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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
© 2011 Bezawit Adane Demisse
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.
iii
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.
iv
This thesis is dedicated to for my all family especially for my Mother, Manyahilishal Abate, and
my Father, Adane Demisse
v
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.
vi
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
vii
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
viii
REFERENCES ........................................................................................................... 45
APPENDICES ............................................................................................................ 51
ix
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
x
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
xi
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
xii
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
xiii
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.
1
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
2
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.
3
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
4
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.
5
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
6
Figure 2-1: Location of Enkulal watershed
Lake
Tana
Gummara
Watershed 6
6
7
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%.
8
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
9
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
10
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
11
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
12
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
13
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
14
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
15
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.
16
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.
17
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.
18
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).
19
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
20
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 ( )
21
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:
22
[ (
)] 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:
[ ]
23
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
24
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 ]
25
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
26
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.
27
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
28
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
29
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
30
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
31
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
32
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
33
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
34
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.
35
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
36
Figure 3-6: Average water table Vs Average slope for each month
37
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
38
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).
39
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
40
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
41
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
42
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
43
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
44
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.
45
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.
46
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Netherlands.
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Sediment dynamics and the role of flash floods in sediment export from
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Awulachew, S. B., Selassie, Y., and Steenhuis, T. S. 2010. Development and
application of a physically based landscape water balance in the SWAT model,
Hydrol. Process.
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framework for runoff and soil loss prediction using GUEST technology. Aust.
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51
Zeleke, G. 2000. Landscape Dynamics and Soil Erosion Process Modeling in the
North-western Ethiopian Highlands. African Studies Series A 16, Geographica
Bernensia, Berne, Switserland.
52
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
53
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
54
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
53
55
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
54
56
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
57
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
58
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
59
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
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