DISCHARGE AND SEDIMENT YIELD MODELING IN...

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DISCHARGE AND SEDIMENT YIELD MODELING IN ENKULAL WATERSHED, LAKE TANA REGION, ETHIOPIA A Project Paper Presented to the Faculty of the Graduate School Of Cornell University In Partial Fulfilment of the Requirements for the Degree of Masters of Professional Studies By Bezawit Adane Demisse August 2011

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DISCHARGE AND SEDIMENT YIELD MODELING IN ENKULAL

WATERSHED, LAKE TANA REGION, ETHIOPIA

A Project Paper

Presented to the Faculty of the Graduate School

Of Cornell University

In Partial Fulfilment of the Requirements for the Degree of

Masters of Professional Studies

By

Bezawit Adane Demisse

August 2011

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© 2011 Bezawit Adane Demisse

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ABSTRACT

Land degradation is a major watershed problem causing significant loss of soil

fertility and productivity in the Ethiopian highlands. Soil erosion is one form of land

degradation. To develop effective erosion control plans and to achieve reductions in

sedimentation, it is important to quantify the sediment yield and identify areas that are

vulnerable to erosion. The objective of this study was to formulate sustainable land

management options that alleviate soil erosion. The study was conducted in a small

watershed located about 80 km North East of Bahir Dar.

The runoff depth was measured and sediment sampling was performed during the

main rainy season of 2010. Twenty-three piezometers were installed and water level

measurements were taken for a 5 month period. In addition, infiltration rates were

measured. A simple saturation excess water balance model was used to simulate the

flow and sediment processes in the watershed and to identify runoff and sediment

source areas. The watershed landscape was divided into saturated, degraded and hill

slopes areas to understand the hydrologic behavior. Finally, the model output was

compared to sediment and runoff data observed at the outlet of the watershed. The

model predicted the daily stream flows and sediment concentration reasonably well.

Twenty-two percent of the watershed consisted of degraded area as the only sources

of surface runoff and sediment. Group discussion discovered that surface runoff from

the lower degraded watershed was the major cause of soil erosion. This was in

agreement with infiltration test measurements, which indicated that infiltration rates

exceeded rainfall rates. Infiltration and recharge were greater on the steep slope

compared with the lower slopes. Piezometer readings indicated that during the rainy

season there was a perched water table, which disappeared after the rain stopped. In

general perched water table depths were greater down slope than upslope but never

reached the soil surface.

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BIOGRAPHICAL SKETCH

Bezawit Adane was born in 1987 GC. She received her BSc degree from Haramaya

University in Soil and Water Engineering and Management on July 29, 2008. She was

employed at the Addis Ababa Water and Sewerage Authority after her BSc degree.

Then she joined Cornell University Masters of Professional Studies (MPS) program in

Integrated Watershed Management and Water Supply in 2010.

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This thesis is dedicated to for my all family especially for my Mother, Manyahilishal Abate, and

my Father, Adane Demisse

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ACKNOWLEDGEMENTS

Above all I thank the lord God for his mercy, without his support and blessings this

piece of work would never have been accomplished.

I would like to express my gratitude to my major advisor Prof. Tammo. S. Steenhuis,

from Cornell University, USA, for his invaluable support, encouragement, supervision

and useful suggestions throughout this research work. He always tried to take care of

every problem I had during my entire study and research period. My sincere thanks

also go to Dr. Amy S. Collick, the coordinator of MPS program. She was so helpful in

all aspect. She helped me organize things, gave me valuable ideas. Thank you so

much. I am thankful to Dr Mikaela Kruskopf, Tana beles project coordinator, for her

constructive ideas during the site selection of my research work, encouragement, and

to get some data. She helped me in facilitating car during data collection. The support

and help I received from Mr. Siefu Admassu, a Cornell PhD student and faculty

member at Bahir Dar University, has been beyond expressionn; he was always

generous, cooperative, and friendly. I am also greatly indebted to Mr. Essayas Kaba

and Mr. Abeyou Wale; both helped me after data collection by analyzing the row data,

sharing their experience, encouragement, valuable comments, etc.

Finally, 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 brother Kirubel Adane, who helped me during the challenging field work. It

was so difficult without his help. Thank you very much.

Last but not least, thanks go to my beloved fiancé, Zeyede Kebede for his comments

and encouragements, and also 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.

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TABLE OF CONTENTS

BIOGRAPHICAL SKETCH ..................................................................................... iii

ACKNOWLEDGEMENTS ......................................................................................... v

TABLE OF CONTENTS ............................................................................................ vi

LIST OF FIGURES .................................................................................................... ix

LIST OF TABLES ....................................................................................................... x

LIST OF ABBREVIATIONS ..................................................................................... xi

LIST OF SYMBOL .................................................................................................... xii

PREFACE .................................................................................................................. xiii

CHAPTER ONE ........................................................................................................... 1

1. INTRODUCTION ................................................................................................. 1

1.1. Background .................................................................................................. 1

1.2. Objectives .................................................................................................... 4

CHAPTER TWO .......................................................................................................... 5

2. MATERIALS AND METHOD ............................................................................ 5

2.1. Description of Study Area ........................................................................... 5

2.1.1. Location and topography .................................................................. 5

2.1.2. Soils ................................................................................................... 7

2.1.3. Land use and cropping pattern .......................................................... 8

2.2. Data Collection .......................................................................................... 10

2.2.1. Metrological Discharge and Sediment data .................................... 10

2.2.2. Ground Water Table Measurements ............................................... 11

2.2.3. Topographic or Wetness index ....................................................... 13

2.2.4. Infiltration Rates ............................................................................. 14

2.2.5. Soil Physical Properties .................................................................. 14

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2.2.6. Field Observations and Interviews .................................................. 15

2.3. Data Checking ............................................................................................ 15

2.4. Data Analysis ............................................................................................. 16

2.5. Different phases of the field work ............................................................. 17

2.5.1. Pre-field work ................................................................................. 17

2.5.2. Field work (primary data collection) .............................................. 17

2.5.3. Post field work ................................................................................ 17

2.5.4. Soil Hydrometer Analysis ............................................................... 17

2.6. Model Development: Conceptual model ................................................... 19

2.7. Model Description ..................................................................................... 20

2.7.1. Sediment model .............................................................................. 23

2.8. Model calibration ....................................................................................... 26

CHAPTER THREE ................................................................................................... 27

3. RESULTS AND DISCUSSION ......................................................................... 27

3.1. Rainfall Amount and Distribution ............................................................. 27

3.2. Rainfall-Runoff Relationships ................................................................... 28

3.3. Soil infiltration rate .................................................................................... 29

3.4. Soil textural property analyses ................................................................... 30

3.5. Ground water and saturated area readings with piezometers ..................... 32

3.6. Hydrology model ....................................................................................... 36

3.7. Sediment model ......................................................................................... 39

3.8. Community understandings of watershed hydrology ................................ 41

CHAPTER FOUR ...................................................................................................... 43

4. CONCLUSIONS AND RECOMMENDATIONS ............................................ 43

4.1. Conclusions ................................................................................................ 43

4.2. Recommendations ...................................................................................... 44

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REFERENCES ........................................................................................................... 45

APPENDICES ............................................................................................................ 51

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LIST OF FIGURES

Figure 2-1: Location of Enkulal watershed .................................................................... 6

Figure 2-2: Slope map of Enkulal watershed ................................................................. 7

Figure 2-3: Soil map of Enkulal watershed .................................................................... 8

Figure 2-4: Percentage of land use in Enkulal watershed .............................................. 9

Figure 2-5: Calculated discharge versus water height ................................................. 11

Figure 2-6: Piezometer location in Enkulal watershed ................................................ 12

Figure 2-7: Piezometers installation at the field ........................................................... 13

Figure 3-1: Five Years rainfall amount and distribution .............................................. 27

Figure 3-2: Monthly runoff, RF, and runoff coefficient in the main rainy season ....... 29

Figure 3-3: Relations of average soil infiltration rate with position in the landscape . 31

Figure 3-4: Average groundwater table elevations for Enkulal watershed .................. 32

Figure 3-5: Average groundwater depth recorded by piezometers at the upper part and

bottom part of the watershed ........................................................................................ 34

Figure 3-6: Average water table Vs Average slope for each month ............................ 35

Figure 3-7: Measured discharge (blue line) and predicted discharge (redline) for the

Enkulal watershed. The solid blue area chart hanging from the top is precipitation ... 38

Figure 3-8: Weekly running average of measured and predicted discharge at the outlet

of the Enkulal watershed .............................................................................................. 38

Figure 3-9 : Predicted (red line) and observed (blue line) of the running 7 day

averaged sediment concentration for the Enkulal Watershed, The discharge is the

green solid chart ........................................................................................................... 39

Figure 3-10: Predicted and observed of the running 7 day averaged sediment

concentration for the Enkulal Watershed ..................................................................... 40

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LIST OF TABLES

Table 2-1: Watershed characterization based on slope (Source: Weigel, 1986) ............ 5

Table 3-1: Annual and monthly rainfall distribution in mm for five years in the study

area ............................................................................................................................... 28

Table 3-2: Average soil infiltration rate at different soil type, slope range and land use

in study area ................................................................................................................. 30

Table 3-3: Textural properties of soils types’ percentage in Enkulal watershed ......... 31

Table 3-4: The validation result in IDW and SPLINE interpolation method .............. 33

Table 3-5: Model input values for surface flow components ....................................... 36

Table 3-6: Model input values for the base flow and interflow ................................... 37

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LIST OF ABBREVIATIONS

SWAT: Soil and Water Assessment Tool

SWAT_WB: Soil and Water Assessment Tool_ Water Balance

SWC: Soil and Water Conservation

DEM: Digital Elevation Model

EMA: Ethiopia Metrological Agency

MOWR: Ministries of Water Resource of Ethiopia

HRUs: Hydrological Response Units

SCS: Soil Conservation Service

GPS: Geographic Positioning System

NRCS: Natural Resources Conservation Service

RMSE: Root Mean Square Error

USLE: Universal Soil Lose Equation

MUSLE: Modified Universal Soil Lose Equation

WEPP: Water Erosion Prediction Project

AGNPS: Agricultural Non-point source portion

PET: Potential Evapotranspiration

AET: Actual Evapotranspiration

IDW: Inverse Distance Weighted

SPSS: Statistical Package for the Social Sciences

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LIST OF SYMBOL

α Recession Coefficient

E Nash-Sutcliffe coefficient, Efficiency Criterion (%)

Observed runoff at the day (mm)

P Daily Rainfall amount (mm)

R Saturation Excess Runoff

Coefficient of determination

S Water stored in the soil (mm)

Stream sediment load

Simulated runoff at the day (mm)

Maximum soil storage capacity

St- t previous time step soil water storage (mm)

Half life of the storage capacity

* Duration of the period after the rainstorm until the interflow ceases

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PREFACE

This thesis report consists of four chapters. In the first chapter a general introduction

is given on hydrological and erosion processes in watersheds in the Ethiopian

Highlands The second chapter the topography, soils, climatic condition and cropping

pattern of the study watershed are presented followed by the experimental procedures

and the description of a simple rainfall runoff model. In the third chapter the results of

the measurements of rainfall, infiltration rates, perched water table depths, runoff and

erosion patterns are presented and discussed. In addition, community understandings

of watershed hydrology, hydrological model calibration and model simulation results

are discussed. The fourth chapter gives the conclusions, recommendations and future

directions.

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CHAPTER ONE

1. INTRODUCTION

1.1. Background

In the Ethiopian highlands, soil and water are the most critical. Nearly 85% of the

population depends on subsistence agriculture. One process that threatens the resource

base is soil erosion. Studies have shown that in Ethiopia billions of tons of soil are lost

annually (USAID, 2000). Due to greater population pressure and consequently more

intensive cultivation, erosion losses have been increasing to an annual areal average

of 7 ton/ha equivalent to depth 0.5 mm (Garzanti et al, 2006). Local erosion rates are

highly spatially variable ranging from less than 1 to over 400 tons/ha/year (Hurni,

1988, Mitiku et al., 2006 and Tebebu et al., 2010).

In order to formulate management options, soil erosion must be considered. Soil loss

from a watershed can be estimated based on an understanding of the underlying

hydrological process, climatic conditions, landforms and soil factors. One option for

formulating management options is to use models to elucidate processes controlling

the hydrologic and sediment fluxes. Erosion rates depend on the rainfall intensity and

the total amount of precipitation after the onset of the rainy season thus adding a level

of temporal complexity to the system. Hence, watershed models that are capable of

capturing these processes in a dynamic manner can be used to provide an enhanced

understanding of the relationship between hydrologic processes, erosion,

sedimentation, and management options. In Ethiopia, soil erosion is a major

challenge, posing a severe threat to the country's economy and development. The

problem also extends to the downstream countries of Sudan and Egypt because the

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Blue Nile drains the Ethiopian highlands and contributes to the sedimentation of

downstream reserviors.

Future development of water resources in Ethiopia and Sudan should include

reduction of soil losses. Several large dams are planned for the Blue Nile basin and

erosion will reduce reservoir capacity such as currently experienced at Roseries and

Aswan High Dams. In addition, in the watershed, erosion from newly developed lands

represents a fertility loss and finally soils that too become too shallow due to erosion

increase surface runoff and reduce interflow (Tesemma et al., 2010).

Erosion models are an important tool in reducing soil loss in the future by predicting

the location of vulnerable areas that need to be managed for reducing soils loss.

Erosion models applied in the Ethiopian highlands range from the empirical

relationships [Universal Soil Loss Equation (USLE)], to physical based models. Hurni

(1985) adapted the empirical USLE for Ethiopian conditions. Eweg et al. (1998) and

Zegeye et al. (2011) showed that the modified USLE can be used to estimate average

annual soil losses but question the reliability of predicting the spatial distribution of

erosion and temporal distribution shorter than a year.

Physically-based erosion models have been applied with some success in various

parts of the Ethiopian Highlands, including the Agricultural Non-Point Source

Polution (AGNPS) model (Haregeweyn and Yohannes, 2003 and Mohammed et al.,

2004), the Soil and Water Assessment Tool (SWAT) (Setegn et al., 2008), the

modified SWAT-WB Water Balance model (Easton et al., 2010) and Water Erosion

Prediction Project WEPP (Zeleke, 2000) are tested for the Ethiopian highlands. These

models except SWAT-WB are applied with the assumption that infiltration excess

runoff mechanism governs the runoff process in all areas.

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Under the prevalent rainfed agricultural production system, the progressive

degradation of the natural resource base, especially in Soil and Water Assessment

Tool (SWAT)-Based Runoff and Sediment Yield Modelling: A Case of the Gumara

Watershed in Lake Tana Sub basin highly vulnerable areas of the high lands coupled

with climate variability have aggravated the incidence of poverty and food insecurity

(Awulachew et al., 2007).

Many of these modeling studies simply predicted sediment on a monthly basis and

were not validated for shorter time intervals. Furthermore, these models do not

include erosion due to concentrated flow channels and gullies (Capra et al., 2005),

hence they fail to predict erosion accurately in the country. Because existing models

simulate neither actual runoff patterns nor erosion phenomena, more realistic models

need to be developed and tested for monsoonal climates (Steenhuis et al, 2009).

Recently Steenhuis et al. (2009), White et al. (2010) and Easton et al. (2010) have

developed simple distributed models that take the terrain topographic features into

account that are suitable for monsoonal climates and can predict the runoff in the

watershed based on a daily basis. The model of Steenhuis et al. (2009) is relatively

simple and divides the watershed up into three distinct areas consisting of the

periodically saturated bottom lands, severely degraded areas with very shallow soils

over an impermeable layer and hillsides. The saturated areas and the degraded areas

produce surface runoff and sediment and the hillside sediment free interflow and base

flow to the river. Ten-day averaged discharge and sediment concentrations were

surprisingly well predicted for the Blue Nile at the border with Sudan. White et al

(2010) modified the SWAT model (SWAT-WB) by redefining the HRU’s based on

topography and soil depth and surface runoff was predicted as any excess rain after

the soil became saturated. SWAT-WB simulated available daily sediment yield data in

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the Blue Nile basin at several scales well (Easton et al., 2010). Input data

requirements, however, for SWAT and SWAT-WB is cumbersome especially in areas

with limited data sources such as in Ethiopia.

Therefore, in this study simple water balance hydrology model was used to predict

sediment yield and also to formulate sustainable land management options that

alleviate soil erosion in the Enkulal watershed.

1.2. Objectives

In Ethiopian watersheds, erosion, sediment transport, and sedimentation are critical

problems. The current level of degradation leading to erosion, and sedimentation are

causing considerable loss of soil. As a consequence, the soils are becoming shallow,

less fertile. In addition, water storage is declining and droughts are becoming more

frequent and intense.

The overall objective of this study is to understand the watershed runoff and soil

erosion by monitoring and simulating the discharge, sediment loss and ground water

levels. Our specific objectives are to 1) determine the spatial and temporal factors that

affect soil erosion and sediment; 2) evaluate a simple model to model soil erosion

losses and the effectiveness of Soil and Water Conservation (SWC) structures.

The study was carried out in the Enkulal watershed a sub watershed of the Gumara

catchment west of Lake Tana.

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CHAPTER TWO

2. MATERIALS AND METHOD

2.1. Description of Study Area

2.1.1. Location and topography

The Enkulal catchment is a small tributary of the Gumara watershed, located

approximately 80 km northeast of Bahir Dar. Enkulal watershed covers an area of 398

ha (Figure 2-1). The gauging station is located at 37o46’E and 11

o37’N. The

watershed is located within Dera woreda (an administrative unit) in Gelawdios kebele.

The watershed topography is described by an elevation range from 2306 to 2528 m, a

222 m altitude difference. More than three quarter of the watershed is in agriculture

generally low yielding and plowed by oxen. Rainfall in the Enkulal watershed is

mono-modal and most of the rainfall is concentrated in the season June to September

and with virtual drought from November through April. The four wettest months

cover 85 percent of the total annual rainfall. The dry season, being from October to

May has a total rainfall of about 15% of the mean annual rainfall (WWDSE, 2007).

With slopes ranging from 0.5% to 40 % (Table 2-1), steep and very steep slope areas

(> 15% slope) cover almost half in the watershed.

Table 2-1: Watershed characterization based on slope (Source: Weigel, 1986)

Slope (%) Description Area (ha) Coverage (%)

0-2 Flat 1.62 0.41

2-10 Sloping 109.35 27.49

10-15 Moderately steep 115.02 28.92

15-30 Steep 162 40.73

>30 Very steep 9.72 2.44

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Figure 2-1: Location of Enkulal watershed

Lake

Tana

Gummara

Watershed 6

6

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Figure 2-2: Slope map of Enkulal watershed

2.1.2. Soils

The soil types in Enkulal watershed were gathered from the World Soils map and

were classified as Haplic Luvisols and Eutric Leptosols (Figure 2-3). The most

dominant soils in the watershed are Haplic luvisols encompassing 88% of the

watershed. The remaining of the watershed has Eutric leptosols 12%.

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Figure 2-3: Soil map of Enkulal watershed

The Enkulal watershed has a relatively mean high annual rainfall of 1577 mm and

remains cloudy during most of the rainy season. Maximum observed daily rainfall is

89 mm. The average annual temperature ranges from

2.1.3. Land use and cropping pattern

Approximately 84% of the Enkulal watershed is cultivated land, while around 14% is

forest and the remaining part is fallow and grazing land. Cereal plough cultivation is

the dominant cultivation system and most of the cultivated fields are usually planted

with barely, teff, wheat, linseed, gibto, peas and beans, the common crops are growing

in the area. Because of increasing land pressure, fallow periods are shortened and the

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fields are given less of a chance to regenerate, this results in decreasing yields due to

decreasing fertility of the soil, increasing erodibility and ultimately total degradation.

(Figure 2-4) shows different type of lands in percentage.

Figure 2-4: Percentage of land use in Enkulal watershed

Farmers have limited income and are unable to buy artificial fertilizers to improve the

productivity of the land and to slow the process of degradation. Furthermore, natural

fertilizers, like livestock dung, are used for cooking fuel.

0.2

83.71

13.64

2.44

Bare Land

Crop Land

Forest

Grass Land

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2.2. Data Collection

2.2.1. Metrological Discharge and Sediment data

Based on the objective of the study, metrological data, daily maximum and minimum

temperatures was collected during the field campaign from Ethiopia Metrological

Agency (EMA) Bahir Dar office.

The runoff stage recording station was located at the outlet of the watershed. Every

morning at 6 AM and evening at 6 PM the river stage was recorded and a sediment

sample taken when there was sediment in the water. On days with storm runoff during

the day, an additional gage level reading and sediment sample were taken at 12 noon.

To decide if a sediment sample should be analyzed a secchi disk with eight dots was

used. If one or more than one of the dots was not visible, a one liter sample was taken

otherwise it was assumed that the water was sediment free and the sample was

discarded.

The amount of sediment load within the sample was determined by oven drying the

one liter grab samples then weighing the oven dried soil. Total soil loss for that

sampling interval was then calculated by multiplying total water flow per time by the

sediment concentration determined from the one-liter sample.

Determining the stage discharge rating curve was cumbersome, because the river

discharges were only measured at low and medium levels. For estimating the

discharges at high river stages, we used the observation of Liu et al (2008) that after

500 mm cumulative effective rainfall the discharge is a constant fraction of the

rainfall. Thus, by trial and error we developed a stage discharge curve that had this

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property and fitted the measured discharge vs river for low to medium levels. The

rating curve equation is shown below.

Where H is the river stage (cm) and Q is the discharge (m3/s).

Figure 2-5: Calculated discharge versus water height

2.2.2. Ground Water Table Measurements

Twenty three piezometers were installed throughout of the watershed and covered

various of land uses such as forest land, cultivated land, and fallow land with steep

slopes ranging between zero and thirty, the locations of piezometers at the lower,

middle and upper parts of the hillslope.

The piezometers consisted of 5 cm diameter PVC pipes. The bottom 30 cm of the

piezometers was perforated and covered with cloth that allows water inflow towards

the tubes but prevents inflow of sediment. The bottom end (opening) of the pipe was

y = 0.3351x2.0858 R² = 0.9807

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

wat

er

stag

e(m

)

Q(m3/sec)

Discharge vs water stage

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closed with a plastic cap and sealed with plastic bandage to block inflow and outflow

of sediment while the above ground opening of the piezometers had a removable cap

prevent rainfall entering in the tube.

Figure 2-6: Piezometer location in Enkulal watershed

The piezometers were installed with an auger. The augured hole had a diameter

slightly larger than the tube. The drilling was stopped when either the impermeable

layer, bedrock, or the ground water was reached. Piezometer depth ranged from 0.72

to 3.32 meters.

Water depth in the piezometers was collected every day at 6:30am in the morning

from the first of August up to middle of November. The water level was measured

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using stick meter (Figure 2-7).The readings helped to identify areas with a high water

table and potential, estimating the impermeable layer depth and to identify the ground

water flow direction and sources of potential saturation excess runoff. Detailed

information (depth to the impermeable layer and daily values of water depth) for each

of the piezometer sites can be found in Appendix 2.

Figure 2-7: Piezometers installation at the field

2.2.3. Topographic or Wetness index

Topography is an important control on hydrological processes. One approach to

quantify this control is the topographic index ln(a/tanβ) where a is the contributing

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area and β is the slope. This index has become widely used in hydrology and con be

derived from the digital elevation model (DEM).

The topographic index for each grid cell was computed based on upslope contributing

area per unit length of contour and topographic slope of the cell. As a result,

bottomlands, with large upslope contributing areas, have higher topographic index

values and have a greater depth of perched water table and therefore prone to

saturation.

2.2.4. Infiltration Rates

Soil infiltration rate was measured at nine different locations throughout the watershed

using a 30-cm diameter single-ring infiltrometer. During the data collection the

infiltrometer was hammered into the ground up to 10-15 cm in order to control for

lateral flow. Then water was added to the ring, and the water depth was measured at

varying time intervals. Here, it should be recognized that infiltration rates measured

with infiltrometers on hill slopes cannot be converted directly to saturated

conductivities since the water depth in the ring is deeper down slope than upslope

(Derib, 2005) and the wetting front after reaching the impermeable layer moved

downhill rather than vertically downward. For shallow soils, the infiltration rate will

underestimate the saturated conductivity.

2.2.5. Soil Physical Properties

The textural composition of soil samples collected from the infiltration test locations

were measured in the laboratory. Soil textural composition was determined using the

simple hydrometer analysis method. Before the field work (data collection) each place

of sampling site in the watershed was identify by using GIS. During the survey, a total

of nine composite soil samples were collected from the top, middle and the bottom

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part of the watershed. All the necessary sample preparation work was done before the

laboratory analyses. The pre-laboratory analyses sample preparation process included

air-drying, crushing of the clods by hand (reduction of the aggregates to the right size,

mostly less than 2 mm), and sieving so as to make the remove stones and other large

organic material.

2.2.6. Field Observations and Interviews

Field observations were made and a survey was administered to the farmers living in

the watershed. Trained field technicians collected the data using structured

questionnaire that can be found in Appendix 3. The field observations and survey

assessment were held to better understand soil erosion, soil loss processes at different

parts of the watershed and SWC (soil and water conservation) structures in reducing

soil loss from the watershed, and to understand the possible reasons for controlling

erosion and sediment transport. It was also an opportunity for the farmers to express

their perception and to present questions. SPSS version 15 for windows was used to

analyze the data from the questionnaire. Descriptive statistics was used to calculate the

percentages from the respondents.

2.3. Data Checking

Data checking was done mainly for the consistency of all collected data. Rainfall, staff

gauge, stream flow, suspended sediment load, and piezometer data were checked with

missing values, time sequence discontinuities, and negative values were checked

before used for analysis. The above primary data collected during the major rainy

season (2010) were incorporated and analyzed using Microsoft Excel spreadsheets.

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2.4. Data Analysis

The statistical criteria selected for comparison of the performance of the model in

predicting discharge were the Nash-Sutcliffe coefficient, E, a dimensionless indicator

widely used to evaluate hydrological models (Nash and Sutcliffe, 1970), coefficient of

correlation, , and root mean square of error (RMSE). The Nash Sutcliffe coefficient

(E) was calculated as:

[ ∑

∑ ̅

]

where Si is the simulated discharge for each time step, is the observed discharge

value, ̅ is is the mean discharge and n is the total number of values with in the

period of analysis. The Nash-Sutcliffe coefficient (E) is a measure of goodness-of-fit

ranging from -∞ to 1, with one indicating a perfect fit while zero indicates that the

model is predicting no better than the average of the observed data. Legates and

McCabe (1999) in Harmel and Smith (2007), mentioned that E is better suited to

evaluate model goodness-of-fit than the coefficient of determination, However,

like , E is overly sensitive to extreme values because it squares the values of paired

differences, as shown in Equation 2.

The root mean square error, RMSE, is well-accepted absolute error goodness-of-fit

indicator that describes differences in observed and predicted values in the appropriate

units (Legates and McCabe, 1999). It is calculated as:

[

]

Where, all the terms have the same meaning as the above equations.

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2.5. Different phases of the field work

2.5.1. Pre-field work

The main activity conducted before the field work was preparing the land cover map

of the Gumara watershed using satellite image, selecting the area, field visit and

preparing an action plan of field work.

2.5.2. Field work (primary data collection)

The required hydrological data were collected from respective offices, The data

consisted of daily discharge, daily rain fall, max and min temperature, sun shine hours

and wind speed and as well as slope, topographic map of the study area . Moreover

during a survey of the catchment, ground truth data was collected using GPS for the

wetted area, girthing land, water table by using piezometers and interviews were

conducted with local people to gather information about the land cover. Sediment data

was collected were collected by using one liter of bottle and then processed on

laboratory. In addition 23 piezometers were installed in transects to measure the water

table depths. Finally, soil depth estimations were taken by field technicians throughout

the watershed and registered using GPS points.

2.5.3. Post field work

The hydrological and metrological data that were collected during field work and the

secondary data’s were processed and used to calibrate the model.

2.5.4. Soil Hydrometer Analysis

A hydrometer is one of the simplest and most rapid methods for mechanical analysis

of soils. This method was used to measure the density of the soil suspension. The

hydrometer was calibrated to measure specific gravity of the suspension.

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The formula we used for calculation of the percentage of original sample in

suspension was:

[(

) ]

Where P is the percentage of soil remaining in suspension, W = oven dry weight of

sample, G = specific gravity of soil particles (2.65), Gl= specific gravity of liquid (1),

Rc = hydrometer reading corrected by the ”composite correction factor" related to

temperature

Where, R is the reading in the soil suspension and is the difference between the

hydrometer reading in distilled water at the same temperature as the settling column,

and the hydrometer reading in a column of the dispersing agent plus hypochlorite.

The diameter of a soil particle corresponding to the percentage indicated by the

hydrometer reading was computed by the equation:

where D is the diameter of the particle in mm, K is a constant that depends on the

temperature of the suspension and the specific gravity of the soil particles, L is the

distance (cm) from the surface of the suspension to the level at which the density is

being measured, and T is the interval of time from the beginning of sedimentation to

the taking of the reading (min).

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The hydrometer reading at 40 sec represents silt and clay in the suspension. Hence, to

determine the amount of sand, subtract this reading from the sample mass. The

following set of equations was used to calculate the percentage of sand silt and clay:

Sand (%) = (Sample mass - 40 sec hydrometer reading / Sample mass) x 100 and

Clay (%) = (40 sec hydrometer reading / Sample mass) x 100

Silt (%) = 100 - (Sand % + Clay %)

2.6. Model Development: Conceptual model

Originally, the conceptual model for the water balance type rainfall-runoff model

developed by Collick et al (2008). It was later refined by Steenhuis et al (2009);

Engda, 2011, and Tilahun et al (2011). Tilahun et al (2011) are then extended the

model to predict sediment concentration in Anjeni Watershed. The following is a

summary form these papers:

Watersheds in the Ethiopian highlands are characterized by relatively flat bottomlands

and gentle to steep sloping uplands The watershed is then divided into three regions:

Two surface runoff source areas consisting of areas near the river that become

saturated during the wet monsoon period and the degraded hillsides with little or no

soil cover. The remaining hillside areas have infiltration rates in excess of the rain fall

intensity (Bayabil et al., 2010 and Engda et al., 2011). A water balance using

Thornthwaite Mather procedure are then applied to each area to simulate overland

flow from saturated and degraded area and base flow and interflow from the hillside.

Inputs to the model are rainfall and potential evaporation and parameter to the model

are the magnitude of the relative areas and the amount of storage in the soil between

witling point and saturation for the runoff producing areas and wilting point and field

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capacity for the hillside. In addition there are three more subsurface parameters, a

maximum storage and half-life for the first order ground water reservoir, the time it

takes for a hill slopes to drain after a rainstorm for the linear interflow reservoir.

In the sediment model, the basic assumption is that erosion is only simulated from

runoff producing source areas: saturated area and degraded area. Erosion is negligible

from the non degraded hillsides because almost all water infiltrates.

The sediment model takes into account both the sediment source areas where the

runoff is generated and the transport capacity of the water. In the model, Sediment

concentration is linearly related to the overland flow velocity from the runoff

producing areas. Erosion rates are greater in the more heavily degraded areas without

plant cover than in the saturated source areas with natural vegetation.

2.7. 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 presented by Steenhuis et al. (2008) for

the whole Blue Nile basin. The basic inputs to the model are daily precipitation and

potential evapotranspiration. The following is taken from the paper by Steenhuis et al.

(2008).

In this model Eto (evapotranspiration) was calculated by using for T-max, the equation

developed by (Temesgen Enku, 2009) in Fogera flood plain. PET, which varies

between 4 mm/day during the dry season and 1 mm/day during the rainy season.

(

) ∗

5

Where ETo is reference ET (mm/day), T-max is daily maximum temperature ( )

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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<PET), the Thornthwaite and

Mather (1955) procedure is used to calculate actual evapotranspiration, AET

(Steenhuis and van der Molen, 1986). In this method, AET decreases linearly with

moisture content, e.g.:

(

)

Where St (mm) is the available water stored in the root zone per unit area and Smax

(mm) is the maximum available soil storage capacity defined as the difference

between the amount of water stored in the top soil layer at wilting point and the

maximum moisture content, equal to either the field capacity for the hill slope soils or

saturation (e.g., soil porosity) in runoff contributing areas. Smax varies according to

soil characteristics (e.g., porosity, bulk density) and soil layer depth. Based on Eq. 9

the surface soil layer moisture storage can be written as:

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[ (

)] For P<PET........................................ 10

In this simplified model, direct runoff occurs only from the runoff contributing area,

when the soil moisture balance indicates that the soil is saturated. Recharge and

interflow originate from the remaining hill slopes. It is assumed that the surface runoff

from these areas is minimal. This will underestimate the runoff during major rainfall

events and, to test its significance, the model was run on a daily, weekly, and monthly

basis.

In the overland flow contributing areas when rainfall exceeds evapotranspiration and

fully saturates the soil, any moisture above saturation becomes runoff, and the runoff,

R, can be determined by adding the change in soil moisture from the previous time

step to the difference between precipitation and actual evapotranspiration, e.g.:

For high infiltration areas on hill slopes the water flows either as interflow or base

flow to the stream. Rainfall in excess of field capacity becomes recharge and is routed

to two Reservoirs that produce base flow or interflow, here it is assumed that base

flow reservoir is filled first and when full, the interflow reservoir starts filling. The

base flow reservoir acts as a linear reservoir and its outflow, BF, and storage, BSt, are

calculated when the storage is less than the maximum storage, BSmax as:

[ ]

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Where α is the half-life of the aquifer, or the time it takes for half of the volume of the

aquifer to flow out without the aquifer being recharged. When the maximum storage,

BSmax, is reached then:

[ ]

Interflow originates from the hill slopes with the slope of the landscape as the major

driving force of the water. Under these circumstances, the flow decreases linearly (i.e.,

a zero order reservoir) after a recharge event.

∑ ∗

(

∗ )

The total interflow, IFt at time t can be obtained by superimposing the fluxes for the

individual events. Where τ* is the duration of the period after the rainstorm until the

interflow ceases, IFt is the interflow at a time t, Perc*t-τ is the percolation on t-τ days.

2.7.1. Sediment model

In calculating the erosion from runoff producing area, we are assuming that rate of

erosion depends on the stream power (Ω) per unit area. The maximum concentration

of sediment that a stream can carry (called the transport limiting capacity Ct (g/L)) can

be derived from the stream power function as shown by Ciesiolka et al. (1995) and Yu

et al. (1997)

𝐶 𝑎 𝑞𝑟

Where qr (mm/day) is the runoff rate per unit area from each runoff producing region,

at (g L mm

-nday

n) is a variable derived from the stream power. The variable at is a

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function of the slope, Manning’s roughness coefficient, slope length, and the effective

depositability (Yu et al 1997). As water depth increases at essentially becomes

independent of the runoff rate per unit area and can be taken as a constant (Yu et al,

1997). The exponential, n, that takes a value of 0.4 assuming both a wide channel and

a linear relationship between sediment concentration and velocity (Ciesiolka et al

1995 and Yu et al 1997). Since the Ekulal is almost 400 ha, the water in the channel

is sufficiently deep so that at is constant.

For erosion of cohesive soils, the sediment concentration will not always reach the

transport limit. Only in cases where, for example, the rills are formed in newly

plowed soils, the transport capacity will be met. Tebebu et al (2010) found that once

the rill network has been fully established, no further erosion will take place and the

sediment source becomes limited and, the concentration, C, will fall below the

transport limit. For the cases when the sediment concentration becomes lower than

the transport limit, Ct,, Ciesiolka et al. (1995) found based on the work of Hairsine

and Rose that the sediment concentration will not decline below the “source limit”, Cs

(g/L):

𝐶𝑠 𝑎𝑠𝑞𝑟

where as is the source limit and is assumed to be independent on the flow rate for a

particular watershed (as compared to plots). Introducing a new variable, H, defined as

the faction of the runoff producing area with active rill formation, the concentration of

sediment from the runoff producing area can then be written as:

𝐶𝑟 [𝐶𝑠 𝐶 𝐶𝑠 ]

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Combining Eq.17 with Eqs. 15 and 16, the concentration from the runoff producing

area becomes

𝐶𝑟 [𝑎𝑠 𝑎 𝑎𝑠 ]𝑞𝑟

Finally, in the calculation of the daily concentration, baseflow and interflow play an

important role. In a monsoon climate, baseflow can be at the end of the rainy season a

significant portion of the total flow. Thus, in the last part of the rainy season the

subsurface flow dilutes the peak storm sediment concentration from the runoff

producing zones when simulated on a daily basis. It is therefore important to

incorporate the contribution of baseflow in the prediction of sediment concentration.

Next, we calculated the concentration of the sediment yield in the stream. Since the

interflow and baseflow are sediment free the sediment load per unit watershed area, Y

(g m

-2day

-1), can be obtained by multiplying Cr in Eq. 18 by the relative area and the

flux per unit area, e.g.,

𝑞𝑟 [[𝑎𝑠 𝑎 𝑎𝑠 ]𝑞𝑟 ] 𝑞𝑟 [[𝑎𝑠 𝑎 𝑎𝑠 ]𝑞𝑟

]

where 𝑞𝑟 and 𝑞𝑟 are the runoff rates expressed in depths units for contributing area A1

(fractional saturated area) and A2 (fractional degraded area), respectively. Assuming

that the saturated and the degraded zones have the same values for transport and

source limiting capacities, the concentration of sediment in the stream can be obtained

by dividing the load Y (Eq. 19) by the total watershed discharge

𝐶 ( 𝑞𝑟

𝑞𝑟 )[𝑎𝑠 𝑎 𝑎𝑠 ]

𝑞𝑟 𝑞𝑟 𝑞 𝑞

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Where qb (mm/day) is the base flow and qi (mm/day) is the interflow per unit area of

the non-degraded hillside, A3 where the water is being recharged to the subsurface

(baseflow) reservoir.

2.8. Model calibration

Evaluation of the hydrologic model behavior and performance is commonly made and

reported through comparisons of simulated and observed values. The next step is to

calibrate daily values of the discharge with the water balance and then subsequently

the sediments concentrations with the sediment model.

For the hydrology model all nine input parameters were calibrated. For partitioning

the rainfall in to surface runoff and recharge for sub-surface reservoirs, they consisted

of the size (A) and the maximum storage capacity (Smax) for the three areas, and for

the subsurface, they involved the half life (t1/2) and maximum storage capacity

(BSmax), of linear aquifer and the drainage time of the zero order reservoir (τ*). In the

sediment model, there are two calibration parameters consisting of the constants for

each two runoff source areas a1 and a2 from Equation 20.

Model calibration was done manually through randomly varying input parameters in

order that the best “closeness” or “goodness-of-fit” was achieved between simulated

and observed river discharge. The calibrated input parameters consisted of maximum

storage Smax of the three regions and the reservoir parameters t*, α, and SBmax.

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CHAPTER THREE

3. RESULTS AND DISCUSSION

3.1. Rainfall Amount and Distribution

Sequential and spatial rainfall (precipitation) characteristics are very important factors

that affect runoff generation. In the 398 ha watershed two rain gauges installed give

temporal effects of rainfall that were obtained from automatic rain gauge readings.

The annual precipitation based on 5 years of observation is 1,577 mm average annual

rainfall (Figure 3-1). Rainfall over the Enkulal watershed is mono-modal and most of

the rainfall is concentrated in the season June to September (keremt). During the

keremt, precipitation exceeds evaporation. The excess leaves the watershed over time.

Figure 3-1: Five Years rainfall amount and distribution

Monthly and annual rainfall amounts are presented in Table 3-1. The monthly rainfall

distribution for each year is highly variable (coefficient of variation, CV > 30%). Year

2006

2008

20100200400600800

1000

1200

1400

1600

1800

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to year monthly rainfall is also highly variable except in months of rainy seasons, July

(11%) and August (25%). Annual total coefficient of variability is too low (CV = 4%)

with an average of 1,577 mm of rainfall and standard deviation of 60 mm. Derib

(2005) states annual rainfall with CV > 30% is an indication of high vulnerability to

drought. Regardless of the higher year to year and annual monthly rainfall variability,

the low variability of total annual rainfall minimizes the risk of drought in the study

area. Hurni and Grunder (1986) verified that drought is not a problem in this area

because of low variability of annual rainfall.

Table 3-1: Annual and monthly rainfall distribution in mm for five years in the study area

Year

2006 2007 2008 2009 2010 Avg. SD CV

(%) Max Min

Total 1634 1532 1605 1494 1617 1577 60 4 1634 1494

Jan 0 19.9 81.4 0 13.1 23 34 148 81 0

Feb 1.4 0.5 0 5.1 0 1 2 153 5 0

Mar 6.8 22.2 1.5 63.2 33.3 25 25 97 63 2

Apr 63.2 87.8 81.9 19.1 52.1 61 27 45 88 19

May 147.3 65.6 211.5 28.2 65.3 104 74 72 212 28

Jun 170 281.4 209.4 66.8 151.2 176 79 45 281 67

Jul 482.2 424.7 376.4 418.3 499.3 440 50 11 499 376

Aug 452.5 439.1 341.8 667.4 527.9 486 121 25 667 342

Sep 255 183.1 228.6 113.2 203 197 54 27 255 113

Oct 47.5 8.1 51.8 107.4 41.4 51 36 70 107 8

Nov 0 0 2.5 3 21.1 5 9 168 21 0

Dec 7.9 0 18.5 2 9.7 8 7 96 19 0

Avg. 136.1 127.7 133.7 124.4 134.8

SD 175.0 166.0 135.2 206.3 187.0

CV

% 128.5 130.0 101.0 165.8 138.7

3.2. Rainfall-Runoff Relationships

The rainfall-runoff data was collected from Enkulal watershed in the main rainy

season of 2010. It helps us to clarify the runoff processes in the watershed. The runoff

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from July is greater than August and September. By dividing the monthly runoff by

the monthly precipitation, the runoff coefficients are obtained. The runoff coefficients

in the rainy season range from 0.5 to 0.2 (Figure 3-2). The result shows that runoff

coefficients fluctuate when rainfall increases during the rainy season.

Figure 3-2: Monthly runoff, RF, and runoff coefficient in the main rainy season

3.3. Soil infiltration rate

The infiltration rates in the watershed range from 8.4 mm/hr to 32.5 mm/hr are shown

below (Table 3-2). It have a greater infiltration rates; IR, than the more downstream

locations that are less sandy and have more clay. The soil infiltration rates are plotted

together with slope for each location in (

0 1 2 3 4 5

0.0

0.1

0.1

0.2

0.2

0.3

0.3

0.4

0.4

0.5

0.5

0

20

40

60

80

100

120

140

160

Jun Jul Aug Sep

Runoff(mm/month)

RF(mm/month)

Runoff coefficients

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Figure 3-3). There is a surprising relationship between slope and infiltration rate with

the greatest infiltration rates associated with the steepest slopes. Note that infiltration

rates in the watershed are in excess of most rainfall intensities expected in the area,

and infiltration excess runoff is unlikely except during the most intense storms.

Table 3-2: Average soil infiltration rate at different soil type, slope range and land use in study area

Testing

site

Location in

watershed

Average IR

(mm/hr)

Slope at hillside

measuring point (%) Soil type Land use

T1 top 32.5 29.5 Loamy

sand fallow grass

T2 top 18.3 16.3 Loamy

sand fallow land

T3 top 12.4 13.4 Sandy clay

loam

terraced and

cultivated

T4 middle 7.3 15.1 Sandy loam fallow grass

T5 middle 12.3 11.8 Sandy clay grass land

T6 middle 16.7 8.4 Clay cultivated land

T7 bottom 13.1 9.6 Sandy clay

loam fallow land

T8 bottom 11.3 6.6 Sandy clay

loam bush land

T9 bottom 8.4 10.1 Sandy clay

loam grass land

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0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

infi

ltra

tio

n r

ate

mm

/min

or

slo

pe

%

Average IR(mm/hr)

slope of hillside(%)

3.4. Soil textural property analyses

The soil sand, silt, and clay percentages from the top, middle and bottom portions of

the watershed and the resulting soil texture classification are given in

Table 3-3. The watershed is sandier than the Soil Conservation Research Program

(SCRP) watersheds, which are a part of a collaborative effort by the Swiss Agency for

Development and Cooperation and the Ethiopian government to combat land degradation. In

the next section we will see that the saturation areas in the Enkulal watersheds are

smaller than in the SRCP watersheds and the sandier nature and the resulting greater

conductivity in the subsoil might be the cause.

Figure 3-3: Relations of average soil infiltration rate with position in the landscape Table 3-3: Textural properties of soils types’ percentage in Enkulal watershed

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3.5. Ground water and saturated area readings with piezometers

The absolute ground water table elevations were averaged and interpolated using

inverse distance weighted (IDW) and spline methods. From the 23 piezometer, 15

were used for interpolation and the remaining eight for validation. The spline method

gave the best results (Table 3-4) and was used to plot the water table elevation in

(Figure 3-4). In general the ground water table elevation was more closely related to

landscape position than to crop type and land use indicating that the catchment was

underlain by an impermeable layer.

Location Sand (%) Clay (%) Silt (%) Soil Texture

Top 84 10 7 Loamy sand

Top 78 16 6 Loamy sand

Top 65 32 3 Sandy clay loam

Middle 79 15 6 Sandy loam

Middle 55 38 6 Sandy clay

Middle 36 58 6 Clay

Bottom 63 28 9 Sandy clay loam

Bottom 65 29 6 Sandy clay loam

Bottom 62 32 6 Sandy clay loam

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Figure 3-4: Average groundwater table elevations for Enkulal watershed Table 3-4: The validation result in IDW and SPLINE interpolation method

Code X Y IDW SPLINE Gwl asl

E4 369815 1286540 2404.08 2407.62 2406.494

E6 369650 1286510 2388.75 2389.85 2385.762

E7 369685 1286415 2388.33 2392.69 2392.913

E8 369604 1286472 2381.30 2382.25 2383.821

E10 369512 1286378 2373.58 2369.28 2371.572

E12 369413 1286413 2367.27 2360.46 2363.55

E22 368879 1285999 2339.61 2340.38 2343.262

E23 369087 1286128 2348.97 2348.17 2344.62

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In order to evaluate the water table response to rainfall the perched water tables height

above the hardpan were considered. After initial analysis, the perched water levels

were divided up in two parts: upper (top) and lower (bottom). The reason was that it

seemed that the two groups acted independent of each other. (Figure 3-5) shows the

division of the watershed and the location of the piezometers located in each part. The

daily perched ground water readings depths for each of the piezometers were averaged

to monthly values and are shown in (Figure 3-6) as a function of the slope of the land.

In August when it rained most (of the 4 months shown), the preached water tables

were the highest. The levels decreased in September when it rained less and in

October only in a few locations a perched water table still existed. The perched water

table completely disappeared in November (figure 3-6 d). For August and September

within each group there was an inverse relationship between slope and perched water

table height. The low-slope areas are generally closer to the river and have the highest

perched water levels. The inverse relationship is expected, because according to

Darcy’s law when the flux is equal when the driving force (slope) decreases the cross-

sectional area (water table height) has to increase.

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In none of the piezometers in Enkulal watershed the water table was at the surface of

the soil. Thus unlike many other watersheds, this watershed has a minimum of

saturated areas. A small area of farmer installed ditches was found in the upper part of

the watershed, indicating that this area could produce surface runoff. Since there is

little overland flow the water falling during the rainy season, on the steep hillsides

infiltrates and flows as saturated subsurface flow in down the slope over the hardpan

to the river as part of the perched water down the hill or leaves the watershed as deep

percolation and provides water to a spring lower down the slope.

Figure 3-5: Average groundwater depth recorded by piezometers at the upper part and bottom part of the watershed

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Figure 3-6: Average water table Vs Average slope for each month

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3.6. Hydrology model

The watershed was divided into three areas with different characteristics: exposed

bedrock (degraded area) and saturated areas that contribute surface runoff or hillsides

that produce recharge when the soil is above field capacity. The model calibration

shows (Table 3-5) that 20% of Enkulal watershed areas consists of the degraded areas,

required little precipitation to generate runoff (i.e., Smax = 10 mm) and 10% of the

areas becomes saturated in the watershed and needed 50mm of effective precipitation

after the dry season to generate runoff (i.e., Smax = 50 mm).The hillside or the

infiltration (recharge) area in Enkulal watershed represents 30% of the total area with

50mm of effective precipitation to reach field capacity from wilting point.

Table 3-5: Model input values for surface flow components

The Enkulal watershed is located in the top of the watershed and some of the

subsurface water passes under the gauging station and provides water for springs

below. The hillside area (30% of the total Watershed) is especially small for is, which

is in accordance with the piezometer readings that indicated that the top part of the

watershed behaved differently than the bottom part. It is likely that the subsurface

flow of the top of watershed became deep percolation. The maximum storage of water

in the root zone was similar to other watersheds. This should not be very surprising

Watershed Component Area

(fraction)

Smax

(mm) Symbol Constant

Enkulal

Hillside 0.3 50

Contributing area

Saturated bottom 0.1 50 at 17

Contributing area rock

outcrops (degraded) 0.2 10 as 5

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since the Smax values only affect the amount of surface runoff in the beginning of the

rainfall season.

Table 3-6: Model input values for the base flow and interflow

Parameter description Value Units

Maximum storage capacity of linear

reservoir,

500 mm

Half life, 150 day

Interflow duration after rainfall, ∗ 100 day

There are two parameters that determine the subsurface flow: interflow and base flow.

While the base flow contribution to stream flow decreases slowly depending on the

amount of water in the aquifer, the interflow decreases linearly for a particular storm

and stops after a time, t*. t* was 100 days for the watershed. The half-life for the

base flow storage was set to be 150 days. (Table 3-6) In addition the parameter SBmax

that determines when interflow occurs was calibrated to be 500 mm.

The running weekly averaged discharge at the outlet is shown in Figures 3-7 and 3-8.

The Nash Sutcliffe efficiency was 0.75, indicating that the simple model was able to

simulate the discharge pattern quite well in this watersheds. The larger than expected

t* for the Enkulal watershed is likely a consequence of missing most of the peak flows

especially later in the rainy season (due to the sample collection timing).

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Figure 3-7: Measured discharge (blue line) and predicted discharge (redline) for the Enkulal watershed. The solid blue area chart hanging from the top is precipitation

Figure 3-8: Weekly running average of measured and predicted discharge at the outlet of the Enkulal watershed

0

10

20

30

40

50

60

70

80

90

1000.0

3.0

6.0

9.0

12.0

15.0

1-J

an

2-M

ar

2-M

ay

2-J

ul

31

-Au

g

31

-Oct

Rai

nfa

ll, m

m/d

ay

Dis

char

ge, m

m/d

ay

y = 0.91x + 0.49 R² = 0.78

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00

Pre

dic

ted

dis

char

ge m

m/d

ay

observed discharge mm/day

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3.7. Sediment model

In simulating the sediment losses, we first define the form of the function of H,

indicating the fraction of ploughed land with active gully formation. Tebebu et al

(2010) found that the erosion is the greatest just after ploughing and stopped after rills

were formed in the field in early August. Cultivation begins after the first rainfall and

then continues for approximately a three to four week period. Therefore, in the model

we assume that the concentration from the runoff areas is at the transport limit (i.e.,

H=1) for the first four weeks after the first rainfall event. Then for another month a

few more fields are being prepared and the H decreases from 1 to zero. Around August

1, the sediment concentration from the runoff areas is at the source limit.

Figure 3-9 : Predicted (red line) and observed (blue line) of the running 7 day averaged sediment concentration for the Enkulal Watershed, The discharge is the green solid chart

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.00.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1-J

an

2-M

ar

2-M

ay

2-J

ul

31

-Au

g

31

-Oct

Dis

char

ge, m

m/d

ay

Co

nce

ntr

atio

n, g

/L

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The sediment concentration shown in Figure 3-9 are calculated according to Eq 20 by

using the H values as specified above and the discharges predicted by the hydrology

model. The value for n was 0.4 as it theoretically should be for a wide field (Tilahun et

al, 2011). The coefficients at and as in Table 1 were calibrated for first and only year

of data. The observed and predicted values for the calibration were 0.76 for the

weekly running averages (Figure 3-9, Figure 3-10).

Figure 3-10: Predicted and observed of the running 7 day averaged sediment concentration for the Enkulal Watershed

The decrease in sediment concentration at the end of the rainy season is also reported

in Tigray, the northern part of Ethiopia by Vanmaercke et al. (2010). They argued that

the low concentration of sediment in the runoff water is due to sediment depletion on

the farmer’s fields. The current model also assumes that the water does not pick up

sediment in the field, and the sediment in generated by raindrop splash. Others

(Descheemaeker et al., 2006 and Bewket W & Sterk G 2003) suggested that the lower

sediment concentrations are due to increased plant cover. Although there could be

y = 0.75x + 0.42 R² = 0.77

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

Pre

dic

ted

sd

mt

con

cen

trat

ion

g/L

Observed sediment concentration g/L

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this effect as well, Tebebu et al (2010) showed for the Debre Mawi watershed that

such a relationship between plant cover and sediment concentration does not exist. In

the Blue Nile Basin, the decreasing daily sediment concentration in the stream flow

can be estimated by assuming that base flow and interflow plays dilutes the sediment

contributed by the surface runoff. Early in the rainy season the baseflow and interflow

are negligible and concentrations are greater.

3.8. Community understandings of watershed hydrology

A total of fifty farmers participated on infield discussion among these 20 of them were

women. The interview was conducted by me and one experienced student. 25% of

participant farmers attended up to grade four, 50% of the participated farmers got

adult education and others did not get any education.

From the total respondents 98% of the study participants considered that their crops

benefitted from using fertilizer. Of these, 95% answered that increasing crop yields

was important in improving the living standard of the House hold. Ninety-three

percent reported increasing average crop yield during the last five years.

Regarding erosion, all participants in the discussion group (farmers and data

collectors) agreed as soil erosion is a serious problem in the Enkulal watershed.

Erosion was most severe from the beginning of June to the beginning of August. All

of them (100%) considered that erosion problem in their farm was getting worse.

Among these, 95% observed greater erosion in the upland than in the down slope area

that was less steep and unlike most other watershed in Amhara did not exhibit

saturated overland flow. According to the 81% of the respondents, the main cause of

erosion was improper tillage, followed by high rain fall (12.5%) and slope/terrain

(6.3%). Most of them (68%) associated soil erosion with yield decline and and some

of them (24%) with increased input demand. The noticeable changes were increase

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soil stoniness 85% and removal of top soil (15%). Sixty one percent participated in

soil and water conservation technologies initiated by the Government or NGOs. Of

these, 43% started before the project and 58% after the project phase out. Among the

participants 33% found it helpful for the reduction of erosion. Despite this 90% of the

farmers reported that, the major cause of soil loss was lack of conservation structures

and it occurred from July to August in the upper part of the watershed. All of the

respondents applied fertilizer to their crop land. Seventy six percent of the farmers

expected 0-5 quintals1 increase in yield from fertilizer application.

1 One quintal is 100kg

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CHAPTER FOUR

4. CONCLUSIONS AND RECOMMENDATIONS

4.1. Conclusions

Infiltration rate and subsurface water depth measurements indicated that most soils

had infiltration rates in excess of the rainfall intensities and most of the discharge in

the stream originated from subsurface flow.

The modified simple water balance model was used to predict runoff and characterize

the runoff mechanism in hill-slope areas with some calibration parameters specific to

the area under consideration. Using these models, it was possible to define the runoff

source areas that also produced the majority of sediment in the streams. The analysis

based on the simulation and water table height observations showed that in the

Enkulal watershed approximately one fifth of the area produced runoff. These runoff

source areas consisted of degraded and shallow soils.

The physical measurements and the modeling results were in agreement with the

observation of the farmers that the bottom lands exhibited low amounts of erosion and

the hillsides were the main areas contributing to sediment loss. In addition, farmers

confirmed that infiltration excess was not a main factor in producing runoff and

erosion because only a minority considered that both rainfall intensity and steepness

were main factors in causing soil erosion. Surprisingly, only one third of the farmers

indicated that outside agencies were helpful in reducing soil erosion from their fields.

This indicates that these agencies need to reconsider their implementation practices for

soil and water conservation practices.

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4.2. Recommendations

Based on the study findings, the following recommendations are made:

For future studies, a detailed water balance model in the catchment can be developed

by taking results of the calibration of watershed area proportion based on runoff

response.

This study enables any further studies to predict the impact of sediment load, or the

percentage transport by different runoff mechanisms. Therefore, developing the model

by considering erosion, runoff as affected by season and an estimate of soil exposed

for erosion will further improve the efficiency of the model.

The performance of the model can be improved by increasing the number of rainfall

and discharge gauging stations within the catchment. It helps to develop a clear

rainfall runoff and soil loss relationship.

From the result of survey questionnaire, we recommend that developing Soil and

Water Conservation (SWC) structure will help to reduce the amount of soil loss.

Onsite training of farmers either by governmental or non-governmental organizations

will help to enhance their awareness and strengthen their skill towards controlling soil

erosion by construction of terrace, soil bund and other management options.

This study hasn’t incorporated the change in productivity due to provision of each

management options. Thus, the effect of these management options on productivity

especially further construction of terraces needs to be studied.

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Zeleke, G. 2000. Landscape Dynamics and Soil Erosion Process Modeling in the

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APPENDICES

APPENDICES 1: Piezometer installation sites and specific locations in the watershed

Piezometer

code

Elevation

asl Depth(m) Land use type

E1 2462 0.75 Grass land

E2 2423 0.73 Cultivated land

E3 2413 0.72 Grass land

E4 2408 1.56 Bush land

E5 2404 0.59 Grass land

E6 2387 1.42 Grass land

E7 2394 1.2 Cultivated land

E8 2385 1.5 Bush land

E9 2382 1.64 Grass land

E10 2373 1.57 Grass land

E11 2370 2.06 Cultivated land

E12 2366 2.6 Bush land

E13 2384 2.08 Grass land

E14 2376 2 Grass land

E15 2389 1.6 Cultivated land

E16 2350 2.63 Bush land

E17 2355 3.32 Grass land

E18 2347 1.98 Grass land

E19 2341 3.3 Grass land

E20 2335 2.53 Cultivated land

E21 2325 1.97 Grass land

E22 2345 1.84 Grass land

E23 2346 1.86 Cultivated land

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APPENDICES 2: Summary of daily piezometeric head data

Code

E

1

E

2

E

3

E

4

E

5

E

6

E

7

E

8

E

9

E

10

E

11

E

12

E

13

E

14

E

15

E

16

E

17

E

18

E

19

E

20

E

21

E

22

E

23

Date

10 20 30 0 20 90 10 100 20 20 17 3 5 90 30 40 50 60 10 3 10 20 30 8/9/2010 10 20 30 0 20 90 10 100 20 20 17 3 5 90 30 40 50 60 10 5 10 20 30 8/10/2010 10 20 30 0 20 90 10 100 20 20 17 3 5 90 30 40 50 60 10 5 10 20 30 8/11/2010 10 20 10 5 10 10 50 30 60 60 45 95 10 100 40 1.2 18 81 90 50 15 30 1.5

8/12/2010 10 20 10 5 10 10 50 10 30 60 60 45 10 100 40 95 1.2 81 90 50 15 30 1.5

8/13/2010 10 20 10 5 10 10 50 30 60 60 45 95 10 100 40 1.2 18 81 90 50 15 30 70

8/14/2010 50 40 30 100 30 0 10 40 20 20 5 5 5 100 70 60 60 30 0 0 10 10 80

8/15/2010 20 20 10 0 5 0 5 10 10 5 20 10 5 100 50 70 90 10 0 0 5 10 70

8/16/2010 18 29 10 0 10 0 5 30 10 5 5 3 5 1.1 40 60 50 20 3 0 10 3 70

8/17/2010 15 20 10 0 8 0 5 20 5 5 10 5 5 70 30 50 50 5 0 0 5 5 40

8/18/2010 40 10 3 0 5 10 3 10 5 3 5 0 5 14 50 60 60 3 0 0 5 3 70

8/19/2010 10 20 10 0 8 0 3 10 5 5 8 5 5 6 20 40 40 5 0 0 5 5 30

8/20/2010 10 20 10 0 8 0 3 10 5 5 8 5 5 6 20 40 40 5 0 0 5 5 30

8/21/2010 10 20 10 0 8 3 10 5 5 8 5 30 3 6 20 30 20 5 0 0 5 5 5

8/22/2010 10 20 10 0 8 3 10 5 5 8 5 30 5 6 20 25 18 5 0 0 5 5 5

8/23/2010 40 3 5 3 5 0 5 50 40 5 5 3 5 30 90 60 50 10 0 0 10 10 50

8/24/2010 40 3 5 3 5 0 5 50 40 5 5 3 5 30 90 60 50 10 0 0 10 5 50

8/25/2010 40 3 5 3 5 0 5 50 40 5 5 3 5 30 90 60 50 10 0 0 10 5 50

8/26/2010 30 3 5 0 3 0 3 20 20 3 3 2 5 85 15 60 50 5 0 0 3 3 70

8/27/2010 30 3 5 0 3 0 3 20 20 3 3 2 5 85 15 60 50 5 0 0 3 3 70

8/28/2010 30 3 5 0 3 0 3 20 30 3 3 2 5 85 15 60 50 5 0 0 3 3 70

8/29/2010 30 3 5 0 3 0 3 20 20 3 3 2 5 85 15 60 50 5 0 0 3 3 70

8/30/2010 16 2 7 3 4 60 5 15 20 5 5 3 4 100 18 60 70 4 10 0 10 7 80

8/31/2010 16 2 17 3 4 60 5 15 20 5 5 3 4 100 20 60 70 4 10 0 10 4 80

9/1/2010 16 2 17 3 4 60 5 15 20 5 5 3 4 80 20 60 70 4 10 0 10 7 80

9/2/2010 10 2 15 31 4 50 5 10 10 5 5 3 4 60 ¨10 40 50 10 10 0 10 7 60

9/3/2010 10 2 15 3 4 50 5 10 10 5 5 3 4 60 10 4 50 10 10 0 10 7 60

9/4/2010 0 3 3 0 1 0 20 15 30 3 5 2 3 100 0 50 55 5 0 0 15 3 70

9/5/2010 0 3 3 0 1 20 15 30 3 5 2 50 3 100 0 55 10 3 0 0 15 3 5

9/7/2010 0 3 3 0 1 0 20 15 30 3 3 50 3 100 0 55 60 3 0 0 15 3 5

9/8/2010 0 3 3 0 1 20 15 3 0 5 50 55 3 100 0 60 5 3 0 0 15 3 3

9/9/2010 0 0 3 0 5 0 3 10 20 0 5 3 3 60 5 50 30 20 0 1 15 20 55

9/10/2010 0 3 3 0 3 0 3 5 3 3 3 3 5 0 0 50 25 1 2 0 10 7 35

9/11/2010 0 0 3 0 5 0 3 10 20 0 5 3 3 60 5 50 30 20 0 1 15 20 55

9/12/2010 0 0 3 5 5 3 10 2 0 5 3 50 3 0 60 30 55 20 1 1 15 0 20

9/13/2010 0 0 3 5 5 3 10 20 5 3 50 30 3 0 60 55 20 0 1 15 3 1 20

9/14/2010 0 0 3 5 5 3 10 20 0 5 3 5 1 0 60 30 50 10 0 10 2 0 20

9/15/2010 0 0 3 5 5 5 3 15 10 5 60 55 3 0 50 20 30 2 20 3 5 3 0

52

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9/16/2010 0 0 3 5 5 5 3 15 10 5 60 55 3 0 50 20 30 2 20 3 5 3 0

9/17/2010 5 6 7 8 4 3 30 15 10 30 15 5 60 30 30 40 10 30 5 10 5 5 5

9/18/2010 5 6 7 10 4 0 3 30 15 10 30 15 5 60 30 30 40 10 30 5 10 5 5

9/19/2010 5 6 7 8 4 0 3 30 15 10 30 15 5 60 30 30 40 10 30 30 10 5 5

9/20/2010 0 2 6 4 3 0 5 5 7 3 10 5 3 0 3 20 30 10 20 20 5 5 3

9/21/2010 0 2 6 4 3 0 5 5 7 3 10 5 3 0 3 20 30 10 20 20 5 5 3

9/22/2010 0 2 6 4 3 0 5 5 7 3 10 5 3 0 3 20 30 10 20 20 5 5 3

9/23/2010 5 10 3 0 10 0 0 3 6 3 5 3 10 0 0 50 40 10 0 0 11 5 50

9/24/2010 5 10 3 0 10 0 0 3 6 3 5 3 10 0 0 50 40 10 0 0 11 5 50

9/25/2010 0 5 4 0 3 0 0 3 10 3 3 2 0 0 0 3 10 3 0 0 2 2 10

9/26/2010 0 5 0 3 0 0 3 10 3 3 2 3 0 0 0 10 10 0 0 2 2 0 3

9/27/2010 0 5 4 0 3 0 0 3 10 3 3 2 0 0 0 3 10 3 2 0 0 2 3

9/28/2010 0 5 4 0 3 0 0 3 10 3 3 2 0 0 0 3 10 3 0 0 2 2 10

9/29/2010 0 5 4 0 3 0 0 3 10 3 3 2 4 0 0 5 10 3 2 0 0 2 10

9/30/2010 0 1 3 0 1 0 0 5 10 2 5 3 5 0 0 30 3 2 1 0 0 3 0

10/1/2010 0 2 2 0 3 0 1 2 15 3 2 2 5 0 0 30 0 0 2 0 0 5 5

10/2/2010 0 2 2 0 3 0 1 2 15 3 2 2 5 0 0 30 0 0 2 0 0 5 5

10/3/2010 0 3 2 0 2 0 0 5 15 3 4 2 5 0 0 25 0 3 0 0 0 5 0

10/4/2010 0 3 2 0 2 0 0 5 15 3 4 2 5 0 0 25 0 3 0 0 0 5 0

10/5/2010 0 2 2 0 4 0 0 3 4 2 3 2 3 0 0 20 0 3 0 0 5 2 0

10/6/2010 0 2 2 0 4 0 0 3 4 2 3 2 3 0 0 20 0 3 0 0 5 2 0

10/7/2010 0 2 2 0 4 0 0 3 4 2 3 2 3 0 0 20 0 3 0 0 5 2 0

10/8/2010 0 2 3 0 1 0 0 3 10 2 2 1 3 0 0 25 0 3 1 0 6 4 0

10/9/2010 0 2 3 0 1 0 0 3 10 2 2 1 3 0 0 25 0 3 1 0 6 4 0

10/10/2010 0 2 3 0 1 0 0 3 10 2 2 1 3 0 0 25 0 3 1 0 6 4 0

10/11/2010 0 2 2 0 1 0 0 2 2 1 2 3 3 0 0 18 0 3 0 0 3 3 0

10/12/2010 0 2 2 0 1 0 0 2 2 1 2 3 3 0 0 18 0 3 0 0 3 3 0

10/13/2010 0 2 2 0 1 0 0 2 2 1 2 3 3 0 0 18 0 3 0 0 3 3 0

10/14/2010 0 2 2 0 1 0 0 1 2 1 2 2 8 0 0 18 0 1 0 0 4 8 0

10/15/2010 0 2 2 0 1 0 0 1 2 1 2 2 8 0 0 18 0 1 0 0 4 8 0

10/16/2010 0 2 2 0 1 0 0 1 2 1 2 2 8 0 0 18 0 1 0 0 4 8 0

10/17/2010 0 2 2 0 1 0 0 1 2 1 2 2 8 0 0 18 0 1 0 0 4 8 0

10/18/2010 0 2 20 1 0 0 1 2 1 2 2 8 0 0 18 0 1 0 0 4 8 0

10/19/2010 0 2 2 0 1 0 0 2 2 1 3 2 3 0 0 18 0 1 0 0 4 2 0

10/20/2010 0 2 2 0 1 0 0 2 2 1 3 2 3 0 0 18 0 1 0 0 4 2 0

10/21/2010 0 2 2 0 1 0 0 2 2 1 3 2 3 0 0 18 0 1 0 0 4 2 0

10/22/2010 0 2 2 0 1 0 0 2 2 1 3 2 3 0 0 18 0 1 0 0 4 2 0

10/23/2010 0 1 1 0 2 0 0 1 2 0 2 0 2 0 0 2 0 3 0 0 2 3 0

10/24/2010 0 1 1 0 2 0 0 1 2 0 2 0 2 0 0 2 0 3 0 0 2 3 0

10/25/2010 0 1 1 0 1 0 0 1 1 0 2 0 3 0 0 2 0 2 0 0 1 2 0

10/26/2010 0 1 1 0 0 0 0 1 1 2 2 0 2 0 0 0 0 2 0 0 2 1 0

10/27/2010 0 1 1 0 0 0 0 1 1 2 2 0 2 0 0 12 0 2 0 0 21 2 0

10/28/2010 0 1 1 0 0 0 0 1 1 2 2 0 3 0 0 12 0 2 0 0 2 2 0

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10/29/2010 0 1 1 0 0 0 0 1 1 2 2 0 3 0 0 12 0 2 0 0 2 2 0

10/30/2010 0 1 1 0 1 0 0 1 2 1 1 0 3 0 0 5 0 2 0 0 2 1 0

10/31/2010 0 1 1 0 0 0 0 1 1 2 2 0 3 0 0 10 0 2 0 0 2 2 0

11/1/2010 0 1 1 0 0 0 0 1 1 2 2 0 3 0 0 10 0 2 0 0 2 2 0

11/2/2010 0 1 1 0 0 0 0 1 1 2 2 8 3 0 0 0 2 0 0 2 2 2 0

11/3/2010 0 1 1 0 0 0 0 1 1 2 2 0 3 0 0 8 0 2 0 0 2 2 0

11/4/2010 0 1 1 1 0 0 0 2 1 0 0 0 3 0 0 0 0 0 0 0 6 2 0

11/5/2010 0 1 1 1 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 6 1 0

11/6/2010 0 1 1 1 0 0 0 2 2 0 0 0 3 0 0 0 0 0 0 0 6 2 0

11/7/2010 0 1 1 1 0 0 0 2 2 0 0 0 3 0 0 0 0 0 0 0 6 2 0

11/8/2010 0 1 1 1 0 0 0 2 2 0 0 0 3 0 0 0 0 0 0 0 6 2 0

11/9/2010 0 1 1 1 0 0 0 2 2 0 0 0 3 0 0 0 0 0 0 0 6 2 0

11/10/2010 0 1 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 2 1 0

11/11/2010 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 2 1 0

11/12/2010 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 2 1 0

11/13/2010 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 11/14/2010 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

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APPENDICES 3: Questioners for Community Perception and Knowledge of

Watershed Hydrology and Soil Erosion Survey

Part I

Background information

Date: ____ Time: ____Zone: ______Name of watershed: _____Name of Respondent’s :_____( not less

than 20 yr)

Woreda/District: ______

HH characteristics N

o

1.1. N

Name of

HH member

1.2. R

elation of

HH members to

head of HH

1. Spouse

2. Children

3. Siblings 4. Relative

1.3. Gender

1. Male 2. Female

1.4. Age of

respondent

1.5. Marital

status

1. Single

2. Married

3. Divorced 4. widow

1.6. Educatio

nal level

1.None

2.Adult

education 3. 1-4

4..5-9

5. 10+ 6. College /

University

1.7. main activities

of the HH

1. Farming

1. House work

3. Herding 4. Bussiness

5. Student

6. Employed 7. Handicraft

8. Disabled

9. Child below 7 yr

1

2

3

4

Part II

Erosion and SWC practices

1. Land holding and farm characteristics

2. How many years you and/or your family been farming?

No Land use type Area (ha) Which part of the watershed

1. Cultivated

land 1.0- ⁄ 2. ⁄ -1 3.1-2

4.2-3 5.>3 1. Upslope 2. Mid slope 3. down slope

2. Fallow land 1.0- ⁄ 2. ⁄ -1 3.1-2

4.2-3 5.>3 1. Upslope 2. Mid slope 3. down slope

3. Grazing land 1.0- ⁄ 2. ⁄ -1 3.1-2

4.2-3 5.>3 1. Upslope 2. Mid slope 3. down slope

4. Home stead

area 1.0- ⁄ 2. ⁄ -1 3.1-2

4.2-3 5.>3 1. Upslope 2. Mid slope 3. down slope

5. Forest (bush) 1.0- ⁄ 2. ⁄ -1 3.1-2

4.2-3 5.>3 1. Upslope 2. Mid slope 3. down slope

No years

1 1-3

2 3-6

3 6-10

4 10-20

5 >20

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3. Are any of crops grows by the HH

No 3.1. Type of

crops

3.2. Do

you grow

this crop

1. Yes

2. No

3.3. If yes

to question

3.2, which

one is a

major crop

(make a

mark)

3.4. Total

production(qt)

3.5. after

using

fertilizer

was the

crop

beneficial?

1.yes

2. no

3.not yet

started

3.6. If yes to

3.5, How was

the importance

of crop

production in

improving the

living standard

of the HH

1. not helpful

2. helped a

little

3. helped a lot

4. helped very

much

With

fertilize

r

1. <5

2. 5-10

3. 10-15

4. >15

W/out

fertilizer

1. <5

2. 5-10

3. 10-15

4. >15

1 Teff

2 wheat

3 barley

4 Maize

5 Sorghum

6 Beans

7 Peas

8 Lentils

9 Millet

10 Chickpea

11 Triticale

12 Chat

13 Gibto

14 Others

(specify)

4. Is there a major change in cropping pattern during the last five years? 1. Increased 2. Decreased 3.

No change 4. Difficult to tell

5. Do you experience erosion problem on your farm? 1. Yes 2. No

6. If yes for question 5, which pert of your plot saw higher erosion?

1. Upland 2. Midsoles 3. Down slope

7. If yes to question 5, what is the main cause of the erosion in your opinion?

8. How do you know soil erosion occurs on your farm land? 1. Yield decline 2. Soil structure and color

change 3. Increase input demand 4. Others (specify)

9. What are the noticeable changes in your farm land you think caused by erosion?

No changes 1.Yes 2.No Remark

1 Removal of the top soil

No cause 1.Yes 2.No

1 Improper tillage

2 Slope/ terrain

3 High rainfall

4 Deforestation

5 Absence of protection measures

6 Over grazing

7 others

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2 Crop productivity reduction

3 Increased soil stoniness

4 Reduced fertilizer response

5 Soil fertility decline

6 Runoff generation

7 Infiltration

8 Reduction in WHC

9 Gully formation/ expansion

10. Which problems exaggerated in your farm land? 1. Erosion 2. Sedimentation 3. Don’t know

11. Where does the major runoff that cause soil erosion gets generated? 1. Top part 2. Middle part 3.

At the bottom part

12. Do you practice soil conservation measures? 1. Yes 2. No

13. Have you ever participated in any SWC technologies work initiated by government or NGOs?

1. Yes 2.No

14. If yes for question 13, which method do you use? No 12.1. Methods

use

12.2. Did you

use this method?

1. Yes 2. No

12.3. If Yes when

did you start

1. Before the

project 2. during the

project

3. after project phase out

12.4. If Yes how

well did it reduce erosion

1. None

2.Some

3. Much

12.5. If Yes how did you learn

these methods? 1. From parents (inherited)

2. From neighbors

3. From extension agent (training)

4. From NGOs

5. From school 6. Others (specify, if any)

1 Terracing

2 Ditches/

trenches

3 Contour planting

4 Stone bund

5 Check dams

6 Soil buns

7 Growing

legumes

8 Strip- cropping

9 Contour farming

10 Mulcting

11 Minimum (zero)

tillage

12 Tree planting

13 Agro-forestry practices

14 Fallowing

15 Vetiver grass

16 Tie ridges

17 Other 1 (specify, if any)

15. If No, for question 13, why didn’t take measures? 1. It is costly 2. High labor demanding 3. I don’t

Know 4. The land is not mining (rented) 5. I have no land right 6. Measures not effective to reduce

erosion 7. Other response (specify)

16. How do you measure the effectiveness of SWC practice? 1. Individually by its effectiveness to trap

soil or to maintain moisture 2. By comparing with different SWC practices 3. I know the

advantage and disadvantage 4. I don’t know 5. Others

17. Do you apply SWC on all your farmland? 1. Yes 2. No

18. If No, how do you select a farm plot for SWC treatment? 1. Depending on slope 2. Randomly 3. I

know which area in my farm land need SWC 4. I don’t know 5.Others

19. Do you think that there is soil fertility problem on your farm? 1. Yes 2. No

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20. Do you apply fertilizer in your farm land? 1. Yes 2. No (If yes, since when)

_____

21. What level of yield advantage you would expect from fertilizer addition? (In qt)

Part III

Erosion and its relation with runoff

22. What season does erosion mostly start? 1. March- April 2. May-June 3. July- Aug 4. Aug-Sep

5. Sep-Oct 6 Others

23. What week of the month is erosion sevier 1. First week 2. Second week 3. Third week

4. Fourth week

24. Where (at what part of the watershed) erosion starts? 1.Upper watershed 2. Middle watershed 3.

Bottom (level slope) of the watershed

25. What are the major causes of soil loss in the watershed?

no causes 1.Yes 2.No Remark

1 Lack of conservation structures

2 Steep land without conservation structures

3 Damaged conservation structures

4 Lack of diversion ditch

5 The land is under steep ridges

6 others

26. Do you understand any relationship between runoff and soil loss? (Explain) 1. Yes 2. No

27. Do you think that Crop types affect runoff generation and soil loss from a cultivated plot? 1. Yes

2. No

28. If Yes for question 27, what type of crop increases runoff generation and soil loss?

No Types of crop 1.Yes 2.No remark

1 Teff

2 wheat

3 barley

4 Maize

5 Sorghum

6 Others1(specify)

29. Is there anything else you would like to tell about rainfall, runoff and erosion in the watershed? Any

questions I might have asked you that I didn’t?

no 0-5 5-10 10-15 15-20 >20

Yes

No