groundwater contribution and recharge estimation … · groundwater contribution and recharge...

GROUNDWATER CONTRIBUTION AND

RECHARGE ESTIMATION IN THE UPPER

BLUE NILE FLOWS, ETHIOPIA

Getachew Hadush Asmerom

March, 2008

Groundwater contribution and recharge estimation

in the Upper Blue Nile flows,

(Ethiopia)

By

Getachew Hadush Asmerom

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. (Maciek) Lubczynski (Chairman)

Dr. ir. P. Droogers (external examiner)

Dr. A.S.M. Gieske (first Supervisor)

Dr.ing. T.H.M. Rientjes (second Supervisor)

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.

DDeeddiiccaatteedd ttoo mmyy ffaatthheerr:: HHaadduusshh AAssmmeerroomm

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Abstract

Stream hydrograph analysis was carried out to determine the baseflow component of the Upper Blue

Nile basin (Lake Tana basin) with recession analysis and baseflow separation techniques. For this

analysis, average daily time series data of hydrometeorological and hydrological data for 12 gauged

rivers with varying record length were tested to quantify the groundwater contribution of the basin

using different approaches: Recursive digital filters and a physically based BASF model. Results of

these analysis indicated that about 15% of the annual flow, using BASF and Eckhardt models, comes

from the shallow groundwater aquifer and the majority of this contribution is obtained from the

southern tributary of the basin, Gilgel Abbay catchment (44%). Together with this, the contribution of

the ungauged catchments to the basin was tried. The result was found to be about 45 mmyr-1 of the

total flow (303mmyr-1) from the ungauged catchments. Thus, in this study the baseflow index (BFI) of

the individual catchments was used as a measure of the baseflow contribution of the basin and

indicates the proportion of baseflow to the total stream hydrographs.

Groundwater recharge of the basin was also estimated by baseflow analysis, rainfall-runoff simulation

using BASF model and by a chemical mass balance method using chloride as a tracer chemical

species. Values of these analyses were different and this makes it difficult to point out the relevant

part. The results from the chemical analysis, BASF model and baseflow separation using the Eckhardt

(2005) model were selected to represent the natural groundwater recharge of the basin and thus, the

value was found vary between 70mm to 120mm per annum.

Another aspect of this study was to determine the annual average water balance of the basin in order

to estimate the gross average annual flow from the ungauged catchments and to estimate the open

water evaporation of Lake Tana. Here Penman open water evaporation method was applied using

Bahrdar and Gondar meteorological stations for the lake. Setting the annual change in lake storage to

zero, the ungauged catchment’s annual average flow was determined. The results of these calculations

indicated that the open water evaporation is 1672mm and the flow is 303 mm on annual basis.

Moreover, an efficient way of determining these water balance variables was applied using the solute

mass balance of the mixing cell modelling approach. The principle behind in applying this method is

the solution method, the singular value decomposition (SVD) algorithm proposed by press et al.,

(1992) and quoted by Gieske and de Vries, (1990). It was found to give satisfactory results. Thus,

open water evaporation was found to be 1665mm and the flow from the ungauged catchment was

determined to be 301mmyr-1.

Key words: Upper Blue Nile, Lake Tana, Baseflow, BFI, Recharge, Mass balance

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Acknowledgements

First of all, I appreciate the chance to receive to pursue my MSc. degree program in ITC (International

Institute for Geo-information Science and Earth Observation) through the Netherlands government

fund. It enabled me to have a precious school life once more again providing financial and academic

support. Besides this I would like to handover my appreciation to my organization at home (Tigray

water resources mine and energy bureau) for giving me the opportunity to study at ITC.

Foremost, my heartfelt gratitude goes to Dr. Ambro Gieske for his kind, continuous support and care

during the period of thesis writing. I appreciate his full time devotion to help and encourage me in

every aspect of my thesis. I am also indebted to Dr.Ing. T.H.M. Rientjes for his unceasing follow up

and conversations. The discussions we had were the guide lines for proper way of scientific thesis

writing and ways how to go through research methodologies. After all, I liked and benefited the

environment I passed through. But for all things I had and I did, I would like to thank a lot Dr. Ambro

Gieske.

I would like to thank for all the WREM staff members specially the program director Arno Van

Lieshout for his keen and wise looking. I appreciate his way of thinking to wards us (students). I have

special appreciation for MSc. Gabriel Parodi and I would like to extend my gratitude towards him. I

am also indebted to Dr. Seifu Kebede from Addis Ababa University for giving me necessary data with

his generous and wise advice before and during the field work.

Extending my regards, I am lucky to have the opportunity to join the international institute and

broaden my area of field and to have friends all over the world. I enjoyed the friendship with all my

classmates I had, especially a precious sister Le Thi Hanh from Vietnam and all others from different

countries.

Finally, I would like to address my happiness to my family. Thanks my father and my mother Desta

Hailu after all. I would like to thank my friends Nigus G/her (Trumbuli), Solomon Weldezgi,

Mebrahitu, Endrias, Tati,… and Lemmlem Wedegergis she helped me in changing the data to digital

format.

For everything, the precious honour goes to Almighty God.

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Table of contents

INTRODUCTION..................................................................................................................................1 1. General..............................................................................................................................................1

1.1. Background of the Upper Blue Nile Basin .............................................................................2 1.2. Problem description ................................................................................................................4 1.3. Objectives of the study ...........................................................................................................4

1.3.1. Main objectives...............................................................................................4

1.3.2. Specific objectives and research questions.....................................................4 1.4. Research hypotheses and main assumptions ..........................................................................5 1.5. Research methodology............................................................................................................5

1.5.1. Pre-field work activities..................................................................................5

1.5.2. Field work.......................................................................................................5

1.5.3. Post-field work................................................................................................6 1.6. Applied methods .....................................................................................................................7 1.7. Structure of the thesis .............................................................................................................8

2. LITERATURE REVIEW ...............................................................................................................9 3. DESCRIPTION OF THE STUDY AREA...................................................................................11

3.1. Location ................................................................................................................................11 3.2. Main tributaries to the Lake..................................................................................................13 3.3. Climate..................................................................................................................................14 3.4. Geology and Hydrogeology..................................................................................................15

3.4.1. Geology.........................................................................................................15

3.4.2. Hydrogeology................................................................................................16 3.5. Lake -Groundwater Interaction.............................................................................................18 3.6. The nature of streamflows ....................................................................................................18

4. WATER QUALITY ASSESSMENT ...........................................................................................21 4.1. General..................................................................................................................................21 4.2. Sample Collection and available data...................................................................................21 4.3. Chemical Analysis ................................................................................................................24 4.4. Field Hydrochemical Analysis..............................................................................................24

4.4.1. Electrical Conductivity .................................................................................24

4.4.2. PH .................................................................................................................25

4.4.3. ITC Laboratory Chemical Analysis ..............................................................25 4.5. Anions analysis .....................................................................................................................26

4.5.1. Chloride ........................................................................................................26

4.5.2. Nitrate, Phosphate, Sulphate and fluoride ....................................................27 4.6. Cations Analysis ...................................................................................................................27

4.6.1. Inductively Induced plasma: ICP-ASE.........................................................27 4.7. Interpretation of the results...................................................................................................28

4.7.1. Ionic balance.................................................................................................28 4.8. Ions Sum Comparison...........................................................................................................29

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4.9. Chemical evolution of groundwaters in the study area........................................................ 30 4.10. Geostatistics ......................................................................................................................... 32 4.11. Determination of spatial autocorrelation in the datasets ..................................................... 32 4.12. Kriging ................................................................................................................................. 33

5. GROUNDWATER CONTRIBUTION IN THE UPPER BLUE NILE FLOWS.................... 35 5.1. Watershed groundwater balance estimation using streamflow recession and baseflow

analysis................................................................................................................................................

35 5.2. Data processing.................................................................................................................... 36 5.3. Algorithms for streamflow recession analysis and baseflow separation ............................. 38

5.3.1. Baseflow recessions ......................................................................................38

5.3.2. Baseflow Separation: Digital Filter Methods................................................40 5.4. Results of the four filter methods ........................................................................................ 43

5.4.1. Baseflow recessions ......................................................................................43

5.4.2. Baseflow separation ......................................................................................46

5.4.3. Comparison of filtered baseflows using the different digital filters .............51

5.4.4. Sensitivity analysis........................................................................................52

5.4.5. Summary and conclusions.............................................................................53 5.5. Baseflows from ungauged catchments................................................................................. 54

5.5.1. Water Balance of Lake Tana.........................................................................54

5.5.2. Open water evaporation (Penman approach) ................................................56

5.5.3. Summary of water balance and conclusions .................................................57 5.6. Numerical Reservoir Modelling – BASF Model ................................................................. 58

5.6.1. Set of equations.............................................................................................60

5.6.2. Baseflow separations.....................................................................................60

5.6.3. Numerical Scheme of BASF Model .............................................................62

5.6.4. Hydrometeorological and hydrological data .................................................63

5.6.5. Results and discussion of BASF model ........................................................66

5.6.6. Performance indicators .................................................................................70 5.7. Chapter summary and main conclusions ............................................................................. 72

6. ESTIMATION OF NATURAL GROUNDWATER RECHARGE IN LAKE TANA BASIN .. ........................................................................................................................................................ 73

6.1. Groundwater recharge from baseflow analysis.................................................................... 73 6.2. Recharge – Runoff Simulation with BASF model............................................................... 76 6.3. Hydrochemical analysis ....................................................................................................... 78

6.3.1. Chloride mass balance analysis.....................................................................78 6.4. Chapter summary ................................................................................................................. 80

7. SOLUTE MASS BALANCE MODELLING ............................................................................. 81 7.1. Mixing Cell Modelling ........................................................................................................ 81 7.2. Theoretical aspects of the model ......................................................................................... 81 7.3. Steady state example with one cell and three tracers .......................................................... 84 7.4. Running the program ........................................................................................................... 88

8. CONCLUSION AND RECOMMENDATIONS........................................................................ 90

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8.1. Conclusions...........................................................................................................................90 8.2. Recommendations.................................................................................................................91

REFERENCES .....................................................................................................................................93 APPENDICES ......................................................................................................................................96

Appendix A-Average daily discharge data of the stations........................................96

Appendix B-BFI as calculated from Eckhardt Model.............................................112

Appendix C-Chemical data.....................................................................................114

C-1_Methods and reagents used in the laboratory....................................114

C-2_Chemical data during field work (8-23, August 2007).......................115

C-3_Geological Survey of Ethiopia............................................................117

C-4_Hydrochemical data by Kebede et al., (2005)....................................118

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List of figures

Figure 1-1. Stream-Groundwater interaction......................................................................................... 1 Figure 1-2. Major tributaries and river gauge locations of the Upper Blue Nile (Conway, 1997)....... 3 Figure 1-3. Framework of the study........................................................................................................ 6 Figure 3-1. Location map of Tana basin............................................................................................... 11 Figure 3-2. The Blue Nile gorge (Blue Nile falls)................................................................................. 12 Figure 3-3. Base map of the study area................................................................................................ 14 Figure 3-4. Simplified Geological map of the study area (Engida et al., 2007)................................... 16 Figure 3-5. Lomi spring........................................................................................................................ 17 Figure 3-6. Types of flows in a particular watershed........................................................................... 19 Figure 3-7. Components of hydrographs.............................................................................................. 19 Figure 4-1. Location map of water samples (08-23 August, 2007). Samples taken from Rivers......... 23 Figure 4-2. Relationship between TDS and EC of the field sample data............................................. 24 Figure 4-3. Linear modelling between EC and Chloride concentrations............................................. 25 Figure 4-4. Correlation of field and lab determined chloride concentrations..................................... 27 Figure 4-5. Comparison of sum of anions and EC/100........................................................................ 29 Figure 4-6. Graph of regression analysis of sum of anions against sum of cations............................. 30 Figure 4-7. Graph of regression analysis of sum of anions and cations in the Lake Tana basin........ 30 Figure 4-8. Samples taken during field work GW (left) and Surface water (right).............................. 31 Figure 4-9. Samples taken from GSE (left) and (Kebede et al., 2005) (right)...................................... 32 Figure 4-10. Semi-Variogram of EC in Tana basin.............................................................................. 33 Figure 4-11. Kriged map of EC values in Lake Tana Basin. EC of the lake was kept constant........... 34 Figure 5-1. Location map of meteorological and gauging stations..................................................... 37 Figure 5-2. Conceptual representation of groundwater storage and its discharge to streams............ 38 Figure 5-3. Recession curve fitting of Ribb catchment......................................................................... 44 Figure 5-4. Recession curve fitting of Gilgel Abbay catchment........................................................... 44 Figure 5-5. Fitting of recession curves and propagating to the entire hydrograph............................. 45 Figure 5-6. Scatter diagram of the parameter τ in the non-linear model in equation (5.4) against Qo

at the start of recession......................................................................................................................... 46 Figure 5-7. Scatter diagram of the storage So and discharge Qo at the start of recession................... 46 Figure 5-8. Hydrograph analysis of the inflow from Gilgel Abbay catchment.................................... 47 Figure 5-9. Daily average total baseflow in the basin......................................................................... 49 Figure 5-10. Pie-chart of the gauged BFI in the basin......................................................................... 50 Figure 5-11. Comparison of BFI computed from Lyne -Hollick and Chapman filters for Ribb.......... 51 Figure 5-12. Comparison of BFI computed from Lyne -Hollick & Chapman filters for Gilgel Abbay52 Figure 5-13. Long term daily average water balance components of Lake Tana................................ 54 Figure 5-14. Annual average rainfall distribution over the lake and meteorological station used..... 55 Figure 5-15. Simplified representation of BASF of model.................................................................... 60 Figure 5-16. Mean annual rainfall distribution over the basin (simple kriging)................................. 65 Figure 5-17. Time series analysis of long term average daily rainfall, potential ETo ......................... 66 Figure 5-18. Hydrograph analysis of the Gilgel Abbay catchment using BASF model....................... 68 Figure 5-19. Hydrograph analysis of the Gumera catchment using BASF model............................... 69 Figure 5-20. Comparison of measured data and simulated data 0f BASF model................................ 71

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Figure 5-21. Comparison of measured data and simulated data of BASF model.................................71 Figure 6-1. Relationship of rainfall and recharge estimated from baseflow separation in..................74 Figure 6-2. Annul rainfall-recharge relationship of the Gilgel Abbay catchment for the period.........75 Figure 6-3. Rainfall-recharge relationship of individual catchments as estimated from baseflow......76 Figure 6-4. Recharge comparisons by two methods in Gilgel Abbay catchment..................................78 Figure 6-5. Chloride mass balance recharge estimation per catchment...............................................79 Figure 6-6. Recharge estimated from chloride-rainfall relationship....................................................80 Figure 7-1. Steady state flow example with one cell and three tracers.................................................84

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List of tables

Table 4-1. Multiple samples for temporal variation test (4 samples)...................................................22 Table 4-2. Rain water chemistry (ITC laboratory, 2007)..................................................................... 23 Table 4-3. Rain water chemistry (Kebede et al., 2005)......................................................................... 23 Table 4-4. ITC laboratory chemical analysis....................................................................................... 26 Table 5-1. List of hydrological stations in Lake Tana Basin ................................................................ 38

Table 5-2. Summary of baseflow characteristics showing values of the three parameters n, Qo and oτ

fitted into equation (5.4). Also the mean of the derived quantity So =Qo oτ is given............................ 45

Table 5-3. Summary statistics of BFI in the basin................................................................................ 48 Table 5-4. Baseflow separation parameters and baseflow index (BFI) for the three recursive digital

filter algorithms used in the study......................................................................................................... 49 Table 5-5. Comparison of baseflow contribution from each catchment............................................... 50 Table 5-6. Results of the sensitivity analysis......................................................................................... 53 Table 5-7. Annual water balance of Lake Tana.................................................................................... 58 Table 5-8. Summary of annual average water budget of the Upper Blue Nile Flows.......................... 58 Table 5-9. Summary of Hydrometeorological stations used in the study............................................. 63 Table 5-10. Inverse distance relationship of the stations and catchments........................................... 64 Table 5-11. Annual average rainfall vs. elevation of the selected meteorological stations................. 65 Table 5-12. BASF model parameters set for Gilgel Abbay catchment................................................. 67 Table 6-1. Catchment characteristics as recharge-rainfall (RF) ratio of the period indicated........... 76 Table 6-2. Recharge-runoff simulation for Megetch catchment........................................................... 77 Table 6-3. Chloride composition in rain and groundwater.................................................................. 79 Table 7-1. Normalized flow components............................................................................................... 85 Table 7-2. Input data for Mixing cell model......................................................................................... 88 Table 7-3. Final results......................................................................................................................... 89

GROUNDWATER CONTRIBUTION AND RECHARGE ESTIMATION IN THE UPPER BLUE NILE FLOWS

1

INTRODUCTION

1. General

The realization that streams and surrounding groundwater exist as a connected resources has helped to

advance the fields of hydrology, biogeochemistry, and aquatic ecology. Stream-groundwater exchange

plays an important role in the processes that affect watershed hydrologic response, water quality, and

subsequent impacts on aquatic biota. The exchange of water between streams and groundwater has

been noted as an important mechanism involved in solute and contaminant transport, dissolved

organic carbon cycling, aquatic-ecosystem functioning and water resources management (Covino,

2005). Most streams gain their recharges from groundwater at the river banks. Monitoring of

dissolved chemical constituents, total dissolve solids (TDS) and electrical conductivity can be used to

determine the contribution of groundwater to stream discharges.

Figure 1-1. Stream-Groundwater interaction

Much research has been carried out on water resources in connection with environmental protection,

flood hazard control, water supply, drought impact and water quality assessments. However, these

works mainly concentrate on surface water and little attention is paid to the study of the interaction

with groundwater. Subsurface water aspects are the most difficult ones to assess and quantify due to


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lack of long term data, geological complexity of the area and their vulnerability to different

environmental aspects.

Shallow aquifer recharge and discharge characteristics are crucial for efficient development and

management of groundwater resources, as well as for minimizing pollution risks to the aquifer and

connected surface waters. Groundwater recharge to shallow unconfined aquifers is complex and

depends on climatic factors as well as the character and thickness of soil and rock above the water

table in combination with surface topography, vegetation, and land use. Thus, this recharge

mechanism shows significant spatial a temporal variability as a consequence of variations in climatic

conditions, land use, irrigation and hydrogeological heterogeneity (Arnold, Muttiah, Srinivasan, &

Allen, 2000).

For large areas in subhumid to humid climates, two water balance methods have been used

extensively by hydrologists: baseflow record estimation and recession curve displacement methods.

These methods are widely applied in groundwater characterization because of the abundance of

streamflow records upon which they are based. Stream hydrograph analysis is a well-established

technique to quantify streamflow components. Separation of streamflows into baseflow, interflow and

surface runoff components is used to estimate the groundwater contribution to streamflows.

Hydrograph separation techniques, especially nowadays the recursive digital filters, have been used to

quantify the groundwater component of hydrologic budgets and to aid in the estimation of recharge

rates. Together with this, baseflow characteristics determined by baseflow separation of hydrographs

from streamflows draining different geologic terrains have been used to show the effect of geology on

baseflows (Ronald, Sloto, & Michele, 1996). Traditionally, hydrographs have been separated

manually. However, these manual methods are subjected to considerable personal bias. In this study,

it is tried to use objective computer codes for baseflow separation, rainfall-runoff modeling and solute

mass balance calculations to obtain a deeper insight in the surface water-groundwater interactions of

the Upper Blue Nile Basin.

1.1. Background of the Upper Blue Nile Basin

The starting point source of the Blue Nile is a spring at Sekela near the foot of Gish Abbay Mountain

at an altitude of 2728 meters a.s.l. This spring is the source of Little Abbay (Gilgel Abbay), which

flows into Lake Tana. There are many larger and smaller rivers flowing into the lake. However, Gilgel

Abbay is the longest and largest river flowing into Lake Tana that has an average elevation of 1784m.


3

The Blue Nile River is the only outflow from the lake at Bahrdar. The Blue Nile flows down in a long

curve through the upland plateau of the Ethiopian highlands from its eastern exit at Lake Tana to

where it enters the Sudan (Fig. 1-2) on the western border of Ethiopia. The climate changes from

humid conditions around Lake Tana to semi-arid conditions in the western Ethiopian lowlands and the

Sudan.

Important tributaries to the upper reaches of the Blue Nile include the Bashilo and Jamma which

always contain some water throughout the year. There are also small tributaries like Guder and Tul

that contribute to the flow seasonally. Tributaries of the lower reach are Didesa and Dabus which

drain the southwest of the Blue Nile and Beles which gets its source from the western escarpment of

the Lake Tana. Didesa and Dabus are thought to be the most important tributaries as they drain the

areas of greatest rainfall. In this study only Lake Tana and the rivers flowing directly into the lake will

be considered. A more detailed description of the lake and its catchment is given in section 3.1.

Figure 1-2. Major tributaries and river gauge locations of the Upper Blue Nile (Conway, 1997)


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1.2. Problem description

The Blue Nile contributes about two-third of the water of the Nile and its source is Lake Tana.

However, it is not well known how much water is contributed by groundwater. To determine the

contribution of groundwater to the Upper Blue Nile, the flows of all rivers into Lake Tana are

separated into the three major components: direct runoff, interflow and baseflow. All flow

components contribute to the lake’s mass balance, which is further determined by direct rainfall,

evaporation and outflow through the Blue Nile.

It is well known that one of the source components of rivers is the contribution of groundwater

through aquifer discharges at their respective banks. This is especially the ultimate source of perennial

gaining streams. Stream-groundwater interaction shows there is mass exchange between the two

interconnected systems depending on the water level of the aquifer (hydraulic head) in the subsurface

and river bottom of the surface interface. Therefore after having established an overall water balance,

it also becomes important to study whether a suitable solute mass balance can be established for the

system.

1.3. Objectives of the study

1.3.1. Main objectives

The main objectives of the study are to quantify groundwater contribution and recharge estimation in

the Upper Blue Nile flows. Lake Tana is taken as a target area to establish stream-groundwater

exchange relationships.

1.3.2. Specific objectives and research questions

� Determine baseflow component of the basin and the effect of changes in basin storage for

stream-aquifer components.

� Estimate the groundwater recharge of the sub-catchments and in the entire basin.

� Determine the contribution of baseflow from different subcatchments and integrate the results

to the whole Upper Blue Nile catchment.

� Test whether river discharge to the lake represents the groundwater contribution in the sub-

basin.

� Test which method is best applicable in the basin and realize the performance of the model

used.


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� Formulate solute mass balance modelling for Lake Tana and its tributaries.

� Establish the annual water balance of Lake Tana.

The achievement of these objectives can be facilitated by introducing the following research

questions.

� Can hydrological data establish a relationship between groundwater and river discharge and

indicate the aquifer characteristics of the basin?

� Can the analysis of field data (hydrogeochemical data) and meteorological data of the stations

be used to estimate evapotranspiration and to understand catchment behaviour?

1.4. Research hypotheses and main assumptions

A number of hypotheses were made to enhance the research questions. These are:

� Dry and wet season discharge measurements can be used to determine the river base flow

components in the study area.

� By the analysis of the stream hydrograph discharges from tributaries into Lake Tana, the

groundwater contribution can be obtained.

� Baseflow separation techniques can be used to indicate groundwater contribution (Baseflow

Index, BFI) to the upper Blue Nile basin.

� The baseflow analysis algorithms can provide recharge estimation of the basin similar to that

obtained by hydrochemical analysis.

� The baseflow component (baseflow index, BFI) determined in the Upper Blue Nile can be

used in the lower reaches of the basin in a similar way.

1.5. Research methodology

Three separate phases were distinguished:

1.5.1. Pre-field work activities

This activity included proposal writing, gathering necessary information about the area, literature

review of work done in the study area and in similar areas elsewhere. It further included preparation

of field materials. ASTER images were obtained through ITC.

1.5.2. Field work

Water samples were collected from different source areas: from groundwaters, lakes, springs and

streams. Some analysis was done in situ like determination of electrical conductivities (EC), total

dissolved solids (TDS), pH and temperature. Chloride was determined using field kits. Other activities

include collection of hydrological, meteorological and hydrogeological data and maps from respective

offices.


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1.5.3. Post-field work

Data processing was the main activity after the field work. Water samples collected in the field were

analyzed in the ITC laboratory. The images were georeferenced with field GPS data. Together with

this, hydrometeorological data collected was prepared and processed for further use in the respective

models. The framework of the study can be summarized in the following flow chart (Fig. 1-3).

Phase Two-D

ata processing

Phase one-

Data collection

Phase Three-Data analysis

Phase Two-D

ata processing

Phase one-

Data collection

Phase Three-Data analysis

Figure 1-3. Framework of the study


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1.6. Applied methods

Numerous analytical and graphical methods have been developed to separate baseflow component

from stream flows hydrographs. Baseflow separation techniques use the time-series record of stream

flow to derive the baseflow signature. The common separation methods are either graphical which

tend to focus on defining the points where baseflow intersects the rising and falling limbs of the

quickflow response, or involve filtering where data processing of the entire stream hydrograph derives

a baseflow hydrograph. The filter separating methods include the digital recursive filters used to

separate baseflow (groundwater) from total stream flow, (Wittenberg & Sivapalan, 1999) and

modified hydrograph recession curve displacement method (Arnold et al., 2000) are among the others.

In this study:

1. A recursive digital filter commonly used in signal analysis developed by Eckhardt (2005) and

a web based hydrograph analysis tool (WHAT) (Kyoung Jae Lim, Bernard A. Engel, & Kim,

2005) were used. A one parameter digital filter developed by Lyne and Hollick, (1979) which

was also described by Nathan and McMahon, (1990) was the base of the model. The digital

filter method has been used in signal analysis and processing to separate high frequency

signal from low frequency signals (Lyne and Hollick, 1979). This method has been also used

in baseflow separations because high frequency waves can be associated with the direct

runoff, and low frequency waves can be associated with the baseflow (Eckhardt, 2008). Thus,

filtering direct runoff from base flow is similar to signal analysis and processing (Eckhardt,

2005).Thus, the ABSCAN (Automated baseflow Separation for Canadian Datasets) software

was used for baseflow separation and analysis of the study (Parker, 2006).

2. BASF model: A physically based hydrograph analysis tool (A.S.M Gieske, 2007).This model,

unlike the digital filters, uses physical parameters of the individual catchments to characterize

them separately. Impute parameters include: rainfall, initial soil moisture content, potential

evapotranspiration, field capacity, saturated hydraulic conductivity, storage coefficients of the

separate groundwater storages. The baseflow component of the streamflow hydrograph is also

decomposed into interflow and deep groundwater flow of the hydrographs. Together with this

hydrochemical analysis of the basin was carried out using aquachem computer code.

3. Estimation of natural groundwater recharge could be performed by different methods: by

water balance methods, baseflow recession analysis, chloride mass balance…etc. In this

study, recharge estimation was carried out using two methods:

• Baseflow separation (hydrograph analysis) which utilizes the daily stream flow records and


8

• Chloride mass balance which uses the measured chloride concentration in rainfall samples

and groundwater samples.

1.7. Structure of the thesis

There are eight chapters in this thesis.

The first chapter of the thesis describes the general introduction of the importance of groundwater

contribution of the shallow aquifer to streamflow hydrographs. It discusses the interaction of streams

and groundwaters in shallow aquifers, background of the Upper Blue Nile and the general activities

performed during the field work.

Chapter two describes the literature reviewed before and during the field work. The literature not only

covers research in the study area but also elsewhere. Chapter three of the thesis deals with the location

and climate of the study area. It also summarizes the main aspects of the lake’s geology and hydro-

geology. Chapter four includes the chemical data analysis in the ITC laboratory.

The main objective of the study was developed in chapter five. It describes the hydrograph analysis of

rivers in the study area applying different approaches. A new model that utilizes physical parameters

of each catchment developed for this purpose was applied in this chapter. Topics of chapter five

include recession analysis, baseflow separation and hydrometeorological analysis of the research.

Chapter six deals with the second part of the objective of the study, natural groundwater recharge

estimation of the basin. It compares methods of recharge estimation that would give approximate

recharge of the area.

A new approach that gave a remarkable result for recharge estimation using chemical constituents

with discharge measurements was applied in chapter seven. This model, the Mixing Cell Model, was

originally intended as an appropriate method for recharge estimation especially in arid environments

with high rate of evapotranspiration. However, it also proved useful in the more humid environment

of Lake Tana.

Finally chapter eight presents the conclusions and recommendations of the study.


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2. LITERATURE REVIEW

Many studies have been done and are currently being done to improve the knowledge of the Upper

Blue Nile basin in relation to water resources management. These studies mainly focus on surface

waters. There are also many studies on the origin of the Lake Tana. Comparing the works done on

surface water balances and groundwater resources, it can be noted that few attempts have been made

so far to explore the groundwater resources in the area. Many studies are available on the geology of

the area around Lake Tana and the geological aspects will be discussed in some more detail in

Chapter three. Here the emphasis is on water related studies.

Conway (1997) studied a grid-based water balance which requires limited data input, few parameters,

and runs on a monthly time step in the upper Blue Nile. Conway calibrated his model to run over a 37

year period (1953-1987) and validated to simulate subcatchment runoff and historical variations in the

basin. He produced 0.74 correlation factor between observed and simulated annual flows over 76

years with mean error of 14%. This model was used to investigate the sensitivity of runoff to changes

in rainfall and potential evapotranspiration.

A recent study in the upper Blue Nile basin flow indicated that the groundwater contribution to the

upper Blue Nile (Lake Tana) constitutes less than 7% of the total inflow (Kebede, Travi, Alemayehu,

& Marc, 2006). The authors observed that lake levels show an annual average variation of 1.5 m.

However, possible relationships between groundwater and lake level variations were not studied. In

an earlier study Kebede et al., (2005) discussed the water types in the basin. It was elaborated that

complementary geochemical and isotope hydrological data show that, in general, there are two types

of groundwater systems in the upper Blue Nile basin. These are the low salinity groundwaters from

the basaltic plateau of the Lake Tana grabens (LTG) and the high TDS thermal groundwater systems

from the deeply faulted grabens in the southern part of the Blue Nile area. He also noted that Ca–Mg–

HCO3 types of waters are often regarded as freshly recharged groundwater which are at their early

stage of geochemical evolution and that Na-HCO3 type are thermal and high TDS groundwaters that

undergone a relatively pronounced degree of groundwater chemical evolution.

On the other hand, it is well known that base flow contributes to a large part of the streamflows,

especially in humid climates. Thus, quantification of shallow groundwater aquifers is important for

sustainable groundwater and surface water exploitation for irrigation and other purposes, and

estimation of contamination impacts in downstream areas of wastewater discharge. Stream-

groundwater interaction study indicated that a considerable amount of mixing exists between these

systems and this interaction is important in studying the aquifer behavior in the river banks.


10

Other studies in similar environments based on baseflow separation and dissolved chemical solids

indicated that a considerable amount of stream-groundwater interaction often exists. In these studies

groundwater contribution was found highly contributing to streams. Covino, (2005) reported in his

work that, 32 - 42 % of total storm discharge was from groundwaters. Another study in the Upper

Mississippi river basin reached the same conclusion (Arnold et al., 2000).

Chemical tracers have also been used as main methods in the study of stream-hydrograph analyses.

These methods are important because they allow the study of hydrological processes at a catchment

scale. Hence hydrograph analysis can be used to separate into two components, pre-event water and

event waters, using mass balance equations for the water types. Isotopes or chemical tracers have been

widely used in hill slope hydrology (Joerin, Beven, Iorgulescu, & Musy, 2002). Chemical mixing cell

modelling method was used in semi-arid areas in the study of chloride mass balance to estimate

groundwater recharge in dolomite aquifers (Adar and Neuman, 1988, Gieske and De Vries, 1990).

As a recently published draft inception report in the hydrological study of the Tana-Beles sub-basins

indicated, there is a new project that will last in the coming 2009. This report addresses its mission by

stating that the aim of the project is to better characterise the water balance of the Tana and Belles

sub-basins of the Blue Nile and to assess the various impacts on the development of water resources

in the two basins (SMEC, 2007). According to the report, there is a need to integrate groundwater and

surface resources in the two subbasins of the area. To address this mission consideration of the

groundwater potentiality of the basins should be investigated. As a constraint, the report indicated

some drawbacks on the accuracy of data measurements in meteorological stations especially on

sunshine, relative humidity, and evaporation in addition to the scarcity of groundwater data. Together

with this there is also inconsistency in the discharge measurements of the Tana sub-basin. This is

more pronounced in the smaller tributaries flowing into the lake. Due to the absence of automatic

water level recorders in the gauging stations flash floods in the smaller catchments have steeply rising

and falling limbs. River Ribb also shows some drawbacks in peak discharge seasons in that it

overflows across the bank and hence induces errors in estimating the daily average discharge

measurements of the gauging station.


11

3. DESCRIPTION OF THE STUDY AREA

3.1. Location

Lake Tana is one of the largest lakes in east Africa. It is located on the basaltic plateau of the North-

western part of the Ethiopia highlands between the UTM coordinates 1283136N-1353324N and

326295E-031531E from the outlet of the Blue Nile at Bahirdar in the south and the Gorgora harbour

in the North. Lake Tana stretches approximately 79km from south to north and 62km from west to

east. The western shore of the lake along its whole length slopes up to an altitude of 2300 meters

along the edge of the escarpment which lies within a few kilometres out of the lake shore (Hurst,

Black, & Simaika, 1959). Lake Tana has a maximum depth of 14 m and an average depth of 9m. The

basin has a total area of 15046 km2. From this total area, Lake Tana covers an area of 3070 km2. It is

believed that the lake has originated from the uplifting process within a plateau setting that averages

approximately 2000m in an elevation (Hautot, Whaler, Gebru, & Desissa, 2006; Kaba Ayana, 2007).

Figure 3-1. Location map of Tana basin


12

To the north, the divide separating the Lake from that of the Atbara does not rise to considerable

height. Hurst et al., (1959) explained that the lake shore is broken by the rocky Gorgora which

separates two marshy stretches: east of the Gorgora lies the Dembia plain which has been formed by

alluvium sediments brought down by Megetch, Dirma and other streams. The northeast part of the

Lake Tana lies down on the foot hills and south of them are the Fogera plains which are large plains

forming a great bay in the hills. These hills to the east of this plain are drained by Gumera and Rib

streams which pour into Lake Tana. The highland mountains continue to the south of the lake and the

Blue Nile flowing out of the lake lies in the deep gorges on its way to the Sudan. The southern

mountains rise to a height of about 4000m and the western slopes fall towards the Gilgel Abbay which

drains an open valley between them. The Gilgel Abbay enters the lake at its south western corner with

a long deltaic arm.

Figure 3-2. The Blue Nile gorge (Blue Nile falls)

The lake itself is situated at a junction of three grabens forming a complex structural complex that

was active during the formation of the mid-Tertiary flood basalt sequence into which the basin is

impressed (Mohr P.A., 1971). Extensive tertiary lavas and tuffs in the surrounding area are derived

from traps that erupted during the main east African rift system. Hurst et al., (1959) also explained the

recent volcanic formation of the Lake Tana. The formation of this recent basaltic lava flow resembles

to the quaternary Aden volcanic series. The youthful appearance of the many volcanic cones on the

southern shore of the lake and the general condition of the crust of Bahrdar Giorgis lava coupled with


13

the fact that the very shallow soil depth enforce to the recent formation of the lake. The lava extends

along the shore of the lake most of the ways from Zege to the outlet and down to the Blue Nile valley

to beyond the first bridge a total distance of almost 40 km. Together with this the soils of the Lake

Tana basin, as part of the Ethiopian highlands, are the result of the decomposition in situ of the

volcanic rocks.

3.2. Main tributaries to the Lake

The Tana basin covers an area of 15046 km2 of which the Lake Tana weighs 20% of the total area.

Lake Tana is fed by more than 40 tributaries. As indicated by Kebede et al., (2005) the four large

rivers, Gilgel-abbay, Ribb, Gumera and Megetch account for 93% of surface water flow to Lake Tana.

Much of the area is ungauged. Figure 3-3 shows the location of the gauge stations.

Determination of water balance in these tributaries is useful for integrating surface and subsurface

systems in the basin. The Gilgel Abbay tributary covers the southwestern part of the lake. This

catchment covers a drainage basin of approximately 4517 km2. Topographically, it is characterized by

rugged plateau plains having highest peaks on its origin at Gishen Abbay an altitude of 2728m a.b.s.l.

while its northern end part decreases to1789m near Lake Tana. It is believed that the actual present

geomorphic surface configuration of the area is the result of structural processes as faulting and

uplifting during the tertiary period. Its formation resembles the uplifting and faulting of the western

highlands of Ethiopia that occurred during the upper Eocene period.

The depression plains around Asabila River in this catchment are formed from the alluviums of the

recent alluvial flats and swamps. There are also a number of flood plain areas scattered in between the

basaltic flows formed by the interaction of several processes and erosional surfaces.

The Gilgel Abbay itself has a number of smaller tributaries. It is mainly drained by River Koga

around Merawi and Kilti that joins downstream before flowing into the lake. Some other tributaries

include Andod, Hawasha, Gudbela and Amerit. There are other numerous shallow seasonal rivers and

drainage channels that also carry large flow of water during a wet season but they dry up quickly in

the dry season.

The eastern part of the Lake Tana is drained by the Ribb and Gumera Rivers. They have an area of

approximately 2156 km2 and 1604 km2 respectively. Far to the east, it is surrounded by the hill sides

that have Termaber basaltic composition.


14

Figure 3-3. Base map of the study area

Most of the hillside slopes, foot slopes and alluvial relief forms are Termaber basalts which rest

unconformably on the older formations, truncated by paleosurface (BCEOM, 1999). Other main

tributaries far to the northern part of the lake are Megetch and Dirma rivers. Around the lake, large

flood plains are present with impeded drainage. During the rainy seasons, the rivers in the flood plain

usually overflow.

3.3. Climate

The fact that the country is located within the tropics combined with high surface altitude means that

pressure and air flow movement determine the large climatic variations in different parts of the

country. The moisture conditions vary from very humid in the western high lands where the study area

is located to arid conditions in the Afar and Ogaden regions.


15

The upper Blue Nile (Lake Tana basin) receives its highest precipitation in the main rainy season

from the Atlantic equatorial western air currents which provide the main rainfall in summer seasons

(from June to September). The rainfall in the dry seasons especially in February, March and April is

very small. Air flowing during the rain season dominated by a zone of convergence in low pressure

systems accompanied by the oscillatory Inter Tropical Convergence Zone (ITCZ) extending from

West Africa through the north Ethiopia moves towards the southeast of the country. The study area

has unimodal rainfall characteristics of peaking in July. In general, the main rain months are from

June to September; where as the dry months are from October to May. As indicated in some

meteorological reports, in the southern part of the sub basin April and May are an intermediate

seasons where minor precipitations occur. Annual rainfall of the basin varies between 900mm and

2600mm and the average annual rainfall is taken as 1376mm.

Temperature varies with altitude in the basin. The climate in the basin is generally temperate at higher

elevations and tropical at lower elevations (Conway, 1997). Conway, (1997) discussed the traditional

classifications of climate in the basin and uses elevation as a controlling factor in his description.

Generally, annual temperature distribution of the basin ranges 15°C to 20°C.

3.4. Geology and Hydrogeology

3.4.1. Geology

In the Geological Evolution of the Main Nile, five river phases have been identified with the Nile

system. However, the ancient beginning of the river has its origin in systems that traversed the Afro-

Arabian swell as early as the Late Cretaceous. The five sequential phases of the Nile basin include the

Eonile in the Upper Miocene, Palaeonile in the Upper Pliocene and the Proto- Pre- and Neonile during

the Pleistocene. The various phases of the river, and their associated climate, erosion, sedimentation

are important in the history of the Nile River. It has been suggested that Lake Tana was first formed

during the early stages of river system development in the Pliocene with the Little Abbay canyon

excavated during Upper Pliocene to early Pleistocene. Although the Blue Nile was integrated into the

Nile system within the mid Pleistocene during the Prenile phase at around 130,000 yrs BP, it

originally developed in isolation towards the end of the Miocene, around 8 Ma BP (Conway, 1997).

Basement rocks in the Tana basin consists Precambrian metamorphic and granitic rocks which

overlain by the extensive deposits of the Permian to Mesozoic sedimentary deposits but outcrop in the

western lowlands.


16

Figure 3-4. Simplified Geological map of the study area (Engida et al., 2007)

The area around the Lake Tana has been the site of volcanic activity since the initiation of the East

African Rift System (EARS). Many studies have been done on the geologic formation of Lake Tana

and explained that the formation resembles to the recent quaternary volcanic formation of the Aden

volcanic series in age, but petrographically and geochemically they belong to the same magmatic

sequence as the trappean lavas. Moreover, King and Chapman, (1972) indicated that the Lake Tana

basin is formed in a similar fashion to the formation of Lake Turkana basin. Other similar studies

(Berhe S.M., Desta B., Nicoletti M., & M.Tefera, 1987) suggested that the lake Tana basin represents

a proto-rift west of the present East African rift System (EARS) and is associated with the oldest

volcanic rocks of the northwestern plateau of Ethiopia (King & Chapman, 1972).

3.4.2. Hydrogeology

The Blue Nile drainage is the result of river intrusions of the Cenozoic basaltic uplift land. Much of

its runoff is contributed from the high lands of the southern central part of the basin. It is

characterized by very high discharge during the wet season and very low discharge during the dry

season. This reflects that the basin gets its major runoff component directly from precipitation rather

than groundwater. As indicated earlier the geologic composition of the upper Blue Nile is the late

Tertiary and early quaternary volcanics. Kebede et al., (2005), explained the main groundwater source

in the basin is from the highly fractured basaltic or metamorphosed rocks. Cold springs emerge from


17

the basaltic plateau of highly dissected and fragmented river erosions as a sign of recent recharge. The

aquifers of the basin include alluvial aquifers and the tertiary basaltic aquifers in the low lying areas at

the eastern and northern side of the lake, the Dembia plain, the thick sequence of quaternary and

underlying tertiary volcanics of the Gilgel Abbay catchment, and that of the regional basaltic aquifers

in the highland areas surrounding the lake.

Alluvial sediments have limited distribution within Lake Tana sub-basin dominantly at the eastern and

northern side of the Lake. The thickness reaches more than 50 meters. The grain size of the sediment

becomes coarser away the Lake. The static water level is very shallow in most areas less than one

meter below the surface. The volcanic rocks which are found widely distributed to the southern part in

the whole Gilgel Abbay catchment are vesicular basalts. It was found that static water level is so

shallow, especially close to the lake shore, and the high discharge springs which serve the water

supply for Bahrdar town, Areki and Lomi, are found in this scoraceous watershed. (Chapter four of

this work discusses about these springs).

Figure 3-5. Lomi spring


18

3.5. Lake -Groundwater Interaction

The degree of groundwater contribution (baseflow index, BFI) of minor and major tributaries of Lake

Tana Rivers was assessed by recession analysis and baseflow separation of the tributaries during dry

weather flows of gauged tributaries. This dry period analysis was made to propagate to the whole

period of the time series streamflow data analysis and was tried to estimate the baseflow composition

in the entire hydrograph of the time series. Lake-groundwater interactions of the Tana basin was

assessed recently by World Bank (Engida Z., Yilma S., & Tuinhof, 2007). The study is summarized as

follows:

1. The alluvial aquifer is recharged from the volcanic aquifers from the upper catchments and

also from Lake Tana during high Lake level periods. The aquifer is discharged to Lake Tana

during low Lake Level and partly to the underlying volcanic aquifer.

2. The volcanic aquifer of quaternary vesicular basalt is recharged from rainfall and most of its

recharge is discharged as springs and baseflow into Lake Tana and some part could recharge

the scoraceous tertiary basalt underlying it.

3. The Tertiary scoraceous basalt is recharged from rainfall within the lake subbasin and

adjacent areas (Choke mountain plateau) and partly from the vesicular basalt and it was

conceptualized that it is mainly discharged to the underlying Mesozoic sedimentary aquifer

The study also indicated that the lake discharges water to the Mesozoic sedimentary succession below

the lake. However this appears unlikely in view of the thick clay layer (80m) lining the lake’s bottom.

Lake water and solute mass balance calculations at present do not show sufficient evidence for

downward leakage either.

3.6. The nature of streamflows

Streamflow is the flow rate in cubic meters per second (m3s-1) along a defined natural channel. It is the

component of the hydrologic cycle which transfers water, originally falling as rain or snow onto a

watershed, from the land surface to oceans. Hence streamflow at a particular point on a channel

system is contributed by runoff from the watershed or drainage basin upstream of that point and return

flow from groundwater aquifer.


19

Figure 3-6. Types of flows in a particular watershed

Streamflow is generated by a combination of

� Baseflow (return flow from groundwater) which is the sustained flow (amount of water) in a

stream that comes from groundwater discharge or seepage. It is the sustained flow between

successive storm events.

� Interflow (rapid subsurface flow through pipes, macro pores and seepage zones in the soil) is

the water that travels laterally or horizontally through the zone of aeration (vadose zone)

without reaching the water table during or immediately after a precipitation event and

discharges directly into a stream or other body of water.

� Direct runoff also called saturated overland flow is a flow from the surface of poorly

permeable or temporarily saturated soil or from permanently saturated zones near the channel

system. It is the quick or flow rapid during and after rainfall of new water.

Discharge (m

3s-1)

Dire

ct runoff

Rising limb

Interflow

Baseflow

Falling limb

Time

Discharge (m

3s-1)

Dire

ct runoff

Rising limb

Interflow

Baseflow

Falling limb

Time

Figure 3-7. Components of hydrographs


20


21

4. WATER QUALITY ASSESSMENT

4.1. General

The objectives of the water quality assessment in this study area were aimed at the determination of

the water composition, water type, source rock deduction and to determine the source of water for

discharge measurements .i.e. surface or groundwater source. The latter one was also important for the

baseflow separation in rivers and streams for groundwater contribution in the upper Blue Nile flow. In

this water quality assessment, special emphasis was given for the determination of chloride content

and electrical conductivity (EC) in the groundwaters, rivers, and the rain water as these are needed for

the recharge estimation of the basin.

Chemical analysis was performed both in laboratories and in the field. Some analyses were conducted

in situ in the field and others in the lab. Hydrochemical parameters that need determination in the field

include; temperature, conductivity, pH, dissolved oxygen, and alkalinity. The last two parameters are

highly dependent on the partial pressure of carbondioxide gas, CO2 and should be analyzed in situ.

4.2. Sample Collection and available data

The samples were collected from different sources: rain, rivers, lakes, springs and wells. Because the

samples were collected in August, rives were flowing strongly. Not many samples could be collected

from wells because of difficulties with access. Springs which emerge in the contact rocks between

porous and nonporous media can be taken as representatives for the local groundwater.

A 15 day field trip from 8 to 23 August 2007 was conducted around the study area. Sites were chosen

on the basis of their importance and proximity to major tributary rivers, springs and wells. It was

observed that a large number of rivers and springs are found surrounding the Lake Tana in all

directions. Besides to this, the water supply of Bahrdar town (2007 population: 175000) depends on

three springs: Areki, Lomi and Tikul Wiha springs (fig.3-5 photo). They are located 8 km west of

Bahrdar, about 3 km west of the airport and are located in the same area approximately 800m apart

from each other at an altitude of 1827m a.b.s.l. Their respective discharge rates are: Areki 140 ls-1,

Lomi and Tikul Wiha 60 ls-1 each. Many springs in the north and south of the Lake Tana are also

present with relatively high discharges. Among the others include the Washay spring southwest of

Bahrdar located at the tip of Engibara Mountain near Kosober town.


22

Water samples were collected in 100ml polythene bottles. These bottles were cleaned properly and

rinsed by the sample water before use. They were acidified and put in the refrigerator for cooling.

Prior to acidification, the samples were filtered to remove suspended materials which could dissolve

when the acid is added. 96 samples were collected and EC, temperature, pH and TDS measurements

were taken in situ at the time of sampling. Out of these, 20 samples were brought to the lab at ITC for

major ion analysis. The results are shown in the appendices. Moreover, multiple measurements were

taken at two sites: at the outlet of the Blue Nile in the Lake Tana and in the inlet of Gilgel Abbay

before it mixes with Koga River at Merawi, to test the temporal variability of the chemical

constituents. The results indicate that EC and TDS standard deviation values of 2.44µ Scm-1 and 2.40

mgl-1 from the Blue Nile outlet and 4.23µ Scm-1 and 3.18 mgl-1 from Gilgel Abbay water samples.

The relatively high deviation in Gilgel Abbay may be attributed to measurement errors or seasonal

intrusions from up streams as it was a rainy season at the time of sample collection.

Table 4-1. Multiple samples for temporal variation test (4 samples)

1 2 3 4 SDLake Tana out late EC (µScm-1) 146.0 141.3 140.6 143.5 2.44

TDS (mgl-1) 65.8 70.0 69.0 70.5 2.10Wetet Abbay EC (µScm-1) 32.1 27.0 31.3 37.3 4.23

TDS (mgl-1) 17.0 11.0 13.0 18.0 3.30

Two types of water samples were collected in the field work for the purpose of chloride and nitrate

determinations in addition to the major ions. Samples used for chloride determination were acidified

or preserved by nitric acid and those used for nitrate determination were preserved by hydrochloric

acid.

In addition to this, samples from previous works in the study were included. 12 groundwater well

samples collected and analyzed between November 2001 and August 2002 (S. Kebede, Travi,

Alemayehu, & Ayenew, 2005) and 20 groundwater well and spring water samples collected and

analyzed between January 18 and March 21, 2006 were included in this study. The latter samples

were obtained from Geological survey of Ethiopia hydrogeology department. Rain water samples

collected and analyzed in 2002 were also added to compare with the data points collected during the

field work. These data were implemented in the chloride mass balance for recharge estimation

calculations.


23

Table 4-2. Rain water chemistry (ITC laboratory, 2007)

Method F- Cl- NO2- Br-

NO3- SO4

-2

IC_001 < 0.02 0.50 < 0.00 < 0.00 0.63 1.24IC_001 < 0.02 1.43 < 0.00 < 0.00 2.03 2.26IC_001 < 0.00 0.65 < 0.00 < 0.00 1.59 1.33

August 8-9August 10-11August 12-13

Year 2007

Table 4-3. Rain water chemistry (Kebede et al., 2005)

Year 2002 Na+ Mg+2 K+ Ca+2 Cl- SO4-2 HCO3

- F- EC (µScm-1)

June 1 - 17 1.52 0.27 0.29 1.39 0.43 0.70 8.51 0.00 8.00June 17 - 30 2.08 0.26 0.33 1.84 0.30 0.65 10.31 0.40 12.70July 1 - 15 1.84 0.21 0.79 1.92 0.65 1.77 9.68 0.00 23.00July 1 - 15 0.50 0.21 0.86 1.66 0.61 1.55 5.78 0.00 23.00Average mgl-1 1.49 0.24 0.57 1.70 0.50 1.17 8.57 0.10 16.68

Figure 4-1. Location map of water samples (08-23 August, 2007). Samples taken from Rivers

(red), Boreholes (black) and springs (yellow) spots (see table 4.1)


24

4.3. Chemical Analysis

A number of parameters were determined in the field. These parameters include: electrical

conductivity (EC), temperature, pH, total dissolved solids (TDS). In this field trip alkalinity and

bicarbonate was not measured but were determined as residuals from the ionic balance. Chloride

concentration was also determined in the field. The purpose of this was to make a cross-check

between field and laboratory hydrochemical determinations and to check the accuracy of field results.

Because laboratory results were considered accurate and more precise, the laboratory chloride results

were used in this study.

4.4. Field Hydrochemical Analysis

4.4.1. Electrical Conductivity

A relation is made between EC and TDS of the samples. Field measured TDS and EC are drawn in the

following graph and show that they are linearly correlated with coefficient of correlation 0.99. This

indicates field measurements were highly reliable. Normally an approximate correlation between

conductance and TDS is given by: TDS = k*EC where k varies between 0.55 and 0.8. However, in

this study the value of k was found 0.43.

y = 0.43x + 3.22

R2 = 0.99

0

50

100

150

200

250

300

350

0 100 200 300 400 500 600 700 800

EC (µScm-1)

TD

S (

mgl

-1)

Figure 4-2. Relationship between TDS and EC of the field sample data


25

As can be seen from the above graph the TDS and the EC are highly correlated. Water sample

collected in the field have TDS values between 5.6 and 892.8mgl-1. Electrical conductivity values are

also used to interpolate the chloride concentrations from samples not analyzed in the field and in the

laboratory. But the linear regression modeling used in this analysis does not indicate a good result.

Therefore there is some uncertainty in the chloride values derived from the EC values by the

regression relation of the figure below.

y = 0.0059x + 1.02

R2 = 0.48

0

1

2

3

4

5

6

7

0 100 200 300 400 500 600 700

EC (µScm-1)

Cl l

ab (

mgl

-1)

Figure 4-3. Linear modelling between EC and Chloride concentrations

4.4.2. PH

The pH of water is the balance between the hydrogen (H+) and hydroxide (OH-) ions in that water and

is defined as the negative logarithm to the base 10 of the hydrogen ion concentration i.e. pH= -log

[H+]. pH measurements in the field were performed. The values indicate that they lie in the range of 5

to 7.5 as in most natural water which exists in neutral solutions. These water samples could be

classified as water types ranging from slightly acidic to slightly basic. It is important to note that pH

values indicate the path ways water encounters the rock types up on going to its destination or where

it originates from.

4.4.3. ITC Laboratory Chemical Analysis

Water samples collected in the field were analyzed for cations and anions in the ITC laboratory. The

ions determined include: Na+, K+, Ca+2, Mg+2, Cl-, SO4-2, HCO3

- and CO3 -2.


26

Table 4-4. ITC laboratory chemical analysis

Sample code EC Cl- PO4

-3 SO4-2 NO3

- F- Ca+2 K+ Mg+2 Na+

µScm-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1

river 146.0 1.7 7.00 0.10 0.29 31.14 5.06 7.44 9.14river 32.1 1.0 7.00 0.80 10.09 4.20 1.48 4.59spring 103.5 2.4 0.00 1.40 13.23 2.74 4.77 5.22borehole 234.0 4.4 0.83 13.39 2.89 4.79 5.41river 65.0 0.9 0.01 9.71 4.42 3.87 5.35spring 64.2 0.5 0.03 0.20 0.13 8.96 3.41 4.26 3.37borehole 527.0 50.0 14.00 2.60 0.17 54.94 4.40 44.09 13.08borehole 442.0 1.0 2.00 70.57 4.21 23.79 18.25borehole 608.0 3.9 0.33 85.08 5.72 26.53 46.04borehole 540.0 6.4 0.39 26.27 15.39 12.68 65.36river 155.9 1.6 16.66 3.71 6.54 5.84spring 295.0 4.4 7.05 4.17 0.17 54.24borehole 625.0 4.2 0.10 100.91 4.36 32.48 24.78lake 144.0 2.2 0.01 0.33 32.48 5.42 7.48 9.22spring 150.0 1.1 0.08 13.42 2.68 4.11 5.76

ITC laboratory analysis (3-5/10/2007)

4.5. Anions analysis

A total of 20 samples were analyzed for anions in the laboratory: 14 samples for chloride and 5

samples for each of the other major and minor ions. A portable data logging spectrophotometer,

HACH DR/2010 was used to determine the content of anions. These include major and minor anions.

In the spectrophotometer individual anions have specific program numbers at a certain wavelength

and different method with its own chemical reagent. See Appendix C for details on the analytical

procedures.

4.5.1. Chloride

A total of 15 Samples collected in the field were analyzed in the laboratory for chloride

concentrations. Chloride concentrations were formerly determined in the field. The relation between

field and laboratory results indicates a second degree (parabolic) correlation. Using linear regression

modelling, they show lower correlation coefficient (0.95) than when using the parabolic modelling

(0.99). Other chloride concentrations were obtained by linear regression modelling from the electrical

conductance measurements as indicated above.

As can be seen from the graph indicated below, it can be observed that the field measurements using

the field kits were overestimating the chloride content as compared to the laboratory results. Perhaps

this could be due to too many drops per measurements in the field might be added during the field

titrations.


27

Cl field vs Cl lab

y = 0.02x2 - 0.13x + 1.64

R2 = 0.99

0

10

20

30

40

50

60

0 20 40 60Cl field (mgl-1)

Cl l

ab (

mgl

-1)

Field vs Lab Cl results

0

20

40

60

80

100

120

C1 C3 C5 C7 C9 C11 C56 C70Samples

Val

ues

(mgl

-1)

Cl lab Cl field

Cl field vs Cl lab

y = 0.02x2 - 0.13x + 1.64

R2 = 0.99

0

10

20

30

40

50

60

0 20 40 60Cl field (mgl-1)

Cl l

ab (

mgl

-1)

Field vs Lab Cl results

0

20

40

60

80

100

120

C1 C3 C5 C7 C9 C11 C56 C70Samples

Val

ues

(mgl

-1)

Cl lab Cl field

Figure 4-4. Correlation of field and lab determined chloride concentrations.

4.5.2. Nitrate, Phosphate, Sulphate and fluoride

These anions are also determined in the laboratory by spectrophotometer. Organic fertilizers and

industrial influents raise the concentration of phosphates in surface waters. But deep groundwaters

hardly contain phosphate compounds. Sources of Sulphates are the mineral pyrite gypsum and

anhydrite. Under some conditions a considerable quantity of sulphates may be obtained from organic

sulphur compounds (e.g. combustion of coal and petroleum, smelting of sulphide ores and

geochemical waters). Nitrates are found in almost all natural waters. They are usually found in low

contaminations unless there is a contamination source. The primary source of nitrates is the

atmospheric nitrogen gas. The methods and reagents used in the analysis of the anions in the

laboratory are tabulated in Appendix C.

4.6. Cations Analysis

4.6.1. Inductively Induced plasma: ICP-ASE

The analysis of cations is conducted in the ICP instrument in ITC laboratory. The results were

checked by analysis with a certified standard Merck Certipur reference solution. The major cations

analyzed are Na+, K+, Ca+2 and Mg+2.


28

4.7. Interpretation of the results

4.7.1. Ionic balance

The accuracy of chemical analysis of water may be readily checked because the solution must remain

electrically neutral. The following relation was used.

100*.%).(

−+

=∑ ∑∑ ∑

anionscations

anionscationsNEtralityElectroNeu [4.1]

The laboratory results together with the borehole results from previous works (Kebede et al., (2005)

and from Geological Survey of Ethiopia are presented as a table in the appendices.

Another useful technique is to compare calculated electrical conductivity with the measured

conductivity as this is related with the concentration of ions in solution. Ionic balance was performed

in which the sum of cations as well as the sum of anions compared to the electrical conductivity

divided by 100(EC/100).

1 1 1( ) ( ) /100( )anions meql cations meql EC Scmµ− − −= =∑ ∑ [4.2]

This equation is valid for EC measurement value up to 2000µ Scm-1 and for dilute solutions the result

should be a straight line. Besides to this, the equation is particularly important when the samples are

transported long distances as there might happen chemical reactions in the sample bottles and

precipitation of some ions present. Based on the above equation a comparison was made between sum

of anions and EC/100 as indicated below.

Generally the total dissolved solids, mostly mineral salts, in waters determines the concentration of

ions and thereby the conductivity of the water as a whole. The specific conductance of the study area

varies spatially from a very small amount (32µ Scm-1) in rivers and to a moderate one (1118µ Scm-

1) in groundwaters. This indicates the samples contain more dilute concentrations of mineral salts

and could be attributed to the insolubility of the minerals salts in the area (they are mostly basaltic

rocks and don’t dissolve readily). Lake Tana’s conductance increases from 142µ Scm-1 to 150µ Scm-

1 as we go from Bahrdar 10 km to the north. Main tributaries to the lake contain low concentration of

ions. Out of these rivers, Megetch River has the highest conductance (156µ Scm-1).


29

0

1

2

3

4

5

6

7

8

9

10

1 11 21 31 41 51 61 71 81 91

Sample points

Val

ue (

meq

l-1 &

µS

cm-1

)

Sum of anions EC/100

Figure 4-5. Comparison of sum of anions and EC/100

Major ions showed high spatial variability in surface waters due to local geological and climatic

conditions. In the basin, the dominant cations are calcium and magnesium ions. They are responsible

for the temporary hardness of the water together with the bicarbonates. But in some groundwaters

(wells) sodium has maximum value (160.5mgl-1). This well also contains the highest value of anions:

Cl-1 (24.1 mgl-1) and CO3-2 (634.4 mgl-1). Bicarbonate values are obtained from previous works and

using a correlation factor between the EC measurements.

Potassium concentrations in natural waters usually range less than 10 mgl-1 (Hounslow, 1995).

Potassium salts are widely used in industry and agriculture and enter surface waters with industrial

discharges and runoff from agricultural land. Chloride concentrations vary from 0.38 mgl-1 in Lake

Tana to 50 mgl-1 at well Bahrdar.

4.8. Ions Sum Comparison

For checking the analysis made, a comparison between ions and the EC/100 was presented in a graph

below. Note that the value (3.73, 8.98 meql-1) is an outlier and could removed from the list. This was

taken from a river surrounded by farm areas in the northern part around Gorgora i.e. in Dembia plain.


30

y = 0.94x + 0.09

R2 = 0.90

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10Sum of anions (meql-1)

Sum

of

catio

ns (

meq

l-1)

Figure 4-6. Graph of regression analysis of sum of anions against sum of cations

y = 0.70x + 0.03

R2 = 0.90

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10Sum of Cations (meql-1)

EC

/100

(µS

cm-1

)

y = 0.73x - 0.08

R2 = 0.99

0

1

2

3

4

5

6

7

8


EC

/ 10

0 ( µ

Scm

-1)

y = 0.70x + 0.03

R2 = 0.90

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10Sum of Cations (meql-1)

EC

/100

(µS

cm-1

)

y = 0.73x - 0.08

R2 = 0.99

0

1

2

3

4

5

6

7

8


EC

/ 10

0 ( µ

Scm

-1)

Figure 4-7. Graph of regression analysis of sum of anions and cations in the Lake Tana basin

4.9. Chemical evolution of groundwaters in the study area

Although the ultimate source of waters is rainfall (through the endless movement of water cycle), the

water type is also determined by the path ways through which it passes to its storage. This is

especially quotable for the source of groundwater as it encounters different aquifer systems on its way

to its destination. For the chemical analysis of groundwater evolution in the study area, only water


31

samples taken from wells and springs were taken. Three types of datasets were used for this analysis

and treated independently:

1. Samples collected during the field work (8-23, August 2007)

2. Samples collected and analysed in 2002 (Kebede et al., 2005) and

3. Samples collected and analyzed between January 18 and March 21, 2006.

In addition to this, the samples taken from lakes and rivers during field work are also treated

separately. Hydrogeological chemical modelling was performed to determine the chemical evolution

of the Lake Tana basin using the aquachem computer code. The three Datasets revealed that all water

samples i.e. groundwaters from the basaltic plateau of the Lake Tana basin are characterized by Ca-

Mg-HCO3 water types. Ca and Mg elements dominate the cation species. Based on the piper diagram

below resulted from the analysis of the samples, the source rocks have high concentration of the

Olivine type basalts containing elements calcium and magnesium. Previous studies also indicate that

these rocks are the low salinity, Ca-Mg-HCO3 type, isotopically relatively enriched cold groundwaters

from the basaltic plateau (Kebede et al., 2005). Hounslow, (1995) has divided water types depending

on their location on the diamond shaped piper plots: The water that plots near the left corner of the

diamond is reach in Ca+2, Mg+2, HCO3- and this is the region of temporary hardness.

Figure 4-8. Samples taken during field work GW (left) and Surface water (right)


32

Figure 4-9. Samples taken from GSE (left) and (Kebede et al., 2005) (right)

The results of the water sample indicate that the water type generally lies with in a temporary

hardness of a water type. The dominant cations are Ca and Mg and that of the anion is HCO3-. The

source rock seems to be hard basaltic rocks. Basalt is a hard, black extrusive igneous rock. It is the

most common type of rock in the earth’s crust and it makes up most of the ocean floor. These rocks

are exposed in the northern, western and south-western of the Lake Tana. In the general groundwater

evolution model, these types of waters are often regarded as at their early stage of geochemical

evolution, rapidly circulating groundwaters which have not undergone a pronounced water-rock

interaction (Kebede et al., 2005), indicating recent recharge and natural conditions without pollution.

4.10. Geostatistics

The spatial distribution of EC values in Lake Tana basin can be determined by point kriging. The

method is applied in two steps:

4.11. Determination of spatial autocorrelation in the datasets

Spatial autocorrelation was determined by the use of Semi-Variogram calculations. The object of this

analysis is to find a theoretical variogram model that best fits the observations.

( )2

)()(*2

1)( ∑ −−= hxZxZ

nhγ [4.3]


33

Where z is the EC value at location x and n is the number of pairs of sampling points in a certain

distance interval h. The objective of this analysis is to find a theoretical model that fits the

observations. The model produces a sill (which is about equal to the variance of the data set), the

range (the distance beyond which there is no longer any spatial correlation) and the nugget (the

random variation between values at sampling sites which are very close together) (A.S.M. Gieske,

Miranzadeh, & Mamanpoush, 2000). The fitted variogram for this analysis is presented below.

Figure 4-10. Semi-Variogram of EC in Tana basin

A spherical model was selected with the following characteristics: the lag distance was limited to

28km in order to include sufficient pairs and the range was 170km. The sill of this semi-variogram

was 1.15*105 µS2cm-2 and the nugget was found as 1.8*104 µS2cm-2.

4.12. Kriging

The kriging interpolation method then makes use of the variogram sill, range, nugget and the distance

values. Simple kriging was selected as the interpolation method and the grid size was made 100m.

Based on the kriged map a number of observations can be made. High EC values were found in the

northern part of the study area. This can be attributed to the samples taken from spring waters. This

also indicates groundwaters in that vicinity have higher ionic concentrations than towards the

southern part. This also seems to agree well with the fact that rainfall is lower in the northern than in

the southern part of the study area. However, in this study the EC value of the lake was kept constant


34

at around 150 µScm-1 by taking more samples with the same value as determined during the sampling

period.

EC

µµµµScm-1

0

200

400

600

800

1000

EC

µµµµScm-1

0

200

400

600

800

1000

Figure 4-11. Kriged map of EC values in Lake Tana Basin. EC of the lake was kept constant

at around 150µ Scm-1

Water samples collected from the southern part were almost all from rivers. As we observe from the

map, they have low EC value. Since the field was done during the rainy season, the composition of the

river water samples is likely close to those of rain sample (sample taken from Gilgel Abbay has 32

µScm-1). Electrical conductivity around the lake is moderate (150 µScm-1). This indicates that the type

of water is fresh. But as there were very limited number of sample points (around the shore in

Bahrdar), further study taking more samples should be conducted to determine water type of the lake

especially with respect to seasonal variations. In general, the relatively saline waters are located north

of Lake Tana but it needs further study taking more samples in all parts of the study area including the

lake it self during all seasons of the year.


35

5. GROUNDWATER CONTRIBUTION IN THE UPPER BLUE NILE FLOWS

5.1. Watershed groundwater balance estimation using streamflow recession and

baseflow analysis

The characteristics of flows in perennial streams during extended dry periods have long been

recognized as different from those experienced during and following storm rainfall events (Tom,

1999). Water types entering streams in response to individual water-input events are identified as

event flows or direct flows or quick flows which are different from the baseflow waters that originate

from persistent slowly varying groundwater sources. It is well known that the observed stream flow of

many rivers in many different hydrological and climatic settings is the outflow originating from

shallow groundwater reservoirs of the associated catchments. Such groundwater reservoirs are an

important water resources both for the maintenance of the natural environment as well as the human

needs (Wittenberg & Sivapalan, 1999). Groundwater discharge from shallow unconfined aquifers is

commonly assumed to be the main contributor to baseflow. Thus, baseflow of rivers originates

predominantly from the saturated zone, the shallow groundwater reservoir, which in most cases is

unconfined. Discharge from these groundwater reservoirs exfiltrates through the river beds (Dingman,

2002).

Many hydrologic and water quality computer models have been developed over the years that are

useful for effective watershed management (Arnold et al., 2000). Some of these models have been

specifically developed for separating the baseflow component from the total stream flows (eg.

HYSEP). In streamflow hydrograph analysis, baseflow separation techniques from the varying

streamflow hydrographs start in identifying the starting and ending points of direct runoff. The start

point is readily identified as when the flow starts to increase while the end point is usually taken as

the time when the plot of logarithm of the flow against time becomes a strait line (Furey, 2001).

Having established the end-points for the separation, a wide range of graphical techniques is available

for defining the baseflow between these points (Chapman, 1999). But these techniques are

inconvenient when separations are to be undertaken on a long continuous record of streamflows,

rather than just a few storm period hydrographs. This has led to the development of numerical

algorithms for baseflow separations.

Baseflow separation uses the time-series record of streamflows to derive the baseflow signature.

Graphical separation methods tend to focus on defining the points where baseflow intersects the rising

and falling limbs of the quickflow response. Filtering methods process the entire stream hydrograph to

derive a baseflow hydrograph. Recursive digital filters, which are common tools in signal analysis, are


36

commonly used to remove the high-frequency quickflow signal to derive a low-frequency baseflow

signal and such filters are simple but the results are very sensitive to the filter parameter, which needs

calibration before the results can be considered valid (Furey, 2001).

Recession analysis focuses on the recession curve which is the specific part of the hydrograph

following the stream peak and rainfall event when flow decreases. Recession segments are selected

from the hydrographic record and can be individually or collectively analysed to gain an

understanding of the processes that influence baseflow. Graphical methods, such as correlation or

matching strip techniques involve plotting multiple recession curves to derive a master recession

curve representing a composite of baseflow conditions (Joko Sujono, 2004). In analytical methods,

equations are applied to fit the recession segments. A storage-outflow model is developed to represent

discharge from one or more natural groundwater storages during the recession phases (Wittenberg,

1999).

Besides to the above points, there is widespread agreement that good correspondence between

measured and calculated streamflows at the catchment outlet is not a sufficient criterion for the

validity of a physically based hydrologic model. Additional knowledge concerning catchments’

internal processes like storativity, permeability of the aquifer properties are needed (Hammond &

Han, 2006). Yet, for many catchments such information is not available and the model’s performance

can only be assessed by comparing calculated and measured stream flows. However, the information

provided by a hydrograph is not limited to the absolute height of the runoff as stated by (Boughton,

1993). For example, a separation of the hydrograph allows for identification of runoff components

originating from different sources of the considered system, and responding differently as delayed and

smoothed flows to rainfall events. An example of the potential benefit of this information for model

verification has been given by Eckhardt, (2005).

5.2. Data processing

The data used for this study were daily average streamflow records collected during the field work

executed from August 08 to September 20, 2007 for the Upper Blue Nile flows (Lake Tana basin)

from the data set of the Regional Water Resources Bureau in Bahrdar, Geological survey of Ethiopia

and Ministry of Water Resources in Addis Ababa for the hydrological data. For the meteorological

data, the National Meteorological Agency (NMA) of Ethiopia was used. The location map of the

meteorological and hydrological stations is indicated in the figure below. Numbers correspond to

discharges stations and are compiled in table 5-1 below.


37

_

• Discharge stations

• Meteo stations

_

_ _

_ _

_ _

__

• Discharge stations

• Meteo stations

_

_ _

_ _

_ _

_

Figure 5-1. Location map of meteorological and gauging stations

The record length of the data collected varies from 7 to 34 years of daily data for the smaller streams

and the main tributaries respectively. Large amount of missing data especially in the minor rivers was

obtained. Filling data gap was performed by regression analysis between the nearest catchments and

the respective years in the catchments. Meteorological data have 12 years of record length but

extremely large missing data especially for sunshine, relative humidity, wind speed, and pan-

evaporation parameters was found. Wind speeds of the recent years showed great consistence than the

other meteorological parameters. For this reason only meteorological data of 12-year length was used

for this study in the calculation of potential evapotranspiration despite the fact that long periods of

discharge data were available. The relative humidity data was obtained for 6:00, 12:00, and 18:00

hours of a day. Moreover, the average daily relative humidity was used in the determination of

potential evapotranspiration (Meteorological data will be discussed in subsequent sections). In line

with this, it is important to explain that the discharge recorded of the down stream catchment was

used if a number of stations are available in the upper catchments as in the case of Ribb and Kilti

catchments for simplicity. A summary of the discharge data set is given in the following table.


38

Table 5-1. List of hydrological stations in Lake Tana Basin

Sl. Main UTM Area

No. Catchment River/ Lake Stations North East km2

1 Abbay Blue Nile Outlet At Bahirdar 1282709 323731 153192 Abbay Gilgel Abbay Near Merawi 1257136 285380 16643 Abbay Koga At Merawi 1257124 287200 2444 Abbay Ribb Near Addis Zemen 1326761 360284 15925 Abbay Gumera Near Bahirdar 1308372 351119 13946 Abbay Megetch Near Azezo 1380370 331553 5147 Abbay Upper Ribb At Debre Tabor 1332169 389339 8448 Abbay Angereb Near Gonder 1396941 335272 419 Abbay Zufil Near Ambessame 1308171 40014610 Abbay Gelda Near Gasai 1293625 351047 3211 Abbay Ribb Near Maksegnit 1304462 407397 5912 Abbay Gemero Near Arb Gebeya 1369249 342362 17413 Abbay Fegora Near Infranze 1286186 365549 2914 Abbay Garno At Merawi 1352620 349526 9415 Abbay Bered At Dangila 1262572 299971 8116 Abbay Amen Near Addis Kidame 1246201 267104 8917 Abbay Quashini Near Addis Kidame 1238826 267050 4218 Abbay Kilti Near Durbete 1269007 276678 74319 Abbay Dirma Near Kola Diba 1374260 317960 377

List of Hydrological Stations In Lake Tana Basin

5.3. Algorithms for streamflow recession analysis and baseflow separation

5.3.1. Baseflow recessions

The hydrograph of a stream during periods with no excess precipitation will decay following an

exponential curve and the stream discharge during these periods is composed of entirely of

groundwater contributions (Fetter, 2001). As the stream drains water from the groundwater reservoir,

the water table falls, leaving less and less groundwater to feed the stream. A lower water table means

that the rate at which groundwater seeps into the stream declines. Imagine a bucket with a hole near

the bottom. As the water drains from the bucket the water level (water table) falls and the stream of

water draining from the bucket (baseflow to streams) declines in volume. The stream of water

draining (baseflow) will not increase until the water in the bucket is replenished (recharged) and the

water level (water table) falls.

Groundwaterstorage, S

Discharge, Qt

Groundwaterstorage, S

Discharge, Qt

Figure 5-2. Conceptual representation of groundwater storage and its discharge to streams


39

Each recession segment of a time series hydrograph is defined as an exponential decay function as

applied in other field of study such as heat flow, radio activity and diffusion (Brodie & Hostetler,

2006). The equation most used for baseflow during non-recharge period in linear reservoir is:

Qt = Qo*e-t/τ = Qo k

t [5.1]

Where Qo, Qt are the flow at times 0 and t, and τ is the residence time or turnover time of

groundwater storage defined as the ratio of storage to flow and k is the recession constant for the

selected time interval. The start of the recession Qo is the day when the flow is dropped from the

preceding flow and continues recessing down in time steps. Tom, (1999) stated that the first and

physically meaningful form of Eq. (5.1) was based on the analysis of Boussinesq, (1887) of flow in

aquifers before its development and application to streamflow and baseflow equations. This

exponential function of the hydrograph recession implies that the recession will plot as a straight line

on the semi-logarithmic axis, with flow on the logarithmic scale against time on the linear scale

(Boussinesq, 1887). The slope of this line is equal to the recession parameter k. However, plotting the

individual recessions on the semi-logarithmic scale usually does not give a straight line but a curved

line. This phenomenon occurs owing to the fact that the recession comes from different flow

components: namely, surface flow, interflow and groundwater flows, with different flow lag

characteristics. This curved line of recession indicates that the storage-outflow relationship of the

aquifer is non-linear (Wittenberg & Sivapalan, 1999). Equation (5.1) is readily shown to be the result

of a linear storage in which the groundwater storage S is related to the stream flow Q by:

Q = S/τ = aS [5.2]

Where a = 1/τ also called cut-of frequency. Linear behaviour of groundwater in a confined aquifer of

constant thickness would be expected from the Darcy equation. Chapman, (1999) indicated that

equation (5.1) can be derived from the equation of one dimensional flow in such an aquifer. He also

mentioned that this could be regarded as a reasonable approximation for unconfined flow when the

underlying impermeable layer is well below the stream bed, resulting in little spatial variation of flow

depth.

According to Chapman, (1999) the spatial variation in the groundwater flow depth must be taken into

account for shallow bed rocks. This is because a falling water table continuously decreases the

effective thickness of the aquifer and decreases the drainage because the transsmisivity becomes less

when the saturated thickness decreases. Declining water tables can also be attributed to groundwater

abstraction and evapotranspiration other than stream discharges. Thus, for the case where the stream


40

bed intersects the impermeable bedrock, Chapman inferred that the flow would be proportional to the

square of the volume of the groundwater storage as:

Q=aS 2 [5.2a]

These results can be generalised into the non-linear relationship (Chapman, 1999)

Q=aSn [5.2b]

Combining equation 2b with the water balance equation:

Q = -dS/dt [5.3]

Results in the recession equation:

( )1

11−

−

−+=

nn

oot

tnQQ

τ [5.4]

The common argument against a linear reservoir approach is that in most catchments, it is unlikely

that all the hydrologic, geologic meteorological factors remain constant over large watersheds. The

dynamic nature groundwater aquifers could be divided into parallel independent storage zones. It

seems more realistic that catchments consist of spatially variable (including layered) systems of

hydraulically communicating pores or fissure systems. Thus according to Wittenberg, (1999), a single

but non-linear reservoir is considered to be more physically realistic and meaningful for recession

calculations.

5.3.2. Baseflow Separation: Digital Filter Methods

The process of baseflow separation, hydrograph analysis, is concerned mainly in the partitioning of

the stream flow records into runoff flow and baseflow components. A number of separating methods

have developed. One of the methods is the recursive digital filter separation method. The digital filter

method has been used in signal analysis and processing to separate high frequency signal from low

frequency signal (Lyne & Hollick, 1979). This method has been used in baseflow separation as high

frequency waves can be associated with direct runoff and low frequency waves with baseflow


41

(Eckhardt, 2005). Thus, the following digital filter equations have been used in baseflow separation

following one after the other. In this study three filter methods obtained from Automated Baseflow

Separation for Canadian Datasets (ABSCAN) software are employed in order to compare the results

from one another and to more understand the behaviour of the catchment under study.

1. Lyne -Hollick

The digital filter method was applied for baseflow separation by Lyne and Hollick (1979) and has

been used in several studies. Time domain filters are most often used and expressed as recursive

formulas (Lynn, 1989). These filters are calibrated to produce baseflow estimates from stream flow

data which means that constants in a filter are adjusted until the calculated time series resembles

baseflow. Lyne and Hollick (1979) were the first to introduce a time domain filter for separating

baseflow from stream flows (Furey, 2001). The filter equation looks like:

( )11 2

1* −− −++= tttt QQqq

αα [5.5]

In this equation (and those that follow below) Qt is the stream flow at time t and qt and bt are the

corresponding runoff and baseflow components. Alpha (α ) is the filter parameter associated with the

catchments. To produce an estimate of baseflow, daily mean streamflow data were filtered forward in

time and the result was filtered backward in time as (Nathan & McMahon, 1990) applied this same

filter forward, backward, and then forward in time to a similar daily stream data set in their study.

2. Chapman

Chapman (1991) pointed out that the Lyne-Hollick, (1979) algorithm incorrectly provides a constant

streamflow Q or baseflow b when direct runoff has ceased. He developed a new algorithm to a form

that is based on the assumption that the baseflow being a simple weighted average of direct runoff and

the baseflow at the previous time (Chapman, 1991). He had reformulated his equation to the total

streamflow as:

( )11 3

1*

3

13−− +

−−+

−−= tttt QQbb

αα

αα

[5.6]

(Chapman & Maxwell, 1996) modified equation (5.6) without changing its characteristics and the

resulting equation produced a similar result to the original one.


42

ttt Qbbαα

αα

−−+

−= − 2

1

2 1 [5.7]

Chapman explained that when the quick runoff has ceased the filter parameter α becomes the

hydrological recession constant a commonly used to describe baseflow recession during dry weather

periods without groundwater recharge. The equation would be:

( )11 2

1−− +−+= tttt qqbb

αα [5.8]

And the resulting baseflow recession would be:

bt = abt-1 [5.9]

3. Eckhardt filter

Eckhardt (2005) reviewed previously developed filters and presented general formulation of one

parameter filter. But the filter seems rather a two-parameter filter in which the baseflow index, BFImax

and the filter parameters α need to be determined for individual catchments. The equation is:

max

max1max

*1

*)1(**)1(

BFI

QBFIbBFIb tt

t ααα

−−+−

= − [5.10]

Subject to bt < Qt

Where α corresponds to the groundwater recession constant and BFImax sets the maximum value of

the baseflow index BFI which is the long term ratio of baseflow to total streamflow. BFImax is a filter

parameter that determines the maximum base flow. In fact it is a subjective parameter that needs to be

determined based on the type of catchment. Eckhardt suggested values for his parameter BFImax based

on the results obtained in his work in different catchments. He mentioned that BFImax values about

0.80 for perennial streams with porous aquifers, 0.50 for ephemeral streams with porous aquifers, and

about 0.25 for perennial streams with hard rock aquifers, but he noted that this should be further

studied in order to determine it especially using tracer experiments.


43

5.4. Results of the four filter methods

5.4.1. Baseflow recessions

Baseflow recession periods were identified as part of the hydrograph in the entire hydrograph of the

individual catchments for the whole periods in which data is available. The recession forecast of the

individual catchments was performed immediately after rainy season ceased. However, the interflows

and bank storage might be contributing considerably to the streamflows. Besides to this, the recession

period identification was done by the method in which the plot of the semi-logarithm of the discharge

against the time remained approximately linear based on the following criteria:

� The recession period was taken at least ten days, but they range mostly from October to

March as the hydrographs of the catchments depict August to September peaks.

� Years with regular recession flows were chosen as recession candidates and years with

intermittent wet periods are excluded.

For some of the catchments, recession periods were identified using long term daily averages of the

entire record period as the recession of the individual segments in individual years does not display

log-linear relationships in the semi-log plots. Some of the catchments which behaved this way

include: Garno, Bered, Gelda and others which are not main tributaries to Lake Tana. The method of

baseflow recession period identification used leads to the log-linear relationship of the hydrograph

recession to identify baseflow sequences. However, a non-linear reservoir model was adapted in the

study. Then each of these baseflow recession periods was fitted to equation (5.4) using the sum of

squares of the residuals or differences between the simulated and observed baseflows as an objective

function for optimization of the parameters n and 0τ . The value of the discharge Qo at the start of the

recession was taken as a parameter to be optimized in the equation.


44

Ribb-1984

0

5

10

15

20

25

30

35

40

260 265 270 275 280 285 290 295 300

Days

Q (

m3 s-1

)

Measured Simulated

Figure 5-3. Recession curve fitting of Ribb catchment

Gilgel Abbay-2004

010

20304050

607080

90100

280 290 300 310 320 330

Days

Q (

m3 s-1

)

Measured Simulated

Figure 5-4. Recession curve fitting of Gilgel Abbay catchment

After fitting the recession part of the hydrograph, it was also tried to extrapolate the model to the

entire hydrograph. To show how the fitting was done, the daily data for Gilgel Abbay catchment is

presented as an example. However this will be discussed in detail in baseflow separation section with

the recursive digital filters.


45

Gilgel Abbay 1973 - 2005

0

100

200

300

400

500

600

700

1 1201 2401 3601 4801 6001 7201 8401 9601 10801

Days

flow

(m

3 s-1)

Q total Q baseflow1975 1980 1985 1995 2000 2005

Years

Gilgel Abbay 1973 - 2005

0

100

200

300

400

500

600

700

1 1201 2401 3601 4801 6001 7201 8401 9601 10801

Days

flow

(m

3 s-1)

Q total Q baseflow1975 1980 1985 1995 2000 2005

Years

Figure 5-5. Fitting of recession curves and propagating to the entire hydrograph

The optimization technique used was the SOLVER optimization method in EXCEL and resulted in a

range of values for n that indicate the spatial and temporal variability in each recession segments that

was reflected in different catchments and within the catchment. Figure (5-6) shows the relationships

between Qo and oτ . From the results summarized in the table below, it can be seen that the mean value

of n obtained from the sampled catchments is greater than one for all the sites except Garno.

Table 5-2. Summary of baseflow characteristics showing values of the three parameters n,

Qo and oτ fitted into equation (5.4). Also the mean of the derived quantity So =Qo oτ is given

No of Qo Storage, So

recessions mean SD mean (104m3d-1) mean (d) mean (106m3)Abbay at Bahirdar 36 1.17 0.49 1553.26 74.80 1161.88Gilgel Abbay 29 4.30 4.31 184.62 136.80 252.55Garno 0.63 2.98 127.68 3.81Kilti 9 2.22 1.79 34.55 47.93 16.56Bered 2.10 2.05 300.03 6.14Geldi 3.06 2.05 350.00 7.17Koga 11 3.78 1.83 26.90 186.27 50.10Dirma 3.47 2.07 66.30 1.37Gumera 22 2.96 2.30 90.71 101.65 92.20Megetch 15 3.05 2.34 30.69 46.93 14.40Gumero 8 2.56 2.80 1.98 64.72 1.28Ribb 22 3.40 3.34 70.59 93.37 65.91

n oτ

The relationship of the storage and the discharge Qo is indicated below and the result indicates the

value of the correlation coefficient R2 is very low in agreement with that the idea of non-linear

relationship between the aquifer storage and the discharge at the beginning of the recession.


46

Gilgel Abbay at Wetet Abbay station

0

50

100

150

200

250

300

350

400

450

0 200 400 600 800 1000 1200

Qo (104m3d-1)

( )o dτ

Gilgel Abbay at Wetet Abbay station

0

50

100

150

200

250

300

350

400

450

0 200 400 600 800 1000 1200

Qo (104m3d-1)

( )o dτ

Figure 5-6. Scatter diagram of the parameter τ in the non-linear model in equation (5.4)

against Qo at the start of recession

Gilgel Abbay

0

10

20

30

40

50

60

70

80

0 200 400 600 800 1000 1200

Qo (104m3d-1)

Sto

rage

, S

o (1

03 m3 )

R2 = 0.08

Figure 5-7. Scatter diagram of the storage So and discharge Qo at the start of recession

5.4.2. Baseflow separation

The three recursive digital filters (Lyne-Hollick, Chapman and Eckhardt) were used to separate the

streamflow data sets in 12 catchments of the Lake Tana basin into their respective components of

baseflow and direct runoff. The separating of the baseflow component from the streamflow


47

hydrograph was done by tuning the filter parameter value between 0.925 and 0.995 until it give a

satisfactory result between the modelled and measured baseflows in the recession part of the

hydrograph. The result was then used to extrapolate to the entire hydrograph as quoted by Chapman,

(1999) and others. It was also tried to fit that these algorithms to the data by minimizing the

differences between the modelled and recorded flows during periods of recession previously

identified (in section 2.1 baseflow recessions) using the same manual optimization technique as

before. The fitting (calibration) was done on 3 years for the minor catchments and 10 years of the

major tributaries. The average value of the filter parameter α (as in Chapman, 1999) then applied to

the whole data set. Thus, the filter parameter value 0.995 was found to give the best estimate in this

study.

Baseflow comparison

0

50

100

150

200

250

300

1 366 731 1096 Days

Flo

w (

m3 s-1

)

Lyne & HollickChapman Eckhardt

0 1973 1974 1975 years

Baseflow comparison

0

50

100

150

200

250

300

1 366 731 1096 Days

Flo

w (

m3 s-1

)

Lyne & HollickChapman Eckhardt

0 1973 1974 1975 years

Figure 5-8. Hydrograph analysis of the inflow from Gilgel Abbay catchment

of the first three years using the three model approaches

In the case of the Eckhardt model approach, the baseflow index, BFI, value was found to be calibrated

in addition to the filter parameterα . The parameter BFImax sets an upper limit to the calculated

baseflow index BFI as discussed in section 5.3.2. The value of the BFImax is dependent on the

hydrological and hydrogeological characteristics of the different catchments as depicted by Eckhardt,

(2005). In this study, a baseflow index value BFImax of 0.25 was used as a predicted value in

accordance with the Eckhardt’s assignment for perennial streams with hard rock aquifers since the

Upper Blue Nile (Lake Tana) basin is characterised mostly by fractured volcanic aquifers. Table 5-4

gives results of the baseflow components as a BFI for the individual catchments contributing to the

Upper Blue Nile (Lake Tana basin) obtained using the recursive digital filtering methods. It is

important to note that in the case of the Blue Nile outlet, it does not mean that the BFI value is the

result of groundwater discharge (contribution) to the flow. Since it is in close connection with Lake


48

Tana, the baseflow from the lake is not equivalent to the groundwater discharge from shallow

unconfined aquifers of the basin. Rather it is the result of the lake storage that persists all the time, so

that other techniques are necessary here. However, for comparison the same filtering technique was

applied to lake outflow as to the river catchments around the lake. The separation practice performed

in the catchments is as follows:

1. Data of individual catchments were fitted to the three model approaches by varying the filter

parameter α between 0.925 and 0.995. The value 0.995 was found to give satisfactory

results.

2. The procedure was applied to individual years in all the gauged catchments thereby

calculating the BFI values of all years.

3. Finally the average annual value of the long term BFI is determined together with maxima,

minima and standard deviations. The results of the statistics performed are summarised

below.

Table 5-3. Summary statistics of BFI in the basin

CatchmentsBlue Nile outlet Bered Dirma

Gilgel Abbay Garno Gelda Gumera Kilti Koga Megetch Ribb Gumero

Sample size 34 5 8 34 19 21 34 8 34 34 34 23Lyne - Hollick

Max 0.85 0.60 0.22 0.39 0.56 0.35 0.46 0.34 0.52 0.45 0.32 0.59Min 0.36 0.43 0.07 0.27 0.19 0.14 0.32 0.26 0.24 0.11 0.18 0.07Mean 0.48 0.51 0.13 0.31 0.32 0.28 0.39 0.31 0.41 0.23 0.25 0.24SD 0.11 0.06 0.05 0.02 0.08 0.06 0.03 0.02 0.06 0.08 0.03 0.12

ChapmanMax 0.55 0.48 0.21 0.32 0.47 0.32 0.31 0.30 0.44 0.45 0.63 0.45Min 0.31 0.36 0.06 0.24 0.18 0.13 0.22 0.24 0.21 0.11 0.17 0.07Mean 0.39 0.42 0.13 0.28 0.29 0.26 0.26 0.28 0.36 0.23 0.24 0.22SD 0.06 0.05 0.05 0.02 0.06 0.06 0.02 0.02 0.05 0.08 0.08 0.09

EckhardtMax 0.29 0.53 0.21 0.17 0.31 0.19 0.20 0.16 0.27 0.32 0.53 0.45Min 0.13 0.42 0.07 0.12 0.09 0.08 0.11 0.12 0.13 0.18 0.08 0.07Mean 0.20 0.48 0.13 0.15 0.16 0.15 0.14 0.14 0.21 0.25 0.13 0.22SD 0.04 0.05 0.05 0.01 0.04 0.03 0.02 0.01 0.04 0.03 0.07 0.09

Using a BFI value of 0.50 in the Eckhardt model is equivalent to using the Chapman model. In the

case of some of the minor catchments, Bered and Gumero, the baseflow component was visually

found to fit Lyne-Hollick better than Eckhardt’s model. A possible explanation for this could be as

follows. Since the Eckhardt’s model approach using a BFImax value of 0.25 is for perennial rivers with

hard rock aquifers and these rivers are not definitely perennial rivers, the model does not seem

suitable for these rivers and their catchments. However, for the sake of comparison the results were

included in the table below.


49

Table 5-4. Baseflow separation parameters and baseflow index (BFI) for the three recursive

digital filter algorithms used in the study

Lyne and Hollick Chapman

0.5 0.25Blue Nile outlet 0.995 0.4829 0.3946 0.3951 0.2049Bered 0.995 0.4980 0.4244 0.4805 0.2089Dirma 0.995 0.1296 0.1252 0.1272 0.0822Gilgel Abbay 0.995 0.3146 0.2796 0.2805 0.1493Garno 0.995 0.3184 0.2854 0.2866 0.1639Gelda 0.995 0.2850 0.2582 0.2595 0.1453Gumera 0.995 0.3897 0.2610 0.2622 0.1418Kilti 0.995 0.3130 0.2816 0.2826 0.1415Koga 0.995 0.4066 0.3589 0.3598 0.2133Megetch 0.995 0.2517 0.2300 0.2314 0.1282Ribb 0.995 0.2479 0.2413 0.2426 0.1336Gumero 0.995 0.2383 0.2153 0.2167 0.1270

Average 0.31 0.27 0.28 0.15

Catchments

Eckhardt

α BFI BFICalculted BFI from

As can be observed from the above table, the groundwater contribution from the gauged catchments of

the Lake Tana basin i.e. the Upper Blue Nile flow is on average 27% to 31% of the annual inflow

using respectively the Chapman and Lyne-Hollick models. This was obtained by averaging the annual

BFI of the separate years from the 11 gauged catchments excluding the Blue Nile outlet BFI value

from the calculation. To indicate the groundwater contribution from the relatively deeper aquifers, a

separate calculation was applied using the Eckhardt, (2005) model approach assigning the BFImax

value of 0.25. This value results in an average 15% of the flow contributed from the hared rock

aquifers. The standard error was found to vary between 0.076 and 0.098 in the entire calculations.

Year one=average

Year two=average

0

50

100

150

200

250

300

350

400

1 365 729Days

Flo

w(m

3s-1

)

Q total Q direct Q Lyne & Hollick Q Chapman Q Eckhardt

Time (days)1 730

Year one= average

Year two= average

Year one=average

Year two=average

0

50

100

150

200

250

300

350

400

1 365 729Days

Flo

w(m

3s-1

)


Time (days)

0

50

100

150

200

250

300

350

400

1 365 729Days

Flo

w(m

3s-1

)


Time (days)1 730

Year one= average

Year two= average

Figure 5-9. Daily average total baseflow in the basin


50

To indicate the percentage contribution of baseflow from each catchment, a comparison was made by

summing the yearly average contribution of baseflow from each catchment and then dividing the each

catchment baseflow contribution to the total annual average baseflow. This comparison indicates the

baseflow from Gilgel Abbay catchment by far exceeds the other catchments followed by the baseflow

from the Gumera catchment. The table below displays this comparison. The Eckhardt’s baseflow was

computed based on the assigned BFImax value of 0.25 to indicate a completely a shallow fractured rock

aquifer.

Table 5-5. Comparison of baseflow contribution from each catchment

Lyne - Hollick Chapman EckhardtQtotal (MCMyr-1) Qbaseflow (MCMyr-1) Qbaseflow (MCMyr-1) Qbaseflow (MCMyr-1) %

G.Abbay 1652.88 517.62 462.71 246.95 43.89Ribb 432.04 107.59 99.81 52.50 9.47Megetch 272.66 61.04 56.52 31.13 5.36Gumera 999.92 286.91 263.55 142.59 25.00Koga 115.06 46.90 41.65 24.51 3.95Kilti 272.03 85.17 76.86 38.35 7.29Gumero 33.91 8.63 7.63 4.31 0.72Gelda 61.81 14.87 13.54 7.36 1.28Garno 27.01 9.63 8.07 4.29 0.77Dirma 195.41 19.59 19.50 12.98 1.85Bered 11.22 5.59 4.54 2.34 0.43Total 4073.95 1163.56 1054.37 567.31 100

From the above table a pie-chart was produces to show the percentage groundwater contribution of

the individual gauged catchments.

Ribb

Gumera

Gelda

G.Abbay

Koga

Kilti

Bered

Megetch

Dirma

Garno

Gumero

Northern

Southern

Eastern

35%

9%

56%

Ribb

Gumera

Gelda

G.Abbay

Koga

Kilti

Bered

Megetch

Dirma

Garno

Gumero

Northern

Southern

Eastern

35%

9%

56%

Figure 5-10. Pie-chart of the gauged BFI in the basin


51

The pie chart of Figure 5-10 illustrates the relative distributions of the total annual flow and the

baseflow. It is clear that most of the surface water originates in the southern and eastern parts of the

lake’s catchment. The gauged contributions from the north and the west are much smaller, as a result

of a drier climate in these regions. This trend should be taken into account when assessing the flow

from the ungauged catchments.

5.4.3. Comparison of filtered baseflows using the different digital filters

In many studies, the results of filtered baseflow were compared with the results from manual

separation or graphical separations and measured baseflow data. (Arnold & Allen, 1999) compared

the baseflow results using the Lyne-Hollick filter with results of measured flow data and found good

correspondence.

For a comparison of filtered baseflows in this study, the figures 5-11 and 5-12 below show the

baseflow into Lake Tana from Gilgel Abbay and Ribb catchments for 34 years from 1973 to 2006

obtained by the Lyne-Hollick and Chapman models (as mentioned earlier the Chapman model is

equivalent to the Eckhardt model with BFImax= 0.50). The figure shows that the different filter

methods produce highly correlated results.

BFI comparison - Ribb

R2 = 0.96

0.15

0.2

0.25

0.3

0.35

0.15 0.2 0.25 0.3 0.35Chapman

Lyne

-Hol

lick

Figure 5-11. Comparison of BFI computed from Lyne -Hollick and Chapman filters for Ribb


52

BFI Comparison-Gilgel Abbay

R2 = 0.93

0.15

0.2

0.25

0.3

0.35

0.15 0.2 0.25 0.3 0.35

Chapman

Lyne

- H

ollic

k

Figure 5-12. Comparison of BFI computed from Lyne -Hollick & Chapman filters for Gilgel Abbay

As a measure of the “goodness of the fit” between baseflows of the Gilgel Abbay catchment simulated

by Lyne-Hollick and Chapman model, the Nash-Sutcliffe coefficient (E) and the coefficient of

determinant R2 was computed the result was found to be 0.95 and 0.99 respectively and the slope was

obtained as a linear fit given by the equation: Y = 0.85x + 0.83.

5.4.4. Sensitivity analysis

The Eckhardt’s model has two parameters: α and BFImax. A sensitivity analysis was carried out in

order to see which one of these parameters influence more the filtered result. For this purpose, one

parameter was changed while keeping the other constant. The sensitivity index I was calculated as:

I = relative change of the calculated mean baseflow index

Relative change of the parameter

Streamflow records from two catchments were used: a hydrograph measured at Wetet Abbay gauging

station of the Gilgel Abbay catchment (BFI= 0.2805) and a hydrograph of the Addis Zemen gauging

station of Ribb catchment (BFI = 0.2426). The filter parameter α is varied by + 0.025 and the aquifer

parameter BFImax by + 0.05. The results of the sensitivity analysis are shown in table 5-6.


53

Table 5-6. Results of the sensitivity analysis

parameters Gilgel Abbay Ribb

α BFImax Calculated

BFI Sensitivity

Index I Calculated

BFI Sensitivity

Index I

0.970 0.50 0.469 0.426 0.970 0.45 0.426 0.860 0.388 0.760 0.97 0.55 0.513 0.880 0.465 0.780 0.945 0.50 0.492 -0.047 0.468 -0.090 0.995 0.50 0.280 -7.560 0.231 -7.800

The filter parameter α exerts a weaker influence on the calculated mean discharge than the BFImax.

This was problematic in that α can be determined by a recession analysis while BFImax is non-

measurable quantity. This results in somewhat subjective determination of baseflow separation.

Clearly there is a need for a more physically based approach to validate and calibrate the BFImax

values to be used.

5.4.5. Summary and conclusions

There is clear exponential baseflow behaviour in all major catchments. Immediately after the rains

there appears to be non-linear flow. However, after some time the linear reservoir approximation

seems to be valid. This can also be shown by plotting logQ against time.

BFI was calculated with three baseflow separation filters and can be ranked as follows as a function

of baseflow produced from the hydrographs

Method Equation BFI

Lyne-Hollick filter [Eq.5.7] 0.31

Chapman filter [Eq. 5.9] 0.27

Eckhardt filter (BFImax= 0.25) [Eq. 5.12] 0.15

From visual inspection of the hydrographs with the modeled baseflow, it appears that the Eckhardt

filter with BFImax= 0.25 best represents the baseflow in the area, with the exception of a few small

catchments near the lake shore.

However, with regard to the somewhat subjective nature of selecting BFImax it is also advisable to

validate and calibrate the values with more physically based methods. A new rainfall-runoff model

(BASF) was tested and used for this purpose. The modeling with BASF is described in section 5.6.


54

5.5. Baseflows from ungauged catchments

5.5.1. Water Balance of Lake Tana

To set up the water balance of Lake Tana all incoming and out going terms should be included for

best estimation. Terms in the water balance of the lake include: precipitation on the lake, evaporation

from the lake (open water evaporation), inflows from gauged and ungauged rivers, the change in

storage of the lake and outflow through the Blue Nile outlet. The inflows from gauged and ungauged

rivers include the direct streamflow and the baseflow of the groundwater aquifer component that

displayed in the hydrograph of the gauge stations for the specified time series. The water balance

equation is given by:

SQEQP outlakeinlake ∆++=+ [5.11]

Where,

Plake = precipitation on the lake

Qin = rive inflows from gauged and ungauged catchments

Elake = open water evaporation from the lake

Qout = outflow through Blue Nile from the lake

∆ S = the change in storage of the lake.

0

5

10

15

20

25

1 365 729

Wat

er b

udge

t va

riabl

es (

mm

)

0 Time (Days) 730

Year one= average

Year two= average

_Qout _Qin (gauged) _Elake _Plake

0

5

10

15

20

25

1 365 729

Wat

er b

udge

t va

riabl

es (

mm

)

0 Time (Days) 730

Year one= average

Year two= average

_Qout _Qin (gauged) _Elake _Plake

Figure 5-13. Long term daily average water balance components of Lake Tana


55

(Two identical years are shown for best illustration of the recession period after the rainy season)

For the estimation of precipitation on the lake, data of ten meteorological stations were used (Fig. 5-

15). A kriging interpolation was applied to estimate the precipitation over the lake and the annual

average rainfall was found to be 1252 mm (see the kriged map of Figure 5-15). Alternatively, using

only the meteorological stations of Gonder and Bahrdar and taking the average precipitation of these

two stations, an annual average value of 1256 mm is found. For the sake of reasons explained below,

the value of 1256 mm was used here for the water balance.

The long term daily average values of observed flows from the gauged catchments were summed up to

produce the annual total inflows from the gauged catchments to the lake. The advantage of using these

daily average value smoothes the outliers in the data set once the data gap have been filled as

described earlier (in baseflow separation). Thus, the total annual inflow from the gauged catchments

was found to be 1345mm. For the outflow, the Bahirdar station (Blue Nile outlet) was used in a

similar manner as done for the inflow component.

1100

1150

1200

1300

1350

1250

1100

1150

1200

1300

1350

1250

1100

1150

1200

1300

1350

1250

Figure 5-14. Annual average rainfall distribution over the lake and meteorological station used


56

5.5.2. Open water evaporation (Penman approach)

In natural water bodies, water-advected heat and change in heat storage may play a significant role in

the energy balance and the magnitude of these components in a particular case depends in large part

on the area, volume and residence time of water in the lake relative to the time period of the analysis

(Dingman, 2002). Following these types of situations, many methods of estimating evaporation from

surface water bodies have developed and these methods formulated theoretical concept of free-water

evaporation as described in Dingman, (2002). This free-water evaporation is an evaporation that

would occur from open water surface such as lakes in the absence of advection and changes in heat

storage.

For the determination of open water evaporation in the Lake Tana, the penman combination open

water evaporation approach was applied as applied in Lake Ziway by (Vallet-Coulomb, Legesse,

Gasse, Travi, & Chernet, 2001). It is formulated as:

γγ

γ +∆+

+∆∆= ** an ERE [5.12]

Where E is the daily evaporation rate (mmd-1), Rn is the net radiation expressed as equivalent

evaporation rate (mmd-1), ∆ is the slope of the saturated vapour pressure curve at the mean air

temperature, γ is the psychrometric constant and Ea is the drying power of the air, given as a daily

rate (mmd-1) by:

Ea = f (u)*(es - ea) [5.13]

where (es - ea) is the saturated vapour pressure deficit, difference between the saturated (es) and the

actual (ea) vapour pressure in kpa and f(u) is the penman’s wind function used by (Brutsaert &

Stricker, 1979) as quoted by Vallet-Coulomb et al., (1979). Thus Ea is given by:

)(*)54.01(*6.2 2 asa eeuE −+= [5.14]

With u2 the wind speed measured at two meters high (ms-1). For the determination of the net radiation

Rn, Rn = Rs*(1- α ) - Rnl, where Rs and Rnl are short and long wave radiation respectively, a water

surface albedo α of 0.06 was used following the case of Lake Ziway. Short and long wave radiations


57

were determined using the average daily meteorological parameters of Bahrdar and Gondar stations

obtained from the National Meteorological Agency (NMA) of Ethiopia.

The equation used in the determination of the drying power Ea is similar to that used in the open water

determination explained by (Maidment, 1993). Maidment, (1993) used the equation:

Ea= (6.43/λ)∗( es - ea) [5.15]

Where λ is given by:

λ = 2.051- 0.002361*ts [5.16]

in units of MJ kg-1 and ts is the surface temperature in 0C. In this case the average surface temperature

of Gonder and Bahrdar was used as there is no lake surface temperature in the indicated stations.

Taking the assumption that the water temperature is 2 0C higher than the mean surface air temperature

the value of λ was found to be 2.46 MJ kg-1 and this gives the right hand side coefficient indicated in

equation (5.15).

Thus, the annual average open water evaporation of Lake Tana was found to be 1672 mm which was

close to the value of 1650 mm obtained by SMEC, (2007). In the determination of the Lake Tana

water balance, the annual average lake evaporation 1672 mm was used.

Moreover, when we calculate the evaporation of the basin using the Hargreaves method and the

meteorological stations of Bahrdar and Gonder and we take the average value of the two, the annual

average result was found 1672 mm similar to what was obtained by the above method. Thus it seems

reasonable to take this result in the study.

5.5.3. Summary of water balance and conclusions

Having set up all the water balance components of the lake and having determined all components of

the lake’s average annual water balance, it becomes possible to estimate the inflow from the ungauged

catchments. It was assumed that on annual basis the average annual change in storage of the lake is

zero. Thus, the water balance equation was solved to give the inflow from the ungauged catchments.


58

Table 5-7. Annual water balance of Lake Tana

Water balance termPrecipitation Plake

Rriver inflow from gauged catchments Qin

Rriver inflow from ungauged catchments Qin

Lake evaporation Elake

Blue Nile outflow Qout

Change in storage ∆S

1672

12310

mmyr-1

1256

1345

303

The table shows the estimated inflow from the ungauged catchments as determined from the annual

average water balance of the lake. However the main target of the water balance determination was to

get the possible baseflow (groundwater) contribution of the shallow aquifer from the ungauged

catchments on annual basis. Of course this is a rough estimation as it does not account individual

catchment’s behaviour.

Then, following the inflow determination from the ungauged catchments from the water balance of

the lake, a relationship was established between the ganged baseflow and total runoff and the

ungauged baseflow and total runoff from the basin. Thus, the baseflow contribution from the

ungauged catchments was determined using the Eckhardt’s baseflow separation model and it was

found that 45mm of the total flow (303mm) was from the baseflow (groundwater contribution) of the

aquifer. The result seams reasonable that even though the ungauged catchment area is high, the main

contribution not only the baseflow but also the total flow comes from the gauged and ungauged

catchments (82% &18%) respectively. The annual average water balance of the Upper Blue Nile is

presented below.

Table 5-8. Summary of annual average water budget of the Upper Blue Nile Flows

Summary of annual basin water balance componets mmyr-1Basin precipitation 1376Basin evapotranspiration (Penman-Monteith) approach 1344Total flow from gauged catchment 1345Baseflow from gauged catchment (BASF & Eckhardt Model) 200Total flow from ungauged catchment 303Baseflow from ungauged catchment 45Average annual inflow of the basin 1648Average annual baseflow of the basin (BASF & Eckhardt Model) 245

5.6. Numerical Reservoir Modelling – BASF Model

Baseflow time series are needed to understand the spatial and temporal variability of runoff processes

in river basins and to extrapolate discharge processes to ungauged catchments. However there is no


59

direct way to continuously measure baseflow in a catchment watershed or continuously monitor

processes that affect baseflow such as overland flow, evapotranspiration, interflow and groundwater

recharge. Consequently, many approaches have been developed to estimate or separate baseflows

from streamflow records (Arnold et al., 2000; Chapman, 1999; Wittenberg & Sivapalan, 1999). But

none of these approaches were physically based under all streamflow conditions. In this section, a

physically based semi-distributed model was introduced that relies on a number of physical

parameters for separating continuous streamflow records. In this way catchment aquifers can be

roughly characterized. The reason for this approach came from the observation that the outflow from

porous aquifers usually follows logarithmic depletion behaviour for modelling water transport

between storages and is controlled by the hydraulic state of the storages.

In the BASF model (Gieske, 2007) the input variables include daily value of precipitation, streamflow

records, and potential evapotranspiration. Soil moisture accounting routines are incorporated in the

model as model parameters. The effective precipitation is partitioned into surface (direct) runoff and

subsurface (interflow and baseflow) runoff depending on the physical parameters that account for the

relative dominance of these processes. Based on conceptual processes, the watershed is divided into

three reservoirs or storage zones. The runoff generation part is the response function which transforms

excess water from the surface or the soil moisture zone to the direct runoff. The direct runoff from the

surface impervious zone arrives at the stream gauge after some delay time and this part is obtained

through quantification of the precipitation by a runoff coefficient. The peak discharge component in

streamflow hydrographs corresponds to this part of the precipitation event.

The soil moisture accounting part is based on the HBV model which is the modification of the bucket

theory (Rientjes, 2006) in that it assumes the statistical distribution of storage capacities in the

catchment aquifer storage. But also it uses the EARTH modelling approach (Van der Lee & Gehrels,

1990). Water infiltrating into the root zone is divided into different components: actual

evapotranspiration and percolation. The percolated water goes to the change in soil moisture storage.

It controls the runoff formation and water infiltration using the soil moisture parameters like initial

soil moisture, saturated soil moisture content, field capacity, saturated hydraulic conductivity, and a

constant beta. The constant especially controls the contribution to the response function (runoff) and

the soil moisture from each precipitation added in the catchment. Actual evapotranspiration from the

groundwater aquifer (soil moisture zone) was calculated according to the governing

evapotranspiration equations (in accordance with the EARTH Model).


60

Precipitation

Direct runoff

Transformation function

Deep groundwater ReservoirBaseflow

Shallow groundwaterReservoirInterflow

Soil reservoir

Root zone

Gauging stationsE

T

(1-ratio)*perc ratio*perc ccc

1- kd Kd

Figure 5-15. Simplified representation of BASF of model

The subsurface flow via the root zone passes two consecutive reservoir zones: the interflow reservoir

zone, which accounts for the delayed flow and the deep groundwater reservoir zones, responsible for

the baseflow. Baseflow and interflow components are characterized by the respective storage

recession coefficients.

5.6.1. Set of equations

5.6.2. Baseflow separations

The rainfall excess (Peff) is divided into three components according to the following equation.

* (1 )eff d eff d effP k P k P= + − [5.15]

eff d iP R R= +


61

Where Rd is direct runoff

effdd PkR *= [5.16.a]

Where Ri is infiltration

(1 )*i d effR k k= − [5.16.b]

The infiltration component Ri enters the soil compartment from which part disappears by evapo-

transpiration (ET), part by percolation (PERC). The balance between the three components changes

the soil moisture in the compartment. The soil moisture accounting and percolation in the soil

compartment (root zone) is carried out in the same way as in EARTH (Van der Lee and Gehrels,

1990). The SWAP model could also be used for this purpose. The percolation (PERC) is again

subdivided into two components: shallow interflow and deep groundwater storage, respectively:

I = (1-RATIO)*PERC and B = RATIO*PERC

The interflow is released from storage according to a linear reservoir model:

i ii i

i

dv VS I

dt γ= − [5.17]

With the recharge (baseflow) is also obtained by flow from reservoir:

b bb i

b

dV VS B

dt γ= − [5.18]

The reasonable assumption is made that the specific yields are the same for interflow and baseflow (Si

= Sb = S). Finally, the streamflow components are given as

,i bi b

i b

V VQ Q

γ γ= = [5.19]

The direct runoff is going into a filter reservoir with storage coefficient 1 and the relations then

become


62

d

dd

d VR

dt

dV

γ−= [5.20]

d

dd

VQ

γ= [5.21]

The simulated hydrograph is given by adding the direct runoff, interflow and baseflow components:

Qcal = Qd + Qi +Qb [5.22]

5.6.3. Numerical Scheme of BASF Model

The finite difference method is a commonly used method to compute groundwater flow and solute

transport. In this case it was decided to solve the three reservoir equations by the implicit method,

which guarantees stability for all possible lengths of time steps. For this case a time step of one day

was selected. Then, the discharge at a specific moment in time is a function of discharge and

precipitation as gauged at the corresponding location. Moreover in line with the HBV model a number

of constants can be introduced to reduce rainfall and evapotranspiration as required. The third

reservoir is used to smooth the direct runoff in the same way as it is done by a triangular filter in the

HBV model.

The finite difference equations for solving the baseflow reservoir are given below assuming a time

step of 1 day

, 1

, 11

*

ti t

i t

b

BV

SV

Sγ

− +=

+ [5.23]

, , /b t b t tQ V γ= [5.24]

The relations for the interflow and direct flow reservoirs are similar.


63

5.6.4. Hydrometeorological and hydrological data

Within the Lake Tana basin a small number of meteorological stations are available and most of the

stations have an incomplete data set. A total of 16 meteorological stations with different classes and

length of record was received. For the BASF model processing, ten meteorological stations were used

which have a relatively satisfactory data set and for which the missing can be filled by regression

analysis using the available stations. A twelve year record was obtained for the selected stations and a

summary of the stations is presented below with the corresponding discharge stations. The area of

each catchment is also added to show the coverage of the stations in the basin while the area of the

Blue Nile outlet is taken to be the entire basin. Furthermore; it is worth mentioning that, for some of

the subcatchments like Bered, Kilti and Dirma, a correction factor was employed in determining the

rainfall in the model from nearby stations. This was because the station used was far away from the

catchment and it was observed that the rain does not fit well with the discharge observed at the gauge

stations.

The determination and accuracy of the potential evapotranspiration depends on the quality of the data

obtained. Some reports indicate that the data of the National Meteorological Agency does not exactly

match the data obtained from FAO’s data set especially in the rainy seasons July and August. The

discrepancy of the data mainly focuses on wind speed and sunshine hours. It is likely that, since a

great deal of missing data was found in these parameters, it is possible that these data are less reliable

than the other data sets. However, even though it was impossible in the present study to compare the

data quality of NMA with data sets used in earlier studies, great care was taken in checking and

determining the meteorological parameters used in this study.

Table 5-9. Summary of Hydrometeorological stations used in the study

Discharge stations Rainfall Stations and Eto Catchments Name Area(km2) start End Start End

B. Nile outlet Bahrdar 15319 1973 2006 Bahrdar 1992 2003 Bered Bered 81 1999 2003 Dangila 1992 2003 Dirma Kola Diba 377 1997 2004 Aykel 1992 2003 Gilgel Abbay Merawi 1664 1973 2006 Dangila 1992 2003 Garno Infranze 94 1987 2003 Infranze 1992 2003 Gelda Gelda 401 1984 2006 Gasay 1992 2003 Gumera Bahrdar 1394 1973 2006 Debre tabor 1992 2003 Kilti Durbete 743 1997 2003 Dangila 1992 2003 Koga Merawi 244 1973 2006 Dangila 1992 2003 Megetch Azezo 514 1973 2006 Gonder 1992 2003 Ribb Addis Zemen 1592 1973 2006 Addis Zemen 1992 2003 Gumero Arb Gebeya 174 1984 2006 Infranze 1992 2003


64

The potential evapotranspiration of the basin was determined for each catchment using Hargreaves

and Penman-Monteith methods. Procedures followed for this calculation were:

1. The 16 stations were compared according to the data they have i.e. based on the data quality,

record length and class classification. From the 16 stations, 10 stations were selected and

rainfall per catchment was produced.

2. The data gap of each station was filled by multiple linear regression equations between

neighbouring stations. Having filled the data gaps, Bahrdar, Dangla, Gonder and Debre Tabor

stations were taken as base stations for estimation of potential evapotranspiration. These

stations were assigned to the corresponding catchments: the Blue Nile outlet, Gilgel Abbay,

Megetch, and Gumera catchments.

3. Evapotranspiration of the four base stations was determined and then an inverse distance

relationship was determined among the stations and the remaining catchment. Thus, the

potential evapotranspiration of the catchments was determined based on the inverse distance

relations. The average annual ETp of the basin from the four base stations based on Penman-

Monteith method was found 1346mm. The inverse-distance relationship is indicated below.

Table 5-10. Inverse distance relationship of the stations and catchments

Weight of stations to the catchment Catchments Bahr Dar Dangila Gondar Debre Tabor

Gelda 0.701 0.046 0.043 0.210 Garno 0.142 0.044 0.565 0.249 Gumero 0.062 0.023 0.822 0.092 Koga 0.447 0.456 0.036 0.061 Megetch 0.009 0.004 0.976 0.011 Ribb Upper 0.018 0.005 0.010 0.967 Gumera 0.115 0.020 0.030 0.835 Gilgel Abbay 0.144 0.802 0.021 0.033 Ribb AZ 0.021 0.006 0.014 0.960 Ribb Gassy 0.055 0.016 0.027 0.902

After the data gaps of the rainfall stations have been filled, the areal annual average distribution map

of the Lake Tana basin was produced by kriging interpolation method as indicated in fig. 5-16. The

long term average annual rainfall of the basin (1992-2003) was observed to vary from 809mm in Delgi

in the north western part of the lake to 2266 mm at Engibara in the southern peak of the basin in

Gilgel Abbay catchment. The mean annual rainfall of the basin was estimated to be 1376 mm. In


65

general, it could be concluded that the rainfall distribution across the basin decreases from the south

to the north.

900

1100

1300

1500

1700

1900

900

1100

1300

1500

1700

1900

Figure 5-16. Mean annual rainfall distribution over the basin (simple kriging)

Table 5-11. Annual average rainfall vs. elevation of the selected meteorological stations

Station name East North Elev_SRTM Annual Avg Rain

Addis Zemen 377034 1339957 2117 1068.60 Aykel 288107 1385819 2160 1174.96 Debre Tabor 392163 1310040 2714 1493.59 Delgi 285688 1352646 1865 809.13 Infranze 356388 1346686 1889 971.05 Gondar 328336 1387763 2074 1081.12 Bahr Dar 327729 1282685 1828 1416.20 Deke Istifanos 311575 1315964 1799 1676.57 Dangla 265059 1245367 2126 1537.67 Engibara 270512 1213353 2580 2266.81 Basin annual average rainfall 1349.57


66

For clarity, the long term average daily rainfall, actual evapotranspiration, Eta calculated from BASF

and Potential evapotranspiration and ETp estimated from Penman Monteith are shown in the following

graph. As we can see from the graph, Eta is nearly equal to ETp during the rainy season (days from

181-271) when the water availability in the catchment is at its maximum.

0

2

4

6

8

10

12

14

16

18

20

1 34 67 100 133 166 199 232 265 298 331 364

Time (Days)

RF

ET a

& E

T o (m

m)

RF ETo ETa

0 35 70 105 140 175 210 245 280 315 350 365

0

2

4

6

8

10

12

14

16

18

20

1 34 67 100 133 166 199 232 265 298 331 364

Time (Days)

RF

ET a

& E

T o (m

m)

RF ETo ETa

0 35 70 105 140 175 210 245 280 315 350 365

Figure 5-17. Time series analysis of long term average daily rainfall, potential ETo

and actual ETa in the Gilgel Abbay catchment

5.6.5. Results and discussion of BASF model

Having set the initial value parameters to the model, the model was run for individual catchments and

then parameters were calibrated until the estimated baseflow components coincided with the recession

part of the streamflow hydrograph. The same general procedure was followed as in the case of the

recursive digital filters. However, the BASF model accounts for different parameters for each

catchment and this is due to the fact that the physical characteristics are different for each basin

catchment. Together with this, the calibration was made by minimizing the difference between the

observed streamflow, Qobs and the simulated flow, Qcal, in the 12 years daily average data set. The

model performance was tested using the coefficient of determination R2 and the Nash-Sutcliff

coefficient E. The calibrated parameters used in filtering of the baseflow in the Gilgel Abbay

catchment are shown in Table 5-11 below


67

Table 5-12. BASF model parameters set for Gilgel Abbay catchment

Parameters Value

Rainfall correction factor 1.00 Evapotranspiration correction factor 0.90 Saturated soil moisture content - fraction 0.30 Residual soil moisture content - fraction 0.05 Field capacity - fraction 0.14 Initial soil moisture content - fraction 0.10 Soil depth-mm 1000 Saturated soil conductivity- mmd-1 1.50 Beta- constant 6.00 Ratio that goes to baseflow and interflow 0.50 Drainage resistance direct runoff - days 2.00 Drainage resistance interflow - days 30.0 Drainage resistance baseflow - days 365 Storage coefficient interflow - unitless 0.25 Storage coefficient baseflow - unitless 0.25

Each of the parameters has a significant role in determining the baseflow component from the

streamflow data. For instance, increasing the constant β in the model increases the direct runoff

depending on antecedent moisture conditions. The soil moisture parameters were the main parameters

in the identification of the catchment behaviour because the antecedent soil moisture conditions of all

catchments depend on soil moisture conditions. Unlike the digital filters, the BASF model produces

different types of results; the long term daily average discharge of the streamflow, interflow, baseflow

component, direct flow and the simulated hydrograph which is the sum of the baseflow, interflow and

direct flow. It also produces the recharge of the catchment in millimetres per day by decomposing the

baseflow into deep baseflow and interflow of the streamflow hydrograph.

Another important aspect of the BASF model is the fact that it allows calculation of the soil moisture

content, percolation below the root zone and the actual evapotranspiration of the catchment. This

actual evapotranspiration was important to compare the results obtained from Penman-Monteith

potential evapotranspiration of the area (see the figure 5-17). None of the filters used in this study

could produce soil moisture, evapotranspiration or the percolation from the streamflow data.


68

Gilgel Abbay

0

10

20

30

40

50

60

70

80

90

100

1 201 401 601 801 1001 1201 1401

Flo

w (

m3 s-1

)

Qob Qi Qb Qcal

1996 1997 1998 1999

Years

Gilgel Abbay

0

10

20

30

40

50

60

70

80

90

100

1 201 401 601 801 1001 1201 1401

Flo

w (

m3 s-1

)

Qob Qi Qb Qcal

1996 1997 1998 1999

Gilgel Abbay

0

10

20

30

40

50

60

70

80

90

100

1 201 401 601 801 1001 1201 1401

Flo

w (

m3 s-1

)

Qob Qi Qb Qcal

1996 1997 1998 1999

Years

Gilgel Abbay

0

2

4

6

8

10

12

14

16

18

20

1 101 201 301 401 501 601 701

Flo

w (

m3 s-1

)

Qobs Qi Qb Qcal

Year one Year two

0 Time (days) 730

Gilgel Abbay

0

2

4

6

8

10

12

14

16

18

20

1 101 201 301 401 501 601 701

Flo

w (

m3 s-1

)

Qobs Qi Qb Qcal

Year one Year two

0 Time (days) 730

Figure 5-18. Hydrograph analysis of the Gilgel Abbay catchment using BASF model

(Long term daily average, above, and daily streamflow of years 1996-1999, below)

The result of the above graph indicates that the calculated, Qcal, long term daily average streamflow

and the four years data set are a good match with the measured streamflow i.e. the calibration of the

model is acceptable so that there is a good correspondence between the modelled and observed values.

However, for some of the catchments, for instant Gumera, there is a large discrepancy between the

estimated and the observed streamflow for individual years of the 12-year data set. But the long term

average daily streamflow is in an excellent agreement. This probably comes from the fact that the

quality of the measured streamflow data is not always good. It also was impossible to calibrate the


69

model for the some of the minor tributaries like Bered, Garno, and Gelda. The calibration results of

the long term daily average streamflow and the entire 12 years data set in the catchment for the

Gumera catchment are shown below (see Fig 5-15).

Gumera

0

5

10

15

20

25

30

35

40

45

50

1 201 401 601 801 1001 1201

Flo

w (m

3 s-1)

Qobs Qi Qb Qcal

1992 1993 1994

Years

Gumera

0

5

10

15

20

25

30

35

40

45

50

1 201 401 601 801 1001 1201

Flo

w (m

3 s-1)

Qobs Qi Qb Qcal

1992 1993 1994

Gumera

0

5

10

15

20

25

30

35

40

45

50

1 201 401 601 801 1001 1201

Flo

w (m

3 s-1)

Qobs Qi Qb Qcal

1992 1993 1994

Years

Gumera

0

5

10

15

20

25

30

1 101 201 301 401 501 601 701

Flo

w (

m3s-1

)

Qobs Qi Qb Qcal

Year one Year two

0 Time (days) 730

Gumera

0

5

10

15

20

25

30

1 101 201 301 401 501 601 701

Flo

w (

m3s-1

)

Qobs Qi Qb Qcal

Year one Year two

0 Time (days) 730

Figure 5-19. Hydrograph analysis of the Gumera catchment using BASF model

(Daily stream flow value of 1992-1994, above, and long term daily average streamflow, below)


70

The figure shows that is a clear discrepancy in the calculated streamflow and the measured flow at the

recession parts of the graph. In some cases the estimated flow is far above the observed value (days

400 to 500, 100 to 900, and 1100 to 1201) and in other years it lies below the observed flow (days at

the beginning of year 1992). On the other hand, a good correspondence is observed between simulated

and observed flows in the long term daily average simulation.

5.6.6. Performance indicators

The performance of the model was tested using two types of performance indicators: the coefficient of

determination R2 and the Nash-Sutcliffe coefficient E. The Nash-Sutcliffe coefficient also called the

coefficient of efficiency is a way to measure the fit between the predicted and measured values. The

computation of E essentially is the sum the deviations of the observations from a linear regression line

with slope 1. If E is between 0 and 1, it indicates deviations between measured and predicted values.

If E is negative, predictions are very poor and the average value of output is a better estimate than the

model prediction (Grunwald & Frede, 1999; Nash & Sutcliffe, 1970). The Nash - Sutcliffe coefficient

E and the coefficient of determination R2 for the observed and simulated values can be computed from

the WHAT software as follows. The Nash-Sutcliffe coefficient is calculated as:

( ) ( )

( )

2 2

1 1

2

1

n n

m mea avg m pi i

n

m mea avgi

Q Q Q QE

Q Q

−= =

−=

− − −=

−

∑ ∑

∑ [5.25]

Where,

E = coefficient of efficiency (Nash-Sutcliffe coefficient)

Qm = the measured value

Qp = the predicted value, and

Qmean_aveg = arithmetic average measured value


71

Gilgel Abbay

R2 = 0.94

0

50

100

150

200

250

0 50 100 150 200 250Simulated Data (m3s-1)

Mea

sure

d D

ata

(m3 s-1

)

E = 0.94

Figure 5-20. Comparison of measured data and simulated data 0f BASF model

Gumera

R2 = 0.95

0

50

100

150

200

250

0 50 100 150 200 250

Simulated Data (m3s-1)

Mea

sure

d D

ata

(m3s-1

)

E = 0.93

Figure 5-21. Comparison of measured data and simulated data of BASF model

The figure above depicts the goodness of tit between the measured data and simulated data of the

Gilgel Abbay and Gumera catchments as evaluated from the WHAT hydrograph analysis tool. It was

found that R2 for both 0.94 and 0.95 and the Nash-Sutcliff coefficient E 0.94 and 0.93 respectively.


72

5.7. Chapter summary and main conclusions

Different non-physically based filter approaches and a physically based rainfall-runoff model have

been applied to separate the baseflow signature from the stream hydrograph of the 12 catchments in

Lake Tana basin. Results indicted that the Chapman and Lyne-Hollick filers was obtained to over

estimate the baseflow part of the hydrographs with BFI 0.27 and 0.31 respectively.

The Eckhardt filter with α = 0.995 and BFImax = 0.25 was found the best to represent the baseflow

separation technique in the basin. The drawback of this model approach is that it does not distinguish

the baseflow with interflow of the hydrographs. In addition to this, since the recursive filter methods

do not have physical meaning with the watershed area under investigation, a physically based

modelling approach was applied to separate the baseflow component from the streamflows and to

calibrate and validate the Eckhardt’s filter parameter BFImax. Thus the BASF model was developed

and tested for this purpose.

During the application of BASF model, the results from Eckhardt filter were found to match with the

BASF model (interflow + baseflow) results. BASF model shows 50% of the Eckhardt’s model is

interflow and deeper baseflow. It was, therefore, concluded that the baseflow from the BASF and

Eckhardt model approaches best represent (90% of the tributaries) with the exception of small

catchments in the basin. Generally, BASF works well in the hilly catchments and initial tests were

satisfactory. More work is required for

� Incorporating antecedent soil moisture conditions in the beginning of the rainy seasons.

� Calibrating conditions for small flat catchments with low discharges.

The aquifers in the basin can be generalised as fractured rocks with shallow soil cover.

In line with this, the water balance of the Lake Tana was setup to determine the flow from the

ungauged catchments. This balance indicated that 303mm of the annual flow comes from the

ungauged catchments. The baseflow from the ungauged catchments was also found 45mmyr-1.


73

6. ESTIMATION OF NATURAL GROUNDWATER RECHARGE IN LAKE TANA

BASIN

Groundwater recharge is a fundamental component in the water balance of any watershed. However,

because it is almost impossible to measure it directly, numerous methods, widely ranging in

complexity and cost, have been used to estimate recharge. In some cases baseflow has been used as an

approximation of recharge with an assumption that it is probably less than the amount of recharging

the groundwater system (Chen & Lee, 2003). Baseflow is that part of a streamflow usually attributed

to groundwater discharge. Some authors say that although baseflow is not an absolute recharge, it

sometimes could be used as an approximation of recharge when underflow, evapotranspiration from

riparian vegetation and other losses of groundwater from watershed are thought to be minimal. When

baseflow is used as a proxy for recharge, it is referred to as effective recharge, base recharge, or

observable recharge to acknowledge that it probably represents some amount less than which

recharged the aquifer (Risser et al., 2005). A common recommendation from different authors is that

the recharge should be estimated by the use of multiple methods like: unsaturated-zone drainage

collected in gravity hypsometers, daily water balance, water table fluctuations in wells. The results

should always be compared carefully (Risser, Gburek, & Folmar, 2005).

The major assumption in using baseflow for estimating recharge is that baseflow equals groundwater

discharge from the aquifer storage and that groundwater discharge is approximately equal to recharge

subject to the losses from gauged watersheds caused by underflow, groundwater evapotranspiration

and abstractions are minimal although different baseflow separation methods produce different

results. In that sense care should be take to determine which estimate is most representative of the

recharge in that watershed area. In this study a number of methods were applied to assess the recharge

of the study area

6.1. Groundwater recharge from baseflow analysis

Several methods have been developed to estimate groundwater recharge from stream flow records.

One of the popular methods is the recession-curve-displacement method which is commonly referred

to as Rorabaugh method (Fetter, 2001). This method estimates total recharge for each streamflow

peak. The disadvantage of this method is the time required to calculate recharge for each peak.

Potential groundwater recharge was shown to approximately equal one half of the total volume that

recharged the system a “critical time” after the peak (Arnold et al., 2000). The recession curve

displacement method uses this approximation, an estimate of critical time and the principle of


74

superposition to estimate total recharge from daily streamflow records. Here in this study, natural

groundwater recharge was performed based on the water balance model. Such estimates of

groundwater recharge during periods of runoff events are sensitive to the non-linearity of the storage

Chapman, (1999). The groundwater recharge is given by:

dtQSSRt

t b∫+−= 2

112 [6.1]

where S1 and S2 are groundwater storages at times t1 and t2, respectively, before and after the periods

of surface runoff. Considering the aquifer storage on all annual bases, the change in storage is

approximately zero and the annual recharge is given by the annual baseflow component of the daily

streamflow hydrograph. That is over periods of equivalent storage, and considering other annual

minimal loses of underflows, groundwater evapotranspiration, and groundwater abstractions, the

recharge to groundwater could be approximated by the baseflow component of the hydrograph

(Simmers, 1988).

Recursive digital filters were used for baseflow analysis in chapter 5, the areal recharge of each

catchment was calculated using the Eq. [6.1]. Annual rainfall and recharge of the Gilgel Abbay

catchment is presented for the years 1992 to 2003 as a bar graph in fig.6-1.

Rainfall and Recharge

0

500

1000

1500

2000

2500

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Rai

n F

all &

Rec

harg

e (m

m)

Rainfall Recharge

Figure 6-1. Relationship of rainfall and recharge estimated from baseflow separation in

Gilgel Abbay catchment (Eckhardt Model)


75

To display the relationship of recharge and rainfall per gauged catchments, the regression analysis of

rainfall-recharge relationship was calculated for the period of 12 years (1992-2003) and the result

indicates in fig. 6-2.

y = 0.141x + 42.34

R2 = 0.55

100

110

120

130

140

150

160

170

180

190

200

1000 1200 1400 1600 1800 2000 2200rainfall (mmyr-1)

rech

arge

(m

myr

-1)

Figure 6-2. Annul rainfall-recharge relationship of the Gilgel Abbay catchment for the period

1992-2003

The long term annual average rainfall and recharge relationship of the individual catchments is also

presented as indicated below. The graph below shows the results for the southern part of the basin

(Gilgel Abbay, Koga and Kilti catchments) that has high rainfall but low recharge. This could be

attributed to the catchment characteristics and the rainfall might disappear as direct runoff. The annual

average rainfall and recharge of the basin were found 1376mm and 70mm respectively.

Together with this, a regression analysis between baseflow and rainfall was carried out for seven

catchments. The slope of the regression line represents baseflow as a proportion of rainfall and the

intercept of the regression line with the rainfall axis at the point where baseflow is zero gives the

minimum critical rainfall for baseflow to have occurred. This value gives insight for the catchment

geologic characteristics where high value indicates the amount of rainfall needed to wet the soil or

regolith, together with the antecedent condition, of the area. This could be observed from the known

fact that Dirma which is located in the north of the lake have high value and the catchment is

characterised by alluvial sediments.


76

Rainfall and Recharge

0

200

400

600

800

1000

1200

1400

1600

1800

Bered Dirma G.Abbay Garno Gelda Gumera Gemero Kilti Koga Megetch Ribb

Catchments

RF

& R

echr

ge

(mm

)

Series2 Series1

Figure 6-3. Rainfall-recharge relationship of individual catchments as estimated from baseflow

(Eckhardt model)

The depth of groundwater recharge (baseflow divided by the area of the catchment) for each

catchment was calculated as an average of rainfall (rainfall per gauged catchment) and the basin

average recharge was found to be 70mm. The results of the calculation for some catchments are

presented in the following table.

Table 6-1. Catchment characteristics as recharge-rainfall (RF) ratio of the period indicated

Gelda Megech GumaraGilgel Abay

Ribb Addis Zemen Dirma Kilti

Period of record 1992-2003 1992-2003 1992-2003 1992-2003 1992-2003 1997-2003 1997-2003Catchment area(km2) 32 514 1394 1664 1592 377 743Rainfall (mm) 1378 1094 1270 1730 1407 1175 1730Baseflow (mm) 279 105 187 287 65 51 103Baseflow as % of RF 20 10 15 17 5 4 6Min. RF for baseflow 179 735 98 617 373 696 573

6.2. Recharge – Runoff Simulation with BASF model

In order to assess recharge more accurately, BASF simulation model was used which requires stream

flow, catchment area, rainfall and evapotranspiration data as input in addition to the catchment

characteristics of soil moisture content, field capacity, the hydraulic conductivity of the aquifer. The

BASF model which was previously used in the baseflow separation was chosen to estimate depth of

groundwater recharge in the basin. The portion of rainfall percolated below the root zone is


77

partitioned among different components. Some part goes as shallow subsurface interflow and some

part to recharge through the vadose zone to the water table according the following equations. Note

that, in the calculations of recharge in the following equation, the area of each catchment was required

to change the recharge obtained for each catchments to effective depth of recharge per year.

As interflow:

( )R 1 *ratio percolation= −∑ [6.2]

As deep groundwater flow:

R *ratio percolation=∑ [6.3]

The rainfall in excess of evapotranspiration is routed downwards to the unsaturated zone depending

on the infiltration capacity of the soil where a fraction becomes runoff by interflow and the remainder

recharges the groundwater.

In this study, a recharge simulation model was developed using daily evapotranspiration and rainfall

data for the period 1992 to 2003 in BASF model for some of the catchments in Lake Tana basin. The

BASF model recharge calculated for Megetch catchment is presented below.

Table 6-2. Recharge-runoff simulation for Megetch catchment

Year Rain - mm Recharge - mm ETa - mmRecharge as % of rainfall

1992 958.51 55.82 475.29 5.821993 1187.81 76.52 517.13 6.441994 1015.50 58.37 452.87 5.751995 977.58 50.18 463.73 5.131996 1186.55 71.96 534.51 6.061997 1131.55 70.59 537.41 6.241998 1055.84 57.87 488.61 5.481999 1212.41 69.04 498.10 5.692000 1131.28 70.92 535.34 6.272001 1184.12 63.67 471.82 5.382002 1019.19 56.38 468.18 5.532003 1070.25 58.77 433.97 5.49

For comparison of the recharges simulated from different methods (BASF model and from baseflow)

and the actual evapotranspiration of the Gilgel Abbay catchment is presented below.


78

0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12BASF Recharge ETa Baseflow recharge Rain

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Var

iabl

es v

alue

mm

Year

0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12BASF Recharge ETa Baseflow recharge Rain

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Var

iabl

es v

alue

mm

Year

Figure 6-4. Recharge comparisons by two methods in Gilgel Abbay catchment

Baseflow (Eckhardt) and BASF models estimations

It was found that the recharge estimated from the baseflow was always higher than the recharge from

BASF model. This is due to the fact that the Eckhardt model does not distinguish between interflow

and deep baseflow but the BASF model does. Thus the recharge based on the digital filters is always

higher than the BASF model.

6.3. Hydrochemical analysis

6.3.1. Chloride mass balance analysis

It has been stated that natural groundwater recharge estimation was possible from chloride mass

balance profiles in the unsaturated zone, and this mass balance technique has been extended to

account recharge estimations in the saturated zones. Chloride mass balance recharge estimation has

been successfully used especially when applied in arid areas (Houston, 2007). The mass balance

equation is given by:

* *p gwP Cl R Cl D= + [6.3]

Where P is precipitation mm, Clp is chlorine content of precipitation in mgl-1, R is recharge mmyr-1 D

is the dry deposition of chloride in mgl-1 and Clgw is chloride content in groundwater in mgl-1. The


79

precipitation can be taken as the annual average precipitation of the basin, but if recharge is needed

per catchment, then the annual average catchment precipitation is used. Here in this study recharge

was calculated in two ways: recharge per catchment and recharge for the basin as a whole. Recharge

calculated per catchment has indicated that, much of the catchments in the southern part of the basin

have higher recharge than those in the northern part of the catchment.

Recharge from Rainfall-Chloride

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Gelda

Garno

Gum

ero

Koga

Megetch

Gum

era

G.A

bbay

Ribb

Bered

Kilti

Dirm

a

Catchments

Rai

n &

rec

harg

e in

mm

Kebede data ITC lab Rain

Recharge = 45 to 75mmyr-1 Kebede Data

Recharge = 93 to 155mmyr-1 ITC lab

Figure 6-5. Chloride mass balance recharge estimation per catchment

Two kinds of data sets have been used for the recharge estimation of the Tana basin. One set of data

was obtained from Kebede et al., (2005) collected on August 2002. The second set of samples was

collected in August 2007 during the field work and the chemical analysis was done in the laboratory

of International Institute for Geoinformation Science and Earth Observation (ITC) in the Netherlands.

Based on these data sets, recharge was estimated for each catchment as indicated above and for the

basin as a whole. Using Kebede’s data set the lowest and highest recharge was found around Gondar

in the northern part at Garno and at Gilgel Abbay catchments with estimated values of 45mm and

93mm annually respectively. The lowest and highest values in the same catchment using ITC

laboratory datasets were 93mm and 155mm respectively.

Table 6-3. Chloride composition in rain and groundwater

Cl mg-1 average StdKebede et al ., (2005) Rain 0.50 0.14Kebede et al ., (2005) Gw 11.52 5.95Itc laboratory Rain 0.86 0.50Itc laboratory Gw 9.59 17.95


80

In the same manner, the lowest and highest values of recharge were estimate for the entire basin for a

12 years period. Values indicated that the lowest was in year 1995 (104mm) and the highest estimate

was in years 1993 and 1999 (139mm). The graph below displays recharge in the basin.

Recharge from Rainfall-Chloride relationship

0

200

400

600

800

1000

1200

1400

1600

1800

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003Years

Rec

harg

e &

Rai

n in

mm

Kebede data ITC lab Rain

Recharge = 60mmyr-1 Kebede dataRecharge = 123mmyr-1 ITC lab

Recharge = 60mmyr-1 Kebede data

Recharge = 123mmyr-1 ITC lab

Figure 6-6. Recharge estimated from chloride-rainfall relationship

6.4. Chapter summary

The chemical data used in the estimation of the recharge confirmed that the Eckhardt’s filter with

filter parameters BFImax = 0.25 and α = 0.995 and the BASF model approach gave similar values.

Thus the recharge of the basin based on the chemical data was found to vary from 75mm to 155mm

and the BASF and Eckhardt model approaches gave on average 70mmyr-1. The overall recharge of the

basin is determined to be between 70mm to 120mm per year.


81

7. SOLUTE MASS BALANCE MODELLING

7.1. Mixing Cell Modelling

The idea of evaluating aquifer recharge components through quantitative analysis of environmental

isotopes and ionic constituents of the groundwater was proposed by (Adar & Neuman, 1988) as

quoted by Gieske and De Vries, (1990). These authors explained that when subdividing the aquifer

into suitable components in which perfect mixing is presumed and determining sets of water and ionic

mass balances for each cell, it can be shown that the problem of finding the unknown recharge

components corresponds mathematically to the minimization of a quadratic expression. Similar

approaches involving general least squares and regression techniques were employed to identify

sources of groundwater pollution for estimation of aquifer transport parameters.

The mixing cell model approach was proposed primarily for arid and semiarid areas and was tested in

the environment of eastern Botswana for groundwater recharge investigations. While trying to apply

the method for several groundwater basins in the eastern part of Botswana by (A.S.M. Gieske & de

Vries, 1990), cases arose where quadratic programming algorithms could not give completely

satisfactory results. Thus an alternative formulation of the problem was given in terms of the general

linear regression theory and this give additional advantage of calculating the errors of the parameters.

Moreover, the implementation of the solution method, the Singular Value Decomposition algorithm

(SVD), was proposed (Press, Flannery, Tenkolsky, & Vetterling, 1992) which has more advantage

that ill-posed problems could be analyzed better, thus giving more insight into the nature of problems

that may arise.

In a similar manner, this modelling approach was conducted in the determination of lake evaporation

and flows from ungauged catchments in Lake Tana basin using chemical constituents such as

chloride, Total Dissolved Solids (TDS) and calcium. Using the known chemical concentrations of the

system together with a number of known flow components, it is possible to calculate one or more

unknown flow components. In this section an example is given how the data can be used

simultaneously to determine two annual flow components: the size of the ungauged flow and the

amount of actual lake evaporation.

7.2. Theoretical aspects of the model

Dividing the aquifer into N cells where perfect mixing is assumed in each cell, the groundwater and

ionic mass balance for cell n (1 < n < N) and for each tracer k can be written as follows (Gieske and

de Vries, 1990).


82

111

nn

nn

J

jnj

In

iinnn e

t

hSqqTQ

n

=∆∆

−−+− ∑∑==

[7.1]

nknk

nn

I

iinink

J

jnjnknnknnk e

t

cVqcqcTcQc

nn

=∆

∆−=−− ∑∑

==

φ11

[7.2]

Where Qn and Tn are respectively source and sink flows of cell n, qin flow from cell i to cell n, qnj flow

from cell n to j, Sn storage capacity of cell n, hn hydraulic head, cnk concentration of tracer k in source

flows Qn, cnk concentration of tracer k entering cell n from i, Vn saturated volume of cell n and nφ

porosity. It is assumed that all quantities have been averaged over the time stept∆ . Because of errors

in averaging and sampling and analytical errors in the evaluation of the tracer concentrations, the right

hand side of the equations [7.1] and [7.2] can not be expected to be zero. The errors in the

groundwater mass balance for cell n and ionic mass balance for tracer k and cell n, are represented by

en1 and enk, respectively.

Gieske and De Vries (1990) showed that all quantities of the aquifer network of cells are known

except for a set of flow components q. Moreover, the above equation can conveniently be written in

matrix form separating the known from the unknown terms, as follows.

nnnn edxC =− [7.3]

where Cn denotes a matrix while xn, dn and en are vectors. Vector xn contains the unknown flow

components of cell n where as dn contains the known terms and en the error terms. Vectors xn are

evaluated by minimizing the weighted sum of the squared errors. Moreover, the actual minimization

can be rewritten in the usual quadratic programming form and the problem is best described as

follows:

xGxcxFMinimizeTT

2

1+−= [7.4]

0≥xtoSubject

Where T indicates the transpose of the vector or matrix, x contains all the unknown flow components,

c is the constant vector and G is the so-called Hessian matrix. It was explained that if there are no

linear dependencies in the tracer and mass balance relations, the Hessian matrix G is positive definite


83

and non-singular which means that a unique solution to the problem exists. If G is singular, no

solution for x can be found, although a minimum of F still exists.

The only constraints in the problem are the non-negativity restrictions given by equation [7.4]. If these

were not present, the problem could be solved simply by inversion of G because at the unrestricted

minimum, the following relation should be hold:

0=+−= xGcxd

dF [7.5]

and then the solution is:

cGx 1−= [7.6]

Because it can be shown that the non-negativity restrictions in Eq. [7.4] are not really important, it is

better to determine the unrestricted minimum according to Eq.[7.5] and Eq.[7.6] with well-known

linear regression methods which offer the additional advantage of obtaining the variances and

covariances of the flow components.

The general linear least squares problem can therefore be formulated as follows. Instead of arranging

and summing the tracer and mass balance relations per cells all relations are arranged in a single

matrix expression.

ebxD =− ` [7.7]

Where D is called the design matrix, x contains the unknown flow components, b the known terms

and e the errors. The Hessian matrix G and design matrix D are related as in the following equations.

(Gieske and de Vries, 1990).

cxG = [7.8]

With

DDG T2=

And

bDc T2=

The solution can then be written as:


84

cGx 1−=

The above equations show how the Hessian matrix G can be calculated from the design matrix D.

However, in practice, there is no need for explicit evaluation G because the Singular Value

Decomposition (SVD) algorithm decomposes D immediately into:

TVSUD = [7.9]

where U is a column orthogonal matrix, V is a square orthogonal matrix and S is a diagonal matrix

with singular values Sij. Making use of orthogonal matrix properties, the solution can be written as:

X = V diag (1/Sij)(UTb) [7.10]

Variances and covariances can be calculated from matrices S and V, which also contain the

information on singular values and null-space. Computer programs to implement the SVD algorithms

are readily available in the literature. Press et al. (1992) give both FORTRAN and Pascal listings. In

this study, an example is given of the solution according to Eq. (7.6) and (7.7)

7.3. Steady state example with one cell and three tracers

To describe the methods and matrix manipulations discussed in the theoretical aspect of the model, a

practical example employed in the determination of flow components with three inflows and two

outflows using three tracer species in Lake Tana basin was applied and illustrated below. The units of

the chemical species and the flows are in mgl-1 and mm respectively. It should be kept in mind that the

concentrations of the chemical species were determined during the field work conducted in August 8-

23, 2007. The situation is represented in figure below and components x1, x3 and x4 represent inflows

to the lake and x2 and x5 represents outflows from the lake. Chloride, calcium and TDS are the

selected chemical species in this example.

Solute mass balance Terms flow (mm) Cl (mgl-1) TDS (mgl-1) Ca (mgl-1)Precipitation - in X1 1256 0.86 8 6Lake Et - out X2 X2 0 0 0Gauged - in X3 1345 2.6 50 17Ungauged - in X4 X4 2.6 50 17Blue Nile outflow - out X5 1231 4 70 30

Figure 7-1. Steady state flow example with one cell and three tracers


85

For this steady state flow example, the solution methods of general least squares will be applied. The

first step in solving the above flow is normalizing the flow and concentration values of the

constituents. Normalization was applied by dividing the individual values of the components by the

last rows of the respective columns. Together with this, the outflows from the cell are always

represented as negatives and inflows to the cells are positives. The normalized values are represented

below in Table 7-1.

Table 7-1. Normalized flow components

Normalized Terms flow (mm) Cl (mgl-1) TDS (mgl-1) Ca (mgl-1)Precipitation - in X1 1.020 0.215 0.114 0.200Lake Et - out X2 X2 0 0 0Gauged - in X3 1.093 0.650 0.714 0.567Ungauged - in X4 X4 0.650 0.714 0.567Blue Nile outflow - out X5 1 1 1 1

As we can see from the table and explained before, the unknown flow components are X2 and X4. In

this example, I used the Hessian and Design matrix to solve the problems.

The flow and tracer equations are:

1. Flow equations

142

142

1,,

113.1

1093.1020.1

exx

exx

eQQQETP outungaugedingaugedin

=−+−=−++−

=−++−

[7.11]

The equations show how the known flow values are substituted in the mass balance equation, leaving

an error value e1, and flow components x2 (ET) and x4 (ungauged flow) to be determined by the

general least squares method.

2. Tracer equation with chloride

2eQCQCQCEtCPC outoungaugediniugaugedinigetp =−++− −− [7.12]

A mass balance must also hold for all conservative ionic species. Moreover, we know that the

concentration of any tracer in evaporation is zero. So, the value of Cet would immediately represent to

be zero. Thus, from the table:

24 07044.0065.00 exxx =−+ [7.13]


86

3. Tracer equation with TDS

3eQdQdQdEtdPd outoungaugediniugaugedinigetp =−++− −− [7.14]

Directly from Eq [7.14], det = 0, and thus after substitution of know values the equation becomes:

342 103.0714.00 exx =−+ [7.15]

4. Tracer equation with calcium

4eQaQaQaEtaPa outoungaugediniugaugedinigetp =−++− −− [7.16]

In the same manner aet = 0. The relation now becomes:

442 103.0714.00 exx =+ [5.17]

From the above four equations, we can construct a design matrix D and vector b as in Eq. [7.7]:

=

−

−

∗

−

4

3

2

1

4

2

177.0

103.0

070.0

113.1

567.0000.0

714.0000.0

650.0000.0

000.1000.1

e

e

e

e

x

x [7.18]

Again from the design matrix, we obtain the Hessian matrix as: G = 2DTD and thus:

−−

=

−

∗

−∗=

51.400.2

00.200.2

57.000.0

71.000.0

65.000.0

00.100.1

57.071.065.000.1

00.000.000.000.12G [7.19]

Vector c is obtained as 2DTb (Eq. [7.8])

−=

−

∗

−∗=

79.1

23.2

14.0

06.0

03.0

21.1

57.071.065.000.1

00.000.000.000.12c [7.20]


87

Then, the unknown vector x is given by: x = G-1c. But first we have to determine the inverse of G as

=

∗

−=−

399.0399.0

399.0899.0

00.200.2

00.251.4

00.2*00.251.4*00.2

11G [7.21]

Thus, the unknown terms are:

=

∗

=

== −

17507.0

28799.1

79.1

23.2

399.0399.0

399.0899.0

4

21

x

xcGx [7.22]

This implies the value of x2 is 1.287999 and x4 is 0.17507. However, in the first step of the procedure

the flow components were normalized. To get the true value of the lake evaporation (x2) and the flow

from the ungauged catchments (x4) we have to multiply by the last row of the respective columns.

Thus, the final values are

x2 lake evaporation = 1.28799*1231=1586 mm yr-1

x4 ungauged catchments = 0.17507*1231= 217 mm yr-1

The lake evaporation by this method is determined as 1586 mm yr-1, whereas the water balance

without solutes (as determined in Chapter 5) has yielded a figure of 1672 mm yr-1. The flow from the

ungauged catchments according to this method is 217mmyr-1 or about 650 MCM yr-1. The water

balance evaluation discussed in Chapter 5 leads to an ungauged flow of about 900 MCM yr-1

(303mmyr-1)

Finally, the solute mass balance method applied here also allows to calculate the error terms in

accordance with Eq. [7.7] as:

Dx-b=e

=

−

−

∗

−

4

3

2

1

18.0

10.0

07.0

11.1

175.0

288.1

57.000.0

71.000.0

65.000.0

00.100.1

e

e

e

e

[7.23]

This leads to the following error vector e


88

−

=

078.0

022.0

043.0

000.0

e [7.24]

Full application of the SVD method would also lead to the variance and covariance matrices of the

flow components determined by this method. However, proper application requires seasonal and

longer term solute concentrations in the river-lake system to be able to determine reliable average

solute input and output into the system. These difficulties notwithstanding, the preliminary results

seem quite acceptable.

7.4. Running the program

For the application of the computer program, Mixing Cell Model, the average hydrochemical data of

the Lake Tana basin with seven chemical species was prepared. As the number of equations with more

variables increases, manual calculation of these variables become tedious and time consuming.

However, computer programs like the mixing cell model provide easy way of solving the unknown

variables from the mass balance components. The model was run to solve the flow components from

the ungauged catchments and the open water evaporation from the lake surface. The mass balance

components are presented in the table below.

Table 7-2. Input data for Mixing cell model

Solute mass balance Precipitation - in Lake Et - out Gauged - in Ungauged - inBalance terms X1 X2 X3 X4flow (mm) 1256 X2 1345 X4Cl (mgl-1) 0.86 0 2.6 2.6TDS (mgl-1) 8 0 50 50Ca (mgl-1) 6 0 17 17K (mgl-1) 0.34 0 3.93 3.93Mg (mgl-1) 0.32 0 6.18 6.18Na (mgl-1) 1.77 0 7.22 7.22SO4

-2 (mgl-1) 1.36 0 4.62 4.62

Blue Nile outflow - outX5

12314

8.709.20

7030

7.669.13

Having set up the input variables, the model was run to solve the problem. It was found that the results

to fit as was obtained from the water balance above. The results of the model also become more

realistic as the number of chemical species increased. This was observed after the manual results with

three chemical species (Lake ET = 1586 mm and flow from ungauged 217 mm) presented and the


89

model results with more chemicals was run. Model results are lake ET 1665mm and flow ungauged

301mm. The model results are presented below.

Vector b and design matrix D

-1.108 -1.000 1.000

0.071 -0.000 0.650

0.104 -0.000 0.714

0.178 -0.000 0.567

0.406 -0.000 0.513

0.235 -0.000 0.677

-0.057 -0.000 0.830

0.342 -0.000 0.502

Singular values W[i]

1 8.30678273761188E-0001

2 2.05574344984875E+0000

If W[i] <1e-10 times Wmax then it is considered a singular value

Matrix v

0.955 0.296

0.296 -0.955

Chi-square = 2.13716437350924E-0001

Correction factor = 1.88731042028475E-0001

Covariance matrix cvm [i, j]

1 1.343 0.343

2 0.343 0.343

To obtain standard deviations diagonal elements of the covariance matrix are used as follows:

Standard deviation (s.d.) = norm*sqrt (cvm [i, i]*chisq/ (m-2))

Norm = 1231.0000

Table 7-3. Final results

No Component number X (mmyr-1) Standared deviation (mmyr-1)1 2 1665 2692 4 301 136


90

8. CONCLUSION AND RECOMMENDATIONS

8.1. Conclusions

Baseflow analysis of stream hydrographs in the Lake Tana Basin was carried out using different

approaches and has indicated how groundwater contribution varies spatially among catchments. Some

separation techniques, especially the Lyne-Hollick model, were found to overestimate the baseflow

contribution while others seem to give reasonable and convincing values related to the flow behaviour

of the catchments in the basin. The overestimation of the baseflow in the case of Lyne-Hollick model

was attributed to the nature of the model because it does not consider the decline of the baseflow after

the quick flow of the stream hydrograph has been ceased. Thus, groundwater contribution in the basin

was found to vary from 26% to 30% in the models of Chapman and Lyne-Hollick, respectively. The

Eckhardt model produced good results with a BFImax value 0.25, which indicates shallow fractured

rock aquifers. Based on this model, the groundwater contribution of the gauged catchments was found

to be 15% of the total annual streamflow contribution from the gauged catchments. However, the

Eckhardt model does not make a distinction between baseflow and shallow interflow. Moreover, the

choice of BFImax as 0.25 is a bit subjective and depends on the nature of the catchment. For these

reasons, tests were made with a physically-based rainfall-runoff model: BASF.

Detailed water balance calculations and hydrograph simulations with BASF confirm the value of

about 15% found with the Eckhardt model. About half of the 15 % is interflow which is released

shortly after the end of the rainy season, while the remaining 7% is infiltrating to the groundwater and

is released exponentially as baseflow during the dry season. The model generally provided good

results for the larger rivers with hilly catchments such as for the Gilgel Abbay, the Gumera and the

Megetch rivers. For these rivers R2 and Nash-Sutcliffe E values were found in the order of 95%. The

results for a number of smaller catchments (for example the Bered) were questionable. Obviously, the

surface reservoirs in the BASF model have to be extended to be able to cope up with relatively flat

marshy catchments and more rigorous antecedent soil moisture conditions.

Despite these minor problems, it can be concluded that the river baseflow in the Lake Tana Basin is

about 15% of the total river flow into the lake. A total of 7% is released as interflow shortly after the

end of the rains whiles the remaining 7% is released as baseflow throughout the dry season. These

values are consistent with the picture that the active groundwater-surface water interaction takes place

from the shallow rock aquifers that form the major part of the catchment area. Not much is known

about the flow patterns of the deep groundwater at present.


91

When looking at the relative contributions of all rivers around the lake, it is obvious that most of the

water is provided from the southern part of the catchment (Gilgel Abbay River and its tributaries).

The eastern part ranks second (Gumera and Ribb Rivers). The contributions from the North and the

West are minor. The same pattern is visible in the baseflow distribution pattern, where the main

baseflow is from the Gilgel Abbay River catchment (44%) and the Gumera River (25%).

It was also tried to determine groundwater contribution from ungauged catchments using two

methods: using annual water balance approach of the Lake Tana and the mixing cell modelling

approach together with the groundwater recharge estimation of the basin. In using these approaches, it

was found that both annual open water lake evaporation and annual lake outflow must be determined.

The total flow of the ungauged catchments is then determined as the rest term in the balance, leading

to a value of about 900 MCMyr-1 (303mmyr-1). It is assumed that the baseflow index (BFI) of the

ungauged catchments is on average the same as the index for the gauged catchments, constituting 15%

of the total flow. Therefore a total ungauged annual baseflow is indicated of approximately 135

MCMyr-1 (45mmyr-1)

Natural groundwater recharge was estimated with different methods. When dealing with the recharge

estimation using the chloride mass balance, the baseflow recharge from the BASF model, the

Eckhardt model and the chloride mass balance method arrive at comparable results in the order of

about 70mm to 120mmyr-1.

Finally, the solute mass balance modelling approach by the Mixing Cell method was found an

efficient way of determining of both the open water lake evaporation (1665+269mmyr-1) and the

ungauged flow component (301+136mmyr-1). However, future more detailed application of the

method will require detailed time series of the chemical components in the system.

8.2. Recommendations

The main objective of this study was to determine the groundwater contribution of the Upper Blue

Nile basin mainly the Lake Tana basin through stream hydrograph analysis (baseflow analysis) and

the natural groundwater recharge of the basin using different approaches. Together with this, it was

tried to determine open water evaporation of the lake, the annual water balance of the Lake with two

methods: water balance model using hydrometeorological data and mixing cell modelling. Thus, the

research concentrated on the interaction of the shallow groundwater aquifer systems with the surface


92

water systems and lacks the description of deep aquifer systems, groundwater flow directions and

seasonal variations of groundwater fluctuations in the entire basin although geological structure

analysis around and with in the lake like fracture zone, faults could explain this to some extent.

For further study of the groundwater resources, the following datasets are important to implement in

the area.

1. Seasonal hydrogeochemical data of the catchments together with the lake chemical data and

from the outflow through the Blue Nile outlet.

2. Although this could be difficult to implement, piezometric data are the most important

datasets to consider when dealing with flow directions of the basin and assessment of

groundwater level distribution in the basin.

3. Groundwater modelling computer codes like Modflow could be applied to determine the

overall groundwater flow patterns and aquifer characteristics of the basin. This computer code

could especially be applied in the northern part of the basin around Gondar, Azezo

subcatchments. This is because some well organized pumping test data, log data could be

found from some organizations like Amhara Regional Water Bureau, Amhara Water

Construction Enterprise and some other enterprises.

4. Apply the methods outlined in points 1, 2 and 3 to the other downstream tributary systems of

the Blue Nile.


93

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96

APPENDICES

Appendix A-Average daily discharge data of the stations

Unit (m3s-1)

Blue Nile Outlet Bered Dirma Garno Gelda

Gilgel Abbay

1 90.199 0.223 0.093 0.228 0.168 5.843 2 87.471 0.223 0.091 0.230 0.165 5.714 3 86.509 0.218 0.093 0.232 0.158 5.587 4 85.486 0.228 0.091 0.232 0.156 5.534 5 84.914 0.228 0.089 0.226 0.154 5.485 6 86.654 0.223 0.095 0.227 0.151 5.372 7 86.728 0.223 0.093 0.225 0.151 5.313 8 86.678 0.223 0.084 0.226 0.148 5.339 9 85.606 0.223 0.083 0.227 0.149 5.230 10 84.699 0.213 0.079 0.226 0.145 5.197 11 83.810 0.222 0.083 0.232 0.148 5.110 12 82.868 0.222 0.083 0.232 0.148 4.996 13 82.293 0.227 0.080 0.241 0.148 4.942 14 81.627 0.222 0.071 0.232 0.147 4.933 15 80.803 0.222 0.069 0.234 0.146 4.915 16 77.733 0.217 0.071 0.232 0.138 4.802 17 75.356 0.222 0.075 0.232 0.142 4.704 18 74.540 0.222 0.072 0.225 0.137 4.650 19 74.093 0.218 0.065 0.215 0.132 4.619 20 73.348 0.226 0.059 0.216 0.136 4.505 21 72.250 0.226 0.061 0.219 0.136 4.481 22 71.860 0.218 0.061 0.215 0.135 4.456 23 70.637 0.213 0.059 0.210 0.132 4.334 24 67.111 0.217 0.059 0.207 0.130 4.269 25 66.968 0.213 0.061 0.209 0.129 4.210 26 66.029 0.213 0.058 0.202 0.128 4.158 27 65.383 0.217 0.056 0.205 0.127 4.115 28 65.061 0.203 0.053 0.202 0.127 4.094 29 64.893 0.195 0.052 0.200 0.123 4.044 30 64.275 0.200 0.049 0.204 0.122 3.936 31 63.742 0.206 0.048 0.200 0.121 3.897 32 63.310 0.201 0.045 0.199 0.120 3.896 33 62.638 0.195 0.049 0.199 0.120 3.843 34 62.034 0.192 0.046 0.198 0.131 3.763 35 62.198 0.203 0.046 0.200 0.130 3.718 36 59.914 0.208 0.042 0.202 0.137 3.669 37 57.977 0.203 0.042 0.201 0.134 3.616 38 56.659 0.208 0.043 0.198 0.133 3.591 39 55.864 0.203 0.038 0.198 0.129 3.555 40 55.546 0.213 0.037 0.191 0.131 3.585


97

average daily discharge data of the stations (continued) Unit

(m3s-1) Blue Nile

Outlet Bered Dirma Garno Gelda Gilgel Abbay

41 55.354 0.221 0.032 0.187 0.133 3.487 42 56.659 0.213 0.035 0.190 0.134 3.535 43 58.757 0.213 0.035 0.181 0.129 3.483 44 60.637 0.221 0.039 0.189 0.128 3.495 45 59.835 0.218 0.039 0.184 0.126 3.465 46 59.085 0.222 0.037 0.191 0.128 3.434 47 58.071 0.228 0.036 0.193 0.126 3.354 48 58.367 0.219 0.028 0.189 0.126 3.269 49 58.233 0.206 0.027 0.193 0.127 3.234 50 57.747 0.218 0.024 0.188 0.123 3.206 51 57.322 0.218 0.023 0.190 0.122 3.146 52 56.771 0.218 0.022 0.189 0.120 3.119 53 52.901 0.231 0.023 0.190 0.115 3.101 54 52.246 0.231 0.022 0.197 0.115 3.094 55 51.871 0.236 0.020 0.196 0.118 3.085 56 51.293 0.224 0.019 0.199 0.117 3.060 57 50.999 0.224 0.018 0.195 0.113 3.051 58 50.809 0.221 0.017 0.196 0.114 3.025 59 50.237 0.231 0.016 0.204 0.115 3.008 60 49.657 0.226 0.013 0.202 0.118 2.946 61 49.367 0.226 0.024 0.203 0.112 2.933 62 49.121 0.226 0.020 0.203 0.110 2.916 63 48.607 0.226 0.020 0.200 0.109 2.976 64 48.144 0.226 0.018 0.200 0.108 3.088 65 47.658 0.231 0.016 0.201 0.110 2.982 66 47.004 0.231 0.014 0.204 0.108 2.939 67 46.025 0.219 0.018 0.204 0.108 2.893 68 44.818 0.231 0.016 0.202 0.108 2.860 69 44.098 0.212 0.016 0.210 0.105 2.905 70 43.581 0.243 0.015 0.215 0.104 2.796 71 43.522 0.277 0.019 0.218 0.102 2.792 72 42.035 0.268 0.018 0.207 0.184 2.726 73 41.921 0.290 0.015 0.236 0.110 2.657 74 42.140 0.319 0.016 0.222 0.109 2.630 75 42.618 0.320 0.017 0.248 0.130 2.664 76 43.509 0.277 0.020 0.248 0.105 2.658 77 44.440 0.205 0.018 0.302 0.105 2.651 78 44.337 0.177 0.017 0.262 0.105 2.614 79 44.370 0.177 0.012 0.215 0.104 2.588 80 43.668 0.177 0.007 0.218 0.100 2.629 81 43.398 0.168 0.010 0.230 0.100 2.606 82 43.936 0.168 0.009 0.238 0.106 2.627 83 43.290 0.163 0.010 0.232 0.106 2.593 84 43.755 0.182 0.015 0.227 0.108 3.016 85 43.363 0.177 0.012 0.241 0.107 3.531 86 43.725 0.172 0.009 0.224 0.108 3.190


98

average daily discharge data of the stations (continued) 87 42.848 0.149 0.007 0.211 0.104 2.864 88 42.362 0.153 0.009 0.208 0.105 2.635 89 40.481 0.157 0.018 0.203 0.109 2.537 90 40.048 0.163 0.019 0.191 0.106 2.510 91 40.504 0.158 0.025 0.184 0.104 2.507 92 40.038 0.154 0.012 0.185 0.105 2.427 93 39.725 0.141 0.037 0.183 0.101 2.386 94 39.375 0.144 0.061 0.190 0.100 2.413 95 39.232 0.148 0.045 0.199 0.103 2.359 96 40.129 0.157 0.023 0.207 0.102 2.516 97 38.706 0.160 0.044 0.212 0.105 2.539 98 39.485 0.150 0.031 0.246 0.108 2.465 99 40.338 0.150 0.025 0.210 0.104 2.514 100 40.300 0.148 0.023 0.212 0.102 2.666 101 40.828 0.158 0.090 0.200 0.101 2.895 102 40.209 0.154 0.065 0.186 0.105 2.591 103 39.443 0.153 0.064 0.178 0.100 2.385 104 38.663 0.141 0.051 0.192 0.100 2.326 105 38.898 0.141 0.050 0.186 0.100 2.292 106 38.130 0.173 0.094 0.181 0.119 2.288 107 38.124 0.174 0.173 0.207 0.103 2.441 108 37.948 0.153 0.111 0.255 0.101 2.642 109 37.627 0.153 0.068 0.220 0.100 2.553 110 37.141 0.157 0.081 0.193 0.100 2.487 111 36.581 0.157 0.065 0.207 0.099 2.526 112 36.902 0.157 0.061 0.214 0.099 2.612 113 35.542 0.160 0.072 0.233 0.099 2.688 114 34.849 0.157 0.098 0.222 0.097 2.838 115 34.462 0.153 0.285 0.207 0.101 3.211 116 35.268 0.160 0.321 0.221 0.100 3.344 117 35.049 0.158 0.526 0.213 0.099 3.767 118 35.777 0.164 0.137 0.203 0.098 3.398 119 34.108 0.162 0.105 0.206 0.099 3.318 120 34.445 0.162 0.110 0.215 0.097 3.023 121 34.054 0.168 0.102 0.208 0.097 3.215 122 32.965 0.165 0.113 0.204 0.097 2.774 123 33.557 0.165 0.113 0.230 0.107 3.009 124 32.666 0.162 0.308 0.213 0.104 3.133 125 31.433 0.162 0.249 0.205 0.101 3.488 126 31.361 0.162 0.443 0.201 0.098 4.099 127 30.831 0.156 0.189 0.206 0.094 4.196 128 30.944 0.170 0.152 0.198 0.130 4.465 129 30.984 0.191 0.114 0.201 0.112 5.829 130 30.655 0.199 0.111 0.206 0.099 4.634 131 30.660 0.164 0.169 0.209 0.094 5.521 132 29.494 0.171 0.103 0.205 0.100 5.242 133 28.118 0.172 0.089 0.188 0.576 9.024 134 28.465 0.170 0.087 0.186 0.358 6.267


99


(m3s-1) Blue Nile


135 28.123 0.163 0.086 0.169 0.124 6.733 136 28.373 0.163 0.153 0.191 0.106 5.684 137 25.451 0.238 0.269 0.276 0.105 6.090 138 25.333 0.266 0.144 0.615 0.098 8.039 139 24.956 0.314 0.105 0.309 0.095 6.769 140 24.839 0.293 1.347 0.247 0.212 7.751 141 24.464 0.297 0.126 0.323 0.192 7.729 142 24.787 0.263 0.104 0.213 0.119 8.267 143 25.015 0.316 0.108 0.199 0.286 9.761 144 24.919 0.403 0.100 0.210 0.200 10.170 145 24.052 0.397 0.078 0.290 0.121 11.829 146 23.925 0.461 0.090 0.214 0.107 11.056 147 24.450 0.394 0.285 0.198 0.102 14.683 148 24.472 0.423 0.691 0.209 0.101 12.941 149 24.803 0.387 0.360 0.216 0.254 12.135 150 24.772 0.390 0.374 0.209 0.100 13.953 151 25.165 0.417 0.839 0.234 0.102 14.704 152 24.860 0.530 0.293 0.230 0.420 17.764 153 24.635 0.328 0.148 0.212 0.235 17.833 154 23.409 0.300 0.128 0.282 0.151 20.158 155 23.664 0.316 0.109 0.369 0.136 23.476 156 23.248 1.109 0.153 0.361 0.142 29.604 157 23.371 0.348 0.202 0.344 0.194 24.047 158 24.224 0.308 0.583 0.441 0.523 30.642 159 24.148 0.292 0.228 0.269 0.231 30.503 160 23.324 0.326 0.380 0.300 0.344 33.944 161 23.970 0.414 0.268 0.294 0.357 30.659 162 24.329 0.323 0.374 0.350 0.640 33.307 163 25.017 0.309 0.655 0.395 0.558 29.625 164 25.915 0.341 0.385 0.394 0.157 34.962 165 26.724 0.325 0.365 0.364 0.363 36.955 166 27.319 0.361 0.703 0.358 0.574 37.761 167 26.706 0.389 1.281 0.304 1.102 39.373 168 27.777 0.672 3.214 0.356 0.414 35.856 169 27.688 0.593 2.323 0.333 2.102 43.452 170 28.075 0.263 2.265 0.299 1.080 47.341 171 27.849 0.343 2.555 0.302 0.687 55.177 172 28.879 0.729 11.337 0.415 1.531 56.776 173 28.267 0.332 8.310 0.335 0.987 56.455 174 28.036 0.292 1.127 0.766 0.483 54.075 175 28.403 0.317 1.189 0.369 0.565 69.085 176 28.271 0.445 20.051 0.356 1.253 66.785 177 29.793 0.294 27.167 0.332 0.622 65.313 178 31.425 0.459 2.405 0.483 3.131 69.642 179 31.997 0.340 21.977 0.384 2.283 81.692 180 33.422 0.261 13.950 0.354 2.617 70.421


100


(m3s-1) Blue Nile


181 34.536 0.351 15.761 0.488 2.236 76.465 182 35.493 0.342 17.303 0.355 1.049 85.725 183 37.979 0.377 4.189 0.391 0.886 88.442 184 37.107 0.823 8.127 0.538 2.386 103.911 185 37.989 0.380 4.830 0.817 2.198 105.499 186 40.965 0.459 30.082 0.850 4.522 105.935 187 39.461 0.454 11.453 1.027 0.676 111.360 188 40.683 0.445 5.570 1.611 1.588 111.276 189 38.821 0.321 7.350 1.576 3.801 121.888 190 39.714 0.715 24.170 0.834 1.206 133.231 191 41.198 0.338 14.902 1.467 1.203 129.991 192 43.865 0.735 51.244 1.642 3.867 145.396 193 45.907 1.313 10.246 1.260 6.252 137.973 194 46.818 0.508 20.558 1.364 2.546 145.873 195 46.020 0.573 17.334 1.954 8.507 148.314 196 46.826 0.763 14.646 0.944 1.832 150.131 197 47.985 0.806 43.584 1.653 2.326 165.392 198 47.928 0.445 25.169 2.369 1.685 170.583 199 50.024 0.795 10.428 2.223 3.945 162.119 200 51.937 0.390 45.928 1.424 3.384 178.910 201 58.470 0.592 31.521 1.918 2.032 176.308 202 56.329 0.594 10.907 1.516 3.083 177.345 203 58.719 0.826 14.210 2.544 3.208 185.902 204 58.197 0.424 7.427 3.445 2.244 173.363 205 58.975 0.686 8.937 2.265 2.511 172.456 206 62.977 1.357 37.155 1.974 3.278 182.603 207 66.771 0.823 15.103 2.844 5.744 201.856 208 67.616 0.622 6.906 1.975 4.287 192.871 209 69.868 1.091 29.719 2.986 4.719 182.090 210 73.094 1.606 27.206 3.337 6.949 183.034 211 76.396 0.756 15.919 2.586 3.217 188.317 212 78.947 1.031 11.757 2.388 3.105 190.836 213 80.889 0.750 17.863 2.193 6.760 175.122 214 85.625 0.599 4.908 2.701 4.916 185.340 215 85.944 0.608 5.509 2.350 4.194 185.708 216 91.486 0.541 93.450 3.108 3.615 188.730 217 88.617 0.634 18.789 3.591 7.378 191.557 218 90.830 0.572 14.626 2.632 7.349 191.386 219 93.180 0.658 40.472 2.209 7.440 187.230 220 98.699 1.018 11.279 3.161 7.180 188.586 221 102.368 0.894 30.091 2.040 5.651 201.947 222 110.170 1.182 34.733 3.104 3.609 210.695 223 133.150 0.729 13.996 7.242 3.657 196.296 224 137.337 0.751 36.727 5.547 5.733 202.022 225 135.566 1.190 48.605 3.456 12.799 211.641 226 137.735 0.498 24.380 3.823 9.696 208.673


101


(m3s-1) Blue Nile


227 144.463 0.516 116.642 2.464 5.077 212.235 228 148.157 0.997 33.980 3.295 17.542 212.956 229 154.319 0.919 30.172 5.338 5.161 209.754 230 175.966 0.643 19.228 5.407 6.006 199.773 231 170.454 0.800 49.677 4.197 4.599 205.322 232 167.475 0.394 59.221 3.533 13.826 193.923 233 181.942 0.723 51.567 4.264 6.713 203.680 234 185.736 0.911 51.159 4.099 10.966 200.570 235 191.349 0.761 34.502 3.665 4.584 196.194 236 198.964 0.642 24.249 4.715 7.619 194.430 237 203.727 0.871 35.535 4.335 15.601 191.534 238 209.926 0.604 39.065 3.188 4.089 193.812 239 215.833 0.370 15.617 2.505 4.011 190.933 240 227.112 0.454 27.091 4.039 4.344 187.754 241 231.848 0.649 8.692 3.823 10.455 183.713 242 240.054 0.599 20.786 1.909 4.658 190.944 243 249.281 0.748 41.061 2.123 8.857 183.506 244 253.781 0.790 13.554 2.075 7.085 187.238 245 263.901 1.243 7.467 1.761 5.222 185.056 246 272.199 1.151 11.077 2.504 4.162 171.485 247 279.303 0.508 6.098 2.340 15.317 172.488 248 282.661 0.472 7.661 2.283 4.471 179.715 249 295.004 0.481 5.013 2.395 8.692 164.790 250 307.418 0.504 8.504 2.042 6.975 168.169 251 310.276 0.501 17.807 1.713 2.750 181.931 252 318.666 0.338 8.555 1.612 5.942 159.111 253 325.063 0.417 6.270 1.499 2.946 167.782 254 330.673 0.659 13.939 1.500 4.588 159.128 255 334.168 0.489 9.810 1.560 3.760 158.864 256 336.785 0.561 11.960 1.155 5.393 160.527 257 336.694 0.360 21.540 1.233 3.267 149.715 258 339.463 0.485 5.319 1.000 8.541 149.215 259 340.941 0.473 12.915 0.958 4.234 151.713 260 342.483 0.392 5.644 0.917 9.449 150.237 261 341.697 0.336 7.091 0.891 2.770 144.418 262 342.809 0.457 1.727 0.906 3.210 139.225 263 343.317 0.554 1.454 1.074 3.484 128.222 264 344.033 0.407 5.983 0.860 4.077 126.732 265 345.292 0.469 14.324 0.875 5.147 134.914 266 345.454 0.683 3.822 0.775 4.644 130.136 267 345.001 0.435 10.564 0.725 3.549 120.837 268 342.562 0.715 7.391 0.691 3.744 111.341 269 341.827 0.510 2.806 0.652 3.972 108.830 270 335.320 0.521 2.090 0.615 2.582 99.074 271 338.688 0.681 4.850 0.594 2.600 102.063 272 339.491 0.784 2.213 0.594 2.965 99.329


102


(m3s-1) Blue Nile


273 337.415 0.520 3.910 0.602 2.250 102.517 274 334.654 0.495 1.968 0.508 2.548 97.774 275 332.582 0.383 5.641 0.467 1.841 90.043 276 328.360 0.302 8.686 0.461 2.292 95.338 277 327.642 0.279 4.970 0.448 2.032 92.206 278 324.626 0.366 2.234 0.435 1.787 80.211 279 321.620 0.361 4.001 0.449 1.701 81.692 280 319.809 0.490 3.311 0.436 1.579 79.237 281 316.597 0.351 2.666 0.411 1.609 71.960 282 312.102 0.350 18.455 0.429 1.627 78.449 283 311.287 0.939 3.016 0.423 4.016 74.358 284 308.506 0.676 14.903 0.468 3.768 78.492 285 304.856 0.344 20.460 0.415 11.315 69.550 286 301.446 0.421 18.642 0.411 2.732 65.512 287 296.984 0.266 7.584 0.406 1.998 58.652 288 294.486 0.312 12.230 0.419 3.187 52.989 289 291.244 0.352 9.876 0.389 1.932 49.311 290 286.938 0.500 21.711 0.402 1.508 47.180 291 284.750 0.539 30.521 0.493 1.360 50.646 292 283.833 0.429 2.547 0.435 3.852 49.332 293 282.563 0.351 4.410 0.596 2.526 46.759 294 280.909 0.552 4.359 0.421 2.004 51.015 295 277.579 0.350 1.426 0.381 1.204 53.513 296 274.863 0.340 1.166 0.369 1.257 50.334 297 271.037 0.283 0.819 0.378 0.916 45.777 298 266.927 0.291 0.678 0.356 1.002 41.510 299 264.925 0.414 0.610 0.333 1.334 42.492 300 261.476 0.375 0.852 0.358 0.865 45.425 301 257.917 0.400 0.781 0.352 0.804 37.311 302 255.040 0.445 0.801 0.304 0.895 39.196 303 248.342 0.319 0.898 0.287 0.737 40.272 304 245.721 0.244 0.772 0.287 0.705 38.503 305 241.720 0.269 0.836 0.289 0.685 33.288 306 238.572 0.263 1.418 0.279 0.787 30.992 307 234.739 0.257 2.008 0.303 0.665 29.743 308 229.694 0.254 0.572 0.288 0.666 26.662 309 226.127 0.250 0.510 0.282 0.681 27.096 310 222.684 0.236 0.440 0.267 1.071 25.553 311 218.903 0.236 0.425 0.254 0.690 24.423 312 214.496 0.236 0.385 0.247 0.822 22.762 313 211.059 0.237 0.354 0.241 0.730 23.612 314 208.367 0.237 0.447 0.233 0.769 23.921 315 203.911 0.262 0.459 0.240 0.787 22.315 316 201.503 0.391 0.427 0.248 0.602 23.034 317 199.602 0.255 0.413 0.224 0.604 21.148 318 195.455 0.246 0.374 0.213 0.585 20.638


103


(m3s-1) Blue Nile


319 193.035 0.240 0.674 0.206 0.552 19.097 320 190.037 0.239 0.708 0.199 0.548 18.499 321 187.102 0.239 0.575 0.192 0.559 22.243 322 184.079 0.233 0.371 0.201 0.569 19.777 323 180.716 0.227 0.342 0.206 0.534 17.184 324 180.577 0.227 0.346 0.203 0.388 16.444 325 175.231 0.227 0.339 0.196 0.370 15.958 326 173.924 0.230 0.311 0.190 0.355 16.155 327 171.221 0.230 0.303 0.183 0.336 15.384 328 167.062 0.224 0.280 0.186 0.356 14.777 329 161.979 0.216 0.264 0.212 0.325 13.805 330 162.697 0.216 0.240 0.201 0.317 12.965 331 163.839 0.221 0.241 0.200 0.304 12.352 332 156.289 0.218 0.202 0.189 0.299 12.440 333 154.549 0.213 0.184 0.186 0.292 12.933 334 151.371 0.207 0.179 0.180 0.271 13.971 335 149.671 0.207 0.189 0.181 0.269 12.381 336 146.709 0.207 0.169 0.174 0.263 12.113 337 143.253 0.207 0.187 0.164 0.262 13.210 338 141.484 0.207 0.169 0.161 0.261 12.211 339 138.800 0.202 0.161 0.160 0.260 11.822 340 136.589 0.202 0.163 0.160 0.252 11.607 341 134.722 0.202 0.157 0.163 0.252 11.634 342 133.369 0.202 0.155 0.168 0.249 11.336 343 130.446 0.219 0.153 0.165 0.249 11.479 344 128.608 0.206 0.152 0.170 0.249 11.185 345 127.010 0.196 0.145 0.170 0.249 10.873 346 125.687 0.196 0.136 0.168 0.243 10.676 347 123.013 0.196 0.141 0.168 0.242 10.485 348 121.393 0.196 0.151 0.163 0.246 10.143 349 119.915 0.202 0.161 0.158 0.253 10.130 350 118.286 0.242 0.153 0.155 0.248 10.189 351 125.230 0.265 0.139 0.155 0.232 10.004 352 111.169 0.278 0.145 0.152 0.229 9.749 353 109.745 0.285 0.126 0.151 0.230 9.539 354 108.302 0.271 0.117 0.151 0.222 9.234 355 107.250 0.271 0.125 0.145 0.231 8.933 356 108.011 0.271 0.118 0.145 0.230 8.734 357 107.405 0.285 0.102 0.143 0.227 8.622 358 106.954 0.332 0.115 0.142 0.226 8.552 359 104.181 0.300 0.108 0.137 0.217 8.362 360 102.990 0.272 0.103 0.134 0.217 8.168 361 101.370 0.275 0.098 0.132 0.213 8.101 362 100.076 0.282 0.106 0.134 0.208 8.020 363 101.022 0.290 0.103 0.140 0.208 7.828 364 99.920 0.294 0.097 0.138 0.205 7.741 365 98.328 0.282 0.095 0.141 0.207 7.712


104


(m3s-1 ) Gumera Gumero Kilti Koga Megetch Ribb 1 4.157 0.181 1.080 1.673 0.722 1.344 2 4.054 0.176 1.104 1.657 0.717 1.253 3 3.947 0.176 1.083 1.633 0.713 1.223 4 3.894 0.171 1.048 1.594 0.686 1.216 5 3.854 0.170 1.064 1.580 0.684 1.204 6 3.759 0.169 0.995 1.564 0.682 1.185 7 3.714 0.167 0.988 1.582 0.680 1.171 8 3.600 0.169 0.973 1.552 0.664 1.163 9 3.539 0.173 1.323 1.517 0.653 1.141 10 3.480 0.177 0.990 1.479 0.653 1.123 11 3.420 0.164 0.949 1.478 0.634 1.115 12 3.364 0.170 0.941 1.470 0.632 1.109 13 3.287 0.174 0.930 1.459 0.639 1.105 14 3.207 0.173 0.888 1.450 0.623 1.080 15 3.179 0.167 0.855 1.457 0.617 0.886 16 3.107 0.164 0.845 1.436 0.609 0.792 17 3.055 0.172 0.815 1.419 0.606 0.774 18 2.982 0.161 0.789 1.412 0.594 0.768 19 2.925 0.159 0.761 1.365 0.603 0.757 20 2.879 0.159 0.839 1.381 0.600 0.770 21 2.822 0.160 0.900 1.377 0.600 0.745 22 2.779 0.387 0.874 1.375 0.592 0.737 23 2.695 0.163 0.860 1.365 0.580 0.734 24 2.654 0.160 0.781 1.353 0.590 0.731 25 2.604 0.154 0.747 1.334 0.575 0.701 26 2.561 0.151 0.728 1.317 0.583 0.687 27 2.531 0.151 0.709 1.303 0.579 0.676 28 2.472 0.152 0.750 1.287 0.586 0.683 29 2.477 0.150 0.726 1.283 0.582 0.671 30 2.422 0.149 0.670 1.270 0.583 0.652 31 2.409 0.185 0.683 1.272 0.567 0.647 32 2.389 0.149 0.645 1.267 0.561 0.637 33 2.340 0.148 0.652 1.261 0.561 0.619 34 2.324 0.148 0.638 1.243 0.564 0.617 35 2.290 0.148 0.616 1.237 0.555 0.596 36 2.200 0.147 0.596 1.240 0.553 0.587 37 2.112 0.148 0.568 1.257 0.548 0.582 38 2.079 0.147 0.567 1.241 0.550 0.566 39 2.064 0.141 0.551 1.209 0.547 0.510 40 2.026 0.141 0.559 1.195 0.552 0.548 41 1.995 0.141 0.601 1.170 0.564 0.549 42 1.961 0.142 0.608 1.160 0.547 0.548 43 1.938 0.139 0.570 1.172 0.532 0.559 44 1.914 0.138 0.530 1.160 0.526 0.571 45 1.897 0.138 0.510 1.158 0.525 0.546 46 1.878 0.138 0.495 1.146 0.520 0.538


105


(m3s-1 ) Gumera Gumero Kilti Koga Megetch Ribb 47 1.836 0.137 0.554 1.137 0.515 0.521 48 1.817 0.137 0.430 1.106 0.502 0.494 49 1.799 0.132 0.371 1.100 0.499 0.521 50 1.770 0.131 0.413 1.078 0.496 0.504 51 1.723 0.131 0.423 1.053 0.489 0.492 52 1.688 0.149 0.414 1.083 0.491 0.481 53 1.667 0.141 0.446 1.051 0.471 0.468 54 1.658 0.135 0.421 1.038 0.490 0.465 55 1.657 0.133 0.331 1.028 0.510 0.460 56 1.626 0.131 0.309 1.017 0.526 0.471 57 1.606 0.128 0.319 1.020 0.523 0.470 58 1.588 0.126 0.332 1.012 0.508 0.465 59 1.566 0.128 0.321 1.001 0.518 0.446 60 1.538 0.126 0.314 1.034 0.518 0.595 61 1.513 0.127 0.295 0.987 0.507 0.559 62 1.522 0.124 0.307 0.969 0.505 0.549 63 1.667 0.125 0.326 0.958 0.508 0.558 64 1.864 0.126 0.414 0.944 0.497 0.413 65 1.841 0.124 0.340 0.939 0.501 0.383 66 1.870 0.125 0.443 0.916 0.505 0.366 67 1.424 0.124 0.426 0.903 0.500 0.350 68 1.387 0.123 0.422 0.922 0.497 0.347 69 1.365 0.123 0.281 0.919 0.494 0.350 70 1.356 0.123 0.266 0.917 0.493 0.331 71 1.343 0.122 0.327 0.913 0.489 0.337 72 1.360 0.130 0.259 0.887 0.520 0.345 73 1.386 0.122 0.256 0.975 0.499 0.412 74 1.470 0.117 0.219 0.962 0.494 0.393 75 1.449 0.115 0.258 0.929 0.538 0.435 76 1.400 0.115 0.229 1.029 0.529 0.646 77 1.389 0.118 0.223 1.049 0.526 0.538 78 1.427 0.122 0.230 1.011 0.568 0.498 79 1.488 0.124 0.238 1.019 0.536 0.706 80 1.432 0.124 0.238 0.996 0.530 0.496 81 1.412 0.134 0.246 0.962 0.528 0.470 82 1.367 0.125 0.239 0.966 0.565 0.499 83 1.362 0.128 0.183 0.946 0.545 0.440 84 1.498 0.128 0.194 0.945 0.532 0.523 85 1.583 0.151 0.180 1.003 0.510 0.477 86 1.453 0.143 0.155 0.963 0.561 0.475 87 1.717 0.131 0.143 0.930 0.537 0.474 88 1.333 0.123 0.159 0.890 0.538 0.470 89 1.260 0.124 0.169 0.884 0.535 0.433 90 1.201 0.124 0.181 0.876 0.533 0.421 91 1.145 0.123 0.167 0.839 0.535 0.470 92 1.151 0.126 0.168 0.828 0.518 0.436 93 1.133 0.124 0.172 0.819 0.513 0.447


106


(m3s-1 ) Gumera Gumero Kilti Koga Megetch Ribb 94 1.145 0.128 0.164 0.800 0.524 0.485 95 1.120 0.126 0.165 0.822 0.530 0.417 96 1.114 0.127 0.218 0.808 0.554 0.413 97 1.105 0.152 0.198 0.815 0.759 0.412 98 1.143 0.129 0.188 0.815 0.747 0.451 99 1.239 0.135 0.186 0.831 0.767 0.494 100 1.325 0.131 0.195 0.816 0.745 0.469 101 1.417 0.123 0.197 0.905 0.644 0.517 102 1.236 0.123 0.184 0.837 0.600 0.465 103 1.101 0.122 0.187 0.806 0.555 0.509 104 1.098 0.123 0.141 0.775 0.549 0.431 105 1.073 0.122 0.190 0.749 0.538 0.368 106 1.096 0.122 0.166 0.772 0.544 0.383 107 1.327 0.272 0.193 0.789 0.536 0.368 108 1.181 0.167 0.157 0.805 0.582 0.440 109 1.085 0.127 0.169 0.807 0.770 0.906 110 1.139 0.179 0.165 0.806 0.644 0.725 111 1.099 0.126 0.154 0.844 0.713 0.654 112 1.058 0.122 0.129 0.830 0.648 0.636 113 1.129 0.122 1.187 0.935 0.605 0.692 114 1.096 0.277 0.448 0.833 0.614 0.723 115 1.106 0.182 0.275 0.857 0.652 0.954 116 1.090 0.140 1.167 0.925 0.816 0.693 117 1.107 0.154 0.461 0.908 0.750 0.625 118 1.187 0.162 0.238 1.001 0.847 0.924 119 1.212 0.132 0.195 0.945 0.642 0.699 120 1.136 0.139 0.237 0.896 0.605 0.893 121 1.348 0.130 0.244 0.864 0.632 0.771 122 1.116 0.182 0.251 0.893 0.648 0.595 123 1.263 0.160 0.237 0.856 0.635 0.526 124 1.372 0.134 0.241 0.918 0.684 0.569 125 1.322 0.135 0.215 1.044 0.620 0.849 126 1.493 0.301 0.226 0.991 0.678 0.730 127 1.391 0.157 0.373 0.955 0.806 0.839 128 1.360 0.189 0.339 1.079 2.314 0.813 129 1.483 0.132 0.450 0.946 0.777 0.750 130 1.248 0.162 0.411 0.938 0.915 0.698 131 1.319 0.162 0.498 0.933 0.999 0.663 132 1.433 0.138 0.456 0.996 0.895 0.671 133 1.812 0.141 1.048 1.306 0.870 0.709 134 1.645 0.131 1.536 1.144 0.921 1.294 135 1.507 0.387 1.136 1.085 0.795 1.159 136 1.705 0.848 0.725 1.197 0.747 1.230 137 1.630 0.871 1.173 1.362 0.908 1.401 138 2.051 0.574 0.676 1.105 0.768 1.858 139 2.290 0.338 0.823 1.167 0.887 2.692


107




108




109




110




111


(m3s-1 ) Gumera Gumero Kilti Koga Megetch Ribb 328 9.859 0.188 3.232 2.866 1.343 3.211 329 9.837 0.224 3.004 2.782 1.426 3.188 330 8.524 0.221 2.874 2.776 1.402 2.659 331 9.204 0.168 2.826 2.723 1.392 2.655 332 9.001 0.198 2.666 2.707 1.456 2.392 333 8.547 0.188 2.909 2.598 1.482 2.333 334 7.734 0.196 2.506 2.583 1.433 2.339 335 7.559 0.175 2.419 2.559 1.289 2.238 336 7.361 0.178 2.297 2.538 1.265 2.256 337 7.135 0.170 2.161 2.520 1.231 2.166 338 7.673 0.168 2.014 2.498 1.211 2.122 339 7.027 0.170 1.904 2.453 1.210 2.133 340 6.833 0.165 1.773 2.429 1.196 2.068 341 6.789 0.163 1.715 2.383 1.185 2.052 342 6.593 0.165 1.723 2.400 1.185 2.118 343 6.525 0.165 1.664 2.406 1.184 2.113 344 6.382 0.162 1.835 2.444 1.180 2.135 345 6.403 0.156 1.810 2.394 1.235 2.100 346 6.080 0.154 1.700 2.397 1.186 1.975 347 6.107 0.152 1.669 2.385 1.117 1.957 348 6.065 0.153 1.656 2.311 1.140 1.967 349 6.063 0.155 1.591 2.276 1.121 1.946 350 5.786 0.154 1.539 2.230 1.092 1.888 351 5.610 0.150 1.479 2.203 1.070 1.839 352 5.597 0.156 1.414 2.196 1.070 1.832 353 5.509 0.151 1.374 2.167 1.047 1.789 354 5.411 0.154 1.343 2.149 1.028 1.786 355 5.356 0.152 1.317 2.105 1.005 1.749 356 5.299 0.153 1.270 2.068 0.996 1.715 357 5.180 0.153 1.255 2.063 0.986 1.686 358 5.136 0.153 1.201 2.056 0.981 1.652 359 5.054 0.154 1.168 2.000 0.962 1.602 360 4.934 0.149 1.156 1.964 0.953 1.570 361 4.779 0.147 1.091 1.947 1.004 1.545 362 4.706 0.143 1.042 1.933 1.002 1.549 363 4.703 0.143 1.008 1.908 0.927 1.526 364 4.620 0.141 1.043 1.904 0.916 1.530 365 4.651 0.141 0.973 1.878 0.905 1.518


112

Appendix B-BFI as calculated from Eckhardt Model

Blue

Nile outlet Gilgel Abbay Gumera Koga Megetch Ribb

Year BFI BFI BFI BFI BFI BFI

1973 0.177 0.151 0.126 0.187 0.086 0.106 1974 0.177 0.151 0.144 0.228 0.088 0.153 1975 0.174 0.150 0.119 0.191 0.061 0.101 1976 0.236 0.173 0.151 0.215 0.066 0.127 1977 0.187 0.163 0.137 0.241 0.094 0.139 1978 0.247 0.158 0.151 0.199 0.144 0.140 1979 0.229 0.163 0.143 0.245 0.093 0.152 1980 0.179 0.163 0.145 0.219 0.102 0.112 1981 0.193 0.148 0.130 0.221 0.107 0.123 1982 0.225 0.162 0.134 0.126 0.095 0.135 1983 0.204 0.151 0.142 0.203 0.108 0.114 1984 0.258 0.148 0.149 0.232 0.116 0.084 1985 0.155 0.141 0.146 0.225 0.094 0.077 1986 0.199 0.153 0.157 0.212 0.119 0.101 1987 0.256 0.157 0.134 0.226 0.136 0.117 1988 0.145 0.153 0.125 0.195 0.127 0.113 1989 0.252 0.151 0.125 0.268 0.094 0.137 1990 0.230 0.154 0.111 0.223 0.113 0.141 1991 0.165 0.139 0.106 0.224 0.120 0.112 1992 0.209 0.151 0.135 0.248 0.130 0.083 1993 0.190 0.155 0.141 0.233 0.123 0.120 1994 0.200 0.155 0.124 0.265 0.090 0.084 1995 0.219 0.146 0.154 0.147 0.085 0.097 1996 0.161 0.134 0.156 0.206 0.115 0.126 1997 0.189 0.148 0.186 0.254 0.137 0.118 1998 0.148 0.141 0.170 0.211 0.083 0.129 1999 0.179 0.138 0.159 0.231 0.148 0.140 2000 0.135 0.138 0.152 0.217 0.261 0.143 2001 0.148 0.144 0.145 0.223 0.189 0.124 2002 0.285 0.141 0.149 0.272 0.166 0.122 2003 0.293 0.122 0.144 0.205 0.192 0.136 2004 0.281 0.130 0.196 0.157 0.226 0.158 2005 0.232 0.135 0.131 0.144 0.226 0.144 2006 0.191 0.169 0.105 0.157 0.226 0.157

Max 0.293 0.173 0.196 0.272 0.261 0.158 Min 0.135 0.122 0.105 0.126 0.061 0.077

Mean 0.204 0.149 0.142 0.213 0.128 0.122 SD 0.042 0.011 0.020 0.035 0.050 0.022


113

BFI as calculated from Eckhardt Model Gumero Gelda Garno Kilti Dirma Bered

Years BFI BFI BFI BFI BFI BFI 1984 0.074 1985 0.122 1986 0.102 0.125 1987 0.142 0.162 1988 0.223 0.080 0.161 1989 0.238 0.115 0.127 1990 0.236 0.173 0.161 1991 0.186 0.185 0.178 1992 0.170 0.160 0.153 1993 0.176 0.146 0.169 1994 0.209 0.153 0.119 1995 0.343 0.191 0.091 1996 0.408 0.136 0.155 1997 0.268 0.138 0.157 1998 0.168 0.088 0.188 1999 0.166 0.130 0.138 0.148 0.044 2000 0.213 0.154 0.137 0.142 0.075 2001 0.178 0.147 0.183 0.144 0.056 2002 0.149 0.183 0.137 0.135 0.082 0.494 2003 0.313 0.096 0.174 0.162 0.071 0.528 2004 0.451 0.168 0.188 0.142 0.122 0.518 2005 0.218 0.177 0.190 0.119 0.096 0.424 2006 0.233 0.147 0.310 0.139 0.112 0.438 Max 0.451 0.191 0.310 0.162 0.122 0.528 Min 0.074 0.080 0.091 0.119 0.044 0.424

Mean 0.217 0.145 0.164 0.142 0.082 0.480 SD 0.092 0.031 0.044 0.012 0.027 0.047


114

Appendix C-Chemical data

C-1_Methods and reagents used in the laboratory

Parameter Method Required Reagents Required Apparatus

Sulphate Sulphate ions react with barium in the SulfaVer 4 Sulphate Reagent and form insoluble barium sulphate turbidity. The amount of turbidity formed is proportional to the sulphate concentration.

SulfaVer 4 Sulphate Reagent Powder Pillows.

Sample Cell, 25 ml, matched pair

Phosphate

Orthophosphate reacts with molybdate in an acid medium to produce phosphomolybdate complex. Ascorbic acid then reduces the complex, giving an intense molybdenum blue colour

PhosVer3 Phosphate Reagent Powder

Test ‘N tube vials; COD vial Adapter, DR 2010; funnel; pipette, tensette, 1.0 to 10 ml; Pipet tips for 19700-10 tensette pipette; test

Nitrate, MR

Cadmium reduction method. Using a powder pillow, Nitrate in the sample reacts and a cadmium deposit will remain after the NitraVer 5 nitrate reagent powder dissolves. The Cd deposit will not affect the results. An amber colour will develop with a maximum

absorbance at 400nm

NitraVer 5 reagent A Test ‘N tubes, NitraVer 5 reagent powder pillow

Chloride

Chloride in the sample reacts with mercuric Thiocyanate to form mercuric chloride and liberate Thiocyanate ion. Thiocyanate ions react with the ferric ions to form an orange ferric Thiocyanate complex. The amount of this complex is proportional to the chloride concentration

Chloride reagent set, ferric ion solution, mercuric Thiocyanate solution, demineralised water

Pipet,volumetric, 1.0 ml; Pipet, volumetric, 2.0 ml, Pipet filler, safety bulb; Pipet, tensette, 0.1 to 1.0 ml; pipit tips, for 19700-01 tensette Pipet; sample cell, 25 ml, matched pair


115

C-2_Chemical data during field work (8-23, August 2007)

Location EC Temp PH TDS Ca+2 K+ Mg+2 Na+ Cl-(lab) SO4-2 NO3

- PO4-3 F- HCO3

-

E N µScm-1 0C mgl-1 meql-1 meql-1 meql-1 meql-1 meql-1 meql-1 meql-1 meql-1 meql-1 meql-1

326295 1283136 146 22.5 6 65.8 1.56 0.13 0.62 0.40 0.05 0.15 0.00 0.00 0.02 1.91

285556 1256983 32.1 23.4 5.2 17.0 0.50 0.11 0.12 0.20 0.03 0.15 0.01 0.00 0.02 0.42

313062 1263498 103.5 23 5 47.6 0.66 0.07 0.40 0.23 0.07 0.00 0.02 0.00 0.02 1.36

340428 1264627 65 21 5.5 31.1 0.49 0.11 0.32 0.23 0.12 0.09 0.00 0.00 0.02 0.85

273312 1214992 64.2 14.8 5 30.7 0.45 0.09 0.35 0.15 0.03 0.08 0.00 0.00 0.01 0.84

324639 1280884 527 23.6 6 229.2 2.75 0.11 3.67 0.57 0.01 0.29 0.04 0.01 0.01 6.91

332813 1285772 442 23.1 6 192.7 3.53 0.11 1.98 0.79 1.41 0.04 0.01 0.00 0.02 5.80

351403 1308822 608 22.8 6.5 263.9 4.25 0.15 2.21 2.00 0.03 0.20 0.01 0.01 0.02 7.97

359233 1319518 540 25.8 6.5 234.8 1.31 0.39 1.06 2.84 0.11 0.19 0.01 0.01 0.02 7.08

366967 1340389 328 22.4 7 143.9 1.88 0.15 1.33 0.93 0.18 0.14 0.01 0.00 0.02 4.30

332813 1285772 235.3 23.1 6 104.1 1.41 0.13 0.95 0.64 0.53 0.12 0.01 0.00 0.02 3.09

332748 1285660 374.1 21 5.5 163.6 2.11 0.16 1.52 1.07 0.00 0.15 0.01 0.00 0.02 4.91

332768 1285624 160.0 20 6 71.8 1.03 0.11 0.64 0.40 0.06 0.11 0.01 0.00 0.02 2.10

332056 1290960 152.6 21.5 5.5 68.7 0.99 0.11 0.61 0.38 0.05 0.10 0.01 0.00 0.02 2.00

336913 1296333 107.6 20.6 5.5 49.3 0.76 0.10 0.42 0.24 0.05 0.09 0.01 0.00 0.02 1.41

336956 1296387 152.1 26 5.5 68.5 0.99 0.11 0.60 0.38 0.05 0.10 0.01 0.00 0.02 2.00

342181 1301260 82.0 23.1 7 38.4 0.63 0.09 0.32 0.16 0.04 0.09 0.00 0.00 0.02 1.08

342186 1301202 123.4 26.7 6 56.1 0.84 0.10 0.49 0.29 0.05 0.10 0.01 0.00 0.02 1.62

342186 1301202 79.7 22.7 5.5 37.4 0.62 0.09 0.31 0.15 0.04 0.09 0.00 0.00 0.02 1.05

342186 1301202 82.5 22.9 5.5 38.6 0.64 0.09 0.32 0.16 0.04 0.09 0.00 0.00 0.02 1.08

342186 1301202 77.4 25.8 5.5 36.4 0.61 0.09 0.30 0.14 0.04 0.09 0.00 0.00 0.02 1.01

342086 1301185 121.5 25.3 5.5 55.3 0.83 0.10 0.48 0.28 0.05 0.10 0.01 0.00 0.02 1.59

348177 1307230 193.0 26.2 5.5 86.0 1.20 0.12 0.77 0.50 0.06 0.11 0.01 0.00 0.02 2.53

351403 1308822 93.6 21 5 43.4 0.69 0.10 0.36 0.20 0.04 0.09 0.00 0.00 0.02 1.23

359629 1325942 135.0 21 5.5 61.1 0.90 0.11 0.53 0.32 0.05 0.10 0.01 0.00 0.02 1.77

362801 1334143 689.0 24.3 6.5 298.6 3.71 0.22 2.81 2.05 0.14 0.22 0.01 0.01 0.02 9.04

362777 1334153 317.0 27 6.5 139.1 1.82 0.14 1.28 0.89 0.08 0.14 0.01 0.00 0.02 4.16

366219 1339280 287.7 24.7 6 126.6 1.68 0.14 1.16 0.80 0.08 0.13 0.01 0.00 0.02 3.77

367020 1340527 206.5 22.4 5.5 91.8 1.26 0.12 0.83 0.55 0.06 0.12 0.01 0.00 0.02 2.71

303952 1265045 94.0 21.9 5.5 43.5 0.70 0.10 0.36 0.20 0.04 0.09 0.00 0.00 0.02 1.23

303962 1264969 104.6 22.1 5.5 48.1 0.75 0.10 0.41 0.23 0.05 0.09 0.01 0.00 0.02 1.37

273231 1214897 56.7 17.1 5 27.5 0.51 0.09 0.21 0.08 0.04 0.08 0.00 0.00 0.02 0.74

272734 1215020 96.5 17 5 44.6 0.71 0.10 0.37 0.20 0.04 0.09 0.01 0.00 0.02 1.27

271586 1219595 43.5 17.6 5 21.9 0.44 0.09 0.16 0.04 0.04 0.08 0.00 0.00 0.02 0.57

285556 1256983 27.0 18 5 11 0.36 0.08 0.09 -0.01 0.03 0.08 0.00 0.00 0.02 0.35


116

Chemical data during field work (8-23, August 2007)

285678 1257115 31.3 18.4 5 13 3.86 0.23 2.93 2.14 0.15 0.23 0.01 0.01 0.02 9.43

271626 1249327 79.0 21 5.5 30 0.38 0.08 0.11 0.00 0.03 0.08 0.00 0.00 0.02 0.41

266964 1230779 13.5 15.3 5.3 5.6 0.62 0.09 0.30 0.15 0.04 0.09 0.00 0.00 0.02 1.04

267029 1230805 91.2 19.9 5 36 0.29 0.08 0.03 0.05 0.03 0.07 0.00 0.00 0.02 0.18

267168 1230953 19.0 15.8 5 7.5 0.68 0.10 0.35 0.19 0.04 0.09 0.00 0.00 0.02 1.20

283458 1200347 44.0 16.3 5.5 18 0.32 0.08 0.06 0.04 0.03 0.08 0.00 0.00 0.02 0.25

291455 1208935 32.0 20.7 5 13 0.44 0.09 0.16 0.04 0.04 0.08 0.00 0.00 0.02 0.58

291465 1208953 46.0 20 6 19.8 0.38 0.08 0.11 0.00 0.03 0.08 0.00 0.00 0.02 0.42

304440 1215238 160.5 16.7 5.5 67 0.45 0.09 0.17 0.05 0.04 0.08 0.00 0.00 0.02 0.60

303075 1213011 62.0 15.8 5 19 1.03 0.11 0.64 0.40 0.06 0.11 0.01 0.00 0.02 2.10

303075 1213011 34.4 15.8 5 14 0.53 0.09 0.23 0.10 0.04 0.08 0.00 0.00 0.02 0.81

304714 1214281 61.0 19 5 25 0.39 0.08 0.12 0.01 0.03 0.08 0.00 0.00 0.02 0.45

304714 1214281 61.0 17 5 24 0.53 0.09 0.23 0.09 0.04 0.08 0.00 0.00 0.02 0.80

304714 1214281 61.0 18.6 5 25 0.53 0.09 0.23 0.09 0.04 0.08 0.00 0.00 0.02 0.80

328759 1387917 141.0 20.3 5.5 58 0.53 0.09 0.23 0.09 0.04 0.08 0.00 0.00 0.02 0.80

331196 1380704 155.9 19.3 5.5 78 0.93 0.11 0.56 0.34 0.05 0.10 0.01 0.00 0.02 1.85

330033 1384286 369.8 24 6.5 150 0.83 0.10 0.54 0.25 0.05 0.10 0.01 0.00 0.02 2.04

328517 1384497 295.0 23.9 8 121 2.09 0.15 1.50 1.06 0.09 0.15 0.01 0.00 0.02 4.85

317960 1374260 114.3 20.5 5.5 47.5 1.71 0.14 1.19 0.82 0.12 0.13 0.01 0.00 0.02 3.87

315015 1371379 167.9 23.1 6 67 0.80 0.10 0.45 0.26 0.05 0.10 0.01 0.00 0.02 1.50

312777 1368477 267.0 26.8 6 110 1.07 0.11 0.67 0.43 0.06 0.11 0.01 0.00 0.02 2.20

308512 1366171 625.0 21.1 7.5 309 5.05 0.11 2.71 1.08 0.07 0.13 0.01 0.00 0.02 3.50

308527 1366191 505.0 25.4 6.5 201 3.38 0.21 2.55 1.85 0.13 0.21 0.01 0.01 0.02 8.20

315310 1353324 154.0 21 6 62 2.78 0.18 2.05 1.48 0.11 0.18 0.01 0.01 0.02 6.62

315310 1353324 147.3 21.1 6 61 1.00 0.11 0.61 0.38 0.05 0.10 0.01 0.00 0.02 2.02

314518 1352983 54.7 22.8 5 22 0.97 0.11 0.58 0.36 0.05 0.10 0.01 0.00 0.02 1.93

313461 1354030 62.1 23.2 5.5 25.4 0.50 0.09 0.20 0.07 0.04 0.08 0.00 0.00 0.02 0.72

312632 1354903 136.5 22.3 5.5 66 0.53 0.09 0.23 0.10 0.04 0.08 0.00 0.00 0.02 0.81

312632 1354903 496.8 25 7.5 202 0.91 0.11 0.54 0.33 0.05 0.10 0.01 0.00 0.02 1.79

324480 1283559 150.0 24.4 6 72 2.73 0.18 2.02 1.45 0.11 0.18 0.01 0.01 0.02 6.52

324900 1285160 146.0 25.4 6 71 0.67 0.07 0.34 0.25 0.05 0.10 0.00 0.00 0.00 1.97

324547 1286034 144.0 25.3 6 71 0.96 0.11 0.58 0.36 0.05 0.10 0.01 0.00 0.02 1.91

324179 1286705 143.0 25.7 6 70 1.62 0.14 0.62 0.40 0.06 0.10 0.01 0.00 0.02 1.89

322239 1290167 143.0 25.8 7 70 0.94 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.88

321596 1291257 144.2 26.9 5.5 70 0.94 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.88

320791 1291708 145.3 28 6 71 0.95 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.89

320906 1289952 143.9 27 5.5 71 0.95 0.11 0.58 0.36 0.05 0.10 0.01 0.00 0.02 1.91

321667 1288189 142.0 23.5 7.5 70 0.95 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.89

320499 1292617 145.4 25 5.5 71 0.94 0.11 0.56 0.35 0.05 0.10 0.01 0.00 0.02 1.86


117

Chemical data during field work (8-23, August 2007)

323455 1286465 142.0 25 6.5 69.9 0.96 0.11 0.58 0.36 0.05 0.10 0.01 0.00 0.02 1.91

321933 1287573 143.6 24.4 6 70 0.94 0.11 0.56 0.35 0.05 0.10 0.01 0.00 0.02 1.86

324901 1284098 146.7 24.5 6 72 0.95 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.88

313424 1285101 172.6 21.1 5.5 85 0.96 0.11 0.58 0.36 0.05 0.10 0.01 0.00 0.02 1.92

297302 1254766 73.7 26 6 37 1.09 0.11 0.69 0.44 0.06 0.11 0.01 0.00 0.02 2.26

285689 1257083 37.3 20 5.5 18 0.59 0.09 0.28 0.13 0.04 0.09 0.00 0.00 0.02 0.97

276290 1256578 102.0 24.8 5 50 0.41 0.08 0.13 0.02 0.04 0.08 0.00 0.00 0.02 0.49

276678 1269007 54.5 20.6 6 26 0.74 0.10 0.40 0.22 0.05 0.09 0.01 0.00 0.02 1.34

276668 1269056 90.0 33.3 6.5 44 0.50 0.09 0.20 0.07 0.04 0.08 0.00 0.00 0.02 0.71

276860 1268985 96.2 22.5 6.5 48 0.68 0.10 0.35 0.18 0.04 0.09 0.00 0.00 0.02 1.18

276917 1268938 84.4 21.6 5.5 41 0.71 0.10 0.37 0.20 0.04 0.09 0.01 0.00 0.02 1.26

280492 1302574 94.5 28.8 6 46 0.67 0.07 0.34 0.25 0.03 0.09 0.00 0.00 0.02 1.11

281498 1306475 225.0 24.2 6.5 110 0.70 0.10 0.37 0.20 0.04 0.09 0.00 0.00 0.02 1.24

285871 1314241 137.5 29 7 67 1.36 0.12 0.90 0.60 0.07 0.12 0.01 0.00 0.02 2.95 326295 1283136 141.3 22.8 6 70 0.92 0.11 0.54 0.33 0.05 0.10 0.01 0.00 0.02 1.80 326295 1283136 140.6 22.7 6 69 0.93 0.11 0.56 0.34 0.05 0.10 0.01 0.00 0.02 1.85 326295 1283136 143.5 22.5 6 70.5 0.93 0.11 0.56 0.34 0.05 0.10 0.01 0.00 0.02 1.84 416521 1297374 192.2 23.5 6.5 94 0.95 0.11 0.57 0.35 0.05 0.10 0.01 0.00 0.02 1.88 415365 1307580 189.5 23.5 6.5 93 1.19 0.12 0.77 0.50 0.06 0.11 0.01 0.00 0.02 2.52

C-3_Geological Survey of Ethiopia

EC HCO3-1 Cl-1 SO4

-2 F-1 NO3-1 Na+1 K+ Ca+2 Mg+2 HBO2 SiO2 CO2 PH TDS

Field No N E µScm-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1

BH 12˚34'25" 37˚26'13" 726 411 31 19 0.17 0.44 57 1 60 28 1.16 39.4 20 7.59 608.77

BH 12˚36'16" 37˚29'07" 276 144 11 1 0.2 1.33 8 10 21 10 <0.14 18 7 7.65 206.53

BH 12˚36'35" 37˚27'24" 840 459 40 20 0.22 <0.04 160 1 35 3 1.07 46.2 5 8.11 719.29

DW 12˚36'49" 37˚28'06" 884 248 65 67 0.19 95.2 49 16 80 25 0.35 37.2 21 7.31 645.74

DW 12˚39'11" 37˚28'19" 515 237 17 12 0.1 46.5 11 0.2 55 22 <0.14 46.6 15 7.52 400.8

DW 12˚44'44" 37˚24'44" 340 212 2 1 0.08 6.65 11 1.3 44 12 <0.14 64.4 20 7.26 290.03

DW 12˚40'23" 37˚30'20" 396 224 8 5 0.09 11.5 16 0.3 40 13 <0.14 47.1 21 7.27 317.89

DW 12˚38'39" 37˚21'29" 216 132 1 2 0.07 3.99 11 2 20 8 0.68 33.8 39 6.72 180.74

CS 12˚36'29" 37˚28'19" 1682 253 135 73 0.12 476 35 3.3 230 55 3.51 60.3 42 6.97 1263.93

CS 12˚36'12" 37˚27'32" 2560 289 283 131 0.1 609 70 2.3 290 85 <.013 54.8 37 7.71 1759.4

CS 12˚38'02" 37˚25'51" 374 237 2 <1 0.11 8.86 9 0.7 43 15 <.013 45.8 9 7.72 315.67

CS 12˚42'21" 37˚27'08" 40 26 <0.5 <1 0.05 0.89 4 2 2 0.5 3.11 25.3 8 6.71 38.55

CS 12˚44'17" 37˚25'18" 144 89 <0.5 <1 0.08 <0.04 8 0.8 13 4 <.013 36.8 15 7.01 114.88

CS 12˚45'06" 37˚24'29" 369 250 <0.5 <1 0.15 1.33 14 2 34 13 0.19 43.7 10 7.73 314.67

CS 12˚41'04" 37˚29'53" 240 154 <0.5 <1 0.11 1.77 9 0.2 23 8 <.013 52.2 5 7.91 196.08

CS 12˚43'20" 37˚30'34" 209 113 1 <1 0.09 11.5 6 0.8 25 8 0.79 27.8 12 7.16 166.18

CS 12˚39'00" 37˚23'26" 207 129 <0.5 <1 0.08 3.1 8 1.4 20 7 0.47 41.1 10 7.39 169.05


118

CS 12˚38'35" 37˚19'12" 462 173 35 8 0.07 44.3 13 2 55 16 0.97 36.4 23 7.08 347.34

CS 12˚38'30" 37˚25'27" 285 181 <0.5 <1 0.1 1.33 11 0.4 30 11 <.013 24 11 7.48 234.83

CS 12˚39'35" 37˚24'59" 319 182 2 <1 0.07 14.2 10 1.4 34 13 0.3 33 27 7.03 256.97

C-4_Hydrochemical data by Kebede et al., (2005)

EC pH TDS K-1 Mg+2 Na-1 Ca+2 HCO3-1 SO4-2 Cl-1 F-1

Source N E

µScm-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1 mgl-1

mgl-

1

CS 1281517 312595 329.99 6.1 231 1.2 10.8 8.4 18.6 130 0.8 1.0 CS 1299941 421690 443.397 6.8 310 11.0 8.1 14.8 37.0 85 18.9 27.6 0.1 CS 1230501 568448 415.305 7.5 291 0.4 7.9 16.6 40.2 213 4.4 4.0 0.2 CS 1212945 584265 1008.59 7.5 706 0.8 38.0 73.5 54.7 480 15.9 12.8 CS 1350000 351500 201.916 7.2 141 1.5 1.5 9.0 21.4 103 1.4 2.3 0.1 CS 1215213 305120 96.7059 7.7 68 1.0 2.5 1.9 8.6 45 0.9 0.6 DW 1230922 328472 565.514 7.5 396 1.1 17.5 25.5 46.5 244 0.1 11.3 0.4 DW 1349950 269459 268.4 6.7 188 3.0 3.7 13.0 28.9 122 0.1 17.1 0.1 DW 1311750 392680 284.586 7.0 199 4.5 7.8 18.0 20.0 143 0.1 4.0 0.1 DW 1387900 334563 440.943 8.2 309 5.0 5.0 40.8 25.7 195 23.1 11.3 0.5 DW 1382400 334470 608.971 8.2 426 0.7 1.0 110.0 4.8 293 0.1 14.0 0.0 DW 1386300 334400 496.871 8.2 348 0.7 10.0 43.0 32.0 244 0.1 14.0 0.0 DW 1387000 329990 659.914 8.2 462 1.2 1.0 120.0 5.5 317 0.1 14.0 0.0 DW 1388000 329980 527.414 8.6 369 1.3 8.8 78.2 12.8 244 0.1 10.6 0.4 DW 1270131 275060 370.329 8.0 259 1.4 4.9 48.4 13.2 183 0.1 7.1 0.1 DW 1281000 324990 1275.39 7.8 893 7.3 31.1 160.5 25.7 634 0.1 24.1 0.5 DW 1313579 285598 464.952 6.7 325 1.1 14.8 10.1 31.2 200 0.8 3.6 0.2 DW 1281000 324500 274.783 6.4 192 0.4 11.3 5.9 16.6 98 5.0 4.4 0.0 DW 1245100 332000 658.393 7.1 461 1.6 18.3 21.6 65.7 281 0.1 13.6


119

groundwater contribution and recharge estimation … · groundwater contribution and recharge...

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