SWAT TO IDENTIFY WATERSHED MANAGEMENT OPTIONS:
(ANJENI WATERSHED, BLUE NILE BASIN, ETHIOPIA)
A Thesis
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Master of Professional Studies
By
Biniam Biruk Ashagre
August 2009
© 2009 Biniam Biruk Ashagre
ABSTRACT
Ethiopia is known for its wealth of natural resources. These result in part from extreme
elevation variation. However, 5,000 years of land cultivation have degraded large
areas of the natural environment. Soil erosion affects 82% of the country. The rich
highland soil, which supports 80% of the total population, only covers 45% of the
country. In these highlands the soil is becoming less fertile; droughts are more
frequent and intense; and water resources are declining, due in part to the soil erosion.
The Anjeni watershed is located in the highlands in the Blue Nile Basin with an annual
soil loss of 18.33 tons/year/ha.
The existence of soil erosion in a watershed is an indication of unsustainable land
management practices. The objective of this study was to formulate sustainable land
management options that alleviate soil erosion in the Anjeni watershed. The SWAT-
WB model that simulates saturation excess flow was applied, and the result showed
that the Anjeni watershed is dominated by saturated excess flow from the shallow soils
rather than infiltration excess flow. The conventional SWAT model uses the SCS-
curve number method which considers only infiltration excess flow. In contrast, the
SWAT-WB model simulates saturation excess flow in order to determine surface
runoff. Hence, SWAT-WB was used to investigate the flow and sediment processes in
the watershed and to compare different potential land management options to alleviate
soil erosion.
The model SWAT-WB was calibrated for flow and performed well with a coefficient
of determination (R2) of 0.92 and Nash-Sutcliffe coefficient (ENS) of 0.91. The model
also performed well in simulating soil erosion on a monthly basis with the coefficient
of determination of 0.56 and the Nash-Sutcliffe coefficient of 0.55. The relatively
poorer performance of the model in simulating soil erosion can be attributed to a gully
in the watershed possibly contributing 30% of the annual soil loss from the watershed.
Model simulation suggests that the existing terraces are saving 2,046 tons/year of soil
loss. If further terraces are constructed, they could save an additional 932 tons/year.
Forestation of degraded areas and bush lands was found to reduce soil erosion by 333
tons/year. Zero-tillage technique for all fields except those covered with teff in the
watershed reduces erosion by only 45 tons/year. If gully rehabilitation work with a
90% erosion control practice is implemented in gullies, an additional 300 tons/year
would be saved. Combining foresting degraded lands and bush lands with
rehabilitation of gullies in Anjeni watershed is predicted to reduce soil loss from the
watershed by 630 tons/year. The impact of further construction of terraces on
productivity and its effect on the overall hydrological balance should be
experimentally investigated before being implemented and if it shows a significant
change, it can be practiced with some measures and innovations on the water
availability during the dry season.
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BIOGRAPHICAL SKETCH
Personal Details Name Sex Date of Birth Nationality Email
Biniam Biruk Ashagre Male 28-Feb-09 Ethiopian [email protected]
Education
Institute Degree Duration Year
conferred Field of Study Arba Minch University B. Sc.
09/2000 - 07/2005 23/07/2005 Civil Engineering
Professional Experience
Employer Duration Position Arba Minch University
23/07/2005 up to date
Lecturer in Civil Engineering Department
Major research Interest
Water, Climate change and Environmental Sustainability
Current research Using SWAT to Identify Watershed Management Options:
(Anjeni Watershed, Blue Nile Basin, Ethiopia)
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This study is dedicated to my beloved wife and my lovely family
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ACKNOWLEDGMENTS
I am grateful to International Water Management Institute (IWMI) for financial
support for this study and for providing with an office and facilitating the study needs.
I would like to acknowledge the Soil Conservation Research Program (SCRP) office
in Addis Ababa and the Amhara Region Agricultural Research Institute in Adit, for
their invaluable provision of information and data.
This thesis is a testimony to the professional and material help and comments from
Professor Tammo Steenhuis, Dr. Zach Easton, Dr. Matthew McCartney, and Eric D.
White. Thanks to all those people who helped see this thesis to completion, they are
too numerous to mention.
Without the help of Dr. Amy Collick, it would have been very difficult to get data
from different offices.
Last but not least, thanks go to my lovely wife for comments and encouragements. I
would also like to thank my family and my friends for their moral support.
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TABLE OF CONTENTS
BIOGRAPHICAL SKETCH.........................................................................................iii
ACKNOWLEDGMENTS..............................................................................................v
LIST OF FIGURES.......................................................................................................ix
LIST OF TABLES .......................................................................................................xii
LIST OF ABBREVIATIONS .....................................................................................xiii
CHAPTER ONE.............................................................................................................1
INTRODUCTION..........................................................................................................1
CHAPTER TWO............................................................................................................5
STUDY AREA............................................................................................................... 5
Location .........................................................................................................................5
Climate...........................................................................................................................6
Hydrology...................................................................................................................... 6
Land use and Soil Conservation..................................................................................7
Geology and Soil ...........................................................................................................9
CHAPTER THREE ......................................................................................................11
METHODS...................................................................................................................11
SWAT Model Description..........................................................................................11
Surface Runoff ..............................................................................................................13
Soil Water .....................................................................................................................15
Lateral Flow .................................................................................................................17
Percolation and Ground Water Return Flow ...............................................................19
Sediment .......................................................................................................................22
Model Input.................................................................................................................26
Digital Elevation Model ...............................................................................................27
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Land Use Map ..............................................................................................................28
Soil Map and Data........................................................................................................30
Weather Data................................................................................................................31
River Discharge and Sediment Yield ............................................................................34
Management Practices and Scenarios .........................................................................36
Tillage Activity..............................................................................................................37
Model Setup ................................................................................................................37
Watershed delineation ..................................................................................................37
HRU Definition.............................................................................................................38
Weather Data Definition ..............................................................................................40
Management Practices .................................................................................................40
Model Sensitivity Analysis, Calibration and Validation ..............................................41
Scenarios ......................................................................................................................47
CHAPTER FOUR ......................................................................................................50
RESULTS AND DISCUSSIONS...............................................................................50
Sensitivity Analysis.....................................................................................................50
Model Calibration and Validation ............................................................................51
Analysis of Results...................................................................................................... 59
Gully Erosion ..............................................................................................................65
Spatial Variation of Runoff and Soil Erosion ..........................................................67
Scenarios...................................................................................................................... 69
Flow..............................................................................................................................69
Sediment.......................................................................................................................72
CHAPTER FIVE ........................................................................................................77
CONCLUSIONS AND RECOMMENDATIONS ...................................................77
APPENDIX .................................................................................................................91
viii
Appendix I: Parameters in SWAT database for each crops in the watershed .....91
Appendix II: Parameters in SWAT database for each soil layers in the watershed
......................................................................................................................................92
Appendix III: Parameters in SWAT database for Urban land uses in the
watershed..................................................................................................................... 95
Appendix IV: A. Sliding of sides of gullies ...............................................................96
Appendix IV: B. Soil piping in channel sides and springs in the watershed.........97
Appendix IV: C. Soil piping in gullies and side sliding...........................................98
Appendix V: Parameters used for Weather Generator in SWAT Model ............99
Appendix VI: Observed and Simulated Flow and Sediment loss in Calibration100
Appendix VII: Observed and Simulated Flow and Sediment loss in Validation103
ix
LIST OF FIGURES
Figure 1: Location of Anjeni watershed.........................................................................5
Figure 2: Percentage of land use of Anjeni watershed based on the recorded land uses
on field investigation (method is provided in chapter three)..........................................7
Figure 3: Graded bund (a) and graded fanya juu bund (b) .............................................8
Figure 4: Conservation practices in the watershed......................................................... 9
Figure 5: Figure 7: Soil Water Characteristics Curve
(http://www.dpi.vic.gov.au/dpi/vro/gbbregn.nsf/pages/soil_hydraulic_pdf/$FILE/Tech
Reportch02.pdf .............................................................................................................16
Figure 6: The digital elevation model of Anjeni watershed ......................................... 28
Figure 7: Google earth image of Anjeni watershed with 20 different control points...29
Figure 8: Land use map of Anjeni using Google image and the recorded land use on
each plot........................................................................................................................29
Figure 9: Soil map of Anjeni watershed (based on FAO classification) ......................31
Figure 10: Land use as reclassified by SWAT into the four letter land use code ........38
Figure 11: Soil Map of Anjeni Watershed as reclassified into four letters soil name in
SWAT database ............................................................................................................39
Figure 12: Newly constructed Fanya Juu terrace (a) and Fanya Juu after five years of
construction (b). * Pictures taken from:
http://www.iwmi.cgiar.org/africa/west/projects/Adoption%20technolgy/rainwaterharv
estin/50-Fanya%20juu.htm...........................................................................................48
Figure 13: Coefficient of determination for simulated flow in calibration on a daily
basis ..............................................................................................................................55
Figure 14: Coefficient of determination for simulated sediment in calibration on a
daily basis .....................................................................................................................55
x
Figure 15: Coefficient of determination for simulated flow in validation on a daily
basis ..............................................................................................................................56
Figure 16: Coefficient of determination for simulated sediment in validation on a daily
basis ..............................................................................................................................56
Figure 17: Coefficient of determination for simulated flow in calibration on a monthly
basis ..............................................................................................................................57
Figure 18: Coefficient of determination for simulated sediment loss in calibration on a
monthly basis................................................................................................................57
Figure 19: Coefficient of determination for simulated flow in validation on a monthly
basis ..............................................................................................................................58
Figure 20: Coefficient of determination for simulated sediment loss in validation on a
monthly basis................................................................................................................58
Figure 21: Hydrograph of the observed and simulated flow from the watershed for the
validation period on a daily basis .................................................................................61
Figure 22: Comparison of observed and simulated sediment loss from the watershed
for the validation period on a daily basis......................................................................61
Figure 23: Hydrograph for Anjeni watershed on a daily basis for the whole period of
calibration showing precipitation, flow from the watershed and sediment loss in a
daily basis for the observed and simulated values........................................................62
Figure 24: Comparison of observed and simulated sediment loss on a daily basis from
Anjeni watershed for the whole calibration period ...................................................... 62
Figure 25: Hydrograph of the observed and simulated flow from the watershed for the
calibration period on a monthly basis...........................................................................63
Figure 26: Comparison of observed and simulated sediment loss from the watershed
for the calibration period on a monthly basis ...............................................................63
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Figure 27: Hydrograph of the observed and simulated flow from the watershed for the
validation period on a monthly basis............................................................................64
Figure 28: Comparison of observed and simulated sediment loss from the watershed
for the validation period on a monthly basis ................................................................64
Figure 29: Location and comparison of the largest gully in the watershed for different
years..............................................................................................................................65
Figure 30: Map of extent of surface runoff in each HRU ............................................68
Figure 31: Map of extent of sediment loss from each HRU.........................................69
Figure 32: Comparison of average monthly flow in each scenario..............................71
Figure 33: Pattern of decrease or increase in flow in different scenarios compared to
the base scenario...........................................................................................................71
Figure 34: Annual average water yield form the watershed in each scenario..............72
Figure 35: Hydrograph in each scenario on a monthly basis (see table in Appendix VI)
......................................................................................................................................73
Figure 36: Sediment yield from the watershed in each scenario on a monthly basis (see
table in Appendix VI)...................................................................................................73
Figure 37: Comparison of Average monthly sediment loss from the watershed in each
scenario.........................................................................................................................74
Figure 38: Pattern of decrease or increase in sediment loss from the watershed in
different scenarios compared to the base Scenario.......................................................76
Figure 39: Annual average water yield form the watershed in each scenario..............76
xii
LIST OF TABLES
Table 1: Percentage of area of soil cover in the Anjeni watershed based on the map
prepared by (Gete Zeleke, 2000) ..................................................................................10
Table 2: Crop calendar used in the Anjeni watershed..................................................37
Table 3: Slope descritization used for creation of HRUs ............................................. 40
Table 4: Review of Calibration of Parameters by variable used by SWAT modelers .42
Table 5: Parameters used for sensitivity analysis in this study .................................... 42
Table 6: Parameters used for Calibration .....................................................................43
Table 7: The most sensitive parameters for flow and sediment ...................................51
Table 8: Output variables; simulated before calibration and observed values in mm..52
Table 9: Values of parameters used for calibration......................................................52
Table 10: Annual average output variables; simulated after calibration and observed 54
Table 11: Coefficient of determination and the Nash – Sutcliffe Coefficients for
calibration and validation both in daily basis and monthly basis .................................54
Table 12: Number of farmers interviewed for identification of the process of runoff
production.....................................................................................................................59
Table 13: Area of the gully in m2 for the three years ...................................................66
Table 14: Calculation of mass of soil loss due to gully erosion for the three different
years..............................................................................................................................67
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LIST OF ABBREVIATIONS
ADJ_PKR: Peak Rate Adjustment Factor
AGNPS: The Agricultural Non-Point Source
AJDYCA: Anjeni-Dystric Cambrisols
AJEURE: Anjeni-Eutric Regosols
AJHAAC: Anjeni-Haplic Acrisols
AJHAAL: Anjeni-Haplic Alisols
AJHALX: Anjeni-Haplic Lixisols
AJHANI: Anjeni-Haplic Nithisols
AJHUAL: Anjeni-Humic Alisols
AJHUNI: Anjeni-Humic Noithisols
AJLILE: Anjeni-Lipthic Leptosols
AJVELU: Anjeni-Vertic Luvisols
ALFA: Alfa Alfa
ALHA_BF: Baseflow Alpha Factor
ArcGIS: Suit consisting of Geographical Information System software products
produced by ESRI
ArcSWAT: Soil and Water Assessment Tool version compataple with ArcGIS
AWC: Available Water Capacity
BARL: Spring Barley
CDE: Center for Development and Environment
CFRG: Coarse Fragment Factor
CH_CV: Channel Cover Factor
CH_EROD: Channel Erodibility
CN: Curve Number
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CN-ASMC: Curve Number – Antecedent Soil Moisture Condition
CORN: Corn
CREAMS: Chemical Runoff, and Erosion from Agricultural Management System
CUSLE: Universal Soil Loss Equation Land Cover and Management Factor
DEM: Digital Elevation Model
EDC: Effective Depth Coefficient
ENS: Nash Sutcliffe Efficiency Coefficient
EPIC: Erosion-Productivity Impact Calculator
ESRI: Environmental Systems Research Institute
FAO: Food and Agriculture Organization
FC: Field Capacity
FLAX: Flax
FRST: Forest-Mixed
GIS: Geographical Information System
GLEAMS: Ground Water Loading Effects on Agricultural Management Systems
GW_REVAP: Groundwater "Revap" Coefficient
GWQ: Groundwater Discharge
GWQMN:
HRU: Hydrologic Response Unit
ISED_DET: Code governing calculation of daily maximum half-hour runoff
KUSLE: Universal Soil Loss Equation Soil Erodability Factor
LSUSLE: Universal Soil Loss Equation Topographic Factor
MUSLE: Modified Universal Soil Loss Equation
NRCS: Natural Resource Conservation Services
PET: Potential Evapo-Transpiration
PRF: Peak rate adjustment factor
xv
PRMS: Precipitation-Runoff Modeling System
PUSLE: Universal Soil Loss Equation Support Practice Factor
R2: Coefficient of Determination
RECHRG_DP: Deep Aquifer Percolation Fraction
REVAPMN: Threshold depth of water in the shallow aquifer for "revap" or
percolation to the deep aquifer to occur
RNGB: Range-Brush
RNGE: Range-Grasses
SCRP: Soil and Water Conservation Research Program
SCS: Soil Conservation Service
SLOPE: Average Slope Steepness
SLSUBBSN: Average Slope Length.
SOL_AWC: Soil Layer Available Water Capacity
SOL_BD: Soil Moist Bulk Density
SOL_K: Soil Saturated Hydraulic Conductivity
SOYB: Soya Bean
SPCON
SPEXP
SURLAG: Surface Runoff Lag Coefficient
SURQ: Surface Runoff
SWAT: Soil and Water Assessment Tool
SWAT-WB: Soil and Water Assessment Tools-Water Balance
SWC: Soil and Water Conservation
SWRRB: Simulator for Water Resources in Rural Basins
TEFF: Teff
USAID: United Sates Agency for International Development
xvi
USLE: Universal Soil Loss Equation
USLE_K: Universal Soil Loss Equation Soil Factor
USLE_P: Universal Soil Loss Equation Management Factor
WEPP: Water Erosion Prediction Project
WP: Wilting point
WWHT: Winter Wheat
WYLD: Water yield
1
CHAPTER ONE
INTRODUCTION
Watershed management strategies are critical to efficiently utilize the natural resource
base while maintaining environmental quality. Of the many at risk resources in the
Ethiopian highlands, soil and water are arguably 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. The average annual rate of soil loss in Ethiopia is estimated to be 12
tons/hectare/year with losses as high as 300 tons/hectare/year (USAID, 2000). In the
Ethiopian highlands, in particular, soil erosion is a major problem with an estimated
loss of 16-50 ton/hectare/year (Abegaz Gizawchew, 1995).
When compared to other regions, the Ethiopian highlands have the highest levels of
soil loss (Fournier 1960, Walling, 1984). The highland areas, which are rural and each
household is dependent on a low level of agricultural productivity, cover 45% of the
country. Due to the potentially high productivity of the region nearly 80% of the total
population lives in the highlands (Patterson, 2007). The level of agricultural
productivity in these areas is highly influenced by erratic and unpredicted rainfall in
addition to degradation of resources, such as soil. Resource degradation, particularly
soil degradation in the form of nutrient depletion, is an important factor in the decline
in the country’s agricultural production (Bekele and Holden, 1998). Therefore,
management techniques practiced to conserve soil are not only related to the
conservation of natural resources but also to the sustainable development of the
agricultural sector. The existence of high rate of soil erosion in a watershed can be
2
taken as an indication of unsustainable land management practices (Herweg and
Stillhardt, 1999).
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 in a watershed, 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. Assessing and mitigating
soil erosion at the watershed level is complex both spatially and temporally. Soil type,
depth, and location, land cover type and management, topology and other factors make
the watershed a complex system where hydrologic and erosive process may differ
greatly over a small spatial scale. 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. There are many models that can
continuously simulate stream flow, erosion/sedimentation, or nutrient loss from a
watershed. However, few models have been developed or tested in the monsoonal
climates of Africa. The Soil and Water Assessment Tool (SWAT) (Arnold et al.,
1998) has recently been adapted to more effectively model hydrological processes in
monsoonal climates such as Ethiopian (White et al., 2008).
Some models developed in temperate regions have been tested in Ethiopia but many
have inherent weaknesses, largely because they were not developed in the country.
The Agricultural Non-Point Source (AGNPS) model was tested on the highlands of
3
the Augucho watershed but the outputs from this model did not accurately simulate
pattern of observed runoff (Haregeweyn and Yohannes, 2003). Haregeweyn and
Yohannes (2003) recommended the integration of AGNPS with Geographical
Information Systems (GIS) for increased efficiency and for the model to handle large
and varied types of data. The Water Erosion Prediction Project (WEPP) erosion model
over-predicts soil loss (Zeleke, 2000). The Precipitation-Runoff Modeling System
(PRMS) was tested by Legesse et al. (2003) for South Central Ethiopia but the model
required extensive calibration to predict monthly runoff. PRMS does not route on a
daily basis thus is limited to analyses where longer time steps are appropriate. In
SWAT, GIS and other interface tools can be used to support the input of topographic,
land use and soil data. SWAT can be easily calibrated and can run on a daily basis and
can easily incorporate changes in land use.
In order to choose a model for a particular watershed, the following factors should be
considered: the level of application, purpose, required accuracy, space and time scale,
and availability of data (Decoursey and Selly, 1988). SWAT is suitable in the
following context:
• Watersheds with no monitoring data can be modeled
• The relative impact of alternative input data (e.g. change in management
practices, climate, vegetation, etc) on water quality and other variables of
interest can be quantified
• A variety of management strategies can be modeled without excessive
investment in time or money.
• Enables user to study long-term inputs (Neitsch et al 2005)
Thus, SWAT was selected to model the hydrological processes and estimate the soil
loss from the Anjeni watershed.
4
This study used SWAT to identify areas of the watershed highly affected by soil
erosion and target these areas for soil erosion control measures. SWAT was also used
to select the best management options to minimize soil loss. In order to formulate
management options, the following objectives were identified:
1. To simulate seasonal and long term sediment yields using SWAT
2. To conduct sensitivity analysis in order to identify hydrological parameters
that most influence surface runoff, base flow, and soil erosion in the watershed
3. To perform calibration and validation for flow and sediment at the outlet of the
watershed
4. To analyze spatial variation of runoff and soil erosion in the watershed. This
helps to identify the sub-watersheds or areas that contribute most sediment to
streams
5. To evaluate the effectiveness of existing Soil and Water Conservation (SWC)
structures in reducing soil erosion
6. To identify the best management options to minimize future soil erosion in the
watershed
5
CHAPTER TWO
STUDY AREA
Location
The Anjeni watershed is located in the Amhara Region which is dominated by
highlands. The watershed is oriented North-South and flanked on three sides by
plateau ridges. It is located at 37o31’E and 10o40’N and lies 370 km NW of Addis
Ababa to the south of the Choke Mountains (Figure 1). Minchet, a perennial river
starts in the watershed and flows towards the Blue Nile Gorge. The lowest point in
elevation in the watershed is found at the outlet (2,406m above sea level). The highest
point in the watershed is found near the Village of Anjeni (2,505 m above sea level).
The research unit Anjeni, which is found in the watershed, was established in March
1984 near the watershed outlet. The research station contains a river station for
hydrological and sediment data collection and a climate station. The catchment area of
Anjeni is 113.4 ha. (Werner, 1986, Bosshart, 1995, SCRP Report, 2000).
Figure 1: Location of Anjeni watershed
6
Climate
Anjeni is situated in the agro-climatic zone “Wet Woyna Dega” with only one rainy
season which lasts from the middle of May to the middle of October (Hurni 1982).
Maximum daily rainfall is 80mm. The mean annual rainfall is 1,690mm with a low
variability of 10%. In the watershed soil erosion is highly influenced by the erosivity
of rainfall. A single intense rainfall event can cause up to 50% of the monthly soil loss
(SCRP report, 2000). Additionally, there is high gully formation at the bottom of the
watershed. Some of these gullies have been rehabilitated by planting trees in the
gullies to reduce further widening and sliding. The daily minimum air temperature
ranges from 0oC to 20oC and the daily maximum air temperature ranges from 12oC to
33oC. The mean daily temperature ranges from 9oC to 23oC.
Hydrology
The catchment drains from the North-East to South-West (Bosshart, 1995). The upper
part of the watershed is dominated by highly compacted and degraded areas. The grass
lands at the bottom the watershed are also compact and have a very low infiltration
rate. Observations show that the degraded areas in the upper catchment and the bottom
grass land areas are those which produce runoff immediately after rainfall starts. An
interview of 50 farmers indicated that runoff in the watershed at the beginning of the
rainy season is not produced immediately after rainfall. In contrast runoff production
in the middle of the rainy season occurs immediately after the rainfall starts. The
runoff production after a rainfall event at the end of rainy season is faster than the
production of rainfall at the beginning of rainy season but not faster than that of the
mid-rainy season. The mid part of the watershed is dominated by cultivated fields.
These fields have a moderate slope which is further reduced by the terraces
constructed in the watershed since 1986.
7
According to this study, the surface runoff contributes around 29% to the river
discharge at the outlet of the watershed. The lateral flow contributes around 49% and
groundwater recharge contributes about 22% of the discharge at the outlet of the
watershed. The mean annual discharge of the Minchet River at the catchment outlet is
730mm.
Land use and Soil Conservation
The watershed is mainly used for agricultural purposes. Cultivated fields cover more
than 65% of the watershed. The summary of the land use in the year 2008 is shown in
Figure 2.
Figure 2: Percentage of land use of Anjeni watershed based on the recorded land uses on field investigation (method is provided in chapter three)
8
Figure 3: Graded bund (a) and graded fanya juu bund (b)
Most of the cultivated fields have small ditches, which serve as drains for the excess
runoff out of the fields. These drainage ditches generally have a depth of 10-20cm and
a width of 20-30cm. The number of ditches and spacing between them in a field
depends on the steepness of the field; the more steep the field, the more runoff
expected hence more ditches have to be made (Werner 1986). This is the traditional
practice that has been practiced in the past and also now. “Fanya Juu Bunds” and
“Graded Bunds” are the measures practiced by the farmers to conserve soil in their
field. “Fanya Juu” is a ditch-wall-combination where the wall is uphill of the ditch
which is on the downhill side. Whereas, the “Graded Bund” is the opposite: the ditch
uphill and the wall downhill, see Figures 3a and 3b. The current conservation practices
in the watershed are presented in Figure 4.
9
Figure 4: Conservation practices in the watershed
Geology and Soil
The geology of the area is flood basalt resulting from a Tertiary Volcanic Eruption.
Thus Trappean Lava covers the Mesozoic Limestone and Sandstone layers below. The
soil of Anjeni developed on the accumulated basaltic lava to form a plateau with soils
varying over short distances. Based on the study made by (Zeleke, 1998) 8 major soil
units and 10 subgroups were identified. Alisols, Nitisols, and Cambisols are the major
types of soil covering more than 80% of the watershed. The bottom part of the
watershed is covered with deep Alisols. The mid-transitional, gently sloping parts of
the watershed are covered with moderately deep Nitisols. Shallow Regosols and
Leptosols cover the high, steepest part of the watershed. The hill top of the watershed
is covered with moderately deep young Dystric Cambisols. The soil in the watershed
can be classified as acidic and low in organic carbon content. The percentage soil
cover in the watershed, see table 1, is determined base on the study (Zeleke, 2000).
10
Table 1: Percentage of area of soil cover in the Anjeni watershed based on the map prepared by (Zeleke, 2000)
Soil Subgroup Area (m2) Percentage Area Vertic Luvisols 42254.7 3.9% Haplic Alisols 206027.9 19.1% Dystric Cambisols 188467.5 17.4% Eutric Regosols 99836.3 9.2% Humic Nitosols 66090.3 6.1% Haplic Nitosols 172074.9 15.9% Haplic Lixisols 48049.4 4.5% Lithic Leptosols 24347.6 2.3% Haplic Acrisols 25799.2 2.4% Humic Alisols 208813.6 19.3%
11
CHAPTER THREE
METHODS
SWAT Model Description
The soil and Water Assessment Tool (SWAT) is a physically-based continuous-event
hydrologic model developed to predict the impact of land management practices on
water, sediment, and agricultural chemical yields in large complex watersheds with
varying soils, land use and management conditions over long periods of time (Arnold
et al., 1998, 2000; Neitsch et al. 2001). It can also be used to simulate water and soil
loss in agriculturally dominated small watersheds (Tripathi et al. 2003).
While the model is not new, it was developed from earlier models: SWRRB
(Simulator for Water Resources in Rural Basins) model (Williams et al. 1985; Arnold
et al., 1990) which is a continuous time step model that was developed to simulate
non-point source loading from watershed, CREAMS (Chemical Runoff, and Erosion
from Agricultural Management System) (Knisel, 1980), GLEAMS (Ground Water
Loading Effects on Agricultural Management Systems) (Leonard et al. 1987), EPIC
(Erosion-Productivity Impact Calculator) (Williams, 1975).
SWAT simulates “subbasins” within a watershed. This helps spatial referencing and is
useful when considering spatiality for watersheds dominated by one land use and soil
type. Input information for each subbasin is organized as: Climate, HRUs (Hydrologic
Response Units), water storage structures, Ground water (a shallow unconfined and
deep confined aquifer), main channel and tributary channels.
Thus, HRUs, ponds, groundwater and channel routing are the components of the
hydrological process (Neitsh et al. 2005).
12
The water balance is the driving force for the simulation of hydrology. SWAT uses
two steps for the simulation of hydrology, land phase and routing phase. The land
phase is the phase in which the amount of water, sediment, nutrient and pesticides
loadings in the main channel from each subbasin are calculated.
Where is the final water content in millimeters (mm), SWo is the initial soil water
content on day i (mm), Pday is the precipitation on day i (mm), Qsurf is the surface
runoff on day i (mm), AET is the actual evapo-transpiration on day i (mm), Qseep is the
water entering the unsaturated zone from the soil profile on day i (mm), and Qgw is the
return flow from the shallow aquifer and lateral flow on day i (mm).
In this study the modified SWAT model SWAT-WB (Soil and Water Assessment
Tools-Water Balance) (White et al., 2008) is used as the result of the different
mechanism of runoff production in the watershed. Liu et al. (2008) shows that the
runoff in most of the Ethiopian highlands is due to saturation excess flow. Saturation
excess flow is one mechanism for runoff generation in areas with shallow soils
characterized by a highly conductive top soil underlain by a dense top soil, and in
regions where the ground water is close to the surface. Runoff is usually generated
from areas that are saturated or become saturated during a storm. Infiltration
measurements and plot studies in the Ethiopian highlands have shown that the
infiltration rates, especially on hillsides with stone cover, can be of the same order of
magnitude or higher than the greatest rainfall intensity (McHugh, 2006). This high
infiltration rate results in the production of runoff for a more extended period for less
intense storms at the end of rainy season compared to the runoff produced
( )∑=
−−−−+=t
igwseepsurfdayot QQAETQPSWSW
1
13
immediately after a large storm (Liu et al., 2008). This phenomenon shows the need to
modify SWAT, which is infiltration excess runoff based, to simulate runoff for
watersheds with saturation excess flow being the dominant runoff process. Therefore,
the SWAT-WB (Soil and Water Assessment Tools-Water Balance, White et al., 2008)
follows a saturation excess approach and uses a simplified water balance approach as
models developed with this approach typically outperform others used in Ethiopia (Liu
et al, 2008 and Collick et al, 2008).
Surface Runoff
SWAT 2005 uses the concept that surface runoff occurs whenever the rate of water
application to the ground surface exceeds the rate of infiltration. Based on this
assumption, SWAT uses two methods for estimating surface runoff: the Soil
Conservation Service Curve Number technique (USDA Soil Conservation Service,
1972) and the Green and Ampt infiltration method (Green and Ampt, 1911). In the
Soil Conservation Service (SCS) curve number method often called the Curve-
Number (CN) method, land use and soil characteristics are lumped into a single
parameter (White et al. 2009). The initial value for CN is assigned by the user for each
HRU then SWAT calculates the lower and upper limit. For this calculation, SWAT
uses a soil classification based on the Natural Resource Conservation Services
(NRCS). This classifies soil into four hydrologic groups (a soil group has similar
runoff potential under similar storm and cover condition (NRCS, 1996)) based on
infiltration characteristics of the soil (Neitsch et al. 2005). After this classification the
model defines three antecedent moisture conditions to determine the appropriate CN
for each day using the CN-AMC (Curve Number – Antecedent Soil Moisture
Condition) (USDA – NRCS, 2004) distribution based on the moisture content of the
soil calculated by the model (Neitsch et al., 2005). This daily CN is then used to
determine a theoretical capacity S (retention parameter) that can be infiltrated.
14
The empirical model used to estimate direct runoff from storm is the SCS runoff
equation.
Where Qsurf is the daily surface runoff in millimeters (mm), Pday is the daily
precipitation (mm), Ia is the initial abstraction which is commonly approximated as
0.2S, and S is the retention parameter.
Thus,
SWAT calculates runoff if and only when the amount of precipitation is greater than
the initial abstractions and the rate of precipitation exceeds the rate of infiltration.
Thus, SWAT indirectly assumes only infiltration excess runoff is created. Whereas,
the daily runoff from a given event in SWAT-WB is equal to the amount of rainfall
minus the amount of water that can be stored in the soil before it is saturated. This
amount of storage is called available soil storage (White et al., 2008).
( )soilsoilD θφτ −=
Where τ is the available soil storage, D is the effective depth of soil profile, Φsoil is the
total soil porosity as expressed as a function of the total soil volume, and θsoil is the
volumetric soil moisture of HRU.
⎟⎠⎞
⎜⎝⎛ −= 1010004.25
CNS
( )( )SIP
IPQ
aday
adaysurf +−
−=
2
( )85.0
25.0 2
+−
−=
aday
daysurf IP
PQ
15
The soil porosity is calculated by SWAT using the relationship between soil porosity
and soil bulk density.
Where ρb is the soil bulk density mg/m3 and ρs is the particle density mg/m3 (based on
researches a default value of 2.65 mg/m3 is used (Neitsch et al, 2005)). The volumetric
soil moisture θsoil varies on each day depending upon plant uptake of water,
evaporation and precipitation (White et al, 2008).
Runoff is produced only when the precipitation is greater than the available soil
storage. Otherwise no surface runoff is produced. In this method, rainfall intensity is
assumed to have a limited impact on runoff production but rainfall volumes are the
ultimate drivers of soil saturation and the total rainfall volume determines the amount
of runoff (White et al., 2008).
Thus, runoff is calculated as
τ−= PQsurf
Where Qsurf is the surface runoff in millimeters (mm), P is the daily precipitation in
mm, and τ is the available soil storage in mm.
Soil Water
Water that infiltrates into the soil profile has several routes to leave the soil. Soil water
may be lost as evapotranspiration, taken by plants, percolated to the bottom of the soil
profile; it may flow laterally in the soil and then contribute to runoff in the main
channel. Water percolated from the root zone ultimately becomes aquifer recharge.
s
bsoil ρ
ρφ −= 1
16
The water content of a soil can range between wilting point to the soil porosity when
the soil is saturated. Between these two states there are two points important for plant-
soil interaction; field capacity and wilting point. Field capacity (FC) is the amount of
water held in the soil after excess gravitational water has drained away and after the
rate of downward movement has materially decreased. The soil water suction values
generally used for field capacity approximation range from 5 kPa for coarse-texture
soils to 10 kPa for samples that retain their original structure (McIntyre, 1974;
Marshall, 1982). Permanent wilting point (WP) is defined as the water content at
which the leaves of a growing plant reach a stage of wilting from which they do not
recover. Different plants have different values of soil water suction at wilting point.
Since the change in water content is small between 800 kPa and 3000 kPa for most
soils, a suction of 1500 kPa based on wilting studies with dwarf sunflower is generally
taken to be an approximation of permanent wilting point (Reeve and Carter, 1991).
Available water capacity (AWC) defined as the amount of water that can theoretically
be extracted by plants from a soil initially at field capacity (McIntyre, 1974).
Figure 5: Soil Water Characteristics Curve (http://www.dpi.vic.gov.au/dpi/vro/gbbregn.nsf/pages/soil_hydraulic_pdf/$FILE/TechReportch02.pdf
17
The amount of water held in the soil between the field capacity and the permanent
wilting point is considered to be the available water for plant extraction, see Figure 5
above. This water available for plants is referred to as the Available Water Capacity
(AWC). It is calculated as:
WPFCAWC −=
Where FC is the field capacity and WP is the wilting point.
SWAT estimates the permanent wilting point for each soil layer as
Where mc is the percent clay content of the layer in percentage (%) and ρb is the bulk
density for the soil layer.
SWAT calculates field capacity (FC) by adding AWC which is an input by the user
and the wilting point (WP). Saturated flow occurs when the water content of a soil
layer surpasses the field capacity for the layer. The excess water from field capacity
water content is available for percolation, lateral flow and surface runoff.
Lateral Flow
This flow is significant in watersheds with soils having high hydraulic conductivities
in surface layers and an impermeable or semi-permeable layer at a shallow depth. The
water collects above the impermeable layer is the source of water for lateral
subsurface flow (Neitsch et al., 2005).
⎟⎠⎞
⎜⎝⎛=
10040.0 bc xm
xWPρ
( )hilld
satexcesslylat Lx
SlpxKxSWxxQ
φ,2
024.0=
18
Where Qlat is the lateral flow, SWly,excess is the drainable volume of water stored in the
saturated layer in millimeters (mm) (A soil is considered to be saturated whenever the
water content of the layer exceeds the layer’s field capacity water content), Ksat is the
saturated hydraulic conductivity (mm/hr), Slp is the increase in elevation per unit
distance equivalent to tanαhill where tanαhill is the slope of the hill slope segment. Slp is
a value which is an input for SWAT (m/m), Φd is the drainable porosity of the soil
layer (mm/mm), and Lhill is the hill slope length (m).
Where SWly is the layer water content on a given day in millimeters (mm) and FCly is
the water content of soil layer at field capacity in mm.
The drainable porosity of the soil layer Φd is calculated as follows.
fcsoild φφφ −=
Where Φsoil is the total porosity of soil layer in mm/mm and Φfc is the porosity of soil
layer filled with water when the layer is at field capacity in mm/mm.
The amount of lateral flow calculated using the above procedures may not reach the
stream on the same day. The lag on lateral flow results in only a fraction of the lateral
flow from each HRU reaching the stream. Thus the daily amount of lateral flow reach
the steam is calculated as (Neitsch et al., 2005).
lylyexcessly
lylylylyexcessly
FCSWifSW
FCSWifFCSWSW
≤=
>−=
0,
,
( )⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−+=
⎟⎠⎞
⎜⎝⎛ −
=lagTT
ilatstorlatlat xQQQ1
1, exp1
19
Where is the amount of later flow discharged to the main stream in a day in mm,
Qlat is the amount of lateral flow generated in the subbasin on a given day in mm, and
TTlag is the lateral flow travel time (days).
The lateral flow travel time TTlag can be calculated by SWAT or the user can define it.
, if HRUs are without drainage tiles.
Where Lhill is the hill slope length in meters and Ksat,mx is the highest layer saturated
hydraulic conductivity in the soil profile (mm/hr) (one of the soil property taken from
previous studies).
, if drainage tiles are present in the HRUs.
Where tilelag is the drain tile lag time in HRUs (hours). Drainage tiles are subsurface
structures for draining water from the soil surface. They usually installed at 90mm
below the soil surface.
Percolation and Ground Water Return Flow
SWAT calculates percolation for each layer in the profile and this process occurs only
when the water content of the soil is more than field capacity.
Where Qper,ly is the amount of water percolating to the underlying soil in mm, SWly,excess
is the drainable volume of water in the soil layer, Δt is the length of the time step in
hours, and TTperc is the travel time for percolation in hours.
mxsat
hilllag K
LTT,
4.10=
24tilelagTTlag =
⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛−= ⎥
⎥⎦
⎤
⎢⎢⎣
⎡ Δ−
percTTt
excesslylyper xSWQ exp1,,
20
sat
lylyperc K
FCSATTT
−=
Where TTperc is the travel time for percolation in hours, SATly is the amount of water in
the soil layer when completely saturated in mm, FCly is the water content of the layer at
the field capacity in mm, and Ksat is the saturated hydraulic conductivity for the layer in
mm/hr.
Recharge to an unconfined aquifer occurs via percolation to the water table from a
significant portion of the land surface. The fraction of the total daily recharge routed to
the deep aquifer is given by.
rchrgdeepdeep QxQ β=
Where Qdeep is the amount of water moving into the deep aquifer on day ‘I’ in mm,
βdeep is the aquifer percolation coefficient (this parameter is defined by the user as
RCHRG_DP), and Qrchrg is the amount of water entering both aquifers on day ‘i'.
Recharge to shallow aquifer can be calculated as follows.
deeprchrgshrchrg QQQ −=,
Where Qrchrg,sh is the amount of water entering the shallow aquifer on day ‘i’.
Depending on the water table height in the shallow aquifer there is a base flow
contribution to the main channel. This flow occurs only when the water stored in the
shallow aquifer is greater than the threshold water level in the shallow aquifer for
ground water contribution to the main channel to occur. This value is defined by the
21
user (aqshthr,q) or in the SWAT interface the variable is presented as GWQMN (Neitsch
et al., 2005).
Thus
Where Qgw,i is the ground water flow into the main channel on day ‘i’ in mm, Qgw,--i-1
is the ground water flow into the main channel on day ‘i – 1’ in mm, and αgw is the
base flow recession constant which is a direct index of ground water flow response to
change in recharge (Smedema and Rycroft, 1983), Δt is the time step (1 day), Qrchrg,sh
is the amount of recharge to the shallow aquifer on day ‘i’ in mm, aqsh is the amount
of water stored in shallow aquifer at the beginning of day ‘i’ in mm, and aqshthr,q is the
threshold water level in shallow aquifer in mm.
When the shallow aquifer receives no recharge the above equation is simplified to;
Where Qgw,i is the ground water flow into the main channel at time t in mm, Qgw,0 is
the ground water flow into the main channel at the beginning of the recession (time =
0) in mm, αgw is the base flow recession constant defined by the user (ALPHA_BF),
and t is the time elapsed since the beginning of the recession (days).
[ ] [ ]( )qsthrshigw
qshthrshgwshrchrggwigwigw
aqaqifQ
aqaqiftxQtxxQQ
,,
,,1,,
0
exp1exp
≤=
>Δ−−+Δ−= − αα
[ ]qsthrshigw
qshthrshgwgwigw
aqaqifQ
aqaqiftxQQ
,,
,0,,
0
exp
≤=
>−= α
22
Sediment
SWAT uses the Modified Universal Soil Loss Equation (MUSLE) to estimate the soil
loss from each HRU.
( ) 56.056.0,8.11 CFRGxLSxPxCxKxhruareaxqxQxSed USLEUSLEUSLEUSLEpeaksurf=
Where Sed is the sediment yield on a given day in metric tons, Qsurf is the surface
runoff from the watershed in mm/ha, qpeak is the peak runoff rate in cubic meter per
second, area,hru is the area of HRU, KUSLE is the USLE soil erodability factor, CUSLE
is the USLE land cover and management factor, PUSLE is the USLE support practice
factor, LSUSLE is the USLE topographic factor, and CFRG is the coarse fragment
factor.
The peak runoff rate is the maximum runoff rate that occurs with a given rainfall
event. SWAT calculates the peak runoff rate with a modified rational method (Neitsch
et al., 2005). A brief description of sediment routing components of SWAT is given
below (Neitsch et al., 2005).
Where qpeak is the peak runoff rate in cubic meters per second, C is the runoff
coefficient is ratio of inflow rate to peak discharge rate, i is the rainfall intensity in
mm/hr, and Area is the subbasin area in square kilometers.
Where Qsurf is the surface runoff in mm and Pday is the rainfall for the day in mm.
6.3AreaxixCq peak =
day
surf
PQ
C =
23
Rainfall intensity is the average rainfall rate during the time of concentration (Neitsch
et al., 2005).
Where i is the rainfall intensity in mm/hr, Ptc is the amount of precipitation during the
time of concentration in mm, and tconc is the time of concentration for a subbasin in
hours.
daytctc PxaP =
Where atc is the fraction of daily precipitation that occurs during the time of
concentration and Pday is the daily precipitation in mm. SWAT estimates the fraction
of daily precipitation during the time of concentration (atc) as a function of the fraction
of daily rain falling in the half-hour of highest intensity rainfall.
( )[ ]5.01ln2exp1 α−−= xtxa conctc
Where α0.5 is the fraction of daily rain falling in the half-hour highest intensity rainfall
and tconc is the time of concentration in hours is the amount of time from the beginning
of a rainfall event until the entire subbasin area is contributing to flow at the outlet
(Neitsch et al., 2005).
chovconc txtt =
Where tov is the time of concentration for overland flow in hours and tch is the time of
concentration for channel flow in hours.
3.0
6.06.0
18 SlpxnxL
t Slpov =
conc
tc
tPt =
24
Where Lslp is the slope length of subbasin in meters, n is the Manning’s coefficient,
and Slp is the average slope in the subbasin in m/m.
Where L is the channel length in meters, n is the Manning’s coefficient for the
channel, Area is the subbasin area in square kilometers, and Slpch is the channel slope
in m/m.
The factors KUSLE, CUSLE, PUSLE, LSUSLE, and CFRG are taken and used based on
previous studies on the watershed and the definition and calculations of the parameters
presented in the SWAT documentation (Neitsch et al., 2005).
After estimating the amount of soil contributed by each subbasin to the main channel
the next step is routing soil loss which has two components; deposition and
degradation. To determine the deposition and degradation maximum concentration of
sediment (Concsed,ch,mx) is compared to the concentration of sediment in the reach at
the beginning of the time step (Neitsch et al., 2005).
( ) exp,,,
sppkchspmxchsed VxCConc =
Where Concsed,ch,mx is the maximum concentration of sediment that can be transported
by the water in kg/lit, Csp is the coefficient defined by the user (SPCON), and Vch,pk is
the peak channel velocity in m/s, and spexp is the exponent defined by user (SPEXP).
The peak channel velocity Vch,pk is calculated as:
375.0125.0
75.062.0
chch SlpxArea
nxLxt =
25
Where qch,pk is the peak flow rate in cubic meters per second (m3/s) and Ach is the
cross-sectional area of flow in the channel in square meter.
The peak flow rate qch,pk is defined as
chpkch qxprfq =,
Where prf is the peak rate adjustment factor (PRF) and qch is the average rate of flow
in m3/s.
If Concsed,ch,i is greater than Concsed,ch,mx, then deposition is the dominant process.
Thus, deposition is calculated as
( ) chmxchsedichseddep VxConcConcSed ,,,, −=
Where Seddep is the amount of sediment deposited in the reach segment in metric tons,
Concsed,ch,i is the initial sediment concentration in the reach in kg/lit or ton/m3,
Concsed,ch,mx is the maximum concentration of sediment that can be transported by the
water in kg/lit or ton/m3, and Vch is the volume of water in the reach segment in m3. If
Concsed,ch,i < Concsed,ch,mx then degradation is the dominant process in the reach
segment. The amount of sediment re-entrained is calculated as
( ) chchchmxchsedichseddep CxKxVxConcConcSed ,,,, −=
Where Seddeg is the amount of sediment re-entrained the reach segment (metric tons)
is the volume of water in the reach segment (m3), Kch is the channel erodability
ch
pkchpkch A
qV ,
, =
26
factor (conceptually similar to the soil erodability factor used in MUSLE), and Cch is
the channel cover factor (defined as the ration of degradation of a channel with a
specified vegetation cover to the corresponding degradation from channel with no
vegetation cover.
Once the deposition and degradation in the reach is determined the next step is to
calculate the suspended sediment in the reach.
deg, SedSedSedSed depichch −−=
Where Sedch is the amount of suspended sediment in the reach (metric tons) and Sedch,i
is the amount of suspended sediment in the reach at the beginning of the time period
(metric tons).
The amount of sediment transported out of the reach Sedout is calculated as
Where Sedout is the amount of sediment transported out of the reach in m3, Sedch is the
amount of suspended sediment in the reach in m3, Vout is the volume of water leaving
the reach segment during the time step in m3, and Vch is the volume of water in the
reach segment in m3.
Model Input
SWAT is a comprehensive model that requires information provided by the user to
simulate runoff and soil erosion. The first step in initializing a watershed simulation is
ch
outchout V
VxSedSed =
27
to partition the watershed into subbasins. The user has the option of allowing SWAT
to automatically delineate the watershed and subbasins using the Digital Elevation
Model (DEM) or the user can provide predefined subbasins. The land area in a
subbasin is divided into hydrologic response units (HRUs). Hydrologic response units
(HRUs) are portions of a subbasin and possess unique land use, slope range, and soil
attributes (Neitsch et al., 2004).
SWAT has different components. Hydrologic components of the model work on the
water balance equation, which is based on surface runoff, precipitation, percolation,
evapotranspiration, and return flow data; Weather is one of the model component that
needs data on precipitation, air temperature, solar radiation, wind speed, and relative
humidity data; Sedimentation is another component of the model that needs
information on surface runoff, peak rate flow, soil erodability, crop management,
erosion practices, slope length, and steepness; Soil temperature, crop growth, nutrient
pesticides and agricultural management are also components of SWAT. Thus, the data
required for the model are DEM, soil data, land use data, precipitation and other
weather data. For calibrating the model and also for validation purposes, river
discharge and sediment yield are required at the outlet of the watershed.
Digital Elevation Model
To delineate the watershed and subbasins and to determine drainage networks SWAT
uses the digital representation of the topographic surface. DEM is the digital
representation of the topographic surface. A 2m by 2m resolution DEM was used from
the Center for Development and Environment (CDE), Institute of Geography,
University of Berne, Switzerland, see Figure 6 below. Subbasin parameters such as
slope gradient, slope length of terrain and the stream network characteristics such as
channel length, width and slope were calculated and used by the model.
28
Figure 6: The digital elevation model of Anjeni watershed
Land Use Map
A map of land use for 2008 was created by recording the crop type on each plot in the
watershed and by identifying the land cover on areas other than cultivated fields. A
Google image of the watershed in 2008 (Figure 7) exactly fits and represents all fields
in the watershed. Hence, the image was used for recording. The digital Google image
was geo-referenced by taking 15 control points around and inside the watershed. The
shape file representing each plot and other land covers was created using the digitizing
tools provided in ArcGIS, ArcMap. A total of 16 different land uses were identified
and mapped as shown in Figure 8.
29
Figure 7: Google earth image of Anjeni watershed with 20 different control points
Figure 8: Land use map of Anjeni using Google image and the recorded land use on each plot
30
Soil Map and Data
Soils in the watershed should be categorized and prepared as a map in a shape file
format and then linked to a customized soil database designed by the user if the soils
are not included in the existing SWAT soil database. The soil map used in this
research was taken from the study made by Zeleke (2000). The soils were classified
based on the FAO (Food and Agriculture Organization) method of soil classification
(SCRP, 2000). Based on this there are ten different soil types in the watershed.
Basic physical properties (percentage sand, clay, and silt; soil texture class; soil
texture class; the percentage of carbon and profile thickness), derived soil properties
(hydraulic conductivity, bulk density, available water capacity, and soil organic matter
content) and the basic properties of each profiles of the ten different soils in the
watershed were obtained from Zeleke (2000), Kejela (1987), and Setegn et al. (2008)
(Figure 9).
31
Figure 9: Soil map of Anjeni watershed (based on FAO classification)
Weather Data
SWAT requires daily or sub-daily meteorological data. The model can either read
these meteorological data from previously measured data stored in tables or can be
generate it using a weather generator model. In this study, measured meteorological
data were used and the weather generator model was set up to estimate any missing
data. The meteorological data used were daily precipitation, daily maximum and
minimum air temperature, daily solar radiation, wind speed, and relative humidity on a
daily basis.
Daily precipitation data from 1984 – 2005 were obtained from the Soil Conservation
Research Program (SCRP), Ethiopia. There were missing precipitation data in some
months in the years 1997 and 1999. In these years the model uses values generated by
32
the weather generator model. SWAT uses WXGEN weather generator model
(Sharpley and Williams, 1990). The model generates precipitation using Markov
chain-skewed (Nicks, 1974) or Markov chain-exponential model (Williams, 1995). A
first-order Markov chain is used to define the day as wet or dry. When a wet day is
generated a skewed distribution or exponential distribution is used to generate the
precipitation amount (Neitsch et al, 2005). Maximum half-hour rainfall is required by
SWAT to calculate the peak flow rate for runoff. This value can be calculated from a
triangular distribution using maximum half-hour rainfall data or using monthly
maximum half-hour rainfall for all days in the month (Neitsch et al, 2005). The
procedure used to generate daily values for maximum temperature, minimum
temperature and solar radiation (Richardson, 1981; Richardson and Wright, 1984) is
based on the weakly stationary generating process presented by Matalas (1967)
(Neitsch et al, 2005). The daily maximum and minimum temperature from the year
1984 to 2005 were obtained from SCRP, Ethiopia. For the month of June and July in
the year 1996, for the month December 1997 and for four days in 1999 data were not
available. So, the weather generator was used to provide values for these dates. Daily
average monthly relative humidity values are calculated form a triangular distribution
using average monthly relative humidity. This method was developed by Williams for
the EPIC model (Sharpley and Williams, 1990; Neitsch et al, 2005). Mean daily wind
speed is generated in SWAT using a modified exponential equation.
( )( )3110 ln rndxmonwndm −= μμ
Where μ10m is the mean wind speed for the day (m/s), μwndmon is the average wind
speed for the month (m/s), and rnd1 is a random member between 0.0 and 1.0
33
SWAT used solar radiation, wind speed and relative humidity to calculate the potential
evapotranspiration (PET) as the model for this particular project used Penman-
Monteith approach for PET calculation. The meteorological station found in Anjeni
watershed has no data on solar radiation, wind speed and relative humidity. Hence,
Daily sunshine hours from the 1997 to 2006, wind speed, and relative humidity from
1994 to 2006 were taken from the nearby station Debremarkos meteorological station.
This station is located 46kms away from the watershed. Daily solar radiation was
calculated from the daily sunshine hour data using the Angstorm-Prescott equation
which is a simple empirical formulae that relates short-wave radiation with other
physical factors, such as extraterrestrial radiation, optical air mass, and turbidity, water
vapor content of the air, amount and type of cloud cover (Njau, 1996; Revfeim, 1997;
Persuad et al., 1997),
Where Qs is the solar radiation in W/m2, Qext is the daily total extraterrestrial radiation
in W/m2, a and b are constants which depend on the location, season, and state of the
atmosphere, n is the actual number of hours of bright sunshine (sunshine hour), and N
is the number of day light hours (since Ethiopia is near the Equator, N is assumed to
be 12).
Wind speed data and the relative humidity were available only from 1994 to 2006 on a
daily basis. So, the weather generator was used to simulate values for the rest years for
the PET calculation to be performed.
⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛+=
NnbaQQ exts
34
River Discharge and Sediment Yield
The river discharge of the outlet Anjeni watershed was obtained from SCRP, Ethiopia.
Data from 1986 to 1993 were available as well as the years 1995, 1996, and 2000.
SWAT does not use these data values in calculations but instead they are used for
comparing observed and simulated values in calibration and validation periods. Data
from 1986 to 1993 were used for calibration and validation and the data for the years
1995, 1996, and 2000 were used for validation.
There was uncertainty surrounding the measured discharge and the sediment yield in
the data from SCRP. For this reason, the river discharge was calculated using the true
water depth measured for those years. The discharge rate at the watershed outlet, in
liters per second, was calculated from the water level height in the main channel which
is measured in each 10minutes during the rainy season. To do this the stage-discharge
relation rating equation for the Minchet River at the Anjeni watershed outlet was used
(Bosshart, 1997):
Where q(H<60) is the discharge of the River Minchet (in liters/second) at the watershed
outlet before the water height gets 60cm, q(60≤H<120) is the discharge of the River
Minchet in liters/second at the watershed outlet when the water level height is between
60cm and 120cm, q(120≤H<360) is the discharge of the River Minchet in liters/second at
the watershed outlet when the water level height is between 120cm and 360cm, is
the water level height or stage in centimeters (cm).
( )
( )
( ) 0.36200.700.14000.567.0
2.1
360120
212060
5.160
−=
+−=
=
<≤
<≤
<
HqHHq
Hq
H
H
H
35
Then the amount of discharge leaving the watershed over a certain time is calculated
as
Where Qi is the amount of water in m3 leaving the watershed in the time interval (tf-ti),
qi is the discharge rate measured using the rating equation when the stage height is Hi,
ti is the initial time in seconds for the river water level height or stage comes to Hi, and
tf is the final time in seconds for the river water level to change from Hi. The amount
of water leaves the watershed in a day is calculated as
∑=
=n
iiday QQ
1
Where Qday is the amount of water in m3 leaving the watershed in a day (24hours) and
Qi is the amount of water in m3 leaving the watershed in the time interval (tf-ti).
The daily discharge is the sum of all the discharge calculated for a particular time in a
particular day. The discharge in this study is considered in mm depth of water in order
to compare the result of the SWAT simulation to the observed values. Thus, the
observed water yield from the watershed is calculated as
Where Qobs is the daily observed water yield from the watershed in mm depth
(observed discharge in mm), Qday is the amount of water in metric cube leaving the
watershed in a day, and A is the area of the watershed in hectares.
The concentration of sediment in grams/liter in the river was obtained from the SCRP
office, Addis Ababa, Ethiopia. The sediment concentration in the river discharge was
( )1000
ifii
ttxqQ
−=
( )AxQ
Q dayobs 10
=
36
measured by SCRP whenever the water level is measured. Thus, the soil loss from the
watershed is calculated as
Where SEDi is the sediment loss in kg from the watershed while the water level height
is Hi, Qi is the amount of water in m3 leaving the watershed while the water level
height is Hi, and Sedi is the measured sediment concentration in gram/liter while the
water level height is Hi.
The daily soil loss from the watershed is the sum of all the calculated soil losses for all
the different water level heights in a particular day.
∑=
=n
iiday SEDSED
1
Where SEDday is the soil loss from the watershed in a day in kg, SEDi is the sediment
loss in kg from the watershed while the water level height is Hi.
Where SEDobs is the observed sediment yield from the watershed in tons/hectare,
SEDday is the soil loss from the watershed in a day in kg, and is the watershed area in
hectares.
Management Practices and Scenarios
SWAT gives options for the user to consider different management practices and the
crop calendar in a watershed. A crop calendar was prepared for this watershed after
interviewing 50 farmers in the watershed (Table 2).
iii SedxQSED =
AxSED
SED dayobs 1000
=
37
Table 2: Crop calendar used in the Anjeni watershed Growing Season Harvesting Season Plowing Crop Starts Ends Starts Ends Start End
Barley 23-May 5-Sep 5-Sep 9-Dec 9-Apr 23-May Teff 27-Jul 19-Dec 19-Dec 9-Mar 7-Feb 27-Jul Wheat 6-Aug 30-Dec 30-Dec 7-Feb 7-Feb 6-Aug Corn 23-May 9-Dec 9-Dec 9-Dec 18-Feb 9-May Soy Bean 6-Aug 9-Dec 9-Dec 8-Jan 8-Jun 6-Aug Nug/FLAX 7-Jun 30-Dec 30-Dec 10-Jan 9-May 7-Jun Sinar/ALFA 27-Jul 29-Dec 29-Dec 7-Feb 8-Jun 27-Jul
Tillage Activity
The farmers make drainage ditches across furrow in their field. After the SCRP start
its activities in the watershed the farmers started using parallel terraces (Fanya juu)
and contour plowing in the year 1986. These tillage practices were considered as
factors and parameters in calibrating the model. The existence of terraces in the
watershed is included in the model setup by considering the resulting slope and slope
length change on the fields due to the construction of terraces. Contour plowing is a
management option to alleviate soil erosion which is taken into consideration during
the model calibration by changing the Universal Soil Loss Equation support practice
factor.
Model Setup
Watershed delineation
SWAT allows the user to delineate the watershed and subbasins using the Digital
Elevation Model (DEM). This tool uses and expands ArcGIS and Spatial Analyst
extension functions to perform watershed delineation (Neitsch et al., 2002). The DEM
of the area is loaded into an ESRI (Environmental System Research Institute) grid
format. Stream network was defined for the whole DEM by SWAT using the concept
of flow direction and flow accumulation. Before defining the stream network, the
38
model processes the DEM map grid to remove all the non-draining zones (sinks). To
define the origin of streams a threshold area was defined. The threshold area defines
the minimum drainage area required to from the origin of a stream. The size and
number of subbasins and details of stream network depends on this threshold area
(Winchell et al., 2007). The threshold area was taken to be 3.4ha, suggested by
ArcSWAT. The threshold area, or critical source area, defines the minimum drainage
area required to form the origin of a stream. The watershed outlet is manually added
and selected for finalizing the watershed delineation. With this information the model
automatically delineated a watershed of 106.5 ha and 13 subbasins were produced.
HRU Definition
The Hydrologic Response Units (HRUs) Analysis tool in ArcSWAT helps to load land
use and soil layers to the project. The delineated watershed by ArcSWAT and the
prepared land use overlapped 100%. The 16 classes land use map was reclassified into
11 classes in order to correspond with the land use in the SWAT interface except teff
which was created in SWAT based on the study Setegn et al. (2008) (Figure 10).
Figure 10: Land use as reclassified by SWAT into the four letter land use code
a) b)
39
The delineated watershed and soil map have an overlap of 99.99%. The soil classes in
the input soil map were decoded using a lookup table (a table created by the user in
.txt format according to SWAT input file format) so that the parameters corresponding
to each soil type could be accessed from the ArcSWAT database (Figure 11).
Figure 11: Soil Map of Anjeni Watershed as reclassified into four letters soil name in SWAT database
HRU analysis in ArcSWAT includes divisions of HRUs by slope classes in addition to
land use and soils. The multiple slope option (an option for considering different slope
classes for HRU definition) was selected for this study. Slope discritization was done
based on the study of Setegn et al., 2008 (Table 3). The slope discritization (0-1, 1-3,
3-5, >5) which accounts for the lower slope ranges is the best discritization option in
considering deposition of soil materials during sediment transportation (Setegn et al.,
2008).
40
Table 3: Slope descritization used for creation of HRUs Classes Slope Range
1 2 3 4
0% - 1% 1% - 3% 3% - 5%
> 5%
Multiple HRUs were defined within a subbasin by ignoring land uses less than 2% of
the subbasin and also ignoring soil types in a subbasin covering less than 5% of the
subbasin. A total of 465 HRUs for 13 subbasins were created.
Weather Data Definition
The WXGEN weather generator model included in SWAT was used to fill in gaps in
measured records. This weather generator was developed for U.S. Since Anjeni
watershed is located outside U.S. the WXGEN weather generator was provided with
all the necessary statistical information from the meteorological records of the
watershed. The parameters needed for the weather generator are listed in Appendix V
(for definition of each parameters listed look at Neitsch et al (2005). These statistical
values were calculated from the meteorological data available in the Anjeni watershed
and Debremarkos station. The number of years for calculating the statistical values
depends on the availability of data in the stations. Other meteorological data (daily
precipitation, daily minimum and maximum air temperature, daily relative humidity,
daily solar radiation and daily wind speed) including the corresponding location table
were prepared according to the SWAT format and integrated into the model using the
weather data input wizard.
Management Practices
ArcSWAT provides two options for defining the management operations in the
watershed; scheduling by date and scheduling by heat units. In this study, scheduling
the management practices by date was preferred because of the lack of information on
41
heat units of crops in the watershed. The basic operations are tillage, growing and
harvest and kill operation. Planting operation or the beginning of growing season used
to designate the time of planting for agricultural crops and initiation of plant growth
for a land cover that requires several years to reach maturity. The tillage operation
redistributes residue, nutrients, pesticides and bacteria. Harvest and kill operation
stops plant growth in a way that the fraction of biomass is removed from the HRU as a
residue on the soil surface. These operations were provided to the model for each crop
type based on the crop calendar prepared beforehand. The tillage practice was done
using the traditional plowing system (conventional agriculture) with a depth of plough
varying from 10cm to 15cm.
Model Sensitivity Analysis, Calibration and Validation
After the model was set up the next step was to run the model. The results from the
simulation cannot be directly used for further analysis but instead the ability of the
model to sufficiently predict the constituent sediment yield and stream flow should be
evaluated through sensitivity analysis, model calibration and model validation (White
and Chaubey, 2005).
The aim of the sensitivity analysis is to estimate the rate of change in the output of a
model with respect to changes in watersheds that result in a clear difference in
hydrologic sensitivity (Reungsang et al., 2005). Sensitivity analyses were conducted
for the Anjeni watershed hydrology to determine the parameters needed to improve
simulation results and thus to better understand the behavior of the hydrologic system
and to evaluate the applicability of the model.
Parameters for sensitivity analysis were selected by reviewing previously used
calibration parameters and documentation from the SWAT manuals (e.g., Neitsch et
42
al., 2005; Werner, 1986; Zeleke, 2000; Bosshart, 1997; Setegn et al., 2008; White and
Chaubey 2005; Kirsch et al., 2002; Arnold et al., 1999 and 2000) as illustrated in
Table 4. The sensitivity parameters selected for this study are shown in Table 5.
Table 4: Review of Calibration of Parameters by variable used by SWAT modelers Output Variables Calibration Parameters
Flow CANMX4 /Crop Growth Routine5 /Curve Number1,2,3,4,5,6,7 /ESCO3,5,6 /Revap Coefficients2,3,4 /Soil Bulk Density5 /Soil Properties1 /AWC6,7 /EPCO3 /Ground Water Parameters5 /Soil Hydraulic Conductivity5
Sediment AMP4 /Channel Cover5 /Channel Erosion5 /CH_N24
/MUSLE Parameters5 /PRF4 /SLSUBBSN4 /SPCON3,4 /SPEXP3,4 /USLE_K(1)4 /SLOPE4 /CH_N14
1Arnold and Allen, 1996; 2Srinivasan et al., 1998; 3Santhi et al., 2001b; 4Cotter, 2002; 5Kirsch et al., 2002; 6Arnold et al., 2000; and 7Arnold et al., 1999
Table 5: Parameters used for sensitivity analysis in this study
Flow Sediment GW_REVAP ALPHA_BF SLSUBBSN USLE_C
GWQMN REVAPMN SPCON USLE_K EPCO ESCO SPEXP USLE_P
SOL_K CANMX Ch_N Blai SOL_AWC GW_DELAY Ch_EROD GWQMN
SOL_Z Blai Ch_K2 SLOPE
Para
met
ers
SURLAG Biomix Ch_COV SURLAG
Model calibration is the modification of parameter values and comparison of predicted
output of interest to measured data until a defined objective function is achieved
(James and Burges, 1982). Parameters for modification are selected from those
identified by the sensitivity analysis. Additional parameters, other than those identified
during sensitivity analysis, are used primarily for calibration due to the hydrological
processes naturally occurring in the watershed. Sometimes it is necessary to change
43
parameters in the calibration process other than those identified during sensitivity
analysis because of the type of miss match of the observed variables and the predicted
variables (White and Chaubey, 2005), as illustrated in Table 6.
Table 6: Parameters used for Calibration
Output Variables Parameters Selected for Calibration
Flow REVAPMN GW_REVAP GW_DELAY ESCO SOL_K SOL_AWC ALPHA_BF RCHRG_DP SURLAG EPCO SOL_BD EDC Sediment SLSUBBSN SLOPE USLE_C USLE_P SPEXP ADJ_PKR Ch_EROD Ch_COV USLE_K PRF SPCON ISED_DET
In this study the calibration was divided into two steps:
i) water balance and stream flow, and
ii) sediment
The calibration for water balance and stream flow was first done for average annual
conditions. Next, calibration was done for the surface flow, groundwater flow, and
lateral flow for average annual conditions. Thus, before starting the calibration the
measured total water yield from the watershed should be sub-divided into base flow
and surface flow. The surface runoff was separated from the total flow using the
following equations.
If ( ) 11 925.09625.0 −− ≥− iSURii QxQQx
Then,
Otherwise,
Once the surface runoff is known the base flow can be calculated as (Hewlett and
Hibbert, 1967; Arnold et al, 1995; Arnold and Allen, 1999).
iSURiiBASE QQQ −=
( ) 11 925.09625.0 −− +−= iSURiiiSUR QxQQxQ
0=iSURQ
44
Where QSUR i is the surface runoff on day i in mm, QSUR i-1 is the surface runoff on day
(i-1) the day before day i in mm, Qi is the total water yield from the watershed on day i
in mm, Qi-1 is the total water yield from the watershed on day (i-1) in mm, and QBASE i
is the base flow contribution to the stream on day i in mm.
For increased accuracy, ground water and lateral flow estimations were considered.
The values GWQ (Groundwater discharge) and SURQ (Surface runoff) in the SWAT
output files cannot be used directly because in stream precipitation, evaporation,
transmission losses etc. will alter the net water yield from that predicted by the WYLD
(water yield) variable. Nevertheless, the Anjeni watershed is a micro watershed and
the effect of precipitation and evaporation from the river is assumed not to have a big
influence. Thus, groundwater and lateral flow were calibrated using GWQ and SURQ
values in the SWAT output files for comparison. While this particular process was not
very accurate, but it is very helpful in a way that the simulated groundwater flow,
lateral flow, and surface water to have similar pattern over time and also the same in
amount to that of the observed one.
The base flow in the watershed was analyzed as described below. The physical
observation of the watershed shows that the low lands are wet until the end of
October. Observation of the base flow from the total water yield using the above
equation for eight years of data (1986 – 1993) showed a rapid decrease of base flow
from 1.02 mm on daily basis (end of October) to 0.73 mm of daily contribution (mid
of November) and then to 0.48 mm of daily contribution (end of November). Once the
base flow becomes 0.48 mm the change in flow decreases, for example, this value
0.48 mm continues without changing for the next three months and then drops to 0.26
mm. These conditions continue for the next three months without change. Finally, this
45
amount changes because of the next rainy season. With this brief analysis of base flow
and with the assumption that the groundwater contribution to streams takes a longer
time than lateral flow, the minimum contribution of groundwater and the maximum
contribution of lateral flow can be calculated. That is, the rapid change in the base
flow contribution is due to the rapid decrease in the lateral flow. Thus, if the daily
base-flow contribution is equal to or less than 0.48 mm then the contribution is totally
from ground water and the contribution of lateral flow is zero. If the daily contribution
of base-flow is greater than 0.48 mm then the groundwater contribution to the base-
flow will be 0.48 mm with the rest coming from the lateral flow. Here, the increase in
groundwater contribution during rainy season is assumed to be zero, which may not be
correct but is a necessary assumption to estimate the minimum contribution of
groundwater. This is an area for further research.
Therefore;
mmQIfQQ iBASEiBASEiGW 48.0>=
And, 0=iLATQ
mmQIfmmOQ iBASEiGW 48.048. >=
And, mmQQ iBASEiLAT 48.0−=
Where QBASE i is the base flow contribution to the stream on day i in mm, QGW i is the
groundwater contribution to the stream on day i in mm, and QLAT i is the lateral flow
contribution to the stream on day i in mm.
By using these observed values the model was calibrated for annual average values.
The fine tuning was achieved by comparing the simulated and the observed values on
a monthly basis followed by comparisons on a daily basis.
46
Sediment calibration was first done using parameters which affect the amount of soil
loss from each HRU. This includes subbasin and management parameters. After this
the sediment loss from the watershed was fine tuned by adjusting parameters
describing the characteristics of reaches.
Model calibration generally consists of statistical tests like Optimization of the Nash-
Sutcliffe Coefficient (ENS) (Santhi et al., 2001a; Cotter, 2002; Grizzetti et al., 2003).
The coefficient of determination (R2) suits model evaluation and is very sensitive to
extreme values (Harmel and Smith 2007). In this study, the coefficient of
determination (R2) and the Nash-Sutcliffe coefficient (ENS) were used.
The coefficient of determination (R2) is the square of the Pearson’s product-moment
correlation coefficient and describes the proportion of the total variance in the
observed data that can be explained by the model. R2 ranges from 0.0 to 1.0 with
higher values indicating better agreement (Legate and McCabe, 1999).
The second objective function used in this study was the Nash-Sutcliffe coefficient of
efficiency ENS which has been widely used to evaluate the performance of
hydrological models (Leavesly et al., 1983; Wilox et al., 1990; Arnold et al., 1999;
Kirsch et al., 2002). This coefficient ranges from minus infinity to 1.0, with higher
values indicating better agreement (Legate and McCabe, 1999). Nash and Sutcliffe
(1970) defined the coefficient as:
( )
⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢
⎣
⎡
⎟⎠⎞
⎜⎝⎛ −
−−=
∑
∑
=
=
N
i i
N
i iiNS
OO
POE
1
_
12
20.1
47
Where Oi is the measured (observed) data, _O is the mean of measured data, Pi is the
modeled (predicted) data.
Model validation is similar to model calibration in that the predicted and measured
values are compared to determine the model fit to the observed data. For validation,
the data set of measured runoff and sediment should be different from that used for
calibration (White and Chaubey, 2005). In this study the data set for validating the
model was taken from the years 1995, 1996 and 2000.
Scenarios
Base Scenario (Scenario I)
This scenario presents the actual condition observed in the watershed. The watershed
is managed by using parallel terraces starting from the year 1986. The Fanya Juu were
adopted in this watershed starting from this time because of the efficiency of Fanya
Juu in decreasing soil loss and its acceptance by the farmers (Werner, 1986). To
include this management practice in the model the slope was assumed to be reduced
by 37.5% and the slope length is reduced by 50% for areas with slope greater than 5%.
Zero-Tillage (Scenario II)
In this scenario the disturbance of soil by tillage was assumed to be zero. The farmers
till their plot more than six times before planting teff (12% of the land in the
watershed grows this crop). In addition to this, the farmers also had a strong belief on
plowing their lands again and again for a better yield. The practicability of zero-tillage
for fields growing teff is doubtful. Some research (Habtegebriel et al., 2007) has
shown the impact of tillage on teff fields. It showed that the yield of grain decreased
by 4.2% to 6.9% when transforming from conventional agriculture to minimum
tillage. Thus, in this scenario zero-tillage is assumed to be practiced for cultivated
fields except in fields growing teff.
48
Parallel Terraces (Scenario III)
In this scenario it was assumed that the farmers reduce the width of their existing and
terraced plot by 50%. Management practices other than this in the watershed are not
changed. Farmers still practice contour plowing, conventional plowing and use the
same crop calendar. To incorporate this, the slope length is further reduced by 50% on
agricultural fields with slope greater than 5% (see illustration in Figure 12a and 12b).
As time goes on the slope of the fields is reduced due to the deposition of soil at the
foot of each terrace. To incorporate this, in this scenario, the slope of each agricultural
plot is further reduced by 25% for areas where the slope is greater than 5%.
a)
b)
Figure 12: Newly constructed Fanya Juu terrace (a) and Fanya Juu after five years of construction (b). * Pictures taken from: http://www.iwmi.cgiar.org/africa/west/projects/Adoption%20technolgy/rainwaterharvestin/50-Fanya%20juu.htm
49
Forestation (Scenario IV)
Foresting all cultivated fields is impractical and impossible for many reasons. Instead,
foresting bush lands and degraded agricultural fields is more feasible. The degraded
agricultural fields in the watershed can easily being identified because the farmers
plant nigger seed on these fields. In some cases these fields have been converted and
used for Eucalyptus planting. This shows that the farmers have already started
changing degraded agricultural fields on the top of the watershed into forest. Thus, in
this study, this scenario was established by replacing the bush lands and degraded
agricultural fields by forest this covers 9% of the watershed in area.
No-Terraces (Scenario V)
In this scenario the farmers in the watershed are assumed not to practice terracing at
all. This provides a way of showing how much soil is lost or conserved and also the
effect of terracing on river discharge. The scenario was developed by ignoring the
slope decrease made in the base scenario and the decrease in the slope length.
50
CHAPTER FOUR
RESULTS AND DISCUSSIONS
Sensitivity Analysis
The groundwater parameters were found to be the parameters to which the flow was
most sensitive, in particular: the base flow alpha factor (ALPHA_BF) in days;
Threshold depth of water in the shallow aquifer required for return flow to occur
(GWQMN) in mm; Threshold depth of water in the shallow aquifer for "revap" or
percolation to the deep aquifer to occur (REVAPMIN) in mm and Groundwater
"revap" coefficient (GW_REVAP). The flow was also found to be sensitive to soil
properties: soil evaporation compensation factor (ESCO); depth from soil surface to
bottom of layer (SOL_Z) in mm and available water capacity of the soil layer
(SOL_AWC) in mm /mm of soil depth. The flow was also sensitive to crop
parameters: maximum potential leaf area index (BLAI) which is a parameter to
quantify the density of the plant and maximum canopy storage (CANMX) in mm H2O.
The most sensitive parameters for the sediment prediction were those used for
calculating the maximum amount of sediment that can be entrained during channel
routing, which includes the exponent, factors and channel properties. The coefficient
SPCON and the exponent SPEXP are defined by the user for calculation of the
maximum sediment concentration in channels. The sediment yield from the watershed
was very sensitive to these values, which affect deposition in channels. The channel
properties, Manning’s ‘n’ value for tributary channels (CH_N) affects the time of
concentration and indirectly the peak discharge in the channel. Factors like the channel
cover CH_COV and the channel erodibility CH_EROD linearly influence the soil loss
from channels. Sediment yield was also very sensitive to effective hydraulic
conductivity in the main channel alluvium (CH_K) in mm/hr. Finally, the sediment
51
predictions were also found to be sensitive to the management practices in the
watershed, which is represented by USLE_P.
The ranking of variables used in the sensitivity analyses for flow and sediment
parameters are listed below (Table 7).
Table 7: The most sensitive parameters for flow and sediment
Flow Parameter Sediment Parameters Ranking Alpha_Bf Spcon 1 Gwqmn Ch_N 2
Esco Spexp 3 Sol_Z Alpha_Bf 4 Blai Ch_Cov 5
Sol_Awc Ch_Erod 6 Revapmin Ch_K2 7
Canmx Blai 8 Gw_Revap Usle_P 9
Sol_K Gwqmn 10
Model Calibration and Validation
Model calibration followed sensitivity analysis. Flow and sediment calibration for the
Anjeni watershed was conducted for the years 1986 to 1993. Two years, 1984 and
1985, were used for model initialization. Likewise, flow and sediment validation for
the Anjeni watershed was carried out for the years 1995, 1996 and 2000. These years
were selected based on the availability of data.
Initially, the model was calibrated on an annual basis. The surface runoff, base flow
and the total water yield were calibrated first. The simulated values for these variables
before calibration are shown below in Table 8.
52
Table 8: Output variables; simulated before calibration and observed values in mm Total Water Yield Base Flow Surface Flow
Actual 700.6 503.2 197.4 SWAT 1137.6 801.9 337.4
The model over estimated the flow; thus parameters (Table 9) were adjusted in order
for the simulated output values to meet the actual annual averages. The parameters
were adjusted further to fine tune the simulation and the changes in parameters are
shown in Table 9.
Table 9: Values of parameters used for calibration
Parameters for flow calibration Parameters for Sediment calibration ALPHA_BF 0.2 USLE_P 0.8 GW_REVAP 0.2 USLE_K Reduced by 0.02 REVAPMN 0.001 CH_COV 0.8 RECHRG_DP 0.65 CH_EROD 0.5 SURLAG 1 PRF 1.2 ESCO 0.8 ADJ_PKR 0.5 EPCO 0.9 SPCON 0.001 EDC 0.43 SPEXP 1 SOL_AWC 10% Increase SLOPE 37.5% Decrease SOL_BD 5% Increase SLSUBBSN 50% Decrease SOL_K 10% Decrease ISED_DET Monthly maximum
ALHA_BF= the baseflow alpha factor; GW_REVAP=groundwater "revap" coefficient; REVAPMN= Threshold depth of water in the shallow aquifer for "revap" or percolation to the deep aquifer to occur; RECHRG_DP= Deep aquifer percolation fraction; SURLAG=surface runoff lag coefficient; ESCO=soil evaporation coefficient; EPCO=plant uptake compensation factor; EDC=Effective Depth Coefficient in the .bee file; SOL_AWC=soil layer available water capacity; SOL_BD=soil moist bulk density; SOL_K=soil saturated hydraulic conductivity; USLE_P=Universal soil loss equation management factor; USLE_K=universal soil loss equation soil factor; CH_CV=channel cover factor; CH_EROD=channel erodibility; PRF= Peak rate adjustment factor for sediment routing in the main channel; ADJ_PKR=peak rate adjustment factor; SPCON= Linear parameter for calculating the maximum amount of sediment that can be re-entrained during channel sediment routing; SPEXP= Exponent parameter for calculating sediment re-entrained in channel sediment routing; SLOPE=average slope steepness (m/m); SLSUBBSN=average slope length.
53
The flow was calibrated using the above parameters to improve the objective functions
(R2 and NSE). The baseflow recession constant (the baseflow alpha factor
(ALPHA_BF)) which is a direct index of groundwater flow response to changes in
recharge was adjusted to 0.2. The groundwater "revap" coefficient (GW_REVAP)
which controls the rate of transfer of water from the shallow aquifer to the root zones
was adjusted to 0.2. The threshold depth of water in mm in the shallow aquifer for
"revap" or percolation to the deep aquifer to occur (REVAPMN), the soil evaporation
coefficient (ESCO), and the plant uptake compensation factor (EPCO) were adjusted
to 0.001, 0.8 and 0.9 respectively. The effective depth coefficient (EDC) parameters
needed to calibrate the water balance. To calibrate flow, the EDC values adjusted
between 0 and 1. The EDC partitions excess moisture between percolation and runoff,
so an EDC of 1 would partition excess to runoff and EDC of 0 to percolation. These
EDC values for each soil type in the watershed were adjusted to be 0.43. These
parameters effectively modify the depth distribution used to meet the soil evaporative
demand to account for the effect of capillary action, crusting and cracks.
Calibration of sediment yield was achieved by decreasing slope (SLOPE) by 37.5%
only for agricultural areas with slope greater than 5% when by considering the terrace
practiced in the watershed since 1986 (SCRP Report, 2000). The slope length of each
plot in agricultural fields with slopes greater than 5% was reduced by 50% to consider
the break in slope length due to the provision of terraces. The reduction of slope
lengths makes the SLSUBBSN for the agricultural areas 30m to 40m which is the
average terrace interval in the watershed. For small plots and micro-watersheds in
particular, the variability of triangular distribution is unrealistic (Neitsch et al., 2005).
So, ISED_DET (a code governing the calculation of daily maximum half-hour runoff)
was assigned to work with monthly maximum half-hour rainfall value. The average
54
annual total flow, the base flow and the surface runoff after calibration are shown
below (Table 10).
Table 10: Annual average output variables; simulated after calibration and observed
Total Water Yield Base Flow Surface Flow Actual 700.6 503.2 197.4 SWAT 712.8 522.7 196.1
The model goodness-of-fit was evaluated both on a monthly and on a daily basis as
shown in Table 11. The linear graphs for the measured and simulated values both for
flow and sediment on daily basis for calibration and validation are shown in Figure 13
to Figure 16. The linear graphs for the measured and simulated values both for flow
and sediment on a monthly basis for calibration and validation are shown in Figures
17 through 20.
Table 11: Coefficient of determination and the Nash – Sutcliffe Coefficients for calibration and validation both in daily basis and monthly basis
Calibration Validation R2 NSE R2 NSE
Flow 0.54 0.47 0.57 0.41 Daily Sediment 0.03 -0.23 0.05 -0.17
Flow 0.92 0.91 0.97 0.93 Monthly Sediment 0.56 0.55 0.89 0.82
55
Figure 13: Coefficient of determination for simulated flow in calibration on a daily basis
Figure 14: Coefficient of determination for simulated sediment in calibration on a daily basis
56
Figure 15: Coefficient of determination for simulated flow in validation on a daily basis
Figure 16: Coefficient of determination for simulated sediment in validation on a daily basis
57
Figure 17: Coefficient of determination for simulated flow in calibration on a monthly basis
Figure 18: Coefficient of determination for simulated sediment loss in calibration on a monthly basis
58
Figure 19: Coefficient of determination for simulated flow in validation on a monthly basis
Figure 20: Coefficient of determination for simulated sediment loss in validation on a monthly basis
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Analysis of Results
The model underestimated peak flow from the watershed in some years and over
predicted in some years. The flow in the dry season which is determined by the
groundwater is also under estimated; see Figures 21 - 24 for comparison of measured
and simulated values on a daily basis. The daily simulated flow is greater than the
observed flow at the beginning of the rainy season. In these times even high rainfall
cannot produce more than 1mm of runoff in a day. This indicates that surface runoff is
produced when the soil is saturated and there is a delay in the lateral flow.
The runoff in the dry season (December to May) is mainly from ground water and
surface runoff if there is rain on that particular day; see Figures 25 - 28 for comparison
of measured and simulated values on a monthly basis. So, the contribution of lateral
flow is zero in this season. The runoff produced after a heavy storm in the dry season
simulated by the model is less than the observed runoff. This shows that there are
some portions in the watershed which produce surface runoff immediately after rain
starts falling because of the very low infiltration rate (based on physical observation in
the watershed). This idea is reinforced by interviewing farmers in the watershed. The
farmers were asked at which time of the rainy season is runoff produced immediately
after rain starts to fall. The result is as shown in Table 12.
Table 12: Number of farmers interviewed for identification of the process of runoff production
Number of Farmers Interviewed Immediate1 Medium2 Slowest3
Beginning of Rainy Season 1 18 31 Middle of Rainy season 43 7 0 End of rainy season 6 25 19
1Number of farmers explaining that the surface runoff production is the most immediate and fastest of all the other two cases 2Number of farmers expressing that the surface runoff production after rain storm is faster (more immediate) than one of the two cases and slower than the other case 3Number of farmers explaining that the surface runoff production is after a storm is the slowest of all the three cases
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The daily runoff production gradually increases, as the rainy season progresses but the
rate of increase in simulated runoff at the beginning of the rainy season is greater than
is actually observed. This is influenced by the initial soil moisture in the soil of the
watershed. Thus, the model over predicts the soil moisture in the watershed. During
August, the month of highest rainfall, the model slightly under predicts the water yield
from the watershed. At the end of the rainy season, the water yield from the watershed
takes longer to come to recession that is the decrease in flow through time is less than
that of actually observed.
The surface runoff production from each HRU was analyzed. Areas in the watershed
with Haplic Lixisols soils were contributed the least surface runoff to the reach. Haplic
Lixisols has the lowest least clay content and the highest sand content of all the soils
in the watershed and are characterized by high saturated hydraulic conductivity,
7mm/hr on in the top layer and 25mm/hr on the lower layers. Most of the areas having
Humic Alisols and Eutric Regosols produce large amount of surface runoff. Humic
Alisols have the lowest hydraulic conductivity (1mm/hr) of all the soils found in the
watershed and high clay content only in the top layer and high percentage of sand in
the lower layers. These soil properties make areas covered with this soil type produce
large amounts of surface runoff as infiltration excess flow.
The model over predicts the soil loss at the beginning of the rainy season as observed
in the whole of the calibration period. The over prediction in runoff at the beginning of
the rainy season resulted in over estimation of soil loss from the watershed at the
beginning of the rainy season. SWAT fails to estimate the peak soil loss in most of the
years. The coefficient of determination (R2) for the daily simulation shows that the
model fails to estimate daily soil loss. However, on a monthly basis the soil loss
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simulated by the model had a similar pattern as that of the measured soil loss from the
watershed, even though it fails to estimate the peak soil loss in each year. The
coefficient of determination (R2) and the Nash-Sutcliffe Coefficient, 0.56 and 0.55
respectively, were satisfactory. So the model can be used for further analysis on soil
loss for different scenarios.
Figure 21: Hydrograph of the observed and simulated flow from the watershed for the validation period on a daily basis
Figure 22: Comparison of observed and simulated sediment loss from the watershed for the validation period on a daily basis
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Figure 23: Hydrograph for Anjeni watershed on a daily basis for the whole period of calibration showing precipitation, flow from the watershed and sediment loss in a daily basis for the observed and simulated values
Figure 24: Comparison of observed and simulated sediment loss on a daily basis from Anjeni watershed for the whole calibration period
63
Figure 25: Hydrograph of the observed and simulated flow from the watershed for the calibration period on a monthly basis
Figure 26: Comparison of observed and simulated sediment loss from the watershed for the calibration period on a monthly basis
64
Figure 27: Hydrograph of the observed and simulated flow from the watershed for the validation period on a monthly basis
Figure 28: Comparison of observed and simulated sediment loss from the watershed for the validation period on a monthly basis
65
Gully Erosion
The model uses the Universal Soil Loss Equation to estimate the soil loss from each
HRU and also considers channel degradation. This means that the model does not
consider gully erosion in the watershed. However, consideration of gully erosion is
important because there is a big gully in the watershed with an average depth of 8m.
Aerial photographs taken in 1957 and 1982 and a Google Earth image from 2008 were
used to estimate the soil loss contribution from the gully to the watershed outlet, see
Figure 29. The resolution of aerial photos was (after digitizing and geo-referencing)
6.00 m X 6.00 m.
Figure 29: Location and comparison of the largest gully in the watershed for different years
The measured annual average soil loss from 1986 to 1993 was 2785.36 tons/yr, with a
minimum soil loss of 663.39 tons/yr and a maximum soil loss of 6979.77 tons/yr. The
average annual soil loss from the watershed due to gully erosion from 1982 to 2008
66
was calculated to be 550.15 tons/yr, which is 83% of the minimum annual average soil
loss from the watershed (663.39 tons/yr), 8% of the maximum annual average soil loss
from the watershed (6979.77 tons/yr) and 20% of the average of all the annual average
soil loss (2785.36). This shows that there is a significant sediment contribution from
gully erosion to the total sediment yield from the watershed. Gully erosion occurred
suddenly and the contribution in each year varies highly. This makes the prediction of
soil loss in each year from gullies difficult. Since the separation of soil loss from the
fields and from gullies is impractical, model calibration is also uncertain. Thus, SWAT
fails to simulate soil loss from the watershed in those years where very low sediment
yield and very high sediment yield were observed. The area of the gully in the
watershed is indicated in Table 13.
Table 13: Area of the gully in m2 for the three years
Area_1957 Area_1982 Area_2008 1055.88 1644.24 2761.73
The farmers in the watershed started constructing terraces in 1984. The above analysis
in Table 14 showed that the rate of gully formation after 1982 increases from 301
tons/yr (during the time when there is no construction of terrace in the watershed) to
550 tons/yr (during the time when the farmers are practicing terraces). Construction of
terraces encourages infiltration. As much water infiltrated to the bottom layers, there
will be a high chance for occurrence of soil piping. Physical observations in the
watershed showed the occurrence of soil piping in the watershed. Soil piping is a
major cause of channel head extension, rilling and gullying in landscapes as diverse as
semi-arid climates from Arizona to East Africa (Jones, 1994). Parker and Higgins
(1990) and Dardis and Beckedahl (1988) all regard piping as a major cause of
67
gullying. In addition to this, gully formation may occur due to the under-cutting of
sides of gullies by a high velocity runoff.
Table 14: Calculation of mass of soil loss due to gully erosion for the three different years
Years Increase in Area (m2)
Volume lost (m3)
Mass lost (tons)
Rate of loss (tons/year)
From 1957 – 1982 588.36 4706.88 7531.01 301.24
From 1982 – 2008 1117.49 8939.92 14303.87 550.15
Spatial Variation of Runoff and Soil Erosion
Areas covered with teff, barely and corn produces the maximum surface runoff in the
watershed. Most of the areas covered with corn produce surface runoff varying from
232 – 393 mm of water in a year. Most of the areas covered with teff produce an
annual surface runoff varying from 532 – 1402 mm of water. Even if most of the plots
covered by barley produced the same amount as the annual average surface runoff
from the watershed (197 mm) some plots produce runoff varying from 531 – 1402
mm. Areas in the watershed covered with forest produce the least surface runoff
varying from 0 – 100 mm of water. Based on location, the middle parts of the
watershed are observed to produce high surface runoff. The top pars of the watershed
with a slope less than 5% are also found to produce high surface runoff. The map in
Figure 30 shows the areal variation of surface runoff.
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Figure 30: Map of extent of surface runoff in each HRU
Most parts of the watershed contribute to soil loss ranging from 0 – 1 tons/ha.
Significant correlation was not observed between a high rate of soil loss and soil types
found in the watershed. The highest sediment loss from fields is observed to be from
teff and corn. Most of the plots covered with teff and corn contribute 50 – 334 tons/ha
soil. Considering fields coved with barely, those that produce high surface runoff are
observed to lose high amount of soil varying from 20– 50 tons/ha. The map in Figure
31 shows the areal variation of sediment loss.
69
Figure 31: Map of extent of sediment loss from each HRU
Scenarios
The different scenarios were compared and analyzed on a monthly basis. The water
yield and the sediment yield from the watershed were analyzed separately.
Flow
Base scenario (Scenario I), Scenario II (no tillage activity) and forestation (scenario
IV) produce essentially the same monthly flow pattern and amount except some slight
change at the end of the rainy season for twenty years’ worth of observed simulated
data, see Figures 32 and 35. Scenario III (Terrace) is observed to reduce the flow from
the watershed and found to give the lowest water yield compared to the other
scenarios. The decrease in runoff due to terrace practice (Scenario III) is considerably
higher at the maximum point of the runoff for a particular year and at also the same at
70
the end of rainy season. No decrease in runoff is observed at the beginning of the rainy
season. But the pattern of runoff in all the scenarios is the same. During the rainy
season, terracing (Scenario III) results a decrease of 1.7% in runoff. This is likely a
direct consequence of changing the slope in the model and therefore likely not realistic
since interflow depends on the overall slope and not on the slope of the terraces.
Unlike Scenario III the percentage decrease in the flow in the dry season is not very
high in Scenario IV resulting in a 3.6% decrease. Thus water consumption during dry
season is not affected by this management option (forestation). The decrease in water
yield from the watershed in the rainy season due to forestation is 1.3% which is not
substantially less than the runoff decrease due to Scenario III.
In Scenario V, avoiding terraces resulted in an increase in flow. The flow increase in
this scenario has the same pattern as the decrease in flow due to the addition of more
terraces (Scenario III) and the runoff in the rainy season increases by 1.24% (Figure
33).
Annually, Scenario III is found to yield the least flow. Forestation conserve further
10mm of water compared to the water conserved by the existing conservation
structures (Figure 34). Avoiding terraces leads to further 15mm loss of water.
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Figure 32: Comparison of average monthly flow in each scenario
Figure 33: Pattern of decrease or increase in flow in different scenarios compared to the base scenario
72
Figure 34: Annual average water yield form the watershed in each scenario
Sediment
Around 99% of the mean annual suspended sediment load is transported between May
and October. The months November, December, January and February are months
when there is no soil loss from the watershed. Thus these months are not the main
concern in the analysis of sediment yield and when comparing scenarios. Unlike the
differences observed in flow among scenarios, the differences observed in sediment
loss among different scenarios have a similar pattern throughout a year (Figures 36
and 37).
73
Figure 35: Hydrograph in each scenario on a monthly basis (see table in Appendix VI)
Figure 36: Sediment yield from the watershed in each scenario on a monthly basis (see table in Appendix VI)
74
Figure 37: Comparison of average monthly sediment loss from the watershed in each scenario
Zero-tillage activities (Scenario II) results in a soil loss decrease from April to
September when compared to the base scenario. There was a 6% decrease in soil loss
at the beginning of the plowing season (from April to June) and a 1.64% decrease
during the rainy season.
In Scenario III (terracing), decreases in soil loss in all the months were observed. In
the months from March to April there is a 61% decrease in soil loss from each HRU
due to the provision of more terraces (0.04t/ha). These are the months when plowing is
started in each year. For the rainy season (June, July, and August) there is a decrease
in soil loss 3.07 t/ha/month (64% of the soil loss in this season in the base Scenario).
For the months at the end of rainy season (September, October and November) the
decrease in soil loss is 0.34% tons/ha/month which is 61% of the soil loss rate in these
months in the base Scenario. Increasing terraces (Scenario III) shows a constant
decrease of soil loss in percentage in all the months under consideration.
75
Forestation (Scenario IV) results in a decrease of soil loss from April to November,
which is from the start of the plowing season to the end of the rainy season. There is
19% decrease in soil loss from each filed compared to the sediment yield in the base
scenario in these months. The soil loss decreased by 0.011 tons/ha/month which is
13% of the soil loss rate in these months in the base Scenario. During the heaviest rain
in the rainy season (June, July and August) this management option resulted in a
decrease of soil loss by 0.86 t/ha/month, which is 18% of the soil loss rate in the base
Scenario in these months. At the end of rainy season (September, October and
November) a decrease in soil loss due to the forestation of some parts of the watershed
is 0.1 t/ha/month which is 21% of the soil loss rate observed in these months in the
base scenario (Figure 38).
The Scenario that did not consider the practice of terraces in the watershed (Scenario
V) resulted in a soil loss increase with the same pattern as the decrease in soil loss due
to the construction of more terraces (Scenario III). But Scenario V resulted in a higher
soil loss compared to the amount of soil conserved by the construction of more
terraces.
The annual soil loss conserved by further terracing is 10t/ha/yr and loss of soil by
avoiding the terraces is 20t/ha/yr. Forestation can reduce soil loss further by 5t/h/yr
(Figure 39).
76
Figure 38: Pattern of decrease or increase in sediment loss from the watershed in different scenarios compared to the base scenario
Figure 39: Annual average water yield form the watershed in each scenario
77
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATIONS
The result from sensitivity analysis showed that the runoff is most sensitive to the
groundwater parameters. Thus, for further accuracy of the model a detailed study of
the groundwater properties (the groundwater depth, the alpha factor etc) are essential.
SWAT-WB approach avoids the SCS curve number method and instead uses water
balance approach and introduce a new coefficient EDC which affect the distribution of
runoff and percolation. EDC needs careful calibration and investigation.
The SWAT-WB model under-estimates flow in the middle of the rainy season. This
could possibly be corrected and the model performance improved by more accurately
determining the value of soil profile depth, of each soil type, which is available for
saturation (D) and is directly influences the EDC.
The runoff production in Anjeni watershed is mainly of saturation excess flow and
infiltration excess flow being a trivial runoff production process in the watershed. This
trivial runoff production process results in the model under-prediction of runoff during
the dry season. As the result of the main runoff production process in the rainy season,
saturation excess flow, high runoff in a day in the watershed is not produced due to
high rainfall but instead it depends on the antecedent moisture conditions.
Simulation of flow by the model is found to be excellent on a monthly basis and
satisfactory on a daily basis. Thus SWAT-WB performs very well and can be used for
runoff simulation for watersheds with the same runoff production process as that of
Anjeni. As studies like Lui et al., 2008 and Collick et al., 2008 have shown, most of
the watersheds in the Ethiopian highlands behave in a way that is similar to Anjeni
78
watershed. That is, the runoff production in these areas is largely due to saturation
excess flow from saturated areas in the landscape. Therefore, this model can be used
as a tool to analyze hydrological processes in Ethiopian highlands.
The sensitivity analysis for soil erosion showed that the soil loss is most sensitive to
channel properties, exponent and factor for calculation of maximum sediment
concentration that can be transported by the water. The channel properties define how
loose and erodible the channel walls are. Generally, sensitivity analysis showed that
the sediment loss from the watershed is more sensitive to channel properties than HRU
properties. 70% of the top 10 parameters for which the model is most sensitive are
those which define channel properties.
In most of the years the simulated value of soil loss from the watershed fails to
estimate the peak soil loss in a year. Gully erosion in the watershed was found to be
very significant. The rate of gully erosion increases by 240 tons/yr after the
construction of terraces starting from 1984. Thus since SWAT has no ability to predict
gully erosion, soil loss tends to be under predicted.
With the assumption that SWAT totally fails to simulate gully erosion, the effect of
application of terraces in the watershed starting from the year 1984 is found to save
2064 tons/yr. This value is calculated with the assumption that the increase in gully
erosion is totally due to the construction of terraces. With the same concept further
construction of terraces in the future (Scenario II) saved 932 tons/year. Even if the
plots are divided into two for further terrace construction the amount that we can
further save is only 932 tons/yr which is 45% of the amount saved by the existing
conservation practices.
79
If gully erosion is controlled with an efficiency of 90% the result would be a savings
of 495 tons/year of soil. Forestation reduces erosion by 333 tons/year. Thus, gully
rehabilitation with an erosion controlling efficiency of 90% and forestation together
would save 828 tons of soil in a year. These two management options are feasible and
can be accepted by the farmers without much effort in addition to saving 88% of the
amount of soil that can be saved by further construction of terraces. Further
construction of terraces in the watershed is not feasible since the plots are only 20m –
30m width and it is also impractical to divide these plots. This management option
also has a negative influence on the availability of water in the dry season. Thus,
further construction of terraces is not a feasible option for conserving soil and
availability of water.
Zero-tillage can save 45 tons of soil loss in a year which is comparatively very small.
This management option also requires considerable effort from farmers and is unlikely
to be acceptable to them. Thus, forestation of degraded lands and bush lands together
with rehabilitation of gullies with 90% efficiency are the best option for controlling
soil erosion and a sustainable development option in the watershed.
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. If there is a
considerable productivity change due to further construction of terraces then this
option can be practiced as it saved 104 tons/yr than the management option
recommended in this study. In addition, the farmers may need to change their practices
to adapt climate change. If implementing the management options is planned,
80
considering the impact of climate change on these management options, formulated in
this study, is very important.
81
REFERENCES
Abegaz Gizachew, 1995. Soil erosion assessment: Approaches, magnitude of the
problem and issues on policy and strategy development (Region 3). Paper
presented at the Workshop on Regional Natural Resources Management
Potentials and Constraints, Bahir Dar, Ethiopia, 11–13 January 1995. Bureau
of Natural Resources and Environmental Protection, Bahir Dar, Ethiopia. 9 pp.
Arnold, J.G., Allen P.M., Muttiah R., and Bernhardt G., 1995. Automated Base-Flow
Separation and Recession Analysis Techniques. Ground Water 33:1010-1018.
Arnold, J.G., and Allen P.M.. 1996. Estimating Hydrologic Budgets for Three Illinois
Watersheds. Journal of Hydrology. 176: 57-77.
Arnold, J.G., and P.M. Allen. 1999. Automated methods for estimating baseflow and
ground water recharge from streamflow records. Journal of the American
Water Resources Association 35:411-424.
Arnold, J.G., J.R. Williams, R.H. Griggs, and N. B. Sammons. 1990. SWRRB - A
Basin Scale Simulation Model for Soil and Water Resources Management.
Texas A & M Press.
Arnold, J.G., Williams, J. R., Srinivasan, R. and King K. W., 1996, SWAT-Soil and
Water Assessment Tool, USDA-ARS, Temple, Texas.
Arnold, J.G., R. Srinivasan, R.S. Muttiah, and J.R. Williams. 1998. Large area
hydrologic modeling and assessment part I: model development. J. American
Water Resources Association 34(1):73-89.
Arnold, J.G., R. Srinivasan, R.S. Muttiah, and P.M. Allen. 1999. Continental Scale
Simulation of the Hydrologic Balance. Journal of American Water Resource
Association. 35(5): 1037-51.
82
Arnold, J.G., R.S. Muttiah, R. Srinivasan, and P.M. Allen. 2000. Regional Estimating
of Baseflow and Groundwater Recharge in the Mississippi River Basin.
Journal of Hydrology. 227: 21-40.
Bekele, S., Holden, S.T. 1998. Resource degradation and adoption of land
conservation technologies in the Ethiopian Highlands: A case study in Andit
Tid, North Shewa. Agricultural Economics 18: 233-47
Bosshart, Urs., 1995. Catchment Dischrge and Suspended Sediment Transport as
Indicators of Physical Soil and Water Conservation in the Minchet Catchment,
Gojam Research Unit. A case study of the northern Highlands of Ethiopia.
SCRP Research Report, Berne University, Addis Ababa, 104p.
Bosshart, Urs, 1997. Measurement of river discharge for the SCRP research
catchments : gauging station profiles. Berne University in Association with
Ministry of Agriculture, Ethiopia. Soil and Conservation Research Program;
Report 31, 1997.
Collick, A.S., Z.M. Easton, E. Adgo, S.B. Awulachew, G. Zeleke, and T.S. Steenhuis.
2008. Application of a physically-based water balance model on four
watersheds throughout the upper Nile River Basin. Paper presented at the
Workshop on Hydrology and Ecology of the Nile River Basin under Extreme
Conditions, June 16-19, Addis Ababa, Ethiopia.
Cotter, A., 2002. Critical Evaluation of TMDL Data Requirements for Agricultural
Watersheds. M.S. thesis, University of Arkansas, Fayetteville, Arkansas.
Dardis, G.F. and H.R. Beckedahl, 1988. Drainage evolution in an ephemeral soil pipe-
gully system, Transkei, Southern Africa, in Dardis, G.F.and Moon, B. P. (eds)
Geomorphological studies in Southern Africa, Balkema, Rotterdam, 247-65.
Decoursey, D. G. and E. H. Selly, 1988. Mathematical models for point sources water
pollution control. Journal of Soil and Water Conservation. 44(2): 568-576.
83
De Ploey J., 1974. Mechanical Properties of Hill slopes and their Relation to Gullying
in Central Semiarid Tunisia, Zeitschrift fur Geomorphologie 21, 177-90.
Easton, Z.M., D.R. Fuka, M.T. Walter, D.M. Cowan, E.M. Schneiderman, and T.S.
Steenhuis. 2008. Re-conceptualizing the Soil and Water Assessment Tool
(SWAT) model to predict run off from variable source areas. Journal of
Hydrology, 348(3-4): 279-91.
Fournier, F., 1960. Climat et Erosion. Presses Universitaires de France, Paris.
Green WH, Ampt GA., 1911. Studies on soil physics; The flow of air and water
through soils. Journal of Agricultural Sciences; 4: 11-24.
Grizzetti, B., F. Bouraoui, K. Granlund, S. Rekolainen, and G. Bidoglio, 2003.
Modelling Diffuse Emission and Retention of Nutrients in the Vantaanjoki
Watershed (Finland) Using the SWAT Model. Ecological Modelling
169(1):25-38.
Habtegebrial K., Singh B.R., Haile M., 2007. Impact of tillage and nitrogen
fertilization on yield, nitrogen use efficiency of tef (Eragrostis tef (Zucc.)
Trotter) and soil properties. Soil and Tillage Research (94) 55–63.
Haregeweyn, N. and Yohannes, F. 2003. Testing and evaluation of the agricultural
non-point source pollution model (AGNPS) on Augucho catchment, western
Hararghe, Ethiopia. Agriculture Ecosystems & Environment 99 (1-3): 201-212.
Hargreaves G. L., Hargreaves G. H., Riley J. P. 1985. Agricultural benefits for
Senegal River basin. Journal of Irrigation and Drainage Engineering 1985;
111(2): 113-124.
Harmel R. D., Smith P. K., 2007. Consideration of Measurement Uncertainty in the
Evaluation of Goodness-of-fit in hydrologic and Water Quality Modeling.
Journal of Hydrology 337, 326-336.
84
Herweg, K.and Stillhardt, B. 1999. The Variability of Soil Erosion in the Highlands of
Ethiopia and Eritrea. Research Report 42. Soil Conservation Research Program
(SCRP), Centre for Development and Environment, University of Berne.
Hewlett, J.D., Hibbert A.R., 1967. Factors affecting the response of small watersheds
to precipitation in humid areas, p. 275-290, In W. E. Sopper and H. W. Lull,
eds. Forest Hydrology. Pergamon Press, Oxford.
Hurni, H., 1982. Inception Report. Soil Conservation Research Project, Vol. I.
University of Berne, Switzerland.
James, L.D. and S.J. Burges, 1982. Selection, Calibration, and Testing of Hydrologic
Models. In: Hydrologic Modeling of Small Watersheds, C.T. Haan, H.P.
Johnson, and D.L. Brakensiek (Editors). ASAE Monograph, St. Joseph,
Michigan, pp. 437-472.
Jones, J.A.A., 1994. Subsurface flow and subsurface erosion: further evidence on
forms and controls, in Stoddart, D.R. (ed.). Process and Form in
Geomorphology, Routledge.
Kefeni Kejela. 1987. Preliminary Assessment of the Impact of Soil Erosion and Its
Implication for Soil Conservation. A Case study based on the Soil Erosion
Data from Anjeni Station. A thesis presented to the School of Graduate Studies
in the Addis Ababa University, Addis Ababa, Ethiopia.
Kefeni Kejela 1985. The Soils of the Anjeni Area – Gojam Research Unit, Ethiopia.
Research Report 27. Soil Conservation Research Project, Centre for
Development and Environment, University of Berne.
Kirsch, K., A. Kirsch, and J.G. Arnold. 2002. “Predicting Sediment and Phosphorus
Loads in the Rock River Basin using SWAT.” Trans. ASAE 45(6): 1757-69.
Knisel, W.G., 1980. ed., CREAMS: A Field Scale Model for Chemicals, Runoff, and
Erosion from Agricultural Management Systems. Washington, D.C.: U.S.
85
Department of Agriculture, Agricultural Research Service Conservation
Research Report No. 26.
Leavesley, G. H., R. W. Lichty, B. M. Troutman, and L. G. Saindon, 1983.
Precipitation-runoff modeling system user’s manual, U.S. Geol. Sur. Water
Resour. Invest. Rep. 83-4238, 207 pp.
Legate D. R., and McCabe G. Jr., 1999. Evaluating the Use of Goodness-of-fit
Measures in Hydrologic and Hydro-climatic Model Validation. Water
Resource Research, Volume 35, No. 1, 233-241.
Legesse D., C. Vallet-Coulomb and F. Gasse. 2003. Hydrological response of a
catchment to climate and land use changes in Tropical Africa: case study South
Central Ethiopia. Journal of Hydrology, 275: 67-85
Leonard, R.A., Knisel, W.G., Still, D.A., 1987. GLEAMS: groundwater loading
effects of agricultural management systems. Trans. Am. Soc. Agric. Eng. 30,
1403–1418.
Liu, B.M., A.S. Collick, G. Zeleke, E. Adgo, Z.M. Easton, and T.S. Steenhuis. 2008.
Rainfall-discharge relationships for a monsoonal climate in the Ethiopian
Highlands. Hydrological Processes, 22(7): 1059-67
Marshall, H.G., 1982. Breeding for tolerance to heat and cold. In: M.N. Christiansen
and C.F. Lewis (Editors), Breeding Plants for Less Favorable Environments.
Wiley, New York, pp. 47-70.
Matalas, N. C. 1967. Mathematical Assessment of Synthetic Hydrology. Water
Resources Research 3(4): 937-945
McHugh OV. 2006. Integrated water resources assessment and management in a
drought-prone watershed in the Ethiopian highlands. PhD dissertation,
Department of Biological and Environmental Engineering. Cornell University
Ithaca NY.
86
McIntyre, DS 1974, in Loveday, J (ed) Methods of Analysis for Irrigated Soils.
Commonwealth Agricultural Bureaux Technical Commmunication No 54,
Farnham Royal, England.
Mohammed, A., Yohannes, F., Zeleke, G, 2004. Validation of agricultural non-point
source (AGNPS) pollution model in Kori watershed, South Wollo, Ethiopia.
International Journal of Applied Earth Observation and Geoinformation 6: 97–
109
Nash, J. E. and J. V. Sutcliffe. 1970, River flow forecasting through conceptual
models part I: A discussion of principles, Journal of Hydrology, 10, 282-290.
Neitsch, S.L., J.G. Arnold, J.R. Kiniry and J.R. Williams. 2001. Soil and Water
Assessment Tool User’s Manual, Version 2000.
Neitsch, S.L., J.G. Arnold, J.R. Kiniry, and J.R. Williams. 2002. Soil and Water
Assessment Tool User’s Manual, Version 2000. Grassland, Soil and Water
Research Laboratory, Temple, Texas GSWRL Report 02-02 Blackland
Research and Extension Center, Temple, Texas BRC Report 02-06. Texas
Water Resources Institute, College Station, Texas TWRI Report TR-192.
Neistch J. R., Arnold J. G., Kiniry J. R., Srinivasan R. and Williams J. R., 2004.
Input/Output File Documentation Version 2005. Grassland, Soil and Water
Research Laboratory, Agricultural Research Service, Blackland Research
Center, Texas Agricultural Experiment Station, Temple, Texas 76502
Neitsch SL, Arnold JG, Kiniry JR, Williams JR., 2005. Soil and Water Assessment
Tool, Theoretical Documentation: Version 2005. Temple, TX. USDA
Agricultural Research Service and Texas A&M Blackland Research Center.
Nicks A D., 1974. Stochastic generation of the occurrence, pattern and location of
maximum amount of daily rainfall. p. 154 – 171 In proc. Symp. Statistical
87
Hydrology Aug – Sept 1971, Tuscon, AZ. US Department of Agriculture,
Misc, Publ. No. 1275.
Njau E. C., 1996. Generalized Derivation of the Angstrom and Angstrom-Prescott
Equations. Renewable Energy, Vol. 7, No. 1, pp. 105-108.
NRCS, Northern Plains Regional Office. 1996. State of the Land for the Northern
Plains Region. USDA, Natural Resources Conservation Service, Northern
Plains Regional Office, Lincoln, NE. 64 p.
Parker, G.G., Sr. and C. G. Higgins, 1990. Piping and pseudokarst in dry lands, in
Higgins, C.G. and Coates, D.R. (eds), Ground water geomorphology: the role
of subsurface water in earth-surface processes and landforms, Geological
Society of America Special Paper 252, 77-110.
Patterson K. P., 2007. Integrating Population, Health, and Environment in Ethiopia,
Population Reference Bureau: Making the Link, 2007.
Persaud N., Lesolle D., Ouattara M., Coefficients of the Angstrijm-Prescott equation
for estimating global irradiance from hours of bright sunshine in Botswana and
NigerAgricultural and Forest Meteorology (88) 27-35.
Reeve, M.J.,Carter, A.D.,1991.Water release characteristic. In:
Smith,K.A.,Mullins,C.E. (Eds.), Soil Analysis. Physical Methods. Marcel
Dekker, New York, pp. 111–160.
Reungsang P., Kanwar R. S., Jha M., Gassman P. W., Ahmad K., and Saleh A., 2005.
Calibration and validation of SWAT for the Upper Maquoketa River
Watershed. Working Paper 05-WP 396. Center for Agricultural and Rural
Development, Iowa State University, Ames, Iowa 50011-1070.
www.card.iastate.edu
Revfeim K.J.A., 1997. On the relationship between radiation and mean daily sunshine.
Agricultural and Forest Meteorology (86) 183-191.
88
Richardson, C. W. 1981. Stochastic simulation of daily precipitation, temperature, and
solar radiation. Water Resource Research 17 (1): 182-190.
Richardson, C. W. and D. A. Wright. 1984. WGEN: a model for generating daily
weather variables. U.S. Department of Agriculture, Agricultural Research
Service, ARS-8.
Santhi, C., J.G. Arnold, J.R. Williams, L.M. Hauck, and W.A. Dugas, 2001a.
Application of a Watershed Model to Evaluate Management Effects on Point
and Nonpoint Source Pollution. Transactions of the American Society of
Agricultural Engineers 44(6):1559-1570.
Santhi, C., J.G. Arnold, J.R. Williams, W.A. Dugas, R. Srinivasan, and L.M. Hauck,
2001b. Validation of the SWAT Model on a Large River Basin With Point and
Nonpoint Sources. Journal of American Water Resources Association
(JAWRA) 37(5):1169-1187.
Setegn, S.G., Srinivasan, R., Dargahi, B. (2008): Hydrological Modeling in the Lake
Tana Basin, Ethiopia using SWAT model. The Open Hydrology Journal Vol 2,
49-62.
Sharpley, A.N. and J.R. Williams, eds. 1990. EPIC-Erosion Productivity Impact
Calculator, 1. Model documentation. U.S. Department of Agriculture,
Agricultural Research Service, Tech. Bull. 1768.
Smedema, L.K. and D.W. Rycroft. (1983). Land drainage-planning and design of
agricultural drainage systems, Cornell University Press, Ithica, N.Y.
Soil Conservation Research Program (SCRP), 2000. Area of Anjeni, Gojam, Ethiopia:
Long-term Monitoring of the Agricultural Environment 1984-1994. Soil
Erosion and Conservation Database. Centre for Development and
Environment, University of Berne.
89
Srinivasan, R., T.S. Ramanarayanan, J.G. Arnold, and S.T. Bednarz, 1998. Large Area
Hydrologic Modeling and Assessment. Part II: Model Application. Journal of
American Water Resources Association (JAWRA) 34(1):91-101.
Steenhuis, T.S., M. Winchell, J. Rossing, J. Zollweg, and M.F. Walter. 1995. SCS run
off equation revisited for variable-source run off areas. J. Irrigation Drainage
Eng., 121(3): 234-8.
Tripathi, M.P., R.K. Panda, and N.S. Raghuwanshi. 2003. Identification and
Prioritisation of Critical Subwatersheds for Soil Conservation Management
using the SWAT Model. Biosystems Engineering (2003) 85(3):365-379,
doi:10.1016/S1537-5110(03)00066-7.
USAID, 2000. Amhara National Regional State food security research assessment
report. Available at: http://crsps.org/amhara/amhara_rpt.PDF
USDA-NRCS. 2004. Part 630: Hydrology. National Engineering Handbook. Available
at: http://policy.nrcs.usda.gov/media/pdf/H_210_630_9.pdf. Accessed 3
January 2008
USDA Soil Conservation Service. 1972. National Engineering Handbook Section 4
Hydrology, Chapters 4-10, 1972.
Walling D.A. (1984) The sediment yields of African Rivers In: Proceedings of the
Challenges in African hydrology and Water Resources. IAHS Publ. No. 144.
Werner C., 1986. Soil Conservation Experiment in the Anjeni Area. Gojam Research
Unit (Ethiopia). University of Berne, Switzerland: Soil Conservation Research
Project 13.
White E.D., Z.M. Easton, D.R. Fuka, E.S. Collick, E. Adgo, M. McCartney, S.B.
Awulachew, Y.G. Selassie, and T.S. Steenhuis. 2008. Adapting the soil and
water assessment tool (SWAT) for the Nile Basin (unofficial data describes
90
that 70% of the cost of operation and maintenance in the Blue Nile part of
Sudan is spent on sediment related and canal maintenance).
White K. L. and Chaubey I., 2005. Sensitivity Analysis, Calibration and Validations
for a Multisite and Multivariable SWAT Model. Journal of the American
Water Resources Association (JAWRA), 41(5):1077-1089.
Wilcox, B. P., Rawls W. J., Brakensiek D. L., Wright J. R., 1990. Predicting Runoff
from Rangeland Catchments: A comparison of two models. Water Resource
Research, (26), 2401 – 2410.
Williams, J.R. 1975. Sediment-yield prediction with universal equation using runoff
energy fac-tor. p. 244-252. In Present and prospective technology for
predicting sediment yield and sources: Proceedings of the sediment yield
workshop, USDA Sedimentation Lab., Ox-ford, MS, November 28-30, 1972.
ARS-S-40.
Williams, J.R., 1995. Chapter 25: The EPIC model. In: V.P. Singh (eds.), Computer
models of Watershed hydrology, pp. 909-1000.
Williams, J.R., A.D. Nicks, and J.G. Arnold. 1985. Simulator for water resources in
rural basins Journal of Hydrology Engineering 111(6): 970-986.
Winchell M., R. Srinivasan R., Di Luzio M. and Arnold J. G., 2007. ArcSWAT
Interface for SWAT2005. Grassland, Soil & Water Research Laboratory,
USDA Agricultural Research Service, Temple, Texas.
Zeleke, Gete, 2000. Landscape Dynamics and Soil Erosion Process Modeling in the
North-western Ethiopian Highlands. African Studies Series A 16, Geographica
Bernensia, Berne.
Zeleke, Gete, 1998. Soil Map of the Anjeni research area. Soil Conservation Research
Program, Centre for Development and Environment, Berne, Switzerland.
91
APPENDIX Appendix I: Parameters in SWAT database for each crops in the watershed
CPNM WWHT FRST RNGE RNGB CORN BARL ALFA SOYB FLAX TEFF CROP NAME
Winter Wheat
Forest-Mixed
Range-Grasses
Range-Brush Corn
Spring Barley Alfalfa Soybean Flax Teff
BIO_E 30 15 34 34 39 35 20 25 15 35 HVSTI 0.4 0.76 0.9 0.9 0.5 0.54 0.9 0.31 0.76 0.9 BLAI 4 5 2.5 2 3 4 4 3 5 4 FRGRW1 0.05 0.05 0.05 0.05 0.15 0.15 0.15 0.15 0.05 0.5 LAIMX1 0.05 0.05 0.1 0.1 0.05 0.01 0.01 0.05 0.05 0.02 FRGRW2 0.45 0.4 0.25 0.25 0.5 0.45 0.5 0.5 0.4 0.89 LAIMX2 0.95 0.95 0.7 0.7 0.95 0.95 0.95 0.95 0.95 0.95 DLAI 0.5 0.99 0.35 0.35 0.7 0.6 0.9 0.6 0.99 0.85 CHTMX 0.9 6 1 1 2.5 1.2 0.9 0.8 6 0.6 RDMX 1.3 3.5 2 2 2 1.3 3 1.7 3 1.3 T_OPT 18 30 25 25 25 25 20 25 30 25 T_BASE 0 10 12 12 8 0 4 10 10 6 CNYLD 0.025 0.0015 0.016 0.016 0.014 0.021 0.025 0.065 0 0.05 CPYLD 0.0022 0.0003 0.0022 0.0022 0.0016 0.0017 0.0035 0.0091 0 0.004 BN1 0.0663 0.006 0.02 0.02 0.047 0.059 0.0417 0.0524 0.01 0.03 BN2 0.0255 0.002 0.012 0.012 0.0177 0.0226 0.029 0.0265 0 0.02 BN3 0.0148 0.0015 0.005 0.005 0.0138 0.0131 0.02 0.0258 0 0.012 BP1 0.0053 0.0007 0.0014 0.0014 0.0048 0.0057 0.0035 0.0074 0 0.002 BP2 0.002 0.0004 0.001 0.001 0.0018 0.0022 0.0028 0.0037 0 0.0015 BP3 0.0012 0.0003 0.0007 0.0007 0.0014 0.0013 0.002 0.0035 0 0.0013 WSYF 0.2 0.01 0.9 0.9 0.3 0.2 0.9 0.01 0.01 0.9 USLE_C 0.15 0.05 0.01 0.05 0.15 0.1 0.15 0.2 0.05 0.3 GSI 0.006 0.002 0.005 0.005 0.007 0.008 0.01 0.007 0 0.008 VPDFR 4 4 4 4 4 4 4 4 4 4 FRGMAX 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 WAVP 6 8 10 10 7.2 7 10 8 8 8 CO2HI 660 660 660 660 660 660 660 660 660 660 BIOEHI 39 16 39 39 45 45 35 34 16 46 RSDCO_PL 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 OV_N 0.14 0.1 0.15 0.15 0.14 0.14 0.06 0.14 0.1 0.3 CN2A 62 36 49 39 67 62 31 67 36 65 CN2B 73 60 69 61 77 73 59 78 60 79 CN2C 81 73 79 74 83 81 72 85 73 84 CN2D 84 79 84 80 87 84 79 89 79 86 FERTFIELD 1 0 0 0 1 1 0 0 0 0 ALAI_MIN 0 0.75 0 0 0 0 0 0 0.75 0 BIO_LEAF 0 0.3 0 0 0 0 0 0 0.3 0 MAT_YRS 0 50 0 0 0 0 0 0 50 0 BMX_TREES 0 1000 0 0 0 0 0 0 1000 0 EXT_COEF 0.65 0.65 0.33 0.33 0.65 0.65 0.65 0.45 0.65 0
92
Appendix II: Parameters in SWAT database for each soil layers in the watershed
SNAM AJLILE
AJVELU
AJEURE
AJHUAL
AJHANI
AJDYCA AJHAAL
AJHAAC AJHALX AJHUNI
NLAYERS 1 6 3 5 4 6 5 5 6 6 HYDGRP A B B C C B C B B B SOL_ZMX 500 1400 1300 1400 2000 1380 2150 1700 1850 1380 ANION_EXCL 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 SOL_CRK 0 0.01 0.01 0.03 0.01 0 0.01 0.01 0.01 0.002 TEXTURE SCL SCL SiL C CSiL L C L-CL SCL C-CL
SOL_Z1 200 200 250 200 200 200 200 200 200 200 SOL_BD1 1.1 1.45 1.08 1.1 1.1 1.1 1.1 1.1 1.45 1.1 SOL_AWC1 0.11 0.11 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11
SOL_K1 5 5 6.8 1 4.34 7 4.34 4.34 7 7 SOL_CBN1 2 0.5 1.6 2 2 2 2 2 0.5 2
CLAY1 50 25 53.6 50 50 50 50 50 25 50
SILT1 33 31 25.7 33 33 33 33 33 31 33
SAND1 17 44 20.7 17 17 17 17 17 44 17
ROCK1 5 0.01 0 5 5 5 5 5 0.01 5 SOL_ALB1 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 USLE_K1 0.22 0.3 0.23 0.22 0.22 0.22 0.22 0.22 0.3 0.22 SOL_EC1 0 0 0.5 0 0 0 0 0 0 0
SOL_Z2 127 280 750 330 900 320 500 350 280 390 SOL_BD2 2.5 1.46 1.15 1.27 1.27 1.45 1.3 1.37 1.46 2.5 SOL_AWC2 0.5 0.11 0.19 0.11 0.11 0.13 0.13 0.09 0.11 0.1
SOL_K2 400 37.2 6.8 1 4.54 13 4.54 5.52 25 7 SOL_CBN2 0.58 0.52 0.3 0.8 1.5 1.1 1.4 0.22 0.52 0.58
CLAY2 5 28 73.6 10 23 32 63 44 22.6 35
SILT2 25 6 15.7 20 50 24 17 4 23 20
SAND2 70 66 10.7 70 27 44 20 52 54.4 45
ROCK2 98 0 0.01 0 0 0 0.01 2 0 0 SOL_ALB2 0.08 0.13 0.13 0.09 0.13 0 0.13 0.13 0.13 0.08 USLE_K2 0 0.3 0.22 0.2 0.22 0.34 0.22 0.11 0.18 0.14 SOL_EC2 0 0 0.045 0 0 0 0 0 0 0
SOL_Z3 0 360 1300 430 1000 650 900 680 450 420 SOL_BD3 0 1.45 1.17 1.28 1.28 1.45 1.3 1.42 1.45 1.5 SOL_AWC3 0 0.11 0.19 0.1 0.11 0.13 0.11 0.15 0.1 0.11
93
SNAM AJLILE
AJVELU
AJEURE
AJHUAL
AJHANI
AJDYCA AJHAAL
AJHAAC AJHALX AJHUNI
SOL_K3 0 34.8 6.8 1 5.16 25 5.16 10.56 25 7 SOL_CBN3 0 0.63 0.1 0.4 1.3 1 1.1 0.21 0.63 1
CLAY3 0 52 71.6 15 60 30 61 43 41.6 24
SILT3 0 8 15.7 24 25 20 20 7 12 16
SAND3 0 40 12.7 61 15 50 19 50 46.4 60
ROCK3 0 0 0 0 0 0 0 1 0 0 SOL_ALB3 0 0.13 0.13 0.09 0.13 0.2 0.13 0.13 0.13 0.12 USLE_K3 0 0.3 0.2 0.2 0.22 0.2 0.22 0.28 0.18 0.14 SOL_EC3 0 0 0.06 0 0 0 0 0 0 0
SOL_Z4 0 710 0 770 2000 900 1600 900 720 710 SOL_BD4 0 1.49 0 1.22 1.22 1.39 1.3 1.49 1.49 1.3 SOL_AWC4 0 0.1 0 0.1 0.11 0.13 0.12 0.1 0.1 0.3
SOL_K4 0 33.6 0 1 4.24 25 4.24 33.6 25 7 SOL_CBN4 0 0.4 0 0.3 0.5 1 0.6 0.2 0.4 1
CLAY4 0 51 0 17 71 30 75 44 51.6 23.1
SILT4 0 5 0 26 20 20 20 6 10 16.5
SAND4 0 45 0 57 9 50 5 50 38.4 60.4
ROCK4 0 0 0 0 0 0 0 0 0 0 SOL_ALB4 0 0.13 0 0.09 0.13 0.2 0.13 0.13 0.13 0.2 USLE_K4 0 0.3 0 0.2 0.22 0.2 0.22 0.3 0.18 0.14 SOL_EC4 0 0 0 0 0 0 0 0 0 0
SOL_Z5 0 1120 0 1400 1000 1220 2150 1700 1470 1000 SOL_BD5 0 1.48 0 1.13 1.5 1.45 1.3 1.48 1.48 1.5 SOL_AWC5 0 0.1 0 0.1 0.4 0.1 0.14 0.1 0.1 0.4
SOL_K5 0 36 0 1 400 24 4.34 36 25 7 SOL_CBN5 0 0.2 0 0.2 2 1 0.4 0.2 0.2 2
CLAY5 0 38 0 25 23.1 25 79 43 54.3 23.1
SILT5 0 8 0 24 17.2 20 14 10 23.4 17.2
SAND5 0 54 0 51 59.7 55 7 47 22.3 59.7
ROCK5 0 0 0 0 0 0 0 0 0 0 SOL_ALB5 0 0.13 0 0.09 0.2 0.2 0.13 0.13 0.13 0.2 USLE_K5 0 0.3 0 0.2 0.14 0.2 0.22 0.3 0.18 0.14 SOL_EC5 0 0 0 0 0 0 0 0 0 0
SOL_Z6 0 1400 0 1635.2 1380 1380 1350 1350 1850 1380 SOL_BD6 0 1.49 0 1.1 1.4 1.53 1.49 1.49 1.49 1.4
94
SNAM AJLILE
AJVELU
AJEURE
AJHUAL
AJHANI
AJDYCA AJHAAL
AJHAAC AJHALX AJHUNI
SOL_AWC6 0 0.1 0 0.11 0.5 0.12 0.2 0.2 0.1 0.5
SOL_K6 0 36 0 4.24 40 50 36 36 25 7 SOL_CBN6 0 0.12 0 1.24 0.9 1 0.12 0.12 0.12 0.9
CLAY6 0 25 0 60 23.6 22 17 17 54.3 24
SILT6 0 5 0 12.7 18.1 16 57 57 24.8 18
SAND6 0 70 0 27.3 58.3 62 26 26 20.9 58
ROCK6 0 0 0 0 0 14 0 0 0 0 SOL_ALB6 0 0.13 0 0.09 0.19 0.11 0.13 0.13 0.13 0.19 USLE_K6 0 0.3 0 0.2 0.14 0.2 0.3 0.3 0.18 0.14 SOL_EC6 0 0 0 0 0 0 0 0 0 0
SOL_Z7 0 1800 0 2422.4 0 0 1800 1800 1800 0 SOL_BD7 0 1.47 0 1.1 0 0 1.47 1.47 1.47 0 SOL_AWC7 0 0.21 0 0.09 0 0 0.21 0.21 0.21 0
SOL_K7 0 36 0 4.04 0 0 36 36 36 0 SOL_CBN7 0 0.1 0 0.34 0 0 0.1 0.1 0.1 0
CLAY7 0 16 0 63.6 0 0 16 16 16 0
SILT7 0 59 0 16.6 0 0 59 59 59 0
SAND7 0 25 0 19.8 0 0 25 25 25 0
ROCK7 0 0 0 0 0 0 0 0 0 0 SOL_ALB7 0 0.13 0 0.09 0 0 0.13 0.13 0.13 0 USLE_K7 0 0.3 0 0.2 0 0 0.3 0.3 0.3 0 SOL_EC7 0 0 0 0 0 0 0 0 0 0
95
Appendix III: Parameters in SWAT database for Urban land uses in the watershed
URBNAME URLD
URBFLNM Residential-Low
Density FIMP 0.12 FCIMP 0.1 CURBDEN 0.24 URBCOEF 0.18 DIRTMX 225 THALF 0.75 TNCONC 460 TPCONC 196 TNO3CONC 6 OV_N 0.1 CN2A 31 CN2B 59 CN2C 72 CN2D 79 URBCN2 98
96
Appendix IV: A. Sliding of sides of gullies
97
Appendix IV: B. Soil piping in channel sides and springs in the watershed
98
Appendix IV: C. Soil piping in gullies and side sliding
Piping
99
Appendix V: Parameters used for Weather Generator in SWAT Model
TMPMX TMPMN TMPSTDMX TMPSTDMN PCPMM PCPSTD PCPSKW PR_W1_ PR_W2_ PCPD RAINHHMX SOLARAV DEWPT WNDAV
25.19 6.48 1.22 1.72 19.16 2.26 4.68 0.1 0.19 1.1 31.6 7.11 1.07 2.11 26.43 7.92 1.4 2.06 15.34 2.18 4.12 0.19 0.49 1 10.8 17.01 -2.72 2.45 26.47 9.65 1.66 1.99 49.855 3.92 3.1 0.21 0.52 4.9 15.6 20.46 2.03 2.36 26.06 10.83 2.1 1.77 64.72 4.36 3.1 0.32 0.69 5.9 18.4 16.06 5.49 2.2 25.11 11.28 2.23 1.56 110.455 6.21 2.29 0.73 0.92 11 30.8 7.68 9.31 2.02 21.76 10.82 2.12 1.9 316.35 10.04 1.2 0.66 0.97 24.6 29.8 1.63 9.28 1.73 19.54 10.65 1.5 1.11 429.12 11.82 1.47 0.65 0.96 29.1 35.3 4.48 12.39 1.5 19.46 10.66 1.6 1.76 392.845 10.18 1.19 0.55 0.9 28.85 36.5 10.4 12.33 1.62 21.03 9.85 1.45 1.3 258.665 7.95 1.25 0.35 0.7 24.25 35.9 15.7 10.61 1.86 22.28 8.89 1.28 1.78 153.93 7.37 2.38 0.16 0.46 12.5 38.4 15.19 8.45 2.15 23.56 7.32 1.13 2.8 36.295 2.68 3.38 0.1 0.28 3.8 13.1 9.78 5.88 2.18 24.29 6.01 1.1 1.52 18.46 1.86 3.78 0.08 0.26 1.6 10.5 2.36 12.22 2.11
100
Appendix VI: Observed and Simulated Flow and Sediment loss in Calibration
Time Qobs Qsim SEDobs SEDsim (mm) (mm) (t/ha) (t/ha)
Jan-86 14.73 4.79 0.00 0 Feb-86 7.24 2.47 0.00 0 Mar-86 8.08 2.28 0.00 0 Apr-86 7.76 1.82 0.00 0 May-86 11.79 1.58 1.77 0 Jun-86 38.30 50.07 3.93 2.59 Jul-86 139.56 150.88 4.92 5.39
Aug-86 174.36 143.89 5.28 2.43 Sep-86 117.20 117.36 2.40 0.71 Oct-86 69.18 71.02 1.38 0.26 Nov-86 18.56 20.67 0.01 0 Dec-86 21.39 10.94 0.00 0 Jan-87 14.51 4.19 0.00 0 Feb-87 7.24 2.73 0.00 0 Mar-87 12.12 3.38 0.00 0 Apr-87 10.76 2.5 0.14 0 May-87 14.44 17.79 0.56 0.69 Jun-87 87.41 135.99 3.07 5.61 Jul-87 173.98 182.35 4.06 6.19
Aug-87 234.71 220.16 5.23 4.5 Sep-87 89.11 118.77 0.63 0.62 Oct-87 44.21 59.12 0.10 0.38 Nov-87 22.00 15.56 0.00 0 Dec-87 14.73 9.41 0.00 0 Jan-88 14.73 4.22 0.00 0 Feb-88 13.78 2.83 0.00 0 Mar-88 9.32 1.72 0.00 0 Apr-88 7.76 1.04 0.00 0 May-88 8.02 2.33 0.00 0 Jun-88 21.45 80.34 0.52 4.28 Jul-88 257.53 169.37 2.09 5.7
Aug-88 192.36 213.1 1.41 4.56 Sep-88 89.67 112.68 0.66 0.47 Oct-88 90.45 118.3 0.74 1.37 Nov-88 28.14 34.34 0.00 0 Dec-88 22.20 16.22 0.00 0
101
Time Qobs Qsim SEDobs SEDsim (mm) (mm) (t/ha) (t/ha)
Jan-89 14.73 8.17 0.00 0.02 Feb-89 7.24 3.18 0.00 0 Mar-89 11.36 3.03 0.00 0 Apr-89 13.56 3.18 0.00 0 May-89 12.05 7.15 0.00 0.06 Jun-89 9.29 35.29 0.02 0.83 Jul-89 153.76 215.98 2.45 8.68
Aug-89 161.06 163.82 1.54 2.95 Sep-89 131.07 137.73 1.01 0.91 Oct-89 42.44 63.64 0.00 0.08 Nov-89 22.93 20.75 0.00 0 Dec-89 18.78 9.71 0.07 0 Jan-90 22.68 6.18 0.00 0.01 Feb-90 11.57 2.69 0.00 0 Mar-90 8.02 2.01 0.00 0 Apr-90 7.76 1.49 0.00 0 May-90 10.48 9.54 0.39 0.17 Jun-90 12.34 29.22 0.57 1.29 Jul-90 135.13 165.7 7.93 6.24
Aug-90 266.41 218.87 16.08 4.94 Sep-90 162.03 170.42 5.08 2.75 Oct-90 47.89 80.62 0.00 0.11 Nov-90 21.94 25.13 0.00 0 Dec-90 17.55 11.38 0.00 0 Jan-91 11.70 4.63 0.00 0 Feb-91 8.97 2.4 0.00 0 Mar-91 8.02 2.14 0.00 0 Apr-91 7.76 1.42 0.00 0 May-91 8.83 2.11 0.03 0 Jun-91 12.35 53.62 0.00 3.99 Jul-91 176.13 218.94 9.88 8.56
Aug-91 187.58 192.54 6.62 3.68 Sep-91 132.73 146.64 3.75 1.48 Oct-91 37.85 51.26 0.06 0.04 Nov-91 21.51 15.85 0.01 0 Dec-91 14.73 7.65 0.00 0 Jan-92 14.73 3.66 0.00 0 Feb-92 12.48 2.02 0.00 0
102
Time Qobs Qsim SEDobs SEDsim (mm) (mm) (t/ha) (t/ha)
Mar-92 8.45 1.73 0.00 0 Apr-92 13.14 5.41 0.94 0.06 May-92 9.89 13.99 0.22 0.3 Jun-92 36.82 64.8 3.67 2.44 Jul-92 117.66 131.1 4.14 2.93
Aug-92 220.39 160.86 2.30 2.96 Sep-92 105.18 98.14 1.33 0.4 Oct-92 109.64 103.35 1.66 0.61 Nov-92 27.24 35.31 0.00 0 Dec-92 18.57 19.66 0.00 0.01 Jan-93 14.73 5.76 0.00 0 Feb-93 11.79 2.96 0.00 0 Mar-93 8.02 2.26 0.00 0 Apr-93 8.02 2.37 0.00 0 May-93 8.57 1.81 0.00 0 Jun-93 57.79 90.3 4.25 5.02 Jul-93 170.70 169.38 7.57 5.21
Aug-93 247.16 241.83 6.82 5.75 Sep-93 218.40 203.13 10.70 4.37 Oct-93 66.26 104.89 0.50 0.31 Nov-93 25.43 38.21 0.00 0 Dec-93 14.73 16.52 0.00 0
103
Appendix VII: Observed and Simulated Flow and Sediment loss in Validation
Time Qobs Qsim Sed obs Sed sim (mm) (mm) (ton/ha) (ton/ha)
Jan-95 14.72 3.98 0.00 0 Feb-95 10.48 2.24 0.00 0 Mar-95 8.01 1.66 0.00 0 Apr-95 8.38 1.9 0.11 0 May-95 12.55 5.71 0.45 0.01 Jun-95 51.03 80.84 8.69 5.23 Jul-95 175.54 187.13 6.69 6.1
Aug-95 170.69 177.73 5.28 2.69 Sep-95 138.45 166.73 5.08 2.56 Oct-95 46.38 71.27 0.00 0.09 Nov-95 18.87 24.32 0.00 0 Dec-95 18.25 10.84 0.00 0 Jan-96 11.99 5.2 0.00 0 Feb-96 10.19 2.77 0.00 0 Mar-96 24.58 7.4 3.65 0.07 Apr-96 24.26 11.57 0.51 0.39 May-96 49.86 36.47 2.77 1.41 Jun-96 100.26 120.44 4.17 4.09 Jul-96 222.31 237.27 10.58 9.52
Aug-96 151.75 170.28 4.29 2.29 Sep-96 89.94 108.83 1.46 0.44 Oct-96 44.82 47.51 0.18 0.04 Nov-96 22.20 18.47 0.00 0.01 Dec-96 15.01 7.89 0.00 0 Jan-00 19.07 3.81 0.00 0 Feb-00 13.77 2.09 0.00 0 Mar-00 14.72 1.47 0.00 0 Apr-00 10.78 2.67 0.00 0 May-00 10.39 3.59 0.00 0 Jun-00 80.05 104.16 7.81 5.7 Jul-00 184.33 218.5 8.03 8.9
Aug-00 156.04 172.36 4.80 2.73 Sep-00 87.74 100 1.75 0.43 Oct-00 76.73 118.33 0.00 1.46 Nov-00 57.11 43.11 0.00 0 Dec-00 33.59 20.74 0.00 0