GROUNDWATER RECHARGE ASSESSMENT IN THE MAKUTUPORA … · GROUNDWATER RECHARGE ASSESSMENT IN THE...

GROUNDWATER RECHARGE ASSESSMENT IN THE MAKUTUPORA BASIN, DODOMA

TANZANIA

Rosemary Athanael Rwebugisa

March, 2008

GROUNDWATER RECHARGE ASSESSMENT IN THE MAKUTUPORA BASIN, DODOMA TANZANIA

by

Rosemary Athanael Rwebugisa

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 Modelling)

Thesis Assessment Board

Dr. Ir. M.W. Lubczynski (Chairman, Associate Professor WRS, ITC)

Dr. Ir.P. Droogers (External examiner, Future Water)

Dr. A.S.M. Gieske (1st supervisor, ITC)

Dr. Ing.T.H.M. Tom Rientjes (2nd supervisor, ITC)

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 parents,

my late father Athanael R. Baitila and my mother Blandina K. Kauta.

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Abstract

The Makutupora basin is located within Precambrian metasediments and fractured crystalline granitic

rocks in the Dodoma region, central part of Tanzania, and has an area of about 1600 km2. It is

characterized by a semi-arid climate with average annual rainfall of about 550 mm and

evapotranspiration of 2000 mm per annum.

The increase in population growth and improvement of life standards has caused an increase in

demand of the water supply for the Dodoma city. This study was carried out to enhance the

sustainable management of the groundwater resource as the reliable source of water in the region.

The recharge flux estimation within the basin was made by the Chloride Mass Balance method,

WTRBLN model, Earth model and hydrograph analysis. A Conceptual model was developed by

combination of satellite imagery information such as Aster image, STRM DEM combined with field

observation data including pumping test data, drilled borehole logs and geological map. A distributed

single layer model was developed. The aquifer system was modelled using PMWIN as pre and post

processor for MODFLOW. It was assumed that the aquifer is under steady state and confined

conditions. The Thornthwaite and Mather water balance model was used to study the hydrologic

regime of the basin on a monthly basis.

The recharge flux was estimated to be about 5 to 12 mmyr-1 equal to 1 - 2% of annual rainfall.

MODFLOW results indicated the annual water budget of the basin reached equilibrium conditions

with recharge from precipitation 8.9, abstraction 7.3, and discharge at the outlet of the basin 1.6 in

MCM per year. The Thornthwaite and Mather water balance model indicated no moisture surplus for

all dry years with low surplus for the wet years. The water quality assessment indicated that, the

Makutupora basin is characterized by CaHCO3 water type, typical fresh water. The hydrochemical

evolution is from less bicarbonate to more bicarbonate composition and the cation composition is

changing from high concentration of Na + K to higher concentrations of Ca and Mg.

It is expected that this study will provide appreciable contribution in sustenance and management of

groundwater resource in the basin. Despite the data scarcity for monitoring wells in the basin, the

developed groundwater model will provide more insight in understanding hydrologic behaviour of the

system in relation to current imposed stress on the recharge flux.

Keywords: Makutupora basin, Recharge flux, Groundwater modelling

ii

Acknowledgements

I wish to thank all organizations and individuals who made it possible for me to complete my studies

in ITC. I am very grateful to the Water Supply Authority management in Shinyanga, Tanzania

(SHUWASA), to have released me to come and further my knowledge here in Netherlands. Special

thanks go to Mr. N. M. Mgozi who introduced ITC to me and encouraged me to apply.

I am deeply indebted to my first supervisor Dr. A. S. M. Gieske for his supervision, encouragement

and guidance he has provided me throughout the research period. He was always willing to listen and

give his opinions to challenges. May God bless you.

I would like to thank my second supervisor Dr. T. H. M. Rientjes for his guidance and encouragement

throughout the entire period of the research work. His critical comments in modelling issues have

really upgraded my knowledge in relation to modelling. My acknowledgement will not be complete

without mentioning Ir. A. M. van Lieshout who allowed me to undertake this research from my home

country. I further extend my acknowledgements to the WREM team in ITC as they all contributed in

one way or another to the completion of my studies.

My field work campaign for data collection could not be fruitful without great help from Prof. A.

Mruma, Mrs. E. Mcharo, Mr. S. Katanga, Mr. E. F. Nahozya, Mr. Frank and all staff of the Regional

Water Office in Dodoma and SHUWASA, special thanks to you all.

My classmates and all friends in the Netherlands have been part of my family during my study period

and they played a big role in this success. I thank you.

Finally I would like to send my sincere gratitude to my family. Special thanks to my husband who has

been taking care of our beloved kids and for encouraging me constantly for the whole period. Special

thanks to my brother Gasper Rwebugisa who encouraged me to knowledge.

Thank you all and be blessed.

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Table of contents

1. Introduction .........................................................................................................................1 1.1 Background.............................................................................................................................1 1.2 Water resources development in Dodoma region...................................................................2 1.3 Problem identification and justification of the research.........................................................2 1.4 Research objectives ................................................................................................................5 1.5 Hypotheses..............................................................................................................................5 1.6 Selected Studies and application of GIS, RS, CMB and Modeling........................................5 1.7 Outline of the thesis ................................................................................................................6

2. The study area......................................................................................................................7 2.1 Location of the study area.......................................................................................................7 2.2 Hydrology and climate............................................................................................................8

2.2.1 Rainfall ...............................................................................................................................8 2.2.2 Temperature........................................................................................................................8 2.2.3 Radiation ............................................................................................................................9

2.3 Evapotranspiration..................................................................................................................9 2.4 Drainage system....................................................................................................................11 2.5 Hydrogeological setting........................................................................................................11 2.6 Geology.................................................................................................................................12 2.7 Geomorphology ....................................................................................................................14

2.7.1 Soil types ..........................................................................................................................14 2.7.2 Topography.......................................................................................................................14

2.8 Vegetation cover/ land use....................................................................................................17 3. Methodology and data .......................................................................................................18

3.1 Methodology.........................................................................................................................18 3.2 Data.......................................................................................................................................19

4. Groundwater resource evaluation......................................................................................20 4.1 Introduction...........................................................................................................................20

4.1.2 Pumping test analysis .......................................................................................................20 4.1.3 Constant discharge pumping test analysis........................................................................22

4.2 Water quality evaluation.......................................................................................................25 4.2.1 Groundwater chemistry ....................................................................................................25 4.2.2 Water quality parameters..................................................................................................26 4.2.3 Sampling points ................................................................................................................26 4.2.4 Field sampling procedure ................................................................................................27 4.2.4 Laboratory determination .................................................................................................27 4.2.5 Chemical analysis results .................................................................................................27 4.2.6 Electrical conductivity and Total Dissolved Solids .........................................................30 4.2.7 Water type deduction........................................................................................................31 4.2.8 Previous hydrochemical studies done on the study area ..................................................32 4.2.9 Hydrochemical evolution of groundwater in the basin....................................................32

5. Groundwater recharge assessment.....................................................................................36 5.1 Introduction...........................................................................................................................36 5.2 Recharge mechanisms in the basin .......................................................................................36

iv

5.3 Recharge estimation methods .............................................................................................. 37 5.4 Chloride Mass balance method............................................................................................ 38

5.4.1 Introduction ..................................................................................................................... 38 5.4.2 Data requirement and calculation procedure................................................................... 39 5.4.3 CMB estimation results ................................................................................................... 40

5.5 The Thornthwaite and Mather method ................................................................................ 40 5.5.1 Introduction ..................................................................................................................... 40 5.5.2 The method calculation procedures................................................................................. 40 5.5.3 WTRBLN: A computer program to calculate water balance .......................................... 42 5.5.4 Preparation of model inputs............................................................................................. 43 5.5.5 Model execution and results............................................................................................ 44

5.6 Analysis of hydrograph of monitoring boreholes ............................................................... 46 5.6.1 Introduction ..................................................................................................................... 46 5.6.2 Relationship between rainfall and recharge .................................................................... 48

5.7 Recharge modelling by Earth............................................................................................... 49 5.7.1 Introduction ..................................................................................................................... 49 5.7.2 Model estimation results.................................................................................................. 50

5.7 General discussion ............................................................................................................... 51 6. Groundwater modeling ......................................................................................................54

6.1 Introduction.......................................................................................................................... 54 6.2 Conceptual model ................................................................................................................ 55

6.2.1 Boundary conditions........................................................................................................ 56 6.2.2 Stratigraphic units............................................................................................................ 57 6.2.3 Surface water body .......................................................................................................... 58 6.2.4 Sinks and sources of the modelled area........................................................................... 59 6.2.5 The modelled area ........................................................................................................... 59

6.3 Aquifer geometry ................................................................................................................. 60 6.4 The model code.................................................................................................................... 60 6.5 Data input for the model ...................................................................................................... 60 6.6 Model execution and calibration ......................................................................................... 60 6.7 Uncertainty of the model calibration ................................................................................... 62 6.8 Sensitivity analysis............................................................................................................... 63 6.9 Model results........................................................................................................................ 64

6.9.1 Recharge and transmissivity........................................................................................... 64 6.9.2 Simulated potentiometric levels ...................................................................................... 64 6.9.3 Water balance of the basin .............................................................................................. 68 6.9.4 Scenario analysis ............................................................................................................ 69

6.10 Two layers model development ........................................................................................... 69 7. Conclusions and recommendations ...................................................................................71

7.1 Conclusion .......................................................................................................................... 71 7.2 Recommendations................................................................................................................ 72

References.................................................................................................................................73 Appendices................................................................................................................................77

Appendix A: Meteorological data ..................................................................................................... 77 Appendix B: Hydrogeological data................................................................................................... 79

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Appendix C: Hydrochemical data and pumping test data .................................................................82 Appendix D: Recharge estimation data .............................................................................................88 Appendix E: Modelling ......................................................................................................................94 Appendix F: The study area ...............................................................................................................95

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List of figures

Figure 1.1 Well abstractions against time ............................................................................................... 4 Figure 1.2: Rainfall variation against time.............................................................................................. 4 Figure 1.3: Groundwater fluctuations with time (BH 234/75)................................................................ 4 Figure 2.1: Makutupora basin location map............................................................................................ 7 Figure 2.2: Variation of rainfall in the basin for the period of 1922 - 2006 ........................................... 8 Figure 2.3: Variation of temperature for the period of 2000 to 2006 ................................................... 10 Figure 2.4: Variation of radiation for the period of 2000 to 2006 ........................................................ 10 Figure 2.5: Variation of wind speed for the period of 2000 to 2006 .................................................... 10 Figure 2.6: Variation of PET in the basin for the period 2000 to 2006 ................................................ 11 Figure 2.7: Geological map of the study area (Source: GST Dodoma, Tanzania) ............................... 13 Figure 2.8: Structural map of the basin (Source: GST Dodoma, Tanzania) ......................................... 13 Figure 2.9: Display of red and black clays soils on top of granitic bedrock (Shindo, 1990)................ 15 Figure 2.10: Contour map of the basin (Source: SRTM DEM, 2007) .................................................. 16 Figure 2.11: Geomorphological map of the basin................................................................................. 16 Figure 3.1: Methodology flow chart ..................................................................................................... 18 Figure 4.1a: Drawdown against time for BH C5................................................................................... 21 Figure 4.1b: Drawdown against time for BH No. 147/75 ..................................................................... 21 Figure 4.1c: Pumping test analysis graphs for BH147 and BH C3....................................................... 24 Figure 4.2: Relationship between transmissivity and specific capacity................................................ 25 Figure 4.3: Field sampling points.......................................................................................................... 26 Figure 4.4: Graph of EC/100 against sum of anions (meq/l) ................................................................ 28 Figure 4.5: Graph of EC/100 against sum of cations (meq/l) ............................................................... 28 Figure 4.6: Nitrate concentration variation in different localities (2007 dataset)................................. 29 Figure 4.7: Nitrate concentration variation in the basin for the period 1983 - 2004. ........................... 30 Figure 4.8: TDS (mgl-1) against EC (µScm-1) for 2007 dataset ............................................................ 31 Figure 4. 9: Piper diagram for the 2007 dataset ................................................................................... 31 Figure 4.10: Piper diagram (Source: Shindo, (1989))........................................................................... 34 Figure 4.11: Piper Diagram (Source: Nkotagu, (1997)......................................................................... 34 Figure 4.12: Variation of TDS in the basin........................................................................................... 35 Figure 5.1: Termite mounds on the pediplain upland (plateau) above the fault scarp of Makutupora. 37 Figure 5.2: One of the termite towers on the mounds within the basin (Source: Shindo, 1990) .......... 37 Figure 5.3: Graphical representation of the water balance calculation results for the year 2000......... 45 Figure 5.4: Graphical representation of the water balance calculation results for the year 2005......... 46 Figure 5.5: Hydrograph of BH 234/75 monitoring borehole ................................................................ 46 Figure 5.6: Rainfall-recharge relationship for monitoring boreholes (BH 234/75) .............................. 49 Figure 5.7: Statistical analysis of recharge in the basin for the period of 1922 up to 2006.................. 49 Figure 5.8: Graph of simulated levels and observed levels against time. ............................................. 50 Figure 5.9: Graphical representation of the Earth modelling results .................................................... 51 Figure 5.10: The situation of the study area in relation to aquifer type distribution ............................ 53 Figure 6.1: Modelling protocol (adopted from Anderson and Woessner, 1992) .................................. 54 Figure 6.2a: Conceptualization of the study area.................................................................................. 55 Figure 6.2b: Considered situation during modelling............................................................................. 56

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Figure 6.3: General head boundary condition along the Little Kinyasungwe river...............................57 Figure 6.4: Stratigraphic units in the basin ............................................................................................58 Figure.6.5: Model discretization ............................................................................................................59 Figure 6.6: Graphical representation of measured against simulated heads (meters)............................61 Figure 6.7: Sensitivity analysis of the recharge flux and transmissivity showing effect of change on

the RMSE of the groundwater level.......................................................................................................63 Figure 6.8: Recharge and Transmissivity maps of the model ................................................................64 Figure 6.9: Potentiometric map of the calibrated model........................................................................65 Figure 6.10: Potentiometric map indicating the situation without any well abstraction .......................65 Figure 6.11: Cross section of the simulated heads versus DEM along point A to B.............................67 Figure 6.12: Conceptualization of two layers model with calibration results .......................................70

viii

List of tables

Table 4.1: Pumping test analysis results ............................................................................................... 23 Table 4.2: Aquifer parameters from 1988 pumping test analysis (Shindo, 1989). ............................... 23 Table 4.3: Reliability check of water quality data ................................................................................ 27 Table 5.1: Summarized recharge estimation data with results.............................................................. 40 Table 5.2: Characteristics of the land groups........................................................................................ 44 Table 5.3: Summary of the model results.............................................................................................. 44 Table 5.4: Summary of recharge flux estimation results ...................................................................... 53 Table 6.1: Water balance of the basin during well abstractions situation ............................................ 68 Table 6.2: Water balance of the basin during no well abstractions ...................................................... 68

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List of acronyms

CMB Chloride Mass Balance

DEM Digital Elevation Model

EC Electro conductivity

GIS Geographic Information system

GST Geological Survey of Tanzania

m.a.s.l Meters above sea level

PET Potential Evapotranspiration

RS Remote Sensing

SRTM Shuttle Radar Topographic Mission

TDS Total Dissolved Solids

WHO World Health Organization

GROUNDWATER RECHARGE ASSESSMENT IN THE MAKUTUPORA BASIN, DODOMA, TANZANIA

1

1. Introduction

1.1 Background

Water resources in the semi-arid areas of Tanzania are under increasing pressure as service is

extended to the increasing population, urbanization and land-use shift to more intensive production of

crops and livestock. Dodoma is the administrative capital city of Tanzania, with a population of

376,530 (2002 population census) and population growth rate of 2.3. Apart from its high population,

it is located in the central part of the country which is semi arid and hence it is vulnerable to water

scarcity. The only alternative is groundwater extraction as surface water sources are increasingly

becoming scarce (Chand et al., 2005). As millions of revenues are invested in water systems to meet

demands and since competition among water users grows, information about groundwater resources is

increasingly important.

Groundwater is a critical resource in much of semi arid or more arid regions, where in recent years,

the use of limited supply has grown year by year in proportion to the improvement of the living

standard (Chand et al., 2005; Shindo, 1990). Optimal management of water resources is difficult,

particularly in a semi arid climate where periods of moderate rainfalls and maximum recharge are

normally followed by periods of drought without significant contribution to groundwater storage

(Gieske, 1992). Forecasting of the amounts of recharge as a function of rainfall events is relatively

important as well as evaluation of long term replenishment of the observed groundwater resources.

Quantification of the rate of natural groundwater recharge is a basic prerequisite for efficient

groundwater resource management. It is particularly important in regions with a large demand for

groundwater supplies where such resources are keys to economic development. Sustainable

management of groundwater resources in undeveloped regions is one of the essential objectives for

the future especially in view of the rising demand for clean water by these fast growing communities

(Kniveton, 2006; Mende et al., 2007).

The sustainability of groundwater resources relies on both quantity and quality. Despite the fact that

the dissolved content in groundwater is normally higher than that of surface water, it is naturally

protected from surface pollution and therefore often potable as the subsurface provides natural

attenuation processes for common contaminants such as bacteria. Groundwater quality study remains

necessary due to continuing change in land use related to human activities.


2

1.2 Water resources development in Dodoma region

In the early period, the water supply in the area was mainly surface water. This was due to the fact

that the groundwater resources were insufficiently known. Imagi dam was built in 1929 at Imagi

stream for Dodoma water supply. In 1943 Imagi reservoir dried up not because of the size of reservoir

but due to increased demand caused by expansion of settlement, cultivation, deforestation,

overgrazing and insufficient rainfall. In 1944 a second reservoir for the Dodoma water supply was

built, the Msalatu dam at the Msalatu stream. An additional reservoir was also constructed at

Mkonze, 7 km south west of Dodoma. It is reported that water demand in the region was growing very

fast in the midfifties. For example water demand for the period of 1950 to 1951 increased by 16%

(Shindo, 1989). This was an indication that surface water source in the semi-arid area of Dodoma

could not meet the permanent water supply of the area.

Since 1948 the Makutupora basin has been the main groundwater supply source for the Dodoma town.

The water demand in the area has increased over time especially after the decision in 1978 to shift the

administrative capital of the country from Dar es Salaam to Dodoma. In order to meet such demand

more boreholes were drilled which resulted in rapid increase in the amount of groundwater extraction

per year.

Relatively few hydrogeological studies have been published in relation to this area. During the period

of 1988 - 1992, the ministry of water, energy and minerals in collaboration with ministry of

Education, government of Japan conducted hydrogeological research in the study area. Three reports

were published; (Shindo, 1989, 1990 & 1991), where hydrological characteristics of the study area

were explained.

1.3 Problem identification and justification of the research

Dodoma is located in the semi desert zone in central Tanzania. The area receives average rainfall of

550mm with an annual potential evapotranspiration of 2000 mm. The area suffers the problem of low

river flows and drying of water reservoirs caused by a long dry period from June to October. The only

reliable water source is the groundwater (Sandstrom, 1995).

Previous hydrogeological and geophysical investigations by Shindo (1989) provided information on

location of predominant recharge areas such as Chenene hills and uplands bordering the faults basin.

The annual recharge was estimated to be 10% of the average annual rainfall. Additionally, from

available well records, refer to Figure 1.3 groundwater level is going down. The drop may be due to

the high abstraction if compared to recharge flux or the climate change factor.


3

In order to optimally manage the groundwater resource, it is highly beneficial to carry out water

balance studies repeatedly. One of the components of the water balance equation that needs to be

determined is the rate of groundwater input as recharge. However, the amounts of abstraction in the

basin seem to be stable for quite some time (Figure 1.1). There are no recent recharge studies done in

the area. It is possible that due to various factors like land use and climate change, the previously

defined recharge flux has changed.

It should be kept in mind that the main replenishment of the aquifer in the study area is predominantly

rainfall, which is generally unreliable (Sandstrom, 1995). From Figures: 1.1, 1.2, and 1.3, it is obvious

that the abstraction for the shown period has the same average amount except for the period 2003 up

to 2004 when the amount of abstraction increased. Around the same period there is decrease in

amount of rainfall received in the area which caused a continuous drop of the groundwater level up to

around 2005.

Since reliability of the groundwater resource is determined by both quantity and quality, water quality

study is also important. This is so because the number of residents in the basin has increased along

with several changes in land use which may affect water quality (Kulabako et al., 2007). Increase in

population is always accompanied by increasing waste disposals along with agricultural activities that

may result in contamination of groundwater. This emphasizes the need for periodical groundwater

quality studies in the basin.

In this study, general assessment of water quality can help to indicate the presence of pollution in the

groundwater contributed by the above factors. Nitrate occurs naturally from mineral sources and

animal waste as well as anthropogenic as a by product of agriculture and human waste (Masetti et al.,

2007). In many polluted aquifers, nitrate is among the dominating contaminants, contributed by its

abundance, mobility and persistence in agricultural contaminants in many shallow groundwater

(Bohlke, 2002). Therefore detection of high nitrate content in groundwater with reference to

background concentration will be a good indicator of human pollution.


4

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Figure 1.3: Groundwater fluctuations with time (BH 234/75)

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1.4 Research objectives

The general objective of this study is to estimate groundwater recharge flux and assess groundwater

quality in the basin. The general objective will be met by achieving the following sub objectives:-

(i) Estimate recharge flux;

(ii) Develop groundwater flow model;

(iii) Determine the water balance of the basin;

(iv) Assess groundwater quality of the basin;

For achievement of the above sub objectives, the following research questions have been formulated:

(i) Can hydrochemical data be used to estimate recharge flux in semi arid areas?

(ii) How do geology, geomorphology, soils and topography contribute to recharge in the area?

(iii) What is the chemical composition of groundwater in the study area?

(iv) Is there any evidence of hydrochemical evolution?

(v) How can groundwater modelling with hydrologic field observation data enhance analysis and

assessment of impact of stress in groundwater flow?

1.5 Hypotheses

(i) The groundwater chemical composition is influenced by human and cattle derived pollution in

the area.

(ii) The lowering of groundwater levels is due to decrease in the amount of rainfall.

(iii) High well abstractions are affecting the groundwater inflow into the Little Kinyasungwe and

the Hombolo dam.

1.6 Selected Studies and application of GIS, RS, CMB and Modeling

The applicability of Geographical Information Science (GIS) and RS is increasing steadily especially

in the fields of hydrology and water resource development. Numeric Modelling together with GIS and

RS has been used by scientists in different places to solve different hydrological problems. The

Chloride Mass Balance (CMB) method is widely used to estimate recharge in semi arid areas (Conrad

et al., 2004; Gieske, 1992; Xu and Beekman, 2003).

GIS has emerged as a decision support system with capabilities of efficient data storage and

convergent analysis of spatial data from diverse sources. There is a strong synergy between remote

sensing and GIS, as remote sensing data is a major source of spatial information in GIS analysis. GIS

data can be used as ancillary information to support remote sensing data interpolation. Digital

elevation models (DEM) are extremely valuable in understanding the properties of the terrain (e.g.

slope, aspect, curvature, flow accumulation, stream ordering). Further, DEM can provide a wealth of

information about the geomorphic and hydrological properties of an area.


6

Saraf et al., (2004) used integrated remote sensing and GIS based methodology to demarcate

groundwater potential zones, identify recharge sites and suitable areas for future artificial recharge in

Silai watershed, West Bengal.

A steady state groundwater flow model developed for the lower part of the Walawa basin in Sri Lanka

(Amarasingha, 2007), was used to study the aquifer system behaviour. The author applied Arc View

3.3 and PMWIN as computer codes in the process of modelling. The developed model provided some

understanding on hydrological characteristics which were not known previously.

A shallow groundwater level in fractured aquifer system in Sweden (Rodhe and Bockgard, 2006), was

modelled by simple water balance recharge model using Darcy’s law. The model results supported the

hypothesis that the bedrock-groundwater at the site is fed by local recharge from the overlying soil

aquifer.

The CMB method has been applied in various places to estimate recharge flux from precipitation.

Houston, (2007) applied the method in Turi basin, within the Toconce formation volcanic-

sedimentary sequence and concluded that the recharge was 15,500m3d-1. It was pointed out that

hydrochemical analysis of groundwater and surface water provided valuable insights on sources and

evolution of water in the hydrologic system.

Hamza, (1993) modelled the Makutupora catchment by using Modflow and observed that

groundwater and surface flow direction are mainly influenced by geological structures in the basin.

However, the model was not validated due to time constraints and therefore it was recommended to

carry out another study in order to validate the model and determine recharge rates.

1.7 Outline of the thesis

Chapter two describes characteristics of the study area including location, hydrology, climate,

evapotranspiration, drainage, hydrogeological setting, geology, geomorphology and land use.

Chapter three describes materials and methodology adopted.

Chapter four describes the groundwater resource evaluation based on pumping test data and

hydrochemical data analysis

Chapter five presents the recharge mechanism, groundwater recharge estimation based on Chloride

Mass Balance, WTRBLN model, Earth model and Hydrograph analysis. Within the same chapter

water balance of the basin is also discussed.

Chapter six describes groundwater modelling starting from conceptualization up to the scenario

analysis and Chapter seven finalizes with the conclusions and recommendations of the study.


7

PROJECTION: UTMELLIPSOID: WGS 1984DATUM: WGS 1984


LegendTopographic low areasTopographic high areasDamRiversAquifer boundaryFaultsBoreholes





2. The study area

2.1 Location of the study area

Makutupora basin is located in the Dodoma region. The basin is located between latitudes 50 36’ 59”

and 60 14’ 50”S and longitudes 350 36’ 36” and 360 01’ 54”E. The main Makutupora well field

(depression) and its surroundings have an area of about 120 km2. The pumping station is

approximately 30km North of Dodoma town (Figure. 2.1)

Figure 2.1: Makutupora basin location map

8

2.2 Hydrology and climate

2.2.1 Rainfall

The region has an average annual rainfall of 550 mm. Rain falls in a single rainy season mostly from

November to April, there is virtually no rainfall from May to October (Sandstrom, 1995). Within the

study area, the distribution of rainfall varies locally, where there is a higher amount of rainfall in the

mountainous side, the Chenene hills, and relatively low rainfall in the western part (Shindo, 1990).

Therefore the rainfall distribution is influenced by the topography.

Figure 2.2: Variation of rainfall in the basin for the period of 1922 - 2006

In Figure 2.2, it is observed that from the year 2002 there is a decreasing trend in the amount of

rainfall received in the basin annually. The average rainfall for the period 1922 to 1999 is 560mm per

annum. From 2000 to 2006 the average rainfall is about 450mm per annum. This has caused a

reduction of 100mm per annum. This is accompanied by an increase in annual potential

evapotranspiration from 2000mm per annum to 2100mm per annum. It is expected that the variation

of amount of rainfall in the area is affecting the recharge flux rate in the area (Scanlon, 2006). The

recharge increases with increase in precipitation. Rainfall data are attached in Appendix A, Table A-1.

2.2.2 Temperature

The monthly maximum temperature is 260C while the monthly minimum is 210C. Figure.2.3. shows

the monthly temperature variation for the period of 2000 to 2006. The minimum temperature occurs

around July while the maximum temperature occurs around February and it starts to rise in August

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1922

1927

1932

1937

1942

1947

1952

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

Time (Years)

Ra

infa

ll in

tens

ity (

mm

)

0

200

400

600

800

1000

1200

1922

1927

1932

1937

1942

1947

1952

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

Time (Years)

Ra

infa

ll in

tens

ity (

mm

)


9

while it gets to its peak in February. Temperature data for the period of 2000 to 2006 is provided in

the Climate data as Table A-1 in Appendix A.

2.2.3 Radiation

Radiation governs the rate of evaporation. In the study area the radiation increases during the rainy

season with monthly maximum value of 25MJm-2 while the minimum is around 19.5MJm-2. The wind

speed varies from 1.67ms-1 to about 4 ms-1. The maximum wind speed occurs during the dry season

and decreases during the rainy season. Available data is provided in the Appendix A, as Table A-1.

Figure 2.4 and Figure 2.5 show variation of radiation and wind speed respectively for the period of

2000 to 2006. Available data is attached in the Appendix A as Table A-1.

From Figures 2.3, 2.4 and 2.5 there is a similar trend between temperature and radiation while a weak

trend is observed for wind speed. In some cases it does not vary in the same way with temperature and

radiation.

2.3 Evapotranspiration

Evapotranspiration is the process in which water is returned to the atmosphere by a combination of

evaporation and transpiration (Allen et al., 1998). The principal weather parameters affecting the

evapotranspiration are radiation, air temperature, humidity and wind speed. The evapotranspiration of

an area is of two types: potential evapotranspiration and actual evapotranspiration.

Potential evapotranspiration is the water loss that will occur under given climate condition with no

deficiency of water for the vegetation while actual evapotranspiration is the amount of water that

actually returns to the atmosphere depending on the availability of water. The evapotranspiration is

determined from weather data as it is difficult to measure from the field (Allen et al., 1998). In the

study area the evapotranspiration values are very high, averaging at 2000 mmyr-1 (Shindo, 1990). This

is about four times the annual rainfall. The high potential evapotranspiration values are experienced

during the early months of the year i.e. January and February. The minimum values are experienced

around the month of July. Figure 2.6 shows the variation of PET for the period of 2000 to 2006.

10

18

20

22

24

26

28

2000 2001 2002 2003 2004 2005 2006

Time (Years)

Te

mpe

ratu

re(0 C

)

200718

20

22

24

26

28

2000 2001 2002 2003 2004 2005 2006

Time (Years)

Te

mpe

ratu

re(0 C

)

2007

Time (Years)

Rad

iatio

n (M

Jm-2))

18

20

22

24

26

2000 2001 2002 2003 2004 2005 2006 2007

Time (Years)

Rad

iatio

n (M

Jm-2))

18

20

22

24

26

2000 2001 2002 2003 2004 2005 2006

Time (Years)

Rad

iatio

n (M

Jm-2))

18

20

22

24

26

2000 2001 2002 2003 2004 2005 2006 2007

1

2

2

3

3

4

4

5

2000 2001 2002 2003 2004 2005 2006

Time (Years)

Win

d S

peed

(m

s-1 )

20071

2

2

3

3

4

4

5

2000 2001 2002 2003 2004 2005 2006

Time (Years)

Win

d S

peed

(m

s-1 )

2007

Figure 2.3: Variation of temperature for the period of 2000 to 2006

Figure 2.4: Variation of radiation for the period of 2000 to 2006

Figure 2.5: Variation of wind speed for the period of 2000 to 2006


11

120.0

140.0

160.0

180.0

200.0

2000 2001 2002 2003 2004 2005 2006

Time (Years)

PE

T (

mm

)

120

140

160

180

200

220

2000 2001 2002 2003 2004 2005 2006 2007120.0

140.0

160.0

180.0

200.0

2000 2001 2002 2003 2004 2005 2006

Time (Years)

PE

T (

mm

)

120

140

160

180

200

220

2000 2001 2002 2003 2004 2005 2006

120.0

140.0

160.0

180.0

200.0

2000 2001 2002 2003 2004 2005 2006

Time (Years)

PE

T (

mm

)

120.0

140.0

160.0

180.0

200.0

2000 2001 2002 2003 2004 2005 2006

Time (Years)

PE

T (

mm

)

120

140

160

180

200

220

2000 2001 2002 2003 2004 2005 2006 2007

Figure 2.6: Variation of PET in the basin for the period 2000 to 2006

2.4 Drainage system

Drainage within the mountain range is structurally controlled by the NW and NE trending faults or

shear zones (Figure 2.8). There are no perennial rivers while ephemeral rivers flow only after heavy

rain storms in the rainy season (Hamza, 1993). Little Kinyasungwe is the main river in the area

originating from the NE part of the catchment (Figure 2.1). Its first part flows in the SW direction and

forms a swamp at the Makutupora wellfield which dries up during the dry season. After forming a

swamp at the well field, the river discharges to the Hombolo dam which is located on the SE part of

the basin.

In areas around Meia Meia, Mtungutu and Mkondai villages, streams disappear and reappear in the

alluvial fans and buried stream channels. In the West part of the wellfield, streams flow to the SW

direction and discharge in the swamp area, see Appendix E, Figure E-13.

2.5 Hydrogeological setting

The water table is at a depth of 40m in the well field area and remains at 2.5m depth around Meia

Meia, generally varying according to topography (Hamza, 1993). From Figure 1.3, it shows that the

aquifer is sensitive to rainfall, characterized by abrupt changes in groundwater levels during wet and

dry seasons. The groundwater level fluctuations data are of paramount importance in the

hydrogeological studies as they provide the information on the behaviour of the subsurface in relation

to water input or output into the system

Main formations are mbuga clay, clay, calcrete, sand, red silt, silt, sand gravel, weathered rock and

basement rock. The top most stratum is called mbuga clay with an average thickness of 40m while the

12

water bearing formations are calcrete, sand, gravel, weathered and fractured granite (Hamza, 1993).

Table B-1 in Appendix B shows the groundwater level fluctuations for the period 2001 to 2006 and

Table B-2 in the same Appendix gives the summary of the lithological logs of the boreholes in the

well field.

At the wellfield, the aquifer is leaky and overlain by up to 40 meters of clay and marl while the

aquifer is unconfined on the upland areas underlain by calcrete and granitic rocks (Shindo, 1990).

From the interpretation of the available borehole logs, there are variations in subsurface strata where

some of layers in various bore logs are missing. A good example is that, most of the logs that are on

the periphery of the well field the calcrete formations are missing. Figure 6.5 shows the stratigraphic

units of the area.

2.6 Geology

Dodoma area lies in a groundwater basin known as Hombolo basin. The basin is located in the

fractured crystalline basement area of the Dodoma Craton (Nkotagu, 1997). Chenene Mountains are

interpreted as a fault block. The mountains have the appearance of an uplifted plateau, and their

surface is thought to have been on a level prior to uplift relative to the flat lying country in the south.

The main topographic features of the area are NW trending Chenene Mountains, which exceed about

2000 m in elevation and the gently rolling plains dotted with inselbergs and "mbuga" with an

elevation of about 1100 m. The metasedimentary rocks in the belt are predominantly quartzites,

ferruginous quartzites, ironstones, micaceous quartzites, and quartzo-feldspathic schists. The well

field is formed by two grabens interconnected with each other. In the north, the Kitope graben extends

in NNE direction parallel to the NE part of the Little Kinyasungwe River. It is bounded by Zanka and

Kitope faults. South of this graben there is a Makutupora graben which follows the Mlemu fault

extending in the NNE direction. Both of them are downthrown for about 100m. The geological map

is provided as Figure 2.7 and structural map is provided as Figure 2.8. The base map is attached as

Figure F-13 in Appendix F.


13

LegendLegend

Sheared synorogenic graniteSynorogenic graniteRed soilsQuartz feldspathic schistClay soils “mbuga”AlluviumUndifferentiated soils

LegendLegend

LegendLegend



LegendLegend

Hombolo damFaultsRivers

Legend

LegendLegend

LegendLegend


Legend


Legend

Figure 2.7: Geological map of the study area (Source: GST Dodoma, Tanzania)

Figure 2.8: Structural map of the basin (Source: GST Dodoma, Tanzania)

14

2.7 Geomorphology

2.7.1 Soil types

There are three principal soil types in the study area: white sandy soil, red loamy soil and black clayey

soil. The dominant soil type is the white sand soil distributed in the upper and lower plateau with

many termite mounds (Shindo, 1989). These are well rounded and finely to coarsely grained. Black

soils cover the seasonal water logged swampy areas while red loam soils are distributed on the hilly

slope of basic rocks such as biotite, gneiss and dolerite dykes (Hamza, 1993). This is typical on some

parts of the upland slopes like Zanka and Mlemu faults. Figure 2.11 shows the geomorphological map

of the study area.

2.7.2 Topography

The topography is dominated by inselbergs and pediment plains underlain by Precambrian basement

rocks, and basin floor. In the area, three main features can be distinguished: mountains and hills,

uplands and lowlands. See Figure 2.10 which displays the contour map of the basin.

The plateau with inselbergs and termite mounds lie at an altitude ranging from 1110 m to 1175 m

around the Chenene hills on the NE part of the study area. The inselbergs are scarce in this basin. The

prominent relief of this region is the termite mounds while the entire surface is covered by termite

mounds. The termite mounds are bare and composed of very fine sands and silt. The diameter of the

termite mounds varies with a diameter of up to 10 m with a height of 2 up to 5m (Shindo, 1989). The

termite mounds are zones of high infiltration.

The uplands are erosional surfaces surrounding the lowlands. Gullies and ephemeral streams are seen

during the rainy season. They are divided into pediplain upland, upland and pediment slopes.

Pediplain upland is characterized by grey sandy soils, low drainage density and termite mounds.

Upland slopes occur as hill slopes (scarps) separating different pediplains and low lands. They are

characterized by granitic outcrop, deep dissecting streams and gullies with thin soil cover. Pediment

slopes are gentle slopes of erosion found on the foot of the feet of residual hills or mountains and

upland areas. They are covered by red loam and grey soils, see Figure. 2.9.

The lowland area has a considerable depth of sediments with depositional characteristics. It is divided

into swampy, alluvial fans and lowland clays. Swamps form during the rainy season on downthrown

sides of faults like at the wellfield area and around the Hombolo dam. Other swamps are found on the

pediplains NW of Meia Meia. Alluvial fans are found around Mtungutu, Mkondai and North of Zenka

covered by fluvial deposits. Alluvial fans consist of sandy and silt soils while silt clay covers most of

stream beds. Termite mounds with flat topography are typical in these areas with dug wells scattered


15

along the buried stream channels. The lowland clay occupies the Kitope graben area around the well

field located between 1060 to 1100 m.a.s.l elevation. It is characterized by mud cracks during the dry

season.

Figure 2.9: Display of red and black clays soils on top of granitic bedrock (Shindo, 1990)

Red soils

Black clay soils

Pediment

16

Figure 2.10: Contour map of the basin (Source: SRTM DEM, 2007)

Figure 2.11: Geomorphological map of the basin

Legend

Legend Legend

MountaneousareasPedimentsRed soilsHombolo damSwampy areasUpland slopesFaultsRivers

MountaneousareasPedimentsRed soilsHombolo damSwampy areasUpland slopesFaultsRivers


17

2.8 Vegetation cover/ land use

In the study area vegetation can be divided into three major types: woodlands, bush lands and

grassland (Hamza, 1993). About 30% of the Chenene hills on the northern part of the catchment are

covered by natural “miombo” woodland forest. The Upland represents the slightly uplifted low

relieved terrain bounded by slopes from the lowlands. The upland occupies the southern part of the

drainage area. 60% is occupied by bush land, thicket and shrubs, mainly acacia trees which are

combined with grasses during the rainy season. 5% in the lowland is covered by an association of

seasonal swamps with grassland. The remaining 5% is used for subsistence farming. However, due to

intense domestic animal grazing, perennial grasses have been depleted leaving bare grounds during

dry seasons with exception of the woodland forest on the northern part.

18

CHLORIDE

MASS

BALANCE

RELIABILITY

CHECK

GROUNDWATER

FLOW STEADY

STATE

AQUACHEM

SOFTWARE

CHEMICAL

LABORATORY

RESULTS

PIPER DIAGRAM

COMPARE WITH

HISTORICAL DATA

TO GET CHEMICAL

EVOLUTION

SENSITIVITY

ANALYSIS

GROUNDWATER FLOW

MODEL

CLIMATE DATASECONDARY

HYDROCHEMICAL

DATA

RECHARGE

FLUX

CONCEPTUAL MODEL

PUMPING TEST

DATA

DIGITIZED

MAPS

SATELLITE

IMAGE/

INFORMATION

YESNO

GENERAL

GROUNDWATER

ASSESSMENT

1989 dataset1997 dataset

WATER SAMPLES

WTRBLN

MODEL

MODEL CALIBRATION

(TARGET – HEAD)

WELL LEVELS

EARTH MODEL

GROUNDWATER

QUANTITY ASSESSMENT

GROUNDWATER

QUALITY ASSESSMENT

HYDROGRAPH

ANALYSIS

3. Methodology and data 3.1 Methodology

Figure 3.1: Methodology flow chart


19

The methodology applied is given in Figure. 3.1 and it is split into two parts. The first part is a

groundwater quantitative part covering the recharge estimation, groundwater modelling and water

balance while the second part deals with water quality. The groundwater recharge flux was

determined by the CMB, analysis of well hydrograph, analysis of relationship between recharge and

rainfall and WTRBLN model by Donker (1987) and recharge modelling by Earth model.

A conceptual model was prepared prior to groundwater model development. All information collected

from the satellite images analyses were incorporated in the conceptualization process. The collected

information includes fault systems and elevations. Field observation data include: pumping test data,

lithological borehole logs and water levels fluctuations. Model calibration was done through trial and

error procedure followed by sensitivity analysis. Imposing scenarios followed, which was done by

changing the amount of well abstractions and observing the water flow behaviour.

A water quality study was made by introducing the chemical analysis laboratory results in to the

AQUACHEM v.5.1 software. The Piper diagram provided the information about water type of the

basin. The (Shindo, 1989) and (Nkotagu, 1997) datasets were imported into Aquachem software to

produce the piper diagrams in order to get insight of the water type of the datasets. Then three datasets

were compared to obtain a hydrochemical evolution trend. Analysis of the 2007 dataset was done to

assess the general water quality status of the basin.

Thornthwaite and Mather (1955) water balance model was used to study the hydrologic regime of the

basin. Monthly climate data were used to calculate the annual water balance of the basin.

3.2 Data

Collection of available information in relation to the present study was done so as to contribute to the

aquifer assessment process. Data collected include Aster image generated on 02/04/2007 at 08:04:27,

SRTM data (90m resolution), climatic data, boreholes elevations, boreholes locations, groundwater

levels fluctuations from 2001 to 2006, pumping test data (constant discharge rate), groundwater

abstractions, boreholes lithological logs, geological map and hydrochemical data (primary and

secondary datasets).

20

4. Groundwater resource evaluation

4.1 Introduction

4.1.2 Pumping test analysis

Evaluation of the groundwater resource is regarded as the second stage after an exploration stage

(Freeze and Cherry, 1979). This stage encompasses the measurement of hydrogeologic parameters,

design and analysis of the wells together with the aquifer yield. This is an important section as it helps

in the sustainability assessment of the groundwater resource or basin under study since it reveals

where wells should be placed, how many, at which pumping rate, under which long term pumping

capabilities of the aquifer and which type of aquifer is present.

In this study, the evaluation of the basin was based on the pumping test data that was collected from

the study area during the field work campaign. Aquifer parameters like transmissivity, hydraulic

conductivity and specific capacity are determined. The aquifer parameters are important as they give

an understanding of groundwater flow in the system. Also it is obvious that one of the objectives of

the resource evaluation is the determination of the maximum possible pumping rates that are

compatible with the hydrogeologic environment where water is taken from. However, the pumping

test data available is based on the wellfield area, which is localized compared to the size of the basin

since boreholes are not equally distributed over the entire basin. Pumping tests were carried out under

a constant discharge rate and for relatively short duration without observation wells. There is no step

drawdown test available. Together with that there is little documentation in relation to the details of

the wells.

This analysis was carried out by AQUIFERTEST V.3.5. The program is designed to analyze data

gathered from pumping tests and slug tests (Rohrich, 2002). The available solution methods cover the

range of all types of aquifers like confined, unconfined and leaky aquifers. Analysis of the drawdown

against time curves for most of the data analyzed indicated the leaky aquifer type except data for BH

147/78 which indicated a more confined aquifer type (Kruseman and Ridder, 1983).

From Figure 4.1a shows that there is a sharp increase of drawdown at the beginning and later

drawdown becomes constant. This may indicate leaky behaviour either from the overlying clay or

from cavity storage. Alternatively the drawdown may have become constant because the water level

was drawn down to the pump position. In the latter case no conclusion can be drawn. From Figure

4.1b, the drawdown continues to increase with time. This behaviour reflects confined aquifer

conditions. From field observations and previous work, it seems that the basin is unconfined in the


21

6

7

8

9

10

1 10 100 1000

Time (min) (Log scale)

Dra

wd

ow

n (

m)

Time (min)

Dra

wdo

wn

(m)

6

7

8

9

10

1 10 100 1000

Time (min) (Log scale)

Dra

wd

ow

n (

m)

Time (min)

Dra

wdo

wn

(m)

0

5

10

15

20

25

1 10 100 1000

Time (min, log scale)

D/d

ow

n (

min

)

Time (min)

Dra

wdo

wn

(m)

0

5

10

15

20

25

1 10 100 1000

Time (min, log scale)

D/d

ow

n (

min

)

Time (min)

Dra

wdo

wn

(m)

recharge areas and it's leaky to confined around the wellfield area. This might be enhanced by

presence of course sands in recharge areas and around the wellfield area there is a clay layer of up to

40m overlying the aquifer.

Figure 4.1a: Drawdown against time for BH C5

Figure 4.1b: Drawdown against time for BH No. 147/75

22

4.1.3 Constant discharge pumping test analysis

The method used in analyzing the pump test is the Hantush Jacob (Walton). The method was

formulated for leaky aquifers. The assumptions based on the method are;

• The aquifer is leaky and has an apparent infinite extent

• The aquifer and the confining layer are homogeneous, isotropic, and of uniform thickness over

the area influenced by pumping

• The piezometric surface was horizontal prior to pumping

• The well is pumped at constant rate

• The well is fully penetrating

• Water removed from the storage is discharged instantaneously with decline in head

• The well diameter is small and well storage is negligible

• The leakage through the confining layer is vertical and proportional to the drawdown.

The Walton solution of the confined aquifer with leakage is provided as equation 4.1

dyyB

ry

yT

Qs

u

−−= ∫

∞

2

2

exp1

4π (4.1)

where:

=B

ruW

T

Qs ,

4π (4.2)

T

Sru

π4

2

= (4.3)

r

Kbb

K

B

r'

'

= (4.4)

KbcB = (4.5)

'

'

K

bc = (4.6)

where:

B- Leakage factor

c- Hydraulic resistance

K, K'- Vertical hydraulic conductivity of the aquifer and leak aquitard respectively

b, b’- Thickness of the aquifer and aquitard respectively

W(u, r/B) is the Leaky well function


23

BH IDAQUIFER

THICKNESS (m)

SWL (m)

YIELD

(m3hr-1)DURATION

(min)

SPECIFIC CAPACITY

(m2d-1)

TRANSMISSIVITY

(m2d-1)

88/75 41 26 16 840 1 9397/75 32 26 52 840 8 841169/75 57 23 79 840 10 671170/75 85 22 28 840 3 490

BH IDAQUIFER

THICKNESSSWL

PUMPING RATE

DURATIONTRANSMISSIVITY

(WALTON)LEAKAGE FACTOR

(m) (m) (m3hr-1) (min) (m2d-1)BH 325/01 (C1) 50 16.5 94 840 765 0.05

BH 327/01 (C3) 42 19.2 94 1020 720 0.5

BH 341/01 (C5) 45 28.9 77 1080 35 0.01

BH 332/01 (C8) 16 20.4 94 1200 42 0.5 - 1

BH 333/01 (C9) 44 20.8 94 1080 470 0.5 - 1

BH 123/75 10 17.8 52 1020 12 0.5

BH 147/78 40 23.5 73 1140 26 0.5

From equation 4.5, when K = 0 (non -leaky aquitards), then r/B = 0 and the solution reduces to the

Theis solution for a confined system.

This method was chosen as it has an advantage of indicating a confined type of aquifer together with

leaky aquifers after fitting a curve. A sample of analysed data is provided as Figures: 4.1a, 4.1b and

4.1c. Summarized results of the analysis are given in Table 4.1. The 1988 pumping analysis results

were taken from Shindo, (1989) and provided as Table 4.2. All graphs of analysis are provided in the

Appendix C, Figure C-1 to C-2.

Table 4.1: Pumping test analysis results

Table 4.2: Aquifer parameters from 1988 pumping test analysis (Shindo, 1989).

From the two tables above, the analysis indicates that the aquifer has a range of transmissivity values

from low to high values with hydraulic conductivity ranging from 1md-1 to higher than 16md-1.

However, the high variability of transmissivity values is causing heterogeneity in the system.

Transmissivity is controlled by hydraulic conductivity and aquifer thickness. Therefore, having high

hydraulic conductivity values along faults will be associated with high transmissivity values.

24

Figure 4.1c: Pumping test analysis graphs for BH147 and BH C3

BH 147/75

BH C3


25

y = 65.179x + 158.47

R2 = 0.722

0

200

400

600

800

1000

0 5 10 15

Specific capacity (m2d-1)

Tra

nsm

issi

vity

(m2 d-1

)


Tra

nsm

issi

vity

(m2

d-1 )

0

200

400

600

800

1000

0 5 10 15

y = 65.179x + 158.47

R2 = 0.722

0

200

400

600

800

1000

0 5 10 15


Tra

nsm

issi

vity

(m2 d-1

)


Tra

nsm

issi

vity

(m2

d-1 )

y = 65.179x + 158.47

R2 = 0.722

0

200

400

600

800

1000

0 5 10 15


Tra

nsm

issi

vity

(m2 d-1

)


Tra

nsm

issi

vity

(m2

d-1 )

0

200

400

600

800

1000

0 5 10 15

Transmissivity is often estimated by using specific capacity data when standard pumping test data are

not available or drawdown is stabilized early (Hamm et al., 2005). Specific capacity is used to

indicate the productivity of the well, defined as the discharge of the well divide by drawdown (Fetter,

2001). Figure 4.2 shows that the transmissivity and specific capacity have linear relationship with R2 =

0.72. The relation is determined by using (Shindo, 1989) dataset due to the low documentation of the

pumping test data collected. This relation has a formula provided as equation 4.7.

T = 158*65 +S

Q (4.7)

where Q = Discharge and S =Drawdown.

Figure 4.2: Relationship between transmissivity and specific capacity

However, the relation above should be applied with caution due to the limited number of samples

available. In this case the relation is valid only where the line exists and extrapolation of the line

should be avoided.

4.2 Water quality evaluation

4.2.1 Groundwater chemistry

Water quality is the composition of water as affected by natural processes and human activities

(Strickland et al., 1997). The major inorganic constituents of water originate when water in the form

of precipitation dissolves atmospheric gases such as carbon dioxide and reacts with minerals on the

surface of the earth (Chapman, 1996; Freeze and Cherry, 1979). The quality of groundwater depends

on the composition of the recharge water, the interactions between the water and the soil, soil-gas and

rocks with which it comes into contact in the unsaturated zone, and the residence time and reactions

that take place within the aquifer.

26

4.2.2 Water quality parameters

The following chemical parameters are considered during assessment of water quality. These

parameters include nitrate, Chloride, pH, acidity, alkalinity and electrical conductivity. According to

(WHO, 1993), the water quality parameters concentration range aims at safe guiding the health of

human being as well as other water use like irrigation, industry and domestic use.

4.2.3 Sampling points

Water samples were collected from deep groundwater boreholes and shallow dug wells. Deep

groundwater samples were collected from the well field area and shallow dug wells were collected

from the nearby village. Some samples were collected from private wells which were located outside

the well field area. A number of boreholes were randomly selected according to the sample

availability. Refer to Figure 4.3, showing sample localities and Appendix C, Table C-1: Field

measurements.

Figure 4.3: Field sampling points

BH C5

BH C8

BH C3BH C7DUG WELL P/S

BH C2DUG WELL VILLAGE

BH 55/82

BH C1

BH C9BH 147/78

PADRES’ COLLEDGE

St. GABRIEL

BH C1

PRIVATE WELL

GOOD HOPE

CPPS

Hon. SHEKIFSALECIAN SEMINARY

WACAPCHIN ASSEMBLIES OF GOD

BH C5

BH C8



BH 55/82

BH C1

BH C9BH 147/78

PADRES’ COLLEDGE

St. GABRIEL

BH C1

PRIVATE WELL

GOOD HOPE

CPPS



BH C5

BH C8



BH 55/82

BH C1

BH C9BH 147/78

PADRES’ COLLEDGE

St. GABRIEL

BH C1

PRIVATE WELL

GOOD HOPE

CPPS




27

4.2.4 Field sampling procedure

Water samples were taken from both productive and non-productive boreholes. For the non

productive boreholes, water was pumped until temperature, pH and electrical conductivity were

stabilized. The shallow groundwater was sampled from dug wells. Parameters which were determined

on the field are temperature, pH, electrical conductivity, TDS and salinity. Duplicate samples were

taken from each point for major ions determination in the laboratory.

4.2.4 Laboratory determination

Water samples were submitted to the Geological Survey of Tanzania (GST) laboratory for chemical

analysis and ten control samples were brought to ITC laboratory to be analyzed. The chemical

laboratory results of ITC control measurements and GST are given in Appendix C, Table A-2 and

Table: A-3 respectively.

4.2.5 Chemical analysis results

• Reliability check

In order to check the quality of measurements done in the laboratory there are various methods used to

indicate the correctness of the results. The following table shows the quality check results according

to Hounslow (1995).

Test Attention

value

Number of

samples

Comments

Anion – Cation balance

∑ ∑∑ ∑

+−

=anioncation

anioncationdiff *100%

>5%

16

The average of overall

samples is 12%

Measured TDS – Measured EC

7.055.0 <<meas

meas

EC

TDS

<0.55&>0.75

-

All samples are averaging at 0.5

++

+

+ KNa

K

>20% 1 All samples are within <20%

except 1 sample with 32%

−+

+

+ ClNa

Na

<50% 7 Except 7 samples,

the remaining are above 50%

−+

+

+ 24

2

2

SOCa

Ca

<50% - All samples are above 50%

Table 4.3: Reliability check of water quality data

Considering these quality factors in the table above, the quality of measurements is fair.

28

y = 0.96xR2 = 0.68

0

5

10

15

20

0 5 10 15 20

Sum anions (meq/l)

EC

(uS

/cm

)E

C (

µS

/cm

)

y = 0.96xR2 = 0.68

0

5

10

15

20

0 5 10 15 20

Sum anions (meq/l)

EC

(uS

/cm

)E

C (

µS

/cm

)

y = 1.07x

R2 = 0.56

0

5

10

15

20

0 5 10 15 20

Sum of cations (meq/l)

EC

(u

S/c

m)

EC

(µ

S/c

m)

y = 1.07x

R2 = 0.56

0

5

10

15

20

0 5 10 15 20

Sum of cations (meq/l)

EC

(u

S/c

m)

EC

(µ

S/c

m)

Figure 4.4: Graph of EC/100 against sum of anions (meq/l)

Figure 4.5: Graph of EC/100 against sum of cations (meq/l)

• Nitrate content

Regarding to all samples taken within the Makutupora basin, nitrate concentrations range between

0.89 mgl-1 and 31.87 mgl-1 for deep boreholes while for samples from shallow depth like dug wells,

the nitrate concentration is very low ranging from below detection limit to 0.44 mgl-1. However, the

nitrate concentration changes to higher values immediately outside the basin. The variation of nitrate

outside the basin is between 10.18 mgl-1 to 150.08 mgl-1.

The nitrate concentration is related to the activities around the area. A Good example is the St.

Gabriel Technical School’s sample which indicated the nitrate content of 150 mgl-1. Around the area

agricultural activities are conducted which are accompanied by application of fertilizers in the farms.

This is among other factors that are contributing to high concentrations of nitrate in the groundwater.

Some areas which indicated nitrate concentration below detection limit include the dug well in the


29

village. At this location, there are neither agricultural nor residential activities. The well was used by

villagers to get water for domestic use.

Water quality has been monitored, although irregularly, on a monthly basis since 1966. From

December 1988, nitrate concentrations raised from normal groundwater values of between 0.1mgl-1 to

10 mgl-1 up to more than 100mgl-1 in some boreholes. By June 1993 the level of nitrate reached 144

mgl-1 in some boreholes. As some of the production boreholes had a low nitrate concentration,

blending of water from different boreholes produced acceptable concentrations of less than 20 mgl-1.

This is in accordance to Tanzanian and WHO water standards.

In order to protect the Makutupora basin from human source pollution, various steps were taken to

minimize the problem, like prohibiting gardening in the vicinity of production boreholes, restriction of

watering livestock at the production boreholes. Boreholes surroundings were cleaned and maintained

to that status to date and then villagers were shifted to new residential area. Hypothesis number one

which says “The groundwater chemical composition is influenced by animal and human habitation in

the basin” is supported by this analysis. Increasing the number of residents in the basin which was

accompanied by grazing activities caused rising of nitrate content in the aquifer. Figure 4.6 shows the

variation of nitrate in the sampled localities.

Figure 4.6: Nitrate concentration variation in different localities (2007 dataset)

0

20

40

60

80

100

120

140

160

BH

C1

BH

C2

BH

C3

BH

C5

BH

C7

BH

C8

BH

C9

Priv

ate

wel

l- V

eyul

a

St.

Gab

riel v

eyul

a

Pas

sion

ist -

veyu

la

BH

MIU

JI

Dug

wel

l (vi

llage

)

Dug

wel

l P/S

CP

PS

Wak

apuc

hini

Msa

lato

St G

aspe

r P

/S

BH

117

/75

BH

55/

82

Sal

ecia

n B

H

Chu

rch

BH

147

/78

Sample localities

Con

cen

trat

ion(

mgl-1

)

Sample localities

Con

cent

ratio

n (m

gl-1)

0

20

40

60

80

100

120

140

160

BH

C1

BH

C2

BH

C3

BH

C5

BH

C7

BH

C8

BH

C9

Priv

ate

wel

l- V

eyul

a

St.

Gab

riel v

eyul

a

Pas

sion

ist -

veyu

la

BH

MIU

JI

Dug

wel

l (vi

llage

)

Dug

wel

l P/S

CP

PS

Wak

apuc

hini

Msa

lato

St G

aspe

r P

/S

BH

117

/75

BH

55/

82

Sal

ecia

n B

H

Chu

rch

BH

147

/78

Sample localities

Con

cen

trat

ion(

mgl-1

)

Sample localities

Con

cent

ratio

n (m

gl-1)

30

0

15

30

45

60

75

1980 1985 1990 1995 2000 2005

Time (Years)

Nitr

ate

con

cent

ratio

n (m

gl-1 )

0

15

30

45

60

75

1980 1985 1990 1995 2000 2005

Time (Years)

Nitr

ate

con

cent

ratio

n (m

gl-1 )

Available chemical laboratory results for monthly water monitoring were collected from the regional

water office. Data collected is for the period 1983 – 2004. However there is little documentation in

relation to water quality monitoring. The average periodical variation of nitrate concentration is

plotted in Figure 4.7.

Frequently deterioration of groundwater quality occurs during the wet season (Kulabako et al., 2007).

From Figure 4.7, the groundwater nitrate contamination can be related to the amount of groundwater

recharge which is predominantly rainfall in the basin. The total rainfall received in the year 1990 was

844 mm. The area receives an average rainfall of 550 mm per year with a standard deviation of 294

mm. This reflects the high nitrate concentration for the year 1993 which supports the source of nitrate

to be related to human activities within the area.

Figure 4.7: Nitrate concentration variation in the basin for the period 1983 - 2004.

(Source: Regional water office-Dodoma)

4.2.6 Electrical conductivity and Total Dissolved Solids

Electrical conductivity is a good estimator of the amount of total dissolved solids (TDS) in water.

TDS in drinking-water originate from natural sources, sewage, urban run-off, and industrial

wastewater. Concentrations of TDS in water vary considerably in different geological regions owing

to differences in the solubility of minerals. The conductivity increases with the concentrations of

TDS and varies as a function of the temperature.

Electrical conductivity is not included in either WHO guidelines or Tanzanian water standards. The

electrical conductivity was ranged from 127.2 to 1582 µScm-1 while TDS ranges from 61.4 to750 mgl-

1. Figure 4.8 shows the relationship between the two parameters in the area. EC and TDS have a good

correlation R2 = 0.99.


31

y = 0.47x

R2 = 0.99

0

200

400

600

800

0 500 1000 1500

The maximum content of TDS in groundwater is 750 mgl -1. This indicates that the Makutupora basin

contains fresh water according to simple groundwater classification based on TDS content by Freeze

and Cherry, (1979).

Figure 4.8: TDS (mgl-1) against EC (µScm-1) for 2007 dataset

4.2.7 Water type deduction

The chemical laboratory results for all sampled points were imported in to AQUACHEM v.5.1

software. Aquachem is a software package developed specifically for graphical and numerical

analysis and modelling of water quality data. The Piper diagram for 2007 dataset is shown in Figure

4.9. Groundwater in the basin is highly mineralized with calcium and magnesium as cations and

bicarbonate as anion. The water type deduced from the piper plot is CaHCO3 water type, typical of

shallow fresh groundwater.

Figure 4. 9: Piper diagram for the 2007 dataset

EC (µScm-1)

TD

S (

mgl

-1)

32

4.2.8 Previous hydrochemical studies done on the study area

Groundwater quality studies were made by Shindo (1989) and rain water hydrochemical analysis was

made by Shindo, (1990). According to Shindo (1989), the analysis was carried out for both

unconfined and confined groundwater. For samples collected within the well field, the groundwater

composition was between Ca + Mg and Na + K water type while the dominant anion was bicarbonate

with the chloride percent higher than that of soil water.

Groundwater composition indicated that chloride concentration range between 27 and 143.5 mgl-1

with a standard deviation of 28.5 mgl-1. Generally higher concentrations were detected at high depth

and increasing with a direction of groundwater flow. The dataset is attached as Appendix C, Table C-

4.

The 1990 dataset contains rain water chemical laboratory analysis. Samples were collected from

different sites including Meia Meia and Chihanga where all localities are within the Makutupora

basin. The chemical composition is dominated by low magnesium content and high bicarbonate. The

chloride content in rain has a standard deviation of 0.3, ranges between 0.5 and 1.1mgl-1 from the

mean. The average concentration was 0.8mgl-1. The rain water is considerably dilute and the study

area is very far from the Indian Ocean about 400km. Table C-5 in Appendix C shows the dataset.

Another groundwater quality study was done by Nkotagu (1997). Water samples were collected from

dug wells, shallow wells, private wells and boreholes. Most of the analyzed samples indicated to be

highly mineralized and dominated by NaCl water type. The total dissolved salts were over 1500 mgl-1.

The average chloride content in the samples is 332.2 mgl-1 with a minimum content of 16.4 mgl-1 and

the maximum of 1104.2 mgl-1. It is most likely that the groundwater composition reflected the

pollution of the 1993 period. The total dissolved salts were high thus characterizing a brackish water

type.

The increase of chlorides in the groundwater might be caused by human activities around the area

which involved a lot of salts like NaCl to penetrate to the saturated zone which resulted to high

sodium and chloride content associated with high TDS values in the 1997 study. Table C-6 in

Appendix C, shows the 1997 dataset.

4.2.9 Hydrochemical evolution of groundwater in the basin

In this analysis of groundwater chemical evolution, three datasets of Shindo (1989), Nkotagu (1997)

and 2007 dataset collected during field work were analyzed.


33

In recharge areas the soil zone undergoes a net loss of mineral matter to the flowing water (Freeze and

Cherry, 1979). As groundwater moves along flow lines from recharge to discharge zones, its

chemistry is altered by the effect of various geochemical processes. Groundwater chemical evolution

can be attributed mainly by two factors namely flow path and the travel time.

In the study area, the underlying bedrock is a granitic rock which is composed of minerals like Quartz,

Feldspar and Biotite. During weathering processes various cations and silicates are produced which

result in the groundwater hydrochemical evolution.

The general trend of chemical evolution is towards the composition of sea water. Freeze and Cherry

(1979) suggested the following major ion evolution sequence:

Travel along the flow path

HCO3- HCO3

- + SO42-

SO42- + HCO3

- SO42- + Cl- Cl-+ SO4

2-

Increasing age

According to the Shindo (1989) dataset, the groundwater was dominated by bicarbonate as anion

ranging between 60 and 80% while chloride was 20 and 60%, hence bicarbonate was dominant anion.

Dominant cation was Na + K with water type between Ca + Mg and Na + K.

As per Nkotagu (1997) dataset, the dominant anion was chloride and dominant cation was sodium,

with NaCl water type. The analyzed samples composition has scattered more or less over the entire

diamond grid thus differing from the 1989 dataset compositionally.

The 2007 dataset indicates the dominant anion to be bicarbonate while the dominant cations are

calcium followed by magnesium. The water type is CaHCO3, typical shallow fresh water type.

Chloride is low as it can be seen in the Figure 4.9.

The chemical evolution that took place in the basin might be indicated by the two datasets of 1989 and

2007. The 1997 dataset is disregarded in the evolution analysis as it might reflect the pollution period

of the nineteen nineties. The groundwater of the basin seems to evolve from less bicarbonate to more

bicarbonate composition. The cations composition changes from Na + K to Ca and Mg.

However, in order to get the temporal as well as spatial variation in solute concentrations, the TDS

contents of the two datasets were compared graphically. The two sample datasets indicated the similar

trend of TDS contents, where spatial variation is identified with no temporal variation. In the

localities that indicated low TDS contents, samples were collected at shallow depth like dug well for

34

the 2007 dataset and at a depth of 25m for the 1989 dataset. Figure 4.12 shows the graphical

representation of TDS distribution. Additionally, TDS content increases together with geochemical

evolution Butler, (2007).

The evolution trend of the basin matches with the Freeze and Cherry (1979) evolution sequence.

Therefore it can be concluded that the groundwater of the basin is considerably young within the first

evolution step.

Figure 4.10: Piper diagram (Source: Shindo, (1989))

Figure 4.11: Piper Diagram (Source: Nkotagu, (1997)


35

Figure 4.12: Variation of TDS in the basin.

50

200

350

500

650

800

TD

S (

mg/

l)

1989_dataset

2007_dataset

Sample

36

5. Groundwater recharge assessment

5.1 Introduction

The groundwater recharge as defined by Freeze & Cherry (1979) is the entry into the saturated zone

of water made available at the water table surface, together with the associated flow away from the

water table within the saturated zone. In a broad sense groundwater recharge may be defined as

addition of water to the groundwater reservoir. There are mainly two types of recharge: artificial and

natural groundwater recharge. This study deals with natural groundwater recharge where the source of

recharge is rainfall.

Groundwater recharge in the basin is controlled by various factors like: climate, geomorphology and

geology. Climate plays a major role in controlling recharge as shown by differences in sources and

rates in humid and arid areas (Scanlon et al., 2002). Climate controls hydrological behaviour of an

area especially the rainfall distribution and net evaporation. Geomorphology involves topography,

vegetation and soil types. Variation in geomorphology reflects differences in topography, soils and

vegetations which in turn affect recharge. In the Makutupora area, recharge generally is considered to

occur in topographic high areas while discharge occurs along the river and in the topographic low

areas at the Hombolo dam. Vegetation cover is important in assessing the recharge potential at a site.

In the basin it was discovered that recharge flux was higher in areas covered by shrubs and bushes

than in areas occupied by woodland and grasses (Hamza, 1993). Geology plays an important role in

fractured rock formations where it enhances preferential flows through the interconnected fractures.

5.2 Recharge mechanisms in the basin

In the study area, previous studies done by Shindo (1989, 1990 and 1991) indicated that recharge

takes place in the Chenene hills located on the NE part of the study area. There are various factors

contributing to this conclusion from the field observation point of view and available satellite image

analysis.

On the hills much runoff is created which infiltrate to the ground at the hill slope areas. Due to the

presence of alluvial fans with coarsely grained sands, enhanced high infiltration rate occurs.

Moreover, because of orographic effects occurring around Chenene hills, the hilly areas receive

comparatively high rainfall as compared to the flat part of the basin.

In fractured rock, flow is often localized in a few main flow paths that control most of the

hydrological response of the aquifer (Borgne et al., 2007). On the other hand, the structure system on

the hills contributes to accumulation of runoff from rainwater on the hill slope areas where the Little

Kinyasungwe River originates. The dominant fracture and fault trending direction is NE.


37

Additionally, on the pediment area there is a number of termite mounds (Shindo, 1989). The termite

mounds are enhancing the recharge process by acting as preferential flows of rain water to infiltrate to

the subsurface and therefore escape the evapotranspiration. This is also termed as bypass recharge

(Rushton, 2003). The termite mounds are also found on the upland slopes adjacent to the fault

systems. This reduces the amount of runoff as much water infiltrates to the subsurface.

Figure 5.1: Termite mounds on the pediplain upland (plateau) above the fault scarp of Makutupora

(Source: Shindo, 1990)

Figure 5.2: One of the termite towers on the mounds within the basin (Source: Shindo, 1990)

5.3 Recharge estimation methods

There are many methods available for quantifying recharge depending on different processes and

sources of recharge. A reliable estimation of recharge in hard rock aquifer is difficult in relation to

wide spatial and temporal variation in hydrological and hydrometeorological conditions (Chand et al.,

38

2005). Each method has its own limitations in terms of applicability and reliability. The reliability of

the recharge estimation method will be determined by the objective of the study (Scanlon et al., 2002).

Water resource evaluation requires information on recharge at a large spatial and temporal scale while

assessment of aquifer vulnerability to pollution requires detailed information at a local and short time

scale (Xu and Beekman, 2003).

Traditional ways of studying recharge are observation of water level fluctuations in boreholes and

monitoring climatological factors such as rainfall, sunshine, wind and temperature changes (Gieske,

1992). These methods require a well distributed network of monitoring wells with automated

equipments for recording measurements, which will be efficient without gaps. In most of the

developing countries, groundwater level data availability is a problem. Usually there are a lot of gaps

which make it difficult to get reliable recharge information by using the traditional method. More

recently chemical and physical ways of determining recharge have been developed and employed with

good recharge estimation results. Among the promising recharge estimation methods for the semi arid

areas, is the Chloride Mass Balance (Bekele et al., 2003; Scanlon et al., 2002; Xu and Usher, 2006).

5.4 Chloride Mass balance method

5.4.1 Introduction

The Chloride Mass Balance (CMB) method was developed by Erickson & Kunakasem in 1969. The

method is based on the assumption of conservation of mass between the input of the atmospheric

chloride and the chloride flux in the subsurface (Gieske, 1992; Xu and Beekman, 2003). Since

chloride is a conservative tracer, water evaporation and plant uptake by transpiration concentrate

rainwater derived chloride in the soil. The profile of chloride in the soil varies with land use. In areas

of native vegetation under arid conditions, the concentration of chloride typically forms a bulge in the

root zone or near the surface. The rate of downward solute transport is determined by rainfall intensity

and is accompanied by solute concentration due to evaporation processes. Some of the solutes may be

taken by plants tissues or taken through mineral precipitation and adsorption and others may be added

to the system by decay of plant materials and weathering activities.

Groundwater recharge estimated from the mass balance of chloride assumes steady-state conditions;

therefore, it is most applicable to areas under native vegetation or cleared areas that have reached

equilibrium (Bekele et al., 2003). However, this will be valid only when there are no additions from

external sources like fertilizers or weathering products which might be associated with a significant

amount of chloride.


39

The chloride concentration in groundwater may originate from different flow components in the

unsaturated zone, where the calculation of groundwater recharge rate using chloride concentrations of

groundwater results in total recharge rate.

The formula for calculating recharge is;-

)(

)(*

gw

pT Cl

DClPR

+= (5.1)

where;

RT = Areal recharge (mm/year)

P = Average annual precipitation (mm/year)

Cl(p) = Chloride content in precipitation (mg/l)

D = Dry deposition of chloride measured during the dry season (mgm-2year-1)

Cl(Choi et al.) = Harmonic mean of chloride concentrations in groundwater (mg/l)

However, in this study there is no documentation in relation to dry chloride deposition (D) on the

study area. Therefore it is assumed to be zero.

The CMB method for the saturated zone is useful in areas where data on groundwater levels are

lacking. The CMB method has various limitations like being inapplicable in areas underlain by

evaporates or areas where up coning or mixing of saline groundwater occurs (Xu and Beekman,

2003). Additionally, the method should be applied with great caution in areas close to the sea where

chloride content in rainfall is highly variable. In fractured rock system applicability where there is

input of chlorides through weathering products or anthropogenic influences, the CMB may

underestimate the recharge of the area.

5.4.2 Data requirement and calculation procedure

The data used for chloride content in groundwater is the Shindo dataset (1989). The chloride content

in groundwater has a standard deviation of 28.5 mgl-1 and a mean of 70.78 mgl-1.The rain data source

is from Shindo, (1990). The chloride content in rain has a standard deviation of 0.3 mgl-1 with a mean

of 0.8 mgl-1. All datasets are provided in Appendix C as Table C-5, Shindo (1990) and Table C-4 is

Shindo (1989) dataset. More description on the datasets is given in section 4.2.9. Table D-1 in

Appendix D shows the calculation procedure with a recharge flux estimate. A summarized table is

provided as Table 5.1 below.

A sample column represents the sample source, the depth represents the depth at which the samples

were collected, Cl(rain)min represents the minimum chloride content from the mean value in the rain

sample, Cl(rain)max represents maximum chloride content from the mean value in the rain sample

40

average chloride concentration in the rain samples, Cl(gw.)min represents the min chloride content in

the groundwater sample from the mean while Cl(gw)max represents the maximum chloride content in

a sample from the mean value, P (annual) represents the average annual rainfall in the study area and

Rech_min represents the minimum recharge flux while Rech_max represents the maximum recharge

flux estimated recharge at the particular point.

Cl (Choi et al.)min Cl (gw)max

Cl (rain)min

Cl (rain)max P(annual) Rech_min

Rech_max

mgl-1 mgl-1 mgl-1 mgl-1 mm mmyear-1 mmyear-1

26.6 143.5 0.46 1.14 550 4.49 10.44 Table 5.1: Summarized recharge estimation data with results

5.4.3 CMB estimation results

The estimated recharge flux is ranging from about 5 to 10 mmyear-1, averaging at 1.3% of the annual

rainfall. However the estimation method might underestimate the recharge flux due to various reasons

like neglecting the dry chloride deposition in the study area. In the calculations it was assumed that

the dry chloride deposition is zero due to lack of records on dry chloride deposition from the study

area. In Botswana, a recharge study by Gieske, (1992) discovered that chloride deposition was about

20% of total dry chloride deposition. Moreover, from Figure 4.7 nitrate concentration started to rise in

1985. In most cases the increase of nitrate in groundwater is associated by anthropogenic activities in

the area which rises along with chloride concentrations. As it should be taken in mind that chloride is

a conservative element, it takes a very long time to get rid of it in the hydrologic system. All these

cases could be the factors to elevate the chloride content in the system and hence resulting to

underestimation of the recharge flux.

5.5 The Thornthwaite and Mather method

5.5.1 Introduction

The Thornthwaite-type monthly water balance models are lumped conceptual models that can be used

to simulate steady state seasonal (climatic average) or continuous values of watershed or regional

water input, soil moisture and evapotranspiration (Dingman, 2002). It is a soil moisture budget

approach of estimating water balance. The method allows computation of groundwater recharge

(Dunne and Leopold, 1978). Input for the model includes monthly values for precipitation and

potential evapotranspiration. Important parameters include latitude, crop factor and the water holding

capacity of the depth of soil for which the balance is to be computed (Calvo, 1986).

5.5.2 The method calculation procedures

The water balance model simulates a monthly total water runoff from the catchment and hence

estimates the total runoff accumulated in the catchment (Kumar et al., 2005).


41

The model assumes that certain fixed percent of rainfall leaves the area as direct runoff (DRO). This

percent is used to obtain the direct storm runoff coefficient (C1) where the remaining coefficient of

rainfall is called the effective rainfall (Peff). If Pi is the amount of rainfall received in a particular

month, then

ii PCDRO = (5.2)

where i = month number.

iieff DROPP −= (5.3)

A portion of Peff is returned to the atmosphere in the form of evapotranspiration. The remaining

portion is known as surface recharge (SRECH). The difference of Peff and potential evapotranspiration

is (ETp) is available for infiltration in to the soil, (if Peff>Etp).

ieffi ETPSRECH −= (5.4)

When SRECH is positive and the soil is not yet at its WHC (Water Holding Capacity), SRECH will

be used up to fill up the SM (Soil Moisture), which is defined by the following formula:

iii SRECHSMSM += −1 (5.5)

After the SM reaches the WHC, the remaining part is available for runoff either as a groundwater

recharge or as surface runoff. This calculation starts from the first wet month.

When SRECH is negative, i.e Peff is less than ETp water is withdrawn from SM. This results into the

exponential depletion of the SM defined by the following formula:

WHC

APWL

i WHCSM = (5.6)

where APWL is the Accumulated Potential Water Loss which is the accumulation of negative values

for the dry season only. It describes the dryness of the soil.

For months with deficit of water (SRECH <0), APWL is calculated by:

iii SRECHAPWLAPWL −= −1 (5.7)

While months with surplus of water (SRECH>0), the APWL is equal to zero indicating no dryness in

the soil.

When Peff is higher than ETp, actual evapotranspiration (ETa) is equal to potential evapotranspiration

(ETp), else it is computed through the following formula:

ieffiai SMPET ∆−= (5.8)

where SM∆ is the difference in SM between the current and the previous month.

The Soil moisture deficit is the difference between the ETp and ETa within the same month.

iaip ETETDeficit −= (5.9)

The soil moisture surplus is the difference between the effective rainfall and the sum of SM∆ and

(ETa). This is the excess rainfall when the soil layer under consideration is saturated with water.

42

[ ]aiieffii ETSMPSURPLUS +∆−= (5.10)

The SURPLUS water percolates into the soil layer and is added to DETENTION. The total

SURPLUS water of the current month plus the DETENTION from the previous month constitutes the

total available water for subsurface runoff (TARO).

iii DETENTIONSURPLUSTARO += (5.11)

The subsurface storage acts as a buffer and causes a delay in groundwater flow (GWF). A fixed

percent of groundwater in the storage will become GWF and the remaining will be detained until next

month the (Meijerink et al., 1994).

5.5.3 WTRBLN: A computer program to calculate water balance

WTRBLN is the computer program that calculates water balance based on the basis of long term

average monthly precipitation, potential evapotranspiration and combined soil and vegetation

characteristics according to the Thornthwaite and Mather method (Donker, 1987).

The following inputs are needed by the WTRBLN model:-

• Direct runoff: which is entered as average monthly figures which will be subtracted from

the monthly rainfall figures

• Reference potential evapotranspiration and the Kc factors: The WTRBLN program uses

Kc factors to convert reference potential evapotranspiration figures to the actual crop

potential evapotranspiration by the following relation;

Tca EKE *= (5.12)

Ea = Actual crop potential evapotranspiration (mm)

Kc = Crop coefficient

ET = reference crop potential evapotranspiration

• Water capacity of root zone in mm

• Precipitation: 12 long term average monthly average precipitation values

• Potential evapotranspiration: 12 long term potential evapotranspiration values


43

5.5.4 Preparation of model inputs

The basin contains three main groups of vegetations: woodland and grasses, shrubs, bushes and crops,

and lastly are grasses which occupies the swampy areas.

• Kc values

Due to scarcity of information in relation to spatial distribution of different vegetations, the crop

factor was assigned based on previous observations and studies. The details on Kc values for various

land cover types are provided by (Doorenbos and Kassam, 1986). From previous studies

(Gebreegziabher, 2004; Hamza, 1993) where the study areas have more or less similar climatic

characteristics and vegetation types, an average Kc value of 0.7 was assigned for the catchment.

• Reference potential evapotranspiration

The most reliable method for calculation of reference PET is the Penman Monteith formula. But due

to lack of humidity data, the method could not be applied. The monthly reference PET values were

calculated by the Radiation method. The method is recommended in areas where available climatic

data include measured air temperature, sunshine hours, or radiation (Doorenbos and Pruitt, 1984). The

formula is provided as equation 5.13

( )RsWcETO .= (5.13)

where,

ETo = Reference crop evapotranspiration in mm per day for the period considered

Rs = Solar radiation in equivalent evaporation in mm/day

W = Weighing factor which depends on temperature and altitude

c = Adjustment factor which depends on mean humidity and daytime wind conditions

The weighing factor (W) is obtained from the relationship between temperature and altitude. The

values of W as related to temperature and altitude are provided in Appendix D as Table D-2. While

the adjustment factor (c) is given by the relationship between the radiation term (W*Rs) and reference

crop evapotranspiration (Kniveton, 2006). It depends greatly on general levels of mean relative

humidity (RHmean) and daytime wind (07.00-19.00 hours) at 2m height above the soil surface.

Figures showing the relation with values are available in Doorenbos and Pruitt, (1984).

The computed evapotranspiration values are attached in Appendix D, Table D-3.

• Water Holding Capacity

The water holding capacity value was assigned according to the rooting depth and water capacity of

root zone characteristics of the land groups. An average value of 150 mm was assigned for the entire

catchment. General characteristics of land groups are provided as Table 5.2

44

LAND COVER GROUP SOIL TYPE ROOTING

DEPTH FIELD

CAPACITY WATER CAPACITY

OF ROOT ZONE

m mm/m mm SWAMPS CLAY 0.5 150 75 WOODLAND FINE SAND 0.15 200 30 SHRUBS, BUSHES AND CROPS FINE SAND 0.5 100 50

Table 5.2: Characteristics of the land groups

5.5.5 Model execution and results

The WTRBLN model was run and the results obtained are attached in Appendix D, Table D-3 to

Table D-9. The summarized model results are provided as Table 5.3. The results indicated no

moisture surplus during dry years and surplus was obtained during the wet years. Dry years are

considered to be years with amount of annual rainfall less than average rainfall which is 550mm per

annum. In this case from 2003 to 2005 the amount of annual rainfall was less than the average annual

rainfall. The remaining years had soil moisture surplus.

Year Precipitation Surplus Runoff GWR mm mm mm mm

2000 739.60 24.00 12.00 12.00

2001 574.70 21.00 10.50 10.50

2002 604.01 61.00 30.50 30.50

2003 445.65 0.00 0.00 0.00

2004 371.30 0.00 0.00 0.00

2005 26.50 0.00 0.00 0.00

2006 609.25 24.00 12.00 12.00

Average 481.57 18.57 9.29 9.29 GWR= Groundwater recharge

Table 5.3: Summary of the model results

Graphical representation of the water balance calculation results is provided for the wettest year i.e

2000 as Figure 5.3 and the extremely dry year 2005 as Figure 5.4. From both Figures it can be seen

that the soil moisture is very low to zero during the dry months starting from around June to October.

But the soil moisture starts to rise one month after the rain season starts depending on the rainfall

intensity. After the maximum soil moisture is reached, the basin receives moisture surplus.

Part of the moisture surplus drains to the groundwater body and eventually to streams and part

remains in the soil and is carried over to the next month. In the study area there is no proper data in

relation to runoff coefficients. According to Thornthwaite and Mather, in case no field data are

available in relation to runoff coefficient, it is recommended that for big catchments 50 percent of the


45

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

50

100

150

200

250

300Precipitation

Soil Moisture

ETa

Moisture surplus

Am

oun

t (m

m)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

50

100

150

200

250

300Precipitation

Soil Moisture

ETa

Moisture surplus

Am

oun

t (m

m)

Time (Months)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

50

100

150

200

250

300Precipitation

Soil Moisture

ETa

Moisture surplus

Am

oun

t (m

m)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

50

100

150

200

250

300Precipitation

Soil Moisture

ETa

Moisture surplus

Am

oun

t (m

m)

Time (Months)

surplus water is available for runoff and the remaining is detained in the subsoil as groundwater

recharge Dunne and Leopold, (1978).

The soil moisture surplus is obtained when the annual rainfall reaches the average annual rainfall

amount. This indicates that it is at such conditions when groundwater recharge takes place. An

example is in the year 2001 and 2002 where the increase in groundwater level was observed, see

Figure 1.2 and 1.3. The remaining period shows a recession of the groundwater level indicating no or

less significant recharge took place in those years. This analysis supports hypothesis number two,

which says “Lowering of water level is due to decrease in the amount of rainfall”. As it shows that

recharge depends on the amount of rainfall received in the basin. As abstraction rate is constant,

decrease in the amount of recharge causes to lowering of groundwater levels. An average recharge is

about 9mm per annum, see Table 5.3.

The Thornthwaite and Mather method accounts for groundwater recharge after the water holding

capacity has been reached. In the basin there is presence of preferential flows created by faults and

termite mounds in the pediment areas. Under such situations the method might under estimate

groundwater recharge. However the method indicates a hydrologic water balance of the basin for

entire period of the year.

Figure 5.3: Graphical representation of the water balance calculation results for the year 2000

46

Time (Months)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

5

10

15

20

25

30

Precipitation

Soil Moisture

ETa

Moisture surplus

Am

ount

(m

m)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

5

10

15

20

25

30

Precipitation

Soil Moisture

ETa

Moisture surplus

Am

ount

(m

m)

Time (Months)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

5

10

15

20

25

30

Precipitation

Soil Moisture

ETa

Moisture surplus

Am

ount

(m

m)

Jan

Mar

May Jul

Sep

Nov

Precipitation

Moisture surplus0

5

10

15

20

25

30

Precipitation

Soil Moisture

ETa

Moisture surplus

Am

ount

(m

m)

Figure 5.4: Graphical representation of the water balance calculation results for the year 2005.

5.6 Analysis of hydrograph of monitoring boreholes

5.6.1 Introduction

It was revealed that analysis of groundwater hydrographs can be used to estimate groundwater

recharge (Bredenkamp, 1988). Among the 5 monitoring boreholes, BH 234/75 was used for analysis

as it has more records compared to others.

The analysis involves a recession curve, which represents periods of no groundwater recharge while

effective rise in the water level indicates groundwater recharge. The effective rise was observed in the

first two years i.e year 2001 and year 2002 with a total increase of 4m (refer Figure 5.5). From year

2003 up to the year 2006, there was a recession interrupted by some minor recharge. But from the

year 2004 to the year 2005 there was a continuous recession.

Figure 5.5: Hydrograph of BH 234/75 monitoring borehole

1050

1052

1054

1056

1058

Wate

r le

vels (m

)

2000 2001 2002 2003 2004 2005 2006 2007

1050

1052

1054

1056

1058

Wate

r le

vels (m

)

2000 2001 2002 2003 2004 2005 2006 2007

Time (Years)


47

From the relation of recharge, storativity and groundwater level fluctuations, the recharge can be

estimated using Equation 5.14:

dt

dhSR *= (5.14)

where;

R = Recharge

S = Storativity

dt

dh = Change in water level per time

1

222

111

86/)2424(

242000*012.02

242000*012.02

−=+=

==∆=→=∆==∆=→=∆

mmyrR

mmhSRmh

mmhSRmh

Then from the recession that occurred during the dry period:

11000 −= mmyrdt

dh

1121000*012.0 −→= mmyrR

From the above calculations it shows that the analysis of water level rise in boreholes gives the lower

estimate of recharge flux compared to recession analysis estimate. Estimated recharge from the

recession is influenced by pumping causing steep slope with high dh/dt value. Therefore the recession

method might overestimate.

However, the storativity value in the study area is not known. Previous hydrological studies (Shindo,

1991) used a range of storativity values from 0.00005 to 0.005 as he classified the aquifer to be

confined. A storativity of 0.1 was applied by Hamza (1993). Since the pumping test results indicated

the aquifer type to be leaky around the wellfield. It is possible that the aquifer contains low storativity

value around the wellfield. Probably it might have higher specific yield up around the source area.

Currently the proper value of storativity is not known which makes it difficult to draw concrete

conclusions from this calculation. However, the estimated flux is expected to increase in case the

storativity value is found to be higher than the applied value of 1.2%.

48

5.6.2 Relationship between rainfall and recharge

It has been revealed that annual recharge could be inferred from annual rainfall by means of equation

5.15 (Bredenkamp, 1988).

( ) ( )( )BIRFAIRE −= (5.15)

where;

RE (I) = Recharge

RF(I) = Rainfall for the year I

A = Fraction of rainfall in excess of the threshold value that represents recharge

B = Threshold value of rainfall

By analysing the available data on rainfall and groundwater level fluctuations, the threshold value was

found to be around 400 mm of rainfall while coefficient A equals to 0.03. The graph was obtained by

plotting change in water levels against annual rainfall for data starting from 2001 to 2006. Figure 5.6

shows the relationship.

Besides the fact that the available data on groundwater level fluctuations was of a short period, the

same trend and similar threshold value was obtained in a groundwater study in South Africa,

Bredenkamp (1988).

By using the relation given as equation 5.15, recharge was calculated from rainfall data from the year

1922 up to 2006. For rainfall values less than the threshold value it gave negative recharge values

which were treated as zero recharge in that particular year. A statistical analysis was made for the

period of about 80 years. An overall average recharge obtained was about 5 mmyr-1 and a histogram is

provided as Figure 5.7. All calculation procedures are attached in Appendix D, Table D-10.

From the statistical analysis it shows that there are years without recharge which are compensated by

few years with high recharge while frequently recharge flux is ranging between 6 to 10 mmyr-1. The

same behaviour was discovered in Botswana (Gieske, 1992) which might be common in many semi

arid areas.


49

0 1-5 6-10 11-15 16-20 >20

S1

0

5

10

15

20

25

30

35

40

Fre

que

ncy

Recharge (mmyr-1)

0 1-5 6-10 11-15 16-20 >20

S1

0

5

10

15

20

25

30

35

40

Fre

que

ncy

Recharge (mmyr-1)

Figure 5.6: Rainfall-recharge relationship for monitoring boreholes (BH 234/75)

Figure 5.7: Statistical analysis of recharge in the basin for the period of 1922 up to 2006

5.7 Recharge modelling by Earth

5.7.1 Introduction

EARTH modelling is a lumped model for simulation of recharge and deep groundwater level

fluctuations (Van der Lee and Gehrels, 1990). It was originally developed for use in the semi arid

climate of Botswana. The model is known to perform well at a time step size of one day. However, in

this study a monthly data time series was used. The model is comprised of four components in which

the first three represent the direct part and the fourth represents the indirect part of the model.

The direct part determines recharge through physical processes above the groundwater table and the

fourth part calculates the groundwater level on the basis of recharge estimates obtained from the

direct part. The model was calibrated by using observed groundwater levels for the period starting

Annual rainfall (mm)

Cha

nge

in w

ate

r le

vels

(m

m)

y = 7.6893x - 3046

R2 = 0.7431

-500

0

500

1000

1500

2000

2500

0 100 200 300 400 500 600 700

Annual rainfall (mm)

Cha

nge

in w

ate

r le

vels

(m

m)

y = 7.6893x - 3046

R2 = 0.7431

-500

0

500

1000

1500

2000

2500

0 100 200 300 400 500 600 700

50

1051

1052

1053

1054

1055

1056

1057simulated level

Observed level

Time (Years)

Wat

er le

vels

(m

)

2001 2002 2003 2004 2005 2006

1051

1052

1053

1054

1055

1056

1057simulated level

Observed level

Time (Years)

Wat

er le

vels

(m

)

1051

1052

1053

1054

1055

1056

1057simulated level

Observed level

Time (Years)

Wat

er le

vels

(m

)

2001 2002 2003 2004 2005 2006

from 2001 to 2006. The model input data included rainfall, potential evapotranspiration and

groundwater level fluctuations under monthly time step.

5.7.2 Model estimation results

The model results of the simulated head with observed head are provided as a Figure 5.8, while the a

complete set of the model output graphs is provided Figure 5.9. From Figure 5.9, the top graph

represents actual evapotranspiration, next is the soil moisture in root zone, percolation from root zone

followed by the recharge arriving at the water table. The last figure is the observed with simulated

water levels.

In the same graph, it shows that the actual evapotranspiration and soil moisture in the root zone have

similar trends. Increase in actual evapotranspiration matches with soil moisture as a result high soil

moisture is lost and only the moisture that escapes evapotranspiration will percolate to the subsurface

as groundwater recharge.

The estimated recharge is averaging at about 5 mmyr-1. It can be seen that the model was not able to

simulate the water levels at the beginning i.e year 2001 while the simulation is matching fairly well

with the observed levels thereafter. This might be contributed by the quality of data applied as the

model estimates well under daily time step. Moreover, monthly data are less accurate compared to

daily data.

One of the input parameters to the model is the storativity value of the aquifer. As explained earlier,

the accurate value of this parameter is not known at the moment. This study applied a storativity value

of 1.2%, a list of parameters used is provided in the Appendix D, Table D-11. Figure 5.8 shows a

good match between the simulated and observed water levels. However, the recharge flux might

increase in case it is found that the storativity value is higher than the applied value.

Figure 5.8: Graph of simulated levels and observed levels against time.


51

0

20

40

60

80

100

Actual evapotranspiration

0

100

200

300

400

Soil moisture in the root zone

0

10

20

30

40

percolation

recharge

1051

1052

1053

1054

1055

1056

1057simulated level

Observed level

2001 2002 2003 2004 2005 2006

mm

mm

mm

mm

0

20

40

60

80

100

Actual evapotranspiration

0

100

200

300

400

Soil moisture in the root zone

0

10

20

30

40

percolation

recharge

1051

1052

1053

1054

1055

1056

1057simulated level

Observed level

2001 2002 2003 2004 2005 2006

mm

mm

mm

mm

Figure 5.9: Graphical representation of the Earth modelling results

5.7 General discussion

Five different methods were applied to determine recharge flux. Table 5.4 gives a summary of the

applied methods with their estimated recharge flux. The methods applied include CMB, Water

Balance, hydrograph analysis, rainfall-recharge relationship and Earth modelling.

The CMB estimated the recharge flux ranging from 5-10 mmyr-1. But due to the fact that there were

no records on dry chloride deposition in the study area, the deposition was assumed to be zero.

Moreover, there was human pollution which was accompanied by chloride uploading in the aquifer

which may have resulted to increase in chloride content in groundwater. This may cause

underestimation of recharge.

52

In relation to Water Balance method, there are no proper data on crop coefficients for types of

vegetations within the basin. Also the spatial distribution of vegetation in the basin was not known.

All these factors may contribute to uncertainty in the estimation.

The accuracy of the hydrograph analysis methods depends on the accuracy of storativity values. Since

at the moment this value is not yet known it makes it difficult to draw definite conclusions. Moreover,

the recession analysis was affected by pumping which makes the slope to be steeper and therefore

results in high dh/dt value. Therefore the recession analysis is more likely to overestimate the

recharge flux.

However, the rainfall-recharge relationship is based on statistical analysis. The available data was

only for six years. Statistically these are relatively few data, despite the fact that its estimation was not

too far from other estimates.

Lastly is the recharge modelling by Earth model. The model estimates quite well under daily time step

data, but due to data availability limitations the monthly time step data was applied. Also the model

requires storativity value as an input parameter which is not well at moment. The model estimated

recharge to be averaging at 5 mmyr-1.

Therefore it can be concluded that the estimated recharge flux lies between 1 – 2% of the annual

rainfall equal to 5 – 12 mmyr-1. The limitations of this study include data scarcity. For example,

storativity values are not known as yet no good pumping test data are available for use in calculation

of the parameters, datasets were short in a way that it was difficult to draw direct conclusions. The

study area is complex geologically with unconfined aquifer conditions on hill slope areas changing to

leaky/ confined conditions around the wellfield area. Figure 5.10 shows the situation of the area in

relation to specific yield/ storativity distribution.


53

METHOD ESTIMATED RECHARGE

ESTIMATED RECHARGE

REMARKS

(mmyr-1) (approx % of annual rainfall)

CMB 5 - 10 1- 2% May be low because of human pollution in the area and neglecting the dry chloride deposition in the calculations.

Water balance 9 2% Crop coefficients are not known and no proper data on vegetation distribution

Hydrograph analysis

8 -12 1.5 - 2% Poor data consistence in relation to storativity/ specific yield. Also the wells are affected by pumping

Rainfall-recharge relationship

5 1% Only few annual data for reliable statistical analysis

Earth model 5 1%

Monthly time step data were applied which is less accurate compared to daily data. Also it depends on accuracy of storativity/ specific yield.

Table 5.4: Summary of recharge flux estimation results

Figure 5.10: The situation of the study area in relation to aquifer type distribution

Unfractured bedrock

Semi confined aquifer

Chenene hills

Water divide

Enhanced recharge conditions

Uncofined conditions (high Sy)

Leaky/ Confined conditions (low S)

Unconfined aquifer

Low recharge

Clay layer

54

6. Groundwater modeling

6.1 Introduction

A model is any device that represents an approximation of the field situation, simulates ground-water

flow and/or solute transport indirectly by means of a set of governing equations thought to represent

the physical processes that occur in the system (Anderson and Woessner, 1992). Groundwater

modelling is used to make predictions about a groundwater system’s response to a stress, to further

our understanding on the hydrological system, to design field studies as well as to be used as a tool for

thinking and analysis.

Good management requires information on the response of the managed system to the proposed

activities. A tool is needed that will provide such information. The model is a tool for understanding

the system and its behaviour and for predicting this response.

The model developed for the Makutupora basin is categorized as an assessive model. The developed

model is aiming at serving as a tool to improve our understanding on the Makutupora basin

groundwater flow system. This includes groundwater quality control and safe yield of the basin.

Figure 6.1 shows the steps that were following during the modelling process.

Figure 6.1: Modelling protocol (adopted from Anderson and Woessner, 1992)


55

6.2 Conceptual model

The purpose of model conceptualization is to create a theoretical model of the system to be simulated,

by simplifying a system to an extent that a logical model approach with appropriate model algorithms

can be defined (Rientjes, 2007). Conceptual models describe how water enters an aquifer system,

flows through the aquifer system and leaves the aquifer system. Briefly a conceptual model describes

the hydrologic system with respect to aquifer properties, flow characteristics and boundary conditions.

In this study the following assumptions were made in the conceptualization process:

(i) The model consists of a single layer.

(ii) The model is two dimensional.

(iii) The aquifer is confined under steady state condition.

(iv) The aquifer has a constant thickness.

Vertical movement of water is considered on the recharge areas. The Chenene hills and the southern

part of the model area were regarded as sources of high recharge, see Figure 6.2a. The aquifer is leaky

around the wellfield but it was assumed that the high amount of recharge enters the aquifer by

horizontal movement by enhanced infiltration from topographic high areas surrounding the basin

while small contribution is from the top clay layer, see Figure 5.9.

Figure 6.2a: Conceptualization of the study area

56

For modelling simplicity purpose the aquifer was conceptualized as Figure 6.2b. The aquifer was

assumed to have a constant thickness of 40m under confined conditions.

Figure 6.2b: Considered situation during modelling

6.2.1 Boundary conditions

Boundary conditions as defined by Anderson and Woessner (1992) are mathematical statements

specifying the dependent variable (head) or the derivative of the dependent variable (flux) at the

boundaries of the problem domain. In steady state simulation, the boundaries largely determine the

flow pattern. Therefore correct selection of boundary conditions is a critical step in model design.

During conceptualization, the model boundary conditions were justified. On the topographic high part

of the basin, the Chenene hills were regarded as the main recharge area. About 40% of total recharge

was assigned on the top cells along the hill slopes. The assigned values were calculated on the bases

of size of the source area and assumption made was that, the surface is rocky which generates high

runoff. Other factors include:

• Orographic effect

• Presence of alluvial fans which are coarse facilitating high infiltration rate

The same boundary is set on the SE part of the lower corner of the model area. About 10% of the

amount of recharge flux that was assigned on the Chenene hills was assigned on this part of the basin.

This is because enhanced local recharge occurs due to runoff created from the adjacent hills.


57

General Head boundary

On the left hand side of the model, the no-flow boundary (Q = 0) was assigned. It was assumed that

the boundary of the model coincides with the water divides as they are located along topographic high

areas. The same boundary condition is set on the opposite side of the model domain except for one

cell at the outlet area of the Hombolo dam with the specified head boundary condition. At the bottom

of the layer, the no-flow boundary was assigned assuming that the boundary coincides with the fresh

granite rock.

The little Kinyasungwe River, which is the main river in the basin, was assigned a general head

boundary condition, see Figure 6.3. This river carries a lot of water during rainy season and it forms a

swamp at the Makutupora wellfield area before it discharges into the Hombolo dam. During dry

season, the river becomes dry.

Figure 6.3: General head boundary condition along the Little Kinyasungwe river.

6.2.2 Stratigraphic units

Stratigraphic units comprise geologic units of similar hydrogeologic properties (Anderson and

Woessner, 1992). This information can be derived from the borehole logs obtained during drilling.

More explanations on the Stratigraphic units of the area are available in section 2.5. On the model, all

layers on top of clay were regarded as clay. Due to low permeability of clay, it was assumed that this

58

layer contributes low amount of recharge to the aquifer. The bottom layer is assumed to be underlain

by fresh unfractured granitic rocks which are completely impermeable. Therefore in the model design

the aquifer is simulated as a single layer. Figure 6.4 displays the Stratigraphic units of one of the

boreholes in the wellfield area. The summary of all borehole lithologies is attached in Appendix B,

Table 2.5.

Figure 6.4: Stratigraphic units in the basin

6.2.3 Surface water body

In the Makutupora basin there are surface water body like swamps and river flows which exist only

during rainy periods and the Hombolo dam which is permanent. The Makutupora well field becomes

flooded during the rainy season which starts around the end of November up to the end of May. This

is attributed by the big river flow, the Kinyasungwe River, which originates from the Chenene hills to

the Hombolo dam. After flooding of the wellfield area, the river discharges to the Hombolo dam.

Another river is the Madihi River located on the west part of the well field. All rivers are ephemeral.

The Little Kinyasungwe River was proved to be longest river in the basin through DEM hydro

processing. Therefore it was assigned a general head boundary during no pumping situation, reflecting

the natural conditions, see Figure 6.3.

0masl

120masl

Mbuga clay

Red silt

Silt

Red silt

Clay

Sand

Clay

Sand

Gravel


59

Source area

(Chenene hills)

Aquifer boundary

Aquifer area

Source area

(Chenene hills)

Aquifer boundary

Aquifer area

6.2.4 Sinks and sources of the modelled area

Rainfall is the only source of water input into the basin. Recharge processes are explained in section

5.2. Through application of recharge package in Modflow, average recharge flux determined by CMB

method was assigned to the model domain. The annual recharge value was converted into daily

values. Evapotranspiration and well abstractions are major sinks of the model. Evapotranspiration

values were not considered through the evapotranspiration package as it was accounted for by the

CMB. The well abstractions were assigned into the model through well package.

6.2.5 The modelled area

The catchment boundary was delineated by the application of DEM hydro processing package in

ILWIS software. The extraction operation constructs catchments: these are calculated for each stream

found in the output map of the drainage network ordering operation (ITC, 2001). However in the

northern boundary it was assumed that the aquifer starts at the foot of the Chenene hills, see Figure

6.5.

Figure.6.5: Model discretization

The model domain has 93 columns and 106 rows with 500m grid size. 4763 is the total number of

active cells and 5095 inactive cells, with a total of 9858 cells, see Figure 6.5. Normally small grid

resolutions are preferred at the problem domain. But due to data scarcity and limitations on computer

memory, same size of grid was applied. The model area is about 1184km2. The background map was

georeferenced with a UTM projection system.

60

6.3 Aquifer geometry

The demarcation of the aquifer extent was made based on the available technical details of the

boreholes. Top of aquifer elevation was assigned based on the datum level of the area. The bottom

elevation of the aquifer was obtained by deducting the aquifer thickness from the TOP elevation.

Aquifer thickness was obtained from the drilled borehole logs data and found to have an average

thickness of 40m.

6.4 The model code

The code selected for this model is the MODFLOW PMWIN two dimensions. The computer code

was developed by the USGS, United State Geological Survey (McDonald and Harbaugh, 1988). This

is the distributed mathematical model. The governing equation for groundwater flow is the Law of

mass balance and Darcy’s law. The governing equation for 2D model is provided as equation 6.1.

*Rdy

dhK

dy

d

dx

dhK

dx

dyx −=

+

(6.1)

where Kx, Ky and are components of the hydraulic conductivity, is the specific storage and R* is the

source/sink term.

6.5 Data input for the model

Information from the geological map like structures, lithologies and stratigraphic units were combined

together with DEM data to create background map. Structures indicate areas of no flow areas while

DEM indicates areas of topographic divides, surface water bodies and elevation of the surface.

Stratigraphic units are necessary in indicating the extent of the aquifer thickness.

All physical data were collected during field work campaign. Data accuracy is a problem as most of

the available data is of poor quality as explained earlier. Therefore to get a good picture of the study

area, various data collected were analysed in combination. For example, to get the boundary of the

aquifer, the satellite images analysis was coupled to the pumping test evaluation and in some areas

where data were incomplete; some values of transmissivity were assumed when necessary. Available

pumping test data were only from the wellfield area as there is no documentation on boreholes

existing away from the wellfield area. Therefore, parameter like and transmissivity was assigned on a

zone basis.

6.6 Model execution and calibration

The execution of the model was accompanied by the entry of prepared data input into the selected

computer code and interpretation of the model results. A number of runs were performed until good

results were obtained. This step was followed by the model calibration.


61

Observed heads (m)

Sim

ula

ted

hea

ds (

m)

Observed heads (m)

Sim

ula

ted

hea

ds (

m)

This process involved adjustment of the model input like parameters, boundary values and stresses in

order to make a good match between the simulated and observed state variables. In order for the

developed model to be a good representative of the real world simulated, the difference between the

simulated and observed state variables should be as minimal as possible. This procedure requires the

calibration target which is referred to as goodness of fit criterion. The calibration target is defined as

calibration value with its associated error (Rientjes, 2007). The error is determined by various

modelling aspects like accuracy of measurements, complexity of the system being modelled and the

applied model resolution.

The present study area is complex in terms of geologic setting thus posing difficulties in delineating

model boundaries. The groundwater level data is not well distributed to the entire model area. Even

the five monitoring wells might not represent the real groundwater levels as they are located very

close to the pumped wells which are under operation for 24 hours. With these challenges, setting the

calibration criterion is a tedious task. Besides the real situation of the field, the calibration was done

manually by trial and error method until the minimum difference between the simulated and observed

groundwater heads was obtained. The changed parameters are transmissivity and recharge flux. The

evaluation of the calibration process was done qualitatively and quantitatively. The plot of measured

heads against simulated heads was produced, which shows good results. The RMSE was calculated

and determined to be 2.2%.

Figure 6.6: Graphical representation of measured against simulated heads (meters)

62

6.7 Uncertainty of the model calibration

Uncertainty in hydrologic modelling may be due to model conceptualization, input parameters and/or

inherited in natural processes. Simply, model uncertainty arises from incomplete understanding of the

system being modelled or inability to accurately reproduce hydrological processes with mathematical

and statistical techniques (Harmel and Smith, 2007). The study area is complex in terms of geological

setting and hydrometeorological processes which give rise to significant heterogeneities and

anisotropy. Additionally, field observed data scarcity is a major problem which makes clarification of

the discontinuities of the aquifer parameters a rather difficult job.

The wellfield located in the graben, is localized on a small area with an approximate area of 120 km2

out of about 1500 km2 total area of the basin. In fractured rock, flow is mainly localized to a few main

flow paths that control most of the hydrological response of the aquifer (Borgne et al., 2007; Saraf et

al., 2004). Due to the fracturing system in the basin, it is expected that there is significant

heterogeneity in the aquifer parameters. Because there is not enough data to adequately describe these

heterogeneities, it is not possible to fully understand the hydrologic behaviour of the system at

present.

Since granitic aquifers are of tectonic origin, the fault systems might extend up to great depth. This

may enhance water withdrawal from the aquifer which is not considered in the conceptualization of

the model. This might contribute to the model uncertainty.

Many digital elevation models (DEMs) have difficulties in replicating hydrological patterns in flat

landscapes (Callow et al., 2007). Errors due to averaging ground surface elevations available on 90m

grid to 500m grid are significant, especially on topographically high areas.

From the calibrated model results, the model shows good calibration results with residual error being

equally distributed at the wellfield but it is not clear on the remaining part of the model area as there

is no measured data to compare with the simulated results. However, in order to check the reliability

of the developed model, a sensitivity analysis was made which is explained in section 6.8 below.


63

Multiplier factor

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Recharge

Transmissivity

RM

SE

in w

ate

r le

vel (

m)

Multiplier factor

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Recharge

Transmissivity

RM

SE

in w

ate

r le

vel (

m)

6.8 Sensitivity analysis

The purpose of sensitivity analysis is to quantify the uncertainty in the calibrated model caused by

uncertainty in the estimates of aquifer parameters, stresses and boundary conditions (Anderson and

Woessner, 1992). This is an essential step in modelling as it enables the modeller to evaluate the

reliability of the developed model. The sensitivity analysis process is associated with stressing and

parameterizing the calibrated model differently from the calibrated conditions. In this case, the model

indicated to predict well in the wellfield area where a number of monitoring wells are located. In areas

where monitoring wells are scarce, it is not possible to check the quality of prediction by using the

graph of simulated heads against observed heads. Then the sensitivity analysis can help in examining

the reliability of the developed model.

During the sensitivity analysis, the recharge and transmissivity values were changed from the

calibrated values one at a time and the variation of the RMSE was noted. The recharge and

transmissivity values were changed by multiplying with factors. Multiplying factor ranged from 0.2 to

1.4, see Figure 6.7.

From Figure 6.7 the model shows to be sensitive to both recharge and transmissivity. With

transmissivity the model shows to be more sensitive at the low percentage and becomes less sensitive

as the transmissivity values increases, while with recharge the model shows to be highly sensitive

with both low and high percentage values. Recharge and transmissivity maps for the model are

provided as Figure 6.8.

Figure 6.7: Sensitivity analysis of the recharge flux and transmissivity showing effect of change on

the RMSE of the groundwater level

64

Transmissivity(m2d-

100150400500

Recharge (m3d-1)

52.50022.9653.275

Transmissivity(m2d-

100150400500

Recharge (m3d-1)

52.50022.9653.275

Recharge (m3d-1)

52.50022.9653.275

Recharge (m3d-1)

52.50022.9653.275

6.9 Model results

6.9.1 Recharge and transmissivity

Figure 6.9a shows recharge map of the area with spatial variability. High recharge is in the elevated

areas surrounding the basin while small recharge is in the flat areas of the basin. Transmissivity is also

varying spatially. Figure 6.9b shows a transmissivity map. There is no clear trend of spatial variability

of transmissivity. But it is expected to be high along the faults since the basin is of granitic origin.

i) Recharge map ii) Transmissivity map

Figure 6.8: Recharge and Transmissivity maps of the model

6.9.2 Simulated potentiometric levels

A potentiometric map is a contour map that represents the top of the ground water surface in an

aquifer. The potentiometric surface is generally the potential energy available to move the

groundwater in the confined aquifer (Strickland et al., 2005). A potentiometric map of an aquifer

provides an indication of the directions of groundwater flow in the aquifer, indicate groundwater

recharge and discharge areas. It should be kept in mind that the potentiometric maps are important

tools in preparing water resource management plans as they assists in technical studies like indicating

high stress areas as well as possible groundwater diversion points. The water levels in wells vary

according to seasonal variations in rainfall, abstraction and recharge rate.

The potentiometric map is provided as a raster map, see Figure 6.9 and 6.10. Contour maps are

provided in the Appendix E as Figures E-1 and E-2. Since water flows from high hydraulic head to

low hydraulic head, the recharge and discharge areas can be obviously seen from the provided maps.


65

Heads (m)Heads (m)Heads (m)

A

B

Heads (m)

A

B

Heads (m)

Figure 6.9: Potentiometric map of the calibrated model

Figure 6.10: Potentiometric map indicating the situation without any well abstraction

66

Recharge area is around the lower part of the Chenene hills and on the hill slopes in the south east.

The discharge is along the river as well as in the Hombolo dam. In Figure E-1, Appendix E, the

stressed areas due to well abstractions are also seen, characterized by relatively wide spaced contours

along the well field area.

However, from the same map it shows that previous groundwater flow direction was following the

longest path from the Chenene hills through the well field area to Hombolo dam, along the

Kinyasungwe River. But under pumping situation the flow path is not followed as it ends around the

wellfield. This is due to the cone of depression created by high pumping rate at the well field area.

Therefore groundwater coming from the lower part of the Chenene hills is trapped in to the well field

area instead of discharging in to the Hombolo dam. The local recharge from the Southern part of the

basin is also trapped in the wellfield area. Due to the huge cone of depression, water fails to flow out

of the well field to the Hombolo dam. As a result the Hombolo dam receives water from nearby hills,

the Mohanga hills located on the ENE side of the basin instead of receiving from the well field area as

well, see Figure F-13 in Appendix F for localities of the area. This proves the second hypothesis to be

true.

Previously Shindo (1991) developed the groundwater model by using Modflow under transient state

conditions. The model boundary coincided with the well field area. The discharge point was located

along the Kinyasungwe River around Chihanga. However the modelled area was too small to get a

clear picture of the groundwater flow.

Hydrogeologic studies coupled with steady state groundwater modelling were carried out by Hamza

(1993) on the same area, which provided the groundwater flow direction. The assumption was; the

recharge takes place out of the model domain. The simulated groundwater flow was from the Chenene

hills towards Hombolo dam.

The groundwater level in the BH 234/75 was 1055.75 m in 2001 and it decreased to 1053.34 m in

2005 year. The difference in water level between 2001 and 2005 is 2.41 m. The difference in water

level between the maximum and the minimum is 1.47 m. This might explain the change of

groundwater flow direction. As due to the drop in water level, there is no enough potential energy to

push water to flow out of the well field area.

It can be realized that under natural conditions the groundwater flows from the Chenene hills to the

Hombolo dam, along the Kinyasungwe River. The current observed cone of depression on the

wellfield area is due to the high amount of abstraction compared to recharge flux. In order to restore


67

the natural groundwater flow, the amount of abstraction should be reduced or the recharge flux should

be increased by practicing artificial recharge. This will contribute to sustainable management of the

basin. Due to the increase in amount of abstractions over time to accommodate increasing water

demand, the input seems to be lower than output hence depletion of the groundwater storage.

An attempt was made to make a 2D view of both situations; no abstraction and with abstraction.

DEM, groundwater levels during no abstraction and groundwater levels with abstraction were

overlaid against distance, see Figure 6.10 and Figure 6.11. From Figure 6.11, around the wellfield

area indicated by dashed cycle shows. The levels representing pumping situation, show a narrow cone

of depression which implies the effect the pumping. From the potentiometric map analysis it was

expected that the cone of depression at the wellfield area could be wide in the cross section which was

not the case. This can be explained by the effect of the size of the grid applied i.e. 500m. It might be

due to a difference between the land surface elevation and the elevation obtained from the SRTM –

DEM. Errors due to averaging ground surface elevations available in 90m resolution grid to 500m

resolution grid are significant that resulting to narrow cone of depression along the wellfield area.

Figure 6.11: Cross section of the simulated heads versus DEM along point A to B

1050

1100

1150

1200

023

0046

0069

0092

00

1150

013

800

1610

0

1840

020

70023

000

2530

027

60029

900

3220

0

3450

036

80039

100

4140

043

70046

000

4830

0

5060

0

Distance from North to south(m)

Ele

vatio

n (m

)

elev_natural

elev_pumping

altitude_dem

A

B

Without pumping During pumping DEM

A

B

Wellfield

area

Levels without pumping

Levels during pumping

DEM levels

68

6.9.3 Water balance of the basin

One of the most basic ways to quantitatively evaluate the movement of groundwater through an

aquifer system is through the water budget for the system. The fundamental equation for a water

budget (or water balance) is that sum of inputs minus the sum of outputs equals the change in storage

of the system:

∑ ∑ ∆=− StorageOutputInput (6.1)

If the system is assumed to be at a steady state, then the change in storage is zero and the water budget

becomes:

∑ ∑= Outputsinputs (6.2)

For a groundwater system, inputs may include direct recharge from precipitation, indirect recharge of

precipitation from surface water runoff, groundwater inflow from outside the system boundary, or

recharge from anthropogenic sources (Lundmark et al., 2007). Groundwater outputs may include

discharge as springs, discharge to surface water bodies and loss to the atmosphere by

evapotranspiration (ET), groundwater outflow to outside the system boundary, and pumping for

domestic, agricultural and industrial uses. The water balance is established based on the modelling

water budget tool in Modflow.

The water balance was established for both situations: with no well abstraction and under well

abstraction situation. Table 6.1 shows the water balance under well abstraction situation while the

water balance at no well abstraction situation is summarized in a table provided in the Appendix E as

Table E-2.

In the Makutupora basin the primary groundwater inputs are recharge from precipitation and the

primary outputs are well abstractions and discharge as groundwater outflow and ET.

Input term Amount(m3/year) Output term Amount(m3/year)

Recharge 8,900,000 Well abstractions 7,300,000

Specified head 1,600,000

Total 8,900,000 8,900,000

Table 6.1: Water balance of the basin during well abstractions situation

Input term Amount (m3year-1) Output term Amount (m3year-1)

Recharge 8,900,000 Head dep. boundary 8,100,000

Specified head 800,000

Total 8,900,000 8,900,000

Table 6.2: Water balance of the basin during no well abstractions


69

6.9.4 Scenario analysis

Scenarios are archetypal descriptions of alternative images of the future, created from mental maps or

models that reflect different perspectives on past, present and future developments (Greeuw et al.,

2000). Scenarios are used to answer the "what if" questions.

In the process of scenario analysis, the situation before the well abstractions was considered. In that

period only natural conditions were referred where the basin was recharged and discharging in the

Hombolo dam. The simulated heads reflecting the situation where no well abstraction is taking place

is provided as Figure 6.10.

Secondly, the abstraction phase reflecting the current situation is represented by the simulated heads

provided as Figure 6.9. In order to see the effect of current abstraction rate, the amount of well

abstractions was changed from low abstraction rates and increased to high rates and the simulated

heads were analyzed.

Generally, the increased abstraction resulted in the widening of the cone of depression around the well

field area resulting in deviation of discharge point for groundwater water from the hills; hence

decrease in the input into the Hombolo dam. Meanwhile the reduction in amount of abstraction

favoured high groundwater flow into the Hombolo dam.

6.10 Two layers model development

An attempt has been made to develop a two layers model. The top layer consists of clay soils with

average thickness 40m lying on top of the aquifer. The bottom layer was regarded as an aquifer with

the same characteristics as applied to the single layer model. The underlying layer was considered to

be fresh unfractured granitic rock which is completely impermeable and therefore not included in the

model.

However, model calibration was not as good as the single layer model calibration. The variation

between the simulated heads and observed heads were 7.4m2 while the single layer had variance of

4.6m2. This is due to the lack of enough data for proper characterization of clay layer. Figure 6.12

shows the model conceptualization and the model calibration results.

70

Observed heads (m)C

alc

ulat

ed

hea

ds

(m)

Observed heads (m)C

alc

ulat

ed

hea

ds

(m)

From simulation results it can be concluded that, for detailed modelling of the basin more pumping

test data equally distributed over the entire basin should be collected in order for the model to reflect

field situation.

Figure 6.12: Conceptualization of two layers model with calibration results

Rainfall (550mm/year)

PET (2000mm/year) Recharge in Chenene hills

Well field

Abstractions

Aquifer layer

Top Clay layer (40m)


71

7. Conclusions and recommendations

7.1 Conclusion

The estimated recharge flux ranges between 1 to 2% of annual rainfall equal to 5 to 12 mmyr-1. This

recharge flux is regarded as the minimum recharge as the period under study was regarded to be dry

period due to significant decrease in the amount of annual rainfall. Also considering the CMB

method, there were human – cattle related pollution that increased the chloride content in groundwater

which contributed to underestimation of recharge flux. Moreover, from statistical analysis it was

concluded that there are number of years with zero recharge which are compensated by few years with

high recharge while frequently moderate recharge is dominant.

The developed regional groundwater model of the basin was capable to indicate the impact of well

abstractions to the groundwater flow. The potentiometric map indicates no groundwater outflow from

the basin to the Hombolo dam under stressed conditions. The cone of depression caused by high stress

at the well field area prevents groundwater from the Chenene hills to flow out of the well field and

discharge to the Hombolo dam. This results into a reduction in the amount of water input into the

Hombolo dam. Therefore the hypothesis for the well abstractions being the major cause for the

dropping of groundwater levels is supported by the modelling results.

Hydrochemical analyses of groundwater can provide valuable insight into the type, sources and

evolution of groundwater in the hydrologic system. It has been observed that the Makutupora basin

consists of HCO3- water type: young groundwater within the first stages of hydrochemical evolution.

Moreover, the groundwater has evolved from less bicarbonate to more bicarbonate composition. The

cations composition changes from Na + K to Ca and Mg content.

Water balance calculations of the basin based on the Thornthwaite and Mather (1955) indicated low

soil moisture surplus for wet years with no surplus for dry years. The decrease in soil moisture surplus

for longer period is contributing to low recharge flux in the basin. It was concluded that the annual

groundwater recharge in the basin is fluctuating depending to the amount of rainfall received in a

particular year. Since no data with regard to aquifer storage seem to exist yet, this limits sound long

term planning.

72

7.2 Recommendations

Since it has been realized that the recharge flux is changing depending on the amount of rainfall

received per year. It is therefore recommended that recharge flux studies should be carried out

frequently for the monitoring purposes. This will enhance sustainability management of the resource

by maintaining the amount of well abstractions less or equal to the input amount.

Future studies will benefit from more monitoring wells which are distant from production wells and

evenly spread which will enhance more effective calibration, and reduce pumping influence from

monitoring wells.

It is important to develop a detailed transient simulation of the aquifer under current research

boundary considerations in order to get more insight into inflows and outflows of the system. Because

recharge, well abstractions and groundwater outflow are natural processes that are strongly time

dependent, their transience influence is felt most under unsteady state modelling.

Due to hydraulic properties discontinuities in the fractured rock aquifers that results into

heterogeneities in aquifer parameters, it is important to undertake well test analysis with monitoring

wells for transmissivity and storativity determination. Moreover, modern geophysics for siting new

holes in the vicinity is highly recommended.


73

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London UK.


77

Year Month Temp mean(0C) Rs(MJm-2) Wind speed(ms-1) Rainfall(mm)2000 Jan 24.7 23.37 1.94 87.02000 Feb 25.1 23.71 2.22 84.82000 Mar 23.4 22.68 1.94 141.92000 Apr 23.1 22.05 2.50 0.02000 May 23.7 21.31 2.78 0.02000 Jun 21.1 20.65 3.33 0.02000 Jul 20.0 20.91 3.06 0.02000 Aug 21.0 21.49 3.33 0.02000 Sep 22.1 22.95 3.61 0.02000 Oct 24.0 23.42 3.89 0.02000 Nov 24.9 22.47 3.33 131.52000 Dec 23.3 20.87 3.06 294.42001 Jan 23.1 22.11 2.78 285.72001 Feb 23.7 25.15 2.22 72.52001 Mar 24.4 23.28 2.50 57.92001 Apr 23.2 22.31 1.94 64.72001 May 22.3 20.85 2.50 0.02001 Jun 20.3 20.23 3.06 0.02001 Jul 20.2 20.56 3.33 0.02001 Aug 20.8 22.53 3.33 0.02001 Sep 22.5 23.14 3.61 0.02001 Oct 24.5 23.38 3.33 0.02001 Nov 25.1 24.09 3.06 0.02001 Dec 25.7 23.06 2.50 93.92002 Jan 23.1 21.72 2.22 323.82002 Feb 23.4 23.54 2.50 75.22002 Mar 23.8 23.73 1.94 95.72002 Apr 23.0 21.62 2.50 16.62002 May 22.7 22.09 2.78 0.02002 Jun 20.8 22.49 3.06 0.02002 Jul 21.0 22.73 3.33 0.02002 Aug 20.8 21.35 3.33 0.02002 Sep 22.4 23.31 3.61 0.02002 Oct 23.8 24.00 3.33 0.02002 Nov 25.4 23.95 3.06 0.02002 Dec 25.2 21.33 2.22 92.72003 Jan 24.5 22.98 2.50 116.12003 Feb 24.7 22.51 1.94 85.12003 Mar 25.4 22.07 2.22 43.52003 Apr 24.3 21.84 2.22 2.52003 May 23.6 20.94 2.50 0.02003 Jun 21.5 21.79 2.78 0.02003 Jul 20.3 21.89 3.06 0.02003 Aug 21.4 23.04 3.33 0.02003 Sep 22.6 22.91 3.61 0.02003 Oct 24.0 24.20 3.33 25.52003 Nov 25.7 24.73 3.61 54.02003 Dec 25.3 24.59 2.50 118.92004 Jan 25.0 23.82 1.94 61.02004 Feb 24.1 22.07 1.67 82.02004 Mar 24.0 21.41 2.22 162.3

Appendices Appendix A: Meteorological data

Table A-1: Climate data for the period of 2000 to 2006

78

2004 Apr 23.1 20.11 2.50 0.02004 May 22.4 20.02 2.50 0.02004 Jun 20.3 22.04 3.06 0.02004 Jul 20.0 21.65 2.78 0.02004 Aug 21.0 22.20 3.06 0.02004 Sep 22.8 22.54 3.33 0.02004 Oct 23.9 23.30 3.61 13.02004 Nov 24.9 23.47 3.33 53.02004 Dec 25.2 21.35 2.78 0.02005 Jan 24.3 21.35 2.22 0.02005 Feb 24.5 22.29 1.94 0.02005 Mar 24.2 21.27 2.22 0.02005 Apr 23.8 20.04 2.50 13.52005 May 22.8 20.69 2.78 0.02005 Jun 21.6 20.21 3.06 13.02005 Jul 20.2 20.37 3.06 0.02005 Aug 20.9 21.25 3.06 0.02005 Sep 22.6 22.57 3.61 0.02005 Oct 23.6 24.10 3.33 0.02005 Nov 25.2 24.12 3.33 0.02005 Dec 26.0 24.18 2.78 0.02006 Jan 25.8 22.73 2.50 35.52006 Feb 26.0 21.80 2.22 14.02006 Mar 23.9 21.40 1.67 228.32006 Apr 23.4 20.73 2.22 18.02006 May 23.0 20.39 2.50 0.02006 Jun 21.2 20.86 2.78 8.02006 Jul 20.4 21.59 3.33 0.02006 Aug 21.7 21.52 3.33 0.02006 Sep 22.5 22.08 3.33 0.02006 Oct 24.0 23.64 3.61 0.02006 Nov 25.1 22.90 3.06 40.02006 Dec 23.8 19.26 1.39 265.5

Table A-1 Continues


79

2001 Level 2002 Level 2003 Level 2004 Level 2005 Level 2006 Level1/15/2001 1054.29 1/1/2002 1056 1/1/2003 1056 1/1/2004 1051.82 1/1/2005 1053.64 1/1/2006 1052.342/1/2001 1054.29 1/15/2002 1/15/2003 1056 1/15/2004 1054.62 1/15/2005 1055.56 1/15/2006 1052.3

2/15/2001 1054.22 1/31/2002 1/31/2003 1056 1/31/2004 1054.6 2/1/2005 1053.72 1/31/2006 1052.242/28/2001 1054.98 2/1/2002 2/1/2003 1056 2/1/2004 1054.582/15/2005 1053.57 2/1/2006 1052.243/1/2001 1055.24 2/15/2002 2/15/2003 1056 2/15/2004 1054.57 2/28/2005 2/15/2006 1052.82

3/15/2001 1055.25 2/28/2002 2/28/2003 1056 2/28/2004 1054.59 3/1/2005 1053.58 2/28/2006 1052.373/30/2001 1055.25 3/1/2002 3/1/2003 1056 3/1/2004 1054.633/15/2005 1053.43 3/1/2006 1052.374/1/2001 1055.74 3/15/2002 3/15/2003 1056 3/15/2004 1054.56 3/31/2005 3/15/2006 1052.42

4/15/2001 1055.75 3/31/2002 3/31/2003 1055 3/31/2004 1054.84 4/1/2005 3/31/2006 1052.444/30/2001 1055.26 4/1/2002 1056 4/1/2003 1055 4/1/2004 1054.6 4/15/2005 1053.34 4/1/2006 1053.215/1/2001 1055.33 4/15/2002 1056 4/15/2003 1055 4/15/2004 1054.48 4/30/2005 4/15/2006 1052.35

5/15/2001 1055.33 4/30/2002 4/30/2003 1055 4/30/2004 1054.56 5/1/2005 1053.32 4/30/2006 1052.325/31/2001 1055.27 5/1/2002 5/1/2003 1055 5/1/2004 1054.555/15/2005 1053.22 5/1/2006 1052.356/4/2001 1055.39 5/15/2002 5/15/2003 5/15/2004 1054.59 5/31/2005 5/15/2006 1052.33

6/18/2001 1055.39 5/31/2002 5/31/2003 5/31/2004 6/1/20051053.06 5/31/2006 1052.267/2/2001 1055.36 6/1/2002 1056 6/1/2003 1055 6/1/2004 6/15/2005 1052.95 6/1/2006 1052.07

7/16/2001 1055.18 6/15/2002 1057 6/15/2003 1055 6/15/20071054.38 6/30/2005 6/15/2006 1052.117/31/2001 1055.24 6/30/2002 1056 6/30/2003 1055 6/30/20041054.35 7/1/2005 6/30/2006 1051.988/1/2001 1055.17 7/1/2002 1056 7/1/2003 1055 7/1/2004 1054.37 7/15/2005 7/1/2006 1052.33

8/15/2001 1055.25 7/15/2002 1056 7/15/2003 1055 7/15/20041054.39 7/31/2005 7/15/2006 1051.788/31/2001 1055.22 7/31/2002 1056 7/31/2003 1056 7/31/20041054.34 8/1/2005 1052.84 7/31/2006 1051.779/1/2001 1055.17 8/1/2002 1056 8/1/2003 1055 8/1/2004 1054.35 8/15/2005 1052.82

9/15/2001 1055.16 8/15/2002 1056 8/15/2003 1055 8/15/20041054.15 8/31/20059/30/2001 1054.99 8/31/2002 1056 8/31/2003 1055 8/31/20041054.08 9/1/2005 1052.6910/8/2001 1054.96 9/1/2002 1056 9/1/2003 1055 9/1/2004 1054.2 9/15/2005 1052.17

10/15/2001 1054.88 9/15/2002 1056 9/15/2003 1055 9/15/2004 1054.1110/31/2001 1054.89 9/30/2002 1056 9/30/2003 1055 9/30/2004 1054.1211/1/2001 1054.88 10/1/2002 1056 10/1/2003 1055 10/1/20041054.04

11/15/2001 10/15/2002 1056 10/15/2003 1055 10/15/2004 1053.9511/30/2001 10/31/2002 10/31/2003 1055 10/31/2004 1053.9112/1/2001 11/1/2002 1056 11/1/2003 1055 11/1/2004 1053.8912/15/01 1054.86 11/15/2002 1056 11/15/2003 1055 11/15/2004 1053.7612/31/01 1054.85 11/30/2002 1056 11/30/2003 1054 11/30/2004 1053.67

12/1/2002 1056 12/1/2003 1055 12/1/2004 1053.6912/15/2002 1056 12/15/2003 12/15/2004 1052.7512/31/2002 1056 12/31/2003 1052 12/31/2004 1052.81

WATER LEVEL (m) -BH 234/75

Appendix B: Hydrogeological data

Table B-1: Groundwater level fluctuations for BH 234/75 (Source: Regional water office, Dodoma)

80

S/N

BH ID

DEPTH IN METRES BELOW THE SURFACE

CLAY SAND GRAVEL CALCRETE WEATHERED BEDROCK

FRACTURED BEDROCK

BED ROCK

WS SWL

1. 12/48 0-68 63-71 47.56 22.25

2. 34/51 0-24 24-39 39-54 54-56 25.0 55.18

25.0

3. 17/53 0-44 44-50 50-61? 61?-75 46.04 23.78

4. 24/53 0-21 22-24 29-31

21-22 24-29

41-56 58-76

31-37 37-64 30.49 23.48

5. 30/53 0-41 57-58 82-87 36.6 42.7 50.3

24.09

6. 31/53 0-61 6-13 Dry

7. 32/53 0-50 50-61 61-69 42.7 50.3

22.9

8. 35/53 0-35 35-60 36.6 42.7 50.3

23.8

9. 39/53 0-40 40-47 47-57 57-60 32. 38.1

24.4

10. 43/53 0-34 38-41 44-55

34-37 70-72

41-44 55-63 67-70 72-76

63-67 35.7 48.8

24.09

11. 44/53 0-44 55-64

44-55 64-75

48.8 25.3

12. 4/54 0-4 4-43 43-53 36.6 31.7

13. 8/54 0-47 75-76

47-66 69-75 75-76

69-75 76-87 48.8 23.8

14. 36/57 0-55 65-73

55-65 73-89 51.8 23.5

15. 37/57 0-44 71-73

64-67 44-64 73-90 50.3 23.5

16. 10/59 0-37 66-88 37-66 88-92 49.7

17. 16/64 0-55 79-84 55-79 64.9 18.2 18. 21/64 0-47 47-61 47.3 21

19. 22/64 0-41 41-61

20. 26/64 0-44 44-61 45.7 20.9

21. 9/65 0-9 15-55

9-15 55-89 41.2 29.3

22. 30/65 0-8 8-18

8-9 19-20

20-27 18.9 15.2

23. 3/71 0-29 29-34 34-55 35-40 40-61 34.5 31.1

24. 43/65 0-32 35.38 32-35 38-40

40-47 47-61 32.6 21.0

25. 14/68 0-6 6-7 7-70 25 19

26. 34/68 0-59 60-61 91-92

59-60 81-91 92-93

61-81 93-99 12.2

27. 97/70 0-63 76-82 63-76 82-85 85-99 65.6 17.7

28. 108/70

0-60 79-85 69-79 60-69 62.5 17.7

29. 2/71 0-61 78-84 61-78 84-93 93-97 22

30. 107/72

0-9 26-44

9-26 23-64

44-63 64-73 73-91 17.4 14.63

31. 217/73

0-67 67-85 85-92

79-85 66.5 81.7 97

18.4

32 151/74

0-63 63-82 82-89 15.3 22.3

33. 188/7 0-4 4-13 24-27 45-73 24.4 14.5

Table B-2: Summary of lithological logs of the boreholes in the basin (Source: Hamza, 1993)


81

32 151/74

0-63 63-82 82-89 15.3 22.3

33. 188/74

0-4 20-23

4-13 23-24 27-45

24-27 45-73 24.4 44.5

14.5

34. 223/74

0-29 29-38 38-51 9.5 18.9

19.2

35. 264/74

0-2 2-4 6-18

4-6 18-20

20-24 24-46 45.8 12.5

36. 53/75 0-9 15-17

9-15 17-61 70-79

79-85

37. 77/75 0-66 66-77 53.4 19.3

38. 88/75 0-46 50-67 46-50 67-73 73-96 70.1 88.4

19.7

39. 89/75 0-52 64-69 76-79

52-55 52-64 88-92

52-55 92-96

69-76 79-88

96-107

40. 97/75 0-43 47-53 78-75

43-47 53-56 79-84

56-78 84-108 80.8 22.8

41. 117/75

0-32 61-66

32-61 76-90

66-76 89-122 49.7 22

42. 118/75

0-41 41-61 61-64 64-78 78-122 76.2 26.9

43. 119/75

0-9 12-14 15-31

9-12 14-15 31-61

61-122 53.4 28.1

44. 122/75

0-26 31-34

26-31 34-50

50-55 55-105 23.7

45. 123/75

0-60 61-64

60-61 64-76 76-92 91-100 93.9 21.65

46. 131/75

0-60 75-76

60-61 61-75 76-85 85-113 85.4 20.5

47. 147/75

0-5 5-32 32-86 29.8 85.4

48. 163/75

0-29 29-31 31-34 34-41 41-107 29 17.45

49. 169/75

0-29 37-41

41-44 44-47 47-104 85.4 20.9

50. 170/75

0-18 18-37 37-44 44-122 36.6 19.4

51. 182/75

0-12 23-24

12-23 35-56

24-35

52. 193/75

0-17 17-35 35-93 45.7 54

30.12

53. 196/75

0-24 24-29 29.50 50-122 36 24.5

54. 197/75

0-6 6-18 18-56 56-61 39.7 28.7

55. 207/75

0-53 56-67

53-56 67-111 111-116 70.1 85.4

28.7

56. 220/75

0-11 24-37

11-24 37-60

60-122 85.4 31.7

57. 234/75

56-61 0-15 15-56 61-98 98-143 89.9

58. 142/76

0-71 73-98

71-73 92-102 102-122

Table 2.5 continues

82

BOREHOLE ID UTM_X UTM_Y pH TEMP (0c) EC (uS/cm) TDS(mg/l)BH-147.78 800516 9338920 8.2 24.7 844.0 411.0BH_C1 802081 9339850 9.5 26.5 870.0 414.0BH_C2 803267 9340848 9.1 29.3 913.0 410.0BH 117/75 804213 9341694 9.9 28.3 980.0 453.0BH_C5 807755 9345584 9.9 28.8 920.0 417.0BH_C7 807469 9342882 9.9 29.0 910.0 410.0BH_C8 806824 9343126 9.5 28.0 895.0 411.0BH_C3 806288 9342938 9.0 24.2 913.0 409.0Dug_W_PS 802876 9341702 8.5 25.7 127.2 61.4BHC9 801619 9339652 9.7 28.0 880.0 404.0Dug_W_V 802365 9340684 7.5 27.5 233.0 105.9BH_55.82 804009 9339288 9.8 25.6 910.0 440.0Padri_colledge 805332 9332676 7.0 25.6 1048.0 509.0St_Gabriel 805044 9332520 7.0 28.6 1222.0 561.0Private_well 805895 9331840 9.0 26.0 913.0 438.0Good_Hope 804016 9325286 9.4 26.1 879.0 420.0CPPS 804713 9323598 9.2 27.3 1582.0 750.0St Gasper 804610 9323638 8.3 26.4 1548.0 745.0Hon_Shekif 804534 9323312 8.2 25.7 1387.0 677.0Salecian_seminary 803662 9322612 8.3 27.5 815.0 378.0St.Frans_Wakap 803421 9322058 8.7 26.0 1061.0 510.0Assmbl._God 804439 9321782 9.1 26.0 780.0 373.0

SAMPLE ID LOCATION Ca K Mg Na Cl- SO4 NO3UTM_X UTM_Y meq/l meq/l meq/l meq/l meq/l meq/l meq/l

DugWell (V) 804068 9340684 0.4283 0.0952 0.2393 0.8620 0.1070 0.0000 0.0016BH C2 803267 9340848 2.9645 0.0002 1.9180 3.9751 0.6394 1.3542 0.0000BH C3 806288 9342938 2.8631 0.0000 1.8875 3.9338 1.3803 1.4583 0.0048BH C5 807755 9345584 3.0013 0.0000 1.9555 4.4840 0.8732 1.3958 0.0000BH147/78-B 800516 9338920 2.9743 0.0000 1.9305 3.9904 2.8169 2.5000 0.0548

Appendix C: Hydrochemical data and pumping test data

C-1: Field measured parameters with sample localities

Table C-2: ITC laboratory results


83

Sample source K+ Na+ Mg2+ Ca2+ SO4 HCO3 Cl No3 pH* Temp* TDS* EC*mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l ^0C mg/l uS/cm

BH C1 4.5 44.0 36.2 68.2 14.3 200.0 150.0 22.1 9.5 26.5 414.0 810.0BH C2 3.7 55.0 34.0 79.0 1.4 366.0 35.5 10.8 9.1 29.3 410.0 913.0BH C3 4.0 46.0 40.9 83.6 7.2 488.0 71.0 8.9 10.4 24.2 409.0 913.0BH C5 4.3 52.0 55.0 84.0 12.1 366.0 35.1 0.9 9.9 28.8 417.0 920.0BH C7 4.0 49.0 64.0 64.0 3.9 732.0 35.5 7.5 9.9 29.0 410.0 910.0BH C8 4.4 51.0 78.0 60.0 9.9 654.0 35.5 7.5 10.7 28.0 411.0 895.0BH C9 4.4 48.0 28.0 49.0 5.5 488.0 35.5 19.5 9.7 28.0 404.0 880.0PW/V 4.8 26.0 39.0 33.0 5.5 488.0 71.0 9.0 26.0 438.0 913.0St. Gabriel veyula 6.5 41.0 45.0 69.0 12.6 400.0 71.0 150.1 7.0 28.6 561.0 1222.0Padre colledge 6.5 40.0 79.0 12.0 5.0 300.0 71.0 92.5 7.0 25.6 509.0 1048.0

Assemblies of God 8.4 62.0 78.0 11.0 9.4 610.0 71.0 9.1 26.0 373.0 780.0Dug well (village) 6.5 20.0 8.5 11.1 11.0 160.0 25.0 0.0 7.5 27.5 105.9 233.0Dug well P/S 6.4 8.0 8.1 13.0 6.9 105.0 35.5 0.4 8.5 25.7 61.4 127.2CPPS 13.6 58.0 55.6 81.6 20.2 688.0 71.0 117.3 9.2 27.3 750.0 1582.0Wakapuchini 3.9 94.0 14.0 57.1 7.7 610.0 35.5 8.7 26.0 510.0 1061.0Good Hope 3.1 37.0 27.0 60.4 12.6 366.0 71.0 19.9 9.4 26.1 420.0 879.0St Gasper P/S 12.4 46.0 61.0 83.1 29.5 666.0 35.5 127.5 8.3 26.4 745.0 1548.0BH 117/75 2.6 9.2 77.0 80.4 17.0 366.0 106.5 31.5 9.9 28.3 453.0 980.0BH 55/82 1.6 47.0 47.0 68.6 15.3 366.0 35.5 21.7 10.7 25.6 440.0 910.0Salecian BH 11.9 54.0 3.2 54.0 20.2 366.0 35.5 10.2 8.3 27.5 378.0 815.0Padre colledge 5.5 30.0 1.8 72.0 23.5 310.0 30.0 9.1 26.0 373.0 780.0BH 147/78 5.9 27.0 0.9 68.9 25.7 244.0 35.5 31.9 8.2 24.7 411.0 844.0

Table C-3: Chemical laboratory results, GST- Tanzania

* Indicates measurements taken in the field

84

SAMPLE DEPTH DATE Na K Ca Mg Fe Al Si P Cl SiO4 HCO3 NO3 Temp EC PH

Units m mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l (C ) (uS/cm)BH 30A/53 24.5 23/12/88 689.81 13.29 5.07 10.42 0.03 0.11 0.5 0.1 52.2 125.8 ND 23.9 9.01 9BH 35/53 30 24/11/88 102.4 7.14 46.09 20.1 0.01 0.09 11.85 0.37 78.5 45.6 347.7 ND 27.8 832 7.2BH 35/53 40 24/11/88 100.71 7.77 47.2 19.9 0.02 0.09 11.71 0.39 77.7 42.7 348.9 ND 27.8 838 7.4BH 35/53 47 24/11/88 101.12 7.89 47.52 20.4 0.03 0.09 11.6 0.46 78.4 45.6 344 ND 28 811 7.3BH 39/53 40 26/12/88 595.21 12.77 140.7 94.66 0.11 0.51 8.91 1.94 370.3 1154 ND 27.9 4040 7.2BH 8/54 40 23/12/88 333.91 13.77 95.78 45.13 0.03 0.18 nd nd 217.9 204.9 646.6 14.6 27.5 2130 7.2BH 97/70 P 23/12/88 118.44 8.04 53.54 22.83 nd 0.1 26.67 0.47 84 66.4 350.1 2.8 28.1 973 7.5BH 88/75 30 23/12/88 48.13 9.03 19.89 9.57 0.02 0.05 0.7 0.26 27.4 20.1 187.9 ND 28 424 8.7BH 88/75 40 23/11/88 47.09 8.22 19.03 9.26 0.01 0.07 0.78 0.26 26.6 19.8 170.8 ND 26.2 391 8.6BH 88/75 50 23/11/88 51.08 9.8 29.44 10.65 nd 0.03 2.23 0.15 30.9 23.2 235.5 ND 26.4 491 80BH 88/75 60 23/11/88 55.89 12.94 45.06 12.19 0.03 0.07 6.02 0.26 39.6 29.2 308.7 ND 27.4 959 7.6BH 88/75 70 23/11/88 58.5 11.46 45.78 12.38 0.02 0.04 6.47 0.22 41.2 30.5 402.6 0.2 26.9 788 7.3BH 88/75 80 23/11/88 88.23 10.88 48.32 17.56 0.03 0.08 13.44 0.34 54.7 45 291.6 0.5 26.3 592 7.6BH 88/75 90 23/11/88 97.69 9.53 51.32 20.02 0.01 0.07 17.19 0.4 58.2 53.5 420.9 0.2 26.9 866 7.3BH 88/75 94 23/11/88 89.3 10.86 49.65 11.45 0.02 0.08 13.18 0.35 54.9 45.3 356.2 0.6 26.1 877 7.8BH 97/75 40 23/11/88 74.5 5.89 32.32 19.15 0.01 0.06 8.97 0.27 43.5 39.1 ND 27 563 7.9BH 118/75 P 20/12/88 94.93 6.06 57.05 25.07 0.02 0.1 21.91 0.43 84.3 63.3 308.7 7.3 28.9 1047 7.8BH 119/75 P 26/12/88 100.66 6.83 57.48 26.42 nd 0.11 23.87 0.52 88.3 77.4 278.2 3.8 28.5 819 7.4BH 123/75 28 22/12/88 82.1 12.9 19.29 14.6 0.02 0.1 5.23 0.34 63.8 12 0.9 26.8 768BH 163/75 25 22/12/88 5.26 5.92 17.67 3.21 0.24 0.06 17.7 0.08 5 nd 102.5 ND 27.5 201 7.6BH 169/75 P 26/12/88 112.36 7.69 15.99 25.85 nd 0.09 10.01 0.45 58.3 60.2 308.7 ND 816 8.5BH 169/75 40 20/12/88 114.38 8.04 34.17 25.18 nd 0.09 9.07 0.47 63.3 63.8 391.6 ND 27.4 920 7.7BH 170/75 P 20/12/88 94.37 8.09 33.36 23.41 nd 0.09 8.91 0.43 48.9 48.4 372.1 ND 29.2 801 8.2BH 182/75 40 26/12/88 93.46 5.54 34.82 23.47 nd 0.06 6.38 0.34 77.8 40.3 ND 26.7 847 8.1BH 193/75 40 22/12/88 107.74 5.54 57.36 24.41 0.01 0.07 20.84 0.36 54.3 45 ND 27.9 899 7.4BH 86/78 30 23/12/88 126.09 10.71 35.98 21.01 nd 0.09 9.88 0.38 105.9 46.8 359.9 ND 26 883 7.7BH 86/78 40 23/12/88 125.47 11.06 36.46 21.99 nd 0.1 10.59 0.44 105.9 48.8 353.8 0.1 26.2 919 7.7BH 86/78 50 23/12/88 123.04 11.54 38.08 22.1 0.02 0.1 11.22 0.44 104.1 50.1 345.3 ND 27 906 7.7BH 86/78 60 23/12/88 124.19 10.83 39.25 23 nd 0.09 12.08 0.49 104.7 52 362.3 ND 28.3 912 7.8BH 86/78 70 23/12/88 123.99 11.06 38.56 22.45 0.01 0.1 12 0.46 103.7 53.8 357.5 ND 28.2 926 7.6BH 103/78 35 14/11/88 133.15 11.08 27.71 27.91 0.01 0.11 3.87 0.54 143.5 23.7 401.4 0.5 25.7 909 7.7BH 103/78 40 14/11/88 113.93 10.56 39.84 30.07 0.02 0.12 6.9 0.62 149.6 27.2 302.6 0.3 25.8 962 7.7BH 103/78 50 14/11/88 129.84 7.87 79.48 38.15 0.03 0.16 18.05 0.77 193.6 61.9 377 1.2 28.6 1295 8BH 103/78 64 14/11/88 98.13 8.29 59.47 26.41 0.03 0.11 25.66 0.53 98.1 80 290.4 3.9 29.3 914 8.1BH 26/79 P 26/12/88 95.16 4.89 55.91 25.4 0.06 0.21 23.03 0.37 84.2 78.2 3.7 28.4 887 7.5BH 55/82 P 23/12/88 119.15 9.67 48.94 28.51 0.07 0.23 17.19 0.48 115.3 61.5 339.2 4.4 27.1 1018 7.7ND = Not detectedP= Pumped

Table C-4: Hydrochemical data, 1989 dataset (Source: Shindo, 1989)


85

SAMPLE ID DATE TEMP EC Na K Ca Mg Fe Al Si Cl SO4 HCO3 NO3Units 0C uS/cm mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l

SITE_A 27/12/1988 0.69 1.90 3.00 0.37 0.00 0.00 0.09 1.20 0.70 0.00SITE_A 6/1/1989 0.27 0.47 0.08 0.07 0.00 0.00 0.00 0.80 0.00 0.90SITE_M 12/1/1989 0.16 1.80 0.50 0.06 0.02 0.05 0.13 0.20 0.00 2.40 0.00SITE_M 14/01/1989 22.40 0.14 0.70 3.60 0.14 0.10 0.01 0.42 0.20 0.00 12.70 0.00SITE_M 19/01/1989 20.30 0.00 4.80 0.91 0.15 0.01 0.03 0.81 0.80 2.10 0.00 1.50SITE_M 20/01/1989 19.11 2.30 1.90 2.40 0.32 0.02 0.06 1.20 1.10 0.00 6.80 2.00SITE_M 22/01/1989 0.90 2.90 0.80 0.11 0.00 0.01 0.07 2.10 1.10 1.80SITE_M 26/01/1989 20.80 2.00 0.47 2.20 0.14 0.00 0.01 0.62 1.10 1.50 5.90 2.00SITE_M 29/01/1989 14.12 1.90 0.55 3.10 0.32 0.21 0.36 1.60 0.70 0.00 11.70 0.50SITE_M 30/01/1989 22.50 2.90 7.80 1.30 0.20 0.01 0.04 0.70 2.20 0.90 2.40 4.90SITE_M 6/2/1989 20.90 16.65 0.30 0.00 0.30 0.09 0.00 0.00 0.13 0.50 0.00 2.00 0.00SITE_M 14/04/1989 15.20 0.69 7.10 2.20 0.00 0.00 3.10 12.50 9.70 29.80 10.70SITE_M 20/04/1989 7.30 1.10 3.30 0.95 0.01 0.00 0.62 4.40 3.10 16.10 4.60RAIN_ZENKA 9/2/1989 0.53 0.30 0.49 0.11 0.00 0.00 0.15 0.70 0.00 2.40 0.30SITE_M 3/12/1989 0.02 0.44 0.07 0.01 0.00 0.00 0.00 0.07 0.02 0.50 0.25SITE_M 5/12/1989 0.15 0.48 0.37 0.08 0.00 0.00 0.00 0.44 0.58 0.55SITE_M 6/12/1989 0.24 0.56 0.38 0.05 0.00 0.00 0.00 0.50 0.22 2.00 0.75SITE_M 7/12/1989 0.33 0.64 0.12 0.08 0.01 0.01 0.00 0.68 0.51 0.68SITE_M 14/12/1989 0.12 0.23 0.12 0.02 0.00 0.00 0.00 0.26 0.02 1.50 0.02SITE_M 15/12/1989 0.12 0.40 0.24 0.02 0.00 0.00 0.00 0.23 0.15 1.50 0.52SITE_M/1 16/12/1989 0.36 1.37 1.10 0.05 0.01 0.00 0.02 0.34 0.02 0.01SITE_M/2 16/12/1989 0.75 0.68 0.70 0.20 0.00 0.02 0.09 1.10 0.15 0.97SITE_M 22/12/1989 0.24 19.10 0.30 0.09 0.00 0.00 0.04 0.38 0.02 0.00SITE_M 23/12/1989 0.36 3.10 0.28 0.07 0.01 0.02 0.00 13.60 0.58 0.44SITE_M 30/12/1989 25.90 17.55 0.18 0.36 0.58 0.04 0.00 0.00 0.00 3.00 0.49 0.43SITE_M 1/1/1990 0.51 4.30 0.33 0.10 0.00 0.01 0.02 0.75 0.12 1.50 0.04SITE_M 4/1/1990 0.15 3.50 0.29 0.03 0.01 0.02 0.00 3.60 0.26 2.90 0.78SITE_M 5/1/1990 0.09 0.50 0.38 0.03 0.00 0.00 0.00 3.00 0.33 2.40 0.59SITE_M 13/01/1990 0.21 0.00 0.32 0.05 0.00 0.01 0.00 0.43 0.36 0.45SITE_M 16/01/1990 0.21 0.20 1.00 0.05 0.00 0.00 0.00 0.47 0.48 0.40SITE_M 10/2/1990 0.30 14.60 1.50 0.09 0.00 0.01 0.03 0.57 0.32 0.62SITE_M 12/2/1990 0.87 3.60 1.60 0.18 0.00 0.01 0.13 11.10 0.42 1.00SITE_M 15/02/1990 35.00 107.40 0.45 0.30 0.44 0.02 0.00 0.01 0.09 3.60 0.11 3.40 0.73SITE_M 17/02/1990 0.15 0.00 0.90 0.05 0.00 0.01 0.00 0.43 0.90 0.53SITE_M 18/02/1990 21.90 8.03 0.09 2.90 0.56 0.13 0.00 0.10 0.13 0.76 0.00 5.90 0.70SITE_M 22/02/1990 30.80 25.60 0.48 7.80 1.20 0.07 0.00 0.01 0.03 2.90 0.18 1.50 0.35SITE_M 25/02/1990 20.50 10.78 0.48 0.75 0.54 0.11 0.00 0.01 0.11 6.40 0.88 2.40 0.87SITE_M 26/02/1990 20.10 0.15 1.30 0.32 0.08 0.01 0.01 0.07 0.45 0.00 0.31SITE_M 28/02/1990 18.80 0.03 2.60 0.58 0.02 0.00 0.02 0.00 0.15 0.42 0.33SITE_M 1/3/1990 0.27 1.50 0.37 0.05 0.01 0.01 0.07 0.71 0.22 0.47SITE_M 3/3/1990 0.09 0.00 1.90 0.06 0.01 0.02 0.06 0.17 0.01 0.52SITE MEIA MEIA18/12/1989 0.42 0.32 0.01 0.02 0.05 0.74 0.02 4.90 0.00SITE MEIA MEIA19/12/1989 25.50 0.00 0.01 0.02 0.00 4.40SITE MEIA MEIA26/12/1989 0.71 0.39 1.90 0.31 0.02 0.00 0.13 1.10 0.62 6.80 0.00SITE MEIA MEIA5/1/1990 0.42 0.00 1.40 0.16 0.01 0.14 0.59 1.20 4.90 0.00SITE MEIA MEIA19/01/1990 25.50 0.51 0.00 1.00 0.17 0.01 0.01 0.03 0.77 0.58 0.00SITE MEIA MEIA26/01/1990 26.40 0.57 0.00 1.50 0.21 0.02 0.05 0.29 0.97 0.04 0.00SITE MEIA MEIA2/2/1990 0.11 0.00 1.20 0.15 0.00 0.01 0.25 0.55 0.12 0.01SITE MEIA MEIA12/2/1990 1.90 1.60 3.20 0.54 0.01 0.27 2.20 2.90 10.20 0.00SITE MEIA MEIA20/02/1990 23.50 68.60 0.81 1.20 1.50 0.21 0.00 0.03 0.45 1.10 0.72 0.00SITE MEIA MEIA27/02/1990 29.10 33.00 0.36 0.24 0.87 0.14 0.01 0.01 0.13 0.80 0.56 2.90 0.00SITE CHIHANGA23/12/1990 1.50 0.94 2.60 0.26 0.01 0.17 0.76 2.00 0.58 8.30 0.01SITE MKONDAI23/12/1990 0.57 2.30 4.70 0.71 0.02 0.02 0.88 0.96 0.30 24.90 0.02SITE CHENENE24/02/1990 25.90 131.50 1.00 1.40 4.00 0.39 0.00 0.01 0.25 1.50 0.02 15.60 0.03

Table C-5: Rainfall dataset (Source: Shindo, 1990)

86

SAMPLE_ID Ca Mg Na K SO4 Cl NO3 HCO3 SiO2mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l mg/l

NZUGUNI POLICE 114.3 57.0 329.0 10.0 240.5 442.1 219.2 592.7 100.4NZUGUNI VILLAGE 53.9 15.7 74.9 7.5 35.5 98.0 35.7 226.6 95.2MAHOMA MAKULU 260.3 72.4 391.3 7.8 118.4 1104.2 130.3 435.8 112.4IPALA VILLAGE 61.4 29.3 123.7 5.3 51.8 131.1 57.5 549.2 61.4HOMBOLO OUTLET 56.5 17.9 93.4 4.5 86.2 105.2 103.1 283.3 90.7HOMBOLO VILLAGE 31.2 35.4 419.7 22.6 324.1 505.0 0.0 370.5 43.4HOMBOLO SPRING 61.6 22.9 29.8 9.8 70.0 59.6 0.0 283.3 52.6HOMBOLO MAKULU VILLAGE 19.7 33.7 172.1 1.0 61.3 273.8 0.0 544.8 83.7HOMBOLO MAKULU SEEPAGE 32.2 9.4 64.6 1.0 20.3 108.9 0.0 260.5 69.8ZEPISA_DCT 7.5 1.9 1315.0 3.2 748.6 1090.2 0.0 1307.5 71.9MAHOMANYIKA 24/75 161.8 113.3 300.6 13.5 435.7 815.5 85.3 501.2 51.7MAHOMANYIKA 263/73 2.4 2.3 102.2 7.2 6.7 61.5 0.0 252.8 0.0MAHOMANYIKA VILLAGE 104.1 115.1 582.0 14.7 650.7 982.4 33.2 571.0 58.0MAHOMANYIKA 234/74 19.2 26.0 253.1 17.4 156.3 277.1 0.0 488.1 15.2NZASA BH 33.5 66.0 239.2 8.3 217.6 372.6 40.6 640.7 70.4NZASA VILLAGE 121.2 118.0 298.9 98.0 450.5 571.7 0.0 745.3 94.4MAHOMANYIKA 2KM 44.1 137.5 372.5 60.1 383.7 675.5 36.5 632.0 70.4KITELELA 115/75 80.1 150.0 324.2 9.4 119.7 1098.8 85.9 318.2 0.0KITELELA SHALLOW WELL 183.4 127.5 63.1 15.0 130.8 501.7 67.6 544.8 102.1MAHOMANYIKA PORT 1.5 4.8 7.8 5.0 18.8 21.7 15.9 65.4 314.6ZEPISA_300m NW of VILLAGE BH 19.2 7.6 101.8 14.4 83.1 125.2 39.5 152.5 113.9ZEPISA_500m WEST OF VILLAGE BH 30.2 30.0 141.0 8.7 62.0 239.2 0.0 418.4 6.9HOMBOLO_163/75 52.5 21.5 122.8 7.2 98.4 112.5 31.0 435.8 55.4RAIN_RWE 1.8 0.4 0.8 8.9 2.2 5.4 0.0 34.9 0.0ZEPISA_BH 92/93 14.1 0.1 0.5 1.1 0.0 16.4 0.0 69.7 0.0IMMACULATE_27.91 41.9 17.6 60.5 9.8 39.5 130.2 174.5 100.2 92.5SHEKIFBH 77.9 25.7 115.9 12.3 77.6 237.8 286.0 82.8 96.1DONBOSCO_BH 51.4 31.5 40.8 8.6 23.3 137.3 115.8 192.8 87.7SALECIAN BH 33.6 13.1 52.2 14.1 62.1 83.2 88.0 113.3 85.7MADINI 46.7 19.0 113.6 5.9 70.0 176.5 48.1 248.8 65.5SIST IVREA_BH 105.8 54.4 103.2 11.0 69.0 279.2 449.1 109.1 89.9TEDDYS CAMPUS 57.4 20.5 107.3 20.0 114.3 194.3 224.5 87.2 92.0ST_JEMA 7.1 3.3 103.7 7.8 83.3 126.7 40.5 183.1 55.2PRECIOUS BLOOD MISSION 21.1 9.6 94.6 19.8 87.6 125.3 89.3 91.5 67.2ADORES OF BLOOD 70.5 42.0 76.2 4.2 69.6 117.9 160.4 383.5 68.7ASSEMBLIES OF GOD 41.7 17.5 63.7 8.4 45.9 118.2 143.6 200.5 99.9MAKOLE 88.0 56.0 220.6 9.5 80.8 593.3 132.8 435.8 64.6FAHARI_BOTTLERS BH 93 136.3 44.6 235.6 21.2 153.4 551.3 331.2 139.5 80.7FAHARI_BH 66.4 21.0 124.1 14.8 97.7 226.9 258.2 109.0 102.5FAHARI BH 103/93 49.5 20.5 195.9 7.4 100.0 368.2 63.3 333.0 74.9FAHARI_50M 55.9 23.0 331.5 10.7 196.2 553.8 39.0 322.5 24.8HURUMA HOMECRAFT 84.2 31.8 296.0 6.1 93.6 327.7 138.1 693.0 80.9SHALLOW WELL_200M 96.5 31.0 291.9 24.7 263.2 442.3 0.0 527.4 35.5SHALLOW WELL_300M 161.7 60.5 492.7 46.3 563.8 814.8 33.0 614.5 29.8MAZENGO SEC SCHOOL OLD BH 73.3 26.6 394.8 4.7 180.3 241.3 206.1 871.7 86.9MAZENGO_NEW BH 67.4 33.5 145.1 9.1 73.8 282.5 33.4 422.8 6.0RETIRED_PRES LODGE 5.6 3.7 31.9 18.2 39.6 31.2 0.0 183.1 0.0RWE_BH 42.4 26.8 138.4 7.3 43.8 302.2 40.1 296.4 13.7ZEPISA DCT 2.1 0.5 3.3 1.4 19.3 19.8 7.6 21.8 0.02

Table C-6: Hydrochemical data, 1997 dataset (Source: Nkotagu 1997)


87

Figure C-1: Pumping test analysis (Walton)

Figure C-2: Graphs of drawdown against time for some boreholes

88

Temp (0C) 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40W at Altitude (m)

0 0.43 0.46 0.49 0.52 0.55 0.58 0.61 0.64 0.66 0.69 0.71 0.73 0.8 0.8 0.78 0.8 0.82 0.83 0.84 0.85500 0.45 0.48 0.51 0.54 0.57 0.6 0.62 0.65 0.67 0.7 0.72 0.74 0.8 0.8 0.79 0.81 0.82 0.84 0.85 0.861000 0.46 0.49 0.52 0.55 0.58 0.61 0.64 0.66 0.69 0.71 0.73 0.75 0.8 0.8 0.8 0.82 0.83 0.85 0.86 0.872000 0.49 0.52 0.55 0.58 0.61 0.64 0.66 0.69 0.71 0.73 0.75 0.77 0.8 0.8 0.82 0.84 0.85 0.86 0.87 0.883000 0.52 0.55 0.58 0.61 0.64 0.66 0.69 0.71 0.73 0.75 0.77 0.79 0.8 0.8 0.84 0.85 0.86 0.88 0.88 0.89

SAMPLE DEPTH Cl (gw)min Cl (gw)max Cl (rain)min Cl (rain)min P(annual) Rech_min Rech_maxm mgl-1 mgl-1 mgl-1 mgl-1 mm mmyear-1 mmyear-1

BH 30A/53 24.5 23.69 80.71 0.46 1.14 550.00 10.72 7.76BH 35/53 30 78.50 78.50 3.23 7.98BH 35/53 40 77.70 77.70 3.27 8.06BH 35/53 47 78.40 78.40 3.24 7.99BH 97/70 P 84.00 84.00 3.02 7.45BH 88/75 30 27.40 27.40 9.26 22.85BH 88/75 40 26.60 26.60 9.54 23.54BH 88/75 50 30.90 30.90 8.21 20.27BH 88/75 60 39.60 39.60 6.41 15.81BH 88/75 70 41.20 41.20 6.16 15.20BH 88/75 80 54.70 54.70 4.64 11.45BH 88/75 90 58.20 58.20 4.36 10.76BH 88/75 94 54.90 54.90 4.62 11.41BH 97/75 40 43.50 43.50 5.83 14.40BH 118/75 P 84.30 84.30 3.01 7.43BH 119/75 P 88.30 88.30 2.87 7.09BH 123/75 28 63.80 63.80 3.98 9.81BH 169/75 P 58.30 58.30 4.35 10.74BH 169/75 40 63.30 63.30 4.01 9.89BH 170/75 P 48.90 48.90 5.19 12.81BH 182/75 40 77.80 77.80 3.26 8.05BH 193/75 40 54.30 54.30 4.67 11.53BH 86/78 30 105.90 105.90 2.40 5.91BH 86/78 40 105.90 105.90 2.40 5.91BH 86/78 50 104.10 104.10 2.44 6.02BH 86/78 60 104.70 104.70 2.42 5.98BH 86/78 70 103.70 103.70 2.45 6.04BH 103/78 35 143.50 143.50 1.77 4.36BH 103/78 64 98.10 98.10 2.59 6.38

4.49 10.44ND = Not detectedP= Pumped average

Appendix D: Recharge estimation data

Table D-1: Recharge flux determination

Table D-2: Values of weighing factor (W) for the effect of Radiation on ETo at different temperature

and altitudes (Source: (Doorenbos and Pruitt, 1984)


89

2000 2001 2002 2003 2004 2005 2006mm mm mm mm mm mm mm

Jan 167 164 161 173 171 161 175Feb 180 188 175 161 157 159 168Mar 159 176 168 169 161 160 151Apr 163 156 160 164 149 150 154May 178 171 182 174 164 171 169Jun 166 161 180 176 176 164 168Jul 166 164 183 174 171 162 172Aug 173 181 169 186 179 171 175Sep 187 190 191 188 186 186 181Oct 196 197 200 203 195 201 198Nov 190 205 204 212 199 205 195Dec 173 198 182 210 182 208 151Total 2100 2149 2155 2191 2088 2096 2056

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 87 85 142 0 0 0 0 0 0 0 132 294 740DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 87 85 142 0 0 0 0 0 0 0 132 294 740REF POTEVP 167 180 159 163 178 166 166 173 187 196 190 173 2098Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 117 126 111 114 125 116 116 121 131 137 133 121 1469P"-PET" (3)-(6) -30 -41 31 -114 -125 -116 -116 -121 -131 -137 -2 173 -729AC POT Wloss -30 -71 0 -114 -239 -355 -471 -592 -723 -860 -862 0 0SM soil moisture 123 93 124 70 31 14 6 3 1 0 0 150dSM change soil m -27 -30 31 -54 -40 -16 -8 -4 -2 -1 0 150AET actual evap 114 114 111 54 40 16 8 4 2 1 132 121 716D soil mst. deficit 3 12 0 60 85 100 109 118 129 136 1 0 753S soil mst. surplus 0 0 0 0 0 0 0 0 0 0 0 24 24TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 4 3 2 2 2 1 1 1 1 1 0 5 22DET detention 15 12 10 8 6 5 4 3 3 2 2 19 0ROTL total runoff (mm) 4 3 2 2 2 1 1 1 1 1 0 5 22ROTL total runoff (m3s-1) 2 2 1 1 1 1 1 0 0 0 0 3WARN=0 WHC not reached 0 1 0 0 0 0 0 0 0 0 0 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 286 73 58 65 0 0 0 0 0 0 0 94 575DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 286 73 58 65 0 0 0 0 0 0 0 94 575REF POTEVP 164 188 176 156 171 161 164 181 190 197 205 198 2151Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 115 132 123 109 120 113 115 127 133 138 144 139 1506P"-PET" (3)-(6) 171 -59 -65 -45 -120 -113 -115 -127 -133 -138 -144 -45 -931AC POT Wloss 0 -59 -124 -169 -289 -401 -516 -643 -776 -914 -1057 -1102 0SM soil moisture 150 101 65 49 22 10 5 2 1 0 0 0dSM change soil m 150 -49 -36 -17 -27 -12 -6 -3 -1 -1 0 0AET actual evap 115 121 94 82 27 12 6 3 1 1 0 94 554D soil mst. deficit 0 10 30 28 93 101 109 124 132 137 143 45 952S soil mst. surplus 21 0 0 0 0 0 0 0 0 0 0 0 21TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 4 3 3 2 2 1 1 1 1 1 0 0 20DET detention 17 13 11 9 7 6 4 4 3 2 2 1 0ROTL total runoff (mm) 4 3 3 2 2 1 1 1 1 1 0 0 20ROTL total runoff (m3s-1) 2.39 1.91 1.53 1.22 0.98 0.78 0.63 0.50 0.40 0.32 0.26 0.21WARN=1 WHC not reached 0 0 0 0 0 0 0 0 0 0 0 0

Table D-3: PET values in mm

Table D-3: WTRBLN simulation results for the year 2000


90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 324 75 96 17 0 0 0 0 0 0 93 0 604DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 324 75 96 17 0 0 0 0 0 0 93 0 604REF POTEVP 161 175 168 160 182 180 183 169 200 204 182 0 1964Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 0 0CROPPOTEVP 113 123 118 112 127 126 128 118 140 143 127 0 1375P"-PET" (3)-(6) 211 -47 -22 -95 -127 -126 -128 -118 -140 -143 -35 0 -771AC POT Wloss 0 -47 -69 -165 -292 -418 -546 -664 -804 -947 -982 0 0SM soil moisture 150 109 95 50 21 9 4 2 1 0 0 0dSM change soil m 150 -41 -15 -45 -29 -12 -5 -2 -1 0 0 0AET actual evap 113 116 111 61 29 12 5 2 1 0 93 0 543D soil mst. deficit 0 7 7 51 99 114 123 116 139 142 35 0 832S soil mst. surplus 61 0 0 0 0 0 0 0 0 0 0 0 61TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 12 10 8 6 5 4 3 3 2 2 1 1 57DET detention 49 39 31 25 20 16 13 10 8 7 5 4 0ROTL total runoff (mm) 12 10 8 6 5 4 3 3 2 2 1 1 57ROTL total runoff (m3s-1) 6.98 5.58 4.47 3.57 2.86 2.29 1.83 1.46 1.17 0.94 0.75 0.60WARN=1 WHC not reached 0 0 0 0 0 0 0 0 0 0 0 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 116 85 44 3 0 0 0 0 0 26 54 119 446DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 116 85 44 3 0 0 0 0 0 26 54 119 446REF POTEVP 173 161 169 164 174 176 174 186 188 203 212 210 2190Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 121 113 118 115 122 123 122 130 132 142 148 147 1533P"-PET" (3)-(6) -5 -28 -75 -112 -122 -123 -122 -130 -132 -117 -94 -28 -1087AC POT Wloss -1092 -1120 -1195 -1307 -1429 -1552 -1674 -1804 -1936 -2052 -2147 -2175 0SM soil moisture 0 0 0 0 0 0 0 0 0 0 0 0dSM change soil m 0 0 0 0 0 0 0 0 0 0 0 0AET actual evap 116 85 44 3 0 0 0 0 0 26 54 119 446D soil mst. deficit 5 28 75 112 122 123 122 130 132 117 94 28 1087S soil mst. surplus 0 0 0 0 0 0 0 0 0 0 0 0 0TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 0 0 0 0 0 0 0 0 0 0 0 0 0DET detention 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (mm) 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (m3s-1) 0 0 0 0 0 0 0 0 0 0 0 0WARN=1 WHC not reached 0 0 0 0 0 0 0 0 0 0 0 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 61 82 162 0 0 0 0 0 0 13 53 0 371DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 61 82 162 0 0 0 0 0 0 13 53 0 371REF POTEVP 171 157 161 149 164 176 171 179 186 195 199 182 2090Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 120 110 113 104 115 123 120 125 130 137 139 127 1463P"-PET" (3)-(6) -59 -28 50 -104 -115 -123 -120 -125 -130 -124 -86 -127 -1092AC POT Wloss -1113 -1141 0 -104 -219 -342 -462 -587 -718 -841 -927 -1055 0SM soil moisture 0 0 50 75 35 15 7 3 1 1 0 0dSM change soil m 0 0 50 25 -40 -20 -8 -4 -2 -1 0 0AET actual evap 61 82 113 25 40 20 8 4 2 14 53 0 422D soil mst. deficit 59 28 0 79 75 104 111 121 128 123 86 127 1041S soil mst. surplus 0 0 0 0 0 0 0 0 0 0 0 0 0TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 0 0 0 0 0 0 0 0 0 0 0 0 0DET detention 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (mm) 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (m3s-1) 0 0 0 0 0 0 0 0 0 0 0 0WARN=1 WHC not reached 1 0 0 0 0 0 0 0 0 0 0 0





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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 0 0 0 14 0 13 0 0 0 0 0 0 27DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 0 0 0 14 0 13 0 0 0 0 0 0 27REF POTEVP 161 159 160 150 171 164 162 171 186 201 205 208 2098Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 113 111 112 105 120 115 113 120 130 141 144 146 1469P"-PET" (3)-(6) -113 -111 -112 -92 -120 -102 -113 -120 -130 -141 -144 -146 -1442AC POT Wloss -1555 -1666 -1778 -1870 -1989 -2091 -2204 -2324 -2454 -2595 -2739 -2884 0SM soil moisture 0 0 0 0 0 0 0 0 0 0 0 0dSM change soil m 0 0 0 0 0 0 0 0 0 0 0 0AET actual evap 0 0 0 14 0 13 0 0 0 0 0 0 27D soil mst. deficit 113 111 112 91 120 102 113 120 130 141 144 146 1442S soil mst. surplus 0 0 0 0 0 0 0 0 0 0 0 0 0TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 0 0 0 0 0 0 0 0 0 0 0 0 0DET detention 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (mm) 0 0 0 0 0 0 0 0 0 0 0 0 0ROTL total runoff (m3s-1) 0 0 0 0 0 0 0 0 0 0 0 0WARN=1 WHC not reached 0 0 0 0 0 0 0 0 0 0 0 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalP rain 36 14 228 18 0 8 0 0 0 0 40 265 609DRO direct runoff 0 0 0 0 0 0 0 0 0 0 0 0 0P-DRO prec - direct roff 36 14 228 18 0 8 0 0 0 0 40 265 609REF POTEVP 175 168 151 154 169 168 172 175 181 198 195 151 2057Kc crop factor 1 1 1 1 1 1 1 1 1 1 1 1 0CROPPOTEVP 123 118 106 108 118 118 120 123 127 139 137 106 1440P"-PET" (3)-(6) -87 -104 123 -90 -118 -110 -120 -123 -127 -139 -97 159 -831AC POT Wloss -87 -191 0 -90 -208 -318 -438 -561 -687 -826 -922 0 0SM soil moisture 84 42 150 82 37 18 8 4 2 1 0 150dSM change soil m -66 -42 108 -68 -45 -19 -10 -5 -2 -1 0 150AET actual evap 102 56 106 86 45 27 10 5 2 1 40 106 584D soil mst. deficit 21 62 0 22 73 90 110 118 125 138 96 0 855S soil mst. surplus 0 0 15 0 0 0 0 0 0 0 0 10 24TL AVAIL wat. avail. runoff 0 0 0 0 0 0 0 0 0 0 0 0 0RO runoff without dir. roff 2 1 3 2 2 1 1 1 1 1 0 2 18DET detention 7 6 12 9 7 6 5 4 3 2 2 9 0ROTL total runoff (mm) 2 1 3 2 2 1 1 1 1 1 0 2 18ROTL total runoff (m3s-1) 1.06 0.84 1.67 1.33 1.07 0.85 0.68 0.55 0.44 0.35 0.28 1.32



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Year precipitation Recharge Rechargemm mm/year mm/year

1922 304.00 -2.88 0.001923 716.00 9.48 9.481924 220.00 -5.40 9.001925 593.00 5.79 5.791926 603.00 6.09 6.091927 531.00 3.93 3.931928 384.00 -0.48 0.001929 591.00 5.73 5.731930 721.00 9.63 9.631931 612.00 6.36 6.361932 413.00 0.39 0.391933 598.00 5.94 5.941934 520.00 3.60 3.601935 626.00 6.78 6.781936 786.00 11.58 11.581937 511.00 3.33 3.331938 578.00 5.34 5.341939 508.00 3.24 3.241940 621.00 6.63 6.631941 742.00 10.26 10.261942 713.00 9.39 9.391943 393.00 -0.21 0.001944 886.00 14.58 14.581945 521.00 3.63 3.631946 308.00 -2.76 0.001947 1083.00 20.49 20.491948 363.00 -1.11 0.001949 500.00 3.00 3.001950 480.00 2.40 2.401951 668.00 8.04 8.041952 449.00 1.47 1.471953 411.00 0.33 0.331954 364.00 -1.08 0.001955 521.00 3.63 3.631956 532.00 3.96 3.961957 541.00 4.23 4.231958 722.00 9.66 9.661959 644.00 7.32 7.321960 632.00 6.96 6.961961 647.00 7.41 7.411962 565.00 4.95 4.951963 436.00 1.08 1.08

1964 630.00 6.90 6.901965 609.00 6.27 6.271966 349.00 -1.53 0.001967 621.00 6.63 6.631968 638.00 7.14 7.141969 284.00 -3.48 0.001970 541.00 4.23 4.231971 610.00 6.30 6.301972 699.00 8.97 8.971973 534.00 4.02 4.021974 424.00 0.72 0.721975 430.00 0.90 0.901976 482.00 2.46 2.461977 396.00 -0.12 0.001978 597.00 5.91 5.911979 529.00 3.87 3.871980 614.00 6.42 6.421981 355.00 -1.35 0.001982 642.00 7.26 7.261983 349.00 -1.53 0.001984 479.00 2.37 2.371985 622.00 6.66 6.661986 543.00 4.29 4.291987 423.00 0.69 0.691988 571.00 5.13 5.131989 917.00 15.51 15.511990 623.00 6.69 6.691991 555.00 4.65 4.651992 592.90 5.79 5.791993 356.00 -1.32 0.001994 645.60 7.37 7.371995 773.50 11.21 11.211996 701.90 9.06 9.061997 932.40 15.97 15.971998 321.60 -2.35 0.001999 527.90 3.84 3.842000 739.60 10.19 10.192001 574.70 5.24 5.242002 604.01 6.12 6.122003 445.65 1.37 1.372004 371.30 -0.86 0.002005 26.50 -11.21 0.002006 609.25 6.28 6.28

Average 551.13 5.08

Table D-10: Recharge calculations from rainfall data (1922 - 2006)


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Earth model parameters:Soil moisture max (mm) 500Soil moisture min (mm) 10Soil moisture init(mm) 250Soil moisture fld (mm) 360Max surf stor (mm) 0Max intc loss (mm) 0Ksat (mm/d) 1Unsaturated recession (d) 0Number of reservoirs 2Saturated recession constant (d) 3500storage coefficient 0.012Initial gw level (m) 0Time step (d) 0Reductor 0Time shift (d) 0

Table D-11: Applied parameters for the Earth modelling

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Appendix E: Modelling

Figure E-1: Contour map of the piezometric levels during well abstraction

Figure E-2: Contour map of the piezometric levels during no well abstraction

Arrows: indicated direction of groundwater flow


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Appendix F: The study area

Plate F-1: At BH 119/75 Plate F-2: Near BH 119/75

Plate F-3: The basin during dry period Plate F-4: Series of “Mibuyu” trees along the

Zanka fault

Plate F-5: Production well opposite to the Pump

house Plate F-6: The Pump house

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Plate F-7: View of the basin from an

elevated point

Plate F-8: Dug well in the village

Plate F-9: The biggest storage tank

before water is pumped to town Plate F-10: One of the pump house in the

Eastern side

Plate F-11: The Kinyasungwe River path

during the dry season

Plate F-12: Dug well near the Primary

school in the village


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Figure F-13: Base map of the study area

Legend

Pump HouseProduction wellsMonitoring wellsMain roadsRivers

Legend



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