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Groundwater resource assessment through distributed steady-state flow modeling,
Aynalem wellfield (Mekele, Ethiopia)
Gebrerufael Hailu Kahsay
March, 2008
Groundwater resource assessment through distributed steady-state flow modeling, Aynalem wellfield
( Mekele,Ethiopia) by
Gebrerufael Hailu Kahsay
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in
partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science
and Earth Observation, Specialisation: (Groundwater Assessment and Modeling)
Thesis Assessment Board
Chairman Dr.Ir. M.W. Lubczynski WRS,ITC,Enschede
External Examiner Dr.Ir.P. Droogers Future Water,Wageningen
First Supervisor Dr.A.S.M. Gieske WRS,ITC,Enschede
Second Supervisor Dr.Ing. T.H.M. Tom Rientjes WRS,ITC,Enschede
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.
Dedicated to my father Hailu Kahsay
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Abstract
The study focused on groundwater recourse assessment through steady-state flow modelling in
Aynalem wellfield northern Ethiopia. Aynalem wellfield is the main source of water for domestic
water supply of Mekele town, capital of Tigray regional state. Despite its importance for the people in
the region, the hydrogeological system of the wellfield is not well understood. The groundwater in the
area is pumped with little consideration to groundwater recharge and effects of climatic forcing on the
recharge. Because the demand for water for domestic and irrigation use is growing fast, the pressure
on the wellfield will be even more serious in the future. Primary and secondary data on geology,
hydrochemistry, geophysics and hydrology are integrated to develop the hydrogeological conceptual
model which is foundation for the development of steady-state groundwater flow model of the area.
The aquifer system was modelled numerically using PMWIN5.3 as pre and post processor of
MODFLOW under a steady-state condition with one layer of 50 meters constant thickness. The model
area which is about one hundred four square kilometres was divided into grid blocks of 250 by 250
meters. The model domain was delineated based on field traverses, topographic maps and DEM
extracted from ASTER image. Aquifer parameters were assigned based on previously reported values
which were then adjusted during the model calibration. The main recharge mechanism considered was
direct recharge from rainfall. Annual recharge of 30-40 mm (4.5-6% of the average annual rainfall) is
estimated by applying the chloride mass balance method. The model was calibrated under non-
pumping and pumping scenarios to static water levels and to averages of three years monitoring water
level respectively. The over all model results were comparable with the measured well data.
The steady-state flow modeling has demonstrated that an average recharge of 42 mm year-1 maintains
the natural equilibrium. On the other hand, the model result with pumping scenario shows that
groundwater abstraction of 7156 m3day-1 resulted in groundwater table decline up to 37 meters in the
wellfield area. The sensitivity of the calibrated model was tested by systematically changing one
parameter or input variable at a time and it was found that the model is highly sensitive to changes of
transmissivity of the aquifer system and recharge rate. The model is associated with a number of
uncertainties resulting from the simplification and assumptions made to the complex field conditions,
poor data quality, and lack of detailed subsurface characterisation of the aquifer system. Hence the
limitations of the model should be taken into consideration prior to applying the model for
groundwater resource management.
Key Words: Aynalem wellfield, Groundwater Resources assessment, Groundwater modeling.
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Acknowledgements
I appreciate ITC (International Institute for Geo-information Science and Earth Observation) for
enabling me to pursue my Master program study by providing academic and financial supports. I am
grateful to my organization, Water Resource Development Bureau for granting my leave of absence to
pursue my study.
Above all I am indebted to my first supervisor Dr. Ambro S.M. Gieske for his unceasing support
guidance and encouragement throughout my study period and thesis time. I benefited a lot from
discussions I had with him owing to which I gained a deeper insight into and understanding of the
factors governing groundwater occurrence and movement. I really appreciate his constructive
criticism and valuable advice which helped me to develop research skills and improve my English. I
would like to thank my second supervisor Dr. Ing. T.H.M. Rientjes for he taught me many of the
aspects in groundwater modeling principles and I am grateful for his critical comments during the
thesis preparation time. I acknowledge the support during my laboratory work to Boudewijn de Smeth
and Remco Dost. It is also my pleasure to thank all WREM staff. Without the impartation of their
knowledge; this work would not have been achieved.
I also wish to express my appreciation to all my class mates for their friendship, support, socialization
and help each other in times of pressure and stress. They were my new family during my stay and it
was a pleasure to be a member of them.
I would like to thank the entire Ethiopian community for providing me environment of home feeling
during my stay here at ITC.
I am most thankful to Teklay, Guesh, Gidena, Aregawi, Yemane,Tesfalem,Solomon and Gebremedhin
who helped me a lot in the secondary data collection during my fieldwork. I particularly appreciate
Teklay that he walked around with me with mud up to his knees and helped me during river flow
measurements.
Finally I owe special gratitude to all my family members and friends back home for always being
there for me.
Thank you all
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Table of contents
1. Introduction ...................................................................................................................................1 1.1. Background.............................................................................................................................1 1.2. Problem statement...................................................................................................................2 1.3. Research objective ..................................................................................................................3 1.4. Research questions..................................................................................................................3 1.5. Methods and Materials ...........................................................................................................3 1.6. Organization of the thesis .......................................................................................................7
2. Literature review...........................................................................................................................9 2.1. Previous works........................................................................................................................9 2.2. Groundwater modeling .........................................................................................................10 2.3. Recharge ...............................................................................................................................11
3. Description of the study area......................................................................................................13 3.1. Location ................................................................................................................................13 3.2. Geomorphology and drainage...............................................................................................13 3.3. Climate..................................................................................................................................15 3.4. Land use, Vegetation and soil...............................................................................................17 3.5. Geology.................................................................................................................................19
4. Analysis and model input data preparation..............................................................................23 4.1. Hydrometeorology ................................................................................................................23 4.2. Hydrochemistry.....................................................................................................................25
4.2.1. Water sampling and analysis........................................................................................25 4.2.2. Reliability check ..........................................................................................................25 4.2.3. Presentation of results ..................................................................................................27 4.2.4. Water type ....................................................................................................................27 4.2.5. Source rock deduction..................................................................................................30
4.3. Chloride mass balance method (CMB).................................................................................31 4.3.1. Chloride in rainwater ...................................................................................................32 4.3.2. Chloride content in groundwater..................................................................................32
4.4. Well abstraction and groundwater level analysis .................................................................35 4.4.1. Well abstraction ...........................................................................................................35 4.4.2. Groundwater level analysis..........................................................................................37
4.5. Pumping test .........................................................................................................................38 4.6. Aquifer characteristics..........................................................................................................42 4.7. Digital elevation model (DEM) ............................................................................................43
5. Conceptual model ........................................................................................................................45 5.1.1. Well log data and geology............................................................................................45 5.1.2. Geophysics ...................................................................................................................48
5.2. Hydrostratigraphy .................................................................................................................49 5.3. Hydraulic proporties of the stratigraphic units .....................................................................50 5.4. Water budget.........................................................................................................................50 5.5. Groundwater flow system.....................................................................................................53 5.6. Model boundaries .................................................................................................................53 5.7. Simplification of the real world............................................................................................54
6. Numerical model..........................................................................................................................57
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6.1. Code selection...................................................................................................................... 57 6.2. Model geometry ................................................................................................................... 57 6.3. Model design........................................................................................................................ 58 6.4. Model calibration................................................................................................................. 61 6.5. Sensitivity analysis............................................................................................................... 66 6.6. Model validation .................................................................................................................. 67
7. Discussion and results ................................................................................................................ 69 7.1. Hydrochemistry.................................................................................................................... 69 7.2. Modeling results .................................................................................................................. 69
7.2.1. Hydraulic properties.................................................................................................... 71 7.3. Groundwater budget............................................................................................................. 72 7.4. Model limitations................................................................................................................. 73
8. Conclusion and recommendations ............................................................................................ 75 8.1. Conclusions.......................................................................................................................... 75 8.2. Recommendations................................................................................................................ 76
9. References.................................................................................................................................... 77 Appendices ........................................................................................................................................... 81
Appendix 1 Hydrometeorological data ............................................................................................. 81 Appendix 1.1. Monthly rainfall (mm) at Mekele airport station.................................................. 81 Appendix 1.2. Long term monthly rainfall (mm) at Mekele airport station................................. 81 Appendix 1.3. Monthly minimum temperatures (0C) ................................................................... 83 Appendix 1.4. Monthly maximum temperature (0C).................................................................... 83 Appendix 1.5. Monthly mean wind speed (m s-1) at 2m height................................................... 84 Appendix 1.6. Mean monthly relative humidity (%) at 1200 local time...................................... 84 Appendix 1.7. Mean monthly sunshine hours .............................................................................. 84 Appendix 1.8. Monthly average piche evaporation (mm)............................................................ 85 Appendix 1.9. Monthly Evapotranspiration (mm) ....................................................................... 85 Appendix 1.10. River discharge Metere gauging station (Aynalem river)................................... 86
Appendix 2 Hydrochemistry ............................................................................................................. 87 Appendix 2.1. Analysis result of rain water ................................................................................. 87 Appendix 2.2. Physical and chemical constituents of water samples .......................................... 87 Appendix 2.3. Comparison of analysis......................................................................................... 87 Appendix 2.4. Major anions and cations ( meq l-1) and water type.............................................. 88
Appendix 3 Well data........................................................................................................................ 89 Appendix 3.1. Well location......................................................................................................... 89 Appendix 3.2. Monthly water production (m3) ............................................................................ 89 Appendix 3.3. Monthly groundwater level monitoring data ........................................................ 91 Appendix 3.4. Static water level record from the wells ............................................................... 91 Appendix 3.5. Lithologic log data of the boreholes ..................................................................... 92
Appendix 4 Geophysical data ........................................................................................................... 97 Appendix 5 Groundcontrol points to correct ASTER DEM......................................................... 100 Appendix 6 Location of all wells in the wellfield........................................................................... 101 Appendix 7 MODFLOW water budget........................................................................................... 102 Appendix 8 Pumping test curve matching....................................................................................... 103 Appendix 9 Plates............................................................................................................................ 105
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List of figures
Figure 1.1. Map showing estimated distribution of groundwater availability .........................................2 Figure 1.2. Flow chart of methodological approach ................................................................................6 Figure 3.1. Location map of the study area............................................................................................13 Figure 3.2. Geomorphologic features and elevation cross-section ........................................................14 Figure 3.3. Drainage map of Aynalem sub-basin...................................................................................15 Figure 3.4. Mean monthly values of climatic variables .........................................................................17 Figure 3.5. Land use map of the study area (after Teklay, 2006) ..........................................................18 Figure 3.6. Soil map of the study area (WWDSE, 2006).......................................................................18 Figure 3.7. Composite stratigraphy of sedimentary succession in Mekele outlier. ...............................20 Figure 4.1. Long-term annual rainfall of the study area ........................................................................23 Figure 4.2. Hydrograph of Aynalem river..............................................................................................24 Figure 4.3. Location of water sample points..........................................................................................26 Figure 4.4. Piper diagram of water samples from boreholes .................................................................28 Figure 4.5. Stiff diagrams of water samples from upper Aynalem........................................................28 Figure 4.6. Stiff diagrams of water samples from lower Aynalem........................................................29 Figure 4.7. Stiff patterns of water samples from Ilala and Chelekot .....................................................29 Figure 4.8. Groundwater abstraction from selected boreholes ..............................................................36 Figure 4.9. Location map of pumping wells ..........................................................................................36 Figure 4.10. Total production of the wellfield.......................................................................................37 Figure 4.11. Groundwater level at selected boreholes ..........................................................................37 Figure 4.12. Average groundwater level trend.......................................................................................38 Figure 4.13. Time drawdown plot of TW1 (2005).................................................................................39 Figure 4.14. Time drawdown plot of TW2 (2005).................................................................................40 Figure 4.15. Time drawdown plot of TW4 (2005).................................................................................40 Figure 4.16. Time drawdown plot of TW6 (2006).................................................................................40 Figure 4.17. Log hydraulic conductivity values.....................................................................................42 Figure 4.18. Scatter plot of ground elevation Vs elevation from ASTER DEM ...................................44 Figure 5.1. Dolerite dyke dissecting the sedimentary rock....................................................................46 Figure 5.2. Tilted sedimentary layers due to dolerite intrusion .............................................................46 Figure 5.3. Lithological log showing depth to dolerite (After Yehdego, 2003) ....................................47 Figure 5.4. Geological map with east- west cross-section (WWDSE, 2006) ........................................48 Figure 5.5. Groundwater level profile....................................................................................................52 Figure 5.6. ASTER DEM indicating the three basins in the Mekele area .............................................54 Figure 5.7. Pictorial representation of the hydrologic system of Aynalem sub-basin ...........................55 Figure 6.1. Model boundary conditions .................................................................................................60 Figure 6.2. Trial and error calibration procedures (Adapted from Anderson and Woessner, 1992).....62 Figure 6.3. Contour map of simulated heads (non-pumping scenario) ..................................................63 Figure 6.4. Scatter plot of observed and simulated hydraulic heads (m)...............................................63 Figure 6.5. Sensitivity plot of the calibrated model with respect to transmissivity...............................67 Figure 6.6. Sensitivity plot of the calibrated model with respect to recharge .......................................67 Figure 7.1. Distribution of hydraulic heads with non-pumping scenario ..............................................70 Figure 7.2. Distribution of hydraulic heads with pumping scenario......................................................70
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Figure 7.3. Comparisons of simulated hydraulic heads for both scenarios .......................................... 70 Figure 7.4. Transmissivity zones applied to the calibrated model ........................................................ 71
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List of tables
Table 4.1. Monthly river discharge (Mm3) of Aynalem river................................................................24 Table 4.2. Summary statistics of the major groundwater constituents ..................................................27 Table 4.3. Parameters used for source rock deduction ..........................................................................31 Table 4.4. Chloride concentration in rain ..............................................................................................32 Table 4.5. Statistics of the chloride concentration in groundwater .......................................................32 Table 4.6. Groundwater chloride content and estimated recharge.........................................................34 Table 4.7. Daily maximum abstraction rate from the wellfield .............................................................35 Table 4.8. Details of pumping test on the test wells ..............................................................................39 Table 4.9. Transmissivity and hydraulic conductivity...........................................................................43 Table 4.10. Summary statistics of transmissivity (m2 day-1) ..................................................................43 Table 5.1. Summery of vertical electrical sounding data.......................................................................49 Table 5.2. Spring inventory data ............................................................................................................51 Table 6.1. Observed and calculated heads for non-pumping scenario...................................................64 Table 6.2. Observed and calculated heads for pumping scenario..........................................................64 Table 6.3. Errors of the calibrated model...............................................................................................65 Table 7.1. Model simulated groundwater budget of the area for the non- pumping scenario ...............73 Table 7.2. Model simulated groundwater budget of the area for pumping scenario .............................73
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1. Introduction
1.1. Background
Groundwater is the subsurface water that occurs beneath the water table in the soils and geologic
formations that are fully saturated (Freeze & Cherry, 1979). Groundwater is one of the key natural
resources of the world. Many major cities and small towns in the world depend on groundwater for
water supplies, mainly because of its abundance, stable quality and also because it is inexpensive to
exploit (Morris et al., 2003). Groundwater use has fundamental importance to meet the rapidly
expanding urban, industrial and agricultural water requirement, especially in arid areas where surface
waters are scarce and seasonal. Uneven distribution of surface water resources resulted in an
increased emphasis on development of groundwater resources. An important objective of most
groundwater studies is to make a quantitative assessment of the groundwater resources in terms of the
total volume of water stored in aquifer or long-term average recharge. Groundwater recharge is
determined to a large extent as an imbalance at the land surface between precipitation and evaporative
demand. When precipitation exceeds evaporative demand by an amount sufficient to replenish soil
water storage, any further excess flows deeper into the ground and arrives at the water table as
recharge.
Groundwater systems have been studied by the use of computer based mathematical models
(Brassington, 1998). These essentially comprise a vast array of equations, which describe
groundwater flow and the water balance in the aquifer. Finite difference method is a commonly used
method to solve the equations. The equations are solved for each node and the movement of
groundwater from one node to its neighbor is calculated. As discussed by Scanlon et al. (2003),
numerical groundwater models are one of the best predictive tools available for managing water
resources in aquifers. These models can be used to test or refine different conceptual models, estimate
hydraulic parameters and, most importantly for water-resource management, predict how the aquifer
might respond to changes in pumping and climate. Groundwater abstractions that exceed the average
recharge, results in a continuing depletion of aquifer storage and lowering of the groundwater table.
Hence safe groundwater abstraction and proper groundwater management is crucial for sustainability
of the resource. Safe yield is the amount of naturally occurring groundwater that can be withdrawn
from an aquifer on a sustained basis, economically and legally, without impairing the native ground
water quality or creating undesirable effects, such as environmental damage (Fetter, 2001).
As most semi-arid areas, Ethiopia is also facing water scarcity particularly in the dry season. Because
of the poor permeability of the crystalline rocks and variable water table depths, the country has
limited supply of groundwater (MacDonald, 2001). The groundwater occurrence is mainly governed
by geology, degree of fracture and topography. As indicated in (Fig.1), the low lands, mainly the rift
valley areas, are characterized by a relatively high potential of groundwater availability. Despite its
scarcity, ambitious development plans, urbanization and rapid population growth lead to over
pumping of the groundwater resource.
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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Aynalem wellfield is one of the areas in Ethiopia that face water table lowering due to increased
withdrawals. The groundwater in the area is pumped with little consideration to groundwater recharge
and effects of climatic forcing on the recharge. As described by Jyrkama & Sykes (2007), quantifying
the future evolution of recharge over time requires not only the reliable forecasting of changes in key
climatic variation but also modeling their impact on the spatially varying recharge process.
Figure 1.1. Map showing estimated distribution of groundwater availability (After MacDonald, 2001)
1.2. Problem statement
Aynalem wellfield is the largest wellfield in Tigray region (Northern Ethiopia) and serves as the only
source of water supply for Mekele town, capital of the regional state. The groundwater table of the
wellfield is continuously declining due to abstraction of water mainly for water supply of the town. As
reported from Water Resources Development Bureau of the region, there has been a significant
groundwater level decline since the implementation the wellfield. On the other hand, population
growth and accompanying development over the past few years in the city have led to an increased
demand for groundwater use from the wellfield. Despite its importance for the people in the region,
the hydrogeological system of Aynalem wellfield is not well understood. The groundwater flow
pattern and the effect of climatic variation on groundwater recharge are not well defined. As an
indictor, the water supply project which was planned for twenty years indicate a drastic groundwater
level decline within three years of service. Because the demand for water for domestic and irrigation
use is growing fast, the pressure on the wellfield will be even more serious in the future. According to
Villholth (2006), abstraction of groundwater has an associated impact on the water balance and hence
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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on the availability of water resources on other parts of the water cycle. Thus understanding of the
aquifer system and assessment of the water balance components of the sub-basin is crucial for the
sustainability of the resource. To understand the effects of abstractions and climatic forcing on the
groundwater flow system, it is worthwhile to develop a groundwater flow model simulating not only
the natural groundwater flow but also abstractions from the underlying aquifer.
1.3. Research objective
General objective
The main objective of the study is to assess groundwater resource of the wellfield and to improve the
understanding of groundwater flow pattern in response to recharge and abstractions using a steady-
state groundwater flow model.
Specific objectives
• To develop a conceptual model representing the hydrogeological condition of the wellfield
based on ground observations and data analysis.
• To assess the water balance components of Aynalem sub-basin.
• To estimate recharge using chloride mass balance method and through model calibration.
• To Set up and calibrate a steady-state groundwater flow model of the wellfield.
• To develop scenarios illustrating effect of different stress conditions on the groundwater
resource.
1.4. Research questions
• What is the dominant hydrologic factor that causes the lowering of the water table in the
wellfield?
• How is the natural flow pattern of groundwater in the wellfield?
• Can a steady state groundwater flow model improve our understanding of flow pattern and
predict the effect of future abstractions?
• How accurate can the groundwater recharge of Aynalem sub-basin be estimated using
chloride mass balance method?
• How accurate are the simulation results from the model developed for the wellfield?
1.5. Methods and Materials
The methods followed in the research process are based on the objectives formulated in section 1.3.
The methodology designed for the research work consists of three major phases (Pre-fieldwork,
Fieldwork and Post- fieldwork).
Pre-fieldwork
In this phase of study, review of previous works on the area and literature related to the method of
recharge estimation and principles of groundwater modeling take a major part. Apart from the
literature review, an archive search for ASTER image and SRTM of the area was also part of the pre-
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fieldwork phase of study. As part of the secondary data collection, Aster satellite image was acquired
for 30 September, 2005 through ITC remote sensing and geo-database. Acquisition of equipment and
preparation of data requirement list and data collection form were the activities conducted before the
field trip.
Fieldwork
A three week field trip starting at the second week of August 2007 was organized to collect relevant
secondary data and ground truth primary data from the field. Meteorological data (Rainfall, Relative
humidity, Pan evaporation, temperature and wind speed at Mekele airport meteorological station was
collected from Ethiopian National Meteorology Service. Three years of groundwater level data with
gaps in the records of some of the boreholes were collected from Water Supply Office of Mekele
town. Monthly River discharge data at Aynalem River gauging station (which is not operational at the
moment and covers only the upper part of Aynalem catchment) was collected for the years 1992 to
2001 from Ethiopian National Meteorology Service. Lithological and geophysical logs, geological
maps and cross-sections, pumping tests data, soil and land use maps were obtained from previous
works mainly conducted by Water Works Design and Supervision Enterprise (WWDSE). As part of
the field work, primary data collection was the major task of the field duration. The primary data
collected include:
• Water samples from boreholes, springs, ponds and rainfall
• Groundwater level measurements at accessible boreholes
• River discharge measurements
• EC, PH, and chloride in situ measurements
• Ground truth observations
Observation points were selected with the help of topographic maps, aerial photographs and ASTER
image of the area. Location readings of the observation points were taken with a hand held Garmin
GPS. The ground truth field observations were focused on the description of the geology,
stratigraphpy, geomorphologic setting, surface water divide, location of discharge, and recharge areas.
Post-fieldwork (data processing and analysis)
At this stage, the primary and secondary data collected during the pre-field stage and fieldwork period
are processed and analyzed. The most important phase here is processing of the data in order to fit the
data input requirement of the intended model (MODFLOW).
For data processing purposes, GIS and geostatistics are used to prepare model input data. For
example, kriging is applied in order to interpolate point measurements of static water levels to prepare
initial hydraulic heads for the entire model. Use of spreadsheet is made to process and prepare input
data for the model. As part of the input data preparation, a DEM is extracted from ASTER image that
was applied to determine surface elevations of boreholes to prepare surface topography cross- sections
and to define top and bottom elevations of layer in the numerical modeling. Pumping test data and
geophysical data from previous studies were analyzed to have an idea about the aquifer parameters
and the geometry of the aquifer system. Well completion data are organized and summarized to
determine the aquifer thickness and vertical extent of layers in combination with geological cross-
section and geophysical log data. The water samples collected during the fieldwork are processed and
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analyzed for the major cations and anions to understand the geochemical properties and source rock
deduction. Results of the laboratory analysis are processed and presented using AQUACHEM
software. By extensive use of Surfer 8 and Global Mapper 5 maps and cross-sections of results are
made. The determination of the chloride in rainwater and concentration in groundwater is part of this
analysis. As major tasks of the post-fieldwork phase of the study, recharge was estimated using
chloride mass balance method, a conceptual model was built based on the existing data complemented
with the data collected in the field and one layer groundwater flow model was developed and
calibrated for pumping and non-pumping scenarios.
Materials and equipments
To collect the required data, a number of materials and equipments were used during the fieldwork
and office work phase of the research. Topographic maps of south Mekele and Quiha sheets and
ASTER image were used for the delineation of the study domain and to select representative ground
observation and sampling points. Field geological equipments including geological hammer, compass
and Garmin GPS system were applied at all times during site visit. Water level meter and current
meter were used to measure depth to water table and river discharge respectively. EC meter pH meter
and chloride titration reagent were used for in situ measurements of the electrical conductivity, pH
and chloride ion of the water samples respectively. Two sample bottles were used for sampling the
water sample from a given spot. One of the sample bottles was acidified with hydrochloric acid and is
used for the analysis of anions except chloride. The second sample bottle is acidified using nitric acid
and is used for the analysis of chloride ion and cations.
Framework of the research
The research has two major tasks: interpretation and analysis of hydro meteorological data, geology,
and hydrogeology, hydrochemistry of the water samples for recharge estimation and preparation of
model input data. The second task of the research is modeling of the groundwater flow system in the
sub-catchment. The sequence of the study process is indicated in Figure 1.2 below.
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Figure 1.2. Flow chart of methodological approach
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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1.6. Organization of the thesis
The thesis is divided in eight chapters and the contents are outlined briefly as:
Chapter1: Describes the introduction of the research that includes the problem statement, the
objective of the research and research questions which the research tries to answer on the basis of the
available data and applied methodologies. Methods followed and materials used are also discussed in
this introductory chapter.
Chapter 2: Stresses the review of previous studies in the area and literature review related to
principle of groundwater modeling and recharge estimation methods are discussed.
Chapter 3: General description of the study area, giving the description of the study area in relation
to location, climate, geology, structure, land use, soil cover and hydro geological setting of the area.
Chapter4: Data processing and analysis. This chapter is devoted to the processing and analysis of
data (primary and secondary data) and involved in synthesizing and screening field data and
translating the data to model input. Water chemistry data, water level data, discharge and recharge
condition of the area are parts of the analysis.
Chapter 5: Development of conceptual model. The main task of this chapter is development of
conceptual model of the area by defining the hydrostratigraphy, water budget and flow systems.
Chapter 6: Numerical modeling, this chapter is mainly designed to discuss the code selection, model
design, model calibration and sensitivity analysis.
Chapter 7: Results and discussion. Illustration and discussion of the modeling results and result of
the recharge estimated by different methods.
Chapter 8: Conclusion and recommendations. Conclusions and recommendations will be made on
the basis of the analysis result. In this final chapter, matters which can not be addressed fully or
partially are out lined and limitation of the research and possibilities of further research are indicated.
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2. Literature review
2.1. Previous works
The lowering of groundwater table in Aynalem wellfield creates a great concern to the region. As a
result, a number of studies were conducted under the supervision of the Tigray Water Resources
Bureau. However, most of the studies focused on selection of prospective borehole sites and
deepening of the existing ones. In the studies, the recharge and other components of the water balance
were not well understood and not considered in the design of the wellfield. Hydrogeological study
was conducted by Beyth (1970) to locate borehole sites for Mekele town water supply. He conclude
that there is no information on the regional groundwater regime in Mekele outlier but the groundwater
availability is probably controlled by the regional and local structures and the potential well sites
were located near the fault lines. The Water resource verification report of DEVECON (1992)
describes the geology structure and hydrogeology of Mekele area. The report indicates that the
groundwater is confined because of alternative layers of shale, marl limestone and dolerite. According
to DEVECON (1992), the main aquifer is dolerite unit, which is not in line with recent studies.
Nowadays days there is acceptance that the main aquifer is the limestone unit and that most of the
productive wells are located near the faults and lineaments where the limestone is highly fractured.
The dolerite dyke plays an important role as it is contributes to the fracturing of the limestone unit in
its way up.
Water Works Design and Supervision Enterprise (WWDSE) carried out a good initial work in 2006.
Unlike the previous studies, this study attempted to assess the hydrological components of the sub-
basin. WWDSE (2006) produce a report containing two volumes, the first volume focuses on geology
and hydrogeology of Mekele vicinity, while the second volume deals with the evaluation of the
groundwater potential in Aynalem wellfield. Both reports contain much useful data and information
including inventory of water points, water quality data, geophysical data and groundwater level
monitoring data of Aynalem wellfield boreholes. According to WWDSE (2006), the main lithologic
units that cover the Aynalem wellfield are shale-limestone intercalation, limestone unit, and Mekele
dolerite. Pockets of calcareous sandstone were also identified but they occupy very small areal
coverage as compared to other lithologic units. The dolerite rocks observed in the area occur mainly
as sills that are very much concordant with the sedimentary rocks. However, there are also dykes of
dolerite offshoots emerging from the sills forming discordant relations.
There is an ongoing study by TAHAL Consulting Engineers which focuses on present water supply
source assessment. According to TAHAL (2007), the current abstraction of about 8000 m3day-1
resulted in groundwater table decline of 15 meters and concluded that the natural recharge of
Aynalem wellfield is quite low which is in the order of 3-5 MCM per annum.
A number of researchers have also conducted scientific research with regard to the overall
groundwater condition of the Aynalem wellfield. Hussien (2000) studied the hydrogeology of the
wellfield and concluded that the annual recharge in the Aynalem catchment is 9 % of the annual
rainfall, which equals about 5.7 M m3year-1. The same author has discussed that the variation in the
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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chemical composition of the groundwater in east-west direction along the valley is attributed to the
variation in the geology of the area. The high concentration of sulphate in the down stream part of the
wellfield is due to the presence of thin layer of gypsum. Yehdego (2003) conducted his research on
the hydrogeologic condition of the Ilala-Aynalem catchments, with particular emphasis given to the
variation in the chemical characteristics of the aquifers, with the application of Isotope hydrology.
The Aynalem sub-catchment shows relatively different water ages, but generally, the boreholes within
the catchments receive recharge from recent rainwater (Yehdego, 2003). Gebregziabher (2003)
conducted a research work in the Aynalem wellfield with the aim to outline fractures and faults
through an integrated geophysical investigation. He concluded that most of the detected fractures and
faults are aligned along NW-SE strike direction similar to the regional structures. He pointed out that
the geological structures (faults, fractures and lithologic contacts) play an important role in the
movement and occurrence of groundwater in the study area. Teklay (2006) carried out a study that
focussed on conjugate use of groundwater and surface water in the sub-basin. In his study he
estimated 35 mm year-1 direct recharge from rainfall which is 5.3% of the annual average rainfall in
the catchment.
2.2. Groundwater modeling
A model is any device that represents an approximation of a field situation (Anderson & Woessner,
1992). As described by Fetter (2001), there are two areas of hydrogeology where we need to rely upon
models of real hydrologic systems: to understand why a flow system is behaving in a particular
observed manner and to predict how a flow system will behave in the future. Groundwater models
have been applied in different parts of the world to solve problems related to groundwater flow, and
solute transport. As explained by Anderson & Woessner (1992), groundwater flow models solve the
distribution of head, whereas solute transport models solve for the concentration of solute as affected
by advection (movement of the solute with the average groundwater flow), dispersion (spreading and
mixing of the solute) and chemical reactions.
The main areas of application of groundwater models are the fields of water quantity and water
quality. In quantitative groundwater modeling only the flow and movement of water are modeled. For
such, model algorithms are applied that are based on the equation of motion (Darcy’s law) and the
continuity of mass equation. These equations are combined and result in the groundwater flow
equation. In the past, groundwater flow behavior has been simulated by scale models, analogue
models and analytical models. However, particularly over the past decades, the use of computer code
is common and allows for the design and development of complex mathematical model approaches.
Several techniques have been developed to solve the partial differential equations describing the
behavior of groundwater flow system. The most popular numerical solution methods are Finite
difference, Finite element and Analytical element method. Finite difference method was the first
method to be used for the systematic numerical solution of partial differential equations. As discussed
by Mehl & Hill (2002), many numerical models of groundwater flow use finite-difference methods to
discretize and solve the governing partial differential flow equation. In finite-difference methods, an
aquifer system is replaced by a discretized domain consisting of an array of nodes and associated
finite difference blocks (cells). Visual MODFLOW, which is based on the finite difference method, is
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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currently the most widely used groundwater flow code in the field of geo-hydrology. Within visual
MODFLOW, the groundwater system is modeled by a set of mathematical equations representing the
flow phenomenon. As described by Carrera-Hernandez & Gaskin (2006), the advantage of
MODFLOW is that it provides different modules to undertake 3-D groundwater flow simulations in
confined and unconfined aquifers as well as in aquifers with variable confinement with both constant
and variable transmissivity values. The modules provided by MODFLOW can be used to simulate the
effect on an aquifer system caused by different stresses such as the presence of extraction and
injection wells, of areal distribution of recharge, evapotranspiration or of different hydrological
features. In the practice of groundwater modeling, often the term numerical model is used to
emphasize that a distributed model domain is applied. Simulation of groundwater flow considering
space variability of stresses and aquifer properties is only possible through distributed-parameter
models (Pulido-Velazquez et al., 2007).
MODFLOW can simulate confined, leaky confined and unconfined aquifers and only simulates
saturated flow in a porous medium with uniform temperature and density (Fetter, 2001). MODFLOW
can not simulate flow in the unsaturated zone and flow in fractured media unless it can be considered
to be an equivalent porous media. However, the discrete heterogeneity of fracture distribution and
hydraulic discontinuity are the primary difficulties in the groundwater modeling practices. Forming a
conceptual model of fractured system requires either a gross simplification or detailed description of
the aquifer properties controlling the groundwater flow (Anderson & Woessner, 1992). Fractured
material is represented as an equivalent porous medium by replacing the primary and secondary
porosity and hydraulic conductivity distributions with a continuous porous medium of equivalent or
effective hydraulic properties. According to Yuri Mun et al.(2004), the equivalent porous medium
(EPM) approach has been frequently applied to simulate flow in fractured media due to its ease of
use. This practice results in some severe limitations such as hydraulic head averaging and an inability
to handle preferred fluid pathways. When fractures are few and far between and the fractured block
hydraulic conductivity is low, the EPM approach may not be appropriate. Simulation of flow through
discrete networks is difficult and data intensive (Snow, 1969). For describing groundwater flow in a
fractured environment, porous media models or continuum approach have been used by increasing the
hydraulic conductivity values of cells where fracture flow occurs.
2.3. Recharge
Groundwater recharge is a process of water movement downward through the saturated zone under
the force of gravity or in a direction determined by the hydraulic condition (Simmers, 1988). Natural
recharge of groundwater could be occurring from precipitation, from rivers and canals and from lakes.
As discussed by Simmers et al. (1997), quantifying the current rate of groundwater recharge is a basic
prerequisite for efficient groundwater resource management and is practically vital in arid regions
where such resources are often the key to economic development.
Groundwater recharge quantification is fraught with problems of varying magnitude and hence
substantial uncertainties. It is therefore desirable to always apply and compare a number of
independent approaches. Various techniques are available to quantify recharge; however, choosing
appropriate techniques is often difficult. According to Scanlon et al.(2002), important considerations
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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in choosing a technique include space or time scales, range, and reliability of recharge estimates based
on different techniques. Each of the methods has its own limitations in terms of applicability and
reliability. Techniques of recharge estimation vary based on source and process of recharge
mechanisms. Simmers et al. (1997) indicate that the procedures to quantify recharge from various
sources are direct measurements, water balance methods, tracer techniques and empirical methods. As
it was applied for the estimation of groundwater recharge in semi-arid climate India by Sharda et al.
(2006), a number of methods were used to estimate the recharge. As part of the study, water table
fluctuation and chloride mass balance methods were applied. The water table fluctuation is based on
the principle that the rise in groundwater level in any aquifer is proportional to the water reaching the
water table. The recharge component contributed to groundwater is expressed as:
Rgw = S WWTA∆ (2.1)
Where, S is storativity,WT∆ is change in water table depth, and AW is area of the watershed. The
chloride mass balance method is based on the assumption of conservation of mass between the input
of atmospheric chloride and the chloride flux in the subsurface (Yongxin & Beekman, 2003).
As described by Bear & Verruijt (1987), the basic equation applicable for the estimation of recharge
using chloride mass balance method is:
Rgw=Pyear
gw
p
Cl
Cl (2.2)
Where, Rgw is the annual recharge rate (mm), Pyear is the average annual rainfall (mm) and Clp and Clgw
are the chloride concentrations of the rainfall and groundwater (mg l-1), respectively. The technique
regards chloride as an inert element, and compared with other inorganic ions, it is not added or
removed by water rock interaction. The element is considered as an inert in the hydrological cycle
having its source from the atmosphere. It has the advantage over tracers involving water molecules in
a sense that atmospheric inputs are conserved during recharge processes allowing a mass balance
approach to be used. Commonly water balance method is applied for recharge estimation in many
climatic zones of the world. Estimation of recharge using this method is largely dependent on the
precision with which the water balance components were determined. The application of the water
balance method in arid and sem-iarid regions is more difficult than in humid regions because
precipitation is frequently only slightly different from actual evapotranspiration, small errors in these
two components cause large errors in recharge estimation. Simmers et al. (1997) identified two
different precipitation mechanisms, diffuse and localized recharge. Direct or diffuse recharge results
from wide spread infiltration of rain water at the point of impact whereas localized recharge resulted
from horizontal flows that occurs into local depression that are not connected to any draining water
courses. The same authors discussed the methods available for estimation of groundwater recharge
directly from precipitation include inflow, aquifer response and outflow methods. Lysimeter
measurements, tracers and soil moisture budget models are considered as inflow methods of recharge
estimation. In the aquifer response method of recharge estimation, groundwater level changes are
transformed to the amount of water by using the specific yield concept. In the outflow method of
recharge estimation groundwater recharge and groundwater discharge are considered equal.
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3. Description of the study area
3.1. Location
Aynalem sub-basin is located in Tigray regional state (northern part of Ethiopia) at about 5kms south
of Mekele town, capital of the regional state. The geographic location of the area is between 39021’ to
39043’East and 13024’to13030’North. Aynalem area is part of the Ethiopian central plateau just to the
west of Afar rift valley, located at about 770 km north of Addis Ababa (The capital city of Ethiopia).
Figure 3.1. Location map of the study area
3.2. Geomorphology and drainage
The study area covers about 104 km2 with a mean altitude of 2200 m above sea level. The altitude of
the catchment varies from 2100 meters above mean sea level at the mouth of the basin to 2540 meters
above mean sea level at the extreme east of the catchment boundary (Fig 3.2). The northern and
southern ends of the catchment are bounded by a chain of dolerite ridges mainly oriented N–W and
the central part is characterized by relatively flat topography of Mesozoic sedimentary terrain. The
Eastern limit of the catchment is physically separated by the dolerite ridge from Afar lowlands.
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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Figure 3.2. Geomorphologic features and elevation cross-section
Aynalem sub-basin is part of the Giba catchment and belongs to the Tekeze drainage system. The
catchment is fed by Aynalem river and a number of ephemeral streams that drains from the adjacent
ridges. The drainage density and pattern is mainly controlled by the geology, topography and
geological structure of the area.
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Figure 3.3. Drainage map of Aynalem sub-basin
3.3. Climate
The fact that Ethiopia is located in the tropics, latitude 30 N to 180 N, combined with the high range of
altitude, -120 to +4650 meters, and the pressure and air flow pattern determines the tremendous
differences in climate which prevail in different parts of the country (Chernet, 1993). Seasonal
variation in pressure systems and air circulation seem to determine the seasonal distribution of rainfall
in Ethiopia. The distribution of rainfall in Ethiopia is characterized by reference to the position of the
Inter-Tropical Convergence Zone (ITCZ), a low pressure area of convergence between tropical
easterlies and equatorial westerlies along which equatorial wave disturbances take place (Gamachu,
1977). During summer the ITCZ is located in northern Ethiopia. Due to the southeast-northwest axis
of weak high pressure system over Ethiopia, the ITCZ descends southwest in northeast part of
Ethiopia, so that it runs nearly parallel to the Red sea coast. During this time except in south eastern
Ethiopia, the remaining part, including the study area is under the influence of the Atlantic equatorial
westerlies, which produce the main rainy season in Ethiopia (Gamachu, 1977). In spring, the ITCZ is
located in southern Ethiopia. The easterly and south easterly moist currents ascend over the highlands
and produce the main rainy season in south eastern Ethiopia, and light rains of spring to most parts of
the country. The area has a semi-arid climate with little or no variation within the study area with
mean annual rainfall of 670 mm. The long-term mean monthly values of the climatic variables are
presented in the figures below.
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Mean monthly rainfall at Mekele airport
0
50
100
150
200
250
JAN
FE
B
MA
R
AP
R
MA
Y
JUN
JUL
AU
G
SE
P
OC
T
NO
V
DE
C
Month
Rai
nfal
l (m
m)
Mean monthly relative humidity at 1200 local time
0
10
20
30
40
50
60
70
80
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
Nov
De
c
Month
Rel
etai
ve h
umid
ity (
%)
Mean monthly wind speed
1
1.52
2.53
3.5
44.5
5
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
Nov
De
c
Month
Win
d sp
eed
(m s
-1)
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Temperature of the area
0
5
10
15
20
25
30
Jan
Feb
Ma
r
Ap
r
Ma
y
Jun
Jul
Aug
Se
p
Oct
Nov
Dec
Month
Tem
pera
ture
(0 C)
Mean max Mean min Mean
Figure 3.4. Mean monthly values of climatic variables
3.4. Land use, Vegetation and soil
Land use is a major controlling factor in watershed hydrology (Pappas et al., 2008). For example
precipitation that falls on roof tops and pavement results in quick runoff instead of infiltrating into
soil as it would generally do in a natural or farmed landscape. The principal land use in the study area
is rain-fed agriculture and mainly crop farming. Grazing land and settlements occupy a considerable
part of the area (Fig.3.5). Aynalem catchment is sparsely vegetated as a result of excessive
deforestation mainly for agricultural land. The sparse vegetation cover results in excessive soil
erosion and resulting in silt deposition problems in water harvesting structures constructed within the
sub-basin.
WWDSE (2006) conducted classification of the soil type in the catchment based on the grain size
distribution. Accordingly, the dominant soil types identified are classified to four classes: sandy loam,
silty loam, clay loam and clay soils (Fig.3.6). The soil types in the area are strongly related to geology
and geomorphology. The steep cliffs are dominated by sandy loam and silty loam soils, whereas the
clay loam and clay soils cover the flat topography and follow the river banks.
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Figure 3.5. Land use map of the study area (after Teklay, 2006)
Figure 3.6. Soil map of the study area (WWDSE, 2006)
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3.5. Geology
Regional geology
According to Mengeasha et al. (1996), the geological units in Ethiopia fall into three major categories.
These are Precambrian basement, late Palaeozoic to early tertiary sediments and Cenozoic volcanic
and associated sediments. During late Palaeozoic to early Mesozoic the northern and eastern parts of
East Africa acted as depositional basins for sediments coming from the higher cratons. This period is
particularly represented by the deposition of the Enticho sandstone and Edagaarbi glacial in Tigray
(Kazmin, 1975). According to this author two major transgression-regression cycles took place during
Mesozoic. It is believed that these cycles are related to major regional tectonic events that have
affected the entire East African region. The Mesozoic sedimentary succession of the Mekele outlier is
the product of transgression-regression cycles and rocks representing a range of sedimentary
environments have been recognized (Bossellini et al., 1997). The first cycle began during early
Jurassic or late Triassic and resulted in the deposition of the Adigrat sandstone consisting mainly of
sandstone and minor lenses of siltstone and Antalo formation consisting mainly fossiliferous
limestone in Tigray Region (Mengesha et al., 1996). The regression of the first phase caused the
deposition of the Agula formation that is constituted of black shale, marl and clay stone with some
beds of black limestone in the Mekele area. Regression of the second phase during late Cretaceous
resulted in the deposition of Amba Aradam formation that is constituted of siltstone, sandstone and
conglomerates. The works of Beyth (1972) and Kazmin (1975), state that these Mesozoic sedimentary
successions unconformably overlies the Precambrian basement, that forms a nearly circular outlier
800 square kilometre, called Mekele outlier. As described by Bosellini et al. (1995), Mekele outlier
consists of a Triassic clastic unit (Adigrat sandstone), Jurassic carbonate-marl-shale succession
(Antalo Suppersequence) and early Cretaceous sandstone (Amba Aradam formation). The stratigraphy
of the Mekele outlier is shown in Figure 3.7. Tertiary flood basalts unconformably overlay the
sedimentary rocks of the area. The basaltic rock around Mekele area is called Mekele dolerite.
According to Levitte (1970) , the dolerite mostly occurs as sills with a thickness of ranging from one
meter to 30 meters and dykes that intruded the sedimentary rocks.
Local geology
Aynalem catchment is located in the central part of Mekele outlier. The main rock formations which
outcropped in the lager part of the study area are shale-limestone intercalation and igneous rocks,
mainly Mekele dolerite, which has intruded into the Mesozoic sedimentary rocks. Stratigraphically,
the shale-lime stone intercalation is the upper most part of the Antalo group. It is composed of shale,
marl and limestone intercalation which rarely contain thin layers of gypsum (Yehdego, 2003). In areas
where there is incoming dolerite intrusion it becomes highly disturbed and the intercalation beds tilted
from horizontal.The dominant sedimentary rock unit outcropping in the study area underlying the
shale and limestone intercalation is the limestone unit. The limestone unit outcrops mainly in the low
land areas of the Aynalem wellfield. Ridge forming dolerite dykes and sill are commonly outcropped
in the study area. The rocks are black, fine to medium grained and in most cases contain phenocrysts
of plagioclase crystals (Hussien, 2000). The existence of dolerite sill is evidenced by the penetration
of the dolerite unit in almost all wells.
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(1). Marl, (2).Coral-stromatoporoid rich limestone, (3). Cross-bedded sandstone, (4). Limestone,(5). Cross-bedded oolitic
limestone, (6). Transgressive system tract, (7). Highstand system tract and (8). lowstand system tract.
Figure 3.7. Composite stratigraphy of sedimentary succession in Mekele outlier.
(After Bosellini et al., 1995).
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Hydrogeological setting
Groundwater occurrence is greatly influenced by the geology, topography and climatic factors that
prevailed in a given area. By the same fact the hydrogeologic condition of Aynalem catchment is
mainly controlled by the geology and geological structure. Geological structures (faults, fractures and
lithologic contacts) play a great role in the movement and occurrence of groundwater in the study area
(Gebregziabher, 2003). According to Beyth (1972), the major faults in the area are divided into two
groups: NW trending faults (supposed to be late Jurassic to early Cretaceous age), N-E to NE- SW
trending faults(associated with the rifting phase. These groups of faults are named as Wukro, Mekele,
Chelekot and Fuceamariam fault belts.
Borehole logs and field descriptions of previous works indicate that the main aquifers in the area are
the limestone unit and weathered and fractured dolerite. The works of Hussien (2000) and Yehdego
(2003) confirm that the main aquifer in the area is limestone. The limestone is commonly outcropped
with inter-beds of shale-marl intercalation and rarely with thin gypsum layers particularly in the
western part of the catchment. The highly jointed part of the limestone bed favors groundwater
storage and movement in the area. As discussed by Hussien (2000), the limestone unit has a hydraulic
conductivity of ranging from 29 m day-1 to 74 m day-1. The highest permeability is at the contact
between limestone and dolerite attributed to intense fracturing by the effect of dolerite intrusion.
The dolerite which has a mod of occurrence of dykes and sills can be considered as an aquifer when
it is fractured (Vernier, 1985). However, the dolerite unit generally has low hydraulic conductivity
ranging 0.02 to1m day-1. The dolerite intrusion in the area caused non uniform and complex vertical
and horizontal distribution of the hydrostratigraphic units forming an interbedded system of
permeable and less permeable layers resulted in a confined to semi-confined aquifer system.
The shale-marl unit is fissile and friable in nature. The intrusion of the dolerite into the shale-marl
intercalation resulted in the development of fractures which increases the porosity and permeability.
As reported from wells drilled in the intercalation, the hydraulic conductivity ranges 1 to 2 m day-1.
This unit is considered as an aquitard which acts as a local confining layer.
Water strike records at the time of drilling and water level monitoring data indicate that the
groundwater table varied spatially as well as temporally with water level rising following the rainy
season. The depth of water table ranges from 7 to 51 meters below ground surface.
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4. Analysis and model input data preparation
4.1. Hydrometeorology
Hydrology encompasses the interrelationships of geologic materials and processes with water (Fetter,
2001). According to Dingman (1994), the movement of water on and under the surface is affected by
the physical and chemical interactions with earth materials accompanying that movement. Defining
the hydrologic boundaries, both surface and subsurface, is a crucial step to perform water budget
analysis of a catchment. Generally water flows from the hydrologic boundaries towards a point of
discharge. Fetter (2001) describes the hydrologic inputs to an area and hydrologic outputs from an
area. Hydrologic inputs include precipitation surface water inflow and groundwater inflow, whereas
the hydrologic outputs from an area include evapotranspiration, groundwater and surface water
outflows and artificial abstractions.
Precipitation
As described in section 3.3, the creation and distribution of precipitation in the study area is highly
influenced by the position of the Inter-Tropical Convergence Zone (ITCZ). The rainfall data for the
study area is available since 1960 (Appendix 1.2). The average annual precipitation for Aynalem
catchment is 670 mm with about 80% falling from June to September. The long–term annual rainfall
record shows inter-annual fluctuations with the driest year 1984 (272 mm) and wettest year 1961
(1106 mm).
Long-term annual rainfall at Mekele airport
0
200
400
600
800
1000
1200
19
60
19
62
19
64
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
92
19
94
19
96
19
98
20
00
20
02
20
04
year
Rai
nfal
l (m
m)
Figure 4.1. Long-term annual rainfall of the study area
Evapotranspiration
The term Evapotranspiration is the total amount of water lost due to the combined effect of
evaporation from the soil and transpiration through the plant leaves. Evapotranspiration could be
explained with the potential and actual evapotranspiration. Potential evapotranspiration is the rate at
which evaporation would occur from a large area completely or uniformly covered with growing
vegetation which has access to unlimited supply of soil water (Dingman, 1994). Actual
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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evapotranspiration is the evapotranspiration at actual field condition. Direct measurement of the
actual evapotranspiration (AET) is difficult, hence it is usually estimated from the potential
evapotranspiration (PET). Teklay (2006) apply the Penman-Monteith method to estimate the
evapotranspiration in the sub-catchment. The estimated evapotranspiration value was 966 mm year-1
which is typical of semi-arid climate.
River discharge
Discharge record of 1992-2001 for Aynalem river is available from the Metere gauging station which
is located at the upper part of the catchment (Appendix 1.10). There is no gauging station at the outlet
of the catchment and thus it was not possible to get river discharge records leaving the catchment. The
Metere gauging station covers only the upper 69 km 2 of the total catchment area of about 104 km 2.
River flow of about 4 m3 s-1 was measured during the fieldwork in August 2007 (at the peak raining
month of the year) right at the outlet of the catchment. But to understand the river flow in response to
different climatic forcing, time series records of the river flow is quite necessary. In the lower part of
the catchment which is down stream of the gauged part there are a number of springs that fed the
river. Thus it is believed that the base flow contribution to the river flow is higher in this part of the
catchment. The average monthly river flow of the Aynalem river at Metere gauging station for the
monitoring period (1992-2001) is shown below.
Table 4.1. Monthly river discharge (Mm3) of Aynalem river
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Q (Mm3) 0.01 0.00 0.00 0.01 0.02 0.05 0.72 1.63 0.54 0.08 0.05 0.03
Hydrograph of Aynalem River at upper part
0.0
0.5
1.0
1.5
2.0
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
No
v
De
c
Month
Dis
char
ge (
Mm
3 )
Figure 4.2. Hydrograph of Aynalem river
Due to the fact that intense rainfall showers occur while there is sparse vegetation cover during the
raining season, frequent flash floods are generated after rainfall events. As it can be observed from the
hydrograph, the stream flow reacts rapidly to rainfall and the peak of the discharge corresponds to the
raining months of July and August and declines sharply in October.
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4.2. Hydrochemistry
Hydrochemistry of groundwater aquifer in a region is largely determined by both the natural
processes, such as precipitation, wet and dry depositions of atmospheric salts, evapotranspiration,
soil/rock–water interactions, and the anthropogenic activities, which can alter these systems by
contaminating them or by modifying the hydrological cycle (Singh et al., 2007). Both the natural
processes and the anthropogenic activities vary in time and space. These variations are reflected in
groundwater hydrochemistry variations showing spatial and temporal fluctuations in a region. The
chemical composition of groundwater is the combined result of the composition of water that enters
the groundwater reservoir and the reactions with minerals present in the rock that may modify the
water composition (Appelo & Postma, 1992). Dissolved constituents in the water provide clues on its
geologic history, its influence on the soil rock masses through which it has a pass, the presence of
hidden ore deposits, and its mode of origin within the hydrologic cycle (Freeze & Cherry, 1979). As
mentioned by Anderson & Woessner (1992), water chemistry data can be used to infer flow
directions, identify sources and amount of recharge and to define local and regional flow systems.
4.2.1. Water sampling and analysis
Representative water samples of boreholes, springs and ponds were collected from Aynalem, Ilala
and Chelekot sub-basins for the analysis of major anions and cations. The well location map of the
Aynalem wellfield is given in Appendix 6 as borehole identification will be used regularly in the
subsequent sections. The location of the water samples are indicated in Fig. 4.3 as Upper Ilala, Lower
Ilala, Lower Aynalem, upper Aynalem and Chelekot. Furthermore rain water samples were collected
from the area to determine chloride concentration in rainfall for the application of chloride mass
balance method of recharge estimation. The groundwater samples were analysed in the Central
Laboratory of the Ethiopian Geological Survey. For comparison purpose, control water samples from
boreholes that are representative of the lower catchment (TW2) and the upper catchment (PW8) were
brought to ITC laboratory and the analysis results from both laboratories were compared (Appendix
2.3). The analysis results for most of the groundwater constituents were comparable in both
laboratories. But there are also differences in the analysis results mainly for the chloride and nitrate
content.
4.2.2. Reliability check
To evaluate the data quality, the accuracy of the water analysis was checked with the anion-cation
balance. The principle of the anion–cation balance is that the sum of cations and sum of anions are
equal because the solution must be electrically neutral. In a electrically neutral solution, the sum of
the cations should be equal to the sum of anions in meq l-1 (Hounslow, 1995).
( ) 100*%∑ ∑∑ ∑
+−
=AnionsCations
AnionsCationstralityElectroneu (4.1)
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Figure 4.3. Location of water sample points
Based on the electroneutrality, analysis of water samples with a percent balance error <5% is
regarded as acceptable (Fetter, 2001). But in very dilute or saline water, up to 10 % error may be
considered as acceptable due to the errors introduced in measuring major ions in dilute groundwater
or in the multiple dilution require for analysis of concentrated groundwater. The analysis result of all
the samples is within the acceptable range of the reliability check of electroneutrality. The cations-
anions balance results are found to be reliable as the balance does not deviate from the 5% criterion
(Appendix 2.4). The analysis results of the water samples indicate that the dominant dissolved cations
in the groundwater of the area are Ca2+, Na+, and Mg2+ with lower levels of K+. And the major
dissolved anions in the groundwater include: SO42- HCO3
- and Cl-. The range and mean of the major
inorganic constituents of groundwater samples from Aynalem and nearby catchments are summarised
below.
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Table 4.2. Summary statistics of the major groundwater constituents
Constituent Minimum Maximum Mean
EC ( µS cm-1) 688.0 2540.0 1267.2
TDS ( mg l-1) 447.0 2068.0 962.1
Total Hardness 308.7 1486.8 667.2
pH 7.3 7.9 7.5
Ca2+ (mg l-1) 109.2 502.2 244.6
Mg2+ (mg l-1) 1.9 44.9 12.6
Na+ (mg l-1) 18.0 81.0 37.0
K+(mg l-1) 1.0 4.3 2.3
HCO3- (mg l-1) 14.6 351.4 248.0
Cl- ( mg l-1) 12.5 73.3 24.9
SO42- (mg l-1) 47.5 1100.0 427.7
4.2.3. Presentation of results
The water analysis result of the major anions and cations are plotted in Piper diagrams and Stiff
diagrams for quick and tentative conclusion of the water type. According to Hounslow (1995), the
position of an analysis that is plotted on a piper diagram can be used to make tentative conclusion as
to the origin of the water represented by the analysis. However, the bicarbonate to silica ratio must
also be considered when making this deduction. The analysis results are also plotted using Stiff
diagrams in which, cations are plotted in meq l-1 on the left of the zero axis and anions are plotted on
the right. As discussed in Fetter (2001), Stiff diagrams are useful in making a rapid visual comparison
between water from different sources. Selected representative water samples from the three
catchments are presented in the plots of Piper and Stiff diagrams (Fig 4.4 to 4.7).
4.2.4. Water type
The major inorganic constituents of water originate when water in precipitation dissolves atmospheric
gasses such as carbon dioxide and reacts with minerals on the surface of the earth (Hounslow, 1995).
The major water types identified from the hydrochemistry analysis of the groundwater samples are
Ca-SO4, Ca-HCO3-SO4, Ca-SO4-HCO3 and Ca-HCO3. The water type in the basin and its
surroundings is not uniform in composition. Lower Aynalem, Chelekot and lower Ilala are dominated
by a Ca- SO4 type of water whereas the upper Aynalem and upper Ilala are dominated by a Ca-HCO3
and Ca -SO4- HCO3 type of water. The chemical ions of the water samples plotted on a piper diagram
show that the major cations composition has a limited range of variation, mainly calcium rich. On the
other hand, the anions composition has a range of variation from sulphate to bicarbonate. As it can be
clearly observed from the stiff pattern, there is variation in groundwater chemistry in the water
samples from upper and lower Aynalem, Ilala and Chelekot. It is possible to relate the variation in
groundwater chemistry with a change in lithology and groundwater flow direction. From the drilling
well logs at the lower Aynalem, there is evidence for the existence of a gypsum layer which attributes
to the Ca-SO4 dominating water chemistry.
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Figure 4.4. Piper diagram of water samples from boreholes
Figure 4.5. Stiff diagrams of water samples from upper Aynalem
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Figure 4.6. Stiff diagrams of water samples from lower Aynalem
Figure 4.7. Stiff patterns of water samples from Ilala and Chelekot
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4.2.5. Source rock deduction
The purpose of source rock deduction is to gain insight into the possible origin of water analysis. The
initial composition of groundwater originates from rainfall which may be considered to be diluted sea
water (Hounslow, 1995). During its return path to the ocean, the water composition is altered by rock
weathering. During rock weathering the major cations and anions are added to the water. The source
rock deduction for the study area is carried out with the help of AQUACHEM software. Water
samples are selected from different areas within the catchment and out of the catchment for the source
rock deduction analysis. The objective of the selection of samples from different areas is to compare
the source rock within the different sub-basins and to see whether there is a connection of Aynalem
groundwater sub-basin with adjacent river basins. As there is no silica analysis result in any of the
samples, simple ionic comparisons are used for the analysis of source rock deduction. The ionic
comparisons applied in the source rock deduction analysis are:
• Na/ (Na+ Cl)
• Ca/ (Ca+SO4)
• TDS
• Cl/Sum of anions
• HCO3/Sumof anions
This simplistic mass balance approach to deduce the source rock is not perfect, but it can be very
helpful in understanding the origin of the groundwater. Based on the Na / (Na+ Cl) ionic ratio, the
water samples show sodium source other than halite (albite, ion exchange) except for the water
sample from upper Ilala (which shows reverse softening). Applying Ca/(Ca+SO4) ionic ratio indicate
that lower Ilala and Chelekot resulted from gypsum dissolution whereas the water samples from
upper Ilala and Aynalem sub-catchment show calcium source other than gypsum (carbonate or
silicate). The source rock deduced based on the TDS is carbonate weathering or brine except for the
upper Aynalem (from silica weathering). And Cl/ (Sum Anion) ionic ratio shows that all of the
samples are result from rock weathering. On the basis of HCO3/ (Sum of anions), the water samples
result from silicate or carbonate weathering and gypsum dissolution. The ionic ratio analysis show
that the source rock of Aynalem is relatively different from the adjacent sub basins. That is the water
from Ilala and Chelekot is rich in gypsum as compared to the Aynalem sub basin. This is supported by
the fact that the water from boreholes of Ilala has a high hardness while the hardness of the water
from Aynalem especially from the upper part is much lower.
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Table 4.3. Parameters used for source rock deduction
Sample name
Na/(Na+Cl)
(meq l-1)
Ca/(Ca+SO4)
(meq l-1)
TDS
(mg l-1)
Cl/SumAnion)
(meq l-1)
HCO3/(SumAnion)
(meq l-1)
Lower Ilala
0.747 0.552 2068 0.114 0.821
Upper Ilala
0.439 0.805 708 0.140 0.618
Upper Aynalem
0.690 0.846 447 0.055 0.819
Lower Aynalem
0.709 0.614 812 0.077 0.505
Chelekot 0.616 0.551 1380 0.090 0.256
4.3. Chloride mass balance method (CMB)
Groundwater resource studies require the estimation of the quantity of water moving downwards from
the soil zones as a potential recharge (Rushton et al., 2006). The methodology selected for the
estimation of recharge should be applicable in a wide variety of climatic and hydrologic situations. In
this study, the Chloride Mass Balance method is applied to estimate the groundwater recharge.
According to Simmers et al. (1997), chloride is the most important environmental tracer and has been
used to estimate rates of groundwater recharge under a wide range of climatic, geologic and soil
conditions. Yongxin & Beekman (2003) added that the chloride mass balance method was applied for
recharge estimation worldwide in recent time. The basic equation used to calculate the annual
groundwater recharge with the assumption negligible chloride dry deposition in an area is based on
equation 2.2. Despite the fact that the method is simple and inexpensive, there are a number of
uncertainties associated with the method in estimating recharge.
In most cases, the long-term average chloride in rainfall is not available. Measured atmospheric input
of chloride, often only short term records of chloride is assumed to be representative for a long period.
But an area of concern as rainfall and chloride deposition during the past may be different from today.
As discussed by Yongxin & Beekman (2003), other areas of concern include the uncertainty in the
measured chloride content of rainfall and rainfall amount. The largest uncertainty associated with
recharge estimation that utilises the chloride mass balance approach is the determination of chloride
concentration in the rainfall. Furthermore rainfall amount is generally difficult to measure, and is
highly variable. The absence of long-term rainfall quality data in the present study is one of the main
limiting factors affecting the accuracy of the method. Another uncertainty source for the chloride
mass balance approach is the sampling density and analysis accuracy of the chloride concentration of
the groundwater. As part of the fieldwork, samples from rainwater and groundwater were collected
and analysed for their chloride content that are utilised in the chloride mass balance method of
recharge estimation.
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4.3.1. Chloride in rainwater
The chloride concentration in the rainwater of the study area has a low detection limit. As a result,
rain water samples that are collected from Mekele and Bahrdar in August 2007 are sent to a special
geochemical laboratory in Utrecht, the Netherlands for the determination of chloride concentration.
Since the rainfall in Bahrdar and Mekele has the same origin (ITCZ), the data set from Bahrdar in
combination to the rainfall water sample from Mekele is used to analyse the standard deviation from
the mean value of the chloride concentration in rain water.
Table 4.4. Chloride concentration in rain
Time of sampling Chloride concentration ( mg l-1) Station
June1-17,2002 0.43 Bahrdar
June17-30,2002 0.30 Bahrdar
July 1-15,2002 0.65 Bahrdar
July 1-15,2002 0.61 Bahrdar
August 9,2007 0.52 Bahrdar
August 10+11,2007 1.42 Bahrdar
August 12,2007 0.62 Mekele
August 12,2007 0.66 Bahrdar
Standard deviation 0.2
Average Cl-1 rain for the samples taken in
August 2007 0.8 ± 0.2 mg l-1
4.3.2. Chloride content in groundwater
Thirty three groundwater samples were collected and analysed for their chloride content. The chloride
content of the collected ground water samples ranges from 10 to 81 mg l-1.
Table 4.5. Statistics of the chloride concentration in groundwater
Chloride concentration in the collected groundwater samples (mg l-1)
Minimum Maximum Arithmetic mean Standard deviation
10 81 24 11
As it is shown by the standard deviation of the chloride concentration, the variability of the chloride
concentration in the groundwater of the area is considerable and this affects the value of the estimated
recharge. Groundwater chloride concentrations may originate from various flow components in the
unsaturated zone, thus the recharge calculation by chloride mass balance gives an average long-term
estimate of recharge. The method has several shortcomings, one of which is that the method can not
be used in environments affected by other sources of chloride other than total atmospheric fallout.
Thus the following assumptions are made in applying the method.
• Precipitation is the only chloride source in groundwater
• Chloride is conservative and will not undergo any chemical reaction with the geologic
material
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According to Eriksson (1985), the average groundwater chloride content should be calculated as
harmonic mean and is given by equation 4.2.
Clgwave
∑=
N
i gwCl
N
1
1 (4.2)
Where
Clgw is the individual chloride concentration of samples (mg l-1)
N is the total number of observations
Based on the collected groundwater samples, the harmonic mean of the chloride content in the
ground water of Aynalem sub-basin is 18 mg l-1.
Summarizing the above results as:
• Average chloride concentration in rain water (0.8 mg l-1 )
• Harmonic mean of chloride content in groundwater (18 mg l-1)
• Average annual rainfall (670mm)
The estimated recharge is 30 mm year-1 which is 4.5% of the average annual rainfall in the area.
Given that input chloride concentrations can vary significantly from site to site within a region of
investigation, it is not surprising that CMB estimations are site specific (Yongxin & Beekman, 2003).
To see the spatial distribution of the recharge in the sub-basin, the average recharge is estimated at
each sample point as indicated in table 4.6.
The estimated recharge value using the chloride mass balance method is sensitive to concentration
variability of the chloride content in the groundwater and rain water. Taking the maximum and
minimum values of chloride content both in rain and groundwater into consideration, an average
recharge of 37mm year-1 which is 5.5% of the average annual rainfall, is estimated while the average
recharge estimated by applying the harmonic mean of the chloride content in the groundwater samples
and the average chloride content in rain water is 30 mm year-1 (4.5% of the annual rainfall). Thus the
estimated recharge may range between 30 to 40 mm year-1 depending on the range of chloride
concentrations in both rain and groundwater.
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Table 4.6. Groundwater chloride content and estimated recharge
UTM East UTM North Clgw (mg l-1) Cl rain (mg l-1) Annual
rainfall (mm)
Recharge
(mm year-1)
558941 1489255 22.20 0.81 670 24.45
558284 1489689 42.40 0.81 670 12.80
557809 1488359 12.50 0.81 670 43.42
556722 1487915 18.30 0.81 670 29.66
558268 1488286 12.50 0.81 670 43.42
557115 1487967 17.40 0.81 670 31.19
553941 1488821 20.30 0.81 670 26.73
552945 1488663 17.40 0.81 670 31.19
552219 1488072 22.20 0.81 670 24.45
552590 1488475 17.40 0.81 670 31.19
552313 1488491 19.30 0.81 670 28.12
552490 1489376 18.30 0.81 670 29.66
552506 1489646 19.30 0.81 670 28.12
559953 1484601 64.60 0.81 670 8.40
564070 1486356 9.70 0.81 670 55.95
554920 1480973 34.70 0.81 670 15.64
552650 1485112 81.10 0.81 670 6.69
557115 1487960 12.48 0.81 670 43.49
553706 1488251 13.44 0.81 670 40.38
551965 1487745 56.64 0.81 670 9.58
553320 1488680 15.40 0.81 670 35.24
560968 1487142 20.00 0.81 670 27.14
558901 1489740 19.30 0.81 670 28.12
555901 1486423 36.70 0.81 670 14.79
556519 1487277 11.60 0.81 670 46.78
553549 1488948 17.28 0.81 670 31.41
555526 1487648 15.36 0.81 670 35.33
559405 1487676 22.10 0.81 670 24.56
556050 1487809 17.28 0.81 670 31.41
557487 1488269 15.40 0.81 670 35.24
561072 1484157 15.40 0.81 670 35.24
559545 1480345 21.20 0.81 670 25.60
552160 1485603 36.48 0.81 670 14.88
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4.4. Well abstraction and groundwater level analysis
4.4.1. Well abstraction
Groundwater has been abstracted from the wellfield since 1999 for the water supply of Mekele town.
At present there are about ten production wells in operation. Additional boreholes are under
construction and the wellfield is expanding to the upstream part of the sub-basin. The pumping
boreholes are concentrated in the lower right bank of the catchment. The abstraction rate of the
boreholes for the water supply of Mekele town indicates that most of the boreholes are not
continuously functional throughout the year. For this study, the average abstraction rate from the
wellfield is determined based on the continuously pumped wells. A significant amount of water is
abstracted from a well implemented in 2005 (TW4-2005). Thus the abstraction rate of this borehole is
considered in the total average of the abstraction rate. Moreover, the abstraction rate of one borehole
(Aviation) which is not owned by the Water Supply Office is considered in the average rate of
abstraction from the wellfield. Based on the abstraction records from the Water Supply office and by
considering the above two boreholes, the average daily abstraction from the wellfield is estimated to
be 7156 m3 day-1. The abstraction rate from each well is summarised in Table 4.7 and the four year
monitoring abstraction data is indicated Appendix 3.2. Table 4.7. Daily maximum abstraction rate from the wellfield
WELL ID UTM E UTM N ALTITUDE (m) Q (m3day-1)
TW-4 (2005) 557116 1487879 2206 1730.4
PW-2 556637 1487727 2224 1045.8
PW-3 553848 1488600 2214 528.2
PW-4B 553608 1488037 2212 472.0
PW-7B 557020 1487774 2233 1107.0
PW-8 557809 1488359 2237 1478.4
PW-11 552379 1489188 2194 161.8
May shibti 554419 1489568 2227 129.9
Abo Tareke 552315 1488492 2175 449.9
Aviation 556596 1488532 2229 52.6
Total daily
production
7156
The abstraction rates of five boreholes that have a continuous record for about three years are
presented in the figure 4.8 below, to illustrate trend of abstraction.
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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Trend of monthly water production
0
10000
20000
30000
40000
50000
60000
70000Ju
l
Aug
Se
p
Oct
No
v
Dec
Jan
Feb
Ma
r
Ap
r
May
Jun
Jul
Aug
Se
p
Oct
No
v
Dec
Jan
Feb
Ma
r
Ap
r
May
Jun
Jul
Aug
Se
p
Oct
No
v
Dec
2004 2005 2006Month
Pro
duct
ion
(m3)
PW3 PW7B PW8 PW11 PW2 Figure 4.8. Groundwater abstraction from selected boreholes
Figure 4.9. Location map of pumping wells
Though, there is no complete production record for all wells that abstract water from the wellfield, a
total average monthly production of about 200000 m3 month-1 is estimated as indicated in Fig 4.10.
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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Total production for the monitored period
0
50000
100000
150000
200000
250000
300000
Jul
Au
Se
pO
ctN
ov
De
cJa
nF
eb
ma
rA
pr
Ma
yJu
nJu
lA
ug
Se
pO
ctN
ov
De
cJa
nF
eb
.m
ar
Ap
rM
ay
Jun
Jul
Au
gS
ep
Oct
No
vD
ec
Jan
Fe
bm
ar
Ap
ril
Ma
yJu
nJu
lA
ug
Se
pO
ctN
ov
De
cJa
nF
eb
ma
rA
pr
Ma
yJu
n
2003 2004 2005 2006
Month
Pro
duct
ion
(m3 )
Figure 4.10. Total production of the wellfield
4.4.2. Groundwater level analysis
Fluctuation of groundwater level in an aquifer is complex and dynamic and is a result of dynamic
responses of the groundwater system to recharge and groundwater abstractions from the system. Three
and half years of groundwater level monitoring data were collected from the Water Supply office of
the region. A trend of groundwater level in selected boreholes which have continuous records for
about three years and the average groundwater level from all wells for the monitoring season are
presented in the figure 4.11 and 4.12 respectively.
Groundwater level at selected boreholes
0
10
20
30
40
50
60
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec
2003 2004 2005
Wat
er le
vel b
gl (
m)
PW6 (not pumped) PW8 PW11
Figure 4.11. Groundwater level at selected boreholes
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Average groundwater level for the monitored period
0
10
20
30
40
50
60
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
2003 2004 2005 2006
Gro
undw
ater
leve
l bgl
(m
)
0
50
100
150
200
250
300
350
Month
Mon
thly
rai
nfal
l (m
m)
Figure 4.12. Average groundwater level trend
As it can be observed from the average groundwater level graph, there is a general groundwater level
decline in response to the ongoing groundwater abstractions. Following the rains of July and August,
there is a groundwater level rise in response to the direct recharge from rainfall. Based on the
groundwater level monitoring data, the average groundwater level is declining at a rate of 4.8 per year
or about 16.6 meters for the monitored period.
4.5. Pumping test
Detailed knowledge of the distribution of hydraulic parameters in the subsurface is a prerequisite for
the solution of many problems in hydrogeology and related fields (Leven & Dietrich, 2006). In
practice, several investigation techniques are commonly employed in order to estimate the distribution
of hydraulic parameters such as hydraulic conductivity, transmissivity, and storage coefficients.
Depending on the hydrogeologic situation and the investigation objectives, short-term or long-term
pumping tests are utilized. The principle of a pumping test is that if we pump water from a well and
measure the discharge of the well and drawdown in the well and in piezometers at a known distances
from the well, we can substitute the measurements into an appropriate well flow equation and can
calculate the hydraulic characteristics of the aquifer (Kruseman & de Ridder, 1991). There are several
equations and associated theoretical models or curve types which have been developed to analyse
pumping test results and choice of the equations depends on the matching of the drawdown pattern of
the well with theoretical models.
In the Aynalem case single well pumping tests at a number of boreholes followed by a few pumping
tests with observation wells were conducted at the implementation time of the wellfield in 1998/99.
Pumping tests were also conducted in the test wells drilled later in 2005 and 2006. For the present
study it was only possible to get raw data on continuous pumping test for the test well boreholes
drilled later in the years of 2005 and 2006. As part of the present work, the collected pumping test
data were analysed to understand the aquifer system behaviour of the area. As there were no reported
step drawdown test data, it was not possible to describe the behaviour response of each well under
varying pumping rates. The test wells which were used for the continuous pumping test analysis
include:
GROUNDWATER RESOURCE ASSESSMENT THROUGH DISTRIBUTED STEADY-STATE FLOW MODELING, AYNALEM WELLFIELD (MEKELE, ETHIOPIA)
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• TW1 (2005) and TW2 (2005) that are located upstream of the current wellfield where
there is less effect of the ongoing pumping.
• TW4 (2005) located in the upper clusters of the pumping wells where there is intensive
pumping.
• TW6 (2006) is located near the outlet of the catchment for which there is less effect of
pumping.
The geographic locations of these wells are indicated in the well location map (Appendix 6).
Table 4.8. Details of pumping test on the test wells
Well name SWL bgl
(m)
Constant
discharge
(l s-1)
Duration of
the test
(hours)
Drawdown
at the end of
the test (m)
Date of test Aquifer
thickness
(m)
TW1(2005) 18.15 36.7 72 0.50 27/02/2006 65
TW2 (2005) 6.30 30.0 72 18.34 02/02/2006 72
TW4 (2005) 53.35 25.0 144 11.93 15/01/2006 54
TW6 (2006) 16.75 6.0 18 12.55 25/05/2006 40
To identify the aquifer system which in turn helps to select appropriate model for calculating the
hydraulic characteristics, semi-log plots of drawdown versus time were constructed in each of the
wells as indicated in the figures 4.13 through 4.16. As can be observed from the time drawdown plots,
the stability of the groundwater level was not yet reached when the tests was terminated. The pumping
in most of the wells has less influence at the initial stage but the drawdown increases with pumping
time. The drawdown behaviour as result of pumping is influenced by the type of aquifer and inner and
outer boundary conditions at different times during the test.
Time drawdown plot of TW1 (2005)
0
0.1
0.2
0.3
0.4
0.5
0.6
1 10 100 1000 10000
Log time (minute)
Dra
wdo
wn
(m)
Figure 4.13. Time drawdown plot of TW1 (2005)
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Time drawdown plot of TW2 (2005)
0
4
8
12
16
20
1 10 100 1000 10000
Log time (minute)
Dra
wdo
wn
(m)
Figure 4.14. Time drawdown plot of TW2 (2005)
Time dradown plot of TW4 (2005)
0
2
4
6
8
10
12
14
1 10 100 1000 10000
Log time ( minute)
Dra
wdo
wn
(m)
Figure 4.15. Time drawdown plot of TW4 (2005)
Time drawdown plot of TW6 (2006)
0
2
4
6
8
10
12
14
1 10 100 1000 10000
Log time (minute)
Dra
wdo
wn
(m)
Figure 4.16. Time drawdown plot of TW6 (2006)
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The common interpretation of pumping test data is based on the assumption that only one aquifer is
pumped and tested. However, the intensive pumping in nearby wells in the wellfield during the test
causes a significant local pressure drop which may cause deviation from the theoretical curves and
may affect the interpretation results considerably. This condition is clearly shown in TW4 (2005),
more likely, the jump in drawdown prevailed at later time of the test is a result of pumping in nearby
wells at the time of pumping test or jumps in the abstraction rate at the well itself during the test. The
time drawdown plot of TW6 (2006) shows an abrupt change of drawdown behaviour which may be
resulted from barrier boundary, fractured storage or change in abstraction rate. Generally it is apparent
that the various combination and amount of groundwater storage in vicinity of the well can result in
almost any type of time drawdown curves. Deviation from theoretical curves is usually due to specific
boundary condition including partial penetration of the well, recharge or impermeable boundaries
(Kruseman & de Ridder, 1991). When the cone of depression reaches recharge boundary, the
drawdown in the well stabilises and impermeable boundary has the opposite effect on the drawdown.
The degree of fracturing also may play a role in the shape of the time drawdown plot. In evaluating
time drawdown curves, a feature that is very common in wells that abstract water from fracture system
is a high or moderate initial yield that decreases rapidly with time. Usually the cause is insufficient
storage of groundwater in the vicinity of the well (Davis & DeWiest, 1966). As discussed by
Kruseman & de Ridder (1991), two systems are recognised in aquifers with double porosity; the
fractures of high permeability and low storage capacity and the matrix blocks of low permeability and
high storage capacity. A characteristic of the flow in such a system consists of:
• Early pumping time, when all the flow comes from the storage in the fracture.
• Medium pumping time, a transition period during which the matrix blocks feed their
water at an increasing rate to the fractures, resulting in a partly stabilizing drawdown.
• Late pumping time, when the pumped water comes from storage in both the fractures
and the matrix blocks.
The drawdown behaviours revealed in the wells are compared with that of the various theoretical
curves to identify the aquifer type. The diagnostic plots of time drawdown plots of the wells under
consideration resemble a confined fractured aquifer of the double porosity type. Cooper & Jacob time
drawdown method (confined aquifer) was applied to the late time data plot to calculate the
transmissivities in the wells. For the analysis purpose AQUITEST software was utilised and the
transmissivity values obtained were 1720, 118, 65 and 74 m2 day-1 for TW1 (2005), TW2 (2005), TW4
(2005) and TW6 (2006) respectively. The curve matchings used for the mathematical solution to
obtain the transmissivities are given in Appendix 8. The interpretation results of the transmissivity
values of these boreholes from the present work are comparable with the previously reported
transmissivities from the same boreholes (Table 4.9).
The boreholes utilised for the present study are only four and they do not fully represent the aquifer
system. Thus the pumping test data are compiled from previous studies mainly from the works of
Hussien (2000), Yehdego (2003), WWDSE (2006) and THAL (2007) in order to characterise the
aquifer system and to prepare model input data. Yehdego (2003) discussed the well interference from
the pumping tests with observation wells. TW4 was pumped at a rate of 10 ls-1 for 72 hours with
observation wells of TW3 and TW5, which are 400 and 290 meters away respectively. The water
level at TW5 started dropping in less than five minutes after pumping was started and it had dropped
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2.93 meter by the end of 72 hours. On the other hand TW3 was not affected for over 24 hours after
pumping started and the water level dropped 0.35 meter by the end of 72 hours of pumping. Well
interference test of TW4 and TW5 show that there was high degree of interference which was
observed in less than five minutes after the start of pumping.
4.6. Aquifer characteristics
Quantitative description of aquifers is vital in order to address several hydrological and
hydrogeological problems. Fluid transmissivity, hydraulic conductivity and aquifer depth are
fundamental properties describing subsurface hydrology. The most effective way of estimating
hydraulic properties of an aquifer is the pumping tests that are carried out on certain boreholes sites.
Nevertheless, a probable sparse spatial distribution of the available boreholes gives rise to significant
problems in modeling the hydrogeological systems. The estimation of hydraulic properties is usually
done by fitting with a relevant type curves. The interpretation results of the aquifer parameters are
reported in WWDSE (2006) and are summarised in able 4.9 for the purpose of the present study.
As is evident from the reported analysis result, the area is characterized by a highly variable and wide
range of transmissivity and hydraulic conductivity. The hydraulic conductivity ranges from 0.02 to
81 m day-1. In some of the wells the hydraulic conductivity is extremely high and in others it is very
low which is indicative of the heterogeneous condition of the subsurface geologic system. The
variability in the hydraulic properties mainly results from the intense fracturing and heterogeneity due
to the existence of dolerite dykes. As it is indicated in the plot of point hydraulic conductivities at the
wells (Fig.4.17), the hydraulic conductivity is not uniform over the catchment. The locations of the
wells applied to illustrate the point hydraulic conductivities are shown in the well location map
(Appendix 6).
Log hydraulic conductivity
0.00
0.50
1.00
1.50
2.00
2.50
PW
-12
PW
-3
PW
-11
PW
-1
Adi
se
lest
e
PW
-4
PW
-6
PW
8
PW
-2
PW
9
PW
-7
TW
1(2
005
)
TW
2(2
005
)
TW
4(2
005
)
TW
5(2
005
)
AR
-1
AR
-2
Le
spe
r
TW
4
TW
5
TW
6(2
006
)
TW
1
Well name
Log
K(m
day
-1)
Figure 4.17. Log hydraulic conductivity values
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Table 4.9. Transmissivity and hydraulic conductivity
Well Id UTM
east
UTM
north
Elevation
(m)
Transmissivity
(m2 day-1)
Aquifer
thickness (m)
Hydraulic
conductivity
(m day-1)
PW-12 553549 1488948 2208 1138 14 81.30
PW-3 553941 1488821 2214 2630 53 49.70
PW-11 552490 1489376 2208 967 30 74.40
PW-1 556050 1487809 2211 138 30 4.60
Adi seleste 555901 1486423 2252 217 12 18.10
PW-4 553706 1488251 2210 92 30 3.10
PW-5 554336 1487216 2189 1 45 0.02
PW-6 555526 1487648 2221 2260 30 74.00
PW-8 557809 1488359 2237 500 34 14.70
PW-2 556722 1487915 2227 24 9 2.82
PW-9 558268 1488286 2243 51 16 3.20
PW-7 557115 1487967 2233 888 20 44.40
TW1(2005) 561057 1487352 2277 1750 66 26.68
TW2(2005) 564439 1485877 2311 100 72 1.39
TW4(2005) 557234 1488028 2228 100 54 1.85
TW5(2005) 552970 1488153 2206 170 60 2.83
AR-1 556406 1488604 2215 409 14 29.20
AR-2 555787 1489875 2256 723 18 40.20
LESPER 551526 1487025 2143 27 24 1.14
TW-4 553140 1488452 2178 24 19 1.28
TW-5 552880 1489018 2183 127 28 4.54
TW6(2006) 549453 1485160 2133 65 40 1.60
TW-1 553845 1487586 2193 23 19 1.25
Table 4.10. Summary statistics of transmissivity (m2 day-1)
Mean Median Mode Standard Deviation Minimum Maximum
540 138 24 753 1 2630
As shown in the summary statistics of the reported well transmissivities (Table 4.10), the
transmissivity ranges from 1 to 2630 m2 day-1.
4.7. Digital elevation model (DEM)
The Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) on-board
NASA’s Satellite Terra is a high-resolution multi spectral sensor that provides along-track stereo
image data of the Earth in the near-infrared wavelength region. A DEM is extracted from Level 1A
product of ASTER using LPS extension in ERDAS IMAGINE to a 15 meters resolution. Ground
control points are prepared from the topographic maps of the study area to assess the vertical accuracy
of the DEM. The vertical accuracy of the DEM is assessed by comparing the extracted elevation value
at a number of check points with those prepared from the topographic maps as ground control
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elevation points. The relationship between the ground elevation and the elevation from ASTER DEM
is shown in figure 4.18. The elevations from ASTER DEM and those from the topographic map show
high correlation with R2 of 0.99 and the comparison at the check points for the study area results in a
root mean square error of 13 meters. Before applying the elevation extracted from the DEM for
further analysis, the elevation is corrected by the regression equation obtained by the built comparison
to the ground control point elevations and the extracted elevation. The regression equation obtained
from the comparison is X = (Y- 47.882)/ (0.9746), where Y is the elevation from ASTER and X is the
elevation from Topomap of the area. The correction process of the elevation from the ASTER is
accomplished by applying this formula in the Map calculation of ILWIS. After correction, the
required array of elevations at the well locations is extracted by applying the map value function of
ILWIS to the ASTER DEM.
Topo elevation vs ASTER DEM elevation
y = 0.9746x + 47.882
R2 = 0.99
1700180019002000210022002300240025002600
1700 1800 1900 2000 2100 2200 2300 2400 2500 2600
Topo elevation(m)
AS
TE
R D
EM
ele
vatio
n(m
)
Figure 4.18. Scatter plot of ground elevation Vs elevation from ASTER DEM
The corrected Elevation from the ASTER DEM is applied to define the ground elevation of the
boreholes, to define the aquifer top and bottom, to construct cross-sections and to define elevation of
the bottom of the riverbed for the use of river package. Furthermore the DEM plays a key role in
defining of the model boundary in the conceptual model formulation.
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5. Conceptual model
The first step in the procedure of modeling is the construction of a conceptual model of the problem
and the relevant aquifer domain. Development of a conceptual model is one of the critical steps in
modeling process. It consists of a set of assumptions that reduce the real problem and the real domain
to simplified versions that are acceptable in view of the objective of the modeling. It is critical that the
conceptual model is a valid representation of the important hydrogeological conditions.
Conceptualization of the system is the basis of the numerical modeling. The nature of the conceptual
model determines the dimension of the numerical model and the design of the grid. Failures of
numerical models to make accurate predictions can often be attributed to errors in the conceptual
model. As discussed by Yihdego (2005), the purpose of developing a conceptual model is to formulate
a better understanding of a site condition, to define the groundwater problem, to develop a numerical
model and to aid in selecting a suitable computer code. The elements of a conceptual model include
defining the extent and characteristic of the aquifer system and developing an understanding of
groundwater flow directions, sources and sinks. The conceptual model of the area is developed by
integrating the available data on hydrostratigraphy, well and geophysical logs, geologic map and
geologic cross–section from previous studies.
5.1.1. Well log data and geology
Well log data has a great importance to develop better understanding of subsurface aquifers and
groundwater flow direction. In other words, the information from well log data contributes for proper
characterization of the hydrogeological condition at a site which is necessary to understand the
relevant flow process. Drilling log data (Appendix 3.5) of the boreholes in the wellfield are collected
from different drilling organizations and applied to demarcate the hydrostratigraphic zones of the
area. The well log data show that the geologic units in the area are not uniform and are highly
heterogeneous in lateral and vertical extent. There are interlayers of permeable and less permeable
geologic units (Limestone shale and dolerite). The geological heterogeneity mainly results from the
existence of dolerite dykes intruded into the sedimentary layers. The occurrence of the dolerite in the
area is mainly in the form of sill. In places, intrusion of dolerite dykes resulted in tilting and fracturing
of the sedimentary layers. The dipping layers in some parts of the Aynalem catchment led to an
increased complexity of the hydrogeological situation. The information from the lithologic log
indicates that the limestone and the fractured and weathered part of the dolerite are the main water
bearing geologic units. In almost all well log data, massive and less permeable dolerite is encountered
at depth which acts as a barrier to groundwater flow.
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Figure 5.1. Dolerite dyke dissecting the sedimentary rock
Figure 5.2. Tilted sedimentary layers due to dolerite intrusion
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Figure 5.3. Lithological log showing depth to dolerite (After Yehdego, 2003)
The cross-section constructed from the lithological log of selected boreholes indicates that the lateral
and vertical distribution of dolerite intrusion is highly variable. Considering the same measuring
reference elevation point, the dolerite at pumping well one (PW-1) is encountered at a depth of 71
meter below ground surface whereas at pumping well two (PW-2) which is 600 meters away, the
dolerite is exposed at the surface. Locations of wells applied for the lithological cross-section are
indicated in the well location map (Appendix 6). Apart from the lithological variation, the dolerite has
an effect on the degree of fracturing of the sedimentary layers. The distribution of the geological units
in the area is indicted in the geological map and cross-section constructed in the east-west direction
following the valley.
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Figure 5.4. Geological map with east- west cross-section (WWDSE, 2006)
5.1.2. Geophysics
Geophysics refers to the study of the earth with special reference to its physical properties, structure
and composition. The geophysical prospecting techniques are based on various fundamental principles
of physics like the law of gravitational attraction, magnetism, optics, refraction and reflection, those
elements of electricity and theory of electromagnetism. The Resistivity method is the method
commonly applied in groundwater studies. Vertical electrical sounding is applied in the study of
horizontal and vertical discontinuity in the electrical properties of the ground, and also in the
detection of three dimensional bodies of anomalous electrical conductivity. The best interpretation
results are generally obtained from a combination of horizontal profiling and electrical sounding data.
Electrical sounding is the process by which depth investigations are made, and horizontal profiling is
the process by which lateral variation in resistivity are detected. However, the results of electrical
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sounding and of horizontal profiling often are affected by both vertical and horizontal variations in the
electrical properties of the ground. For the present study, geophysical survey results (VES) and
Seismic refraction data were collected from previous studies mainly conducted by WWDSE (2006)
and Gebregziabher (2003). The resulting analysis of these surveys is used as supportive data for the
lithologic log and geological cross-section for identifying the hydrostratigraphic units. The
interpretation results of the vertical electrical soundings are calibrated by comparing with the
lithologic log and the resistivity value of the subsurface material at the same location in order to have
an idea of the resistivity values of geologic formation where there is no well log data. The geophysical
survey data is given in Appendix 4 and the interpretation results for the vertical electrical soundings
are summarised in table 5.1.
Table 5.1. Summery of vertical electrical sounding data
No. Estimated
resistivity range
(Ωm)
Main geological
formation
Description Remarks
1 10-60 Shale 10-25, wet and
25-60 dry
2 60-280 Limestone Weathered and
fractured
Water bearing
3 200-450 Limestone Hard, less
fractured.
4 100-300 Dolerite Fractured
6 300-600 Dolerite Slightly
fractured
7 >600 Dolerite Massive, hard dry
5.2. Hydrostratigraphy
Hydrostratigraphic units comprise geologic units of similar hydrogeological properties. In formulating
the hydrostratigraphy, several geologic formations may combine into a single hydrostratigraphic unit
or a geologic formation may be subdivided into aquifers and confining units (Anderson & Woessner,
1992). Understanding the lateral and vertical extent and relationship between the hydrogeological
units is crucial for constructing an accurate conceptual groundwater flow model. Based on available
resistivity and seismic data, drilling records and geologic knowledge gained from the previous studies
the following hydrostratigraphic units are identified.
• Limestone-shale–marl intercalation
• Limestone
• Dolerite
The main water bearing unit in the area is the limestone unit, but the weathered and fractured part of
the dolerite also acts as a conduit for occurrence and movement of groundwater. As discussed in a
recent study conducted by TAHAL (2007), the groundwater in the area exists under confined to semi-
confined conditions because of the interbedded fractured limestone and shale beds. Moreover, the
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reported storage coefficients for some of the wells are in the order of 10- 4 which suggests a confined
aquifer system.
5.3. Hydraulic proporties of the stratigraphic units
Hydraulic properties including both horizontal and vertical hydraulic conductivities and
transmissivities are key components of the conceptual groundwater model. The hydraulic property
data for the Aynalem aquifer system is derived from aquifer pumping tests carried out in the previous
studies. The pumping test results have been documented on a number of published and unpublished
reports. Hydraulic properties of the different geologic units have been reported by Hussien (2000).
The hydraulic conductivity of limestone unit ranges from 29 to 74 m day-1. The limestone has a high
hydraulic conductivity at the contact between the limestone and dolerite due to the fracturing effect of
the dolerite intrusion. The Mekele dolerite has low primary porosity and permeability. Due to the
secondary porosity as a result of fracturing, the dolerite can also be considered as an aquifer where it
is fractured Vernier (1985). The hydraulic conductivity of the unfractured dolerite is reported to be
0.02-1 m day-1. As a result of geologic heterogeneity and the effect of fracturing, the Aynalem
catchment does not have uniform hydraulic properties. In the area there are zones with high
contrasting transmissivities and hydraulic conductivities attributed to the geologic heterogeneity and
degree of fracturing. As described in section 4.5, the pumping test analysis result shows wide ranges
of hydraulic properties with an average transmissivity of 540 m2 day-1.
5.4. Water budget
Identifying the source of water to the system as well as outflow from the system is part of the
development of conceptual model. In preparing water budget to the system, the expected outflow
directions and exit points are considered.
Recharge
Major factors contributing to groundwater recharge of an area include rainfall, evapotranspiration and
soil type. Groundwater recharge could take place by direct percolation through the vadose zone in
excess of moisture deficits and evapotranspiration or indirect recharge from streams and reservoirs.
The recharge in the area occurs from rainfall and seasonal floods generated from the topographic
elevated ridges surrounding the area. Isotope analysis of the groundwater in the Aynalem catchment
carried out by Yehdego (2003) indicates that the groundwater recharge mechanism is mainly direct
recharge from rainwater. The recharge process of Aynalem catchment is highly controlled by the
topography, geology and structure which direct the infiltrated water towards the discharge area. As
the mechanism of recharge process differs based on the climatic zones, selection of appropriate
recharge estimation technique requires conceptualisation of the recharge process. For this research,
recharge is estimated by applying the chloride mass balance method (section 4.3). The estimated
recharge by applying this method is 30 to 40 mm year-1 (4.5-6% of the average annual rainfall). As
none of the recharge estimation methods are absolutely accurate, the results of recharge estimated
from different methods will be compared with the recharge obtained from the model calibration to end
up in a reasonable estimate of the recharge.
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Ephemeral channel transmission loss represents an important groundwater surface water exchange in
arid and semi-arid regions and is potentially a significant source of recharge at the basin scale.
Indirect recharge from ephemeral streams and seepage from reservoirs are other areas of groundwater
recharge mechanisms in the catchment. At the upstream parts of the sub-catchment, there are small
ephemeral streams that loose water downward from the stream bed to the water table as part of
groundwater recharge. Recently a number of small scale reservoirs were constructed in the Aynalem
catchment for the purpose of supplementary irrigation of the rain-fed agriculture. Seepage is a
common phenomenon of the area as the underlying geology is affected by fracturing and bedding
plane of the sedimentary beds. But for the purpose of the modelling it is assumed that the indirect
recharge from reservoirs and ephemeral streams is integrated in the areal recharge estimates, based on
the chloride mass balance method.
Groundwater discharge
The mechanism of groundwater discharge from the aquifer system is mainly discharge to streams,
well abstractions and groundwater outflow through the western outlet. The Aynalem river is well
connected to the aquifer system which feeds water to the river as base flow during the dry season. The
groundwater discharge from the aquifer is expressed as springs and seepages along the river banks
mainly in the lower reaches of the river and marshy areas in the flat laying parts of the valley. As part
of the natural groundwater discharge, a number of low yield springs are identified during the field
work. Most of the springs are gravity springs, discharged right at the contact of permeable and less
permeable units (limestone and shale).
Table 5.2. Spring inventory data
Spring UTM E UTM N Elevation (m) Date of measurement Discharge
(l s-1)
Discharge
(m3 day-1)
1 552160 1485603 2184 24/08/2007 0.3 25.92
2 555605 1491726 2247 24/08/2007 0.2 17.28
3 554457 1485491 2258 24/08/2007 0.2 17.28
4 551805 1487990 2176 24/08/2007 1.0 86.40
5 552077 1485518 2180 24/08/2007 0.2 17.28
6 557061 1486928 2202 25/08/2007 0.5 43.20
7 560160 1487371 2277 25/08/2007 0.2 17.28
8 560226 1487267 2274 25/08/2007 0.2 17.28
The main groundwater abstraction presently takes place through production wells for the water supply
of Mekele town. As described in section 4.4, about 7156 m3 of groundwater is abstracted daily from
the wellfield. Though there are no detailed studies of the unsaturated zone in Aynalem catchment, it is
assumed that the influence of evapotranspiration is limited to a depth of several meters above the
water table. Evapotranspiration from the saturated zone is therefore not considered in the conceptual
model as groundwater discharge.
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Figure 5.5. Groundwater level profile
A profile showing surface topography, groundwater level elevation before pumping and during
pumping was constructed to have insight into the flow system and to see the effect of pumping in the
wellfield. As can be observed in the profile constructed based on the groundwater level
measurements, the general groundwater gradient follows the topography. Associated errors of
groundwater level measurements mainly, measuring device errors, operator errors and transient effects
during measuring resulted in noisy static water level elevations. However, the overall hydraulic
gradient is from east to west following the topography. Using this profile, the estimated natural
hydraulic gradient in the sub-basin is 0.012 or 1.2%. The groundwater level in the wellfield is
declining due to the ongoing abstractions resulting in a local cone of depression. The groundwater
level monitoring data indicate that there is a groundwater decline of up to 40 meters in response of the
well abstractions.
The major natural groundwater outflow from the sub-basin is through the western outlet under the
saturated aquifer depending on the hydraulic gradient in the wellfield. About 12960 m3 daily
groundwater outflow is estimated by applying Darcy law. Average transmissivity of 540 m2 day-1
(from reported pumping test results table 4.9) by assuming the well transmissivity to be valid also for
the aquifer transmissivity, hydraulic gradient 0.012 and two kilometres groundwater outflow zone are
considered in the calculation. The equation applied to estimate the groundwater outflow is given as:
Q =TW *dh/dx (5.1)
Where,
Q = groundwater outflow (m3 day-1)
T = Transmissivity (m2 day-1)
W = width of the zone of groundwater outflow (m)
dh/dx = hydraulic gradient
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Similarly TAHAL (2007) applied Darcy’s law to calculate groundwater flux that feeds Aynalem
wellfield from the eastern side. Their basic assumptions were groundwater recharge occurs only from
the eastern part and no interaction between groundwater and surface water. TAHAL (2007) calculated
the groundwater flow that apparently feed Aynalem wellfield by considering 5km front width, average
gradient of 1.5% and transmissivity value of 180 m2 day-1 which they claim as the median value of the
well transmissivities reported by WWDSE (2006). Based on these considerations the calculated
groundwater flow to the wellfield was 13500 m3 day-1.
The present study considerations differ from that of TAHAL (2007) in that:
• Calculation is for the groundwater outflow that leaves the catchment with front width of 2km
groundwater outflow zone in the western end of the catchment.
• The recharge is not only occurring in the eastern part.
• There is groundwater-surface water interaction as the groundwater discharge is expressed by a
number of springs and measurements of stream flow even in dry seasons.
• Transmissivity value was considered as the average value of the reported transmissivities.
5.5. Groundwater flow system
A groundwater flow system is a set of flow paths with common recharge and discharge areas. 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 model. As it is pointed out by
different researchers (Gebregziabher 2003, Hussien, 2000), the groundwater flow system in the area is
mainly controlled by extensive faults and fractures. The local flow system is controlled by the
topography and geology and degree of fracturing. To construct groundwater flow direction, hydraulic
head information is required. Head measurements are also needed to establish initial conditions for
the numerical groundwater modeling and for model calibration. As it can be observed in the static
water level records from the wells (Appendix 3.4), under natural condition, the groundwater level in
the area ranges from 7 to 51 meters below ground surface.
5.6. Model boundaries
Model boundary is the interface between the model calculation domain and surrounding environment.
In modeling, we are interested in a specific part from the continuous real world system. Thus, the
effect of the real world in terms of hydrological influences at the model boundaries must be described.
Correct selection of boundary conditions is a critical step in model design because the boundaries
largely determine the flow pattern (Anderson & Woessner, 1992). In other words, mass exchanges
across the boundaries are simulated and hence wrong boundary conditions generate wrong water
balance of the system under study. In groundwater flow system, model boundaries could be physical
(impermeable geologic formations and surface water bodies) or hydraulic boundaries (groundwater
divides and flow lines). Establishment of the model boundaries is based on the site specific
knowledge acquired from the geology topography and flow system prevailing in the area. Physical
boundaries including impervious geologic formation, tight fault escarpments, topography and surface
water divides are used in defining the boundaries of the model domain. As it can be seen in the map
prepared from the ASTER DEM (Fig.5.6), Aynalem sub-basin is physically separated by the dolerite
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ridge lines from the adjacent sub-basins of Ilala and Chelekot. Dolerite ridge lines form the Eastern,
Southern and Northern boundaries of the study domain. Those parts of the model boundaries are
defined as no flow boundary assuming that the groundwater fluxes across the water divide are
negligible. As described in section 5.4, the groundwater flow from the sub-basin is considered through
the western outlet depending on the hydraulic gradient in the wellfield. General head boundary is
selected as the appropriate boundary condition for the head dependent flow through the western
outlet.
Figure 5.6. ASTER DEM indicating the three basins in the Mekele area
5.7. Simplification of the real world
Simplification is necessary because complete reconstruction of the field system is not feasible. The
conceptual model should be simplified as much as possible while it is still remains complex enough to
represent the system behaviour (Anderson & Woessner, 1992). Despite heterogeneous hydrogeologic
condition in the sub-basin, a simplified conceptual hydrogeological model of the groundwater system
of Aynalem was developed on the basis of information about geology, hydrogeology and hydrology.
For the modelling purpose, the weathered and fractured dolerite and the limestone unit were combined
to form a single aquifer system. On the other hand, the massive dolerite sill and inter-bedded shale are
considered as barriers to the groundwater occurrence and movement in the wellfield.
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In order to simplify the complexity of the real hydrogeological system, some basic assumptions about
the study area have to be made. The following plausible assumptions are made about the model area
of Aynalem:
• The system is considered in a steady-state throughout the year.
• The geological formations of concern are considered horizontal.
• Since there are flow measurements, during the dry season the Aynalem river is assumed to be
discharging groundwater (gaining stream) on average.
• There is no groundwater inflow from the adjacent sub-basins.
Groundwater occurrence, distribution and flow regime in the Aynalem sub-basin is highly governed
by dolerite sills which categorise the groundwater into shallow and deep aquifer systems. Due to
absence of data for the deep aquifer system, this study is concentrated on the shallow aquifer system.
Based on the lithological and geophysical logs 50 meters of average aquifer thickness is considered.
The conceptual system of the sub-basin is shown in Fig 5.7 below.
.
Figure 5.7. Pictorial representation of the hydrologic system of Aynalem sub-basin
Q out
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6. Numerical model
6.1. Code selection
Primary governing factor for selection of code depends on the objective and reliability of the code.
Since the main focus of the study is to understand the groundwater flow system in response to
recharge and discharge, the selected model is MODFLOW. MODFLOW is a modular three
dimensional finite-difference groundwater flow model of the U.S. Geological Survey, to describe and
predict the behaviour of groundwater flow system. The code is based on the flow equation of Darcy
and mass continuity equation. The partial differential equation in which MODFLOW is based on is:
t
hSW
z
hk
zy
hk
yx
hk
x szzyyxx ∂∂=−
∂∂
∂∂+
∂∂
∂∂+
∂∂
∂∂
(6.1)
Where,
kxx, kyy, kzz represents hydraulic conductivity along x, y and z coordinate axes (LT-1), which are
assumed to be parallel to the major axes of hydraulic conductivity, h represents the potentiometric
head (L), W is flux per unit volume representing sources and/or sinks of water (T-1), Ss represents the
specific storage of the porous material (L-1) and t is time. For steady state condition there is no change
in storage with time and hence the right-hand side of the equation is set to zero. Furthermore the
consideration of the present study is two-dimensional model, hence the third component of the
equation goes to zero.
MODFLOW numerically uses block-centred finite differences to solve the flow equations in three
dimensions. It consists of a main program and independent subroutines called modules (McDonald &
Harbaugh, 1988). The modules are grouped into packages and each package deals with a specific
hydrogeologic feature to be simulated.
MODFLOW is selected for the following reasons:
• MODFLOW is one of the most widely used groundwater flow code in the field of
hydrogeology and is able to simulate steady state and transient flow conditions in one.
• The model can simulate recharge, flow to wells, flow to drains and flow through riverbeds.
• MODFLOW is extensively tested in various environments under different conditions.
• The theory behind the model is well documented and relatively easy to understand.
6.2. Model geometry
As discussed in the conceptual model development section, the model domain is delineated based on
the surface topography and local physical boundaries.
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Horizontal extent The horizontal extent of the model domain is 8 by 20 km bounded by 548820 to 569251 m UTM East
1482054 to 1490288 m UTM north. The irregular shape of the study area reduces the model domain
to an area of about 104 square kilometres.
Vertical extent As described in the conceptual model, a simplified one layer model was used to represent the geologic
materials in the study area. In groundwater modeling, the number of model layers, which are
considered in the discretized domain, depends on the hydrogeological stratification of the system. In
many model approaches hydrogeological layers of a real world system can be simulated by a single
model layer. Based on the geologic and geophysical logs and well completion data a layer with a
constant average thickness of 50 meters is considered to model the Aynalem aquifer system. The top
and bottom elevations of the aquifer system are defined based on the lithologic logs and the DEM
extracted from the ASTER image.
6.3. Model design
At this stage of model development, the conceptual model approach is transformed to a form suitable
for mathematical modeling. The most important activities of model design include the design of
spatial domain, selection of initial conditions and setting the boundary conditions.
Discretization
In numerical models, the continuous natural phenomenon is replaced by a discretized domain, the so
called grid. Grid size depends on hydraulic gradient, degree of aquifer heterogeneity, size of the
model area, level of detail required and availability of data. Selecting the size of the nodal spacing is a
critical step in grid design (Anderson & Woessner, 1992). The size of the nodal spacing in horizontal
dimension is a function of the expected curvature in the water table or potentiometric surfaces. Finer
nodal spacing is required to define highly curved surfaces. A grid with a smaller number of nods is
preferred in order to minimise data handling, computer storage and computation time. Yet, it is
desirable to use a large number of nodes to represent the system accurately. By making simplifications
and assumptions of the actual field condition, the model area is discretized to one layer with a regular
grid of (250m by 250m, 32 rows by 82 columns) consisting of 1492 active cells.
Mathematical representation of boundaries
The mathematical representation of the boundaries in the model is important because many hydrologic
boundary conditions can be mathematically represented in more than one way. Boundary conditions
are mathematical statements specifying the dependent variable (head) or the derivative of the
dependent variable (flux) at the boundary of the problem domain. In the field of hydrogeology three
types of mathematical boundaries are applied.
• Specified head (Dirchlet condition) for which head is given.
• A specified flow (Neumann condition) for which the derivative of the flux across the
boundary is given.
• Head dependent flow boundary (Cauchy condition) for which flux across the boundary is
calculated given a boundary head and conductance values.
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For the present study specified flow (no flow) boundaries and head dependent flow boundaries
(Cauchy boundary conditions) were applied. A general head boundary package was employed to
simulate groundwater outflow through the western outlet of the sub-basin. General head boundary is a
generic form of the head dependent boundary condition. General head boundaries are normally used
along the edge of the model to allow groundwater to flow into or out of the model under a regional
gradient. If the water elevation rises above the specified head, water flows out of the aquifer. The
expression applied for the head dependent flow in the general head flow boundary is:
Qb=Cb*(hb-h) (6.2)
Where
Cb = hydraulic conductance of the boundary (L2 T-1)
hb = hydraulic head at or beyond the boundary( L)
h = hydraulic head in the aquifer (L)
As discussed in the conceptual model formulation, Aynalem river is in hydraulic contact with the
groundwater. In the lower reaches of the river groundwater is discharged to the river as springs and
seepages along the river banks and river beds. The flow of water between an aquifer and overlying
river is commonly simulated using river package. The expression for which river package is based on
is:
QRIV = CRIV * (HRIV - h) h>RBOT (6.3)
QRIV = CRIV * (HRIV - RBOT) h<= RBOT (6.4)
CRIV = (K * L * W) / M (6.5)
Where,
QRIV is the rate of leakage between the river and the aquifer (L3 T-1), CRIV is hydraulic conductance
of the river bed, (L2T-1), HRIV is head in the river (L), h is hydraulic head in cell (L), RBOT is
elevation of the bottom of the riverbed (L), K is the hydraulic conductivity of the riverbed material
(LT -1), L is the length of the river within a cell (L), W is the width of the river (L) and M is the
thickness of the riverbed (L).
The groundwater drained to the river can be simulated by setting RBOT equal to HRV in the river
package, for this case the river package acts the same as the drain package. The drain package works
in a much the same way as the river package, except that leakage from the drain to the aquifer is not
allowed (Anderson & Woessner, 1992). Due to the absence of river level measurement data, drain
package is applied to simulate the groundwater discharge to the gaining reaches of the river. The
recharge to the aquifer from the loosing reaches of the river is assumed to be take place by the vertical
areal recharge which is integrated in the chloride mass balance method.
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Figure 6.1. Model boundary conditions
Initial conditions
Initial conditions refer to the hydraulic head distribution in the system at the beginning of the
simulation and thus are boundary condition in time (Anderson & Woessner, 1992). The initial
conditions in numerical groundwater models are initial head distributions and have only to be entered
to fulfil the convergence criteria of the numerical scheme. If the hydraulic gradient between heads of
boundary elements and non boundary elements become too large, many computer codes will fail in
their calculations by numeric instabilities. So for steady state models initial heads should be only in
the range with the values of the hydraulic head conditions at the boundary element of the model. For
the present case, the static water level records of the wells are interpolated within the model to obtain
the initial hydraulic heads for the entire model.
Representation of aquifer parameters
The primary hydraulic parameters required by a steady-state groundwater flow model are either
transmissivity or hydraulic conductivity in a distributed fashion across the model grid. Zonation
schemes of the aquifer parameters were applied to better approximate spatial distribution of the
hydraulic parameters and for accommodation of the heterogeneity. Zonation for the input parameters
was carried out based on geological information, point hydraulic conductivity and transmissivity data
of the pumping tests. Initially, the hydraulic parameters estimated from pumping test results of
previous studies were applied, later the parameters were adjusted during the calibration process.
Boreholes and observation wells
The majority of the boreholes are concentrated on the lower right bank of the catchment. The data
base of the boreholes was collected from the Water Supply Office of Mekele town and from previous
reports on the area. There are no proper data records of all the boreholes and it is hardly possible to
obtain a continuous time series monitoring groundwater level data for all of the boreholes. In almost
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all wells, the pumping well serves as an observation well. That means that the monitoring
groundwater level data is highly affected by the transient effect of the pumping at the time of water
level measurements. In the modelling area the well abstractions indicated in section 4.4, were
implemented using the well package of MODFLOW.
Recharge
Considering direct recharge from rainfall, the recharge estimated by the chloride mass balance
method which is 4.5-6% from the average rainfall of 670mm, was utilised as a model input. This
recharge is uniformly applied to the top most active cell by using the recharge package of
MODFLOW. The recharge value is adjusted to 6% of the average annual rainfall of the area during
the model calibration.
6.4. Model calibration
In most cases, the model will not give satisfactory results because the input data to the model do not
reflect the real world with enough accuracy mainly due to ambiguity of input data. Thus in order to
improve the reliability of the model, adjustments in the model input data are required. In the
procedure of parameter value adjustment, the values are adjusted within a pre-determined range of
error criterion until the model produces results that approximate the set of field measurements
selected as the calibration target.
Calibration target and uncertainty
Prior to the calibration process, setting of calibration target is required. Calibration target is a
calibration value and its associated errors. In the Aynalem case, hydraulic heads obtained from
groundwater level measurement data were used as calibration values. And the calibration target was to
match hydraulic heads calculated by the model with measured head points. Hydraulic heads were
obtained from groundwater level monitoring of three years data and from static water level records
measured during drilling time of 1998/99. Water level measurements from some of the wells (PW2
and PW6) are erratic and are believed as outliers due to large measurement errors and are left out
during calibration. It should be noted that most of the field measured head data are associated with
errors due to the following reasons.
• The static water level measurements were taken just after well completion with out
stabilization.
• There are no independent monitoring wells, the water level observations are carried out on the
pumping wells.
• Measurement errors related to measuring instrument accuracy and operator errors.
Thus the field measurement of head values may not represent the actual water level of the field
condition. The combined effect of these errors is assumed to result in a maximum of 10 meters
difference between the observed and calculated heads.
The model calibration was carried out for the scenarios with and without abstraction. The first step
towards model calibration is to check the model reliability in generating field condition, when it is
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subjected only to the natural regime. The static water levels records before time of pumping were used
in calibrating the natural regime of the groundwater flow system. Average groundwater levels of three
years monitoring data were used to calibrate the steady-state model with pumping scenario.
Trial and error calibration
Trial and error calibration is the process of manual adjustment of input parameters until the model
produce field measured heads within the range of the error criteria. The model was calibrated for
natural steady-state conditions, assuming constant recharge and steady discharge neglecting seasonal
fluctuations. Calibration was conducted through trial and error by varying aquifer hydraulic
parameters and comparing calculated heads to those measured in wells. During the calibration
transmissivity, recharge and boundary conditions were modified manually and trial runs were carried
out until the model output was within the range of the pre-defined error criterion.
The best fit results were achieved when the study area was divided into regions with different
transmissivity zones. The use of zones of uniform property value as a basis of spatial parameter
definition can be quite unsatisfying for a number of reasons, including the fact that the design of a
tentative zonation scheme for the model must be undertaken ahead of the actual parameter estimation
process. Although geological mapping can help in this regard, there are many instances where
geological boundaries are only approximately known. The transmissivity values applied during the
calibration ranges from 30 to 135 m2 day-1. The transmissivity values attained during the calibration
process are by far smaller than the reported transmissivities from previous studies. The procedure
followed during the trial and error method of calibration is indicated in Figure 6.2.
Figure 6.2. Trial and error calibration procedures (Adapted from Anderson and Woessner, 1992)
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Calibration results
The calibration results of the model are presented graphically and in a table form showing the
distribution and comparisons of the observed and model calculated hydraulic heads. A listing of
measured and simulated heads together with their differences and average of the differences is a
common way of reporting calibration results.
Figure 6.3. Contour map of simulated heads (non-pumping scenario)
Figure 6.4. Scatter plot of observed and simulated hydraulic heads (m)
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Table 6.1. Observed and calculated heads for non-pumping scenario
Borehole
name
UTM east UTM north Observed
head (m)
Computed
head (m)
Difference (m)
PW-1 556050 1487809 2209.48 2210.26 -0.78
PW3 553941 1488821 2185.15 2195.59 -10.44
PW4 553706 1488251 2184.95 2192.92 -7.97
PW5 554336 1487216 2186.67 2196.56 -9.89
PW7 557115 1487967 2215.80 2215.39 0.41
PW8 557809 1488359 2222.99 2227.12 -4.13
PW9 558268 1488286 2222.14 2231.66 -9.52
PW11 552490 1489376 2187.60 2184.11 3.49
PW12 552945 1488948 2188.83 2187.14 1.69
TW3 552945 1488955 2177.62 2186.18 -8.56
TW5 553207 1488955 2188.47 2189.43 -0.96
TW1(2005) 561057 1487352 2258.35 2263.56 -5.21
TW2(2005) 564439 1485877 2311.30 2317.73 -6.43
Table 6.2. Observed and calculated heads for pumping scenario
Borehole
name
UTM East UTM North Observed
had (m)
Computed
head (m)
Difference (m)
TW4(2005) 557234 1488028 2182.40 2178.48 3.92
PW3 553941 1488821 2162.70 2169.51 -6.81
PW7b 557188 1488028 2182.90 2176.74 6.16
PW8 557809 1488359 2202.30 2193.81 8.49
PW11 552490 1489376 2162.40 2159.74 2.66
Mu 552511 1489648 2159.60 2160.23 -0.63
TW3 552945 1488955 2160.20 2160.39 -0.19
PW6 555529 1487648 2172.00 2180.17 -8.17
PW12 552945 1488948 2165.80 2161.64 4.16
TW1(2005) 561057 1487352 2258.35 2255.91 2.44
TW2(2005) 564439 1485877 2311.30 2309.12 2.18
Evaluation of calibration
The results of the calibration should be evaluated both qualitatively and quantitatively (Anderson &
Woessner, 1992). The calibrated results were evaluated based on the calibration target and assessment
of the mass balance of the system.
The calibrated model was evaluated qualitatively and quantitatively, in which hydraulic head
distributions have been used as quantitative calibration targets whereas flow directions have been
used as qualitative calibration targets. Qualitatively, the contour maps of the simulated heads were
analyzed to see whether the results are comparable with the actual field condition. Flow direction was
determined based on the simulated head distribution and comparison is made with the flow direction
determined in the conceptual model. A scatter plot of measured against simulated heads is another
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way of showing the calibrated fit (Fig.6.4). The scatter plots are visually examined whether points in a
plot deviated from the straight line in a random distribution. Deviation of points from the straight line
should be randomly distributed. Furthermore the calibrated model outputs were evaluated by applying
the three common ways of error quantifying methods (Mean error, Mean absolute error and Root
Mean Squared error).
Mean error is the mean of the difference between the measured head (hm) and simulated head (hs)
ME = is
n
im hh
n)(
1
1
−∑=
(6.6)
Mean absolute error is the mean of the absolute value of the difference between the measured head
(hm) and simulated head (hs)
MAE =∑=
−n
iism hh
n 1
)(1
(6.7)
Root Mean Squared error is the square root of the average of the squared difference between the
measured head (hm) and simulated head (hs)
RMSE =
−∑ =ni ism hh
n 12)(
15.0
(6.8)
Where
n = number of calibration values
Table 6.3. Errors of the calibrated model
Error (m)
Model scenario ME MAE RMSE
Non-pumping 4.48 5.35 6.42
Pumping 1.34 4.12 4.92
The evaluation of the calibrated model result shows that:
• Most of the simulated heads were within the pre-established calibration target.
• Water balance discrepancy was zero (Appendix 7).
• The overall results of the groundwater model are comparable with the measured well data and
in agreement with conceptual model.
• The measure of errors evaluated by ME, MAE and RMSE are in the acceptable range
according to the pre-determined error criteria.
Though the overall result of the model was comparable with the measured well data, few observations
which are not uniformly distributed over the model domain are utilised in the calibration process.
Ideally calibration values should be measured at a large number of points uniformly distributed over
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the model domains. Thus it is not possible to conclude that the calibration is accurate by only
quantifying the errors using ME, MAE and RMSE with out considering the distribution of the
residuals. Comparison between contour maps of measured and simulated heads provides a visual
qualitative measure of the similarity between patterns, thereby giving some idea of the spatial
distribution of errors in the calibration.
6.5. Sensitivity analysis
Sensitivity analysis is an essential step in all modeling applications. As discussed by Anderson &
Woessner (1992), the purpose of a sensitivity analysis is to quantify the sensitivity of the model
simulations in the calibrated model caused by uncertainty in the estimates of aquifer parameters, stress
and boundary conditions. Sensitivity analysis provides information on which model parameters are
most important to the simulated system. Sensitivity analysis is also inherently part of model
calibration. The most sensitive parameters will be the most important parameters for matching the
model result with the observed values.
To assess the sensitivity of the simulations in the calibrated model, a sensitivity analysis was
performed with respect to the pumping scenario. The sensitivity analysis was performed by
systematically changing one calibrated parameter at a time while noting the observed changes in
hydraulic heads. A number of sensitivity analyses were conducted to test the effects on model results
due to changes in input parameters or boundary conditions.
It was identified that the most sensitive factors are recharge depth and transmissivity of the aquifer.
The calibrated values of these input variables were multiplied by factors of 0.5.0.8, 0.9, 1.1, 1.2, 1.3
and 1.5. The resulting hydraulic heads were then compared with the observed hydraulic heads and
mean average error, absolute average error and root mean squared error were calculated for each
parameter. Then the calculated average errors in the hydraulic heads were plotted against the
multiplying factors as shown in figures 6.5 and 6.6. The magnitude of change in heads from the
calibrated solution is the measure of the sensitivity of the solution to that particular parameter. And
the results of the sensitivity analysis are reported as the effects of the parameter change on the average
measure of error selected as a calibration criterion (in this case mean average error, absolute average
error and root mean squared error).
It was found that slight changes in either the aquifer transmissivity or slight changes in recharge rate
affect dramatically the distribution of hydraulic head throughout the area. The sensitivity plots show
that the recharge generates non-linear sensitive response while sensitivity towards transmissivity
generates linear response. The model is equally sensitive to both increase and decrease of
transmissivities on the other hand, the calibrated model is more sensitive to recharge fluxes reduction
than to recharge rate increment.
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6.6. Model validation
In model validation, the normal procedure is to define a set of measurements or observations of
system variables, where part is used for model calibration and the remaining part is used for model
validation. Unfortunately it is often impossible to validate a model because usually too short set of
observed state data is available, which is already required for calibration. For the same reason model
validation was not accomplished here.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7
Transmissivity change factor from calibrated value
Ero
rr o
f hy
daru
lic h
ead
(m)
ME MAE RMSE
Figure 6.5. Sensitivity plot of the calibrated model with respect to transmissivity
-40
-20
0
20
40
60
80
100
120
140
0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7
Recharge change factor from calibrated value
Err
or o
f hy
drau
lic h
ead
(m)
ME MAE RMSE
Figure 6.6. Sensitivity plot of the calibrated model with respect to recharge
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7. Discussion and results
This chapter is designed to analyse the findings and discuss the overall results of the study with more
attention given to modelling results.
7.1. Hydrochemistry
Water chemistry differs depending on the source of water, the degree to which it has been evaporated,
the types of rock and mineral it has encountered, and the time it has been in contact with reactive
minerals. The chemical constituents of groundwater give important clues with regard to the geological
history of the enclosing rocks, the velocity and direction of water movement (Freeze & Cherry, 1979).
As discussed in section 4.2, water samples from the existing wells and springs were collected and
their physical and chemical characteristics were analysed. Chemical analysis results of water samples
collected from wells drilled in Agula shale show high concentrations of Ca2+, SO42- and Na+. This
high concentration is caused by dissolved gypsum and limestone minerals which are found
interbedded in the Agula shale. On the other hand, chemical analysis of water samples collected from
the wells drilled in Mekele dolerite generally show low concentrations of Ca2+, SO42- and Na+. The
plots of chemical analysis results on Piper diagram, (Fig 4.4) and Stiff diagrams (Fig 4.5 to Fig 4.7)
show that the groundwater samples are calcium rich. Among the anion facies a majority of the water
samples does not fall in any dominant class and varies from sulphate to bicarbonate. The analysis
shows that groundwater in the sub-basin is Ca-HCO3 type in the upper catchment, changing to Ca-
HCO3-SO4 type along the groundwater flow direction and finally becoming Ca-SO4 type near to the
outlet of the catchment. Groundwater in the upper zone of the catchment has a low concentration of
total dissolved solids (TDS) that ranges from 400 to 700 mg l-1, whereas, the groundwater in the lower
part of the catchment has high total dissolved solids (TDS) that ranges from 800 to 1500 mg l-1. The
groundwater in the lower part or outlet of the catchment is a combination of water that infiltrates
every where in the catchment and has a more chance to interact with the rock materials along its flow
path which contributes for the high concentration of total dissolved solids toward the outlet of the
catchment. In other words, the increase in TDS to ward the western outlet is resulted from water with
longer residence time.
Water type and source rock deduction analysis of groundwater samples from Aynalem and adjacent
catchments was attempted to see whether there is groundwater connectivity between the catchments.
As there is no silica analysis result in any of the samples, simple ionic comparisons were used for the
analysis of source rock deduction. In the water type identification the bicarbonate to silica ratio is not
considered. Thus, with this simple analysis and with the data at hand, it is not possible to give a clear
conclusion whether the similarity in water type is attributed to lithology or groundwater connectivity.
7.2. Modeling results
The maps of spatial distribution of the simulated hydraulic heads for the non-pumping and pumping
scenarios are shown in Figures 7.1 and 7.2 respectively.
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A
B
Figure 7.1. Distribution of hydraulic heads with non-pumping scenario
A
B
Figure 7.2. Distribution of hydraulic heads with pumping scenario
Hydraulic head for pumping and non-pumping scenarios
2100
2150
2200
2250
2300
2350
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
distance (m)
hydr
aulic
hea
d (m
)
head with non-pumping head with pumping
A
B
Figure 7.3. Comparisons of simulated hydraulic heads for both scenarios
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A cross-section is constructed from east to west direction following the valley to see the hydraulic
head distribution in the system with both scenarios. In both cases the general hydraulic gradient in the
sub-basin follows the surface topography and the gradient is east-west which is in agreement with the
flow system defend in the section on the conceptual model of the area. As it is clearly shown in the
hydraulic head cross-section with pumping scenario, there is a groundwater level decline which
results in a cone of depression in the wellfield area. The effect of pumping is largely on the wellfield
area where there is extensive abstraction and with less effect towards the upper and lower end of the
catchment. As is indicated in the cross-section comparing the simulated hydraulic heads for the
pumping and non-pumping scenarios, the groundwater abstraction in the wellfield area results in a
groundwater level decline up to 37 meters. The groundwater level decline as a result of pumping
estimated by the model is in agreement with the observed decline (up to 40 m).
7.2.1. Hydraulic properties
The aquifer parameters obtained from the pumping test result show very high contrasting hydraulic
properties spatially. There are extremely high and low values of transmissivity and hydraulic
conductivity values obtained from nearby wells. The assumption of homogeneity and infinite
horizontal extent of aquifer usually considered in pumping test data analysis are highly violated due
the geological heterogeneity of the area. Thus it is tried to optimize the hydraulic properties during the
calibration process using the calibration values (hydraulic heads). The average transmissivity value
reported from the well pumping test results was 540 m2 day-1. The model calculated transmissivities
were much lower than those reported from pumping test result data of the previous studies. To better
address the heterogeneity, zones of transmissivities were applied (Fig 7.4). The transmissivity values
adjusted in the calibrated model are ranging 30 to 135 m2 day-1.
Figure 7.4. Transmissivity zones applied to the calibrated model
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7.3. Groundwater budget
The input term to the groundwater considered for the present study is direct recharge from rainfall.
Whereas the output terms considered are well withdrawals, groundwater drains to the river and head
dependent groundwater flow out of the aquifer system through the western boundary.
Recharge
Recharge is very difficult to estimate reliably and in many cases more than one recharge estimation
method is required. There are as many methods available for quantifying groundwater recharge as
there are different sources and processes of recharge. Each of the methods has its own limitations in
terms of applicability and reliability (Yongxin & Beekman, 2003). As described in section 4.3, the
recharge is estimated using chloride mass balance method as part of the present study. The estimated
recharge value may vary depending on errors associated to the method and the standard deviation of
the chloride content measurements both in the groundwater and the rain water. Yongxin & Beekman
(2003) discuss the uncertainties associated with the chloride mass balance method as:
• Uncertainties in the measured chloride content, both in rainfall and groundwater
• Uncertainty in the measured rainfall amount, depending on the type of rain gauge used and
analytical errors introduced.
The optimised recharge rate by model calibration is 42 mm (6% of the mean annual rainfall), while
the chloride mass balance shows that the recharge is in the order of 30-40 mm year-1 (4.5-6% of the
mean annual rainfall in the area). Considering the standard deviation in the chloride concentration of
the groundwater samples, the recharge estimated by chloride mass balance method is in the same
range as the recharge determined by the model calibration.
Previously, the groundwater recharge in the area has been estimated by different studies (Hussien
2000, Yehdego, 2003 and Teklay, 2006). The studies of Hussien (2000) and Yehdego (2003)
estimated the recharge by applying a water balance method as 9% of the average annual rainfall. By
applying the same method, Teklay (2006) estimates the recharge as 5.3% of the annual rainfall. The
reported recharge values vary widely. This report estimates the groundwater recharge in the area in
the range of 4.5 to 6% of the annual rainfall.
Groundwater abstraction
Groundwater pumping is frequently the least measured water balance component (Ruud et al., 2004).
The groundwater abstraction in Aynalem wellfield is poorly documented and the recorded data shows
many gaps. The operating periods of the boreholes are also not well known, thus it is difficult to get
the accurate abstraction rate from the wellfield. By examining and analysing the available abstraction
records, 7156 m3 of average daily groundwater abstraction from the wellfield is estimated and this
value is implemented in the steady-state model through the well package.
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Model simulated groundwater budget
The groundwater budget can be quantified on the basis of the calibrated model output. The
groundwater flow budgets calculated by the model for the non-pumping and pumping scenarios are
indicated in table 7.1 and table 7.2 respectively.
Table 7.1. Model simulated groundwater budget of the area for the non- pumping scenario
Flow term IN (m3 day-1) OUT (m3 day-1)
Recharge 11982
River 7236
Head dependent flow through the western boundary 4746
Well withdrawals
Total 11982 11982
Table 7.2. Model simulated groundwater budget of the area for pumping scenario
Flow term IN (m3 day-1) OUT (m3 day-1)
Recharge 11982
River 810
Head dependent flow through the western boundary 4015
Well withdrawals 7156
Total 11982 11982
There are several indicators that the overall result of the calibrated steady-state groundwater flow
model developed for the area is realistic.
• The deviation of the simulated heads from the observed heads is within the pre-established
calibration target.
• The simulated groundwater level drawdown due to pumping in the wellfield area is in
agreement with the observed groundwater levels.
• The model calculated inflow and outflow terms are balancing.
• Groundwater flow direction simulated by the model is reasonable and in agreement with the
flow direction defined in the conceptual model.
Nevertheless, there are a number of limitations and uncertainties in the steady-state groundwater flow
model developed here.
7.4. Model limitations
Numerical models of groundwater flow are limited in their representation of the physical system
because they contain simplifications and assumptions that may or may not be valid. Results from
groundwater flow models have a degree of uncertainty primarily because of uncertainties in many
model input parameters (most importantly hydraulic conductivity and transmissivity) and boundary
conditions applied. The various steps in the modelling process may each introduce errors, converting
the real world into conceptual model and converting conceptual model into mathematical model.
The built model of the present research is associated with a number of uncertainties. First of all the
hydro geological heterogeneity caused difficulties in the conceptual simplification of the field
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condition due to lack of detailed description of the heterogeneity. Despite the complex and
heterogeneous nature of the aquifer system, assumptions and simplifications were made during the
conceptualization of the system. Definitely uncertainties will be introduced as the result of these
assumptions and simplifications of the field conditions. Various forms of heterogeneity in the porous
media properties can be very different from the fluid flow behaviour in the individual zones (Das &
Lewis, 2007). There are generally few locations where observations are available, and the geological
structure of the aquifer is only partially known. Lack of proper site characterization may result in a
model that is calibrated to a set of conditions which are not representative of actual field conditions.
The main constraints in the modeling process were data gaps and poor quality of the available data.
The data which have a key role in defining the model geometry (screen length and aquifer thickness)
are not well documented. The available records of the water level measurements are not continuous
and mostly are only single measurements. Furthermore calibration values (heads) are highly
associated with measurement errors. The available hydraulic head data is applied for model
calibration, thus there was insufficient independent data for model validation.
Another area of uncertainty is resulting from defining the boundary conditions of the model domain.
The boundary conditions were defined based on the surface physical features such as impervious
geology and surface water divide. The locations of the groundwater catchment boundaries are
uncertain since they might not coincide with their surface expressions. This uncertainty is highest in
sedimentary terrain where there is preferential occurrence of secondary porosity along bedding planes
as in the case of the study area. The area is conceptualized as a single layer assuming impermeable
dolerite sill separating the upper aquifer from the deep aquifer system. In reality the separating layer
may be partially impervious layer due to localized fractures. Hence additional uncertainty may be
introduced as the result of this assumption.
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8. Conclusion and recommendations
8.1. Conclusions
Primary and secondary porosity and permeability of the main water bearing geologic unit (limestone)
plays a most important role in controlling the natural groundwater occurrence and movement in the
Aynalem catchment. Due to the inter-layering of the limestone unit with less permeable shale and
dolerite rocks, the groundwater in the area occurs under confined to semi-confined conditions. In
response to the geological heterogeneity, broad ranges of aquifer parameters (transmissivity and
hydraulic conductivity) are reported from the results of pumping test analysis. As major part of the
study, a steady-state groundwater flow model was developed with and with non-pumping scenarios to
assess the groundwater resource of Aynalem sub-basin.
The principal mechanism of groundwater recharge in the area is direct recharge from rainfall. The use
of chloride mass balance method to estimate recharge shows that the annual recharge in the catchment
is in the order of 30-40 mm year-1 from mean annual rainfall of 670 mm. The model simulates a mean
annual recharge of 42 mm year-1. Although the accuracy of the chloride mass balance method is
dependent on the measurement accuracy of the chloride content both in groundwater and rainfall, it
can be applied to estimate groundwater recharge in the region.
From the hydrochemical data analysis, a conclusion can be made that there exist at least two classes
of water types in Aynalem catchment. Ca-CHO3 dominated water type at the upper catchment and Ca-
SO4 dominated water type at the lower western extreme of the catchment are present with a clear
evolutionary trend between the two types.
The steady-state flow modeling has demonstrated that an average recharge of 42 mm year-1 maintains
the natural equilibrium. On the other hand, the model results with pumping scenario show that a
groundwater abstraction of 7156 m3 day-1 resulted in a groundwater level decline up to 37 meters in
the wellfield area. The high rate of groundwater withdrawals in the lower right bank of the catchment
has created a local cone of depression (Fig 7.3). The cone of depression due to pumping is limited to
the current wellfield area and has less effect on the upstream and downstream areas of the catchment.
According to the overall groundwater resource assessment carried out by integrating geological,
hydrological data and steady-state flow modelling it is concluded that water balance components that
play important role in the groundwater table fluctuation of the wellfield are recharge and groundwater
abstraction from wells. The steady-state numerical model is suitable as a tool to improve our
understanding of the groundwater flow system in response to recharge and abstractions and to
estimate aquifer properties in the area without data. But the limitations of the model discussed in
section 7.4 should be taken into account prior to applying the model for groundwater resource
management.
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8.2. Recommendations
This study is not the end of groundwater flow modeling in the area rather it is a good starting work for
detailed modeling in the future. With additional data, further refinement of the model is possible,
which is expected to improve the accuracy of the model. Further extensive field-based observations
combined with down hole geophysical well logging and hydraulic testing techniques, detailed
delineation of fractures and other secondary porosity is required to compile the hydrogeologic
framework for each geological sequence. Prior to detail groundwater modeling, detail structural
mapping is required which will have a great importance in aquifer characterization and definition of
boundary conditions.
The transmissivity to which the model is highly sensitive requires better future characterization. The
cell size applied in the discretization of the problem domain (250 by 250m) is not small enough to
adequately represent the drawdown in the wells. Thus the model can be further developed by
integrating additional data and by applying finer grids in the pumping areas. To improve the
uncertainties on the model boundaries, regional scale steady–state groundwater flow modeling is
recommended so that the steady-state solution of the regional model is applied to set the boundary
condition for the model at the local scale.
In the present study, only the upper shallow aquifer system is modeled as there is no data for the
deeper aquifer system, thus future studies should consider the deeper aquifer system and study the
relation to the regional groundwater flow system provided that additional data is obtained. The
fracturing in the limestone resulting from faulting and dolerite intrusion as identified by Hussien
(2000) and Yehdego (2003) shows fracture flow. It was tried to simulate this in the model by
assigning high transmissivity values that may not fully represent the fractured flow system. Thus a
groundwater flow model which accounts for both diffuse and fracture flow should be used to improve
modeling of groundwater flow in the fractured aquifer system.
Once an improved steady-state model is obtained, transient simulation should be carried out for better
predictions of pumping effect and for better recharge modeling (response of groundwater levels to
good/bad raining seasons). To improve calibration values, groundwater level should be monitored at a
set of monitoring wells that are evenly distributed in the sub-catchment. The time series
documentation of the abstraction rates should be improved.
Finally it is advised to consider the following points in relation to groundwater resources development
in the wellfield.
• Future groundwater resource development plans in the wellfield should take into account the
balance between the groundwater recharge and the intended abstraction rates to ensure the
sustainability of the resource in the catchment.
• It is advisable to redistribute the pumping wells from the narrow right lower bank of the
catchment to the upper catchment to reduce the groundwater stress resulting from localised
pumping.
• The drawdown in the wellfield should not become so high that the groundwater levels in the
wellfield is becoming lower than the level at the outlet.
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Appendices
Appendix 1 Hydrometeorological data
Appendix 1.1. Monthly rainfall (mm) at Mekele airport station
Appendix 1.2. Long term monthly rainfall (mm) at Mekele airport station
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Appendix 1.3. Monthly minimum temperatures (0C)
Appendix 1.4. Monthly maximum temperature (0C)
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1993 21 22 23.6 22.2 24 25.3 22.3 22.9 24.4 23 22 22
1994 22 23 24.2 24.9 26.1 25.7 21.3 21 22.5 23 22 21
1995 22 24 24.6 24.6 25.8 28.2 22.9 22.1 23.9 23 23 23
1996 23 25 24.9 25.6 24.8 24.4 23.2 22.5 24.9 24 22 22
1997 23 24 25.7 25.5 26.6 26.6 22.8 23.1 25.6 23 23 23
1998 24 25 26.2 27.3 27.0 27.9 22.4 21.3 23.9 23 22 22
1999 22 25 25.0 26.3 27.9 27.9 21.7 21.4 23.5 23 22 22
2000 23 24 24.7 25.6 27.5 27.6 23.6 22.4 23.9 24 23 22
2001 23 25 24.5 26.5 28.1 25.5 24.6 21.9 24.6 25 23 23
2002 22 25 25.8 26.6 28.7 27.3 25.5 23.3 24.8 25 24 23
2003 25 26 25.7 26.6 28.2 26.9 23.4 22.3 24.3 24 23 22
2004 25 24 25.0 25.9 28.2 26.5 24.8 22.9 25.1 21 23 23
2005 24 26 26.1 26.3 26.4 27.4 23.2 23.3 24.6 23 23 22
2006 24 25 25.5 25.0 26.0 27.1 23.6 22.3 24.5 24 23 22
mean 23 24 25.1 25.6 26.8 26.7 23.2 22.3 24.3 23 23 22
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Appendix 1.5. Monthly mean wind speed (m s-1) at 2m height
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 3.6 4.0 3.79 4.08 2.96 1.92 2.04 1.64 2.00 3.3 3.6 3.7
1997 4.4 4.9 3.86 3.79 3.63 2.25 1.68 1.39 2.32 3.7 3.8 3.8
1998 3.5 3.2 4.36 4.78 3.42 2.39 2.33 2.31 1.58 2.7 3.3 3.6
1999 3.5 5.7 17.5 4.25 2.9 2.83 1.86 2.20 1.49 2.7 3.4 3.6
2000 3.7 4.4 4.72 3.44 2.99 2.17 2.05 1.89 1.71 2.8 3.1 3.3
2001 2.8 3.5 3.22 3.74 2.67 1.89 2.31 1.56 1.79 2.8 3.2 3.5
2002 3.5 3.6 3.19 3.43 2.67 2.01 1.54 1.52 1.96 3.0 3.4 3.1
2003 3.9 3.3 3.81 3.63 3.07 2.01 1.99 1.61 1.49 3.6 3.9 4.1
2004 3.6 4.0 4.16 4.04 3.01 1.87 1.49 1.48 2.01 3.3 4.0 4.1
2005 3.4 4.6 4.51 4.46 2.95 3.78 2.11 1.48 1.45 2.8 3.6 4.2
2006 4.0 4.3 3.79 4.04 2.91 2.00 1.71 1.65 1.47 3.2 3.8 3.9
Appendix 1.6. Mean monthly relative humidity (%) at 1200 local time
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1993 48 42 38 68 65 67 72 69 53 50 54 44
1994 43 47 46 34 31 39 69 74 56 34 52 42
1995 38 38 40 45 39 39 63 67 40 36 35 44
1996 47 34 42 35 43 49 60 70 44 33 39 33
1997 37 31 34 32 30 45 68 62 33 42 45 35
1998 45 33 33 29 29 28 69 76 48 36 28 29
1999 36 20 28 24 20 26 72 75 47 45 39 42
2000 30 21 34 28 26 30 63 73 44 42 38 35
2001 38 29 37 29 24 38 70 76 44 38 34 30
2002 45 31 34 27 20 33 56 68 41 36 34 43
2003 31 32 34 34 24 36 64 74 46 36 34 34
mean 40 33 36 35 32 39 66 71 45 39 39 37
Appendix 1.7. Mean monthly sunshine hours
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1993 9.7 8.4 9.5 8.4 9.3 7.1 5.4 5.9 7.1 8.5 10.4 10.2
1994 10.3 9.9 9-0 9.5 10.1 6.4 4.4 5.1 7.9 10.5 9.8 10.2
1995 10.3 8.9 9.3 9.1 9.3 9.2 5.4 5.1 8.6 9.8 10.0 9.4
1996 9.0 9.6 8.2 9.2 8.4 5.9 6.1 5.7 7.7 9.8 9.0 9.9
1997 9.5 9.9 8.6 9.1 9.6 8.0 6.0 6.5 8.4 8.1 8.9 10.0
1998 8.4 8.7 9.1 9.4 9.4 7.3 4.9 4.1 7.1 9.2 10.2 10.3
1999 9.3 10.3 9.7 10.4 9.9 6.9 3.9 5.2 8.1 8.9 10.4 9.9
2000 10.1 10.0 10.0 7.8 9.6 7.7 6.7 6.3 6.9 9.0 9.0 9.4
2001 9.6 9.7 6.4 9.6 10.1 9.8 6.5 6.9 8.6 9.3 10.3 10.1
2002 9.3 10.1 8.9 10.4 10.6 11.9 6.2 7.4 8.5 10.3 10.0 9.6
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Appendix 1.8. Monthly average piche evaporation (mm)
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
1993 135 153 212 128 179 149 69 82 135 201 220 225 1887.5
1994 250 194 202 266 308 178 85 111 153 229 195 207 2377.8
1995 220 195 197 219 287 186 93 59 171 249 204 190 2268.5
1996 151 247 177 133 219 174 119 62 203 232 213 262 2192.1
1997 219 295 263 303 353 253 132 118 225 242 188 222 2810.8
1998 166 244 302 374 391 420 128 75 174 240 317 354 3184.4
1999 295 432 328 451 365 287 89 69 140 152 218 178 3003.2
2000 213 264 275 234 257 240 105 63 142 141 153 167 2255.7
2001 119 189 174 258 254 170 74 53 139 173 180 198 1981.2
2002 128 213 208 253 328 240 140 79 162 239 223 142 2354.6
2003 177 196 237 271 323 201 114 69 131 222 245 219 2403.5
2004 182 263 346 210 331 224 145 81 170 240 236 199 2625.5
2005 177 283 233 275 234 235 98 92 129 214 196 278 2442.4
2006 192 185 220 247 254 236 103 65 149 219 192 115 2175.7
mean 187 239 241 259 292 228 107 77 159 214 213 211 2425.9
Appendix 1.9. Monthly Evapotranspiration (mm)
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1992 3.7 4.4 5.7 7.5 7.9 8.6 3.6 1.7 3.9 5.8 5.4 4.1
1993 4.3 5.3 6.8 4.3 5.8 5.0 2.2 2.7 4.5 6.5 7.3 7.3
1994 8.1 6.7 6.5 8.9 9.9 5.9 2.7 3.6 5.1 7.4 6.5 6.7
1995 7.1 6.7 6.3 7.3 9.3 x 3.0 1.9 5.7 8.0 6.8 6.1
1996 4.9 8.5 5.7 4.4 7.1 5.8 3.8 2 6.8 7.5 7.1 8.5
1997 7.1 10.2 8.5 10.1 11.4 8.4 4.2 3.8 7.5 7.8 6.3 7.1
1998 5.3 8.4 9.7 12.5 12.6 14 4.1 2.4 5.8 7.7 10.6 11.4
1999 9.5 14.9 10.6 15.0 11.8 9.6 2.9 2.2 4.7 4.9 7.3 5.7
2000 6.9 9.1 8.9 7.8 8.3 8.0 3.4 2.0 4.7 4.6 5.1 5.4
2001 3.8 6.5 5.6 8.6 8.2 5.7 2.4 1.7 4.6 5.6 6.0 6.4
2002 4.1 7.4 6.7 8.4 10.6 8.0 4.5 2.6 5.4 7.7 7.4 4.6
2003 5.7 6.8 7.7 9.0 10.4 6.7 3.7 2.2 4.4 7.2 8.2 7.1
2004 5.9 9.1 11.1 7.0 10.7 7.5 4.7 2.6 5.7 7.8 7.9 6.4
2005 5.7 9.7 7.5 9.2 7.5 7.8 3.1 3.0 4.3 6.9 6.5 9.0
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Appendix 1.10. River discharge Metere gauging station (Aynalem river)
UTM E-553925
N-1486920 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1992 I 0.00 0.00 0.02 0.00 0.07 0.00 0.17 0.79 0.04 0.00 0.02 0.00
II 0.00 0.00 0.13 0.00 0.36 0.00 0.52 2.03 0.07 0.00 0.07 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1993 I 0.00 0.00 0.00 0.10 0.08 0.01 0.17 1.08 0.44 0.02 0.01 0.00
II 0.00 0.00 0.00 0.41 0.20 0.04 0.20 9.10 4.15 0.01 0.04 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.01 0.00 0.00 0.00
1994 I 0.00 0.00 0.00 0.00 0.00 0.04 0.19 3.84 1.93 0.03 0.00 0.00
II 0.00 0.00 0.00 0.02 0.00 0.36 0.76 15.15 7.59 0.02 0.00 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00
1995 I 0.00 0.00 0.00 0.00 0.01 0.10 0.15 0.55 0.99 0.02 0.00 0.00
II 0.00 0.00 0.03 0.00 0.07 0.23 0.20 0.96 3.81 0.01 0.00 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00
1996 I 0.01 0.00 0.00 0.00 0.02 0.08 0.35 0.22 0.04 0.00 0.00 0.00
II 0.00 0.00 0.00 0.00 0.22 0.22 2.47 0.27 0.04 0.00 0.00 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00
1997 I 0.00 0.00 0.00 0.00 0.05 0.04 1.70 0.51 0.26 0.00 0.00 0.00
II 0.00 0.00 0.00 0.00 0.41 0.03 10.19 1.35 0.68 0.00 0.00 0.00
III 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00
1998 I 0.00 0.00 0.00 0.00 0.00 0.27 2.02 2.35 0.49 0.18 0.20 0.15
II 0.00 0.00 0.00 0.00 0.00 1.10 4.71 6.83 1.35 0.09 0.10 0.07
III 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.08 0.06 0.05 0.03
1999 I 0.05 0.01 0.00 0.00 0.00 0.00 0.98 3.90 0.97 0.47 0.24 0.15
II 0.03 0.01 0.00 0.00 0.00 0.00 1.00 6.68 2.8 1.96 0.10 0.07
III 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.12 0.10 0.07 0.04
2000 I 0.05 0.00 0.00 0.00 0.00 0.00 0.18 0.81 0.04 0.00 0.00 0.00
II 0.04 0.00 0.00 0.00 0.00 0.01 0.72 0.96 0.09 0.00 0.00 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2001 I 0.00 0.00 0.00 0.00 0.00 0.00 1.32 2.22 0.20 0.06 0.03 0.00
II 0.00 0.00 0.00 0.00 0.00 0.00 6.25 5.45 0.12 0.04 0.02 0.00
III 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.01 0.00 0.00
I is monthly Runoff in million m3 II is maximum Discharge in m3s-1 III is minimum Discharge in m3s-1
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Appendix 2 Hydrochemistry
Appendix 2.1. Analysis result of rain water
Appendix 2.2. Physical and chemical constituents of water samples
Appendix 2.3. Comparison of analysis
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Appendix 2.4. Major anions and cations ( meq l-1) and water type
Well
Na+ K+ Ca 2+ Mg 2+ Cl - SO4 2- NO3
- HCO3 - Water type Ionic
balance
PW11 2.1 0.1 15.90 0.76 0.5 14.29 0.04 2.93 Ca-SO4 -2.9
PW4 0.9 0.0 7.56 1.05 0.4 3.63 0.20 4.37 Ca-HCO3-SO4
-5.3
PW8 0.8 0.0 5.45 0.71 0.4 0.99 0.31 5.23 Ca-HCO3 -1.0
PW2 1.3 0.0 9.64 0.63 0.5 5.22 0.24 4.75 Ca-SO4-HCO3
-3.8
TW2 1.5 0.1 10.50 0.76 0.6 6.59 0.04 4.08 Ca-SO4-HCO3
-6.2
TW3 1.7 0.1 12.60 1.05 0.5 9.89 0.08 3.55 Ca-SO4-HCO3
-5.2
L.Ilala 3.5 0.1 25.10 3.69 2.1 22.92 0.04 4.56 Ca-SO4 -4.6
PW6 2.4 0.1 21.40 0.84 0.4 18.58 0.05 3.31 Ca-SO4 -5.0
PW9 0.9 0.0 7.55 0.46 0.4 3.13 0.23 4.46 Ca-HCO3-SO4
-4.7
TW1 2.6 0.1 15.90 3.23 1.6 18.68 0.06 0.24 Ca-SO4 -3.1
Pw3 1.4 0.0 8.80 0.16 0.6 5.83 0.23 4.71 Ca-SO4-HCO3
4.5
PW7 0.9 0.0 7.13 0.76 0.5 1.32 0.32 5.76 Ca-HCO3 -5.5
U.Ilala 0.9 0.0 8.38 0.84 1.2 2.03 1.05 5.28 Ca-HCO3-SO4
-3.3
Dandera1 0.7 0.1 5.46 1.11 0.3 1.43 0.18 5.62 Ca-HCO3 0.9
Tw1(2005) 0.9 0.1 10.50 0.35 0.6 5.83 0.20 5.24 Ca-SO4-HCO3
0.5
PW-12 1.2 0.2 7.56 1.06 0.5 4.29 0.17 4.87 Ca-HCO3-SO4
-0.8
PW-7B 1.6 0.2 8.40 0.72 0.4 7.69 0.07 2.50 Ca-SO4-HCO3
-1.3
PW-1 1.0 0.1 2.69 1.53 0.5 3.19 0.08 0.82 Ca-Mg-Na-SO4
-7.9
piz-1S 1.4 0.1 7.29 1.07 0.4 5.27 0.23 3.02 Ca-SO4-HCO3
-4.8
Mu 2.4 0.2 18.5 0.81 0.6 16.90 0.05 2.54 Ca-SO4 -4.4
piz-3S 1.1 0.1 6.72 0.55 0.6 3.90 0.55 3.31 Ca-SO4-HCO3
-0.7
Endbotarek 1.6 0.2 10.50 0.55 0.6 6.77 0.05 4.03 Ca-SO4-HCO3
-5.7
piz-2D 0.7 0.1 6.08 0.93 0.4 0.83 0.47 5.28 Ca-HCO3 -5.4
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Appendix 3 Well data
Appendix 3.1. Well location
Appendix 3.2. Monthly water production (m3)
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Appendix 3.3. Monthly groundwater level monitoring data
Appendix 3.4. Static water level record from the wells
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Appendix 3.5. Lithologic log data of the boreholes
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Appendix 4 Geophysical data
Vertical electrical sounding data Where
• NMN/2 is potential electrode separation
• AB/2 is electrical electrode separation
• Ra is apparent Resistivity in ohm meter
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Figures showing the vertical electrical sounding interpretation using Ipi2 software
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Seismic profile and cross-sections (Gebregziabher, 2003)
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Appendix 5 Groundcontrol points to correct ASTER DEM X Y Z topo Z STER Corrected
Z
X Y Z topo Z
ASTER
Corrected
Z
540422 1490899 2261 2256.5 2266.2 553616.9 1485035 2285 2251.5 2261
540868.2 1489632 2245 2236.7 2245.9 554988.1 1485712 2275 2256.5 2266.2
540650.5 1491505 2267 2245 2254.4 555695.4 1490205 2304 2272.8 2282.9
541308.9 1491673 2225 2220.2 2228.9 554521.1 1491978 2326 2299 2309.8
542631.1 1491154 2170 2166.2 2173.5 557702.7 1490473 2226 2219.2 2227.9
543726.2 1490352 2147 2145.9 2152.7 559819.3 1491648 2149 2141.9 2148.6
544118 1488900 2162 2140 2146.6 559672.4 1488481 2254 2251.8 2261.4
546674.2 1488451 2110 2109.9 2115.8 559694.2 1486015 2278 2265.3 2275.2
547974.6 1488337 2127 2125.3 2131.6 558399.2 1477005 2231 2227.3 2236.2
548148.7 1489755 2147 2133.4 2139.9 558562.4 1472989 2398 2385.4 2398.4
548932.2 1488976 2167 2157.7 2164.8 556696.1 1470466 2209 2194.5 2202.6
549737.5 1488158 2164 2158.5 2165.6 558078.2 1468716 2148 2145.3 2152.1
547120.3 1486634 2097 2097.9 2103.4 556756 1476148 2292 2296.7 2307.4
548165 1487294 2109 2109.4 2115.2 556951.9 1483344 2295 2288.4 2298.9
548578.6 1486449 2107 2099.2 2104.8 557017.2 1484806 2385 2366.8 2379.4
549628.7 1486439 2110 2110.6 2116.5 563859.5 1488514 2346 2331.3 2342.9
546124.6 1485479 2154 2146.4 2153.2 562368.6 1493072 2290 2278.8 2289.1
548143.3 1484845 2168 2167.4 2174.8 563500.4 1495800 2386 2384.6 2397.6
550920.2 1487249 2154 2147.4 2154.2 564664.8 1499378 2344 2352.1 2364.3
551116.1 1485159 2172 2163 2170.2 564788.2 1496998 2487 2489.9 2505.7
550827.7 1486448 2126 2117.2 2123.2 562932.8 1497925 2418 2419.8 2433.7
564167.9 1501861 2347 2335.1 2346.8
566567.6 1499879 2332 2337.6 2349.4
565016.9 1494060 2384 2385.7 2398.7
565746 1489546 2380 2354.7 2366.9
566866.9 1485316 2385 2368.8 2381.4
564494.5 1484656 2339 2327.2 2338.7
563351.9 1483234 2332 2318.5 2329.8
565517.5 1483261 2430 2412.6 2426.3
565675.3 1482498 2393 2368.2 2380.8
562889.4 1478739 2300 2298.3 2309.1
562867.1 1476554 2267 2263.8 2273.7
560581.8 1475748 2256 2247.7 2257.1
556134 1473132 2335 2330.4 2342
549398.9 1472842 2228 2212.7 2221.2
548544.6 1472815 2337 2303.8 2314.7
549562.1 1471753 2230 2213.6 2222.2
548365.1 1478871 2005 2002.2 2005.3
547707.5 1479872 2048 2038.2 2042.2
548844.7 1480607 2150 2129.8 2136.2
Topo elevation vs ASTER DEM elevation
y = 0.9746x + 47.882
R2 = 0.9933
1700
1900
2100
2300
2500
2700
1700 1900 2100 2300 2500 2700
Topo elevation(m)
AS
TE
R D
EM
ele
vati
on
(m
)
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Appendix 6 Location of all wells in the wellfield
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Appendix 7 MODFLOW water budget
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Appendix 8 Pumping test curve matching
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Appendix 9 Plates
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