per liter. Sources include
both natural (bed rock geology) and anthropogenic (mining activities, livestock feed additives,
pesticides, and arsenic trioxide wastes and stockpiles). Increased solubility and mobility of
arsenic is promoted by high pH (>8.5), competing oxyanions, and reducing conditions
(Robertson 1989).Volcanic rocks and sediments derived from them are associated with elevated
arsenic levels in ground water (Hem, 1992; Robertson, 1989). This may be true not because
volcanic rocks contain more arsenic than other types of rocks, but because the arsenic is more
readily mobilized from volcanic rocks and derived sediments, as groundwater flows through
such systems, weathering reactions remove dissolved CO2, increasing water pH, as pH increases,
arsenic becomes more soluble.
In the study area concentration of arsenic tends to increase along the general groundwater flow
direction from the plateau to the rift (Fig.6.7 and Table 6.15), but it also seems to be controlled
by the pH-Eh condition of the area. This is evidenced by sample from Dimajelewa well located
in the transitional part (between the plateau and the rift), where a unique pH-Eh condition is
observed during field insitu measurement. The pH of the well was relatively low (6.5) and the Eh
was 12.6 being the only well among the sampled deep wells having a positive valve (oxidizing
condition), all the others show reducing conditions (negative Eh). The arsenic concentration of
this well is low (0.3µg/l). From the arsenic concentration and pH trends of Figure 6.7, it is
possible to conclude that in addition to the bed rock geology, the pH-Eh condition plays a role on
the arsenic concentration of the groundwater.
So far, high arsenic concentrations above WHO guidelines are not reported for the groundwater
systems (springs, wells, rivers etc.) in the study area., arsenic concentrations in the groundwaters
of Akaki catchment, one of the major tributary of the Upper Awash basin, are reported to be
less than 3µg/l (Demlie, 2008). This study is the first of its kind to sample the deep aquifer from
the exploratory wells which were drilled related to the ABGREP project in Upper Blue Nile and
Awash river basins, so the result of the trace element analysis which shows high arsenic
concentrations above the WHO guideline in most of the wells tapping the lower aquifer is a
101
signal so as to consider the deep aquifer not only in terms of potential but also with respect to
water quality for different purposes (drinking, agricultural, industrial). The average analytical
accuracy during analysis of Arsenic was ±0.2µg/l.
Modjo
D/zeit
Dukem
Melkaku
Jewalo
Dimajel
Holeta
30
25
20
15
10
5
0
As conc.
(microgram/litre)
As
9
8.5
8
7.5
7
6.5
6
Inchini
pH
pH
Water wells
Figure 6.7 Arsenic concentrations versus pH plot for wells along the NNW-SSE (Inchini-Modjo)
direction
Well Names
pH
Eh
Temp (ºC)
As (µg/l)
TDS (mg/l)
Inchini
8.3
-12.9
28.5
160
9.2
Holeta
8.8
-45.4
28.8
238
12.3
Dimajelewa
6.5
12.6
23
192
0.3
Jewalokora
7.3
-33.6
24.2
394
18.5
Melkakunture
7.3
-98.7
30.8
572
16.2
Dukem
7.5
-33.7
27.4
360
18.1
D/zeit
7.9
-69.3
23.6
540
24.1
Modjo
7.5
-44.7
25
434
19.2
Table 6.15 Arsenic concentrations, pH-Eh of some selected water wells falling on the NNW-SSE
direction in the study area
Boron
Present and future availability of drinking and irrigation water has become fundamental for the
economic and social development of Ethiopia. The problem is more serious because Ethiopia is a
country repeatedly affected by series of droughts and now a day in addition to the use of
102
groundwater for water supply purposes there is a great need to use groundwater for irrigation for
food security purposes.
In semi-arid regions like the study area, groundwater used for irrigation contains high levels of
boron related to the origin and composition of the background lithology (Lubick, 2004).
Naturally occurring boron is present in groundwater primarily as a result of leaching from rocks
and soils containing borates and borosilicates (Goldberg, 1997). Anthropogenic sources of boron
are related to release of boron containing products into water through municipal sewage systems
(because borate compounds are ingredients of domestic washing agents) and mining and
processing of boron minerals (WHO, 2008; Fox, 2002). Concentrations of boron in groundwater
throughout the world range widely, from <0.3 to >100 mg/l (WHO, 2008).
When humans consume large amounts of boron-containing food or drinking water, the boron
concentrations in their bodies may rise to levels that can cause health problems. Boron can infect
the stomach, liver, kidneys and brains and can eventually lead to death; therefore a guideline
value of 510µg/l is designated in drinking water (WHO, 2008).
.
Boron concentration in irrigation water is of particular interest because of its beneficial and toxic
effects in plants. Boron deficiency is an uncommon disorder affecting plants growing in deficient
soils and is often associated with areas of high rainfall and leached soils. The range between
deficiency and toxicity symptoms is narrow, necessitating accurate quantification. For the
majority of plants boron concentrations between 150-500µg/l are desired. Depending on plant
sensitivity boron can be toxic at soil test concentrations above 1000µg/l (Nable, 1997).
All the samples analyzed have boron concentrations above the WHO guideline values for
drinking water; more than 80% have concentrations less than 1000µg/l, and 4 samples have got
concentrations greater than 1000µg/l. Therefore, the quality of the deep groundwater has to be
taken into consideration, particularly with respect to boron which is very crucial for using the
water for irrigation purposes. As it is previously mentioned, the knowledge on the distribution
and concentrations of toxic trace elements, particularly in the deep aquifer system, is inadequate.
So, further investigation has to be made to rectify the findings of this study particularly on the
103
concentrations of toxic trace elements such as boron and arsenic. Therefore, it is highly
recommended to undertake further investigation to validate this finding for the high
concentrations of boron in the samples owing to the fact that boron is not expected to be high in
surrounding rocks and soils of the study area, of course, this has to be also further supported by
analyzing the rocks and soils for their toxic trace element contents.
Aluminum
Health effects of Al have been linked to the Alzheimer disease, a neurological illness which
results in brain damage causing forgetfulness (WHO, 2008). Elevated Al concentrations could
also be e toxic to plant roots (Bohn, 1985). Owing to data limitations on its effect on humans a
health based guideline cannot be derived but 200µg/l or less is indicated as practicable level
(WHO, 2008).
Aluminum usually exists as hydrated form as Al(H2O6)3+, the other ionic species of Al form
hydroxy complexes whose stability is dependent on pH that is the stability increases with rising
pH of the water (Drever, 1992). pH and Al concentration trend is presented in Figure 6.8 The
concentration of Al will depend on the availability and extent of weathering of the
aluminosilicates such as clays, pyrophyllites, feldspars, micas and other related minerals. High
Al concentrations therefore could probably be due to a more intensive weathering of the
surrounding volcanic rock minerals such as feldspars and mica. All the samples analyzed have
Al concentrations above the WHO guideline values for drinking water. Al showed similar
concentration and pH trends (Fig.6.8) as that of boron in the study area. Due to very high
concentrations in all of the samples analyzed, Similar to that of boron the author recommends
further investigations, otherwise this is the first observation as far as the trace element
concentrations in the deep aquifers is concerned. The average analytical accuracy was ±12.5 and
8.7µg/l for boron and Aluminum respectively.
104
Al
10
2000.0
1750.0
1500.0
1250.0
1000.0
750.0
500.0
250.0
pH
9
8
7
DPL15
DPL16
DPL21
DPL22
DPL23
DPL29
SHT37
DT43
DT44
DT46
DT47
DT48
DT49
DT50
DR71
DR72
DR73
DR81
DR82
6
Al conce.
(micrograms/litre)
pH
Samples
Figure 6.8 Aluminum concentration and pH trends in the study area
Zinc
The concentration of zinc is found to be high in 3 samples, but as zinc is used as an anticorrosion
agent where it is coated on iron casings, pipes and pump materials to protect them against
corrosion, could also lead to Zn being released into the groundwater. This is evidenced in this
study by the high Zn concentration sample (DPL29, Shegole) from an operational well with all
the pipes, fittings and pump parts in place. All the other sampled wells were exploratory wells
sealed after drilling but still with metal casings installed. Sample DT47 (Dimajelewa), is found
exceptionally rich not only in zinc but also in manganese and also some extent in barium, this
could be related to the exceptional Eh-pH condition in that part of the aquifer which has got
relatively lower pH and oxidizing condition.
105
6.4.3 Correlation of trace and major element compositions
In this study, as it is mentioned earlier, in addition to the water quality aspects, the trace element
analysis result is also interpreted to understand the possible geochemical processes that can
influence the distribution and mobility of the trace element composition in the groundwater flow
system. From the trace element analyses result, it was established that the groundwater quality is
not uniform throughout the study area. This means that different processes can influence the
distribution and mobility of the trace element composition in the groundwater from different
depths or locations. In order to investigate geochemical processes which may influence the trace
element distribution in groundwater of the study area, an attempt made to separate the different
water samples from each other.
In this respect an attempt is made to plot some hydrochemical parameters such as TDS and major
element concentrations versus trace elements in order to see the trends with respect to each other
owing the fact that fluxes of chemical elements during chemical processes closely depend on the
instability of primary minerals with respect to the solution, and on the formation of secondary
minerals. Representative plots of major ions and/or hydrochemical parameters versus trace
element concentrations are presented based on similarity in chemical behaviors of these elements
(Fig. 6.9). For example, concentration trend of aluminum is found to resemble that of boron
(Fig.6.9(a)) and lithium that of rubidium (Fig. 6.9(b)) owning the fact that these elements are
members of the same group in the periodic table which show similar patterns in their electron
configuration, especially the outermost shells resulting similar trends in chemical behavior
(IOUPAC, 1985).
Since the analyzed trace elements are quite many, (n=20), basic statistics (correlation) and
multivariate statistical methods were also used so as discriminate the samples from each other.
As it is explained earlier, the aim of multivariate analysis is to interpret the governing processes
through data reduction and classification. The effectiveness of this method stems from its ability
to reveal hidden inter-variable relationships and allows the use of virtually limitless numbers of
variables. The correlation matrix of the trace elements, TDS, pH and some major ions is
presented in Table 6.16.
106
Variables pH
pH
TDS
NA
CA
CL
HCO3
Li
B
Al
Ti
V
Cr
Mn
Co
Ni
Cu
Zn
As
Rb
Sr
Mo
Cd
Cs
Ba
Pb
U
1.00
TDS
-0.56 1.00
NA
-0.28 0.62 1.00
CA
-0.64 0.47 -0.12 1.00
CL
-0.09 0.47 0.51 -0.36 1.00
HCO3
-0.53 0.76 0.90 0.30 0.26 1.00
Li
-0.49 0.71 0.68 0.37 0.12 0.83
1.00
0.06 1.00
B
0.14 0.22 0.37 -0.42 0.60 0.16
Al
0.48 -0.12 -0.05 -0.42 0.46 -0.29 -0.43 0.68 1.00
Ti
0.44 -0.04 -0.02 -0.23 -0.08 -0.09 -0.09 -0.09 -0.01 1.00
V
-0.16 -0.17 -0.26 0.08 -0.19 -0.20 -0.33 -0.19 -0.10 0.18 1.00
Cr
0.06 0.11 -0.08 0.13 0.04 -0.04 -0.20 0.20 0.31 0.29 0.22 1.00
Mn
-0.35 0.37 -0.02 0.50 -0.18 0.20
0.68 -0.24 -0.48 -0.11 -0.23 -0.16 1.00
Co
-0.33 0.38 -0.03 0.49 -0.15 0.19
0.67 -0.23 -0.45 -0.10 -0.24 -0.15 1.00 1.00
Ni
-0.38 0.54 0.04 0.33 0.27 0.17
0.50 0.11 -0.10 -0.09 -0.25 0.19 0.74 0.77 1.00
Cu
-0.14 -0.16 -0.11 -0.26 0.26 -0.20 -0.20 0.26 0.25 -0.14 0.25 0.06 -0.13 -0.11 0.17 1.00
Zn
-0.33 0.33 -0.05 0.51 -0.22 0.18
0.66 -0.31 -0.53 -0.06 -0.22 -0.16 0.99 0.99 0.73 -0.08 1.00
As
0.11 -0.13 -0.07 -0.28 0.30 -0.22 -0.36 0.73 0.58 -0.46 -0.00 0.10 -0.38 -0.37 -0.11 0.25 -0.43 1.00
Rb
-0.58 0.83 0.52 0.48 0.17 0.72
0.84 0.10 -0.28 -0.10 -0.14 -0.04 0.59 0.59 0.60 -0.01 0.56 -0.30 1.00
Sr
-0.70 0.44 -0.03 0.81 -0.31 0.32
0.49 -0.34 -0.56 -0.24 0.08 -0.05 0.62 0.60 0.36 -0.06 0.62 -0.20 0.46 1.00
Mo
0.07 0.42 0.15 -0.29 0.77 -0.03 -0.07 0.52 0.46 0.13 -0.13 0.20 -0.15 -0.12 0.38 0.19 -0.18 0.24 0.21 -0.40 1.00
Cd
-0.03 0.34 0.06 -0.21 0.64 -0.05 -0.07 0.31 0.32 0.26 0.03 0.34 -0.07 -0.04 0.52 0.42 -0.05 0.05 0.16 -0.26 0.83 1.00
Cs
-0.34 0.54 0.33 0.41 0.24 0.45
0.33 0.23 0.27 -0.10 0.10 0.02 0.04 0.05 0.12 0.01 0.00 -0.02 0.55 0.11 0.19 0.06 1.00
Ba
-0.21 0.43 0.13 0.39 -0.14 0.31
0.73 -0.17 -0.46 0.28 -0.20 -0.03 0.91 0.90 0.67 -0.19 0.91 -0.51 0.60 0.52 -0.09 0.02 0.05 1.00
Pb
0.00 -0.01 -0.08 -0.20 0.20 -0.12 -0.15 0.11 0.16 0.17 0.47 0.31 -0.11 -0.10 0.15 0.72 -0.06 -0.01 0.05 -0.12 0.32 0.60 -0.04 -0.07 1.00
U
-0.36 0.18 -0.22 0.55 -0.19 -0.02 -0.22 -0.18 -0.07 -0.14 0.59 0.24 -0.14 -0.14 -0.13 -0.12 -0.16 0.18 0.04 0.41 -0.13 -0.15 0.30 -0.21 -0.09 1.00
Table 6.16 correlation matrix of trace elements (µg/l), pH and some major ions (mg/.l), significant correlations are marked
107
c
e
Conce. (micrograms/l)
conce.(microgram/l)
DPL16
DPL21
DPL21
DPL16
DPL22
DPL22
DPL21
DPL23
DPL23
DPL22
DPL29
DPL29
DPL23
SHT37
SHT37
DPL29
DT43
DT44
DT46
DT48
DR71
DT49
DT49
DR72
DR71
DR71
DR72
DR72
DR73
DR73
DR81
DR81
DR81
DR82
Al
DT47
DT48
DR73
B
Ba
DT49
DT46
DT47
Mn
DT48
DT44
samples
DT46
SHT37
Sr
V
DT44
DPL15
DT43
samples
DT43
1800
1600
1400
1200
1000
800
600
400
200
0
DPL16
Ti
Samples
DR82
DR82
f
conce. (m icrogram/l)
DPL23
DPL22
DPL29
DPL23
SHT37
DPL29
DT43
SHT37
DPL23
DPL29
DT43
DT44
DT48
DT49
DR71
DT49
DR72
DR71
DR73
DR73
DR72
DR81
DR81
DR73
DR82
DR82
DR81
DR82
Cu
108
DR71
DR72
Cs
DT47
Ni
DT47
Rb
DT46
DT46
DT48
Li
DT44
SHT37
samples
DT43
Co
DT49
Pb
DT48
DPL22
Cr
DT47
Samples
DT46
Cd
Samples
DT44
250
DPL21
DPL22
200
DPL16
DPL21
150
DPL21
50
DPL15
DPL16
100
0
60
50
40
30
20
0
10
DPL15
Mo
DPL16
b
Conce. (m icrogram s/l)
DPL15
d
Conce. (micrograms/l)
35
30
25
20
15
10
5
0
Figure 6.9 Concentration trends of trace elements in the study area
DPL15
a
900
800
700
600
500
400
300
200
100
0
450
400
350
300
250
200
150
100
50
0
conce. (microgram/l)
DPL15
From Figure 6.10 it is evident that TDS, the measure of salinity, tends to increase from the
plateau (DPL) towards the rift (DR) along the N-S and/or NNW-SSE general groundwater flow
direction of the area discussed in section 5.3, but Al did not show a clear trend. From the
correlation matrix Al responds negatively with TDS, even though it is not strong. The fact that
the low TDS plateau wells (minimum rock-water interaction) have got high Al concentrations
and relatively high pH (Fig.6.8), may be due to the sensitivity of solubility and mobility of
aluminum with Eh-pH conditions which seems to have the upper hand over the lithology and
rock-water contact time. It is also evident from the correlation matrix that Al is positively
correlated with pH.
Al
TDS (mg/l)
600
1700
1500
1300
1100
900
700
500
300
100
500
400
300
200
DPL15
DPL16
DPL21
DPL22
DPL23
DPL29
SHT37
DT43
DT44
DT46
DT47
DT48
DT49
DR71
DR72
DR73
DR81
DR82
100
Al conce.
(microgram/l)
TDS
samples
Figure 6.10 TDS versus aluminum concentration of groundwater in the area
Calcium and strontium exhibit similar behavior along the flow path (Fig. 6.11(a)) but with a
slightly negative correlation with Na (Table 6.16 and Fig.6.11 (b)). Owing to the fact that silicate
hydrolysis and ion exchange process being the main activities for the enrichment of Na in the
groundwater along the flow, as it is previously discussed, calcium will be consumed by
carbonate precipitation and also exchanged for sodium on clay structures which are encountered
during drilling in some of the exploratory wells especially on the plateau areas.
109
100
Ca (mg/l)
80
60
40
20
700
600
500
400
300
200
100
0
DR81
DR82
DT50
DR71
DR72
DR73
DT46
DT47
DT48
DT49
SHT37
DT43
DT44
DPL21
DPL22
DPL23
DPL29
DPL15
DPL16
0
700
600
500
400
300
200
100
0
Sr (microgram/l)
Sr
Sr (microgram/l)
Ca
a
samples
Na
b
Sr
600
Na (mg/l)
500
400
300
200
100
DR81
DR82
DT50
DR71
DR72
DR73
DT46
DT47
DT48
DT49
SHT37
DT43
DT44
DPL21
DPL22
DPL23
DPL29
DPL15
DPL16
0
samples
Figure 6.11 plots of Ca versus Sr (a) and Na versus Sr (b)
Concentrations trends of arsenic is not similar with that of the trends of TDS (Fig. 6.12) but the
deviations are not clear to explain mainly because deviations are related neither on parameters
such as pH nor on location; due to the fact that minimum concentrations of arsenic are associated
with both low (DT47, pH=6.5) and high (DR72, pH=9.7) pH conditions. In addition the samples
are also located in different regions; both in the transition and rift part of the study area. Arsenic
is positively correlated with both boron and aluminum (Table 6.16 and Fig 6.12(b)).
110
DR82
DR81
DR73
DR72
DR71
DT49
DT48
DT47
DT46
DT44
DT43
SHT37
DPL29
DPL23
DPL22
DPL21
45
40
35
30
25
20
15
10
5
0
DPL16
700
600
500
400
300
200
100
0
DPL15
TDS (mg/l)
As
As (micrograms/l)
TDS
a
samples
As
1400
1200
1000
800
600
400
200
0
45
40
35
30
25
20
15
10
5
0
DPL15
DPL16
DPL21
DPL22
DPL23
DPL29
SHT37
DT43
DT44
DT46
DT47
DT48
DT49
DT50
DR71
DR72
DR73
DR81
DR82
B
conce.(micrograms/l)
B
As
conce.(micrograms/l)
b
Samples
Figure 6.12 plot of TDS versus arsenic (a) and boron versus arsenic (b)
Other trace elements such as Li, Cr and Mn shows similar concentration trends as that of TDS in
the groundwaters of the study area (Fig. 6.13 (a, b and c))., but Pb seems to have some what a
different trend as compared to TDS (Fig.13 (d). Pb is found to be correlated with Cu and Cd.
111
b
10
5
d
DR82
DR81
DR73
DR72
DT49
DR71
DT48
DT47
25
20
15
10
5
samples
samples
Figure 6.13 plots of TDS versus some of the trace elements
Cluster analysis was used to divide the water samples into groups based on their trace element
concentrations (Fig.6.14 (a)). All variables were brought into the same range by scaling the
values for each constituent. The similarity measure was the Euclidean distance between groups.
The groups of highest similarity were connected by the unweighted pair group average method.
This algorithm results in clusters containing samples with close values (Brown, 1998). Principal
component analysis (Fig.6.14 (b)) and correlation analysis (Table 6.16) were also applied to
determine what chemical processes play important roles in the trace element distribution of the
groundwater in each cluster.
112
DR82
DR81
DR73
DR72
DR71
DT49
DT48
DT47
DT44
DT46
DT43
SHT37
DPL29
DPL23
DPL22
0
Pb (microgram/l)
30
DPL21
DR82
DR81
DR73
DR72
DR71
DT49
DT48
DT46
DT44
DT43
SHT37
DPL29
DPL23
DPL22
DPL21
0
Pb
700
600
500
400
300
200
100
0
DPL16
50
TDS (mg/l)
100
Mn (microgram/l)
150
TDS
DPL15
Mn
200
DPL16
DT46
samples
700
600
500
400
300
200
100
0
DPL15
TDS (mg/l)
TDS
DT43
SHT37
DPL29
0
Samples
c
Cr (microgram/l)
15
DPL15
DR82
DR81
DR73
DR72
DT49
DR71
DT48
DT47
DT46
DT44
DT43
SHT37
DPL29
DPL23
DPL22
DPL21
DPL16
0
20
DPL23
50
25
DPL22
100
Cr
700
600
500
400
300
200
100
0
DPL21
150
TDS (mg/l)
200
Li (microgram/l)
250
TDS
DT44
Li
700
600
500
400
300
200
100
0
DPL15
TDS (mg/l)
TDS
DPL16
a
Cluster analysis of the water samples based on trace elements resulted in two broad groups,
which are shown in Fig 6.14(a). One of the group contains only sample DT47 (Dimajelewa),
where it is found exceptionally rich in zinc, manganese, lithium and barium probably indicating
that the partitioning of these trace elements into the solution is enhanced by an acidic- oxidizing
environment, in the contrary arsenic and aluminum are found relatively low in this well. The
second group contains all the other samples which are further subdivide into very closer
subgroups, in terms of Euclidean distance, based on certain differences among them. Principal
component analysis on the other hand separated the samples into three groups (Fig. 6.14(b),
which is enables to discriminate some differences between the second group members of the
cluster analysis result. In this case the samples are found to cluster in three, DT47 in one corner,
DT49 and DT50 in other edge and all the remaining samples almost in one place. DT49 (Asgori)
and DT50 (CMC) are wells whose water is found relatively warm (thermal) with a temperature
of about 40ºC according to measurements during drilling (WWDSE, 2008), so it could be the
warm temperature in these wells that might be responsible for their trace element concentrations,
for example both DT49and DT50 wells have got very high boron concentrations of 1205 and
1034µgm/l respectively.
On the basis of the correlation matrix (Table 6.16) Li, Rb and Cs display good correlation with
TDS, Na and HCO3 and all these six ions are negatively correlated with pH. Sr showed strong
positive correlation with Ca and strong negative correlation with pH. Mn, Zn and Ni display
negative correlation with pH which supports the fact that these ions are anomalously high in
DT47 which has a relatively lower pH.
113
a
b
Figure 6.14 Dendrogram of the trace element data (a) and projection of the samples on the factor
plane (1x2) of the principal component analysis (b)
114
6.5 Isotope hydrology
6.5.1 General
Isotopes are forms of a given chemical element that have different atomic masses. For a
particular element, the isotopes have the same numbers of protons, and so have the same atomic
number. However, each isotope has a different number of neutrons and therefore has a different
atomic mass. Stable isotopes are those isotopes that do not undergo radioactive decay; so their
nuclei are stable and their masses remain the same. However, they may themselves be the
product of the decay of radioactive isotopes. In hydrological studies, the stable isotopes of
interest generally relate to H, C, N and O. In terms of the water molecule itself, oxygen has three
stable isotopes, 16O, 17O, and 18O; and hydrogen has two stable isotopes, 1H and 2H (deuterium).
The relative abundances of the lighter isotopes of hydrogen (1H=0.999) and oxygen (16O=0.997) are
naturally high. The stable isotopes of
18
O (oxygen-18) and 2H (deuterium) are used to provide
information on hydrological processes, including groundwater-surface water interactions. Detail
information on the application of environmental isotopes in hydrological investigations can be
obtained from Fritz and Fontes (1980), Fontes and Edmunds (1989), Coplen (1993), Gat (1996),
Mazor (1997), Clark and Fritz (1997) and Cook and Herczeg (2000).
Fresh water is vital to life and a prudent balance between its use and assessment of its
availability is imperative to properly manage the resource. A critical component in assessing
fresh water is knowledge of the water cycle; how water supplies are recharged (occurrence),
accumulation and distribution of groundwater resources. Water undergoes phase transitions,
interacts with minerals and the atmosphere, and participates in complex metabolic processes
essential to life. The isotopes of hydrogen and oxygen undergo large fractionations during these
processes, providing a multiple isotopic tracer record of diverse phenomena (Clark and Fritz,
1997).
In the hydrologic cycle, hydrogen and oxygen ratios provide conservative tracers, uniquely
intrinsic to the water molecule that elucidates the origin, phase transitions, and transport of H2O
(Dansgaard,1964). As a result, in different parts of the hydrologic cycle, water is naturally tagged
115
with isotopic "fingerprints", which vary according to the history of a particular body of water and
its pathway through the hydrologic cycle. Isotope techniques in water management provide
important and sometimes unique tools for obtaining critical information. Isotopes could be used
to get information on the source of recharge, age and rate of movement of groundwater.
The isotopic composition of water is commonly expressed in per mill deviation from the
standard mean ocean water (Craig 1961); the standard solution is later modified as Vienna
Standard Mean Ocean Water, commonly abbreviated as VSMOW. These deviations are denoted
by δ2H for deuterium and δ18O for 18O, and expressed as:
  2H 

 


sample
 H 


δ2H‰ =  2 
− 1 * 1000
  H VSMOW

 H 




  18O 

  16  sample



  O

18
δ O‰ =  18
− 1 *1000
  O VSMOW

  16 O 




6.2
6.3
Where water with less 2H (deuterium) and 18O than VSMOW has negative δ2H and δ18O and vice
versa.
6.5.2 δ2H and δ18O composition of precipitation in the study area
18
O and 2H are integral parts of natural water molecules that fall as rain or snow (meteoric water)
each year over a watershed and, consequently, are ideal tracers of water (Kendall and
McDonnell, 1998). Unlike most chemical tracers, oxygen and hydrogen isotopes in water are
relatively conservative in reactions with catchment materials, waters recharged at different times,
in different locations, or that followed different flow paths are often isotopically distinct; in other
words, they have distinctive "fingerprints".
Time series rainfall isotopic data is totally lacking for most of the meteorological stations in the
study area except Addis Ababa, which is located in the north central part of the study area. From
116
the long-term δ18O and δ2H measurements of precipitation at the Addis Ababa GNIP station of
IAEA (Data from 1961-2005), the mean isotopic composition of precipitation is δ18O = -0.32‰
and δ 2H = 9.7‰, δ2H is positively correlated with δ18O measurements. The line of best fit
through data points representing δ2H vs. δ18O values (Fig.6.15) is given by:
δ2H = 7.13δ18O +11.98,
(R2=0.96)
(6.4)
This is comparable to the global meteoric water line (GMWL) of Craig (1961), which is given by
δ2H = 8 δ18O + 10‰ SMOW, and later modified by Rozanski et al. (1993) using the regression
line of IAEA GNIP stations as: δ2H = 8.17 (±0.07) δ18O + 11.27 (± 0.65) ‰ VSMOW.
The isotope value of precipitation is controlled by, isotope value of source, rate of evaporation
of source, isotopic evolution of air mass, relative humidity during precipitation (Gat., 1996). As
an air mass leaves its source it cools as it rises above the continents. This cooling induces
precipitation that distills the heavy isotopes from the water vapor in the air mass. The remaining
vapor becomes progressively depleted in
18
O and 2H. Precipitation with relatively high isotope
values (compared to the air mass vapor) falls from the clouds. This results in the so-called
continentality effects and orographic effect that produces precipitation with very low values at
high altitudes (Dansgaard, 1964).
117
100
LMWL of Addis Ababa
60
δ2H = 7.13δ18O +11.98
C
C
C
C
CC C
CC
C
CC
C
C
CC
C
CCCC
CC
C
C
C
CC
CCCC
C
C
C
CC
C
C
C
C
CC
C C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
CC
CC
CC
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
CC
C
C
C
C
C
CCC
C
CCC
C
C
C
C
C
C
C
C
CC
CC
C
C
CC
C
C
C
C
C
C
CCCC C
CC
C
C
C
C
C
C
C
C
C
C
C
C C
C
CC
CC
CC
C
C
CCC
C
C
C
C
C
C
C
C
C
C
C
CC
C
CC
CCC
C
C
C
C
C
C
C
C
C
CC
C
CC
CC
CCC
CC
C
CC CC
C
C
C
2H (‰)
20
-20
C
-60
GMWL
δ2H = 8.2δ18O +10.8
-100
-10
-6
-2
2
6
10
18O (‰)
Figure 6.15 Plot of δ18O‰ versus δ2H‰ of precipitation at Addis Ababa GNIP station (Data
1961-2005, IAEA), the blue regression line through data points represents the local meteoric
water line (LMWL) for Addis Ababa and the red line represents the global meteoric water line
(GMWL)
This being the normal trend, the local meteoric line at Addis Ababa (Fig.6.15) plot above the
global meteoric water line and cross each other at a coordinate of (1.1, 19.8) ‰. The difference
in intercept may reflect a higher kinetic effect during evaporation (nonequilibrium evaporation)
of the moisture along the trajectory of the two main moisture sources of the region; the monsoon
from east Atlantic, West Indian Ocean and/or both for the precipitation over the Addis Ababa
region. This is observed by other researchers by comparing the stations lying along the trajectory
of the Atlantic monsoon, which shows an increase in δ-values from the coast to Addis Ababa.
Most of the stations fall right on the GMWL except for Addis Ababa which is at 2360masl. This
station is off because of substantial evaporative enrichment. Some people have suggested that
118
recycling of water by transpiration in the Congo Basin has a major role in the high values at
Addis Ababa. Others have suggested that input of Indian Ocean moisture may play a role as well
(Gat et al, 2003). This phenomenon is best illustrated by Figure 6.16.
Figure 6.16
6.5.3 Spatial variation of 18O and 2H isotopes in the waters of Upper Awash basin
An interpretation is made on a data set of stable hydrogen (H) and oxygen (O) isotope, as far as
the original data is concerned, especial emphasis is given during data collection to the newly
drilled exploratory deep wells. Due to their great depth, these wells penetrated into the lower
aquifer, therefore this is an intention to see the isotopic signatures of the deep aquifer system of
the study area. In addition to the original data generated in this study, complementary stable
isotope data is also used from previous works (Azagegn, 2008, Demlie, 2007; Kebede, 2005;
Gizaw, 2002). The spatial distribution of the stable isotope data points in the study area is
presented in Figure 6.17.
119
Figure 6.17 Spatial distributions isotope data points of 18O and 2H, the red irregular lines mark
physiographic regions used in the interpretation of the isotope data
Comparison of the stable isotope data for surface water and groundwater samples relative to the
global or local meteoric water lines can provide information on hydrological processes. For
example, isotopically light water molecules evaporate more efficiently than isotopically heavy
water molecules. Due to this variability in isotopic vapour pressures, evaporation produces
residual water enriched in the heavier isotopes relative to the initial isotopic composition.
Therefore water that has undergone evaporation lies to the right of the local meteoric water line
due to this enrichment (Coplen, 1993). The ‘d-excess’ is another useful parameter to decipher
120
the effect of evaporation in modifying the isotopic character of rainwater prior to groundwater
recharge, ‘d-excess’ is defined as the excess deuterium that cannot be accounted by equilibrium
fractionation between water and vapour. Since condensation is most often an equilibrium process
‘d-excess’ is an indicator of kinetic fractionation during evaporation, governed by molecular
diffusivity of isotopic molecular species (Dansgaard, 1964; Clark and Fritz, 1997).
Using the concept of the deuterium excess (d) defined as (d=
2
H - 8*
18
O, Dansgaard, 1964)
and assuming rains at Addis Ababa as representative of the precipitation over whole of the study
area, an attempt is made to infer the stable isotopic signatures of the precipitation and the
groundwater in the area. Based on the isotope data of the groundwaters of the study area, the
majority of the samples except samples from the deep aquifer system have increased deuterium
excess, the mean being about 13.5‰. For atmospheric moisture not influenced by secondary
evaporative processes, the d-excess approximates the y-intercept of the GMWL of 10
(Dansgaard 1964) and by analogy to y-intercept of the LMWL for groundwaters recharged from
that precipitation, 11.98 in our case. Increased deuterium excess in groundwater can arise from
the evaporation of the rainwater before it percolates to the underground. Based on the fact that if
water from precipitation is evaporated on its way before it percolates to the subsurface, the water
molecules with lighter isotope will contribute preferentially to the isotopic composition of the
water vapor and this, in turn, leads to an enhanced deuterium excess in the percolating water and
there by the groundwater. Groundwaters from the deep aquifer system of the area generally have
lower values of d-excess having a mean value of 10.5‰ which is less than the y-intercept of the
LMWL, 11.98‰. The low d-excess values in most of the wells tapping the lower aquifer
indicate a cold climate isotopic signal, probably due to cooler climate during recharge from
higher altitude. d-excess values locally more than 11.98% indicate evaporative influence on the
rain water of the area.
The δ18O versus δ2H plot of waters from different sources in the study area revealed certain
trends. Most of the samples except that of the plateau scatter on the right (below) of the LMWL
signifying evaporation of rainwater before groundwater recharge (Fig. 6.18). Most of the
samples from the plateau area scatter on the left (above) of the LMWL signifying minimal
evaporative fractionation impact on the groundwater isotope signal of the plateau waters may be
121
due to the colder climate in that part of the area. From the plot it is possible to observe at a
glance that there are two major groups of waters, group I and II (Fig. 6.18). Group I comprises
waters from rivers, springs, and wells tapping the upper unconfined shallow aquifer and the
majority of the isotopic compositions concentrate around the rain isotopic composition of the
area (Addis Ababa), which signifies that the aquifers they represent are getting their recharge
from the modern precipitation.
10
Legend
K
B
D
A
D
A
2
LMWL
δ H = 7.13δ18O + 11.98
-6
D
D
D
K
D
A
K
D
K
K Group I
A
2
2H (‰)
A
AA
A
A
B
A B
-14
A
J
AJJ
C
K K
C
C
B
K
C
C
C K
K
C
J
JK
A J
K K
J
-22
-30
-6.0
-4.8
A
C
D
B
K
J
D
K
D
B
Deep Plateau
Thermal
Shallow Plateau
Shallow Transition
Deep Transition
Deep Rift
K
A
GMWL
δ H = 8.2δ18O + 10.8
2
Group
II
A
J
J
K
-3.6
-2.4
-1.2
0.0
18O(‰)
Figure 6.18 plot of ²H versus 18O‰ of waters in the Upper Awash basin along with the
LMWL of Addis Ababa station and GMWL
Water wells tapping the lower aquifer in all the three physiographic regions fall in group II.
Groundwaters in group II are relatively depleted in their heavy stable isotope composition when
compared to present day precipitation of the area. Plateau deep wells: Segnogebeya (DPL31),
Chancho (DPL32) and Inchini (DPL33); wells from Becho plain (located in the transitional part
122
of the study area): Asgori (DT68), Dimajelewa (DT69), Jewalokora (DT70), Melkakunture
(DT73) and Kimoye (DT74); Rift wells: Abusera (DR82), Bishefitu (DR85), Dukem (DR86),
Borora (DR87), Modjo Ude (DR88) belongs to this group. Groundwaters from the lower aquifer
system of the area generally have lower values of δ2H, δ18O and d-excess indicating a colder
climatic signal during recharge. . In addition to this the similar isotopic signatures of the deep
system in all the three physiographic regions of the study area imply similar origin of recharge
and presence of hydraulic connectivity.
This is also supported by litho-hydrostratigraphic relationships constructed from lithologic logs
obtained during drilling of the exploratory wells as discussed in section 4.2. In the lithological
correlation it was established that the scoraceous basalt (Tarmaber, .Amba Aiba formation)
which is the main aquifer in the plateau part of the study area and the Blue Nile plateau wells,
was also encountered in the deep wells of the transitional and the rift part of the study area at
different depths. It was therefore proposed that this hydrostratigraphic unit serves as a conduit
for the movement of groundwater from the Blue Nile plateau all the way to the rift in the Awash
River basin. The presence of artesian conditions in some of the wells tapping the lower aquifer in
the transitional part of the study area and their respective hydraulics analysis (head differences at
the proposed recharge area and head at the artesian well) also support their recharge from higher
altitudes. The isotopic signature therefore, strengthens the idea that the Blue Nile and Awash
basins are hydraulically connected and groundwater flows from Blue Nile plateau to Awash via
the lower scoraceous basalt aquifer which is encountered in all the three physiographic regions
during exploratory deep drilling. Thermal wells tapping the Filwuha fault belt in central Addis
Ababa belong also to group II. The depleted isotope content in the thermal groundwater could be
justified in a similar manner as that of the deep groundwaters of the area i.e., recharged from a
different precipitation regime having depleted isotope content. Another possible cause proposed
by previous researchers for the depleted isotope composition of the thermal groundwaters is that
the present meteoric water could have circulated deep following the opening along the fault and
the isotope signal has been altered by isotope exchange at higher temperatures with fluids
originating from deeper sources (Gizaw, 2002, Demlie et al, 2006 and Kebede et al, 2007).
123
However, it has to be recalled that the construction of the deep wells is not controlled in the way
that it doesn’t exclude waters from the overlaying shallow aquifers, so the effect of mixing is a
prevailing condition in the wells and there by on the isotopic signal of their waters, this is
evidenced by the wide scattering of water samples within the groups in Figure 6.18. The
sampling is done with a special instrument which allows to take samples from the required depth,
it works like a bailer such that it goes open till the required depth, when the releasing weight is
dropped at the required depth the instrument cuts the column of water at that point and
immediately shuts its part, in this way the effect of mixing during sampling is minimized. In
some wells samples were taken at different depths based on the screen arrangement owing the
fact that the sampled wells are nonoperational wells which were sealed after drilling and are
more or less stagnant, so the intention is to see the water chemistry from the different inter
layered aquifer system with in the same well to know whether it exists a chemical stratification
or not. This part is presented separately at the end of this chapter.
6 .5.4 Radioactive Isotopes
Radioactive isotopes have unstable nuclei that decay, emitting alpha, beta, and sometimes
gamma rays. Such isotopes eventually reach stability in the form of non-radioactive isotopes of
other chemical elements, termed radiogenic daughters. Decay of a radionuclide to a stable
radiogenic daughter is a function of time measured in units of half-lives. The decay constants (λ)
and half-lives (t1/2) of radioactive isotopes that are frequently used as environmental tracers in
the field of hydrology are listed in Table 6.16. Radioactive isotopes are useful indicators of the
time that water has spent in the groundwater system.
124
Isotope
Decay Constant
(Year-1)
Half-life
(day-1)
(year)
(day)
Rubidium (87Rb)
1.46 x 10-11
4.00 x 10-14
4.75 x 1010
1.73 x 1013
Uranium (238U)
1.55 x 10-10
4.24 x 10-13
4.468 x 109
1.63 x 1012
Iodine (129I)
4.41 x 10-8
1.21 x 10-10
1.57 x 107
5.73 x 109
Chlorine (36Cl)
2.3 x 10-6
6.30 x 10-9
3.01 x 105
1.10 x 108
Krypton (81Kr)
3.03 x 10-6
9.03 x 10-9
2.29 x 105
8.36 x 107
Carbon (14C)
1.21 x 10-4
3.31 x 10-7
5730
2.09 x 106
Radium (226Ra)
4.33 x 10-4
1.19 x 10-6
1600
5.84 x 105
Argon (39Ar)
2.58 x 10-3
7.06 x 10-6
269
9.83 x 104
Silicon (32Si)
4.95 x 10-3
1.36 x 10-5
140
5.11 x 104
Strontium (90Sr)
0.0241
6.65 x 10-5
28.78
1.05 x 104
Hydrogen (3H)
0.0558
1.53 x 10-4
12.43
4540
Krypton (83Kr)
0.0644
1.77 x 10-4
10.756
3929
Radium (228Ra)
0.121
3.31 x 10-4
5.75
2100
Sulphur (35S)
2.89
7.92 x 10-3
0.240
87.51
Argon (37Ar)
7.23
1.98 x 10-2
0.0959
35.04
Radon (222Rn)
66.0
0.181
0.0105
3.8235
Table 6.16: Decay constants and half-lives of selected radioactive isotopes with application to
hydrology (from Browne and Firestone, 1999)
6 .5.4.1 Tritium (3H)
Tritium is introduced into the hydrologic cycle by both natural and human sources. Atmospheric
tritium is formed when cosmic rays bombard nitrogen to yield 3H (Ferronsky 1982).This occurs
according to the following reaction:
14
N + n → 12C + 3H
6.5
125
Where n is a neutron from cosmic radiation. About 3 to 5% of all neutrons in the upper
atmosphere react with nitrogen to form 3H. The natural concentration of tritium in the
atmosphere is uncertain, because few measurements were made prior to nuclear testing.
However, the natural tritium concentration is estimated to be between 4 to 25 TU depending on
location (Nir 1964). When tritium is formed it often combines with oxygen in the form HTO,
where H is 1H or 2H; T is 3H; and O is oxygen.
Since the beginning of nuclear testing, large quantities of man made 3H have been introduced
into the atmosphere. In 1954-55, 1958, and 1961-62 large amounts of tritium were released into
the atmosphere. Before 1957, most of the 3H released during testing only reached the
troposphere; the nuclear explosions during this time were relatively small and were carried out at
relatively low altitudes. Precipitation quickly removed this pulse of tritium. After 1957, the
nuclear explosions were greater in magnitude and carried out at higher altitudes. This large pulse
of tritium, which reached the stratosphere, has had a long term affect on the concentration of
tritium in precipitation (Fritz and Fontes, 1980). Other sources of tritium are weapons production
industries, nuclear industries, and digital watch manufacturers. These industries release tritium
into the lower atmosphere and directly into the hydrologic cycle.
Tritium concentrations in precipitation have been greatly influenced by atmospheric testing of
atomic weapons. Tritium concentration is expressed in units of TU (tritium unit). One TU is a
3
H/1H ratio equal to 10-18. The use of tritium as a hydrologic tracer is therefore related to the
release of large quantities of tritium into the atmosphere during the atomic weapons testing in the
1950s and early 1960s. The atmospheric testing peak provides an absolute time marker from
which to estimate groundwater age. However, because radioactive decay and hydrodynamic
dispersion have greatly reduced maximum tritium concentration in groundwater, identification of
the 1960s atmospheric testing peak has become increasingly difficult (Neven Kresic, 1997). The
interpretation of ages from tritium data alone is further complicated by the fact that water wells
are commonly screened over intervals that represent a wide range of groundwater ages.
Therefore, tritium data is used only as a qualitative indicator of groundwater age (Rowe, 1999).
The most accurate use of tritium data is to indicate pre-or post-1952 groundwater recharge.
126
Assuming that piston flow conditions (no dispersion or mixing) are applicable, Clark and Fritz
(1997) provide the following guidelines for using the tritium data:
1. Groundwaters that contains a tritium content of less than 0.8TU are recharged prior to
1952
2. Waters with a tritium content of 0.8 to 4UT may represent a mixture of water that
contains components of recharge from before and after 1952.
3. Tritium concentration from about 5 to 15TU may indicate recharge after about 1987.
4. Tritium concentration from about 16 to 30TU are indicative of recharge since 1953 but
can not be used to provide a more specific time of recharge.
5. Water with more than 30TU probably is from recharge in the 1960s or 1970s.
6. Water with more than 50TU predominately is from recharge in the 1960s.
Accordingly, based on the tritium content of analyzed deep well water samples in the study area,
they are found to be recharged either prior to 1952 or have mixture of recharge from before and
2.8
2.4
2
1.6
1.2
0.8
0.4
0
2.8
2.4
2
1.6
1.2
0.8
0.4
0
Sululta
Inchini
Holeta
Segnogebeya
Chancho
Shegole
CMC
Aba Samuel
Asgori
Dimajelewa
Jewalokora
Tefki
Melkakunure
Kimoye
Bishefiu
Dukem
Abusera
Borora
Modjo Ude
Tritium (TU)
after 1952 (Table 6.17 and Figure 6.19).
Samples
Figure 6.19 Tritium concentrations of deep wells in the study area
127
Sample ID
AY1
AY2
AY3
AY4
AY5
AY6
AY7
AY8
AY9
AY10
AY11
AY12
AY13
AY14
AY15
AY16
AY17
AY18
AY19
Local name
Sululta
Segnogebeya
Chancho
Asgori
Dimajelewa
Jewalokora
Tefki
Bishefitu
Dukem
Abusera
Borora
Modjo Ude
Melkakunture
Inchini
Holeta
Kimoye
CMC
Shegole
Abasamuel
3
H (TU,±0.3)
2.66
0.63
0.14
0.67
0.33
0.6
0.25
1.2
0.93
1.7
0.94
0.82
0.78
1.6
1.05
0.07
1.59
2.21
2.61
Table 6.17 Tritium content of deep wells in the study area
The tritium concentration of wells tapping the lower basaltic aquifer is found to be very low,
<0.8TU for most of these wells (Fig. 6.19). The low tritium content is exhibited in all the three
physiographic regions of the study area: Chancho and Segnogebeya in the plateau; Asgori,
Dimajelewa, Tefki, Melkakunture and Kimoye in the transition; and Dukem, Borora and Modjo
Ude in the rift part of the study area. This again agrees with what is evident in the stable isotope
signatures of these wells. Therefore, it can be said that these wells represent relatively old
waters; hence they could have long resident time underground and/or have long flow paths. In
addition to this their similar tritium signature all the way from the plateau to the rift could also be
an additional evidence to support the conclusion that the wells tapping the lower aquifer would
have been recharged from a similar source (both in time and space) and are hydraulically
interconnected. Therefore the likely recharge area for the deep aquifer system could be the
relatively elevated and cool climate Blue Nile plateau located at the northern watershed
128
boundary of the study area, which is evidenced by the plateau wells located in Blue Nile basin
showing similar stable and tritium isotopic signatures with the waters of the transitional and rift
part of Upper Awash.
In addition to this from the complementary data of tritium obtained from previous works
(appendix 4), there are some springs and boreholes which are located at the foot of Intoto
mountain (the northern watershed boundary of Awash basin with Blue Nile basin) which have
also very low or zero TU (Sample No.AY20-28; Appendix 4.2). The east-west distribution of
these low tritium waters at the foot of Intoto with a very small upstream catchment area may
verify the presence of deep circulating water coming through north-south running faults which
cut the acidic Intoto mountain and intercept the east-west Addis Ababa (Filwuha)- Ambo fault.
More over, the high yield of recently drilled boreholes for the water supply of the city Addis
Ababa along the foot of Intoto ridge in Keranio, Burayu, Asko, Shegolemeda, Gojamber,
Shiromeda, Ferensai Legasion, Kotebe, Tafo and Ayat is also in agreement with the idea that
deep circulating low tritium waters flow from the Blue Nile plateau to Awash basin (Azaegegn,
2008). From this, it can be said that deep circulating water from the Blue Nile plateau flows
towards Awash River basin via the north –south running fault systems which serve as a
hydrogeological window that connects the two basins. The low tritium content of the Filwuha
thermal wells in central Addis Ababa (sample No.AY29-38, Appendix 4.2) can also be justified
in a similar manner.
A tritium concentration measurement of precipitation is available in Addis Ababa, IAEA GNIP
station. Tritium concentration of precipitation ranging from a maximum (peak) of 189TU
129
measured in the year 1965 to a present day average value of about 10TU (an average value of the
last two decades) has been recorded. From the tritium measurement it appears that tritium
content of precipitation has almost reached a steady state level, despite the small annual and
monthly fluctuations. However, the continuous depletion of the artificial tritium in the
environment will likely reduce its future usefulness in the groundwater studies.
6.6 Chemical stratification in wells
Based on measurements done on some wells, a sort of vertical chemical stratification is
observed. TDS and HCO3 show increment with depth (Table 6.18) may be due to the simple
reason that the upper part of the aquifer receives recharge and get diluted. The magnitude of the
difference in TDS and/or in bicarbonate ion is bigger for samples taken from large intervals; in
Tefki well the samples were taken from 12m and 250m below the ground surface, the difference
in TDS between the shallow part and the deep part of the aquifer is about 122mg/l; on the other
hand, in Modjo Ude well measured at 30m and 80m depth the difference is very small about
2mg/l.. In the contrary, the tritium content of the lower part of the aquifer is found to be greater
than the upper part may be due to different sources of recharge for the different aquifer layers
within the well.
wells
Sample ID Depth (m) pH
Eh
Sululta
A01A
210
8.0
-72.4 272
263
2.66
A01B
108
7.9
-64.7 264
244
2.64
Jewalokora A07A
100
7.27 -33.3 394
416
1.09
A07B
surface
7.27 -33.6 392
414
0.6
A08A
250
7.2
-27.9 322
302
0.56
A08B
12
8.0
-76.7 230
180
0.25
Modjo Ude A13A
80
7.5
-44.7 434
414
2.56
A13B
30
7.6
-54.1 420
412
0.82
Tefki
TDS HCO3 tritium Remarks
Artesian well
Table 6.18 Chemical parameters showing vertical chemical stratification in some wells
130
6.7 Water chemistry monitoring and groundwater-surface water interaction
The chemistry of six boreholes, four lakes and the Awash River at Melkakunture and Hombole
gauging stations (Fig.6.20, see Appendix 5 for monitoring data) were monitored for two years
from January 2006 to December 2007. The monitored water point sites were chosen in a way
that they can give clues about the interaction of groundwater with Debrezeit Lakes and Awash
River. Major ions and parameters such as TDS, EC, alkalinity, and dissolved oxygen were
monitored on monthly basis. This data has been used to study the temporal hydrochemical
variations and surface water and groundwater interactions. The water chemistry monitoring data
indicated temporal variations of the water quality and the interactions of the different water
bodies in the study area.
Figure 6.20 Water quality monitoring points in the study area
131
6.7.1 Groundwater and Lakes
All of the lakes have similar ion concentration and conductivity trends, but with different
amplitudes (Fig. 6.21 and Appendix 5). Generally the major ion concentration and electrical
conductivity value of the lakes is relatively high in dry season and low in the rainy season. This
is due to the simple reason that there exists high evaporation during the dry months that leads to
concentrate the lakes and inflow of fresh water from rainfall and surface runoff in the rainy
season which dilutes the lakes. Lake Bishoftu Guda shows similar ion concentration and EC as
that of the surrounding area groundwater chemistry, which indicates that the lake is a transit for
the groundwater system of the area. Lake Hora Hoda has a very high ion concentration and
conductivity and is a terminal lake. The annual amplitude of conductivity variation of this lake is
relatively high, about 400µS/cm, while the amplitude of Lake Hora and Lake Bishoftu is about
150 and 100µS/cm respectively.
6000
Legend
C C
CC C C C
C
C C C CC C C
C CC
C
C CC
CC
C
D
4800
Cond (uS/cm)
A
B
Bishofitu Guda
Bishofitu
Hora Hoda
Hora
3600
2400 D B
DD D
D D
D D D D D
D D DD D D D
D D D DD
B D
BB B B B B B B B B B B B B B B B B B B B B
A A
AA A A A A A A A A A A A A A A A A A A A A
1200
0
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2006-2007
Figure 6.21 Conductivity of monitored Lakes of Debrezeit
132
In D/Z Air force well, which is located about 0.5km down stream of Lake Bishoftu (Fig.
6.20), the electrical conductivity increases in the rainy season and until some delay time
after it (June to October) and decreases in the dry months from January to May (Fig. 6.22).
This is due to the fact that the level of Lake Bishoftu relatively increases in the rainy season
and the inflow from the lake to the groundwater occurs due to increase in hydraulic
gradient. Due to the relative decline of the lake level in the dry season the influence of the
lake on the chemistry of the groundwater decreases which is evidenced by the relative
decrease of ion concentration and conductivity in the wells water chemistry during this
period.
1400
Legend
D
D
DD D D D
D
D
D
D
D
A
D D
D D
B
D
C
D
1200
E
H
D
Cond (uS/cm)
D
D
D
D D
1000
D
H
H H
HH
H
H
H
H HH H H H H H H H H
HH
Dukem_MBI
Tede_Mojo
D/Z_Well4
D/Z_AirForce
Gafat_BH10
Dire
H HH
800
E
E
E
E E E E E E
E E
E E
EE
E E
EE E
EE
C C
A
B A
CC C C C C C C C C C C C C C C C C C C C C
A
AA A A A A A A A A
A A A A A A A A A AA
BB B
B
BB
B
B B B B B B B B BB
B
B
BB
E
600
B
400
B
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2006-2007
Figure 6.22 Conductivity of monitored Boreholes
133
The influence of the lakes decreases as distance from the lakes increases. This is evidenced by
the similar trend but smaller amplitude of ion concentration and conductivity (Fig. 6.22) in the
wells Dire and Gafat, which are 12 and 16km far downstream of the lakes respectively (Fig.
6.20). In addition to this, Tede well which is found far down stream of Debrezeit lakes (about 30
km) do not exhibit the trend as that of the wells near to the lakes. Debrezeit well 4
(Shimbrameda) and Dukem_MBI wells which are found 3 and 16km upstream of the Debrezeit
lakes showed relatively no variation in ion concentration and EC values which indicates that
these wells are not affected by inflows from the lakes due to the flow direction of the
groundwater system, i.e. groundwater flows from north either to SSW or SSE up on its arrival to
the Bishoftu Lakes region (WWDSE, 2008). Generally, a seasonal fluctuation of the
hydrochemical behavior was observed in wells located in down gradient of the flow path with
respect to the lakes and those in the up gradient are not affected.
6.7.2 Groundwater and rivers
During the rainy season from July up to September, the electrical conductivity values measured
at the two river monitoring stations, Melkakunture and Hombole are nearly similar, less than
150µS/cm (Fig. 6.23), which signifies a diluted chemistry from rainfall and surface runoff.
During dry season (October to May), the electrical conductivity and major ion concentration
values measured at the two stations show similar trend but a significant difference in amplitude
(having EC range of 350-420µS/cm at Melkakunture and 500-620µS/cm at Hombole
respectively). Considering the groundwater chemistry of exploratory wells, for example
Melkakunture mapping well at a distance less than 20m from the river monitoring station, its EC
is 536µS/cm. The head in the well is lower than the river bed for its upper aquifer system and
higher than the river bottom for the lower artesian, confined aquifer system. Therefore, from the
observation at Melkakunture exploratory well and monitoring station, it can be said that Awash
River might recharge upper aquifer system on condition that the river bed rock permeability
permits infiltration. The lower EC value at Melkakunture river in the dry season while the
deep groundwater having higher EC signifies that the river is not fed by the deep
groundwater and/or vice versa due the fact that the area is covered by thick relatively
134
impervious acidic volcanics, mainly ignimbrite, that separates the deep groundwater from
the river and/or the shallow system.
The dry season EC and major ion concentration value measured at Hombole which is almost
similar with that of the surrounding deep wells verify the fact that the river is being
recharged by the regional groundwater. The big elevation drop between these monitoring
points (Melkakunture and Hombole, more than 300m) also supports this idea, whereby the
river is in a position to cut the impervious ignimbrite and intersect the lower aquifer so that
interaction occurs between the two systems.
700
Legend
D
D
D
D
D
560
D
J
Awash_Hombole
Awash_Melka
D
D
D
D
D
D
D
Cond (uS/cm)
D
J
420
J J J
J
J
J
J
D
D
D
J
J
J
J
J
280
J
J
J
D
140
D D
J
D
J
DD
J J
D
J
J
J J
0
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2006-2007
Figure 6.23 Electrical conductivity of Awash River monitored at Melkakunture and Hombole
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7. Hydrogeological framework of Upper Awash basin: Generalized from
converging evidences of results
The hydrogeological framework of the basin has been established by converging evidences from
the different approaches/methods used. The result is briefly described below.
7.1 Aquifer system and hydraulic characteristics
The volcanic rocks of central Ethiopia in general and that of the study area in particular are
hydrogeologically inhomogeneous. Proper understanding of the physical framework of these
rocks within which groundwater localizes and moves is a key to the hydrogeology of these rocks.
Detailed hydrogeological investigation supported by exploratory drillings was carried out in the
study area. The study reveals the occurrence of two basaltic aquifer systems (upper and lower) in
the northern half of the study area (upstream of Melkakunture and Dukem areas). These basaltic
aquifers are separated by thick impermeable acidic volcanics (See section 4.1).
The upper basaltic aquifer is overlain by ignimbrites and tuffs in Becho and Legedadi areas. It
forms confined and unconfined aquifer system, locally the lateral extension is obliterated by
trachytic and rhyolitic volcanic centers and ridges. The lower aquifer is confined in
disposition. The lower basaltic aquifer is characterized by highly weathered and fractured
scoraceous lava flows; while the upper basaltic aquifer is fine to coarse grained, porphyritic, and
in places vesicular in nature. The lower basaltic aquifer has a higher transmissivity and storage
coefficient than the upper basaltic aquifer and, as a consequence, wells tapping the groundwater
system of the lower basaltic aquifer have higher yields than wells tapping the upper basaltic
aquifer. Acidic volcanics (rhyolites, trachytes, ignimbrites and Pyroclastics) commonly act as
local aquicludes by compartmentalizing the upper basaltic aquifer. However, in places these
units are found to sustain low yielding water wells along weathered and fractured zones. In
addition to the dominant basaltic aquifers, there are patches of aquifers related to Quaternary
alluvial and lacustrine deposits. The alluvial and lacustrine aquifers are found dominantly in
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the southeast around Debrezeit and Modjo towns, and locally in the northwestern part of the
Becho plain and along the main perennial river courses.
The volcanic rocks of the region have undergone extensive faulting often having a general trend
of NE – SW, E-W and at places NW-SE. The study area is situated at the intersection of the two
major regional structures namely the NE-SW trending Main Ethiopian Rift (MER) and the E-W
trending Addis Ababa-Nekemit (Yerer-Tuluwelel) volcanic lineament. The density of faults and
lineaments increases to the southeast towards the rift valley. Drilling and water quality
monitoring in the southern part of Upper Awash basin showed that the upper and lower
aquifer forms one unconfined regional aquifer system south of Melkakunture and Dukem
areas may. This is probably due to the intensive faulting and fracturing in this part of the
study area.
The shallow aquifer system in Upper Awash is recharged by local precipitation. Rainwater that
soaks through the weathered mantle and/or soil and that is not captured and transpired by plants
can seep through the unsaturated zone to recharge the aquifers. The deep aquifer system is
recharged in the high mountains and plains of Addis Ababa area and its environs. Based on the
stratigraphic relationships constructed from the drilling data of the exploratory boreholes along
and observations of geological structures, it can be deduced that aquifers in different areas could
be connected to each other through the permeable and porous scoraceous basaltic unit. It is
believed that in Upper Awash side and/or the transition and rift valley part of the study area, this
unit is downthrown by the regional east- west running Ambo Fault. The scoraceous lower basalt
formation together with the tectonic structures is therefore responsible in conveying the recharge
from the adjacent Blue Nile plateau to the Upper Awash aquifer system.
The interpolation of depth to static water level of the deeper aquifer demonstrates complex
situations, from artesian conditions to deep water levels. Generally, depth to static water level
increases from north to south except in some localities where geologic structures and local
barriers dislocate aquifers and consequently also depth to static water level and flow. Maximum
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water level depth has been recorded in the southern sector of the basin near the rift. This could be
due to the intensive fracturing of the area by regional faults (See section 5.3).
The regional groundwater flow direction is from north to south following the regional
topographic gradient. The interpolated head suggests the existence of transmissivity contrast as
reflected by variable hydraulic gradient (non-uniform flow pattern). This is due to strong
variability in lithology and partly due to variation in density and attitude of fractures. Thus the
extreme variations of lithology and fracture conditions have resulted not only in considerable
variation of productivity of the volcanic aquifers but also caused considerable variation in
hydraulic characteristics (transmissivity, hydraulic conductivity, storativity etc...). Interpretations
made on the transmissivity and specific capacity of the volcanic aquifers produced an empirical
relation between transmissivity and specific capacity. The fact that specific capacity is correlated
with groundwater flow patterns can simplify parameter estimation mainly because specific
capacity values are more abundant in groundwater databases than values of transmissivity or
hydraulic conductivity, and offer alternative approach to estimate hydraulic parameters of the
aquifers of the area (See section 5.4).
7.2 Water chemistry
The waters of the study area are classified into five major groups of chemical facies based on the
dominant cations and anions. These are Ca-Mg-HCO3, Ca–Na–HCO3, Na-Ca-Mg-HCO3, NaHCO3 and Ca–HCO3 types. Water groups represented by Ca–Mg-HCO3, are associated with the
shallow systems (springs, rivers and wells of depth less than 150m). Waters in this group are
characterized by dilute chemistry (TDS<340mg/l) and known to circulate in the upper basaltic
aquifers. Chemical facies of type Ca–Na–HCO3 are associated with moderately mineralized
waters (TDS< 500mg/l) and are mainly distributed to the transition and rift part of the study area.
This part of the area is characterized by intercalations of acidic volcanics (rhyolites, ignimbrites,
tuff, trachytes and pyroclastics), where water systems are tapping the weathered and fractured
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mantle rich in Ca- and Na- plagioclases. The Na–Ca–HCO3 type waters are mainly encountered
in two different physiographic regions, the plateau and the rift. In the plateau part of the study
area these waters are associated with deep wells having a diluted chemistry (TDS<235mg/l.
Cation exchange is most likely the process responsible for such a composition. In the rift, this
type of facies is associated with wells having a moderate mineralization (TDS<600 mg/l). The
reason for this could be groundwater evolution along its flow path (See section 6.3 and Figure
6.2).
Na-HCO3 type waters are found to exist in the three different regions. Some diluted chemistry
deep wells in the plateau area have this type of water in which a similar reason could be adopted
as those plateau wells of type Na-Ca-HCO3, which can probably be related to intensive ion
exchange that replaces the calcium in the water. Some wells in the southern part (rift) of the
study area are also found to have this facies which could be an outcome of groundwater
evolution along the regional flow path. The other systems with this facies are the highly
mineralized (TDS>1600mg/l) thermal wells which are located along the Filwuha Fault of central
Addis Ababa and elsewhere in the study area. From their association to acidic volcanics in
places, these waters might have evolved from Ca–Na–HCO3 and Ca–HCO3 type water-silicic
rock interaction at elevated temperatures where Ca is lost through CaCO3 precipitation. The
other process could be deep circulation and longer residence time which leads to further
hydrolysis of silicate minerals in the Ca–Mg–HCO3 type waters whereby the concentration of
Na, K and HCO3 increase. Ca–HCO3 type waters are also encountered in the study area
representing groundwaters that are recently recharged and/or contain waters at the early stages of
geochemical evolution which have not undergone significant water–rock interactions; this is
further evidenced by the association of these water types to the shallow systems of the plateau
area.
Generally, the ionic concentration of the waters of the study area show systematic variation of
the chemical facies. The recharge zones in the plateau have a relatively dilute hydrochemistry
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which gradually evolves to a relatively concentrated hydrochemical composition as groundwater
moves to the transition and rift zones of the study area along the flow path, which assumes a
north-south general direction (See Table 6.9). Inverse geochemical modeling shows that the
dissolution of pyroxenes, olivine, plagioclases, K-feldspars, K-micas and gaseous CO2, and
precipitation of calcite, chalcedony and clay minerals are the major chemical processes to derive
the observed natural groundwater chemistry in the study area. (See section 6.3.4 and Table 6.13)
The groundwater in the basin is fresh, of an alkali-bicarbonate type and is suitable for domestic
and/or irrigation use. However, this study reveals data where the concentration of As, Al and B
are higher than the permissible limits, which may be hazardous to public health and/or crops (See
Section 6.4).
7.3 Isotopic signatures
The δ18O versus δ2H plot of waters from different sources in the study area revealed the presence
of two groups of waters (Figure 6.18). The first group comprises waters from rivers, springs, and
wells tapping the upper unconfined shallow aquifer and the majority of the isotopic compositions
scatter around the rain isotopic composition of the area, which signifies that the aquifers they
represent are getting their recharge from the modern precipitation. The second group contains
water wells tapping the lower aquifer in all the three physiographic regions of the study area.
Groundwaters in this group are relatively depleted in their heavy stable isotope composition
when compared to present day precipitation of the area. The lower values of δ2H, δ18O and dexcess in these waters might indicate a colder climatic signal during recharge. In addition to this
the similar isotopic signatures of the deep system in all the three physiographic regions of the
study area imply similar origin of recharge and presence of hydraulic connectivity which is in
agreement with the conclusion obtained from the litho-hydrostratigraphic correlation that the
Blue Nile and Awash basins are hydraulically connected and groundwater flows from Blue Nile
plateau to Awash via the lower scoraceous basalt aquifer which is encountered in all the three
physiographic regions during exploratory deep drilling. The tritium data further constrains this
idea such that the tritium content of the wells tapping the lower basaltic aquifer is found to be
very low, <0.8TU for most of these wells. Further more, the low tritium content is exhibited in
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all the three physiographic regions of the study area, similar to the stable isotopic signatures (See
section 6.5.4 and Figure 6.19).
In addition to this, there are some springs and boreholes which are located at the foot of Intoto
Mountain (the northern watershed boundary of Awash basin with Blue Nile basin) which have
also very low or zero TU. The east-west distribution of these low tritium waters at the foot of
Intoto with a very small and/or almost zero upstream catchment area may verify the presence of
deep circulating water coming through north-south running faults which cut the acidic Intoto
mountain and intercept the east-west Addis Ababa (Filwuha)- Ambo fault. More over, the high
yield of recently drilled boreholes for the water supply of the city Addis Ababa along the foot of
Intoto ridge is also in agreement with the idea that deep circulating low tritium waters flow from
the Blue Nile plateau to Awash basin.
From all this converging evidences, it can be said that deep circulating water from the Blue Nile
plateau flows towards Awash River basin via the north –south running fault systems and the
lower scoraceous basalt aquifer which serve as a hydrogeological window to connect the two
basins.
7.4 Groundwater occurrence and circulation conceptual model
A conceptual model is a pictorial representation of the groundwater flow system, commonly in
the form of a simplified diagram or hydrogeologic cross-section. The conceptualization of how
and where water originates in the groundwater flow system and how and where it leaves the
system is critical to the development of an accurate numerical model. In developing a conceptual
model, the extent of the flow domain to be analyzed is expanded vertically and horizontally to
coincide with physical features of the groundwater system that can be represented as boundaries
(Thomas, 2001). The hydrogeological conceptual model is a result of the combination of static
(the rocks and soils: lithology, geological structures, etc.) and dynamic (the water: hydrology,
hydrochemistry, isotope hydrology, etc.) components and is necessary to give answers about the
influence of tunneling on the hydrogeological environment (Winkler et al., 2003). Hence, the
construction of groundwater models depend on an understanding of the conceptual
hydrostratigraphic model and the development of such a model involves synchronizing data
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obtained from different approaches such as field investigations (i.e., drilling activities and/or
geophysical surveys), water chemistry, isotope hydrology and etc. The appropriate conceptual
model is the base of any mathematical or numerical modeling. Any numerical modeling of
aquifers is a mathematical realization of the input parameters described within the
hydrogeological conceptual model.
Based on converging evidences from exploratory drilling, litho-stratigraphic relationships, water
quality monitoring, water chemistry and isotope signatures, the groundwater occurrence and flow
in Upper Awash and adjacent Blue Nile plateau can be conceptualized as shown in figure 7.1.
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L
g
e
n
d
Approximate water level of the regional Aquifer
Lower basaltic Aquifer
Blue Nile Plateau
e
Upper basaltic Aquifer
Regional ground flow direction
Blue Nile Basalt
Lacustrine/alluvial aquifers
Rechrage
Fualts and Fractures
Mesozoic sedimntary formation
Local aquiclides ( Volcanic riges)
2900m
Exploratory Wells
Regional aquicludes (Ignimbrites)
Becho area
Debrezeit- Modjo area
2200m
1500m
60km
120km
180km
Figure 7.1 Northwest-Southeast schematic conceptual model of groundwater occurrence and circulation in the Upper Awash basin,
central Ethiopia (not to scale). Exploratory well log data were used to construct the section.
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8. Numerical groundwater flow model of Upper Awash basin:
Preliminary results
8.1 Introduction and purpose
Tools are needed to analyze the effects of different water abstractions and asses the
dynamics of hydrological system. A groundwater computer model is one such tool that
can be used to examine effects from pumping on water levels, spring flows, and flow
paths (directions of groundwater flow). These effects can affect water availability, waterdependent ecosystems, and the movement of contaminants. A computer model can be a
valuable tool for managing the water resources and for addressing complex socioeconomic and political issues resulting from conflicting water uses.
As a consequence, numerical groundwater flow models are important tools in
hydrogeological studies in different parts of the world. However, such practice is rarely
exercised in Ethiopia, mainly due to limitations in pertinent hydrogeological data.
Recently groundwater simulation models have received attention related to the
hydrogeological system analysis of the Akaki catchment (AAWSA, 2000; Ebba, 2006;
Ayenew et al, 2008), one of the major tributaries of the Awash river and the source of
water supply for the Addis Ababa city. In this study, the first ever unified finitedifference regional groundwater flow model of the Upper Awash aquifer system has been
developed. Although not suitable to be used as a groundwater management tool at this
stage, the model does function as valuable tool in providing an initial concept on the
groundwater flow system and occurrence in the basin, to evaluate the system responses
and to understand the groundwater-surface water interactions. Generally, the modeling
work at this stage focuses more on groundwater flow system analysis, rather than
developing a well calibrated model that can readily be used as a management tool.
Upper Awash basin represents a large groundwater system that incorporates many small
catchments. Due to the large size of the study area and the inherent heterogeneity of the
volcanic aquifers of the region, attempt is made to develop conceptual and simplified
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regional numerical flow model as a preliminary modeling work. Simulations are made
under steady state conditions to obtain a general quantitative representation of the
hydrogeological system dynamics.
The regional groundwater flow model developed in this study with a relatively coarser
grid size could be used for future modeling works in the region as a base for local models
with a finer-scale grid of an area within a regional-scale model.
8.2 Concepts about groundwater models
Hydrogeological investigations are uncertain, as it is not possible to observe subsurface
geological features and underground water flow systems directly. A groundwater flow
system is dynamic, continuously changing with respect to time and space, which makes
the situation even more complex. Despite all the advances that have been made in remote
sensing, ground-probing radar and other techniques for exploring the subsurface, our
knowledge of what goes on underground is still very limited (Beven, 2001). Groundwater
models provide a scientific means to synthesize existing data into numerical
characterization of groundwater system. Due to the complexity of the real physical world,
simplifications are introduced to groundwater models in terms of assumptions to obtain a
quantitative solution for a given problem. Due to these assumptions and many
uncertainties in the input data, a model must be viewed as an approximation not as exact
duplication of field conditions (Mandle, 2002). Anderson and Woessner (1992) indicated
that modeling is an excellent way to organize field data, but it is only one component of
the general hydrogeological assessment, not and end in itself. Thus, groundwater flow
models are not an alternative for field investigation but are valuable tools to synthesize
the existing hydrogeological understanding of a given system. Groundwater models can
be used as a predictive tool to predict the future of groundwater flow system or as a
generic to investigate groundwater flow processes.
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Models are simplified descriptions of physical reality and can be verbal descriptions,
graphical representations, physical or mathematical expressions. Physical models such as
laboratory sand tanks simulate groundwater flow directly, whereas mathematical models
simulate flow indirectly by means of a governing equation that represents the physical
processes that occur in the system (Anderson and Woessner. 1992). Groundwater flow
equations that describe three-dimensional groundwater movement in a porous medium
are developed from the application of Darcy’s law and the continuity equation. The
concept of the continuity equation explains that the sum of all inflows into and out of the
cell equals the rate of change in the storage within the cell (McDonald and Harbaugh,
1988). The water balance equation is mathematically combined with Darcy’s law to
derive the following equation that describes the three-dimensional subsurface
groundwater flow process in porous medium (Anderson and Woessner. 1992).
(8.1)
Where
•
Kxx, Kyy and Kzz are the values of hydraulic conductivity along the x, y and z
coordinate axes (L/T)
•
h is the potentiometric head (L)
•
W is a volumetric flux per unit volume representing sources and/or sinks of
water, where negative values are extractions, and positive values are injections
(T−1)
•
•
SS is the specific storage of the porous material (L−1); and
is time (T)
In groundwater models, the governing equations are solved by numerical methods though
discretization of the model domain in to finite differences or finite element units. Finitedifference grids are regular rectangular grids (Fig. 8.1 a) and finite-element grids are
irregular polygonal subdivisions (Fig. 8.1 b); these grid types reflect the mathematical
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techniques used to solve the groundwater flow equations. These grids can represent one-,
two-, or three-dimensional systems.
a
b
c
d
Figure 8.1 Finite-difference grids with mesh centered nodes (a), finite- element mesh
with triangular elements (b), mesh centered (c) and block centered (d) finite- difference
modes
There are two approaches in using the finite-difference technique, known as blockcentered and mesh-centered. The name of the technique refers to the relationship of the
node to the grid lines. Head is computed at the intersection of grid lines (the mesh) in the
mesh-centered technique (Fig. 8.1 c), Conversely, head is computed at the center of the
rectangular cell in the block-centered approach (Fig. 8.1 d). The finite-difference grid is
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designed by manipulating rows, columns, and layers of cells. A series of cells oriented
parallel to the x-direction is called a row. A series of cells along the y-direction is called a
column (Fig 8.1c, d). A horizontal two-dimensional network of cells is called a layer.
Groundwater models are used to calculate the rate and direction of groundwater
movement for both aquifers and hydraulically connected surface waters. They simulate
groundwater flow by means of the governing equation that represents the physical
process in the system, together with equations that describe heads or flows along model
boundaries (Anderson and Woessner. 1992). For groundwater systems, model input
consists of the areal extent, thickness, and altitude of aquifers and confining units and the
hydrologic properties of these units. Model input also consists of the amount, location,
and by which method water enters (recharges) and leaves (discharges) the modeled
system. The definition of the amount, the location, and the method by which water enters
or leaves the groundwater system describes the boundary conditions. Typical outputs of
groundwater flow models are hydraulic heads and flow rates, which are in equilibrium
with hydrogeological conditions (hydrogeological framework, hydrologic boundaries,
initial and transient conditions, hydraulic properties and sources or sinks). In other words,
when the groundwater flow equation is solved for each cell, the water level and flow is
calculated for each cell. The equation can be solved for equilibrium (steady state) or
changing (transient) conditions. These modeled water levels and/or flows then can be
compared to measured water levels in wells and/or measured flows. Model calibration
consists of adjusting model inputs until there is a good match between the modeled and
measured water levels/flows. Detailed descriptions of these concepts in particular and
numerical models in general can be referred to McDonald and Harbaugh (1988);
Anderson and Woessner (1992); Franke and Reilly (1987); and Harbaugh and McDonald
(1996).
Numerical modeling of groundwater flow in fractured aquifer system has got a special
challenge, as it is not that clear how to describe heterogeneity associated with fractures.
Fractured media consists of solid rock with primary porosity cut by a system of cracks,
microcracks, joints, fractures and shear zones, which create secondary porosity
(Anderson and Woessner, 1992). Three common approaches are available to numerically
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model fractured aquifers: (i) equivalent porous medium approach (EPM), (ii) dual
porosity approach (DP), and (iii) discrete fractures approach (DF) at the scale of
individual fractures (Cook, 2003). The EPM approach defines a fractured system as a
single continuum or series of continua, where parameter values are affected by the
presence of fractures, but fractures are not modeled explicitly (Stafford et al 1998). The
fractured rock system is represented by one flow equation and matrix blocks are
represented by another in the dual porosity approach and applied when dealing with
systems involving high matrix porosity (Cook, 2003). The discrete fracture approach, on
the other hand, is typically applied to fractured media with low primary porosity, as the
model assumes that the water moves only through the fractured network (Anderson and
Woessner, 1992). Geology of the model area, scale of interest and purpose of the model
are important factors that should be considered when conceptual models are developed
for fractured rock aquifers. These factors could play an important role when selecting
suitable approaches to numerically represent fractured aquifers by one of the
aforementioned methods (NRC, 1996).
In the case where fracture densities are very high, it may be possible to treat the system as
one continuum where hydraulic parameters are represented by a lumped sum accounting
both fractures and matrix blocks (Cook, 2003). In addition to this as the scale increases,
the more appropriates is to employ equivalent porous media modeling approaches, where
extensive regions of an aquifer are represented by uniform hydrogeological properties. In
this case, standard finite difference and finite element codes used for porous media can be
applied to the EPM that represents the fractured system (Anderson and Woessner, 1992).
The MODFLOW model (McDonald and Harbaugh, 1988) is an example of such
application for groundwater simulations, and has been successfully applied to fractured
rock aquifers (Cook, 2003). Intensive fracturing and connectivity of fractures in the
Upper Awash basin provide the base for applying EPM approach. This approach is
commonly applied for modeling at a basin scale, where local matrix/fracture interactions
are neglected.
Accordingly,
in this study the numerical simulation utilizes the modular finite-
difference block-centered groundwater flow code, MODFLOW (McDonald and
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Harbaugh, 1988), running under the groundwater vistas v.5.19 graphical interface (ESI,
2007). Since its original development in the 1980’s the USGS has released four main
MODFLOW versions, MODFLOW-88, MODFLOW-96, MODFLOW-2000, and
MODFLOW-2005. Currently, MODFLOW is the world’s most widely used program for
simulating groundwater and transport flow equations. Groundwater vista (GV) facilitates
the use of complex three dimensional groundwater models through a flexible user
interface that allows the modeler to create a model in a variety of ways. However, no
software package can be totally flexible and GV is no exception (ESI, 2007).
Groundwater vista incorporates all the different MODFLOW versions.
8.3 Model design
Multi-disciplinary and conventional hydrogeological approaches were used, as presented
in earlier chapters, to investigate the complex volcanic hydrogeology of the study area.
As a result the groundwater occurrence and circulation of Upper Awash basin is
conceptualized based on converging evidences of hydrostratigraphic relationships
obtained from exploratory drilling, water chemistry and isotopic signatures. In addition to
this, an attempt is made to gain knowledge on this complex hydrogeology through
simplified numerical groundwater flow model. In this preliminary modeling work, the
response of the Upper Awash basin regional groundwater flow system will be outlined
with a two dimensional, steady state condition considering one layer unconfined aquifer
system.
This single layer model grid has 65 rows and 85 columns oriented north-south (Fig.8.2)
defining the 10841 square kilometer surface area of the Upper Awash aquifer system. A
uniform spacing of 2km (both rows and columns) is used, that is each cell is 4 kilometer
square. Such coarse resolution is acceptable in a regional groundwater model whose
primary purpose is groundwater flow system analysis (Buchanan, 1999). It would be
inappropriate to use this model for any other purpose requiring a finer resolution grid
without significant modifications.
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5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
5
10
15
20
25
30
35
40
45
50
55
60
65
Figure 8.2 Finite-difference model grid. The irregular line with in the mesh outlines the
Upper Awash basin boundary
8.4 Boundary conditions
A boundary condition can be defined as a constraint put on the active model grid to
characterize the interaction between the active simulation grid domain and the
surrounding environment. In groundwater models, which are used for analyzing
groundwater flow analysis, the specification of the boundary conditions usually defines
the source of water to the system and its ultimate manner of discharge. Thus, boundary
conditions are one of the key aspects in the proper conceptualization of a groundwater
system and representation of that system in a numerical computer model (Thomas, 2001).
151
85
There are generally three types of boundary conditions; specified head (First Type or
Dirichlet), specified flow (Second Type or Neumann), and head-dependent flow
(Third/mixed Type or Cauchy) (Franke et al, 1987). Specified head boundary cells are
called constant head cells and are assigned for a head that does not vary throughout the
simulation. Specified flux boundary cells are represented using no-flow, wells, or
recharge. Mixed-type boundary conditions are represented by rivers, general-head
boundaries, streams, or evapotranspiration. Constant flux boundary conditions are called
wells in groundwater vista and/or MODFLOW. It is specified in a cell by entering the
volumetric flow rate (L3/T) that the model will extract or inject into that cell. The sign of
the flow rate (positive or negative) depends upon the model. For example, MODFLOW
assumes that negative flow rates indicate pumping and positive refers to injection.
Recharge is a form of constant flux boundary conditions; however, it is normally
distributed over large areas of the model. No-Flow boundary conditions, a form of
constant flux boundaries, are applied to cells that are outside the computational domain of
the model. These are termed inactive cells in MODFLOW (IBOUND = 0). Head and
concentration are not computed in cells designated as no-flow.
In the case of mixed-type or head-dependent flux boundary conditions (for example
general-head boundary; the generic form of the head-dependent flux boundary condition
in GV and MODFLOW) computes the flux of water into or out of the model and assigns
that flux to the cell. The other types of head-dependent boundary conditions (drains,
rivers, and streams) modify this flux term depending upon the relationship of boundary
head to model-computed head in the cell. The drain boundary condition will only allow
water to be removed from the system if the head computed by the model is greater than
the head in the boundary (drain); if the head computed by the model is less than the head
in the boundary (drain), the boundary condition is turned off. The river boundary
condition also limits the amount of water injected into the aquifer if the aquifer head
drops below the bottom of the river (McDonald and Harbaugh 1988).
Accordingly in this model, in addition to the areas outside the computational domain of
the model, the western boundary bordering the Ambo-Weliso Mountains and south
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western boundary bordering the Gurage Mountains coincide with a groundwater divide
and as a result were assigned as no-flow boundary based on the basic assumption that
these mountains coincide with the surface as well as groundwater divides between the
study area and the respective watersheds. The acidic volcanic ridges and centers: like
Intoto, Wechecha, Furi, Yerer, Bedegebaba and Ziquala, which act as local barriers for
the groundwater movement and circulation, were also treated as no-flow.
The southern boundary of the model domain around Lake Koka is treated as a general
flow boundary owning the fact that this part of the area is the exit corridor for the
groundwater from the study area as evidenced by groundwater level and flow direction
maps. The northern, eastern and north western plateau part of the study area is
represented as a constant flux boundary through which groundwater is recharged from the
adjacent Blue Nile basin to the study area as per the conclusions made from converging
evidences of lithostratigraphic relationships, water chemistry and isotopic signatures
discussed in the preceding chapters. The amount of water recharged from the Blue Nile
basin to Upper Awash is estimated to be 216mcm/year (WWDSE, 2008). All natural and
artificial lakes in the study area are considered as constant head cells.
Wells
A total of 388 abstraction wells are considered with a pumping rate varying between 22
and 7567 m3/day (for basic data of these wells see Appendix 6). There are no
injector/recharging wells.
Rivers
From water chemistry monitoring results the Awash River has been known to both gain
and lose water from and to the aquifer system. In this numerical simulation of the aquifer
system the relationship between the Awash River and the aquifer is modeled using the
“Rivers” package. In order to include a river in the simulation, MODFLOW requires the
location, bed elevation, stage (river head) and conductance of the river. The conductance
is computed according to the following equation
CRIV=
KxLxW
M
(8.2)
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Where: CRIV= Hydraulic conductance of the riverbed (L2/T), K= Hydraulic conductivity
of the riverbed sediment ([L/T), L= Length of the river within a cell (L), W= Width of the
river within a cell (L) and M= Thickness of the riverbed sediments (L).
However, mainly due to absence of field data to quantify the conductance term in every
cell in the vast river network of the study area, it is assumed that the hydraulic
conductivity of the riverbed and the river channel sides is the same thereby similar to the
hydraulic conductivity of the surrounding rocks and soils in which average values are
assigned in the hydraulic conductivity zones of the model described earlier. In each river
node, based on the bed elevation, thickness, conductance and river stage the model
determines whether the node is gaining or losing.
The main Awash River and its
perennial tributaries are considered in the model. Figure 8.3 illustrates the boundary
conditions considered in the model.
8.5 Model input parameters
8.5.1 Aquifer properties
Aquifer properties refer to the hydraulic conductivity/transmissivity and storage
coefficient of an aquifer that governs groundwater flow rates and localization.
For the steady state model, the primary parameter to be estimated and distributed across
the model grid is the hydraulic conductivity. As discussed in preceding chapters, the
aquifer properties of the study area are characterized by inherent heterogeneities.
Hydraulic conductivity values of water bearing formations in the study area were derived
from pumping test data collected from exploratory wells and existing data. Extrapolation
and estimation at unmeasured points were made by geostatistical analysis (kriging) to
provide initial estimates for the model. The model domain was divided into hydraulic
conductivity zones based on similarity in aquifer properties as obtained from the
geostatisticaly predicted hydraulic conductivity map (Chapter 5).
154
Figure 8.3 Model boundary conditions
155
8.5.2 Groundwater recharge
Because an evaluation of groundwater availability is largely dependent upon recharge
(Freeze, 1971), it is crucial model input parameter warranting careful examination and
meaningful implementation. Groundwater recharge refers to the process by which the
volume of water is percolated and crosses the water table to become part of the
groundwater flow system. Recharge is a complex function of precipitation rate and
volume, soil type, water level and soil moisture, topography, and evapotranspiration
(Freeze, 1969). Precipitation, evapotranspiration, water table elevation, and soil moisture
are aerially and temporally variable. Soil type, geology, and topography are spatially
variable. As a result, reliable tools for specification of recharge at watershed scale, or the
regional model scale is always a problem. Groundwater recharge has been estimated by
different researchers and different approaches in the study area. Using the water balance
(WATBAL) model developed by David Yates (Yates, 1996) the mean annual recharge
over the study area is estimated to be about 47mm (WWDSE, 2008). The mean annual
recharge estimated using semi-distributed catchment soil-water balance model
(Thornthwaite and Mather, 1957: Alley 1985) and chloride mass balance method
(Sharma and Hughes, 1985) for the Akaki catchment part of the study area resulted
105.4 and 265mm respectively (Demlie, 2007). Groundwater table fluctuation method
of recharge estimation based on data obtained from groundwater monitoring records,
using data loggers installed in deep wells in relation to the present work, resulted an
average annual recharge of about 85mm. All the methods produce different results; the
chloride method used in Akaki catchment seems to overestimate the recharge. The value
obtained by groundwater table fluctuation method recharge estimation (chapter 3) was
used as a representative recharge value for the current modeling work. Since the
groundwater level is far from the surface, evaporation from the groundwater system is
assumed to be negligible.
8.5.3 Aquifer geometry
Aquifer geometry refers to the specification of the top and bottom elevation of the aquifer
as well as thickness and areal extent of the water bearing layer. The model layer is
156
equivalent to the hydrostratigraphic unit, which comprises lithologic units of similar
aquifer properties. Aquifer geometry information is used by the model to define aquifer
properties (transmissivity and storage coefficients) and to decide whether the given
aquifer is confined or unconfined by comparing the top elevation of the layer with
simulated heads (Middlemis, 2000). The bottom layer elevation is used by the model to
identify when a cell is drained or goes dry. As it is explained earlier, in this model the
basin will be treated as single unconfined hydrostratigraphic unit which is represented by
a two dimensional horizontal model. The digital elevation model (DEM) of the model
domain was derived from the Shuttle Radar Topography Mission (SRTM) satellite data
and specified as the top elevation of the aquifer. The average thickness of the aquifer is
estimated from lithologic logs of boreholes and used to derive the bottom elevation from
the DEM of the area. In this modeling work, the basin is represented by an average
aquifer thickness of 350m. But in, reality, as it is explained in chapter 4, two distinct
basaltic aquifers are encountered in the central, northern and western part of upper
Awash, i.e. upper confined/unconfined and lower confined basaltic aquifer. How ever,
drilling and water quality monitoring in the southern part of upper Awash showed that
the upper and lower aquifer forms one unconfined regional aquifer system south of
Melkakunture and Dukem areas may be due to the intensive faulting and fracturing
in that part of the study area.. The average aquifer thickness is generalized from deep
drillings performed in the region so far.
8.6 Model simulations and Sensitivity analysis
After designing the model, assigning boundary conditions and input parameters, the
groundwater regime of Upper Awash basin was simulated under steady state. In stead
state MODFLOW simulation, an equilibrium solution is desired for heads such that all
inflows and outflows to the aquifer domain are in perfect balance (Anderson and
Woessner, 1992). The steady state solution provides one set of head field that represent
long term and average boundary conditions of the model domain.
157
Sensitivity analysis is the process of identifying the model parameters that have the most
effect on model calibration or on model predictions. A sensitive parameter is one that
changes the calibration statistics by a large amount. By identifying the most sensitive
parameters in a model, one can streamline the calibration process by focusing the efforts
on the most important aspects of the model.
GV has two methods of performing a sensitivity analysis, (1) single sensitivity runs, and
(2) an automated sensitivity analysis. In an individual sensitivity run, you change one
parameter or boundary condition by a small amount and evaluate the change in
calibration statistics. A single sensitivity run is made by multiplying a single parameter
zone by a given multiplication factor.
In an automated sensitivity analysis, GV runs MODFLOW several times and computes
calibration statistics for each simulation. An automated sensitivity analysis is performed
using the parameters recharge, hydraulic conductivity and river conductance. The model
is found to be sensitive to hydraulic conductivity and recharge, but less sensitive for
changes in river conductance (Fig. 8.4). In fractured rock aquifers, pump test parameters
are usually uncertain and represent only a small area around boreholes. Hence hydraulic
conductivity is set as calibration parameter during calibration and recharge, even though
it is found to be very sensitive in the model, is kept constant due to the fact that
simultaneous estimation of hydraulic conductivity and recharge for a steady state model
using measured water level as the only observation data set could result misleading
outcomes (Doherty, 2004).
158
K-zone 11
K-zone
Sum of Squared Residuals
1374686
1372232
K- zone 2
1369779
K-zone 3
1367325
Recharge
1364872
River Cond1
1362418
River Cond2
1359964
River Cond3
1357511
1355057
1352604
1350150
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
Multiplier
Figure 8.4 Result of the auto sensitivity analysis
8.7 Model calibration and results
To demonstrate that groundwater models are realistic, field observations of aquifer
response (such as groundwater levels) are compared to corresponding model simulated
values in model calibration, with the general objective of reducing the difference.
Calibration involves adjustment and refinement of parameter structure and values to
provide the best match between measured and simulated hydraulic heads and flows. It
checks how the simulation is reproducing to field measured heads and flows (McDonald
and Harbaugh 1988). Calibration requires development of calibration targets and
specification of calibration measures.
Calibration statistics are computed by first calculating the error associated with each
target and then calculating simple statistics on the population of targets. The error is
called a residual and is computed by subtracting the model-computed value (head,
drawdown, concentration, or flux) from the target value. Negative residuals indicate that
the model is calculating the dependent value too high and a positive residual is where the
159
model value is too low. Calibration can be carried out by manual trial and error and
automatic optimization. The trial and error technique implies a manual parameter
adjustment where initial parameter values are adjusted in a sequential model run to match
simulated heads to observed ones. Automatic calibration involves the use of numerical
algorithm to obtain an estimate of system parameters that yield the closest match between
observed data and simulation results. The trial and error adjustment may become highly
subjective and inefficient procedure; automatic calibration, in contrast, is fast and less
subjective. In this study, PEST (Parameter Estimation), an inverse solution automatic
calibration method is used within the groundwater vistas graphical interface. PEST is
model independent and uses a nonlinear iterative estimation technique known as the
Gauss-Marquardt-Levenberg method, which has the strength of estimating parameters
using fewer model runs than any other estimation method (Doherty, 2004). A detailed
description of PEST software and its parameter estimation methods can be found in the
PEST manual (Doherty, 2004).
Calibration results are evaluated by using qualitative and quantitative performance
measures. Qualitative assessment (pattern matching) involves comparisons of contour
maps and hydrograph of measured and simulated head, while quantitative performance
measure involves mathematical or statistical description of residuals (Middlemis, 2001).
The performance of any model should be evaluated by combining both qualitative and
quantitative measures. The water balance is one of the quantitative performance measures
that are used to check solution accuracy or the total residual error in the calibration
solution by comparing total simulated inflows and outflows. The water balance error is
expressed in percentage, representing the difference between total inflows and total
outflows including change in storage divided by total inflows or outflows. The error
should be less than 1% for the entire model to ensure the calibration solution is
numerically stable and acceptable (Anderson and Woessner, 1992; Middlemis, 2001).
Lumped-sum comparison methods are the other commonly used quantitative measures
employed to measure the average difference of residuals. However, these performance
indicators provide lumped measures of calibration that do not indicate the spatial or
160
temporal distribution of the error (Anderson and Woessner, 1992). These methods
include mean error of residuals, absolute mean error to the residuals, and root mean
squared error.
In this steady state simulation, as it is indicated earlier, hydraulic conductivity is used as
calibration parameter. The primary calibration target in groundwater modeling is
hydraulic head (water level). Accordingly, in this study, steady-state calibration was
made using static water level observations of 388 wells. The effectiveness of calibration
was evaluated by visual matching of measured groundwater level contours with
simulated ones, the water balance error and lumped quantitative performance measures
such as the mean error, the mean absolute error, and the root mean square error. The
mean error (ME) is the mean of the differences between measured heads (hm) and
simulated heads (hs):
1 n
ME = ∑ ( hm − hs ) i
n i =1
8.3
Where n is the number of calibration measurements. Because both positive and negative
residuals are used in the calculation, this value should be close to zero for a good
calibration. In other words, the positive and negative errors should balance each other.
The mean absolute error (MAE) is the mean of the absolute value of the differences
between measured heads and simulated heads:
MAE =
1 n
∑ (hm −hs) i
n i =1
8.4
The root mean square (RMS) error is the square root of the average of the squared
differences between measured heads and simulated heads:
RMS =
1 n
2
 ∑ (hm − hs ) i 
 n i =1

0 .5
8.5
The RMS is used as the basic measure of calibration for heads. Uncertainty in head
measurements can be the result of many factors including, measurement error, scale
161
errors, and various types of averaging errors, both spatial and temporal. The required
calibration criterion for heads is an RMS that is equal to or less than 10 percent of the
observed head range in the aquifer being simulated (Anderson and Woessner, 1992). A
root mean squared error of 6% was achieved at the end of the calibration for the observed
head range in Upper Awash aquifer (Table 8.1). The mean error (ME) and the mean
absolute error (MAE) are also characterized by low values indicating that the model was
well calibrated. The value of the correlation coefficient (R2) indicates a good
performance of the model.
Model performance measures
Values
Mean error (ME)
-0.89
Mean Absolute error (MAE)
0.04
Root mean square error (RMS)
0.06
Correlation coefficient (R2)
0.96
Table 8.1 Summary of calibration errors
In addition to visual matching of simulated contours (Fig. 8.5) to those of observed
contours (Fig. 5.2 (b)), scatter plots are used in assessing the quality of calibration
simulations. Observed target values (measurements) are plotted versus the values
computed by the model. In an ideal calibration, the points will fall on a straight line with
a 45 degree slope; i.e., the computed value equals the measured value. The degree of
scatter about this theoretical line is a measure of overall calibration quality (Anderson
and Woessner, 1992). In our case, the correlation coefficient (R2) between the observed
and simulated values was found to be = 0.96 (Fig. 8.6 (a)). A similar scatter plot can be
created with observed value on the X-axis and residual on the Y-axis. In this case, the
scatter of points should not have a pattern (it should be random). Both scatter plots for the
calibration simulations of the Upper Awash aquifer system are shown in Figure 8.6.
162
2300
2300
2300
2300
21
00
2100
1900
19
2100
00
1900
0 2300
0
3
2
1700
Figure 8.5 Simulated groundwater level contours
163
2636.0
a
Model Value
2377.2
2118.3
R2 = 0.96
1859.4
1600.6
1600.6
1859.4
2118.3
2377.2
2636.0
Observed Value
305.0
b
203.3
Residual
101.7
0.0
-101.7
-203.3
-305.0
1600.6
1859.4
2118.3
2377.2
2636.0
Observed Value
Figure 8.6 Scatter plot of observed vs. computed target values (a) and observed vs.
residuals (b)
164
Water balance
The water balance is based on the principle of the conservation of mass for boundaries
defined in space and time (Oosterbaan et al 1994); it states the rate of change in water
stored in a system, such as a drainage basin, is balanced by the rate at which water flows
into and out of the system and can be written as:
Inflow = Outflow + ∆W
(8.6)
Where, ∆W is the change in water storage, when the change in storage is positive, the
water content increases and, when negative (i.e. there is depletion instead of storage), it
decreases.
When the water balances are made for fairly long periods (e.g. per season or per year), in
many cases the ∆W values (changes in storage) are small compared to the values of the
other factors (components) of the water balance. Then, the ∆W values can be ignored, so
that the water balances can be simplified. When ∆ is taken zero, i.e. the amounts of all
incoming and all outgoing water are equal, one obtains a steady state. Therefore, over
fairly long periods of time, the water balance can often be considered in steady state.
Water balance is a valuable assessment tool, as it provides a measure of the relative
importance of each component to the total budget. Following visual evaluation of water
level contours, statistical and graphical evaluation of the model out put, the steady state
calibration water balance of the groundwater regime of Upper Awash basin was
analyzed. The water balance of the groundwater domain (table 8.2) shows that river
leakage is the main component that plays a major role both as a source of groundwater
and also acts as a discharging media to the surrounding flow system. Recharges from
precipitation, groundwater inflow from the adjacent Blue Nile basin, leakage from
reservoirs are also sources of water to the groundwater regime in the study area.
Extraction wells, groundwater leaving the aquifer across the southeastern and southern
general head boundary are the major outflows from the aquifer.
165
Water balance component
Inflow
Outflow
(m3/year)
(m3/year)
Constant Head
1.05x109
9.4 x108
Constant flux
2.16x108
0
Wells
0
1.5 x108
Recharge
8.6 x108
0
River Leakage
3.1 x1010
3.2 x1010
Head dependent Boundaries
2.5 x109
2.6 x109
Storage
0
0
Total
3.6092 x1010
3.6087 x1010
In - Out
5x106
Percent Discrepancy
0.01
Table 8.2 Simulation result of long-term annual water balance of Upper Awash basin
groundwater domain
166
9. Conclusions and recommendations
9.1 Conclusions
The Awash River rises on the high plateau near Ginchi town west of Addis Ababa in
Ethiopia and flows along the rift valley into the Afar triangle, and terminates in salty
Lake Abbe on the border with Djibouti, being an endorheic basin. The Awash River basin
has been divided in to three main sub-basins: Upper (upstream of Koka Dam station),
Middle (between Koka and Awash station), and Lower (comprising the deltaic alluvial
plains in Tendaho, and the terminal lakes area). The Upper Awash basin is located in
central Ethiopia at the western margin of the Main Ethiopian Rift (MER). The study area
is confined within the limits of 8º15’-9º15’N latitude and 38 º-39 º 15’ E longitudes.
The main objective of this work was to characterize the hydrogeological system of the
Upper Awash basin by giving special emphasis on the inter-basin water transfer across
the Blue Nile and Awash River basins watershed boundary. To achieve this objective
converging evidences from conventional hydrogeological investigation, exploratory
drilling, litho-hydrostratigraphic relationships, water quality monitoring, water chemistry,
isotope hydrology and numerical modeling were used to set up the hydrogeological
framework of the study area.
Based on evidences from the exploratory drilling in the central, northern and western
part of the basin, two distinct basaltic aquifers are encountered, i.e. upper and lower
basaltic aquifer. The lower aquifer is confined and the upper is confined at some
places and unconfined in others. However, drilling and water quality monitoring in the
southern part of Upper Awash showed that the upper and lower aquifer forms one
unconfined regional aquifer system south of Melkakunture and Dukem areas may be
due to the intensive faulting and fracturing in that part of the study area.
Based on the litho-hydrostratigraphic relationship correlated from the drilling data of the
exploratory boreholes coupled with the respective geological structure scenarios,
167
groundwater movement could be connected to each other through the permeable and
porous scoraceous lower basaltic aquifer all the way from the Blue Nile Plateau to the
study area. From the constructed relationship, the scoraceous Tarmaber and/or Amba
Aiba formation together with the tectonic structures is therefore responsible in conveying
the recharge from the adjacent Blue Nile plateau to the Upper Awash groundwater
system.
The data logger record of the temporal fluctuation of the groundwater levels were used to
determine the groundwater recharge by using the water table fluctuation method. The
average aerial annual recharge of the study area is estimated to be 85mm, which accounts
about 8.7% of the mean annual aerial precipitation of the study area.
Based on the dominant cations and anions, the waters of the study area were classified
into five major groups of chemical facies: Ca-Mg-HCO3, Ca–Na–HCO3, Na-Ca-MgHCO3, Na-HCO3 and Ca–HCO3. The shallow systems (springs, rivers and shallow wells)
in all the three physiographic regions of the study area are represented by Ca–Mg-HCO3
type waters, having a dilute chemistry (TDS<340mg/l) and known to circulate in the
upper basaltic aquifer. Moderately mineralized waters (TDS< 500mg/l) are having Ca–
Na–HCO3 facies and are mainly distributed to the transition and rift part of the study area
and associated mainly with intercalations of acidic volcanics. The Na–Ca–HCO3 type
waters are mainly encountered in the plateau and the rift part of the study area. Cation
exchange process responsible for the diluted chemistry (TDS<235mg/l) of the plateau
deep systems and groundwater evolution along its flow path, for the rift waters having
similar facies but moderate mineralization (TDS<600mg/l). Na- HCO3 type waters in the
area are associated with diluted chemistry deep wells in the plateau area and wells in the
southern part (rift). Ca–HCO3 type waters were associated with the shallow systems of
the plateau area; representing groundwaters that are recently recharged and/or contain
waters at the early stages of geochemical evolution which have not undergone significant
water–rock interactions.
168
Trace element analysis of some deep groundwater sources show concentrations above the
permissible limits of WHO guidelines of toxic elements such as born and arsenic. Hence,
it is a signal to consider the deep aquifer not only in terms of potential but also with
respect to water quality for different purposes (drinking, agricultural, industrial).
Therefore, it is highly recommended to undertake further investigation to validate these
findings for the high concentrations of toxic elements in the samples owing to the fact
that these elements are not expected to be high in surrounding rocks and soils of the study
area, of course, this has to be also further supported by analyzing the rocks and soils for
their toxic trace element contents.
The stable isotopic signatures (δ18O and δ2H) of different water sources revealed further
evidences for the ideas concluded from the litho-stratigraphic relationships obtained from
exploratory drilling. Water wells tapping the lower aquifer in all the three physiographic
regions show similar isotopic signatures. Groundwaters from this lower basaltic aquifer
are found to be relatively depleted in their heavy stable isotope composition when
compared to present day precipitation of the area. Deep wells from the Blue Nile Plateau,
wells from Becho plain (located in the transitional part of the study area) and wells from
the rift part scatter in the same group. Groundwaters from the lower aquifer system
generally have lower values of δ2H, δ18O and d-excess indicating a colder climatic signal
during recharge. In addition to this the similar isotopic signatures of the deep system in
all the three physiographic regions of the study area imply similar origin of recharge and
presence of hydraulic connectivity, thereby inter-basin transfer across the watershed
boundary of the two basins, Blue Nile and Awash.
The evidence obtained from the tritium data analysis also strengthens the ideas obtained
from the litho-hydrostratigraphic relationships and the stable isotopic signatures. The
tritium concentration of wells tapping the lower basaltic aquifer is found to be very low,
<0.8TU. The low tritium content is exhibited in all the three physiographic regions
tapping this particular aquifer. This again agrees with what is evident in the stable isotope
signatures of these wells. Therefore, it can be said that these wells represent relatively old
waters; hence they could have long resident time underground and/or have long flow
169
paths. In addition to this their similar tritium signature all the way from the plateau to the
rift could also be an additional evidence to support the conclusion that the wells tapping
the lower aquifer would have been recharged from a similar source (both in time and
space) and are hydraulically interconnected. Therefore the likely recharge area for the
deep aquifer system could be the relatively elevated and cool climate, Blue Nile plateau,
located at the northern watershed boundary of the study area, which is evidenced by the
plateau wells located in Blue Nile basin showing similar stable and tritium isotopic
signatures with the waters of the transitional and rift part of Upper Awash.
In addition to this, there are some springs and boreholes which are located at the foot of
Intoto Mountain (the northern watershed boundary of Awash basin with Blue Nile basin)
which have also very low or zero TU. Moreover, there are also high yielding recently
drilled boreholes for the water supply of the city Addis Ababa along the foot of Intoto
ridge. The east-west distribution of these low tritium high yield waters at the foot of
Intoto with a very small upstream catchment area may verify the presence of deep
circulating water coming through north-south running faults which cut the acidic Intoto
Mountain and intercept the east-west Addis Ababa (Filwuha) - Ambo fault. This leads
again to the conclusion that deep circulating water from the Blue Nile plateau flows
towards Awash River basin via the north–south running fault and the lower basaltic
aquifer systems which serve as a hydrogeological window that connects the two basins.
Finally, a numerical groundwater flow model was developed for the watershed of Upper
Awash basin by taking into consideration the groundwater inflow from the adjacent Blue
Nile basin. The modeling exercise was an attempt to see the response of the basin aquifer
system for model input parameters and its sensitivity for them. The steady state model
provided valuable information on the groundwater balance, groundwater–surface water
interactions and flow patterns. The model estimated groundwater head distribution
reasonably agrees with the regional groundwater contour map constructed from field
measurements. Having all the uncertainties in the estimation of model input parameters,
the overall water balance can be used for the groundwater system analysis of the area.
170
However, owing to the scale of the model, data limitations on aquifer parameters and
thickness, the steady state model could only be used for system response evaluations. At
this stage, the model can not be used for detailed groundwater management purposes. But
the accuracy of the model could be improved by making more detailed estimation of
model input parameters in a finer model grid, using multi layer aquifer system (the well
lithologic logs in the northern and central part of the study area show different volcanic
layers representing different hydraulic characteristics, lower and upper basaltic aquifer)
and by treating temporal variations under transient conditions. The results obtained here
are system responses made in single layer aquifer system using fairly estimated model
input parameters in a coarser model grid. Hence, the results should be interpreted and
applied considering all the limitations and drawbacks.
9.2 Recommendations
This study benefited from the hydrogeological data set obtained from different sources,
mainly due to the location of the study area, Upper Awash basin, in the vicinity of the
capital Addis Ababa and due to the fact that recently more attention is given to this part
of the region both for water supply and agricultural development projects, particularly for
the realization of the exploratory drillings from which original hydrogeological data were
obtained. However, the work also faced limitations with regard to both data quantity and
quality as well as to the spatial and temporal coverage of hydrological, hydrogeological,
hydrochemical and isotope data. To better understand and increase the knowledge on the
hydrogeological framework of a complex system like the study area, proper utilization of
existing records and generating new data having both temporal and spatial coverage is
very crucial. The following points, if addressed, will help in alleviating the problem in
future groundwater studies.
1. Groundwater monitoring has been given a very little and/or no attention in the
country. But it is a very essential step towards evaluating and managing
groundwater resources. Automatic data loggers can easily be integrated with
171
manual groundwater observation techniques to establish a network of
groundwater level monitoring stations.
2. Although
numerous
boreholes
were
constructed
by
different
bodies
(governmental, non governmental organizations, individuals, firms, etc.), a central
groundwater database, either in a national and/or regional level is not established
in the country to organize the records generated during the construction of these
wells. Data are collected in a non standardized and non systematic manner, most
of the time not reliable to use for research and/or scientific purposes.
3. Isotopic and hydrochemical data of both rainfall and groundwater are useful in
understanding a hydrologic system. But spatial and temporal coverage of these
data are very limited. So, coupling of conventional hydrogeological methods and
chemical and isotopic signatures is essential to have a better understanding on the
groundwater dynamics of a system.
172
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Appendices
186
Appendix 1: Lithologic log of some selected boreholes in Upper Awash basin and
adjacent Blue Nile plateau (Source: ABGREP database)
1.1 Plateau wells
Well name
Lithologic log
Depth (m)
Chancho
Segnogebeya
Legedadi
Bekie
Sululta
Lithologic description
From
To
0
38
Black top soil
38
164
Slightly to highly weathered scoraceous basalt
164
176
Massive basalt
176
186
Fractured basalt
186
188
Highly weathered scoria
188
210
Massive basalt
210
240
Moderately to Highly weathered scoria
240
312
Massive to highly weathered basalt
312
320
Moderately weathered scoria
320
324
Massive basalt
0
6
Black top soil
6
34
34
90
Moderately to highly
scoraceous basalt
Massive basalt
90
250
250
273
Slightly weathered to highly weathered and
fractured scoraceous basalt
Shale
0
8
Black top soil
8
116
Moderately to highly weathered rhyolite and tuff
116
170
Slightly to moderately weathered basalt
170
282
Moderately to highly weathered ignimbrite
282
354
Slightly weathered scoraceous basalt
0
7
Clay
7
30
Scoraceous basalt
30
115
Highly weathered Tuff
115
298
Fractured basalt
298
300
Scoraceous basalt
0
8
Black top soil
weathered and fractured
187
Well name
Lithologic log
Depth (m)
Inchini
Holeta
Lithologic description
From
To
8
56
Highly weathered rhyolite
56
94
Moderately weathered and fractured basalt
94
110
Highly weathered ignimbrite and rhyolite
110
182
Massive to highly weathered and fractured basalt
182
184
Clay
200
266
Highly weathered & fractured basalt
266
272
Clay
272
278
Highly weathered basalt
278
304
Scoraceous basalt
0
12
Fractured basalt
12
24
Highly weathered and fractured basalt
24
50
Moderately weathered and fractured basalt
50
76
Slightly fractured basalt
76
92
Massive basalt
92
96
Highly weathered and fractured basalt
96
146
Massive basalt
0
6
Red top soil
6
14
Slightly fractured basalt
14
26
Red clay
26
38
Slightly fractured basalt
38
42
Black clay
42
60
Slightly fractured basalt
60
72
Red clay
72
106
Slightly to Moderately fractured basalt
106
114
Massive basalt
114
122
Fractured & weathered basalt
122
126
Massive basalt
126
132
Highly weathered basalt
132
236
Moderately weathered to Massive basalt
236
300
Scoraceous basalt
188
1.2 Wells in the transitional part of the study area
Lithologic log
Depth (m)
Well name
Kimoye
Dimajelewa
Asgori
Tefki Harojila
Lithologic description
From
To
0
8
Top soil
8
36
Clay
36
46
Highly weathered
46
52
Coarse gravel
52
64
Highly weathered basalt
64
70
Coarse gravel
70
74
Silty clay
74
82
Massive basalt
82
86
Clay
86
110
Moderately to highly weathered basalt
110
138
Tuff
138
243
Fractured & weathered basalt
0
52
Tuff & Pumice
52
172
Moderately weathered basalt
172
300
Tuff & Loose pyroclatic deposit
300
311
Scoraceous basalt
0
1
Black top soil
1
136
Moderately weathered tuff, ignimbrite and ash
136
154
Moderately weathered basalt
154
164
Massive basalt
164
224
224
225
Moderately to highly weathered ignimbrite and
pumice
Paleaosol
225
308
Scoraceous basalt
0
22
Weathered pyroclastic deposit
22
80
Slightly weathered to massive trachyte
80
122
Scoraceous basalt
122
132
Ignimbrite
132
170
Scoraceous basalt
170
280
Scoria
189
Lithologic log
Depth (m)
Well name
Melkakunture
Akaki
CMC
Lithologic description
From
To
0
2
Black top soil
2
34
Rhyolite, tuff and ignimbrite
34
54
Highly weathered & fractured basalt
54
90
Massive basalt
90
110
Fractured basalt
110
192
Rhyolite, ignimbrite and pumice
192
290
Scoraceous basalt
0
4
Black top soil
4
202
202
262
Slightly to moderately weathered & fractured
basalt
Moderately weathered scoraceous basalt
262
266
Silt with minor clay
266
328
Highly weathered scoraceous basalt
0
100
100
104
Slightly to moderately weathered tuff and
ignimbrite
Clay
104
208
208
262
Moderately to highly weathered ignimbrite and
pryroclastics
Massive basalt
262
368
Moderately weathered ignimbrite
0
4
Black top soil
1.3 Wells in the rift part of the study area
Well name
Lithologic log
Depth (m)
Adulala
Dukem
Lithologic description
From
To
0
4
Top soil
4
8
Scoraceous basalt
8
12
Paleaosol
12
60
Scoraceous basalt
60
128
Tuff
128
212
Scoraceous basalt
212
225
Highly fractured basalt
0
26
Weathered tuff and pyroclastic deposit
26
40
Moderately weathered trachyte
190
Well name
Lithologic log
Depth (m)
Borora
Modjo Ude
Bore-Tina (ADTW1)
Lithologic description
From
To
40
104
Highly weathered trachyte
104
132
Moderately weathered trachyte
132
178
Scoraceous basalt
178
180
Paleaosol
180
206
Scoraceous basalt
206
226
Weathered tuff
226
242
Weathered basalt
242
254
Massive basalt
254
264
Scoraceous basalt
264
282
Massive basalt
0
14
Black top soil
14
34
Highly weathered basalt(clay)
34
50
Weathered scoraceous basalt
50
62
Black fractured aphantic basalt
62
70
Paleaosol
70
98
Scoraceous basalt
98
120
Black fractured basalt
120
128
Moderately tuff
128
130
Paleaosol
130
300
Scoraceous basalt
0
6
Black top soil
6
46
Highly weathered trachyte
46
80
Fractured trachyte
80
142
Weathered scoria
142
194
Fractured trachyte
194
278
Scoraceous basalt
0
4
Top Soil
4
24
Slightly Weathered Scoraceous basalt
24
38
Tuff
38
58
Weathered Scoraceous Basalt
58
78
Massive Basalt
78
140
Weathered Scoraceous Basalt
140
158
Slightly Weathered Trachy Basalt
158
188
Fresh Ignimbrite
191
Well name
Lithologic log
Depth (m)
Serdo (ADTW2)
Modjo Mudasenkele
Lithologic description
From
To
188
200
Weathered and altered Ignimbrite
200
218
Slightly Weathered Trachy Basalt
218
250
Slightly Weathered Tuff
250
274
Slightly Weathered Basalt
274
300
300
316
Weathered Trachy Basalt
316
352
Scoria dominated Scoraceous Basalt
352
362
Weathered and fractured Basalt
0
2
Top Soil(black Cotton Soil)
2
30
Massive basalt
30
56
56
72
Slightly to moderately fractured Scoraceous
Basalt
Moderately weathered Trachy Basalt
72
158
158
170
Slightly to moderately weathered and fractured
Scoraceous Basalt
Scoria
170
210
Moderately fractured Scoraceous Basalt
210
218
Weathered Tuff
218
228
Moderately fractured Scoraceous Basalt
228
248
Moderately fractured Trachy Basalt
248
300
Moderately fractured Scoraceous basalt
0
12
Top soil
12
24
Moderately weathered trachyte
24
102
Highly weathered tuff
102
146
Weathered and fractured trachyte
146
225
Highly weathered tuff
225
230
Circulation loss
230
320
Highly weathered tuff
320
330
Scoraceous basalt
Scoria
192
Appendix 2: Hydrochemical data of water samples in Upper Awash basin (“a” in the data sources column represent the original
data generated in this study and for others the data source is the AGREP database of WWDSE, ministry of water resources unless
other wise indicated, , units in mg/l unless indicated)
SampleID
Locality
utmE
utmN
Elv
(m)
Well_Depth
(m0
Cond
µS/cm)
TDS
PH
NA
K
CA
MG
CL
NO3
F
HCO3
CO3
SO4
SHPL1
Holeta-Ag
445773
1001323
2389
75
168
130
8
10
2
32
19
11
15
1
196
0
0
SHPL2
Holeta-Wo
448532
1008047
2525
50
258
174
6
6
1
45
5
8
25
0
118
0
6
SHPL3
Nano Galg
441584
1003445
2453
50
263
170
7
7
2
46
8
1
7
0
179
0
1
SHPL4
Badeg Adi
431584
998853
2313
0
415
269
7
10
2
63
20
2
8
0
287
0
2
SHPL5
Adabega S
432432
1024464
2603
70
475
312
8
25
2
84
3
11
10
1
322
0
0
SHPL6
Ginchi we
404656
997733
2230
81
663
434
8
12
2
114
15
16
25
0
384
0
9
SHPL7
Chancho t
471304
1027754
2552
65
257
146
7
21
1
32
4
2
3
0
154
0
1
SHPL8
Fiche-Gor
482263
1038145
2570
66
235
134
7
14
1
7
23
9
0
1
137
0
2
SHPL9
Gefersa E
452124
1002590
2629
0
240
148
7
5
1
41
5
5
9
0
149
0
1
SHPL10
Menagesha
454124
1005990
2629
0
272
172
7
6
2
44
9
0
10
0
172
0
0
SHPL11
Tatek Mil
459689
998340
2592
48
196
132
7
9
5
30
4
1
8
0
123
0
1
SHPL12
AA-Hillto
474175
996550
2381
120
3359
2049
8
840
15
6
2
43
0
21
2198
0
55
DPL13
Holota
447549
1007893
2508
203
164
110
8
18
2
16
2
3
3
1
98
0
2
DPL14
Holota-to
445987
1001623
2378
330
252
158
9
35
1
19
5
6
1
0
143
5
2
DPL15
Inchini
421795
1040108
2457
146
252
158
8
31
3
19
6
6
0
1
146
0
1
DPL16
Holota
440274
1006055
2525
300
260
164
9
54
1
5
1
13
0
1
113
12
1
DPL17
AA-Americ
473900
1001050
2568
200
227
139
9
29
5
24
11
14
2
0
171
12
0
DPL18
AA-Asko-9
465578
999808
2481
193
341
206
9
76
3
4
1
8
0
3
181
12
0
DPL19
AA-Burayu
464031
1002909
2583
200
273
172
7
32
5
26
3
7
0
2
154
0
3
DPL20
AA-Yekami
477515
997474
2410
216
284
182
7
30
6
26
4
10
2
1
169
0
8
DPL21
Chancho
473911
1031930
2543
324
166
108
8
30
1
5
1
2
1
0
90
0
7
DPL22
Segnogeb
455620
1026514
2610
273
237
152
8
34
2
16
1
7
3
0
119
5
13
DPL23
Sululta
474421
1013070
2610
304
289
192
8
55
1
6
3
26
0
1
70
24
26
DPL24
WWDSE
477940
995029
2330
200
210
132
6
11
3
26
6
3
3
0
129
0
1
DPL25
Legadadi,
493518
1004421
2468
354
380
242
8
32
7
36
9
6
0
1
221
0
16
DPL26
Bekie map
507086
1012954
2578
300
412
235
9
80
19
9
2
29
8
1
137
19
19
DPL27
AA-Ayat-9
489127
999697
2441
280
524
344
7
77
22
17
3
33
0
2
226
0
30
DPL28
Onoda
513157
1025381
2904
348
258
170
8
29
1
25
4
17
1
1
104
10
13
DPL29
AA-Shegol
469322
1001428
2573
240
311
174
7
8
2
42
13
8
0
1
197
0
0
DPL30
AA-Filwuh
473276
996535
2350
504
3380
2240
9
930
16
3
1
8
2
28
1874
101
92
SHT31
Wajitu Ha
439632
993521
2186
39
447
300
7
10
2
78
12
6
23
0
256
0
7
Data
source
a
a
a
a
a
a
193
SampleID
Locality
utmE
utmN
Elv
(m)
Well_Depth
(m0
Cond
µS/cm)
TDS
PH
NA
K
CA
MG
CL
NO3
F
HCO3
CO3
SO4
SHT32
Dimajalew
413137
973900
2090
60
1153
798
8
152
16
118
30
61
1
1
630
0
SHT33
Awash Mel
456740
962388
2005
39
510
350
7
42
10
63
9
7
8
1
333
0
106
3
SHT34
Addis Ale
430267
987498
2071
38
630
380
7
42
4
83
14
19
7
2
350
0
22
SHT35
Tefki tow
444624
978143
2065
65
726
455
7
116
7
36
6
57
5
4
346
0
16
SHT36
Alem Gena
466690
976790
2087
93
588
376
7
67
10
42
14
20
33
1
266
0
17
SHT37
Abasamuel
469142
966835
1946
0
571
371
7
46
6
42
22
9
6
1
304
0
6
SHT38
Alem Gena
460464
974637
2090
61
376
236
7
16
2
47
14
6
12
3
227
0
1
SHT39
Galetti P
474800
984700
2146
71
550
354
8
36
9
33
12
11
0
1
299
0
0
DT40
Alem Gena
445643
973409
2121
142
333
219
7
17
4
50
8
3
8
1
236
0
1
DT41
Muti Dayu
451590
954524
2117
156
430
280
7
20
11
51
15
8
12
2
274
0
1
DT42
Kusaye Ti
462875
950361
2077
187
383
250
8
28
10
42
9
6
5
1
239
0
0
DT43
Melkakunt
456314
962592
2014
290
536
360
7
41
11
65
12
9
2
1
312
0
14
DT44
Tefki
450359
981037
2084
280
487
312
8
49
5
55
4
13
5
1
256
0
8
DT45
Asgori du
427126
971361
2075
308
229
142
8
27
2
19
6
5
0
0
117
7
6
DT46
Kimoye
427395
992768
2109
243
430
282
9
102
1
3
1
35
6
1
159
14
16
DT47
Dimajalew-
413137
973900
2090
311
822
540
8
69
14
94
21
4
7
2
589
0
2
DT48
Jawaro-
433200
959670
2111
194
603
404
7
52
8
77
14
8
4
1
439
0
2
DT49
Asgori
427126
971361
2075
308
874
572
7
160
23
10
3
63
0
2
366
0
44
DT50
CMC
484821
994284
2320
368
2370
1601
8
505
32
29
20
33
1
1
1495
0
113
DT51
AA-Bole L
484152
989566
2205
182
363
240
7
20
5
46
10
6
1
1
223
0
4
DT52
AA-TW4
489950
976019
2072
220
537
315
8
40
11
56
17
7
8
0
317
0
1
DT53
AA-TW3
484475
975622
2110
220
672
408
8
40
4
97
18
7
10
0
444
0
1
DT54
AA-TW5
485798
968308
1905
220
627
344
7
35
7
62
25
6
11
0
386
0
1
DT55
AA-Water
481200
980000
2161
173
530
385
8
41
5
67
16
14
12
1
366
0
0
DT56
AA-TW1
477945
976985
2069
300
964
530
9
180
4
10
2
0
0
1
149
0
51
SHR57
Red Fox F
503210
930527
1600
100
1930
915
9
442
7
7
0
63
0
19
830
0
9
SHR58
Woliso Ne
498233
939885
1646
8
1621
1193
8
276
11
4
1
38
0
27
657
0
22
SHR59
Galiyee,
485970
935007
1708
98
453
253
8
42
12
32
14
10
10
0
271
0
3
SHR60
D/Z-Healt
497100
968198
1896
92
453
306
8
42
12
32
14
10
10
0
271
0
3
SHR61
Kusaye Ad
498233
939885
1646
8
1621
1036
8
345
20
21
11
85
0
4
801
0
38
SHR62
D/Z-Girma
495561
968574
1906
60
464
303
7
25
7
43
20
0
0
0
320
0
1
SHR63
Modjo-Biy
507714
955875
1849
33
728
476
7
66
12
74
24
15
10
1
461
0
7
SHR64
D/Z-Veter
500078
968505
1907
56
1041
635
8
138
12
36
36
0
0
1
625
0
6
SHR65
Modjo#1
512011
949196
1766
90
697
449
8
70
13
47
11
14
1
1
342
18
0
Data
source
a
a
a
a
a
a
a
a
a
194
SampleID
Locality
utmE
utmN
Elv
(m)
Well_Depth
(m0
Cond
µS/cm)
TDS
PH
NA
K
CA
MG
CL
NO3
F
HCO3
CO3
SO4
SHR66
Shimbira
500424
974376
1903
82
615
397
8
33
9
48
15
18
2
1
256
18
0
SHR67
Meki-Grab
483833
904045
1666
52
1783
1184
8
420
20
31
7
122
5
8
1017
0
1
DR68
Ziquala-A
481162
935226
1808
200
354
232
7
30
7
38
7
4
7
1
226
0
0
DR69
Mukiye, Z
478740
933707
1773
155
400
260
8
40
10
33
6
7
7
2
239
0
1
DR70
Adulala R
490444
951336
1765
225
599
364
8
64
10
55
16
12
2
2
395
0
1
DR71
D/Z-Oromi-
491980
965840
1957
250
343
238
8
44
18
25
5
14
3
1
231
0
2
DR72
Abusera
478990
955803
1830
330
616
400
8
105
24
21
3
13
0
2
363
0
13
DR73
Dukem
490336
970789
1924
282
500
350
8
75
22
28
5
9
0
2
340
0
1
DR74
Modjo Mud
506464
941989
1697
268
854
578
8
170
15
17
3
53
1
8
424
0
26
DR75
Nazareth-
527667
941523
1615
152
700
470
8
106
15
51
7
8
18
1
451
0
8
DR76
Shoki Ziq
483243
961360
1879
170
503
330
8
39
7
56
17
6
14
2
320
0
0
DR77
Dukem
487900
972421
1949
135
632
386
7
32
7
62
31
12
12
0
383
0
8
DR78
Gafat#10-
507950
951364
1819
150
675
439
7
51
14
68
19
17
7
2
398
0
7
DR79
Tuludimtu
515693
979674
2390
230
673
440
7
32
7
82
25
13
3
1
419
0
14
DR80
Tuludimtu
515449
979174
2108
230
728
478
7
41
9
96
14
10
5
1
483
0
0
DR81
ModjoUde
506765
957179
1836
278
664
414
8
54
13
81
12
16
4
1
404
0
2
DR82
Borora
504878
970766
1879
300
534
348
8
54
14
47
18
14
0
1
351
0
1
DR83
Modjo Lum
512957
947774
1771
134
519
376
7
52
13
53
11
6
11
1
333
0
4
DR84
Tede-m
518458
946916
1865
214
451
293
8
46
16
40
8
7
4
1
282
0
4
DR85
Kile Doyo
482579
924575
1705
131
354
240
8
38
7
34
6
5
5
2
226
0
0
DR86
Bore-Tina
507013
968324
1877
370
1610
1068
6.57
230
49
88.2
40.29
32.9
1.3
1065.6
-
96.3
DR87
Serdo
503928
965114
1885
303
677
440
7.5
73
10.9
60.48
19.38
16.48
2.6
1.1
0.69
425.3
412.5
=
7.99
DR88
Denkaka-Algae
507861
960487
1848
324
670
438
7.61
61
13
57.96
23.46
19.6
6.13
-
7.24
1.3
345.87
-
2.57
DR89
Chiricha
510700
967092
1908
384
565
372
7.11
55
14.6
58.8
12.75
11.33
-
1.9
Data
source
a
a
a
a
a
a
195
Appendix 3: Groundwater trace element data (µgm/l) in Upper Awash basin (all original, generated in this study)
SampleID
Local Name
Li
DPL15
Inchini
3.949
B
Al
697.3
831.6
Ti
DPL16
Holeta
2.040
636.4
1470.0
15.320
DPL21
Chancho
1.247
1208.0
1583.0
23.860
DPL22
Segnogebeya
1.216
606.9
633.3
259.500
DPL23
Sululta
1.996
1107.0
1547.0
16.590
7.586
V
Cr
Co
Ni
Cu
2.963
3.304
43.920
Mn
0.243
2.928
5.898
Zn
78.29
As
Rb
Sr
Mo
Cd
Cs
Ba
Pb
U
12.280
2.597
151.900
0.762
0.028
0.054
99.570
1.776
0.186
2.614
2.902
58.880
1.244
3.605
5.386
1.777
8.593
27.580
0.546
9.819
14.920
145.20
9.197
2.194
16.880
0.554
0.033
0.059
95.490
3.789
0.146
70.42
39.930
2.927
13.360
1.997
0.058
0.065
149.900
3.609
17.080
7.259
56.650
0.401
5.569
0.061
6.609
657.20
1.384
3.324
82.590
1.537
0.125
0.034
260.700
3.637
0.974
4.380
5.635
59.130
0.679
10.500
48.600
79.75
32.030
6.868
270.800
1.802
0.067
0.078
150.700
4.313
1.036
DPL29
Shegole
0.932
529.6
648.2
4.703
3.039
2.170
19.180
0.380
9.997
39.620
4139.00
11.540
5.336
220.800
0.224
0.190
0.038
107.500
10.630
0.091
SHT37
Aba Samuel
6.807
807.1
728.1
4.692
15.990
2.651
10.720
0.279
5.813
10.480
41.37
31.960
14.720
415.200
2.476
0.037
0.075
124.400
3.260
4.228
DT43
Melkakunure
16.070
660.3
765.7
6.035
9.107
3.451
10.940
0.223
2.530
3.207
198.10
16.230
25.610
311.200
4.040
0.058
0.158
108.400
1.593
4.600
DT44
Tefki
2.726
686.0
1033.0
11.380
1.224
6.407
121.300
0.562
8.216
9.664
164.50
13.610
4.586
376.200
1.638
0.081
0.059
135.500
2.016
1.092
DT46
Kimoye
3.848
984.6
1260.0
16.000
1.520
5.260
18.400
0.293
3.706
5.088
142.10
27.720
2.614
19.240
12.490
0.058
0.061
151.300
2.408
0.107
DT47
Dimajelewa
204.200
633.3
286.1
0.746
0.227
3.226
6619.000
22.700
42.890
8.275
29800.00
0.285
69.460
632.900
0.264
0.071
0.184
829.400
2.934
0.420
DT48
Jewalokora
10.230
789.0
1123.0
9.274
8.081
22.050
36.010
0.654
17.790
10.050
584.40
20.540
24.050
327.300
3.664
0.140
0.139
137.000
5.568
3.541
DT49
Asgori
35.300
1205.0
1711.0
32.750
3.196
9.153
179.600
1.841
34.770
33.350
241.00
28.200
36.550
46.580
29.580
0.673
0.267
156.500
13.560
0.542
DT50
CMC
197.000
1034.0
772.8
4.651
0.933
3.454
83.520
0.359
5.692
8.878
149.50
12.900
56.990
249.900
1.154
0.025
0.352
242.100
2.152
0.112
DR71
Bishefiu
9.042
851.5
1119.0
16.970
22.590
9.696
24.110
0.440
6.770
52.500
829.40
18.120
22.210
244.400
4.078
0.213
0.155
120.500
26.770
1.655
DR72
Abusera
37.810
852.5
1222.0
418.200
4.872
11.880
41.720
0.624
8.886
11.300
1967.00
1.747
23.900
155.300
10.030
0.285
0.141
395.100
11.420
0.444
DR73
Dukem
38.830
880.8
1055.0
10.730
2.612
3.058
69.080
0.527
11.130
25.220
282.60
14.390
49.690
117.100
12.250
0.111
0.277
119.500
5.214
0.321
DR81
Mojo Ude
27.930
881.8
1628.0
24.900
11.750
6.381
50.750
0.707
6.718
13.080
111.20
19.200
31.030
265.300
3.819
0.046
0.764
141.000
2.327
3.639
DR82
Borora
51.770
974.8
1220.0
14.900
2.170
5.189
52.220
0.412
4.603
5.576
621.40
24.130
15.610
303.800
5.439
0.096
0.106
151.600
4.590
0.255
196
Appendix 4: Environmental isotope data of water samples in Upper Awash basin
4.1 Stable isotopes of Hydrogen and oxygen
Sample ID
SHPL1
SHPL2
SHPL3
SHPL4
SHPL5
SHPL6
SHPL7
SHPL8
SHPL9
SHPL10
SHPL11
SHPL12
SHPL13
SHPL14
SHPL15
DPL16
DPL17
DPL18
DPL19
DPL20
DPL21
DPL22
DPL23
DPL24
DPL25
DPL26
DPL27
DPL28
DPL29
DPL30
DPL31
DPL32
DPL33
DPL34
DPL35
DPL36
DPT37
DPT38
DPT39
DPT40
DPT41
DPT42
DPT43
DPT44
Data Source
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Original
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Original
Original
Original
Original
Original
Original
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Locality
Girmachew
Asco Gebr
Kidanemih
Abo Tsebe
Gefersa R
Tinishu A
Keranyo M
Ledeta Ri
Yeka Mich
Lege Dadi
Bosena
Entoto
Awash
Dire
Abo Tebel
Sululta
Timekete
Kara
Tsebay Maremia
Tsesfa Di
Kara
Anwar Mes
Addis Aba
Addis Aba
Gedera #2
Ghion Hot
PrisonCom
J.J.KOTAR
Chancho W
Ayer Tena
Segnogebe
Chancho
Inchini
Holeta
Shegole
CMC
Filweha W
Filweha W
Filweha W
Filweha W
Hilton We
Hilton We
Ghion Hot
Ghion Hot
utmE
466022
467212
475208
478048
457710
465829
466896
471038
477012
496840
491550
468825
404143
485155
486203
474513
473399
484759
471428
466425
464923
471418
473358
478161
486219
473417
471106
472599
471407
466083
455702
473819
421722
440379
469414
485027
473739
473747
473762
473735
474539
474345
473384
473429
utmN
1003592
1004259
1003085
1006634
1002800
1000681
996961
995131
997769
1001912
1014346
1005462
997941
1012616
1044859
1013274
1005100
998607
998607
994760
992809
998260
992343
995248
1001227
996301
995449
1012751
1027967
993808
1027242
1031909
1040053
1006281
1001640
994606
996201
996190
996176
996188
996372
996472
996279
996367
Source
Spring
Spring
Spring
Spring
River
River
Spring
River
Spring
Dam
River
Spring
River
Dam
Spring
Borehole
Borehoe
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Bore well
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
δ18O
‰
-3.0
-3.0
-4.0
-3.0
-2.0
-1.0
-2.0
0.0
-1.0
-1.0
-1.0
-2.0
-1.0
-2.0
-4.0
-2.4
-3.0
-3.0
-3.0
-3.0
-3.0
-2.0
-2.0
-3.0
-3.0
-2.0
-3.0
-2.0
-3.0
-3.0
-3.5
-4.1
-3.6
-2.9
-2.4
-5.3
-5.0
-5.0
-5.0
-5.0
-5.0
-5.0
-4.0
-5.0
δH
‰
-8.0
-6.0
-13.0
-6.0
-1.0
5.0
0.0
8.0
-2.0
-2.0
-1.0
-4.0
6.0
2.0
-10.0
-8.5
-10.0
-13.0
-7.0
-8.0
-10.0
3.0
2.0
-9.0
-7.0
-10.0
-7.0
-2.0
-12.0
-9.0
-16.3
-21.2
-14.8
-8.4
-4.9
-27.8
-25.0
-23.0
-22.0
-23.0
-22.0
-23.0
-17.0
-23.0
Temp
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
cold
cold
cold
warm
Thermal
Thermal
Thermal
Thermal
Thermal
Thermal
Thermal
Thermal
197
d-excess
‰
16
18
19
18
15
13
16
8
6
6
7
12
14
18
22
10.7
14
11
17
16
14
19
18
15
17
6
17
14
12
15
11.7
11.6
14
14.8
14.3
14.6
15
17
18
17
18
17
15
17
Sample ID
DPT45
DPT46
DPT47
DPT48
SHT49
SHT50
SHT51
SHT52
SHT53
SHT54
DT55
DT56
DT57
DT58
DT59
DT60
DT61
DT62
DT63
DT64
DT65
DT66
DT67
DT68
DT69
DT70
DT71
DT72
DT73
DT74
SHR75
DR76
DR77
DR78
DR79
DR80
DR81
DR82
DR83
DR84
DR85
DR86
DR87
DR88
Data Source
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Azagegn, 2008
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Azagegn, 2008
Azagegn, 2008
Original
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
AAWSA, 2000
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Original
Original
Original
Original
Original
Original
Original
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Original
Original
Original
Original
Locality
Saint Jos
National
National
HiltonHot
Tinishu A
Tiliku Ak
Abune Are
Roge spri
Awash on
Abasamuel
Lafto Han
Akaki EPSidamo Aw
Merino We
EP-5 (Aka
Fanta Spr
Tinshu Ak
Dansei Sp
TraconTra
Bole Lemi
Bole Lemi
Asgori we
AwashMelk
Asgori
Dimajalew
Jewalokor
Tefki
Abusera
Melkakunt
Kimoye
LibenGadu
Abudiya
Ejersatow
Ilan tot
Tsede 10
EthioAmer
Bekerjo,
AdulalaZu
DuloloJil
AlemTena
Bishefitu
Dukem
Borora
Modjo Ude
utmE
473476
473226
473467
474188
473218
476381
474754
457026
418884
469142
471985
479459
480252
479283
478707
478849
472633
472633
483260
482871
484158
427207
456307
427210
413274
433511
450472
478904
456318
427491
493374
512271
506559
518533
518533
493647
494137
488939
489365
496700
492064
490434
504969
506841
utmN
995836
996424
996420
996842
987047
981142
983714
981375
963344
966835
987830
977350
976967
974400
979650
981223
983922
983922
976089
990027
989585
971573
962581
971573
974205
959708
981241
984263
962583
992970
937202
946944
937360
941374
947038
968789
951700
943693
948128
917100
966025
970999
977094
957324
Source
Borehole
Borehole
Borehole
Borehole
River
River
Spring
Spring
Hand dug
spring
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Bore well
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Hand dug
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
δ18O
‰
-5.0
-5.0
-5.0
-5.0
0.0
-1.0
-1.0
-5.0
-3.0
-2.6
-2.0
-2.0
-1.0
-1.0
-2.0
-2.0
0.0
-2.0
-2.0
-4.0
-5.0
-5.0
-5.0
-4.9
-4.9
-3.7
-3.1
-4.0
-4.6
-5.3
-3.0
-5.0
-4.0
-4.0
-5.0
-3.0
-3.0
-4.0
-4.0
-1.0
-3.3
-4.9
-5.1
-3.1
δH
‰
-21.0
-26.0
-23.0
-24.0
8.0
4.0
4.0
-23.0
-10.0
-9.9
-4.0
-5.0
5.0
0.0
-1.0
-1.0
8.0
-3.0
-7.0
-18.0
-26.0
-23.0
-23.0
-28.7
-26.7
-18.1
-14.3
-24.4
-24.9
-29.2
-13.0
-26.0
-23.0
-24.0
-27.0
-12.0
-12.0
-16.0
-16.0
2.0
-16.6
-28.2
-30.0
-15.9
Temp
Thermal
Thermal
Thermal
Thermal
Cold
Cold
Cold
Cold
Cold
cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
warm
cold
cold
cold
cold
cold
cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
Cold
cold
cold
cold
cold
198
d-excess
‰
19
14
17
16
8
12
12
17
14
10.9
12
11
13
8
15
15
8
13
9
14
14
17
17
10.5
12.5
11.5
10.5
7.6
11.9
13.2
11
14
9
8
13
12
12
16
16
10
9.8
11
10.8
8.9
4.2 Tritium data
Sample ID
AY1
AY2
AY3
AY4
AY5
AY6
AY7
AY8
AY9
AY10
AY11
AY12
AY13
AY14
AY15
AY16
AY17
AY18
AY19
AY20
AY21
AY22
AY23
AY24
AY25
AY26
AY27
AY28
AY29
AY30
AY31
AY32
AY33
AY34
AY35
AY36
AY37
AY38
AY39
AY40
AY41
AY42
AY43
AY44
AY45
AY46
Local name
Sululta
Segnogebeya
Chancho
Asgori
Dimajelewa
Jewalokora
Tefki
Bishefiu
Dukem
Abusera
Borora
Modjo Ude
Melkakunure
Inchini
Holeta
Kimoye
CMC
Shegole
Aba Samuel
Quesquam
Eyasu
Kidanemihiret
Menbere Kibur
Kara Alero
HannaMariam
Sansuzi
Kara
Merino
Filweha
Filweha
Filweha
Filwehal 1
Hilton 1
Ghion Hotel
Ghion Hotel 3
Hilton 2
Palace #1
Palace #2
Abo Tsebel
Aregawi
Cement 1
Ambassador
Mesegid 2
Asco Gebriel
Bosenar
D'Ariq Hotel
utmE
474513
455702
473819
427210
413274
433511
450472
492064
490434
478904
504969
506841
456318
421722
440379
427491
485027
469414
469142
473310
466648
475208
471746
484759
471985
465794
464923
479283
473735
473762
473747
473739
474345
473417
473429
474539
473226
473467
478048
474754
473358
473012
471418
467212
491550
471942
utmN
1013274
1027242
1031909
971573
974205
959708
981241
966025
970999
984263
977094
957324
962583
1040053
1006281
992970
994606
1001640
966835
1004162
1001993
1003085
1003727
998607
987830
1002766
992809
974400
996188
996176
996190
996201
996472
996301
996367
996372
996424
996420
1006634
983714
992343
996595
998260
1004259
1014346
996209
3
H (TU)
2.66
0.63
0.14
0.67
0.33
0.6
0.25
1.2
0.93
1.7
0.94
0.82
0.78
1.6
1.05
0.07
1.59
2.21
2.61
0.5
0.1
0
0
0
0.5
0.1
0.6
0.4
0.3
0.6
0
0
0
0.3
0
0.1
0
0.7
20.2
9.2
2.4
6.9
10.5
4.9
1.6
8.3
Source
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
spring
Spring
Spring
Spring
Spring
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Borehole
Spring
Spring
Borehole
River
Borehole
Spring
River
Borehole
Data source
original
original
original
original
original
original
original
original
original
original
original
original
original
original
original
original
original
original
original
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
199
Sample ID
AY47
AY48
AY49
AY50
AY51
AY52
AY53
AY54
AY55
AY56
AY57
AY58
AY59
AY60
AY61
AY62
AY63
AY64
AY65
AY66
AY67
AY68
AY69
Local name
EP-5
Fanta
Abo Tsebel
Gedera #2
Gefersa
Gesho
Girmachew
Kebena
Medhanem
Kotebe Gebrel
Ledeta River
Lege Dadi
Gebriel
Menilik
Ras Hotel
Ras Seyum
Sidamo
Tiliku Akaki
TimeketeBahir
Tinishu Akaki
Maremia
Urael
Yeka Michael
utmE
478707
478849
471215
486219
457710
470578
466022
475490
466896
484183
471038
496840
470934
474009
472675
477868
480252
476381
473399
465829
471428
475405
477012
utmN
979650
981223
992561
1001227
1002800
1003754
1003592
996083
996961
999230
995131
1001912
992809
998554
996208
1006233
976967
981142
1005100
1000681
998607
995881
997769
3
H (TU)
1.1
2
6.2
1.3
7.7
1.4
1.1
6.1
3.4
0.8
7.3
7.4
0.2
11.3
9.9
18.7
7.3
3.9
3.4
8.5
1.3
7.3
6.5
Source
Borehole
Borehole
Spring
Borehole
River
Spring
Spring
River
Spring
Borehole
River
Dam
Borehole
Borehole
Borehole
Spring
Borehole
River
Borehoe
River
Borehole
Spring
Spring
Data source
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
Azagegn, 2008
200
Appendix 5 Water chemistry monitoring data (Data source: WWDSE, ministry of water resources, conductivity in µS/cm and
other measurements in mg/l unless other wise indicated)
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Station
Date
Tede_Mojo Jan-06 518458 946916
1865 308.0 471.0 7.2 0.1
Na
K
Total Ca Mg
Hardness
43.0 18.0
134.5 40.0 7.7
Fe Mn
F
0.0 0.0 1.6
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
5.7
7.5
234.6
D_
Oxygen
0.0 286.2 3.0 0.1
6.0
Gafat_ BH10
Jan-06 507950 951364
1819 402.0 659.0 7.0 0.1
53.0 12.7
253.9 76.6 14.2
0.0 0.0 1.1 12.4
11.5
326.4
0.0 398.2 5.8 0.1
6.0
D/Z_AirForce
Jan-06 499131 965767
1909 774.0 1276.0 7.9 0.2 214.0 19.5
256.1 53.9 27.4
0.0 0.0 0.2 66.5
12.5
607.9
0.0 741.7 1.1 0.2
7.0
Dire
Jan-06 487670 960425
1939 520.0 856.0 7.0 0.1 113.0 13.4
199.6 53.9 14.7
0.0 0.0 5.5 15.2
8.5
412.1
0.0 502.7 1.9 0.1
7.0
D/Z_ Well4
Jan-06 500494 974376
1914 316.0 530.0 7.4 0.2
28.0
7.0
234.4 68.7 13.9
0.0 0.0 1.0
4.8
8.0
265.2
0.0 323.5 0.6 0.3
5.0
Dukem_MBI
Jan-06 483567 976037
2133 272.0 498.0 7.6 0.1
25.0
5.1
214.8 52.2 18.7
0.0 0.0 0.2
6.7
17.5
232.6
0.0 287.7 0.8 0.1
6.0
Hora Hoda
Jan-06 497710 960839
1857 3556.0 5580.0 9.6 0.3 1240.0 228.0
99.8 15.7 13.7
0.0 0.1 14.0 551.9
11.5
2172.6 616.8 1396.4 0.8 3.5
7.0
Bishoftu
Jan-06 498623 965965
1860 1012.0 1643.0 9.1 0.4 272.0 36.5
273.4 11.3 55.4
0.0 0.1 1.1 91.2
8.0
799.7 129.6 712.1 1.4 0.4
5.0
Hora
Jan-06 499489 967975
1872 1510.0 2330.0 8.6 0.4 420.0 46.5
347.2 81.8 32.3
0.0 0.1 1.0 159.6
8.5
1048.6 64.8 1147.5 1.9 0.7
6.5
Bishoftu Guda
Jan-06 499346 971614
1876 504.0 882.0 8.7 0.3 100.0 25.0
240.9 26.1 38.9
0.0 0.1 1.1 18.1
12.5
440.6 38.4 459.5 1.1 0.1
6.0
Awash@MelkaKu
Jan-06 456575 962147
2000 230.0 316.0 8.0 0.2
27.0
6.2
160.6 41.8 12.5
0.0 0.0 0.0
5.7
8.0
191.7
0.0 233.9 1.1 0.1
5.8
Awash Jan-06 476347 925646
@Hombolle
Tede_Mojo Feb-06 518458 946916
1687 366.0 617.0 8.4 0.3
56.0 15.0
184.5 54.8 10.6
0.1 0.0 1.0 30.4
21.5
214.2 12.0 236.9 15.4 0.7
6.0
1865 298.0 453.0 7.5 0.0
40.0 14.6
128.0 39.2 7.4
0.0 0.0 1.4
5.8
8.5
220.3
0.0 268.8 9.9 0.3
5.0
Gafat_ BH10 Feb-06 507950 951364
1819 449.0 672.0 7.6 0.1
50.0 14.3
243.0 68.7 17.5
0.0 0.0 1.0 12.5
10.0
322.3
0.0 393.2 3.6 0.2
6.0
D/Z_AirForce Feb-06 499131 965767
1909 813.0 1340.0 7.9 0.2 204.0 23.0
249.0 56.6 26.5
0.0 0.0 0.2 67.2
12.5
612.0
0.0 744.6 3.0 0.5
6.0
Dire Feb-06 487670 960425
1939 550.0 840.0 7.3 0.1 110.0 13.4
195.3 55.7 13.8
0.0 0.0 5.2 15.4
10.0
406.0
0.0 495.3 1.9 0.2
8.0
D/Z_ Well4 Feb-06 500494 974376
1914 340.0 517.0 7.6 0.1
26.0
6.9
225.7 64.4 15.9
0.0 0.0 0.8
6.7
8.5
261.1
0.0 318.6 0.5 0.4
6.0
Dukem_MBI Feb-06 483567 976037
2133 301.0 474.0 7.8 0.1
25.5
5.2
206.2 57.4 15.4
0.0 0.0 0.5
5.8
17.5
224.4
0.0 273.8 1.0 0.1
6.0
Hora Hoda Feb-06 497710 960839
1857 3518.0 5570.0 9.7 0.7 1200.0 248.0
69.4 16.5 6.9
0.0 0.0 13.6 725.8
16.3
2080.8 576.0 1367.4 8.3 3.5
7.0
Bishoftu Feb-06 498623 965965
1860 1517.0 2350.0 8.7 0.4 410.0 56.5
353.7 83.5 35.5
0.0 0.1 0.8 167.0
10.5
1089.4 40.8 1246.1 2.0 1.1
7.0
Hora Feb-06 499489 967975
1872 1043.0 1656.0 9.1 0.4 268.0 46.0
277.8 12.2 60.4
0.0 0.0 1.1 93.1
14.3
683.4 96.0 638.6 1.5 0.5
6.0
Bishoftu Guda Feb-06 499346 971614
1876 557.0 856.0 8.8 0.4
99.0 23.5
243.4 28.7 42.9
0.0 0.1 1.1 23.0
10.5
448.8 48.0 449.9 1.4 0.2
6.0
Awash@MelkaKu Feb-06 456575 962147
2000 276.0 420.0 7.8 0.1
31.5
7.7
160.6 47.0 10.6
0.0 0.0 0.2
4.8
12.5
199.9
0.0 243.9 1.2 0.3
6.0
Awash Feb-06 476347 925646
@Ombolle
Tede_Mojo Mar-06 518458 946916
1687 434.0 640.0 8.3 0.3
57.0 15.5
193.1 60.9 10.1
0.1 0.2 1.0 44.2
26.8
222.4
7.2 256.6 15.4 0.8
7.0
1865 310.0 456.0 7.4 0.1
41.0 14.3
138.6 40.9 9.2
0.0 0.0 1.1
7.7
7.0
228.7
0.0 279.0 8.0 0.2
6.0
Gafat_ BH10 Mar-06 507950 951364
1819 430.0 671.0 7.1 0.3
49.0 13.9
253.0 74.8 16.2
0.0 0.0 1.7 16.3
11.0
323.2
0.0 394.3 3.9 0.2
7.0
D/Z_AirForce Mar-06 499131 965767
1909 784.0 1284.0 8.2 0.2 218.0 26.0
250.8 57.9 26.4
0.0 0.0 0.2 65.3
10.0
614.3
9.6 729.9 2.7 0.2
6.0
Dire Mar-06 487670 960425
1939 566.0 836.0 7.3 0.1 107.0 13.3
202.4 56.3 15.1
0.0 0.1 4.2 16.3
10.5
429.0
0.0 523.4 1.6 0.3
7.0
D/Z_ Well4 Mar-06 500494 974376
1914 350.0 521.0 7.6 0.3
28.0
6.6
244.2 66.0 19.4
0.0 0.1 0.8
5.8
8.5
270.3
0.0 329.7 0.4 0.3
7.0
Dukem_MBI Mar-06 483567 976037
2133 286.0 475.0 7.6 0.3
24.5
5.0
215.6 69.5 10.3
0.0 0.0 0.2
5.8
16.3
234.4
0.0 285.9 1.1 0.3
7.0
Hora Hoda Mar-06 497710 960839
1857 3650.0 5520.0 9.8 0.6 1220.0 236.0
74.8 16.9 8.1
0.0 0.0 10.8 629.8
14.2
2230.2 775.2 1144.6 0.7 3.4
7.0
Bishoftu Mar-06 498623 965965
1860 1050.0 1627.0 9.3 0.4 278.0 45.0
286.0 8.8 64.8
0.0 0.1 1.1 90.2
12.5
818.9 163.2 666.6 0.6 0.3
6.0
201
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Na
K
Total Ca Mg
Station
Date
Hardness
Hora Mar-06 499489 967975
1872 1502.0 2330.0 8.9 0.4 450.0 41.0
349.8 73.9 40.5
0.0 0.1 1.4 164.3
8.5
Bishoftu Guda Mar-06 499346 971614
1876 542.0 832.0 9.1 0.4
98.0 33.0
0.0 0.1 1.2 25.0
11.8
Awash@MelkaKu Mar-06 456575 962147
2000 140.0 200.0 7.6 0.9
12.0
4.1
85.8 25.5 5.4
0.1 0.1 0.5
0.0
10.0
98.3
0.0 119.9 1.0 0.2
7.0
Awash Mar-06 476347 925646
@Ombolle
Tede_Mojo May-06 518458 946916
1687 248.0 357.0 7.5 0.4
24.0
8.2
123.2 40.1 5.9
0.0 0.1 0.8 17.3
18.3
119.1
0.0 145.3 20.1 0.3
6.0
1865 294.0 450.0 7.5 0.1
41.5 14.2
136.4 41.8 8.1
0.0 0.0 1.1
5.9
6.8
222.5
0.0 271.5 8.5 0.1
7.0
Gafat_ BH10 May-06 507950 951364
1819 440.0 662.0 7.2 0.2
45.0 13.8
261.8 75.7 18.4
0.0 0.1 1.7 16.3
10.8
304.8
0.0 371.9 4.0 0.2
6.0
D/Z_AirForce May-06 499131 965767
1909 872.0 1328.0 7.9 0.2 204.0 21.5
261.8 40.9 39.4
0.0 0.0 0.2 69.1
10.1
626.5
0.0 764.3 3.0 0.4
4.0
Dire May-06 487670 960425
231.0 37.0 34.0
Fe Mn
F
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
D_
Oxygen
1109.4 60.0 1231.5 2.0 0.7
6.0
455.5 81.6 389.8 1.2 0.2
7.0
1939 572.0 878.0 7.6 0.2 112.0 14.1
211.2 56.1 17.8
0.3 0.1 4.1 20.2
9.8
435.7
0.0 531.7 1.8 0.4
6.0
D/Z_ Well4 May-06 500494 974376
1914 340.0 517.0 7.6 0.3
27.0
6.7
233.2 62.3 19.4
0.0 0.0 0.8
6.7
8.6
259.1
0.0 317.1 8.8 0.3
5.0
Dukem_MBI May-06 483567 976037
2133 310.0 467.0 7.7 0.3
24.9
5.0
206.8 44.5 23.8
0.0 0.0 0.2
6.7
16.3
226.3
0.0 276.1 1.2 0.3
6.0
Hora Hoda May-06 497710 960839
1857 3842.0 5540.0 9.8 0.6 1180.0 255.0
74.8 16.0 8.6
0.1 0.2 10.7 706.6
11.4
2431.0 888.0 1160.2 0.7 3.3
5.0
Bishoftu May-06 498623 965965
1860 1060.0 1624.0 9.3 0.5 258.0 41.5
286.0 12.5 62.6
0.0 0.1 1.2 99.8
12.1
822.8 177.6 642.7 0.6 0.4
4.0
Hora May-06 499489 967975
1872 1454.0 2310.0 8.9 0.5 445.0 46.0
358.6 17.8 71.5
0.0 0.1 1.3 169.0
8.4
1090.2 105.6 1115.3 2.0 0.8
5.0
Bishoftu Guda May-06 499346 971614
1876 530.0 810.0 9.2 0.3
80.0 30.0
235.4 20.3 47.0
0.1 0.1 1.2 25.0
14.3
446.9 110.4 379.3 3.2 0.6
5.0
Awash@MelkaKu May-06 456575 962147
2000 144.0 217.0 7.7 0.3
13.5
4.6
88.0 26.7 5.4
1.9
9.7
99.1
0.0 120.9 1.0 0.2
5.0
Awash May-06 476347 925646
@Ombolle
Tede_Mojo Jun-06 518458 946916
1687 298.0 520.0 8.1 0.3
30.0 10.7
165.0 55.2 7.0
0.0 0.1 0.8 42.2
9.1
170.2
0.0 207.6 22.8 0.3
5.0
1865 284.0 434.0 8.1 0.1
41.0 14.2
123.2 35.6 8.6
0.0 0.1 1.2
6.7
210.9
0.0 257.3 9.9 0.2
5.0
0.2 0.0 0.5
4.8
Gafat_ BH10
Jun-06 507950 951364
1819 414.0 647.0 7.3 0.3
41.0 13.8
242.0 62.3 21.6
0.0 0.0 1.6 15.4
9.9
288.6
0.0 352.1 6.8 0.2
5.0
D/Z_AirForce
Jun-06 499131 965767
1909 820.0 1281.0 8.0 0.2 200.0 20.2
253.0 36.5 38.3
0.0 0.0 0.2 68.2
10.2
568.0
0.0 692.9 3.5 0.4
3.0
Dire
Jun-06 487670 960425
1939 550.0 838.0 7.1 0.3 106.0 13.0
200.1 52.5 17.3
0.3 0.0 4.0 13.4
9.2
407.0
0.0 496.5 1.7 0.4
5.0
D/Z_ Well4
Jun-06 500494 974376
1914 340.0 513.0 7.6 0.2
27.0
6.4
239.8 62.3 21.1
0.0 0.0 0.8
5.8
8.2
255.3
0.0 311.5 8.3 0.5
4.0
Dukem_MBI
Jun-06 483567 976037
2133 308.0 463.0 7.8 0.2
22.0
4.8
217.8 46.3 25.4
0.0 0.1 0.1
5.8
16.2
227.6
0.0 277.6 1.5 0.3
5.0
Hora Hoda
Jun-06 497710 960839
1857 3482.0 5420.0 9.9 0.5 1180.0 175.0
88.0 24.0 7.0
0.1 0.1 11.0 610.6
11.2
2183.0 685.8 1394.5 0.8 3.4
4.0
Bishoftu
Jun-06 498623 965965
1860 1118.0 1605.0 9.4 0.3 256.0 42.5
286.0 16.0 60.5
0.1 0.1 1.2 93.1
13.1
795.5 144.0 677.7 0.7 0.6
4.0
Hora
Jun-06 499489 967975
1872 1566.0 2240.0 9.0 0.3 425.0 38.5
352.0 16.0 76.7
0.1 0.1 1.1 162.2
13.8
1045.3 105.6 1060.5 4.1 0.1
7.0
Bishoftu Guda
Jun-06 499346 971614
1876 540.0 803.0 9.4 0.4
228.8 16.0 46.4
0.1 0.1 1.3 29.8
8.2
416.3 84.0 337.0 7.2 0.6
5.0
Awash@MelkaKu
Jun-06 456575 962147
2000
64.0
94.0 7.2 0.3
4.3
3.1
46.2 10.7 4.8
0.2 0.1 0.1
1.9
8.9
42.6
0.0
51.9 1.4 0.2
4.0
Awash
@Ombolle
Tede_Mojo
Jun-06 476347 925646
1687
98.0 132.0 7.4 0.2
5.7
3.5
63.8 16.9 5.4
0.0 0.1 0.8
9.6
7.6
44.4
0.0
54.2 20.8 0.3
4.0
Jul-06 518458 946916
1865 284.0 434.0 8.1 0.1
41.0 14.2
123.2 35.6 8.6
0.0 0.1 1.2
4.8
6.7
210.9
0.0 257.3 9.9 0.2
5.0
Gafat_ BH10
Jul-06 507950 951364
1819 414.0 647.0 7.3 0.3
41.0 13.8
242.0 62.3 21.6
0.0 0.0 1.6 15.4
9.9
288.6
0.0 352.1 6.8 0.2
5.0
D/Z_AirForce
Jul-06 499131 965767
1909 820.0 1281.0 8.0 0.2 200.0 20.2
253.0 36.5 38.3
0.0 0.0 0.2 68.2
10.2
568.0
0.0 692.9 3.5 0.4
3.0
Dire
Jul-06 487670 960425
1939 550.0 838.0 7.1 0.3 106.0 13.0
200.1 52.5 17.3
0.3 0.0 4.0 13.4
9.2
407.0
0.0 496.5 1.7 0.4
5.0
D/Z_ Well4
Jul-06 500494 974376
1914 340.0 513.0 7.6 0.2
27.0
6.4
239.8 62.3 21.1
0.0 0.0 0.8
5.8
8.2
255.3
0.0 311.5 8.3 0.5
4.0
Dukem_MBI
Jul-06 483567 976037
2133 308.0 463.0 7.8 0.2
22.0
4.8
217.8 46.3 25.4
0.0 0.1 0.1
5.8
16.2
227.6
0.0 277.6 1.5 0.3
5.0
Hora Hoda
Jul-06 497710 960839
1857 3482.0 5420.0 9.9 0.5 1180.0 175.0
88.0 24.0 7.0
0.1 0.1 11.0 610.6
11.2
2183.0 685.8 1394.5 0.8 3.4
4.0
84.0 24.0
202
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Na
K
Total Ca Mg
Station
Date
Hardness
Bishoftu Jul-06 498623 965965
1860 1118.0 1605.0 9.4 0.3 256.0 42.5
286.0 16.0 60.5
Fe Mn
F
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
D_
Oxygen
795.5 144.0 677.7 0.7 0.6
4.0
0.1 0.1 1.2 93.1
13.1
Hora
Jul-06 499489 967975
1872 1566.0 2240.0 9.0 0.3 425.0 38.5
352.0 16.0 76.7
0.1 0.1 1.1 162.2
13.8
1045.3 105.6 1060.5 4.1 0.1
7.0
Bishoftu Guda
Jul-06 499346 971614
1876 540.0 803.0 9.4 0.4
228.8 16.0 46.4
0.1 0.1 1.3 29.8
8.2
416.3 84.0 337.0 7.2 0.6
5.0
Awash@MelkaKu
Jul-06 456575 962147
2000
64.0
94.0 7.2 0.3
4.3
3.1
46.2 10.7 4.8
0.2 0.1 0.1
1.9
8.9
42.6
0.0
51.9 1.4 0.2
4.0
Awash Jul-06 476347 925646
@Ombolle
Tede_Mojo Sep-06 518458 946916
1687
98.0 132.0 7.4 0.2
5.7
3.5
63.8 16.9 5.4
0.0 0.1 0.8
9.6
7.6
44.4
0.0
54.2 20.8 0.3
4.0
84.0 24.0
1865 306.0 452.0 7.4 0.2
39.0 14.5
142.6 41.4 9.5
0.0 0.0 1.8
6.7
6.5
218.5
266.6 9.1 0.2
5.0
Gafat_ BH10 Sep-06 507950 951364
1819 412.0 670.0 7.3 0.4
46.0 14.2
264.5 69.0 22.4
0.0 0.0 1.6 17.3
8.6
303.6
370.4 16.1 0.2
4.0
D/Z_AirForce Sep-06 499131 965767
1909 734.0 1287.0 8.0 0.7 196.0 25.0
266.8 27.7 48.2
0.1 0.1 0.4 75.8
10.3
547.4
667.8 9.5 0.8
4.0
Dire Sep-06 487670 960425
1939 560.0 838.0 7.3 0.4 105.0 13.4
211.6 53.4 19.0
0.0 0.0 3.7 20.2
8.4
391.0
477.0 10.5 0.5
6.0
D/Z_ Well4 Sep-06 500494 974376
1914 304.0 520.0 7.5 0.2
25.0
6.6
248.4 62.6 22.4
0.0 0.1 0.8
5.8
8.3
259.9
317.1 8.9 0.4
5.0
Dukem_MBI Sep-06 483567 976037
2133 270.0 475.0 7.6 0.3
23.0
5.1
213.9 46.0 24.1
0.0 0.0 0.1
6.7
16.2
220.8
0.0 269.4 11.0 0.2
6.0
Hora Hoda Sep-06 497710 960839
1857 3446.0 5400.0 9.8 0.7 1120.0 230.0
156.4 15.6 33.0
0.1 0.2 11.2 537.6
11.4
2185.0 600.0 1445.7 0.9 3.3
4.0
Bishoftu Sep-06 498623 965965
1860 1042.0 1596.0 9.5 1.4 266.0 41.5
289.8 9.2 65.0
0.2 0.2 1.5 97.0
10.6
761.3 139.2 645.8 89.6 0.2
5.0
Hora Sep-06 499489 967975
1872 1428.0 2220.0 8.9 1.0 405.0 48.5
351.9 41.4 75.6
0.1 0.1 1.5 161.3
12.5
993.6 79.2 1051.2 5.0 0.4
6.0
1876 482.0 774.0 9.2 0.6
230.0 9.2 50.4
418.6 84.0 339.9 4.3 0.3
5.0
Bishoftu Guda Sep-06 499346 971614
Awash@MelkaKu Sep-06 456575 962147
Awash Sep-06 476347 925646
@Ombolle
Tede_Mojo Oct-06 518458 946916
0.1 0.1 1.2 24.0
8.3
94.0 136.0 7.4 2.7
7.8
3.7
59.8 15.6 5.0
1.3 0.2 0.1
2.9
8.1
57.5
1687 118.0 189.0 7.6 0.7
7.5
4.0
71.3 18.4 6.2
0.1 0.1 0.7
6.7
7.8
2000
91.0 32.5
0.0
70.2 11.5 0.2
5.0
64.4
78.6 8.8 0.1
5.0
1865 304.0 454.0 7.6 0.2
46.0 16.0
135.7 40.5 8.4
0.1 0.0 2.0
5.8
5.2
211.6
0.0 258.2 8.8 0.2
5.0
Gafat_ BH10
Oct-06 507950 951364
1819 488.0 744.0 7.6 0.4
66.0 16.5
278.3 68.1 26.3
0.1 0.0 1.8 18.2
7.2
349.6
0.0 426.5 15.5 0.3
3.0
D/Z_AirForce
Oct-06 499131 965767
1909 748.0 1287.0 8.0 0.7 200.0 23.5
276.0 29.4 49.3
0.1 0.2 0.4 67.2
9.1
554.3
0.0 676.2 12.3 0.7
3.0
Dire
Oct-06 487670 960425
1939 538.0 844.0 7.2 0.3 110.0 13.6
211.6 53.4 19.0
0.1 0.0 4.1 18.2
7.6
397.9
0.0 485.4 9.4 0.2
5.0
D/Z_ Well4
Oct-06 500494 974376
1914 354.0 520.0 7.6 0.4
27.0
7.0
243.8 64.4 20.2
0.1 0.1 0.8
9.6
9.2
253.0
0.0 308.7 9.6 0.4
5.0
Dukem_MBI
Oct-06 483567 976037
2133 294.0 469.0 7.9 0.3
23.0
5.1
218.5 45.1 26.9
0.1 0.0 0.1
8.6
14.2
218.5
0.0 266.6 10.0 0.2
6.0
Hora Hoda
Oct-06 497710 960839
1857 3445.0 5460.0 9.7 0.8 1280.0 222.0
75.9 12.9 10.6
0.3 0.1 10.2 556.8
0.4
2070.0 696.0 1110.2 0.9 4.0
4.0
Bishoftu
Oct-06 498623 965965
1860 1072.0 1613.0 9.4 1.5 264.0 41.5
296.7 7.4 67.8
0.1 0.1 1.4 96.0
0.5
754.4 158.4 598.3 92.0 0.2
5.0
Hora
Oct-06 499489 967975
1872 1471.0 2260.0 8.9 0.9 440.0 46.0
368.0 12.9 81.8
0.2 0.0 1.4 173.8
10.2
1016.6 98.4 1040.2 6.3 0.3
6.0
Bishoftu Guda
Oct-06 499346 971614
1876 512.0 793.0 9.2 0.3
95.0 29.5
239.2 12.0 51.0
0.2 0.1 1.1 26.9
0.8
407.1 84.0 325.9 6.7 0.3
6.0
Awash@MelkaKu
Oct-06 456575 962147
2000 214.0 329.0 8.2 0.3
18.5
5.0
149.5 42.3 10.6
0.2 0.1 0.1
8.6
7.4
147.2
0.0 179.6 12.1 0.1
6.0
Awash Oct-06 476347 925646
@Ombolle
Tede_Mojo Nov-06 518458 946916
1687 235.0 347.0 8.5 0.8
20.0
8.4
165.6 47.8 11.2
0.1 0.1 0.5 24.0
5.2
154.1
9.6 168.5 6.2 0.1
6.0
1865 302.0 453.0 7.4 0.2
47.0 14.4
132.0 39.7 8.1
0.5 0.0 1.4
8.6
4.0
218.4
0.0 266.4 7.6 0.2
6.0
Gafat_ BH10 Nov-06 507950 951364
1819 438.0 666.0 7.1 0.3
52.0 13.7
264.0 61.7 27.0
0.0 0.1 1.7 15.4
8.1
314.4
0.0 383.6 13.0 0.3
6.0
D/Z_AirForce Nov-06 499131 965767
1909 798.0 1312.0 7.9 0.5 206.0 23.0
261.8 26.5 48.1
0.0 0.1 0.6 71.0
11.5
607.2
0.0 740.8 11.2 0.7
4.0
Dire Nov-06 487670 960425
1939 584.0 851.0 7.2 0.3 114.0 13.1
206.8 52.0 18.9
0.1 0.0 3.8 19.2
6.2
398.4
0.0 486.0 8.5 0.2
5.0
D/Z_ Well4 Nov-06 500494 974376
1914 340.0 522.0 7.5 0.4
28.0
6.7
233.2 60.0 20.5
0.1 0.0 0.8
7.7
8.5
252.0
0.0 307.4 9.4 0.4
4.0
Dukem_MBI Nov-06 483567 976037
2133 306.0 472.0 7.8 0.4
27.5
5.0
211.2 39.7 27.5
0.1 0.0 0.1
6.7
17.6
225.6
0.0 275.2 11.5 0.2
5.0
203
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Na
K
Total Ca Mg
Station
Date
Hardness
Hora Hoda Nov-06 497710 960839
1857 3560.0 5460.0 9.8 0.7 1220.0 216.0
74.8 15.0 9.2
Fe Mn
F
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
D_
Oxygen
2280.0 720.0 1317.6 0.8 3.7
5.0
0.0 0.0 10.0 725.8
0.8
Bishoftu Nov-06 498623 965965
1860 1048.0 1618.0 9.4 1.2 266.0 22.0
294.8 7.1 68.0
0.1 0.1 1.6 95.0
0.5
768.0 132.0 668.0 87.2 0.2
4.0
Hora Nov-06 499489 967975
1872 1470.0 2280.0 8.7 1.1 385.0 41.0
352.0 8.8 81.0
0.1 0.0 1.3 165.1
6.7
1024.8 84.0 1078.0 10.2 0.3
5.0
Bishoftu Guda Nov-06 499346 971614
1876 538.0 809.0 9.1 0.5
88.0 30.0
222.2 13.2 64.4
0.1 0.1 1.0 23.0
0.4
412.8 60.0 503.6 8.0 0.4
5.0
Awash@MelkaKu Nov-06 456575 962147
2000 254.0 375.0 8.1 0.5
24.0
7.2
158.4 44.1 11.9
0.1 0.0 0.3
8.6
1.1
187.2
0.0 228.4 4.0 0.1
5.0
Awash Nov-06 476347 925646
@Ombolle
Tede_Mojo Dec-06 518458 946916
1687 350.0 520.0 8.2 0.5
51.0 11.9
211.2 61.7 14.0
0.1 0.1 0.5 63.4
8.2
206.4
9.6 232.3 7.5 0.1
5.0
1865 290.0 455.0 7.4 0.2
42.0 13.2
132.0 41.5 7.0
0.1 0.0 1.5
7.7
3.2
220.8
0.0 269.4 8.5 0.3
5.0
Gafat_ BH10 Dec-06 507950 951364
1819 430.0 665.0 7.0 0.3
50.0 12.5
253.0 70.6 18.9
0.1 0.0 1.7 15.4
6.9
312.0
0.0 380.6 12.2 0.1
5.0
D/Z_AirForce Dec-06 499131 965767
1909 658.0 1078.0 7.7 0.7 135.0 15.0
248.6 35.3 39.4
0.1 0.1 0.7 51.8
9.9
468.0
0.0 571.0 11.2 0.6
3.0
Dire Dec-06 487670 960425
1939 542.0 840.0 7.2 0.3 107.0 12.0
198.0 55.6 14.6
0.0 0.1 3.2 15.4
7.4
408.0
0.0 497.8 7.0 0.2
5.0
D/Z_ Well4 Dec-06 500494 974376
1914 336.0 531.0 7.1 0.3
24.0
5.8
242.0 67.0 18.4
0.0 0.0 0.9
5.8
7.6
259.2
0.0 316.2 9.4 0.5
5.0
Dukem_MBI Dec-06 483567 976037
2133 288.0 474.0 7.4 0.3
25.0
4.5
215.6 46.8 24.3
0.0 0.0 0.2
3.8
15.7
223.2
0.0 272.3 10.2 0.3
5.0
Hora Hoda Dec-06 497710 960839
1857 3520.0 5520.0 9.7 1.0 1190.0 222.0
66.0 17.6 5.4
0.1 0.1 11.2 825.6
0.7
2040.0 552.0 1366.4 1.0 3.1
4.0
Bishoftu Dec-06 498623 965965
1860 1048.0 1604.0 9.2 1.2 256.0 36.5
275.0 12.4 59.9
0.1 0.1 1.4 87.4
0.5
782.4 108.0 734.9 62.8 0.3
4.0
Hora Dec-06 499489 967975
1872 1472.0 2300.0 8.6 0.8 400.0 45.0
358.6 41.5 62.6
0.2 0.0 1.4 159.4
5.8
1003.2 48.0 1126.3 9.4 0.4
6.0
Bishoftu Guda Dec-06 499346 971614
1876 528.0 820.0 8.9 0.6
76.0 28.0
237.6 20.3 45.9
0.1 0.1 1.1 22.1
0.2
441.6 48.0 441.2 8.0 0.3
4.0
Awash@MelkaKu Dec-06 456575 962147
2000 264.0 419.0 7.7 0.5
23.0
9.6
169.4 48.3 11.9
0.1 0.1 0.3
9.6
1.3
192.0
0.0 234.2 5.4 0.2
4.0
Awash Dec-06 476347 925646
@Ombolle
Tede_Mojo Jan-07 518458 946916
1687 360.0 604.0 8.0 0.5
52.0 12.0
193.6 60.9 10.3
0.1 0.1 0.7 51.8
10.5
211.2
0.0 257.7 9.5 0.1
4.0
1865 260.0 408.0 7.5 0.1
44.0 14.2
105.8 30.0 7.2
0.1 0.0 1.3
5.8
6.2
199.2
0.0 243.1 2.7 0.1
6.0
Gafat_ BH10
Jan-07 507950 951364
1819 420.0 654.0 7.1 0.3
54.0 13.9
262.2 72.8 18.7
0.1 0.0 1.2 18.2
12.2
312.0
0.0 380.6 7.2 0.2
6.0
D/Z_AirForce
Jan-07 499131 965767
1909 806.0 1280.0 7.9 0.2 212.0 26.0
271.4 33.7 44.6
0.0 0.0 0.2 68.2
13.5
636.0
0.0 775.9 10.6 0.2
4.0
Dire
Jan-07 487670 960425
1939 544.0 841.0 7.1 0.2 110.0 13.3
211.6 57.3 16.0
0.0 0.0 5.2 15.4
8.2
420.0
512.4 1.1 0.1
6.0
D/Z_ Well4
Jan-07 500494 974376
1914 330.0 519.0 7.5 0.3
30.0
6.6
243.8 70.1 16.0
0.0 0.0 0.9
4.8
7.8
256.0
313.3 0.8 0.3
5.0
Dukem_MBI
Jan-07 483567 976037
2133 320.0 490.0 7.6 0.1
20.0
4.1
216.2 50.1 21.5
0.0 0.0 0.2
5.7
16.5
264.0
322.1 0.9 0.1
6.0
Hora Hoda
Jan-07 497710 960839
1857 3632.0 5580.0 9.8 0.3 1360.0 230.0
92.0 19.1 10.5
0.1 0.1 12.3 604.8
12.1
2352.0
0.0 1503.0 0.9 3.2
7.0
Bishoftu
Jan-07 498623 965965
1860 1062.0 1620.0 9.2 0.5 266.0 49.0
310.5 14.6 65.5
0.1 0.1 1.2 105.6
9.2
808.8 139.2 703.7 1.3 0.8
5.0
Hora
Jan-07 499489 967975
1872 1490.0 2290.0 8.7 0.5 430.0 48.0
395.6 39.1 71.0
0.1 0.1 1.0 171.8
9.2
1094.4 72.0 1188.8 1.4 0.5
6.0
Bishoftu Guda
Jan-07 499346 971614
1876 554.0 821.0 9.1 0.3 100.0 28.0
253.0 24.6 45.7
0.0 0.1 1.3 25.0
12.1
439.2 67.2 399.2 1.3 0.2
5.0
Awash@MelkaKu
Jan-07 456575 962147
2000 260.0 410.0 8.1 0.2
30.0
7.0
170.2 47.3 12.1
0.1 0.0
9.6
8.8
199.2
243.0 0.8 0.1
6.0
Awash Jan-07 476347 925646
@Ombolle
Tede_Mojo Feb-07 518458 946916
1687 392.0 620.0 8.2 0.2
52.0 14.1
225.4 74.6 8.8
0.1 0.0 0.9 57.6
10.3
225.6
0.0 275.2 10.2 0.8
5.0
1865 294.0 446.0 7.6 0.2
49.0 14.0
134.2 40.5 8.1
0.1 0.1 1.4
6.6
5.6
227.7
0.0 277.8 4.3 0.1
4.0
Gafat_ BH10 Feb-07 507950 951364
1819 401.0 665.0 7.0 0.3
55.0 13.4
250.8 73.9 16.2
0.0 0.1 1.4 16.1
11.7
310.5
0.0 378.8 8.3 0.2
6.0
D/Z_AirForce Feb-07 499131 965767
1909 640.0 993.0 7.7 0.6 114.0 48.0
275.0 44.0 40.5
0.0 0.0 0.4 49.1
12.8
434.7
0.0 530.3 14.2 0.4
4.0
Dire Feb-07 487670 960425
1939 504.0 842.0 7.0 2.0 116.0 12.9
209.0 55.4 17.3
0.0 0.0 5.9 18.0
6.3
404.8
0.0 493.9 1.6 0.1
6.0
1914 306.0 521.0 7.3 0.3
237.6 66.0 17.8
0.0 0.0 0.6
7.2
257.6
0.0 314.3 3.7 0.1
5.0
D/Z_ Well4 Feb-07 500494 974376
28.0
6.6
6.6
204
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Station
Date
Dukem_MBI Feb-07 483567 976037
2133 269.0 473.0 7.5 0.3
Na
24.0
K
Total Ca Mg
Hardness
4.9
220.0 48.4 24.2
Fe Mn
F
0.0 0.0 0.1
Cl NO2 NO3 Alkalinity
230.0
CO3 HCO3 SO4 PO4
D_
Oxygen
0.0 280.6 4.5 0.1
5.0
6.6
10.0
Hora Hoda Feb-07 497710 960839
1857 3558.0 5580.0 9.7 0.5 1260.0 220.0
77.0 17.6 8.1
0.1 0.1 13.2 633.0
12.5
2300.0 696.0 1390.8 2.4 3.5
6.0
Bishoftu Feb-07 498623 965965
1860 990.0 1658.0 9.2 0.6 266.0 43.5
286.0 14.1 61.6
0.1 0.0 0.7 96.4
8.2
793.5 122.4 719.2 0.5 0.2
5.0
Hora Feb-07 499489 967975
1872 1467.0 2290.0 8.6 0.7 450.0 45.0
363.0 28.2 71.8
0.1 0.1 0.9 160.7
8.5
1048.8 72.0 1133.1 1.3 0.2
5.0
Bishoftu Guda Feb-07 499346 971614
1876 486.0 812.0 8.9 0.5 102.0 33.5
228.8 16.7 45.9
0.1 0.1 1.1 23.6
11.8
414.0 64.8 373.3 0.5 0.2
5.0
Awash@MelkaKu Feb-07 456575 962147
2000 253.0 420.0 7.5 0.4
30.5
9.0
160.6 49.3 9.2
0.1 0.1 0.2 14.4
0.8
188.6
0.0 230.1 5.6 0.1
5.0
Awash Feb-07 476347 925646
@Ombolle
Tede_Mojo Mar-07 518458 946916
1687 344.0 569.0 7.9 0.6
54.0 13.2
193.6 55.4 13.5
0.1 0.1 0.7 35.0
9.4
207.0
0.0 252.5 32.0 0.3
6.0
1865 290.0 451.0 7.8 0.2
47.0 14.3
129.8 39.6 7.6
0.0
1.2
6.7 0.0 5.8
213.6
0.0 260.6 4.5 0.2
5.0
Gafat_ BH10 Mar-07 507950 951364
1819 412.0 676.0 7.8 0.3
52.0 14.2
255.2 74.8 16.7
0.1
1.4 16.5 0.0 12.7
304.8
0.0 371.8 7.8 0.4
5.0
D/Z_AirForce Mar-07 499131 965767
1909 615.0 1001.0 7.7 0.8 127.0 17.0
264.0 37.8 41.6
0.1
0.8 50.9 0.0 11.6
424.8
0.0 518.3 11.8 0.5
4.0
Dire Mar-07 487670 960425
1939 510.0 843.0 7.5 1.5 118.0 13.5
206.8 53.7 17.8
0.0
5.2 16.3 0.0 6.1
410.4
0.0 500.7 1.9 0.2
5.0
D/Z_ Well4 Mar-07 500494 974376
1914 302.0 515.0 7.5 0.1
30.0
6.9
233.2 64.2 17.8
0.0
0.8
6.7 0.0 6.8
259.2
0.0 316.2 3.2 0.2
5.0
Dukem_MBI Mar-07 483567 976037
2133 274.0 475.0 7.7 0.1
25.0
5.1
217.8 44.0 26.5
0.1
0.2
6.7 0.0 9.8
230.0
0.0 281.1 5.1 0.2
5.0
Hora Hoda Mar-07 497710 960839
1857 3566.0 5600.0 9.7 0.8 1260.0 222.0
94.6 20.2 10.8
0.1
9.4 648.9 0.0 0.5
2256.0 672.0 1385.9 2.9 3.4
6.0
Bishoftu Mar-07 498623 965965
1860 1013.0 1638.0 9.3 0.7 280.0 44.5
283.8 13.2 61.6
0.1
0.7 97.0 0.0 8.5
796.8 129.6 708.6 0.5 0.2
5.0
Hora Mar-07 499489 967975
1872 1472.0 2330.0 8.8 0.8 425.0 48.0
369.6 22.9 76.7
0.1
0.9 169.0 0.0 8.1
1068.0 792.0 1141.9 1.3 0.3
5.0
Bishoftu Guda Mar-07 499346 971614
1876 492.0 817.0 9.1 0.4 104.0 30.0
233.2 16.7 47.0
0.0
1.2 25.0 0.0 10.8
439.2 69.6 394.3 0.5 0.1
6.0
Awash@MelkaKu Mar-07 456575 962147
2000 190.0 293.0 7.7 0.8
20.0
6.9
116.6 37.0 5.9
0.1
0.4
7.7 0.6 0.7
132.0
0.0 161.0 8.8 0.2
4.0
Awash Mar-07 476347 925646
@Ombolle
Tede_Mojo Apr-07 518458 946916
1687 340.0 551.0 7.9 0.4
54.0 12.4
178.2 50.2 13.0
0.1
0.5 36.5 0.1 8.9
211.2
0.0 257.7 15.0 1.1
6.0
1865 296.0 453.0 7.8 0.2
46.0 15.0
143.0 41.4 9.7
0.0
1.4
8.6 0.0 6.1
220.8
0.0 296.4 3.2 0.2
5.0
Gafat_ BH10
Apr-07 507950 951364
1819 447.0 667.0 7.7 0.3
52.0 14.5
213.2 51.0 21.1
0.1
1.4 16.3 0.0 11.3
336.0
0.0 409.9 5.3 0.2
4.0
D/Z_AirForce
Apr-07 499131 965767
1909 662.0 1056.0 8.0 0.9 127.0 19.0
288.2 41.4 45.4
0.1
0.9 55.7 0.0 10.5
489.6
0.0 596.3 9.9 0.4
4.0
Dire
Apr-07 487670 960425
1939 578.0 846.0 7.3 1.3 108.0 12.8
200.2 53.7 16.2
0.1
4.5 18.2 0.0 6.5
444.0
0.0 541.7 1.5 0.2
6.0
D/Z_ Well4
Apr-07 500494 974376
1914 344.0 509.0 8.2 0.3
30.0
6.4
253.0 70.4 18.9
0.2
0.7
9.6 0.0 6.4
276.0
4.8 327.0 2.7 0.1
5.0
Dukem_MBI
Apr-07 483567 976037
2133 298.0 473.0 7.9 0.2
29.0
5.0
209.0 44.0 24.3
0.1
0.3
5.8 0.0 10.6
232.8
0.0 284.0 3.7 0.1
5.0
Hora Hoda
Apr-07 497710 960839
1857 3858.0 5560.0 9.8 0.7 1250.0 226.0
77.0 15.8 9.2
0.1
8.2 633.6 0.0 0.5
2472.0 720.0 1551.8 5.2 3.2
5.0
Bishoftu
Apr-07 498623 965965
1860 1068.0 1648.0 9.4 0.9 268.0 40.5
310.2 8.8 70.7
0.0
0.7 101.8 0.0 8.2
856.8 132.0 776.9 0.8 0.3
5.0
Hora
Apr-07 499489 967975
1872 1622.0 2320.0 8.9 1.1 450.0 43.5
382.8 17.6 83.2
0.1
1.1 174.7 0.0 8.7
1147.2 86.4 1223.9 2.3 0.3
6.0
Bishoftu Guda
Apr-07 499346 971614
1876 580.0 826.0 9.2 0.6
99.0 31.0
248.6 18.5 49.7
0.0
1.0 25.0 0.0 9.7
451.2 72.0 404.1 0.7 0.1
5.0
Awash@MelkaKu
Apr-07 456575 962147
2000 240.0 380.0 7.7 1.2
25.0
9.5
140.8 50.2 3.8
0.1
0.5 15.4 0.6 0.7
156.0
0.0 190.3 8.2 0.1
4.0
Awash Apr-07 476347 925646
@Ombolle
Tede_Mojo May-07 518458 946916
1687 322.0 501.0 7.8 0.5
45.0 10.3
176.0 52.8 10.8
0.1
0.7 36.5 1.9 11.0
192.0
0.0 234.2 17.5 0.3
5.0
1865 310.0 450.0 7.4 0.2
47.0 16.0
145.2 44.0 8.6
0.1
1.5
7.5 0.0 5.2
247.2
0.0 301.6 1.1 2.1
5.0
Gafat_ BH10 May-07 507950 951364
1819
D/Z_AirForce May-07 499131 965767
1909 626.0 999.0 7.8 0.6 132.0 17.0
277.2 45.8 40.0
0.0
1.1 55.6 0.0 9.8
460.8
0.0 562.2 12.4 0.5
5.0
Dire May-07 487670 960425
1939 564.0 838.0 7.2 0.5 114.0 13.8
198.0 55.4 14.6
0.1
5.6 18.9 0.0 5.6
424.8
0.0 518.3 1.9 0.4
5.0
205
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Station
Date
D/Z_ Well4 May-07 500494 974376
1914 380.0 517.0 7.7 0.1
29.0
Total Ca Mg
Hardness
6.8
253.0 71.3 18.4
0.1
0.8 13.9 0.0 6.6
26.0
5.2
213.4 45.8 24.3
0.0
0.4
Hora Hoda May-07 497710 960839
1857 3582.0 5620.0 9.8 0.7 1280.0 240.0
74.8 20.2 5.9
0.0
9.1 605.7 0.1 0.4
Bishoftu May-07 498623 965965
1860 1070.0 1637.0 9.3 0.8 274.0 42.5
292.6 12.3 64.3
0.1
0.8 106.3 0.0 6.6
868.8 112.8 830.6 1.2 0.3
4.0
Hora May-07 499489 967975
1872 1638.0 2340.0 8.9 0.9 440.0 46.0
385.0 32.6 74.5
0.1
1.0 189.7 0.0 7.7
1188.0 96.0 1254.2 1.9 0.5
6.0
Bishoftu Guda May-07 499346 971614
1876 518.0 806.0 9.1 0.4 101.0 26.0
222.2 13.2 46.4
0.8
1.0 26.8 0.0 8.8
432.0 60.0 405.0 0.4 0.1
5.0
Dukem_MBI May-07 483567 976037
2133 290.0 472.0 7.7 0.1
Na
K
Fe Mn
F
Cl NO2 NO3 Alkalinity
7.0 0.0 12.2
CO3 HCO3 SO4 PO4
288.0
D_
Oxygen
0.0 351.4 2.4 0.3
6.0
232.8
0.0 284.0 2.8 0.3
5.0
2280.0 744.0 1268.8 7.2 3.8
5.0
Awash@MelkaKu May-07 456575 962147
2000 160.0 243.0 7.5 1.1
14.0
7.6
94.6 32.6 3.2
0.1
0.7
9.9 0.5 0.8
110.0
0.0 134.7 5.2 0.1
5.0
Awash May-07 476347 925646
@Ombolle
Tede_Mojo Jun-07 518458 946916
1687 292.0 475.0 7.8 0.6
42.0
8.8
143.0 44.0 8.1
0.1
0.5 35.8 0.8 9.8
189.0
0.0 219.6 16.0 0.2
6.0
1865 296.0 453.0 7.7 0.3
51.0 15.5
132.0 43.1 5.9
0.0
1.1
7.0 0.1 0.1
230.4
281.1 3.4 0.2
Gafat_ BH10
Jun-07 507950 951364
1819 380.0 673.0 7.4 0.5
40.0 13.0
255.2 79.2 14.0
0.0
1.5 17.9 0.0 5.9
320.4
390.9 5.8 0.0
D/Z_AirForce
Jun-07 499131 965767
1909 816.0 1251.0 8.1 0.5 186.0 19.5
259.6 40.5 38.9
0.0
1.9 65.5 0.0 1.9
583.2
711.5 0.1 0.5
Dire
Jun-07 487670 960425
1939 548.0 838.0 7.3 0.2 112.0 14.0
224.4 64.2 15.7
0.0
10.2 18.9 0.0 1.8
432.0
527.0 2.2 1.8
D/Z_ Well4
Jun-07 500494 974376
1914 342.0 522.0 8.0 0.1
27.0
6.9
242.0 67.8 17.8
0.0
0.7
7.0 0.0 5.4
284.0
347.0 0.3 0.3
Dukem_MBI
Jun-07 483567 976037
2133 312.0 475.0 7.9 0.1
25.0
5.1
226.6 58.1 20.0
0.0
0.3
6.0 0.0 9.4
250.2
305.2 3.8 0.1
Hora Hoda
Jun-07 497710 960839
1857 3620.0 5590.0 9.9 0.7 1200.0 240.0
77.0 14.1 10.3
0.1
19.1 546.2 0.0 0.3
Bishoftu
Jun-07 498623 965965
1860 1066.0 1632.0 9.5 1.1 264.0 45.5
290.2 14.1 62.6
0.0
3.9 102.3 0.0 0.3
Hora
Jun-07 499489 967975
1872 1516.0 2310.0 8.9 0.1 395.0 46.0
352.0 22.9 72.4
0.0
1.1 170.8 0.0 0.1
1125.0 132.0 1104.1 1.4 0.6
441.0 93.6 347.7 4.3 0.2
Bishoftu Guda
Jun-07 499346 971614
1876 528.0 808.0 9.2 0.2
Awash@MelkaKu
Jun-07 456575 962147
2000
231.0 19.4 44.8
0.0
0.7 22.8 0.0 0.4
3.3
46.2 11.4 4.3
0.2
1.0
Awash
@Ombolle
Tede_Mojo
Jun-07 476347 925646
1687 298.0 520.0 8.1 0.3
30.0 10.7
165.0 55.2 7.0
0.0 0.1 0.8 42.2
Gafat_ BH10
Jul-07 518458 946916
1865 298.0 455.0 7.1 0.4
44.0 17.5
138.0 39.2 9.4
0.1
1.2
Jul-07 507950 951364
1819 430.0 673.0 7.2 0.4
48.0 17.5
246.0 62.4 21.9
0.0
D/Z_AirForce
Jul-07 499131 965767
1909 824.0 1304.0 8.0 0.5 200.0 23.5
254.0 41.6 36.5
0.0
74.0 110.0 7.7 1.0
95.0 29.0
6.9
2.0 1.0 5.6
2286.0 768.0 1227.3 3.0 3.2
792.0 177.6 605.1
0.2
43.2
52.7 8.9 0.1
9.1
170.2
0.0 207.6 22.8 0.3
7.0 0.0 4.5
225.0
0.0 274.5 1.7 0.2
5.0
1.4 16.9 0.0 5.6
334.8
0.0 408.5 5.9 0.1
5.0
1.1 73.5 0.0 7.6
657.0
0.0 801.5 10.5 0.7
4.0
Dire
Jul-07 487670 960425
1939 584.0 847.0 7.1 0.3 110.0 16.0
200.0 56.0 14.6
0.0
5.8 16.9 0.0 3.9
459.0
0.0 560.0 1.8 0.4
4.0
D/Z_ Well4
Jul-07 500494 974376
1914 342.0 526.0 7.9 0.4
27.0
7.8
218.0 62.4 15.1
0.0
0.6
6.0 0.0 5.2
275.4
0.0 336.0 1.8 0.3
4.0
Dukem_MBI
Jul-07 483567 976037
2133 286.0 476.0 7.4 0.2
28.0
5.7
202.0 46.4 20.9
0.0
0.4
6.0 0.0 12.0
235.8
0.0 287.7 2.8 0.2
5.0
Hora Hoda
Jul-07 497710 960839
1857 3658.0 5400.0 9.7 0.7 1220.0 238.0
72.0 22.4 2.9
0.1
7.9 575.9 0.0 0.4
2250.0 768.0 1183.0 5.8 3.3
5.0
Bishoftu
Jul-07 498623 965965
1860 1096.0 1604.0 9.3 0.7 244.0 53.5
266.0 13.6 56.4 Trace
0.6 100.3 0.0 5.1
828.0 187.2 516.0 1.4 0.2
5.0
Hora
Jul-07 499489 967975
1872 1526.0 2280.0 8.7 0.9 410.0 56.0
354.0 27.2 69.5
0.1
0.9 179.7 0.0 5.7
1159.0 110.4 1189.7 1.1 0.5
6.0
Bishoftu Guda
Jul-07 499346 971614
1876 546.0 798.0 8.9 0.7
220.0 13.6 45.2
0.0
0.5 23.8 0.0 6.4
450.0 103.2 339.2 0.3 0.1
4.0
Awash@MelkaKu
Jul-07 456575 962147
2000
82.0 138.0 7.5 0.5
5.4
3.3
56.0 16.0 3.9
0.2
0.8
4.0 0.0 1.3
57.6
0.0
70.3 8.5 0.2
4.0
Awash Jul-07 476347 925646
@Ombolle
Tede_Mojo Aug-07 518458 946916
1687
98.0 132.0 7.4 0.2
5.7
3.5
63.8 16.9 5.4
0.0 0.1 0.8
9.6
44.4
0.0
54.2 20.8 0.3
4.0
1865 300.0 448.0 7.3 0.1
47.0 16.0
130.2 39.5 7.7
0.1
1.4
4.0 0.0
235.8
0.0 287.7 1.4 0.2
5.0
Gafat_ BH10 Aug-07 507950 951364
1819 480.0 724.0 7.7 0.4
57.0 15.0
247.8 67.2 19.4
0.1
1.4 15.9 0.0
360.0
0.0 439.2 4.5 0.5
5.0
D/Z_AirForce Aug-07 499131 965767
1909 840.0 1285.0 7.9 0.6 206.0 20.5
249.9 61.3 23.5
0.1
1.0 66.5 0.0
630.0
0.0 768.6 8.7 0.7
6.0
89.0 35.5
7.6
206
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Na
K
Total Ca Mg
Station
Date
Hardness
Dire Aug-07 487670 960425
1939 620.0 947.0 7.4 0.2 138.0 16.5
195.3 52.1 15.8
0.1
6.2 21.8 0.0
504.0
D_
Oxygen
0.0 614.8 1.4 0.1
5.0
D/Z_ Well4 Aug-07 500494 974376
1914 350.0 528.0 7.2 0.1
30.0
6.8
247.8 68.0 18.9
0.1
0.7
7.9 0.0
279.0
0.0 340.4 1.5 0.2
5.0
Dukem_MBI Aug-07 483567 976037
2133 310.0 470.0 7.6 0.3
28.0
5.2
218.4 53.8 20.4
0.1
0.4
6.0 0.0
246.6
0.0 300.8 2.4 0.1
6.0
Hora Hoda Aug-07 497710 960839
1857 3450.0 5230.0 9.8 0.5 1240.0 210.0
73.5 16.8 7.7
0.1
8.4 572.0 0.0
2196.0 768.0 1117.5 4.5 3.4
5.0
Bishoftu Aug-07 498623 965965
1860 1020.0 1559.0 9.4 1.0 286.0 29.0
273.0 63.9 8.1
0.1
0.8 95.3 0.2
792.0 213.6 531.9 1.3 0.2
5.0
Hora Aug-07 499489 967975
1872 1450.0 2220.0 8.9 0.7 435.0 42.0
342.3 75.6 37.2
0.1
1.4 194.6 0.0
972.0 12.0 1161.4 0.9 0.4
4.0
Bishoftu Guda Aug-07 499346 971614
1876 510.0 780.0 9.0 0.5 107.0 33.0
207.9 44.5 23.5
0.1
0.8 21.8 0.2
412.2 81.6 337.0 0.5 0.2
5.0
Awash@MelkaKu Aug-07 456575 962147
2000
Fe Mn
F
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
72.0 108.0 7.5 0.6
3.9
3.2
58.8 18.5 3.1
0.3
0.7
2.0 0.7
52.0
0.0
63.7 7.8 0.5
5.0
Awash Aug-07 476347 925646
@Ombolle
Tede_Mojo Sep-07 518458 946916
1687 105.0 171.0 7.1 3.0
7.9
4.7
67.2 21.8 3.1
0.3
0.3
6.0 0.1
72.0
0.0
87.8 2.7 0.1
4.0
1865 308.0 451.0 7.5 0.1
44.0 17.0
128.1 37.0 8.7
0.0
1.5
5.0 0.0
234.0
0.0 285.5 1.4 0.3
4.0
Gafat_ BH10 Sep-07 507950 951364
1819 446.0 684.0 7.7 0.3
52.0 17.0
247.8 67.2 19.4
0.0
1.5 17.9 0.0
351.0
0.0 428.2 3.2 0.4
5.0
D/Z_AirForce Sep-07 499131 965767
1909 802.0 1293.0 7.7 0.7 194.0 24.5
266.7 69.7 22.4
0.0
0.8 72.5 0.1
669.6
0.0 816.9 7.8 0.8
4.0
Dire Sep-07 487670 960425
1939 518.0 866.0 7.0 0.2 112.0 15.0
216.3 59.6 16.3
0.0
6.1 20.9 0.0
450.0
0.0 549.0 1.5 0.3
4.0
D/Z_ Well4 Sep-07 500494 974376
1914 316.0 516.0 7.6 0.2
28.0
6.9
241.5 69.7 16.3
0.0
0.6
7.9 0.0
289.8
0.0 353.6 1.5 0.3
5.0
Dukem_MBI Sep-07 483567 976037
2133 282.0 474.0 7.5 0.5
29.0
7.9 0.0
243.0
5.1
205.8 48.7 20.4
0.0
0.6
0.0 296.5 2.0 0.1
5.0
Hora Hoda Sep-07 497710 960839
1857 3446.0 5200.0 9.6 0.4 1100.0 208.0
71.4 14.3 8.7
0.1
8.8 611.7 0.1
2088.0 912.0 693.0 3.8 3.5
5.0
Bishoftu Sep-07 498623 965965
1860 994.0 1575.0 9.4 0.9 260.0 35.4
279.3 11.8 60.7
0.1
1.0 100.3 0.0
835.2 280.8 447.9 1.2 0.1
5.0
Hora Sep-07 499489 967975
1872 1428.0 2200.0 8.8 0.7 410.0 45.0
342.3 16.8 72.9
0.1
1.2 204.6 0.0
1116.0 120.0 1117.5 0.9 0.2
4.0
1876 506.0 778.0 8.9 0.4
226.8 16.8 44.9
0.0
1.2 27.8 0.0
450.0 115.2 314.8 0.9 0.1
5.0
Bishoftu Guda Sep-07 499346 971614
Awash@MelkaKu Sep-07 456575 962147
Awash Sep-07 476347 925646
@Ombolle
Tede_Mojo Oct-07 518458 946916
2000
95.0 27.0
72.0 108.0 7.5 0.6
3.9
3.2
58.8 18.5 3.1
0.3
0.7
2.0 0.7
52.0
0.0
63.7 7.8 0.5
5.0
1687 110.0 167.0 7.3 0.9
8.4
6.4
77.7 19.3 7.1
0.3
0.3
7.0 0.1
82.8
0.0 101.0 2.2 0.2
5.0
1865 266.0 451.0 7.9 0.2
45.0 15.5
123.9 39.5 6.1
0.0
1.3
7.0 0.0
241.2
0.0 294.3 1.7 0.2
5.0
Gafat_ BH10
Oct-07 507950 951364
1819 420.0 672.0 7.9 0.2
54.0 14.0
258.3 74.8 17.3
0.1
1.4 18.9 0.0
360.0
0.0 439.2 2.4 0.4
5.0
D/Z_AirForce
Oct-07 499131 965767
1909 854.0 1307.0 8.0 0.4 204.0 23.5
256.2 42.0 36.7
0.1
1.0 73.4 0.1
667.8
0.0 814.7 5.2 0.7
4.0
Dire
Oct-07 487670 960425
1939 558.0 843.0 7.5 0.2 116.0 13.8
210.0 53.8 18.4
0.1
6.4 20.9 0.0
475.2
0.0 579.7 1.2 0.0
4.0
D/Z_ Well4
Oct-07 500494 974376
1914 320.0 517.0 7.6 0.2
31.0
6.9
241.5 63.8 19.9
0.0
0.6
8.9 0.0
302.4
0.0 368.9 1.6 0.3
6.0
Dukem_MBI
Oct-07 483567 976037
2133 268.0 473.0 7.8 0.2
24.0
5.3
205.8 54.4 22.4
0.1
0.6
7.9 0.0
244.8
0.0 298.7 3.0 0.2
5.0
Hora Hoda
Oct-07 497710 960839
1857 3394.0 5270.0 9.8 0.3 1220.0 220.0
67.2 16.8 6.1
0.1
9.2 542.2 0.1
2250.0 912.0 890.6 4.9 3.3
6.0
Bishoftu
Oct-07 498623 965965
1860 1056.0 1597.0 9.4 0.8 262.0 42.0
283.5 13.4 60.7
0.1
1.2 103.3 0.0
846.0 240.0 544.1 2.3 0.2
4.0
Hora
Oct-07 499489 967975
1872 1466.0 2260.0 8.8 0.5 410.0 49.5
357.0 33.6 66.3
0.1
1.4 173.8 0.0
1198.8 129.6 1199.0 1.4 0.7
4.0
Bishoftu Guda
Oct-07 499346 971614
1876 510.0 787.0 9.2 0.4
98.0 34.5
231.0 13.4 47.9
0.0
0.8 26.8 0.0
468.0 84.0 400.2 1.2 0.2
5.0
Awash@MelkaKu
Oct-07 456575 962147
2000 214.0 331.0 7.9 0.6
12.5
7.2
147.0 42.0 10.2
0.2
0.5
180.0
0.0 219.6 3.6 0.3
5.0
Awash Oct-07 476347 925646
@Ombolle
Tede_Mojo Nov-07 518458 946916
1687 240.0 358.0 8.3 0.5
23.0
6.7
147.0 43.7 9.2
0.1
0.5 20.9 0.1
171.0 16.8 174.5 15.5 0.2
6.0
1865 306.0 453.0 7.8 0.0
54.0 17.5
128.1 38.1 7.1
0.0 0.0 1.5 10.9 0.0 0.1
250.2
0.0 305.2 1.1 0.2
5.0
Gafat_ BH10 Nov-07 507950 951364
1819 432.0 668.0 7.3 0.2
59.0 14.5
243.6 64.8 18.4
0.0 0.0 1.5 20.8 0.0 0.7
333.0
0.0 406.3 2.8 0.1
5.0
8.9 0.1
207
Monitoring
Analysis
x
y Elevation TDS Cond pH NH4
Na
K
Total Ca Mg
Station
Date
Hardness
D/Z_AirForce Nov-07 499131 965767
1909 749.0 1144.0 7.9 0.5 190.0 21.5
249.9 36.1 38.8
0.0 0.0 1.0 70.4 0.0 2.1
541.8
D_
Oxygen
0.0 661.0 5.1 0.5
5.0
1939 556.0 838.0 7.4 0.1 121.0 13.7
199.5 51.0 16.3
0.0 0.0 6.5 21.8 0.0 0.3
442.8
0.0 540.2 1.3 0.2
4.0
D/Z_ Well4 Nov-07 500494 974376
1914 338.0 515.0 7.8 0.1
31.0
6.8
233.1 75.6 10.7
0.0 0.0 0.8 10.9 0.0 5.2
297.0
0.0 362.3 1.5 0.3
5.0
Dukem_MBI Nov-07 483567 976037
2133 308.0 473.0 7.8 0.1
30.0
5.2
210.0 43.7 23.5
0.0 0.0 0.5 11.9 0.0 0.9
244.8
0.0 298.7 1.6 0.1
6.0
Dire Nov-07 487670 960425
Fe Mn
F
Cl NO2 NO3 Alkalinity
CO3 HCO3 SO4 PO4
Hora Hoda Nov-07 497710 960839
1857 4042.0 5420.0 9.8 2.4 1200.0 224.0
71.4 13.8 8.7
0.0 0.1 8.9 595.8 0.0 0.1
2124.0 912.0 736.9 3.1 3.3
6.0
Bishoftu Nov-07 498623 965965
1860 1048.0 1598.0 9.3 0.9 315.0 47.0
273.0 12.2 58.7
0.1 0.1 0.8 100.1 0.0 0.5
819.0 211.2 569.7 1.0 0.1
4.0
Hora Nov-07 499489 967975
1872 1512.0 2310.0 8.5 1.0 430.0 46.5
346.5 28.4 66.3
0.1 0.0 1.0 174.4 0.0 0.3
1072.8 72.0 1162.4 1.2 0.2
5.0
Bishoftu Guda Nov-07 499346 971614
1876 542.0 816.0 9.0 0.5 108.0 32.0
220.5 19.3 41.8
0.1 0.0 1.0 27.7 0.0 0.2
460.8 81.6 396.3 1.2 0.1
6.0
Awash@MelkaKu Nov-07 456575 962147
2000 262.0 391.0 7.9 0.3
27.0
8.2
151.2 44.6 8.7
0.1 0.0 0.8 10.9 0.1 0.3
201.6
0.0 246.0 3.3 0.1
5.0
Awash Nov-07 476347 925646
@Ombolle
Tede_Mojo Dec-07 518458 946916
1687 382.0 579.0 8.2 0.3
62.0 10.0
184.8 51.8 12.2
0.1 0.1 0.8 54.5 0.2 7.7
203.4
7.2 233.5 10.0 0.1
5.0
1865 298.0 451.0 8.0 0.5
49.0 15.0
136.5 40.3 8.7
0.0 0.1 1.3
4.0 0.0 1.2
276.0
0.0 336.7 0.7 0.1
5.0
Gafat_ BH10 Dec-07 507950 951364
1819 444.0 664.0 7.7 0.3
56.0 14.5
245.7 70.6 16.8
0.0 0.0 1.2 16.9 0.0 1.2
330.0
0.0 402.6 2.1 0.1
5.0
D/Z_AirForce Dec-07 499131 965767
1909 864.0 1308.0 8.3 0.2 218.0 17.5
254.1 29.4 43.9
0.1 0.0 0.9 70.5 0.1 2.2
632.0 12.0 746.5 0.7 0.4
4.0
Dire Dec-07 487670 960425
1939 556.0 838.0 7.4 0.1 121.0 13.7
199.5 51.0 16.3
0.0 0.0 6.5 21.8 0.0 0.3
442.8
0.0 540.2 1.3 0.2
4.0
D/Z_ Well4 Dec-07 500494 974376
1914 338.0 515.0 7.8 0.1
31.0
6.8
233.1 75.6 10.7
0.0 0.0 0.8 10.9 0.0 5.2
297.0
0.0 362.3 1.5 0.3
5.0
Dukem_MBI Dec-07 483567 976037
2133 308.0 473.0 7.8 0.1
30.0
5.2
210.0 43.7 23.5
0.0 0.0 0.5 11.9 0.0 0.9
244.8
0.0 298.7 1.6 0.1
6.0
9.7 1.1 1340.0 236.0
86.1 18.5 9.7
0.1 0.1 8.6 615.7 0.0 0.2
2200.0 840.0 976.0 2.6 0.8
5.0
Bishoftu Dec-07 498623 965965
1860 1048.0 1598.0 9.3 0.9 315.0 47.0
273.0 12.2 58.7
0.1 0.1 0.8 100.1 0.0 0.5
819.0 211.2 569.7 1.0 0.1
4.0
Hora Dec-07 499489 967975
1872 1512.0 2310.0 8.5 1.0 430.0 46.5
346.5 28.4 66.3
0.1 0.0 1.0 174.4 0.0 0.3
1072.8 72.0 1162.4 1.2 0.2
5.0
Bishoftu Guda Dec-07 499346 971614
1876 542.0 816.0 9.0 0.5 108.0 32.0
220.5 19.3 41.8
0.1 0.0 1.0 27.7 0.0 0.2
460.8 81.6 396.3 1.2 0.1
6.0
Awash@MelkaKu Dec-07 456575 962147
2000 262.0 391.0 7.9 0.3
8.2
151.2 44.6 8.7
0.1 0.0 0.8 10.9 0.1 0.3
201.6
0.0 246.0 3.3 0.1
5.0
Awash Dec-07 476347 925646
@Ombolle
1687 402.0 630.0 7.9 0.2
15.5
197.4 58.0 12.8
0.0 0.0 0.7 47.7 0.0 8.6
250.0
0.0 305.0 12.8 0.2
6.0
Hora Hoda Dec-07 497710 960839
1857 3682.0
27.0
208
Appendix 6 Water extraction wells used in the numerical model
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Ziquala-Abuloya
478772
958888
1911
220
102.22
1808.78
267.84
Ziquala-Abusera
475705
956271
1906
220
104.25
1801.75
630.72
Hirpo Qamo
454568
880156
1824
179
131.4
1692.6
535.68
Rosa Kontola
457077
888976
1793
200
135.66
1657.34
86.4
Meki-Abosa
469799
886240
1666
51
31.5
1634.5
224.64
Qiltu Ombole
475571
924071
1752
154
91.62
1660.38
604.8
Red Fox Flowers
503210
930527
1610
100
7.8
1602.2
1900.8
Koka
506728
937459
1669
129.66
53.27
1615.73
345.6
Modjo Abudab stab
512159
946729
1792
141
72.52
1719.48
717.12
Dukem NOC
480641
977009
2121
134
98.3
2022.7
3369.6
Dukem MBI
483567
976037
2133
172.7
125.05
2007.95
933.12
Modj-Golge dildima
521269
949730
1914
283
207.6
1706.4
345.6
RMI Steel factory
494320
965101
1995
153
110.44
1884.56
1209.6
172.8
D/Z-Oxford
493179
968613
1919
108
74.37
1844.63
Dukem Gedera hotel
487881
973470
1995
133.4
59.63
1935.37
777.6
D/Z-Hospital
492987
967055
1928
108
42.45
1885.55
440.64
1076.544
Shimbra Meda
501422
973727
1910
80
24.1
1885.9
Shimbira Meda
500766
973335
1905
80
22.64
1882.36
613.44
Shimbira Meda
500494
974376
1914
80.5
24.87
1889.13
1442.88
Shimbira meda
500424
974376
1914
81.7
30.6
1883.4
565.92
D/Z-Airforce
499228
964796
1910
80
43.07
1866.93
691.2
Gafat#1
507491
952694
1862
102
40.1
1821.9
483.84
Gafat#2
508079
952013
1822
114.5
7.9
1814.1
388.8
Gafat#4
508995
951614
1810
150
31.26
1778.74
432
Gafat#7
509385
950325
1994
150
26.08
1967.92
345.6
Gafat#8
509020
950736
1795
150
28.9
1766.1
345.6
Gafat#9
508684
951058
1806
146.2
23.91
1782.09
345.6
Gafat#10
507950
951364
1819
150
25.55
1793.45
345.6
Modjo Lume#3
512957
947774
1783
134
36.7
1746.3
734.4
Modjo#4
511927
948292
1770
113.5
8.3
1761.7
388.8
Modjo#2
511976
948671
1786
80
35.34
1750.66
1105.92
Modjo#3
512408
948682
1782
80
34.14
1747.86
432
Modjo#1
512011
949196
1785
90
31.77
1753.23
388.8
Modjo Bekele Mola
513423
948940
1784
100
37.2
1746.8
535.68
Modjo Ethio Japan
513773
949998
1795
148
39.52
1755.48
406.08
Meki-Choroke
471448
898126
1706
93
78
1628
345.6
Meki-Graba Phila
483833
904045
1677
52
33.6
1643.4
190.08
Nazereth tec College
536113
947062
1693
195
160
1533
267.84
Awash Melkasa ELPA
537332
928166
1536
126
88
1448
432
Nazereth Trans
530429
941358
1595
196
162.1
1432.9
108
Naz.rehab.center
530748
940960
1593
192
149.7
1443.3
216
Wonji Shewa
523310
928661
1553
114
6.9
1546.1
440.64
Nazereth-Yerer Flour
527667
941523
1626
152
99.1
1526.9
172.8
Nazereth Metal Works
526928
943518
1656
202
162.7
1493.3
216
Koye Jajaba
477019
917872
1724
123.4
78.5
1645.5
216
Koka Ethi-Cutting
502329
929321
1606
130
5.4
1600.6
2160
Holota-Tsedey
447200
998889
2290
126
0
2290
1209.6
Holota-Jerico flowers
453522
1001474
2562
115.25
57.84
2504.16
1261.44
Holota-MetroLux
445942
1001323
2250
99
0
2250
1209.6
Ambo#2-TWS
378447
991365
2189
130
32.89
2156.11
172.8
Ginchi
404656
997733
2235
81
21.5
2213.5
302.4
209
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Addis Alem TWS
434015
1000129
2554
147
4.8
2549.2
Holota-Agri Flowers
440110
1001337
2419
153
36.62
2382.38
129.6
129.6
Alem Gena-Geja Dera
461930
970844
2201
183.2
144.4
2056.6
198.72
Suden Muchucha
459928
941554
2175
134
77.9
2097.1
216
Silti Boze
426788
887491
2102
173.68
72.32
2029.68
293.76
Butajira-Debub
435268
897823
2006
105
40.7
1965.3
285.12
Shereshera Ele
439018
898838
1910
85
25.66
1884.34
434.592
635.04
Butajira-new-TWS
430858
896035
2070
80
8.01
2061.99
AA-Burayu Spring
462743
1002521
2620
250
35
2585
259.2
Soloke
458775
911576
1936
295
151.87
1784.13
388.8
Sebeta-Aztoki Imp
463635
988675
2296
83
26.82
2269.18
328.32
Sebeta Tal Flowers
455450
983014
2110
120
14.56
2095.44
570.24
Sebeta Tal Flowers#1
455426
982736
2099
150
16.33
2082.67
462.24
Tefki
444624
978143
2074
65.4
9.6
2064.4
64.8
D/Z-Health College
497100
968198
1901
92
36.4
1864.6
864
Galiyee
485970
935007
1712
98
56.56
1655.44
509.76
Ziquala-Annate
481162
935226
1815
200
151.88
1663.12
198.72
Derba-1
463286
1038986
2420
78
4.2
2415.8
276.48
Korke Robe
473871
1024156
2605
15
6
2599
172.8
Legadadi-Dire
494036
1010902
2549
154
21.35
2527.65
259.2
Bekie
507408
1013586
2592
110
29.46
2562.54
432
D/Z-New well
492803
969204
1906
116.3
64.3
1841.7
388.8
Tafki golden Rose#
442842
977555
2074
100
14.15
2059.85
293.76
Woliso Prison
386183
943986
2046
108.4
0
2046
432
Woliso#2TWS
387549
945858
2066
93
0
2066
959.04
Busa-Boda#2
404050
980200
2150
98.5
9.95
2140.05
691.2
Kelecho Gerbi
407400
972711
2140
172
19.45
2120.55
345.6
AA-Sebeta Shootin
463599
988583
2283
130
19.46
2263.54
345.6
Addis Alem-Siet
436160
1000453
2370
153
36.62
2333.38
129.6
Addis Alem- Flower
435382
1000251
2358
206
9.93
2348.07
681.696
D/Z-Sunshine Con
488408
973431
1984
150
42.52
1941.48
432
D/Z-Dugda PLC
502364
970907
1893
74
23.78
1869.22
2108.16
Modjo lume#1( WS)
512282
951356
1762
123
7.34
1754.66
717.12
Modjo Lume#2(WS)
512355
951516
1777
124.83
9.25
1767.75
717.12
Holota-Agri
445180
1002459
2393
101
23.3
2369.7
302.4
Incini Adaa Berga
432432
1024464
2598
161
11.45
2586.55
216
D/B-Dalocha DBH#4
561897
1069503
2796
125
9.2
2786.8
432
D/Z-Green Star
494598
967157
1896
80
66.78
1829.22
578.88
AAWSA Ras Kassa
475000
1001300
2542
168
73.54
2468.46
1555.2
AA-Anwar Mosque
471300
998200
2445
87.5
16.5
2428.5
43.2
Nazereth Soap F
530792
940526
1618
210
172.6
1445.4
527.04
AA-Armay hospital
469800
996300
2350
51.9
16.5
2333.5
362.88
AA-Awash Winery
469900
996000
2335
67.1
13.7
2321.3
362.88
Legadadi
491300
1004800
2456
94.55
5.42
2450.58
216
AA-Civil Aviation
469800
996200
2342
60
7
2335
21.6
AA-Ethio-Plastic
478450
995600
2353
171
86.65
2266.35
129.6
Malmalle
515109
943100
1772
160
67.58
1704.42
483.84
Holota-Dandi Boru
440595
1000333
2374
170
81.46
2292.54
639.36
Maru Sambo
402350
951544
2440
60
26.73
2413.27
15.552
Maru Renda
393964
957477
2497
61.25
39.12
2457.88
12.096
Woliso-Wolenso
397276
954511
2400
61.25
17.45
2382.55
47.52
Seto
397540
953574
2393
60
17.93
2375.07
12.96
Woliso-Desta
396861
952816
2380
36
20.48
2359.52
187.488
210
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Sarie
398182
951093
2350
62
14.13
2335.87
112.32
AA-Defence Insury-2
471300
995050
2326
114
0
2326
1442.88
Woliso-Andode#2
400409
954207
2379
48
21.58
2357.42
345.6
Woliso Abeyi
398901
951499
2402
60
24.43
2377.57
112.32
Busa-Ebicha Derera
400317
931223
2148
66
38.35
2109.65
10.368
Senbeti
394200
944338
2207
62
36.5
2170.5
13.824
D/B-Dalocha DBH1
562106
1070685
2790
108
12.79
2777.21
604.8
D/B-Dalocha DBH2
562015
1071000
2794
95
11.68
2782.32
432
AA-Former Golf Club
469700
994500
2332
152.4
6.7
2325.3
172.8
D/B-Beresa BBH2
559369
1067725
2769
100
6.25
2762.75
1322.784
D/B-Beresa BBH4
559318
1066823
2757
93
6.25
2750.75
1296
D/B-Beresa BBH#5
559432
1066077
2758
102
2.85
2755.15
1473.12
D/B-DalochaDBH#6
560802
1070783
2782
76
11.45
2770.55
1036.8
Bantu-Areda Leka
424000
952736
2169
112
5.37
2163.63
864
Asgori-Gurara
381500
933000
1922
100
7.33
1914.67
483.84
AA-Bambis Sunshine
474395
995211
2335
153
59
2276
216
AA-Anbessa/Walya
471200
993700
2300
85
23.3
2276.7
432
360.288
AA-Abay Mesk Soft
473000
992700
2292
121.2
110.6
2181.4
AA-Misrak flour & oil
472900
992500
2280
156.2
89.6
2190.4
172.8
AA-United Oil mills-1
473200
992400
2287
68.5
29
2258
220.32
AA-Adey Abeba
473848
990072
2247
100
39.3
2207.7
34.56
AA-Addis Tyre
473915
989015
2215
201
34.5
2180.5
287.712
AA-National Road
475000
987800
2180
172
27.8
2152.2
345.6
Meher Fiber Factory-2
475335
980717
2075
179.4
17.1
2057.9
224.64
Akaki Indo-Europian
476500
981300
2055
53.3
3.7
2051.3
119.232
Akaki Indo-Europian
476600
981500
2070
126.2
3.5
2066.5
360.288
Akaki Mesfin Zelwlew
481507
976220
2100
132
120
1980
432
AA-Ethio-Metal Meal-
476400
980700
2058
126
53.4
2004.6
302.4
Akaki Ethio-Fiber-1
477400
979500
2080
96
27.4
2052.6
216
Akaki Tele
476600
978200
2065
79.2
46.4
2018.6
183.168
535.68
AA-Kality Military
475310
983810
2105
93
0
2105
AA-US Embassy
473900
1001050
2564
200
14.55
2549.45
172.8
AA-Kality Military
475300
984000
2100
128.5
0
2100
496.8
AA-Glass and Bottle
467200
1001017
2517
150
35.3
2481.7
392.256
AA-Anbessa
468400
1001016
2580
192
3
2577
129.6
Alem Gena-
463600
988200
2280
64
27.5
2252.5
259.2
AA-Coca Cola factory
470000
996400
2338
44
14
2324
576.288
AA-National Palace-2
473400
996300
2352
249
0
2352
432
AA-Ghion Hotel-1
473300
996100
2342
77.7
0
2342
359.424
359.424
AA-Ghion Hotel -2
473300
996200
2344
56.4
7.6
2336.4
AA-Brewey-1
471600
995800
2345
34
19
2326
86.4
AA-Brewery-2
471500
995900
2345
34
17
2328
233.28
AA-Brewery-3
471400
995800
2345
64
12
2333
69.12
AA-Brewery-4
471400
996000
2345
32.4
23
2322
216
AA-Brewery-7
471500
995800
2345
52
16
2329
432
AA-Brewery-8
471300
995800
2345
85
7.6
2337.4
665.28
AA-Water III BH25-2
477162
976038
2060.8
135
42
2018.8
7566.912
AA-Water III BH21
477856
976402
2063.6
151
44.7
2018.9
6471.36
AA-Hana Mariam-2
471700
986600
2220
81
26.1
2193.9
51.84
AA-Stars Business
481205
976968
2155
184
121
2034
604.8
AA-Anbessa
471200
995700
2343
192
3
2340
86.4
AA-Alert-3 Well
468100
993200
2300
83
45
2255
133.92
AA-Sheraton BH-1
473334
997204
2360
355
4.2
2355.8
86.4
211
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
AA-Cement Factory-1
473100
991800
2280
93.9
56.4
2223.6
AA-Cement Factory-2
473100
991900
2270
153.9
112.7
2157.3
125.28
216
AA-Addis Tyre
473900
989000
2224
201.5
45.4
2178.6
317.088
AA-Hilton Hotell
474050
996650
2373
400
9.8
2363.2
190.08
AA-Ethio-Spice
473300
987700
2161
103
44.7
2116.3
78.624
AA-WWDA Ware
473300
987300
2163
120
35.39
2127.61
43.2
AAWSA Kality Well
475000
985800
2112
120
0
2112
2160
Meher Fiber Factory-1
475662
980783
2055
51.8
27.4
2027.6
129.6
Akaki Indo-Europian
476369
981717
2062
63.7
7
2055
287.712
AA-Ethio-Metal meal-
476400
980600
2056
120
16.9
2039.1
483.84
AA-Kality Airforce-1
476400
984800
2125
90
12
2113
298.08
Galetti Project
474800
984700
2140
71.3
0.5
2139.5
578.88
AAWSA Asko
465507
1002282
2555
178
1.95
2553.05
518.4
AA-Kotebe metal
480629
998771
2471
96
13.6
2457.4
544.32
233.28
AA-Old Airport-2
470500
994500
2320
170
41.7
2278.3
AA-Gulele Glass-
466900
1001005
2517
150
20.4
2496.6
216
AA-Brewery-9
471400
995900
2345
88
16.8
2328.2
544.32
AA-Beverage
478462
977721
2090
150
50
2040
345.6
AA-NMWC Pump
477608
978689
2090
116
57.7
2032.3
255.744
AA-Kality Metal
474225
982650
2150
177.8
30.78
2119.22
227.232
AA-Prefabrication
474429
986829
2177
187
40.5
2136.5
129.6
AA-Artificial
475300
983800
2120
140
0
2120
432
Akaki Textile Mill
476350
981300
2060
65
7.4
2052.6
216
1036.8
AA-EELPA,Kotebe
480431
998457
2452
181
15.4
2436.6
AA-Meta Abo Brewery
455000
985200
2200
126
50.86
2149.14
302.4
AA-Darge-Suq,WSSA
464300
990600
2290
52
14.4
2275.6
380.16
172.8
AA-Abay Mesk Soft
473010
992710
2292
90
23.67
2268.33
AA-US Embassy-3
474050
1000875
2550
156
14.6
2535.4
172.8
Aa-Ethio-Meat
473326
986813
2180
86.7
25.2
2154.8
171.936
AA-Ethio iron BH1
476426
980749
2060
43.7
4
2056
259.2
AA-Ethio Iron BH-2
476430
980669
2060
62
6.5
2053.5
518.4
302.4
Sunshine Terminal
483093
976323
2159
207
137.6
2021.4
Tatek Tor Sefer-5
459700
998075
2580
67
10.2
2569.8
432
AA-Water lll Test-B3
463700
988500
2280
130
19
2261
345.6
AA-Water lll Test-B4
486200
1001042
2450
100
10
2440
129.6
AA-Water lll Test-B5
481210
980010
2150
150
11
2139
259.2
AA-Water lll Testl-B6
470800
982900
2110
114
0
2110
259.2
AA-Water lll testl-B7
473566
978610
2070
122
23.5
2046.5
345.6
AA-Water lll Test-B9
481615
982915
2205
120
35.1
2169.9
259.2
AA-Water lll Test-B10
461500
1001023
2630
110
82
2548
86.4
AA-Water lll Test-B11
466200
988800
2246
100
0
2246
345.6
AA-Water lll Test-B12
466400
987600
2252
125
18.3
2233.7
216
AA-Water lll Test-T1
481200
980000
2150.8
173
8.9
2141.9
129.6
AA-Water lll Test-T5
481600
982900
2205
120
37.33
2167.67
259.2
AA-Water lll Test-B13
479425
981425
2133.4
100
2.7
2130.7
345.6
AA-Water lll TestB14
480900
978800
2126.4
160
86
2040.4
216
AA-Water lll TestT2
479400
981400
2133.5
74
2.8
2130.7
1010.88
Akaki Water S. EP-1
479340
981400
2131.33
108.7
0.73
2130.6
2289.6
Akaki Water S. EP-2
481600
982850
2203.98
136
33.48
2170.5
959.04
Akaki Water S. Ep-3
479740
981400
2133.88
126
3.38
2130.5
820.8
AA-water lll Test-B15
473069
979881
2057.4
116
5.8
2051.6
216
AA-Watet lll Test-T4
473108
979851
2058.34
103
7.07
2051.27
43.2
AA-Water lll BH01
477972
974859
2078.5
133
59
2019.5
7566.912
212
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
AA-Water lll BH02
478399
975589
2072.5
122
53
2019.5
AA-Water BH3b
478713
974977
2083
130
64
2019
7566.912
7566.912
AA-Water lll BH05b
476574
975607
2070.3
142
51.4
2018.9
7563.456
AA-Water lll BH06
479696
976936
2086.7
145
67.8
2018.9
7566.912
AA-Water lll Bh07
479405
976735
2086
151
67.2
2018.8
5963.328
AA-Water lll BH08
479061
976370
2086.5
144
67.2
2019.3
5443.2
AA-Water lll e BH09
479246
977104
2077.5
146
58.7
2018.8
5963.328
AA-Water lll BH12
478808
976867
2070.6
152
47.5
2023.1
7007.04
AA-Water lll BH14
478580
976051
2078.6
130
59.2
2019.4
5140.8
AA-Water lll BH16
478347
976752
2067.5
148
47.5
2020
7566.912
AA-Water lll BH17
478199
976361
2065.3
144
45.9
2019.4
7563.456
AA-Water lll Bh18
478154
975966
2073.5
140
54.1
2019.4
7563.456
AA-Water BH19
478019
977985
2070.2
150
51.5
2018.7
7566.912
AA-Water lll BH20
477945
976985
2068.3
148
49.9
2018.4
7566.912
AA-Watter lll BH22
477651
975923
2066.8
142
47.9
2018.9
6214.752
AA-Water lll BH23
477477
977216
2064.3
145
44
2020.3
7566.912
AA-Water lll BH24
477330
976793
2061.6
130
42.9
2018.7
7566.912
Akaki Water s. EP-6
479526
977468
2090
129
71.7
2018.3
3024
Akaki Water S. EP-7
479021
977596
2090
126
73.48
2016.52
2858.112
Akaki Water S. EP-8
478998
977937
2090
130
73.48
2016.52
3568.32
Dukem-Arena
487900
972421
1948.4
135
75.75
1872.65
361.152
D/Z-Dire Clinic
488391
961066
1940
180
148
1792
207.36
AAWSA/Kotebe Kara
484190
998500
2480
140
45.02
2434.98
1641.6
AA-Kotebe, Selam
482070
999823
2526
117
79.56
2446.44
216
AA-Sansuzi, AAWSA
465900
1002875
2600
110
15.75
2584.25
691.2
AA-Burayu, ethio-
464600
1003075
2620
96
14.45
2605.55
172.8
AA-Kotebe , Submmit
483750
994550
2340
230
50.87
2289.13
691.2
AA-Meta Abo BH5
455300
985250
2218
101
37.5
2180.5
384.48
AA-Meta abo BH9
455550
983750
2138
181
46.8
2091.2
345.6
Tefki-Golden Rose#1
444000
977700
2055
100
17.12
2037.88
34.56
AA-Sebeta Agro No.1,
460850
985850
2260
106
43
2217
561.6
AA-Sebeta Agro No.2
460500
986500
2285
100
27.12
2257.88
786.24
AA-Bingham
468650
999800
2460
172
68.5
2391.5
43.2
AA-Netherlands Emba
468800
996600
2360
124
50.35
2309.65
172.8
AA-Dire Tannery BH1
468200
1001600
2578
150
45.9
2532.1
328.32
Korea Embassy
468425
996350
2320
68
19.6
2300.4
777.6
AA-Hagbes PLC.,
468875
993750
2298
130
11.16
2286.84
429.408
AA-Vatican
470950
993300
2290
120
18.93
2271.07
345.6
AA-Hillton Hotel
474175
996550
2365
120
40.25
2324.75
414.72
AA-Nigeria Embassy,
472700
999800
2485
120
11.7
2473.3
129.6
AAWSA Shegole
468100
1001625
2585
150
31.69
2553.31
1036.8
Tafo, Ropack
487800
1002200
2488
80
24.8
2463.2
483.84
AA-Gulele Misionery 1
465651
1001575
2540
76
7.3
2532.7
578.88
AA-Gulele Misionery.2
465600
1001855
2545
104
13.2
2531.8
691.2
AA-Kera
472150
993300
2270
150
50.5
2219.5
535.68
AA-TW2
473576
972821
2081
150
74
2007
734.4
AA-TW4
489950
976019
2067
220
91
1976
1572.48
TW5 Test well No.5
485798
968308
1905
217
69.4
1835.6
1641.6
AA-Tadele Gelecha
465243
1003930
2615
115
26.17
2588.83
276.48
AA-Zak Ethiopia
474957
982383
2140
135
24
2116
518.4
AA-Jehova Well No.1
483350
999064
2487
116
59
2428
518.4
AA-Tikur Abbay Shoe
466350
1001000
2550
153
15.65
2534.35
345.6
AA-Algeria embassy
469727
993542
2324
120
22.24
2301.76
276.48
213
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Alem Gena-Balz af
460121
985966
2239
120
39.7
2199.3
AAWSA Mekanissa.3
470316
991064
2222
170
9.5
2212.5
576.288
864
AAWSA Keraniyo.1
467000
996300
2340
132
51.75
2288.25
518.4
AAWSA Keraniyo.2
463908
995127
2450
147
40
2410
259.2
AA-Ato Tahas Burayo
465410
1002944
2560
124
11.22
2548.78
259.2
AA-Woreda 17 K23
476963
994106
2349
132
72.5
2276.5
259.2
Dukem-Industrial Park
490000
968000
1900
115
94.85
1805.15
259.2
AA-Burayu Simachew
465161
1003103
2610
66.7
18.24
2591.76
552.96
AA-National Bank
472541
996743
2378
90
51.75
2326.25
259.2
AA-Repi Enyi General
464538
991302
2300
94
5.33
2294.67
406.08
AA-Samson PLC near
469804
993691
2330
170
26.62
2303.38
604.8
AA-Worwdw 17 k 17
476235
995275
2357
151
84
2273
86.4
AA-Motor Engineering
477463
994346
2335
170
91.86
2243.14
172.8
D/Z-Airforce No.2
499500
964500
1890
72
30.5
1859.5
388.8
D/Z-Sahilu
494829
967088
1917
80
64.1
1852.9
291.168
D/Z-Girma Gebre
495561
968574
1906
60
49.9
1856.1
209.952
D/Z-Almaz Ayele
499320
970175
1890
68
18.9
1871.1
518.4
D/Z-Blue Nile Plastics
495765
966397
1910
94
46.6
1863.4
388.8
D/Z-Hora Tannery
498633
970028
1890
74
34.1
1855.9
864
D/Z-Veternary College
500078
968505
1880
56
14.5
1865.5
1330.56
AA-Kotebe Selam
481878
1000148
2546
140
75.95
2470.05
457.92
AA-usEmbassy-4
474000
1001000
2562
201
39.7
2522.3
203.04
Legadadi-NAS Food
488150
1002300
2489
175
26
2463
138.24
AA-Sebeta-Dragados
457030
984617
2200
140
88.52
2111.48
432
Akaki Beverly
480895
977403
2120
104
84.8
2035.2
381.024
AA-Kality-Get-as
474788
982924
2150
124
23.54
2126.46
432
AA-Batu Tannery
473466
987247
2165
65
9.25
2155.75
993.6
Sendafa-Said Ali
488243
1002102
2486
96
25.93
2460.07
172.8
AA-Mekanisa-Santa
470983
992553
2235
96
32.58
2202.42
172.8
Legadadi-Dini
486000
1000707
2485
54
12
2473
103.68
Akaki BABRGUBA
474645
985501
2130
68
92.3
2037.7
1555.2
AA-kality Elsa Flour
474641
985622
2155
68
8.2
2146.8
380.16
Sululta Depot
472975
1011144
2650
114
11.7
2638.3
190.08
AA-ZAF
480965
977576
2139
131.5
98.87
2040.13
561.6
AA-Tibebu Hospital
471799
999371
2484
114
8.45
2475.55
2376
AAWSA F7 at Koye
481337
982304
2190
135
23.35
2166.65
3532.032
AAWSA F1 at Fanta
479000
981400
2120
120
10
2110
3456
AA-Military Food
473900
985100
2165
72
8
2157
155.52
AA-Burayu-1-99
463972
1000788
2514
174
0
2514
1421.28
Alemgena
462260
984901
2294
180
82.75
2211.25
576.288
AA-Lafto-99
471500
990500
2255
200
12.28
2242.72
1434.24
AA-Yekamichael-99
477515
997474
2388
216
18.86
2369.14
1252.8
AA-Burayu-99
464031
1002909
2584
200
0
2584
1531.008
AA-Asko-99
465578
999808
2434
193
0
2434
1425.6
AA-Kidane Mihret-99
466050
993650
2525
200
71
2454
1728
AA-Mekanisa-1-99
470277
989578
2269
230
12.7
2256.3
1296
AA-Mekanisa-2-99
469245
990260
2264
240
0
2264
2592
AA-Repi-1-99
465295
990132
2282
200
9.8
2272.2
1728
AA-Mikililand-99
466600
1001250
2274
196
7.26
2266.74
1382.4
AA-Shegole Mesgid
469414
1001640
2571
240
0
2571
1296
AA-Burayu-99
464090
1002920
2513
200
0
2513
1529.28
AA-Keranio-99
463595
995915
2475
200
25.51
2449.49
1728
AA-Sebeta Fishery
460295
986769
2222
158
31
2191
1468.8
214
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Inchini-
421795
1040108
2457
146
20.45
2436.55
Holota
440274
1006055
2525
300
12.33
2512.67
1382.4
3067.2
Asgori
427126
971361
2075
308
4.2
2070.8
3067.2
Melkakunture
456314
962592
2014
290
0
2014
3067.2
Abusera
478990
955803
1830
330
38.47
1791.53
172.8
Segnogebeya
455620
1026514
2610
273
69.75
2540.25
181.44
Chancho
473911
1031930
2543
324
1
2542
518.4
Legadadi
493518
1004421
2468
354
27.75
2440.25
1313.28
Bekie
507086
1012954
2578
300
26.49
2551.51
527.04
Woberi
501332
1068067
2654
209
71
2583
1382.4
Dukem
490336
970789
1924
282
89
1835
1123.2
Sululta
474421
1013070
2610
304
18.2
2591.8
1365.12
Tefki
450359
981037
2084
280
11
2073
1641.6
CMC
484821
994284
2320
368
42
2278
501.12
Jawaro
433200
959670
2111
194
0
2111
1658.88
Dima Jalewa
413137
973900
2090
122
8.5
2081.5
1446.336
Modjo Ude
506765
957179
1836
278
19
1817
1442.88
Borora
504878
970766
1879
300
20.45
1858.55
3067.2
Onodo
513157
1025381
2904
348
96.61
2807.39
302.4
Adulala
490444
951336
1765
225
98.35
1666.65
1382.4
Modjo Muda
506464
941989
1697
268
74.6
1622.4
731.808
Kimoye
427395
992768
2109
220
4
2105
1509.408
Ferencay
474424
1001212
2505
260
39.84
2465.16
1296
Ferencay
475068
1001254
2532
260
106.36
2425.64
1555.2
Ferencay
475945
1000580
2473
252
43.34
2429.66
3715.2
Ayer Tena
466842
993128
2307
250
37.58
2269.42
432
Summit
481681
994296
2280
250
12.15
2267.85
1209.6
Summit
485664
994125
2295
250
1
2294
1641.6
Kotebe
482994
998429
2437
203
44
2393
2937.6
Gojam Bar
470504
1002135
2639
232
3
2636
2764.8
Summit
482349
995063
2321
210
52
2269
1209.6
Gojam Bar
470218
1001886
2608
233
0
2608
864
Gojam Bar
471026
1002023
2633
222
13.44
2619.56
2678.4
East Bole
481061
992052
2252
270
28
2224
1641.6
East Bole
480760
992453
2261
260
22
2239
1641.6
Shiromeda
472608
1002066
2537
170
4
2533
604.8
Bolelemi
482696
990537
2222
270
7
2215
1598.4
Ferencay
475945
1000580
2473
232
23
2450
3084.48
Shiromeda
474238
1001465
2525
200
68
2457
2419.2
Kotebe
481462
998906
2451
250
71.3
2379.7
2730.24
4060.8
Kotebe
480321
997370
2399
180
10
2389
Akaki
478050
981860
2115
250
10
2105
2160
Burayu
480604
992935
2266
250
20
2246
1468.8
1468.8
Burayu
465836
999320
2457
280
20
2437
Shegole
468207
1001645
2565
250
20
2545
1296
Sansuzi
465832
1002965
2590
250
20
2570
1296
Alemgena
464527
990017
2278
260
13
2265
1641.6
Summit
482109
993423
2278
250
20
2258
1728
East Bole
480604
992935
2266
250
20
2246
1728
Summit
484883
994701
2320
250
16
2304
1425.6
Summit
485228
994341
2312
250
12.4
2299.6
1728
Summit
482849
991509
2236
270
7
2229
1598.4
East Bole
481101
991646
2250
250
25
2225
1468.8
215
Well Name
utmE
utmN
Elv.
(m)
Depth
(m)
SWL
( m)
Gw elev
(amsl)
Discharge
(m3/d)
Legetafo
486790
1001357
2291
250
3
2288
2332.8
CMC
482732
996914
2357
210
15
2342
1641.6
Mekanissa
471085
988117
2212
280
20
2192
86.4
Ayat
487881
997599
2367
250
15
2352
1728
Gurdshola
481434
997787
2413
260
10
2403
1036.8
Akaki
478077
981842
2110
250
25
2085
1728
Burayu
466306
1000367
2490
250
10
2480
1728
Kerssa Desso
480553
984520
2190
250
25
2165
1728
Kerssa Desso
479591
984268
2170
280
25
2145
1728
Kerssa Desso
480161
983987
2181
265
25
2156
1728
old Airport
468505
995156
2296
250
15
2281
1728
Keranyo
467660
996912
2334
250
15
2319
1728
Mekanissa
466550
986929
2265
235
20
2245
1728
Keranyo
468268
996584
2323
290
15
2308
1728
Mekanissa
467059
987492
2264
240
20
2244
1728
Mekanissa
467103
987041
2269
230
20
2249
1728
Legetafo
486775
1000441
2414
250
3
2411
2160
Burayu
463431
1000712
2513
200
20
2493
1468.8
216
Appendix 7 Communications
217
7.1 Paper presented during the annual meeting of the Geological Society of America
2009 Portland GSA Annual Meeting (18-21 October 2009)
Paper No. 59-8
Characterization of Volcanic Aquifers and Assessment of the Movement of
Groundwater in the Upper Awash Basin, Central Ethiopia
YITBAREK, Andarge and RAZACK, Moumtaz, Hydrogeology, University of Poitiers, France, University of
Poitiers ( UMR CNRS 6532, HYDRASA), 40, Avenue du Recteur Pineau, Poitiers, 86022, France,
[email protected]
The groundwater flow system and mechanism of recharge of different aquifers has been studied
using conventional hydrogeological field investigations, hydrochemical and isotope techniques in
the volcanic terrain of central Ethiopia. Litho-hydrostratigraphic relationships were constructed
from lithologic logs obtained from exploratory drilling of deep boreholes. The result indicates quite
complex flow pattern and hydraulic characteristics of the different volcanic aquifers. The analysis
of the temporal and spatial variation of water samples from different places revealed clear
groundwater-surface water interactions. New evidences have also emerged on the inter-basin
groundwater transfer. Two distinct regional basaltic aquifers (Upper and lower) were identified
showing distinct hydrochemical and isotopic signatures. In the southern part of the study area the
upper and lower aquifer forms one unconfined regional aquifer system. In the other hand in the
northern and central part of the basin, it appears that the two systems are separated by regional
aquiclude forming confined aquifers, in places with artesian wells. The groundwater from the
deep exploratory wells (>250m) tapping the lower basaltic aquifer and wells located in the south
were found to be moderately mineralized (TDS 400-650mg/l) and with relatively less enriched
stable isotope composition. In contrast the upper shallow aquifer has lesser ionic concentration
and more isotopically enriched. Evidences from the different methods indicate that there is likely
groundwater transfer from the northern adjacent Blue Nile basin to the Upper Awash basin. This
has enormous practical implication in finding large groundwater reserve at a greater depth that
can solve water supply problems for many cities including the capital Addis Ababa. It will also
have important role in finding more regional aquifers along the plateau-rift margins in many areas
having similar hydrogeological setup as the Upper Awash basin.
Session No. 59
Hydrologic Characterization and Simulation of Neogene Volcanic Terranes
Geological Society of America Abstracts, Vol. 41, No. 7, p. 177
© Copyright 2009 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the
author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted
to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for
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prohibited without written permission from GSA Copyright Permissions.
218
Résume
Une approche utilisant plusieurs méthodes convergentes a été mise en œuvre pour étudier le cadre
hydrogéologique du système aquifère volcanique fracturé et complexe du bassin supérieur du fleuve Awash
situé sur le bord du Rift éthiopien. L’écoulement des eaux souterraines et les mécanismes de recharge des
différents aquifères ont été étudiés à l’aide de méthodes conventionnelles de terrain, de l’hydrochimie, de
l’hydrologie isotopique et de la modélisation numérique des flux souterrains. Des relations lithohydrostratigraphiques ont été établies à partir des logs lithologiques de forages exploratoires profonds. Les
résultats montrent un modèle d'écoulement et des caractéristiques hydrauliques des différents aquifères
volcaniques très complexes. La corrélation litho-hydrostratigraphique indique que l’aquifère basaltique
inférieur, constitué de scories poreuses et perméables, est continu tout le long depuis le Nil Bleu jusqu’à la
zone étudiée. L’analyse de la variation temporelle et spatiale des échantillons d’eau provenant d’endroits
différents a révélé des interactions nettes entre l’eau souterraine et l’eau superficielle. De nouvelles
évidences des transferts d'eau inter-bassins sont apparues. Deux aquifères basaltiques régionaux (l'aquifère
supérieur et l'aquifère inférieur) ont été identifiés, montrant des signatures hydrochimiques et isotopiques
bien distinctes. Dans la partie sud de la zone étudiée, l’aquifère supérieur et l’aquifère inférieur forment un
système aquifère régional non confiné. Dans les parties nord et centrale du bassin au contraire, il apparaît
que les deux systèmes sont séparés par un aquiclude régional, donnant lieu par endroits à des puits
artésiens. Les eaux souterrainex provenant des puits d’exploration profonds (plus de 250 m) pénétrant
l’aquifère basaltique inférieur et des puits situés au sud se sont révélées modérément mineralisées (TDS
400-650 mg/l), avec une composition isotopique stable, relativement moins enrichie et avec presque pas de
tritium. Par contre, l’aquifère supérieur superficiel a une concentration ionique moins importante,
davantage enrichie isotopiquement. Les résultats des différentes méthodes montrent clairement qu’il existe
un transfert d’eau souterraine du nord du bassin adjacent du Nil Bleu vers le bassin supérieur du fleuve
Awash. Les résultats convergent également pour attester de l’origine commune de la recharge et de la
continuité hydraulique de l’aquifère basaltique inférieur exploité par des forages. Ceci peut avoir des
implications pratiques capitales car l'existence d'importantes ressources d’eau souterraine en profondeur
peut résoudre les problèmes d’approvisionnement de nombreuses villes, y compris la capitale, Addis
Ababa. Ces résultats pourront aussi contribuer à mettre à jour d’autres aquifères régionaux le long des
limites du rift dans des zones ayant une structure hydrogéologique similaire à celle du bassin supérieur du
fleuve Awash.
Mots-clés : Ethiopie, Bassin supérieur Awash, aquifère volcanique, hydrostratigraphie, recharge des
nappes d’eau souterraine, hydrochimie, isotopes, modélisation numérique.
ABSTRACT
Integrated approach has been used to investigate the hydrogeological framework of a complex fractured
volcanic aquifer system in the Upper Awash river basin located at the western shoulder of the Ethiopian
rift. The groundwater flow system and mechanism of recharge of different aquifers have been studied using
conventional hydrogeological field investigations, hydrochemistry, isotope hydrology and numerical
groundwater flow modeling techniques. Litho-hydrostratigraphic relationships were constructed from
lithologic logs obtained from exploratory drilling of deep boreholes. The result indicates quite complex
flow pattern and hydraulic characteristics of the different volcanic aquifers. The litho-hydrostratigraphic
correlation indicates that the permeable and porous scoraceous lower basaltic aquifer is extended laterally
all the way from the Blue Nile Plateau to the study area. .The analysis of the temporal and spatial variation
of water samples from different places revealed clear groundwater-surface water interactions. New
evidences have also emerged on the inter-basin groundwater transfer. Two distinct regional basaltic
aquifers (Upper and lower) are identified showing distinct hydrochemical and isotopic signatures. In the
southern part of the study area the upper and lower aquifers form one unconfined regional aquifer system.
In the northern and central part of the basin, it appears that the two systems are separated by regional
aquiclude forming confined aquifers, in places with artesian wells. The groundwater from the deep
exploratory wells (>250m) tapping the lower basaltic aquifer and wells located in the south were found to
be moderately mineralized (TDS: 400-600 mg/l), with relatively depleted stable isotope composition and
with almost zero tritium. In contrast, the upper shallow aquifer has lesser ionic concentration, more
isotopically enriched. Evidences from the different methods clearly indicate inter-basin groundwater
transfer from the Blue Nile basin to the Upper Awash basin. The evidences also converge to testify
common origin of recharge, presence of hydraulic connectivity for systems tapping the lower basaltic
aquifer. This has enormous practical implication in finding large groundwater reserve at a greater depth that
can solve the current water supply problems of the community including the capital Addis Ababa. It will
also have important role in finding more regional aquifers along the plateau-rift margins in many areas
having similar hydrogeological setup as the study area.
Key words: Ethiopia, Upper Awash Basin, Volcanic aquifer, Hydrostratigraphy, Groundwater recharge,
Hydrochemistry, Isotope, Groundwater flow modeling