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MODELING RAINFALL-RUNOFF RELATIONSHIP AND ASSESSING IMPACTS OF SOIL CONSERVATION RESEARCH PROGRAM INTERVENTION ON SOIL PHYSICAL AND CHEMICAL PROPERTIES AT MAYBAR RESEARCH UNIT, WOLLO, ETHIOPIA

A Thesis

Presented to the Faculty of the Graduate School

of Cornell University

in Partial Fulfillment of the Requirements for the Degree of

Masters of Professional Studies

By

Haimanote Kebede Bayabil

August 2009

© 2009 Haimanote Kebede Bayabil

ABSTRACT

This study focuses on characterizing subsurface water flow and ground water table fluctuations in response to rainfall that leads to saturation excess runoff, the basic principle of variable source area hydrology. In particular, this study concentrates to develop a model that efficiently simulates the location of saturated runoff areas and predict river discharge, which finally could help in realistic planning of watershed interventions. Furthermore, the study assesses the impact of soil conservation research program intervention on selected physical and chemical soil properties of the study area. Long-term discharge and rainfall data was available at the watershed outlet and for four test plots. In addition, 29 piezometers were installed in 2008 and water table measurements were taken during the main rainy season. Based on major runoff mechanisms identified at the catchment-level, a conceptual rainfall-runoff model was developed to compute runoff. The model incorporates saturated excess overland flow from both bottomlands and subsoil exposed areas and baseflow and interflow from the hillsides. The model was tested on a daily, weekly, and monthly basis and fitted well the discharge data at the bottom of the watershed. In addition, the distributed model output agreed well with the ground water table measurements. The watershed was saturated (and produced runoff) in the flat areas near the river while the hillsides were unsaturated with a perched water table that responded rapidly to rainfall. Data from test plots showed that flatter areas produced more runoff than test plots at steeper slope areas. The model has potential to predict runoff in ungauged basins but should be further tested to do so. On the other hand, soil samples were tested for selected physical and chemical properties. The result indicated that AP and % OC contents of the soil were found in lower amount than before/early project intervention period, while the Db value has shown an increase.

BIOGRAPHICAL SKETCH

Haimanote Kebede was born and raised in East Gojjam, in 1981. After successfully completing his secondary school study at Debre Markos C.S.S. School, he joined Alemaya Universiy in 1999. During his four-year stay at university, he studied plant science and received his Bachelor degree in June 2003.

In September 2003, he was employed by Elfora Agro Industries P.L.C. where he served as Junior Agricultural Expert for eight months. Then in May 2004, he moved to Finchaa Sugar Factory as a Plantation Section Manager and was assigned to manage a plantation section, including the 1300-hectare farm cultivated under irrigation and all the working staff under this section.

In April 2005, he had an opportunity to participate in a training entitled ‘Sugarcane Micro-Propagation Techniques’ conducted in Havana, Cuba for six months.

After his return to Ethiopia, he continued to work at the Finchaa Sugar Factory until he left the organization in November 2007 for his Masters degree.

ACKNOWLEDGMENTS

First and for most I am ever grateful to the Almighty God, without his support and blessings, this piece of work would never have been accomplished.

My sincere gratitude goes to my major advisor Prof. Tammo. S. Steenhuis, from Cornell University, USA, for his sympathy, encouragement, endless patience, and strong belief in me. He has been sensitive and softhearted; he always tried to take care of every problem I had during my entire study and research period. He was more like a friend than just a professor.

Dr. Amy S. Collick was so wonderful. She helped me organize things, gave me valuable ideas, edited my manuscript, and was always in the front line to help me during hard times. Thank you so much.

Very special thanks go to my co-advisor Dr. Ingr. Sileshi Bekele, East Africa Director of International Water Management Institute (IWMI), for his constructive ideas, encouragement, and worthy comments.

I am thankful to ARARI, for allowing me to work at the Maybar research site and providing long term hydrological and sediment data.

The support and help I got from Mr. Derese G / Wold, former SCRP staff, was more than I could express; he was always generous, cooperative, and friendly. He is an amazing person with never changing smile.

International Water Management Institute and Bahir Dar University are acknowledged for their financial support and Dr Ayalew Wondie helped me in facilitating the financial issues with Bahir Dar University finance division.

I am also greatly indebted to the technicians at Maybar Research Station: Gash Ali Ahmed, Seid Hussien, and Seid Belay. They were welcoming and allowed me to share everything they have. The life experience I got from them was invaluable.

I would also like to thank my family for the unconditional love and support they provided me throughout my life and in particular, I must acknowledge my younger sister Nitsuh Kebede, who has always believed in my potentials, and she was the reason and my strength to join this program.

Finally, I would like to express my gratitude for all my friends, who have been helping and encouraging me by telephone and e-mail during the study and thesis writing periods.

TABLE OF CONTENTS

BIOGRAPHICAL SKETCH iii

ACKNOWLEDGMENTS v

TABLE OF CONTENTS vii

LIST OF FIGURES x

LIST OF TABLES xii

LIST OF ABBREVIATIONS xiii

1. CHAPTER ONE 1

RESEARCH BACKGROUND 1

STUDY AREA 3

Location and Topography 3

Soils 5

Agro Climate, Land Use, and Cropping Pattern 5

REFERENCES 9

2. CHAPTER TWO 10

INTRODUCTION 10

MODEL DEVELOPMENT 12

MATERIALS AND METHODS 16

Discharge from Runoff Plots 17

Saturated Area Delineation 20

Data Checking and Analysis 20

MODEL EFFICIENCY EVALUATION 20

Calibration and Validation of Rainfall – Runoff Model 20

RESULTS AND DISCUSSION 21

Rainfall Amount, Intensity and Infiltration Capacity 21

Rainfall Intensity and Soil Infiltration Rate 23

Runoff from Test Plots 25

Groundwater in the watershed 28

Ground water level at different slope range 30

Ground water level at different land use areas 31

Simulating Watershed Discharge 32

Calibration and simulation 33

Runoff Source Area in the watershed 43

CONCLUSION 45

REFERENCES 46

3. CHAPTER THREE 51

INTRODUCTION 51

RESEARCH METHODS 52

Soil Sampling Techniques, Site Selection, and Sample Preparation 52

Laboratory Analyses 53

Soil Chemical Property Analyses 53

Organic Matter Content (%OC) 54

Available Phosphorous (AP) 54

Soil Physical Property Analysis 55

Bulk Density Determination (Db) 55

Statistical Analysis 56

RESULTS AND DISCUSSION 56

Available Phosphorous (AP) 56

Percentage Organic Carbon (% OC) 57

Bulk Density (Db) 58

CONCLUSION 59

REFERENCES 61

APPENDICES 63

LIST OF FIGURES

Figure 1-1: Digital terrain map of the Maybar Watershed. Low elevation at the southern end of the watershed, near the outlet, is indicated by blue while high elevation is indicated by red. 4

Figure 1-2: Soil map of Maybar watershed (Source: Weigel, 1986) 5

Figure 1-3: Mean annual rainfall, river discharge, and suspended sediment yield. 7

Figure 1-4: Long term daily climate record 8

Figure 1-5: Land use map of Maybar watershed (2008 cropping calendar 2nd crop) 8

Figure 2-1: Structure of the conceptual water balance model by Steenhuis et al. (2008) 13

Figure 2-2: Location of piezometer transects at different slope range in the watershed. 19

Figure 2-3: Long-term rainfall amount and distribution 22

Figure 2-4: Long-term average annual hydrograph 23

Figure 2-5: Long-term rainfall amount and intensity values 24

Figure 2-6: Infiltration test results (Source: Derib, 2005) 25

Figure 2-7: Average annual plot runoff and rainfall values 26

Figure 2-9: Plot runoff coefficients at different slope gradients 27

Figure 2-10: Comparison of simulated runoff from saturated area and plot runoff (Plot-1) on daily basis. 28

Figure 2-11: Comparison of average response of piezometers to rainfall from upper and lower watersheds. 30

Figure 2-12: Water level at different slope ranges calculated above the impermeable layer. 31

Figure 2-13: Water levels at different land use types 32

Figure 2-14: Comparison of daily model calibration simulated and measured discharge 35

Figure 2-15: Scatter plot of daily model calibration simulated and measured discharge 36

Figure 2-16: Comparison of daily model validation simulated and measured discharge against rainfall amount 36

Figure 2-17: Scatter plot of daily model validation simulated and measured discharge result 37

Figure 2-18: Comparison of weekly model calibration output 38

Figure 2-19: Scatter plot of weekly model calibration simulated and measured discharge results 39

Figure 2-20: Comparison of weekly model validation simulated and measured discharge against rainfall amount 39

Figure 2-21: Scatter plot of weekly model validation simulated and measured 40

Figure 2-22: Comparison of monthly model calibration simulated and measured discharge against rainfall amount 41

Figure 2-23: Scatter plot of monthly model calibration simulated and measured discharge values 41

Figure 2-24: Comparison of monthly model validation simulated and measured discharge results 42

Figure 2-25: Scatter plot of monthly model validation simulated and measured discharge results 42

Figure 2-26: Map of runoff source area 44

LIST OF TABLES

Table 1-1: Watershed characterization based on slope (Source: Weigel, 1986) 4

Table 1-2: Soil labels and their descriptions (Source: Weigel, 1986) 6

Table 1-3: Soil types and their area share 6

Table 2-1: Slope range, runoff coefficient, and land use type of test plots 27

Table 2-2: Optimized values of model parameters 33

Table 2-3: Summary of data used during modeling and model efficiency results for three time setups 38

Table 3-1: Statistical analysis result for AP 57

Table 3-2: Average AP, %OC, and Db values for different land use areas. 57

Table 3-3: Statistical analysis result for (%OC) 58

Table 3-4: Statistical analysis result for (Db) 59

LIST OF ABBREVIATIONS

ARARI: Amhara Regional Agricultural Research Institute

EIAR: Ethiopian Institute of Agricultural Research

SCRP: Soil Conservation Research Program

SDC: Swiss Agency for Development and Cooperation

TP: Test Plot

UNDP: United Nations Development Program

WDR: World Development Report

CHAPTER ONE

RESEARCH BACKGROUND AND STUDY AREA

RESEARCH BACKGROUND

In the 21st century, agriculture continues to be fundamental to the overall economy, food security, and poverty reduction in Sub-Saharan Africa countries (WDR, 2007). In Ethiopia, agriculture is mainly rain-fed, traditional and small scale with low inputs, which often leads to low crop productivity and yield. Furthermore, Ethiopia’s low crop productivity is further aggravated by water shortage due to scarce rainfall and land degradation caused by excessive soil erosion.

Worldwide awareness of water scarcity has put an emphasis on finding better approaches to meet water demand (Anonymous, 2000b quoted by Bastiaanssen et al., 2003) and reduce erosion (Nyssen et al., 2008). Soil erosion and water scarcity are the major problems in the Ethiopian highlands, affecting the livelihoods of millions as the associated sedimentation and flooding or drought cause additional problems for downstream populations.

The majority of the Ethiopian human and livestock population reside in the Ethiopian highlands where soils are degraded due to exacerbated soil erosion reaching up to 400 tons/hectare/year (UNDP, 2002). Increasing population pressure coupled with declining land productivity has led to a demand for additional food production. To meet the demand, all land types, irrespective of their suitability, are intensively cultivated using poor management practices. In the period between 1950 and 2000, the population in the Ethiopian highlands was estimated to have increased nearly four times from about 16 million to about 65 million (Hurni et al., 2005). In addition to excessive population pressure, the rain-fed, low-input subsistence agriculture of the highlands is further worsened by erratic and unpredictable rainfall resulting in drought or flood conditions. The rainfall in the highlands ranges from very little rain creating extreme drought conditions to excessive rainfall producing floods. Both extremities result in severe crop damage and sometimes complete crop failure. As a result, the Ethiopian highlands have become very fragile, sensitive to slight environmental changes, and food insecure.

In Maybar, located in the northeastern escarpment of the central highlands of Ethiopia with attributes similar to the other highland areas in the country, farming practices are suffering from severe land degradation and acute water scarcity problems. Taking these problems into consideration, the Soil Conservation Research Program (SCRP) was implemented in 1981 by the Ethiopian Ministry of Agriculture (MoA) in collaboration with the University of Berne, Switzerland and with the support of the Swiss Agency for Development and Cooperation (SDC). Under this program which lasted from 1981-1987, a total of seven research sites were established with the Maybar research station being SCRP’s first research site (SCRP, 2000). The underlying objective was to provide measures that could be implemented to alleviate the aggravated land degradation and water scarcity problems. During the implementation, soil and water conservation measures, such as physical structures, area closures and biological structures, were put in place through a “food for work” campaign.

Since the establishment of the site, fine resolution data on climate, hydrology and suspended sediment, from both river and test plots, has been collected and an expansive database was established that serves as a data source to carry out hydrological, soil erosion, and conservation research activities at regional, national, and international levels.

The data collected in this watershed has been analyzed by the Amhara Regional Agricultural Research Institute (ARARI) and the Ethiopian Institute of Agricultural Research (EIAR) researchers, national and international students at Masters and PhD levels, and other researchers. The research activities, under taken in the watershed, differ both spatially and temporally depending on the objectives and intended outcomes.

This study analyzes the data of the Maybar watershed, but it bases the analysis on specific hydrological processes, specifically from the perspective of variable source area hydrology that relies on saturation excess runoff mechanism. To aid with the analysis, 29 piezometers were installed and ground water levels of the area were measured during the main rainy season of 2008. Furthermore, this study includes the impact assessment of the soil conservation research program intervention on selected physical and chemical soil properties of the study area.

This thesis has three chapters. This chapter gives insight into the complete research project and provides detailed information about the research site. Chapter Two focuses on the hydrological modeling that incorporates the explanation of the major hydrological processes, identification of the major runoff mechanisms, and determination of the runoff sources and recharge areas in the watershed. This information was further used to model the rainfall-runoff relationships in the area. Finally, Chapter Three addresses the impact assessment of SCRP interventions on selected physical and chemical properties of soils of the Maybar Watershed.

STUDY AREA

Location and Topography

The study area consists of the Kori Sheleko catchment, which is found in the Maybar Watershed. It is the first of the SCRP research sites established and is located in the northern eastern part of the central Ethiopian highlands situated in the Southern Wollo administrative region, approximately 20 km south-southeast of Dessie town. The gauging station lies at 39o39’E and 10o51’N. The area is characterized by highly rugged topography with steep slopes ranging between 2530 and 2860 meters above sea level (masl), a 330 meter altitude difference within a 112.8 ha catchment area (Figure 1-1). Steep and very steep slope areas (> 25% slope) cover about 74% of the watershed. Table 1-1 defines and describes the slope classes, their area coverage and percent share of the watershed.

Figure 1-1: Digital terrain map of the Maybar Watershed. Low elevation at the southern end of the watershed, near the outlet, is indicated by blue while high elevation is indicated by red.

Table 1-1: Watershed characterization based on slope (Source: Weigel, 1986)

|Slope class | |

|[%] |[o] |Description |Area (ha) |Coverage (%) |

|6.1-13.0 |3.5 – 7.4 |Sloping |6.8 |6 |

|13.1 – 25.0 |7.5 – 14.0 |Moderately steep |22.5 |20 |

|25.1 – 55.0 |14.1– 28.8 |Steep |42.9 |38 |

|>55.0 |> 28.8 |Very steep |40.6 |36 |

Soils

The soil types in Maybar research unit have developed from the alkali-olive basalts and tuffs of the Ashangi group, which are part of the tertiary volcanic trap series (Weigel, 1986). Figure 1-2 includes the soils map of the Maybar Watershed, and Table 1-2 defines the soil labels found in the map’s legend. Table 1-3 clearly indicates that the watershed area is dominated by shallow depth soils classified as phaeozems and phaeozems associated with lithosols and covering more than 93% of the total area in the watershed.

Figure 1-2: Soil map of Maybar watershed (Source: Weigel, 1986)

Agro Climate, Land Use, and Cropping Pattern

The Maybar research watershed receives an average annual rainfall of 1370mm, of which only 1148 mm is effective rainfall (rainfall contributing directly to runoff and recharge), and has an average annual river discharge of 407 mm. The mean annual suspended sediment rate was estimated to be 951 tons/year, which is approximately 8.4tons/ha/year. Figure 1-3 provides a graphical representation of the annual rates of rainfall, discharge, and sediment yield from 1989 to 2004.

|Soil type |Label |Soil mapping units |Descriptions |

|Phaeozems |a |Hh1ls |Hapllic phaeozems very shallow (10-25 cm), very stony, (sandy) |

|associated with | | |clay loams. |

|Lithosols | | | |

| |b |Hh2ls |Hapllic phaeozems shallow to very sahllow (10-50 cm) stony |

| | | |phase, (sandy) clay loams. |

|Phaeozems |c |Hh2s |Hapllic phaeozems shallow (25-50 cm) stony phase, clay loams. |

| |d |Hh3s |Hapllic phaeozems moderately deep (50-100 cm) stony phase, |

| | | |(sandy) clay loams. |

| |e |Hh4s |Hapllic phaeozems deep to very deep (> 100 cm) stony phase, |

| | | |(sandy) clay loams. |

|Regosols |g |Re2s |Eutric regosols ver shallow to deep (10-100 cm) stony phase, |

| | | |clay loams. |

| |h |Gm1w |Mollic Gleysols water table during growing periods with in < 20 |

| | | |cm of the surface, clay loams. |

|Gleysols | | | |

| |i |Gm2v |Mollic Gleysols water table during growing periods with in 20-50|

| | | |cm of the surface, clay loams. |

Table 1-2: Soil labels and their descriptions (Source: Weigel, 1986)

Table 1-3: Soil types and their area share

|Soil type |Area (ha) |Share (%) |

|Phaeozems associated with Lithosols |63.2 |56.0 |

|Phaeozems |42.4 |37.6 |

|Gleysols |2.8 |2.5 |

|Fluvisols |2.5 |2.2 |

The Kori River, the main river in Kori Sheleko catchment in Maybar, is the main inlet to Lake Maybar, which is approximately 0.5 km below the gauging station. The whole of the Maybar Watershed drains to the Borkena River, ultimately flowing to the Awash River basin, a subcatchment of the central Ethiopian Rift Valley.

Figure 1-3: Mean annual rainfall, river discharge, and suspended sediment yield.

The area is typical for the “Dega” thermal zone with an average daily temperature of 16 OC. The rainfall pattern commonly follows a bi-modal distribution (Figure 1-4): the first rainy season, the shorter of the seasons, around mid-March to April and the second often begins around June/July and ends usually in September. The Maybar area is known to be a low agricultural potential, intensively cultivated, oxen-ploughed cereal belt of the north-eastern escarpment region of the central Ethiopian highlands (Boshart, 1997).

According to Hurni et al. (2005), approximately 60% of the total catchment area is cultivated whereas 20% is woodland and the remaining 20% is grassland (Figure 1-5). There exists two cropping seasons and the predominant crops are cereals and maize, hence there exists two cropping seasons: the first, “Belg”, is the small rainy season in spring and the second, “Kremt”, the main rainy season during the summer and autumn. During the “Belg” season cereals are predominantly planted while in the “kremt” season pulses are dominant (SCRP, 2000).

Figure 1-4: Long term daily climate record

Figure 1-5: Land use map of Maybar watershed (2008 cropping calendar 2nd crop)

REFERENCES

Bastiaanssen, W.G.M. and L. Chandrapala, 2003. Water balance variability across Sri Lanka for assessing agricultural and environmental water use, Agricultural Water Management 58(2)171-192

Bosshart, U. 1997. Measurement of River Discharge for the SCRP Research Catchments: Gauging Station Profiles. Soil Conservation Research Programme, Research Report 31, University of Berne, Switzerland.

SCRP. 2000. Area of Maybar, Wello, Ethiopia: Long-term Monitoring of the Agricultural Environment.1981-1994. Soil Conservation Research Programme, University of Berne, Switzerland.

Hurni H., Tato K., Zeleke G.2005. The Implications of Changes in Population, Land Use, and Land Management for Surface Runoff in the Upper Nile Basin Area of Ethiopia. Mountain Research and Development. 25(2)147-154.

Nyssen J., Poesen J., Deckers J. 2008. Land degradation and soil and water conservation in tropical highlands. Soil & Tillage Research,article in press. Journal homepage: locate/still.

United Nations Development Program. 2002. Human Development Report. Oxford University Press, Inc. 198 Madison Avenue, New York, New York, 10016.

Weigel,G. 1986. The Soils of Maybar Area. Soil Conservation Research Programme (SCRP), Report no. 7. Berne, Switzerland: University of Berne.

World Development Report. 2007. Agriculture for development. 1818 H Street. NW. Washington D.C 20433.

CHAPTER TWO

MODELING RAINFALL - RUNOFF RELATIONSHIPS ATMAYBAR RESEARCH UNIT: WOLLO, ETHIOPIA

INTRODUCTION

The increasing acuteness of water scarcity problems worldwide requires efforts towards revising and mitigating the approaches of water supply and demand (Bastiaanssen et al., 2003). In areas where water is scarce, efficient use of all water resources, surface and ground water, is important. Groundwater and surface water are not isolated components of a watershed hydrologic system, but instead interact in a variety of physiographic and climatic landscapes (Sophocleous, 2002). The interactions between groundwater and surface water and the resulting exchange fluxes are often characterized by high temporal and spatial variability. Commonly the type of interaction is classified by the direction of the exchange fluxes: influent (flowing in) fluxes and effluent (flowing out) fluxes (Kalbus et al., 2006; and Zehe, 2007).

There is a real need for improved concepts to determine the source and timing of flow by studying the drainage morphology: from such knowledge watersheds can be evaluated as intermediaries of water flow, and future behavior under specific conditions may be predicted with greater precision (Hewllet and Hibbert, 1963). Efficient prediction of quantitative runoff and river flow occupies a central place in the technology of applied hydrology (Nash and Sutcliff, 1970; Calvo, 1986; Hosking and Clarke, 1990; and Cabus, 2008) since these values are useful to avoid risk for water resource planning, flood forecasting, pollution control and many other applications.

The modeling of rainfall-runoff relationships is not a simple task. It requires sufficient knowledge and good understanding of the hydrological processes, rainfall characteristics, runoff mechanism, and the identification of runoff source areas within the watershed, which in turn are determined by the physical properties of the basin (Shakya and Chander, 1998; and Gomi et al., 2008). If the relationships between these properties and the hydrological behavior could be defined, the hydrological responses of basins could be easily predicted (Acreman and Sinclair, 1986).

Total rainfall falling in a given area will not be directly converted to runoff because before runoff is generated rainfall has to pass different steps (Hewllet and Hibbert, 1966; Wang et al., 1992; and Huang et al., 2008). As a result, the rainfall – runoff relationship in watersheds is non-linear (Szilagyi, 2007; and Leh et al., 2008).

Generally, there are two types of runoff mechanisms: saturation excess runoff and infiltration excess (Hortonian) runoff (Kubota and Sivaplan, 1995; Sen et al., 2008; and Wickel et al., 2008). Saturation excess runoff volume is dependent on the aerial extent of saturation within a watershed and the rainfall depth, but it is independent of rainfall intensity. In contrast, infiltration excess runoff volume is directly dependent on rainfall intensity and will not occur at low intensities (Walter et al., 2000). Identification of runoff generation processes within the watershed requires close observations and detailed investigations, but characterization of dominant runoff processes is not an easy task, especially when such processes occur below the soil surface (Beven, 1989 quoted in Latron and Gallart, 2008).

Computer-based rainfall-runoff models at different resolutions have been developed for several decades (Jayakrishnan et al., 2005) with the objective of elucidating the complex and dynamic hydrologic processes and simulating runoff and river discharge from watersheds throughout the world. Most of the models attempt to simulate the complex hydrological processes that lead to the transformation of rainfall into runoff, with varying degree of abstraction from different physical processes (Jacquin and Shamseldin, 2006). The models differ not only in their level of complexity, but also in their level of applicability, efficiency, and specific data requirements. The efficiency of all the models that simulate the amount of runoff from a given rainfall depends on the ability of the model to simulate, all factors that affect the rainfall-runoff process in a given area (Jacquin and Shamseldin, 2006).

Although hill slopes are responsible for generating 95% of the water in the streams (Shakya and Chander, 1998), hill slope hydrologic response to rainfall is not well studied (Meerveld and Weiler, 2008). Most of early models describing rainfall runoff processes relied on Horton’s (1933) infiltration excess principle (Shakya and Chander, 1998), but the Horton concept failed to predict runoff on vegetated hill slopes (Meerveld and Weiler, 2008). To better simulate runoff from hills slopes, Hewlett (1961) introduced the variable source area concept, which is based on saturation excess runoff mechanism.

In Ethiopia, saturation excess overland flow has been identified as one of the mechanisms for generating storm flow (Lui et al., 2008). This study in the Ethiopian highlands focuses on characterizing subsurface water flow and ground water table fluctuations in response to rainfall that leads to saturation excess runoff. In particular, based on these processes, the goal is to develop a model that efficiently simulates the location of saturated runoff areas and predict river discharge. The results of this study will help in realistically planning watershed interventions.

MODEL DEVELOPMENT

Conceptual Watershed model: Watersheds in the Ethiopian highlands are characterized by relatively flat bottomlands and gentle to steep sloping uplands. In our conceptual watershed model, the watershed is divided into two areas, based on slope steepness, soil depth, and infiltration capacity of the soil: runoff source areas near the river and recharge source areas on the hills. The runoff source area was further divided in to two sub-groups based on relative difference in soil depth and amount of moisture required to initiate runoff.

Figure 2-1 illustrates the structure of the watershed model developed in this study. The basic assumption made was that hill slope areas have very high infiltration capacities and all the rainfall above field capacity percolates downward due to gravity. On the other hand, the excess rainfall when the soil is saturated from runoff source areas (flatter areas) becomes overland flow. In addition the flatter areas remain wet even during the extreme dry months of the year, only the top most soil layer will dry due to small amounts of water percolating downward from the hills. And hence these areas need only a small amount of rainfall, to start generating surface runoff.

Figure 2-1: Structure of the conceptual water balance model by Steenhuis et al. (2008)

Model Description: A water balance model was modified from the model in Collick et al. (2008) for small watersheds in the upper Blue Nile basin and in Steenhuis et al. (2008) for the whole Blue Nile basin. The basic inputs to the model are daily precipitation and potential evapotranspiration. Model outputs include daily runoff, interflow, and base flow according to the type and proportion of area under consideration within the watershed.

The amount of water stored in the topmost layer (root zone) of the soil, S (mm), for hill slopes and the runoff source areas were estimated separately with a water balance equation of the form:

Where P is precipitation, (mm d-1); AET is the actual evapotranspiration, (mm d-1), St-Δt, previous time step storage, (mm), R saturation excess runoff (mm d-1), Perc is percolation to the subsoil (mm d-1) and Δt is the time step.

During wet periods when the rainfall exceeds potential evapotranspiration, PET (i.e., P>PET), the actual evaporation, AET, is equal to the potential evaporation, PET. Conversely, when evaporation exceeds rainfall (i.e., P ................
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