SIMULATION OF RUNOFF BY SWAT MODEL AT BULULO
WATERSHED, KERSA, EAST HARERGE
M.Sc Thesis
WENDWOSEN AKALU
November, 2014
Haramaya University
SIMULATION OF RUNOFF BY SWAT MODEL AT BULULO
WATERSHED, KERSA, EAST HARERGE
A Thesis Submitted to School of Graduate Studies through Institute of
Technology, School of Natural Resource and Environmental Engineering,
Haramaya University
In Partial Fulfillment of the Requirement for the Degree of
MASTER OF SCIENCE IN IRRIGATION ENGINEERING
By
WENDWOSEN AKALU
November, 2014
Haramaya University
SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As thesis research advisor, I hereby certify that I have read and evaluated this Thesis prepared,
under my guidance, by Wendwosen Akalu entitled Simulation of Runoff by SWAT Model
at Bululo Watershed, Kersa, East Harerge.
I recommended that it be submitted as fulfilling the M.Sc thesis partial requirement.
Prof. Shoeb Quraishi (Dr,Eng.)
Major Advisor
_______________
________________
Signature
Date
As members of the Board of Examiners of the MSc Thesis Open Defense Examination, we
certify that we have read, evaluated the thesis prepared by Wendwosen Akalu and examined
the candidate. We recommended that the thesis can be accepted as fulfilling the thesis
requirement for the degree of Master of Science in Irrigation Engineering.
_________________________
Chairperson
_________________________
Name of Internal Examiner
_________________________
Name of External Examiner
________________
Signature
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Signature
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Signature
ii
_______________
Date
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Date
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Date
DEDICATION
This thesis manuscript is dedicated to the memory of my mother, Mamitu Kidane, who
emphasized the importance of education and taught me important lessons throughout her life;
and to the memory of my father, Akalu Ergete, who left this world. And also for my wife
Yetnayet Shmelis and our children, Kalab and Eyosiyas Wendwosen for their affection, love
and their moral support during my studies.
iii
STATEMENT OF AUTHOR
First, I declare that this thesis is my work and that all sources of materials used for this Thesis
have been duly acknowledged. This thesis has been submitted in partial fulfillment of the
requirements for an advanced MSc degree at the Haramaya University and deposited at the
University Library to be made available to borrowers under rules of the Library.
Brief quotation from this thesis is allowable without special permission provided that accurate
acknowledgement of sources is made. Requests for permission for extended quotations from
or reproduction of this manuscript in whole or in part may be granted by the head of the major
department or the dean of the School of Graduate Studies when in his or her judgment the
proposed use of the material is in the interests of scholarship. In all other instances, however,
permission must be obtained from the author.
Signature: -------------------------
Wendwosen Akalu
Haramaya University
Haramaya
iv
ABBREVIATIONS AND ACRONYMS
AMC
Antecedent Soil Moisture Conditions
ANN
Artificial Neural Network
ARS
Agricultural Research Service
BFI
Base Flow Index
DEM
Digital Elevation Model
DWSM
Dynamic Watershed Simulation Model
FAO
Food and Agriculture Organization
FAST
Fourier Amplitude Sensitivity Test
GDP
Gross Domestic Product
GIS
Geographic Information System
HDI
Human Development Index
HRU
Hydrologic Response Unit
HSPF
Hydrological Simulation Programme -Fortran
IFPRI
International Food Policy Research Institute
LH
Latin Hypercube
Mm3
Million meter cube
MoWR
Ministry of Water Resource
NMSA
National Meteorological Services Agency
NSE
Nash-Sutcliffe Efficiency
OAT
One Factor at a Time
OIDA
Oromiya Irrigation Development Authority
PET
Potential Evapotranspiration
SMDR
Soil Moisture Distribution and Routing
SNNPR
Southern Nations, Nationalities and People Region
SWAT
Soil and Water Assessment Tool
UNDP
United Nations Development Program
UK
United Kingdom
UKIH
United Kingdom Institute of Hydrology
USGS
United States Geological Survey
v
BIOGRAPHICAL SKETCH
The author was born on September 1, 1975 in West Harerge, Hirna town. He attended his
elementary and junior secondary school education from 1982-1989 in Hirna Number 1 and
Hirna Number 2 schools, respectively. From 1990-1994, he attended his secondary school
education at “Hirna High School”. In September 1995, he was admitted to the Wendo Genet
College of Forestry, now Hawasa University and graduated with diploma in Forestry in 1996.
Soon after his graduation, he was employed by the Agriculture Bureau of the Oromia Regional
State served for two years as Forest Development expert in Eastern Harerge Administrative
Zone in Gole Oda district. Then, he was transferred to Bedeno district and worked as soil and
water conservation expert and process owner of natural resource division in different times.
He joined Haramaya University in 2005 and graduated with B.Sc. degree in Soil and Water
Engineering and Management on September 10, 2009 summer in service program.
In year 2011, he joined the School of Graduate Studies of the Haramaya University to pursue
his studies in the field of Irrigation Engineering.
vi
ACKNOWLEDGEMENT
My first words of gratitude must go to my major advisor, Prof. Dr. Eng. Shoeb Quaraishi for
his valuable comments for the preparation of the manuscript and for his significant
contribution throughout the research work. In total, the work was shaped to its present form
with his genuine support.
I am indebted to Oromia Agricultural Bureau financed my study and gave me the chance and
arranged all steps of the process. Especially, the indisputable assistance I got from this
organization is the one, which I never got elsewhere.
I would like to extend my recognition to Ethiopian National Meteorological Agency, Ministry
of Water Resource, Kersa district Land Administration Office, for the provision of all relevant
data, documents and information essential for the study.
I would like to forward my appreciation and special thanks to Mr. Wendu Alemayehu for his
personal interest he showed in supporting on Arc GIS concepts, and for all the material
support he did.
I would like to give my great thanks to Mr. Bushira Alyi, who fully provided me his Lap Top
computer until the last date of graduation.
I would like to extend my deepest gratitude to my mother-in-law W/ro Bekelu Tamirat, and
my friend Mr. Dereje Ayele for their support and creating wonderful environment for my
study.
At last but not the least, I extend my appreciation to my wife, Yetnayet Shmelis and to my
children Kalab and Eyosiyas, who kept me in their prayers and waited so long patiently and
who have shared the many uncertainties, challenges, and sacrifices for completing my
graduate study. I have no words to appreciate for their strength and patience paying for my
success.
vii
TABLE OF CONTENTS
DEDICATION
iii
STATEMENT OF AUTHOR
iv
ABBREVIATIONS AND ACRONYMS
v
BIOGRAPHICAL SKETCH
vi
ACKNOWLEDGEMENT
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF TABLES IN THE APPENDIX
xiii
LIST OF FIGURES IN THE APPENDIX
xiv
ABSTRACT
xv
1. INTRODUCTION
1
2. LITERATURE REVIEW
4
2.1. Rainfall-Runoff Relationship
4
2.1.1. Surface runoff process
4
2.1.2. Factors affecting runoff
5
2.2. Runoff Coefficients
7
2.3. Probability Analysis of Rainfall
7
2.4 .Estimation of Flood Peak
8
2.4.1. Rational method
9
2.4.2 .The US Soil Conservation Service (SCS) method
9
2.4.3 .The unit hydrograph method
12
2.5. Description of SWAT Model
12
2.5.1. Components of SWAT model
14
2.5.2. Comparisons of SWAT with Other Models.
17
2.5.3. Review of past works on SWAT model
19
2.5.3. SWAT Strengths and Limitations
21
2.6. Sensitivity Analyses, Calibration and Validation of SWAT Model
viii
23
TABLE OF CONTENTS (Continued)
2.6.1. Sensitivity analyses
23
2.6.2. Calibration approach
24
2.6.3. Validation
25
2.7. Geographical Information System
25
2.8. Rainbow Software Package
26
2.9. Estimation of Baseflow
28
2.9.1. Smoothed minima method or USGS BFI method
28
2.9.2. Recursive digital filter technique
29
3. MATERIALS AND METHODS
31
3.1. Description of the Study Area
31
3.2. Methods
32
3.2.1. SWAT input data and analysis
32
3.2.1. Digital elevation model (DEM) of the study area
32
3.2.2. Digitized stream networks.
34
3.2.3. Land use/Land cover
34
3.2.4. Soil data
36
3.2.5. Meteorological data
36
3.2.6. Hydrological data
37
3.3. Arc SWAT Model Set Up
38
3.3.1. Watershed delineation
38
3.3.2. Hydrologic response unit analysis
39
3.3.3. Defining weather database
40
3.3.4. Sensitivity analysis
40
3.3.5. Model calibration
41
3.3.6. Model validation
44
3.3.7. Model evaluation
44
4. RESULTS AND DISCUSSION
45
4.1. Watershed and Channel Delineation
45
4.2. Hydrologic Response Units
46
ix
TABLE OF CONTENTS (Continued)
4.3. Contribution of Rainfall Stations for the Catchment
49
4.4. Homogeneity Test of Annual Rainfall Data
50
4.5. Base Flow Separation for Dawe Gauge station
52
4.6. Sensitivity Analysis
52
4.7. Model Calibration
53
4.8. Model Validation
56
4.9. Model Evaluation
59
4.10. Water Yield Simulation of Bulullo Watershed
61
4.11. Average Annual Water Yield of sub watersheds
62
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS.
65
5.1. Summary
65
5.2. Conclusion
66
5.3. Recommendations
67
6. REFERENCE
68
7. APPENDICES
77
7.1. Appendix Tables
78
7.1.1. Meteorological data
78
7.1.2. Soil data parameters
83
7.1.3. Hydrological data and SWAT out put
85
7.2. Appendix Figure.
86
x
LIST OF TABLES
Table
Page
3 1. Land use/land cover of the study area and redefinition according of SWAT Code
35
4.1. Area covered by sub-watershed
46
4.2. Probability of rejecting homogeneity of annual rainfall (Kulbi station)
52
4.3. Sensitive parameter ranking and final auto-calibration result
53
4.4. Calibrated average annual stream flow and model evaluation statistics for calibration
period 1992-1998.
55
4.5. Calibrated monthly stream flow model evaluation statistics for (1992-1998)
56
4.6. Validated annual stream flow and model evaluation statistics for (1999-2003).
58
4.7. Validated monthly stream flow model evaluation statistics for (1999-2003)
59
4.8. Summary of the model performance evaluation statistical result for calibration and
validation.
60
4.9. Simulated monthly water yield (Mm3).
61
4.10. Water yield in Mm3 for different probabilities of Exceedance.
62
4.11. Water yield in Mm3 for each sub watershed
62
xi
LIST OF FIGURES
Figure
Pages
3.1. Location map of the study area.
31
3.2. DEM used for the study Area
33
3.3. Stream network map of the study area
34
3.4. Land Use/Land Cover Map of the study area
35
3.5. Location map of weather stations.
37
3.6. Flow calibration procedure used in this study (Snathi et al., 2001)
43
4.1. Bululo Sub Watershed map.
45
4.2. Land use /Land cover map of the study area.
47
4.3. Soil map of study area.
48
4.4. Slope map of study area.
49
4.5. Contribution of Rainfall Stations for the Catchment (Thiessen Polygon)
50
4.6. Rescaled cumulative deviation of annual rainfall at Kulbi station
51
4.6. Probability plot of annual rainfall at Kulbi Station
52
4.8. Scatter plots for annual model calibration period 1992-1998.
54
4.8. Mean annual observed and simulated flow of calibration period 1992-1998.
54
4.10. Scatter plots for monthly model calibration period (1992-1998).
55
4.11. Observed and simulated flow hydrograph for calibration period (1992-1998)
56
4.12. Scatter plots for mean annual flow during model validation period (1999-2003)
57
4.13. Mean annual observed and simulated stream flow of validation period (1999-2003)
57
4.14. Scatter plots for monthly model validation period (1999-2003)
58
4.15. Comparison between observed and simulated monthly stream flow for validation period
(1999-2003).
59
F4.16. Average annual water yield generated from each subbasins
63
xii
LIST OF TABLES IN THE APPENDIX
Appendix Table
Pages
1. Statistical Precipitation Data of Kulbi Station (1990-2010)
78
2. Kulbi total monthly precipitation (mm).
79
3. Statistical Precipitation Data of Kersa Station (1995-2010)
80
4. Kersa total monthly precipitation (mm).
80
5. Statistical Precipitation Data of Girawa Station (1990-2010)
81
6. Girawa total monthly precipitation (mm).
82
7. Soil Parameter Value for Chromic Luvisols
83
8. Soil Parameter Value for Rendzic Leptosols
83
9. Soil Parameter Value for Humic Nitosols
84
10. Soil Parameter Value for Eutric Cambisols
84
11. Hydrological data for Dawe gauging station (m3/s)
85
12. SWAT Monthly Stream Flow out Put for the Calibration and
85
13. Probability of rejecting homogeneity of annual rainfall (Girawa station)
86
14. Probability of rejecting homogeneity of annual rainfall (Kersa station)
86
xiii
LIST OF FIGURES IN THE APPENDIX
Appendix Figure
Page
1. Rescaled cumulative deviation of annual rainfall at Girawa station
86
2. Probability plot for annual rainfall (1990-2010) at Girawa station
87
3. Rescaled cumulative deviation of annual rainfall at Kersa station
87
4. Probability plot of annual rainfall at Kersa station
88
xiv
SIMULATION OF RUNOFF BY SWAT MODEL AT BULULO
WATERSHED, KERSA, EAST HARERGE
ABSTRACT
Investigation of the runoff yield of the catchment is essential for management and utilization
of water resource. Due to the spatial and temporal heterogeneity in soil properties, vegetation
and land use practices, hydrological behavior of watershed is a complex phenomenon. Soil
and Water Assessment Tool (SWAT) model is one of the semi distributed and computationally
efficient model. The objective of this study was to estimate the runoff yield of Bululo
Watershed as a whole and individual sub-watershed. Twenty six years meteorological, twelve
years hydrological, land use, soil and 30m by 30m grid DEM data were used. The
performance of the model was evaluated using coefficient of determination and Nash and
Sutcliff techniques. Model calibration and validation indicated a good fit between the
observed and simulated discharge values. Values of coefficient of determination for
calibration were 0.956 and 0.873 for the annually and monthly time steps, respectively.
Similarly, Nash and Sutcliff values of 0.853 and 0.779 were obtained respectively. Validation
of the model was also done with independent observed stream flow data from 1999 to 2003.
The performance evaluation statistics for validation showed the values of coefficient of
determination as 0.971 for annual and 0.802 for monthly time steps. The corresponding NSE
values were 0.788 for annual and 0.512 for monthly time steps. After calibration and
validation of the model the mean annual simulated water yield of the watershed was estimated
as 16.24Mm3. Bululo watershed was divided in thirteen sub-watersheds. Runoff yield for each
sub-watersheds were quantified. Relatively large amount of runoff yield and maximum
drainage ratio were observed on those sub-watersheds which had cultivated lands on steep
slopes.
xv
1. INTRODUCTION
Over the past three decades, Ethiopia has been challenged by lack of food security. In
Ethiopia, the trend in growth of domestic food production matched population growth only in
the 1960s (Markos, 1997). The per capita domestic food production has steadily declined over
the last four decades. Between 1971 and 2000, a simple average of year-to-year growth of per
capita production was –1.15 percent with average growth rates during the 1970s, 1980s and
1990s estimated at -0.84, -1.98 and -0.64 percent, respectively (FAO, 2001).
Ethiopia is among the poorest and most food insecure countries of the world. On the Human
Development Index (HDI) of the United Nations Development Program (UNDP), it ranks 157
out of 169 countries in the world, and about 39 percent of its population lives below the
poverty line (FAO, 2010). Today, Ethiopia faces high levels of food insecurity, ranking as one
of the hungriest countries in the world, with an estimated 5.2 million people needing food
assistance in 2010 (IFPRI, 2009).
The main stay of the Ethiopian economy is agriculture that generates 46 percent of the GDP,
40 percent of the foreign exchange earnings, and provides 80 percent of employment (FDRE,
2011). Ethiopia is the second most populous nation in Sub-Saharan Africa with a population
of about 85 million of which 83 percent are rural (CSA, 2007).
Food insecurity and poverty in Ethiopia are attributed to the poor performance of the
agricultural sector, which in turn is attributed to both policy and non-policy factors (Wolday,
1995). Among the non-policy factors, recurrent drought is mentioned as the number one cause
of food shortage in Ethiopia.
The majority of the populations in the arid and semi-arid areas depend on agriculture and
pastoralist for subsistence. These activities face many constraints due to predominance of
erratic rainfall patterns, torrential rainfall which is majority lost to run-off, high rate of
evapotranspiration further reducing yields, weeds growing more vigorously than cultivated
1
crops and competing for scarce reserves of moisture, low organic matter levels and high
variables responses to fertilizers (CASL, 2006).
Most of highlands of Ethiopia receive between 510 to 2000 mm of rainfall annually for crop
production (Awulachew et al., 2005). However, rainfall is rarely available in amounts
sufficient to meet with crop needs. Excess rainfall and drought are common occurrences. The
erratic nature of the rainfall resulted a pronounced dry period even in those highlands.
Particularly in arid and semi-arid regions of Ethiopia, unpredictability and erratic distribution
of rainfall resulted in total crop failure, the loss of human and animal life in the past decades.
The increasing food demand in the country can be met in one or a combination of three ways:
increasing agricultural yield, increasing the area of arable land, and increasing cropping
intensity. Expansion of the area under cultivation is a finite option, especially in view of the
marginal and vulnerable characteristic of large parts of the country’s land. Irrigation is one
means by which agricultural production can be increased to meet the growing food demands in
Ethiopia (Awulachew et al., 2005).
There is a need of a more efficient capture and use of the rain water harvesting. An
optimization of the rainfall management, through water harvesting in sustainable and
integrated production systems can contribute for improving the small-scale farmers’ livelihood
by upgrading the rain fed agriculture production.
Rainwater harvesting is taken as a means, particularly in drought prone areas of the country, to
alleviate food insecurity. Oromiya Regional State made efforts for the implementation of
water harvesting technologies in rural areas affected by food shortage and poverty (MoWR,
2002).
The current government food security strategy is designed to address both the supply and
demand sides of the food equation: availability and entitlement, respectively within the
framework of the national agricultural and rural development strategies (FDRE, 2002). The
2
new strategy is mainly targeted and focused on water harvesting and the introduction of high
value crops.
In order to address the above mentioned development strategies a proper investigation of the
runoff yield of the catchment is essential for management and utilization of water resource. If
these are not investigated the life of the water harvesting structures is shortened by
sedimentation on the farm land and, it makes difficult to design the appropriate and
economical structures. Moreover, it makes difficult to select the appropriate crop type
scheduling with respect to available runoff.
Hence, the modeling of runoff, soil erosion and sediment yield are essential for sustainable
development. Further, the reliable estimates of the various hydrological parameters including
runoff and sediment yield for remote and inaccessible areas are tedious and time consuming
by conventional methods. So it is desirable that some suitable methods and techniques are
used for quantifying the hydrological parameters. Due to the spatial and temporal
heterogeneity in soil properties, vegetation and land use practices a hydrological cycle is a
complex system. As a result, use of mathematical models and geospatial analyses tools for
responses to land use and climatic changes is the current trend (Sanjay et al., 2009).
Models which give a comprehensive picture of the various hydrologic processes are called as
integrated watershed models. There are a number of integrated physically based distributed
models. Among them, researchers have identified Soil and Water Assessment Tool (SWAT)
as the most promising and computationally efficient model (Neitsch et al, 2005).
The general objective of this study is to estimate the runoff yield from Bululo watershed at
Kersa Wereda, East Harerge zone, using SWAT model.
Specific objectives: 
To estimate the water yield generated from Bululo watershed as a whole and individual
sub-watersheds.
3
2. LITERATURE REVIEW
2.1. Rainfall-Runoff Relationship
2.1.1. Surface runoff process
Runoff is the total surface flow from a given drainage area. Before runoff can occur,
precipitation must satisfy the demands of evaporation, interception, infiltration, surface
storage, and surface detention and channel detention. Rainfall duration, intensity and aerial
distribution influence the rate and volume of runoff. Total runoff of a storm is clearly related
to the precipitation intensity.
The amount of runoff from a given drainage area depends on many inter related factors.
Watershed characteristics such as slope, shape and size, cover of soil and duration of rainfall
have a direct effect on the peak flow and volume of runoff from any area (Chandler and
Walker, 1998).
Intensity of rainfall has dominating effect on the runoff yield. If the intensity is greater than
the infiltration rate of the soil, then surface runoff is generated rapidly, while in case of low
intensity rainfall, a reverse trend is found. Fernandez (1996) conducted a study to investigate
the impacts of long-term trends and fluctuations in rainfall characteristics as runoff from the
Little Washita River watershed.
The land use pattern or land management practices used have great effect on the runoff. There
are a few studies conducted to evaluate the influence of climatic and catchment characteristics
on runoff generation (Faucette et al. (2004), Gilley et al. (1998), Zhang (1998)). Savabi (2004)
conducted a study to find out the influence of soil type on runoff generation.
Rain water recharge pits can improve the field availability of water and hence replenishment
of the groundwater table. Runoff harvesting is going to be the most applicable method for
meeting the water demand in the future. Hydrological analysis is unavoidable in any water
4
harvesting structure design, and hence the present study was intended to analyze the rainfallrunoff characteristics and to derive a representative unit hydrograph for the selected rural
region for future runoff calculations to design water harvesting structures.
There are different methods for runoff estimation (Mc Cool et al., 1995), but these
sophisticated methodologies are not suitable for rural areas with limited data. Most of the
rainfall-runoff models need historical data for the calibration to get efficient results. Therefore
it is significant to develop simple methodology for the efficient hydrological analysis for
regions with limited data set.
2.1.2. Factors affecting runoff
Apart from rainfall characteristics, i.e., intensity, duration and distribution, there are a number
of site-specific factors, which have a direct bearing on the occurrence and volume of runoff.
Some of these factors include: watershed size, shape and orientation relative to storms,
topography (slope, depression areas, etc.), geology and soils, human factors (agriculture,
silviculture, dams, development) and water status of soil. The major factors that affect the rate
and volume of watershed runoff are described below (Yitebitu, 2004).
2.1.2.1. Soil type
Soil functions essentially as medium that provides a large number of passageways for water.
Water flow in soil depends on the size and permanency of the pores. The size of the conduits
depends on the size of the soil texture, the degree of aggregation and the arrangements of
particles and aggregates (Silveira et al., 2000).
The infiltration capacity is also dependent on the porosity of a soil, which determines the
water storage capacity and affects the resistance to water flow into deeper layers (Critchley et
al., 1991).
5
The infiltration capacity depends also on the moisture content of the soil at onset of a
rainstorm. The initial high infiltration capacity decreases with time (provided the rain does not
stop) until it reaches a constant value as the soil profile becomes saturated (Finkel and
Sergerros, 1995; Lonard et al., 2005).
2.1.2.2. Vegetation
The amount of rain water intercepted by the foliage depends on the kind of vegetation and its
growth stage, the effect the vegetation on the infiltration capacity of the soil being more
significant. A dense vegetation cover shields the soil from the raindrop impact and reduces the
crusting effect. In addition, the root system as well as organic matter in the soil increases the
soil porosity thus allowing more water to infiltrate. Vegetation also retards the surface flow
particularly on gentle slopes, giving the water more time to infiltrate and to evaporate
(Critchley et al., 1991).
2.1.2.3. Slope and watershed size
Investigations made earlier indicate that steep slopes in the headwaters of drainage basins tend
to generate more runoff than do lowland areas or gentle slopes (Sharma et al., 1986).
On gentle slopes, water may be temporarily pond and later soaked in, but on steep slopes,
water tends to move down the slope more rapidly. Soils tend to be thinner on steep slopes.
With limited storage capacity of water, and where bedrock is exposed, little infiltration can
occur. In some cases, however, accumulations of coarse sediment at the base of steep slopes
soak up runoff from the cliffs above, turning it into subsurface flow.
In addition, it was observed that the quantity of runoff decreased with slope length to some
extent (Ben Asher, 1988). The runoff efficiency (volume of runoff per unit of area) increases
with the decreasing size of the watershed i.e. the larger the size of the watershed the larger the
time of concentration and the smaller the runoff efficiency (Critchley et al., 1991).
6
2.2. Runoff Coefficients
In addition to the above-mentioned site-specific factors, which strongly influence the rainfallrunoff process, it should also be noted that watershed will not be homogenous (Yitebitu,
2004). Even at the micro level, there are differences in slopes, soil types, vegetation covers
etc. A micro-watershed has therefore its own runoff response at different rainstorm events.
The proportion of total rainfall that becomes runoff during a storm event represents the runoff
coefficient.
The runoff coefficient can be calculated using the following formula (Finkel, 1987; Critchley
et al., 1991; Colombo and Safatti, 1997; Yitebitu, 2004).
C=
𝑅𝑢
(2.1)
𝑅
where, C= runoff coefficient
Ru= depth of runoff in mm
R= rainfall depth in mm
2.3. Probability Analysis of Rainfall
Probability analysis method is a rather simple, graphical method to determine the probability
or frequency of occurrence of yearly or seasonal rainfall. For the design of water harvesting
schemes, this method is as valid as any analytical method described in statistical textbooks.
The first step is to obtain annual rainfall totals for the cropping season from the area of
concern. In locations where rainfall records do not exist, figures from stations nearby may be
used with caution. It is important to obtain long-term records (Critchley et al., 1991).
The probability of occurrence P (%) for each of the ranked observations can be calculated
from the following Weibull Eq. (Colombo and Safatti, 1997; Subramanya, 1997; Garg, 1999;
Van der Molen et al., 2007):
7
𝑚
P (%) =𝑁+1 ∗ 100
(2.2)
where, P= probability in % of the observation of the rank m
m= the rank of the observation
N= total number of observations used
The return period T (in years) can easily be derived once the Exceedance probability P (%) is
known from the Eq.s:-
T=
100
P
(year) =
N+1
m
1
=P
(2.3)
Plotting the ranked observations against the corresponding probabilities on normal probability
paper, and from the curve fitted to the plotted observations it is possible to obtain the
probability of the occurrence or exceedance of rainfall value of the specific magnitude.
2.4 .Estimation of Flood Peak
Flood peak vary from year to year at a given location and their magnitude constitutes a
hydrologic series which enable one to assign a frequency to a given flood peak value. In
design of a hydraulic structure, the peak flow that is expected with a given frequency is a
primary importance to properly dimension the structure to accommodate the effect.
Rational formula, empirical formula, unit hydrograph techniques and flood frequency studies
are alternative methods available to estimate the magnitude of peak flood. Their use depends
up on the design objective, the availability of the data and the importance of the project
(Subramanya, 1997).
8
2.4.1. Rational method
Consider a rainfall of uniform intensity and very long duration occurring over a basin. The
runoff rate gradually increases from zero to a constant value. The runoff increases as more and
more flow from remote areas of the catchment reach the outlet. The longest time will apply to
water traveling from the most hydraulically remote point is called time of concentration and it
is important hydraulically characteristics (Subramanya, 1997; LMNO, 2003; Van der Molen et
al., 2007). It is obvious that if the rainfall continues beyond time of concentration, the runoff
will be constant and at the peak value.
𝑄𝑝𝑒𝑎𝑘 =
𝐶𝐼𝐴
(2.4)
360
𝑤ℎ𝑒𝑟𝑒, 𝑄 𝑝𝑒𝑎𝑘 = 𝑝𝑒𝑎𝑘 𝑟𝑢𝑛𝑜𝑓𝑓 𝑟𝑎𝑡𝑒 (𝑚3 𝑠 −1 ),
C= runoff coefficient,
I= average maximum rainfall intensity (mm/hr) equal to or more than time of
concentration and
A= watershed area (ha).
2.4.2 .The US Soil Conservation Service (SCS) method
The U.S. Soil Conservation Service (SCS) method is widely used for estimating floods on
small to medium sized ungauged drainage basins in the USA and in many other countries.
While there is an extensive literature on the method, little quantitative information is available
on the data base from which the method was developed, and the manner in which this base
was used in the development. Rallison (1980) gives a general description of the origin and
evolution of the method from infiltrometer tests.
The procedure as currently employed is described by US SCS (1985). The basic relationship
in the method is between depths of runoff and rainfall in a flood event, utilizing a runoff curve
number (CN) as a primary variable.
9
An equation involving the calculated runoff depth and lag, time of concentration, and rainfall
duration provides an estimate of the peak discharge. Many variations of the procedure have
been proposed and employed in practice. A series of papers presented at an international
symposium on rainfall-runoff modeling (Singh, 1982) gives a recent review of the method.
The SCS curve number (CN) method is a simple, powerful, widely used and efficient method
to determine the approximate amount of runoff from a rainfall. It is used for planning the
structures meant to store water and control erosion and flood. The method requires numerous
watershed characteristics which are the basis for watershed runoff determination. The basic
requirements for this method are rainfall amount and curve number.
The SCS curve number is based on the area's hydrologic soil group, land use type, vegetation
cover, soil conservation measures and antecedent soil moisture conditions. Although the
method is designed for a single storm event, it can be scaled to find average annual runoff
values (Svoboda, 1991; L-THIA, 2003).
A curve number is an index that represents the combination of a hydrological soil group, landuse and treatment class. A curve number is a function of soil group, land-cover complex and
antecedent moisture conditions (Rejoice, 2006). The SCS curve number method has been
applied to estimate daily and yearly surface runoff depth.
The general Eq. for the SCS curve number is (SCS, 1972):
(𝑃−𝐼 )2
𝑄 = (𝑃−𝐼 𝑎
(2.5)
𝑎 +𝑆)
where, Q = accumulated runoff depth or rainfall excess (mm),
P= rainfall depth (mm),
Ia= initial abstractions (mm), which includes surface storage, interception and
infiltration prior to runoff, and
S= retention parameter (mm) or the maximum potential difference between rainfall and runoff
in mm, starting at the time the storm begins.
10
Runoff will only occur when P > Ia. The retention parameter varies spatially due to changes in
soils, land use, management and slope and temporally due to changes in soil water content.
The retention parameter, S is defined as:
S = 25.4 (
1000
CN
− 10)
(2.6)
where, CN is the curve number for the day.
The initial abstractions, Ia, is commonly approximated as 0.2S and Q becomes:
𝑄 =
(𝑃−0.2𝑆)2
(𝑃+0.8𝑆)
, for P>0.2S
(2.7)
Runoff volume of the watershed is calculated using the formula (Colombo and Safatti, 1997):
Qv = 10 x Q x A
(2.8)
where, Qv= runoff volume (m3),
Q= runoff depth (mm),
A= watershed area (ha)
The SCS curve number is a function of the soil’s permeability, land use and antecedent soil
moisture conditions (AMC) and grouped into three antecedent moisture conditions:
AMC I; dry, AMC II; average moisture and AMC III; wet. The AMC I curve number is the
lowest value the daily curve number can assume in dry conditions. The curve number for
AMC III and I are calculated using the Eq. below (Silveira et al., 2000).
CN1 = CN2 − (100−CN
20(100−CN2 )
2 +exp[2.533−0.0636(100−CN2 )])
CN3 = CN2 exp[0.00673(100 − CN2 )]
(2.9)
(2.10)
11
where, CN1= curve number for AMC I,
CN2= curve number for AMC II,
CN3= curve number for AMC III.
The curve number for AMC II is assumed to be appropriate for 5% slopes. Williams (1995)
developed an Eq. to adjust the curve number to a different slopes (Kulkarni et al., 2004):
𝐶𝑁3 −𝐶𝑁2
𝐶𝑁2𝑠 = (
3
) [1 − 2𝑒𝑥𝑝(−13.86𝑠𝑙𝑝 )] + 𝐶𝑁2
(2.11)
where, 𝐶𝑁2𝑆 is the moisture condition II curve number adjusted for slope.
slp is slope of the watershed.
2.4.3 .The unit hydrograph method
Rainfall-runoff modeling using linear unit hydrograph methods is widely used for flood
estimation in engineering practice. The method generally uses either an empirically-derived
unit hydrograph or some standard shape defined by one or two parameters, such as the time to
peak. These parameters may be derived either from data or from catchment characteristics.
The UK Flood Studies Report (NERC, 1975) recommends that such methods should not be
used on catchments with an area greater than 500 km2, where the assumption of nearly
uniform net rainfall over the catchment cannot be justified.
The unit hydrograph concept is based on the assumption that the time base of all floods by
rainfall of equal duration is the same; hence, the hydrograph for any rainfall of a given
duration is obtained by multiplying the ordinates of the unit hydrograph by the storm depth
(Linsley et al., 1982).
2.5. Description of SWAT Model
SWAT is the acronym for Soil and Water Assessment Tool, a river basin, or watershed, scale
model developed by Arnold et al., (1998) for the USDA Agricultural Research Service (ARS).
SWAT was developed to predict the impact of land management practices on water, sediment
12
and agricultural chemical yields in large complex watersheds with varying soils, land use and
management conditions over long periods of time (Neitsch et al., 2005).
In recent years, SWAT (Soil and Water Assessment Tool) model developed by Arnold et al.,
(1998) has gained international acceptance as a robust interdisciplinary watershed modeling.
SWAT is currently applied worldwide and considered as a versatile model that can be used to
integrate multiple environmental processes, which support more effective watershed
management and the development of better informed policy decision (Gassman et al., 2005).
SWAT is a basin-scale, continuous-time model that operates on a daily time step and is
designed to predict the impact of management on water, sediment, and agricultural chemical
yields in ungauged watersheds. The model is physically based, computationally efficient, and
capable of continuous simulation over long time periods. Major model components include
weather, hydrology, soil temperature and properties, plant growth, nutrients, pesticides,
bacteria and pathogens, and land management.
In SWAT, a watershed is divided into multiple subwatersheds, which are then further
subdivided into hydrologic response units (HRUs) that consist of homogeneous land use,
management, and soil characteristics. The HRUs represent percentages of the subwatershed
area and are not identified spatially within a SWAT simulation. Alternatively, a watershed can
be subdivided into only subwatersheds that are characterized by dominant land use, soil type,
and management (Gassman et al., 2007).
13
2.5.1. Components of SWAT model
2.5.1.1 Hydrological components of SWAT model
The Simulation of the hydrology of a watershed is separated into two divisions. One is the
land phase of the hydrological cycle that controls the amount of water, sediment, nutrient and
pesticide loadings to the main channel in each subbasin. Hydrological components simulated
in land phase of the Hydrological cycle are canopy storage, infiltration, redistribution,
Evapotranspiration, lateral subsurface flow, surface runoff, ponds, tributary channels and
return flow. The second division is routing phase of the hydrologic cycle that can be defined
as the movement of water, sediments, nutrients and organic chemicals through the channel
network of the watershed to the outlet.
In the land phase of hydrological cycle, SWAT simulates the hydrological cycle based on the
water balance equation:
SWt = SWo + ∑ti=1(R day − Qsurf − Ea − wseep − Qgw )
(2.12)
where SWt is the final soil water content (mm), SW0 is the initial soil water content on day i
(mm), t is the time (days), Rday is the amount of precipitation on day i (mm), Qsurf is the amount
of surface runoff on day i (mm), Ea is the amount of Evapotranspiration on day i (mm), wseep is
the amount of water entering the vadose zone from the soil profile on day i (mm), and Qgw is
the amount of return flow on day i (mm).
The subdivision of the watershed enables the model to reflect differences in
Evapotranspiration for various crops and soils. Runoff is predicted separately for each HRU
and routed to obtain the total runoff for the watershed. This increases accuracy and gives a
much better physical description of the water balance.
Brief description of some of the key model components are provided in this study. More
detailed descriptions of the different model components are listed in Arnold et al., (1998) and
Neitsch et al., (2005).
14
Surface runoff occurs whenever the rate of water application to the ground surface exceeds the
rate of infiltration. When water is initially applied to a dry soil, the application rate and
infiltration rates may be similar. However, the infiltration rate will decrease as the soil
becomes wetter. When the application rate is higher than the infiltration rate, surface
depressions begin to fill. If the application rate continues to be higher than the infiltration rate
once all surface depressions have filled, surface runoff will start. Surface runoff occurs
whenever the rate of precipitation exceeds the rate of infiltration. SWAT offers two methods
for estimating surface runoff: the SCS curve number procedure (USDA-SCS, 1972) and the
Green & Ampt infiltration method (Green and Ampt, 1911) as cited in (Neitsch et al., 2005).
Using daily or sub daily rainfall, SWAT simulates surface runoff volumes and peak runoff
rates for each HRU.
SWAT calculates the peak runoff rate with a modified rational method. There are many
methods that are developed to estimate potential Evapotranspiration (PET). Three methods are
incorporated into SWAT: the Penman-Monteith method (Monteith, 1965), the Priestley-Taylor
method (Priestley and Taylor, 1972) and the Hargreaves method (Hargreaves et al., 1985).
Groundwater balance in SWAT model is calculated by assuming two layers of aquifers.
SWAT partitions groundwater into a shallow, unconfined aquifer and a deep confined aquifer
and it simulates two aquifers in each sub basin. The shallow aquifer is an unconfined aquifer
that contributes to flow in the main channel or reach of the sub basin. The deep aquifer is a
confined aquifer. Water that enters the deep aquifer is assumed to contribute to stream flow
somewhere outside of the watershed (Arnold et al., 1993). The water balance for a shallow
aquifer in SWAT is calculated with:
aq sh,i = aq sh,i−1 + wrchrg − Qgw − wrevap −wdeep − wpump,sh
(2.13)
where, aqsh,i is the amount of water stored in the shallow aquifer on day i (mm), aqsh,i-1 is the
amount of water stored in the shallow aquifer on day i-1 (mm), wrchrg is the amount of
recharge entering the aquifer on day i (mm), Qgw is the groundwater flow, or base flow, into
the main channel on day i (mm), wrevap is the amount of water moving into the soil zone in
15
response to water deficiencies on day i (mm), wdeep is the amount of water percolating from
the shallow aquifer into the deep aquifer on day i (mm), and wpump,sh is the amount of water
removed from the shallow aquifer by pumping on day i (mm).
The steady-state response of groundwater flow to recharge is (Hooghoudt, 1940):
𝑄𝑔𝑤 =
800∗𝐾𝑠𝑎𝑡
𝐿2𝑔𝑤
ℎ𝑤𝑡𝑏𝑙
(2.14)
where Qgw is the groundwater flow, or base flow, into the main channel on day i (mm), Ksat is
the hydraulic conductivity of the aquifer (mm/day), Lgw is the distance from the ridge or
subbasin divide for the groundwater system to the main channel (m), and hwtbl is the water
table height (m).
A water table fluctuation due to non-steady-state response of groundwater flow to periodic
recharge is calculated (Smedema and Rycroft, 1983):
𝑑ℎ𝑤𝑡𝑏𝑙
𝑑𝑡
where
𝑑ℎ𝑤𝑡𝑏𝑙
𝑑𝑡
=
𝑤𝑟𝑐ℎ𝑟𝑔 −𝑄𝑔𝑤
(2.15)
800∗𝜇
is the change in water table height with time (mm/day), wrchrg is the amount of
recharge entering the aquifer on day i (mm), Qgw is the groundwater flow into the main
channel on day i (mm), and 𝜇 is the specific yield of the shallow aquifer (m/m).
Assuming that variation in groundwater flow is linearly related to the rate of change in water
table height, equations 2.14 and 2.15 can be combined to obtain:
𝑑𝑄𝑔𝑤
𝑑𝑡
𝐾
= 10 𝜇∗𝐿𝑠𝑎𝑡
2 (𝑤𝑟𝑐ℎ𝑟𝑔 − 𝑄𝑔𝑤 ) = 𝛼𝑔𝑤 ∗ (𝑤𝑟𝑐ℎ𝑟𝑔 − 𝑄𝑔𝑤 )
(2.16)
𝑔𝑤
where, 𝛼𝑔𝑤 is the baseflow recession constant or constant of proportionality. The baseflow
recession constant, 𝛼𝑔𝑤 is a direct index of groundwater flow response to changes in recharge
(Smedema and Rycroft, 1983). 𝛼𝑔𝑤 varies from 0.1-0.3 for land with slow response to
recharge to 0.9-1.0 for land with a rapid response. Although the baseflow recession constant
16
may be calculated, the best estimates are obtained by analyzing measured streamflow during
periods of no recharge in the watershed.
2.5.1.2. The Routing Face of the Hydrological Cycle
Open channel flow is defined as channel flow with a free surface, such as flow in a river or
partially full pipe. SWAT uses Manning’s equation to define the rate and velocity of flow.
Water is routed through the channel network using the variable storage routing method or the
Muskingum River routing method. The details of the water routing methods are discussed in
(Neitsch et al., 2005).
The peak channel velocity, vch,pk, is calculated:
𝑣𝑐ℎ,𝑝𝑘 =
𝑞𝑐ℎ,𝑝𝑘
(2.17)
𝐴𝑐ℎ
3
where qch,pk is the peak flow rate (m /s) and Ach is the cross-sectional area of flow in the
2
channel (m ).
2.5.2. Comparisons of SWAT with Other Models.
Borah and Bera (2003, 2004) compared SWAT with several other watershed-scale models. In
the 2003 study, they report that the Dynamic Watershed Simulation Model (DWSM) (Borah et
al., 2004), Hydrologic Simulation Program - Fortran (HSPF) model (Bicknell et al., 1997),
SWAT, and other models have hydrology, sediment, and chemical routines applicable to
watershed-scale catchments and concluded that SWAT is a promising model for continuous
simulations in predominantly agricultural watersheds. In the 2004 study, they found that
SWAT and HSPF could predict yearly flow volumes and pollutant losses, were adequate for
monthly predictions except for months having extreme storm events and hydrologic
conditions, and were poor in simulating daily extreme flow events. In contrast, DWSM
17
reasonably predicted distributed flow hydrographs and concentration or discharge graphs of
sediment and chemicals at small time intervals.
Shepherd et al. (1999) evaluated 14 models and found SWAT to be the most suitable for
estimating phosphorus loss from a lowland watershed in the UK.
Van Liew et al. (2003a) compared the streamflow predictions of SWAT and HSPF on eight
nested agricultural watersheds within the Little Washita River basin in southwestern
Oklahoma. They concluded that SWAT was more consistent than HSPF in estimating
streamflow for different climatic conditions and may thus be better suited for investigating the
long-term impacts of climate variability on surface water resources.
Saleh and Du (2004) found that the average daily flow, sediment loads, and nutrient loads
simulated by SWAT were closer than HSPF to measured values collected at five sites during
both the calibration and verification periods for the upper North Bosque River watershed in
Texas.
Singh et al. (2005) found that SWAT flow predictions were slightly better than corresponding
HSPF estimates for the 5,568 km2 Iroquois River watershed in eastern Illinois and western
Indiana, primarily due to better simulation of low flows by SWAT.
Nasr et al. (2007) found that HSPF predicted mean daily discharge most accurately, while
SWAT simulated daily total phosphorus loads the best, in a comparison of three models for
three Irish watersheds that ranged in size from 15 to 96 km2.
Srinivasan et al. (2005) found that SWAT estimated flow more accurately than the Soil
Moisture Distribution and Routing (SMDR) model (Cornell, 2003) for 39.5 ha FD-36
experimental watershed in east central Pennsylvania, and that SWAT was also more accurate
on a seasonal basis.
18
SWAT estimates were also found to be similar to measured dissolved and total P for the same
watershed, and 73% of the 22 fields in the watershed were categorized similarly on the basis
of the SWAT analysis as compared to the Pennsylvania P index (Veith et al., 2005). Grizzetti
et al. (2005) reported that both SWAT and a statistical approach based on the SPARROW
model (Smith et al., 1997) resulted in similar total oxidized nitrogen loads for two monitoring
sites within the 1,380 km Great Ouse watershed in the U.K. They also state that the statistical
reliability of the two approaches was similar, and that the statistical model should be viewed
primarily as a screening tool while SWAT is more useful for scenarios. Srivastava et al.
(2006) found that an artificial neural network (ANN) model was more accurate than SWAT
for streamflow simulations of a small watershed in southeast Pennsylvania.
2.5.3. Review of past works on SWAT model
The SWAT hydrologic subcomponents have been refined and validated at a variety of scales.
Arnold and Allen (1996) used measured data from three Illinois watersheds, ranging in size
from 122 to 246 km2, to successfully validate surface runoff (r2=0.74 to 0.94), groundwater
flow (r2=0.38 to 0.51), groundwater, ET in the soil profile, groundwater recharge, and
groundwater height parameters. Santhi et al. (2001, 2006) performed extensive streamflow
validations for two Texas watersheds that cover over 4,000 km2. Arnold et al. (1999b)
evaluated streamflow and sediment yield data in the Texas Gulf basin with drainage areas
ranging from 2,253 to 304,260 km2. Streamflow data from approximately 1,000 streams
monitoring gauges from 1960 to 1989 were used to calibrate and validate the model. Predicted
average monthly streamflows for three major river basins (20,593 to 108,788 km2) were 5%
higher than measured flows, with standard deviations between measured and predicted within
2%. Annual runoff and ET were validated across the entire continental U.S. as part of the
Hydrologic Unit Model for the U.S. (HUMUS) modeling system. Rosenthal et al. (1995)
linked GIS to SWAT and simulated ten years of monthly streamflow without calibration.
SWAT underestimated the extreme events but produced overall accurate streamflows.
Bingner (1996) simulated runoff for ten years for a watershed in northern Mississippi. The
SWAT model produced reasonable results in the simulation of runoff on a daily and annual
basis from multiple sub-basins, with the exception of a wooded subbasin. Rosenthal and
19
Hoffman (1999) successfully used SWAT and a spatial database to simulate flows, sediment,
and nutrient loadings on a 9,000 km2 watershed in central Texas to locate potential water
quality monitoring sites. SWAT was also successfully validated for streamflow for the Mill
Creek watershed in Texas for 1965-1968 and 1968-1975 (Srinivasan et al., 1998). Monthly
streamflow rates were well predicted, but the model overestimated streamflows in a few years
during the spring/summer months. The overestimation may be accounted for by variable
rainfall during those months.
Qi and Grunwald (2005) forwarded that, in most studies, SWAT had usually been calibrated
and validated at the drainage outlet of a watershed. In their study, they calibrated and validated
SWAT for four subwatersheds at the drainage outlets. They found that spatially distributed
calibration and validation accounted for hydrologic patterns in the subwatersheds.
Spruill et al. (2000) calibrated and validated SWAT with one year of data each for a small
experimental watershed in Kentucky. The 1995 and 1996 daily NSE values (0.19 and -0.04)
reflected poor peak flow values and recession rates, but the monthly flows were more accurate
(0.89 and 0.58). Their analysis confirmed the results of a dye trace study in a central
Kentucky karst watershed, indicating that a much larger area contributed to streamflow than
was described by topographic boundaries. Coffey et al. (2004) reported similar statistical
results for the same Kentucky watershed.
Benham et al. (2006) observed that SWAT
streamflow results did not meet calibration criteria for the karst-influenced 367 km2 Shoal
Creek watershed in southwest Missouri, but that visual inspection of the simulated and
observed hydrographs indicated that the system was satisfactorily modeled. They suggest that
SWAT was not able to capture the conditions of a very dry year in combination with flows
sustained by the karst features.
Santhi et al. (2001) successfully calibrated and validated SWAT for streamflow and pollutant
loss simulations for the 4,277 km2 Bosque River in Texas. They suggested a general
procedure, including a flowchart, for manual calibration that could identify sensitive input
parameters (15 were used), realistic uncertainty ranges, and reasonable regression results (i.e.,
R2 > 0.79 and NSE > 0.62 values). A combined sensitivity and calibration approach is
20
described by White and Chaubey (2005) for SWAT streamflow and pollutant loss estimates
for the 3,100 km2 Beaver Reservoir Watershed, and three subwatersheds, in northwest
Arkansas. They also reviewed calibration approaches, including calibrated input parameters,
for previous SWAT studies.
SWAT was used to calibrate and validate a hydrologic component on Erer river discharges at
gauging station and predict the water and sediment yield of Ija Galma Waqo spate irrigation
by Eyob (2010). The coefficient of determination (R2) and Nash-Sutcliffe model efficiency
(NSE) were used to evaluate model calibration and validation. The estimated results for
coefficient of determination and NSE found were satisfactory for the gauging station (R2 =
0.65 and. NSE = 0.56 for calibration and R2 = 0.73 and. NSE = 0.5 for validation period).
Wendu (2011), calibrated and validated SWAT model at Erer river discharge and predicted
runoff at Erer proposed dam site. The coefficient of determination (R2) and Nash-Sutcliffe
model efficiency (NSE) were used to evaluate model calibration and validation. The estimated
results for coefficient of determination and NSE found were satisfactory for the gauging
station (R2 = 0.959 and 0.82. NSE = 0.81 and 0.79 for annual and monthly, respectively, for
calibration and R2 = 0.81 and 0.79. NSE = 0.72 and 0.76 for annual and monthly, respectively,
validation period).
2.5.3. SWAT Strengths and Limitations
According to DiLuzio et al. ( 2002), SWAT model has the following strength
1. Watersheds with no monitoring data (e.g., stream gauge or water quality data) can be
modeled.
2. The relative impact of alternative input data (e.g. changes in management practices, climate,
vegetation, or land use) on water quality or other variables of interest can be quantified.
3. The model uses readily available inputs. While SWAT can be used to study more
specialized processes such as bacteria transport.
4. SWAT is computationally efficient. Simulation of very large basins or a variety of
management strategies can be performed without excessive investment.
21
5. The model enables users to study long-term impacts. Many of the problems currently
addressed by users involve the gradual buildup of pollutants and the impact on downstream
water bodies. To study these types of problems, results are needed from runs with output
spanning several decades.
6. The capability of generating the required database through its interface known as AVSAWT
The major limitations of the SWAT model (Arnold et al., 1998) are:
1. Spatial variability associated with precipitation (rain gauges often far apart), which can
cause considerable error in runoff estimation if one gauge is used to represent an entire subwatershed or if one attempts to “spatially weight” precipitation for a sub-watershed; data
files are difficult to manipulate and often contain days of missing records
2. Daily weather generator parameters are available for generating weather sequences at a
point; however, spatially correlated generators required for large area hydrologic simulation
have not been developed
3. SWAT does not simulate detailed event-based flood and sediment routing- Sediment
routing equations are simplistic and assume channel dimensions are static throughout
simulation (may be unrealistic since simulations may be made for 100 years or more)
4. The simplistic description of channel bed does not account for cohesive, non cohesive or
armored channels
5. Reservoir outflow calculations are simplistic and do not account for controlled operations.
6. Large watershed can be divided into hundreds of HRUs resulting in many hundreds of input
files, which are difficult to manage and modify without a solid interface
7. The tool does not check meaningfulness of the values entered by the user. The user is
responsible to ensure that any physical parameters entered are correct and meaningful.
8. The ground water component currently in SWAT is one dimensional and does not consider
flow between sub-basins. Work is ongoing to link SWAT to an existing three dimensional
numerical ground water model.
22
2.6. Sensitivity Analyses, Calibration and Validation of SWAT Model
2.6.1. Sensitivity analyses
Sensitivity analysis is a method of identifying the most sensitive parameters that significantly
affect on model calibration or on model prediction. Sensitivity analysis describes how model
output varies over a range of a given input variable (Dilnesaw, 2006).
SWAT is a complex model with many parameters that makes manual calibration difficult.
Hence, sensitivity analysis was performed to limit the number of optimized parameters to
obtain a good fit between the simulated and measured data. Sensitivity analysis helps to
determine the relative ranking of which parameters most affect the output variance due to
input variability (van Griensven et al., 2002) which reduces uncertainty and provides
parameter estimation guidance for the calibration step of the model.
Spruill et al. (2000) performed a manual sensitivity/calibration analysis of 15 SWAT input
parameters for a 5.5 km2 watershed in Kentucky, which showed that saturated hydraulic
conductivity, alpha base flow factor, drainage area, channel length, and channel width were
the most sensitive parameters that affected streamflow.
Numerous sensitivity analyses have been reported in the SWAT literature, which provide
valuable insights regarding which input parameters have the greatest impact on SWAT output.
A two-step sensitivity analysis approach is described by (Francos et al., 2003), which consists
of: (1) a “Morris” screening procedure that is based on the One factor at a time (OAT) design,
and (2) the use of a Fourier amplitude sensitivity test (FAST) method. The screening
procedure is used to determine the qualitative ranking of an entire input parameter set for
different model outputs at low computational cost, while the FAST method provides an
assessment of the most relevant input parameters for a specific set of model output. Holvoet et
al. (2005) presented the use of a Latin hypercube (LH) OAT sampling method, in which initial
LH samples serve as the points for the OAT design. The LH-OAT method has been
incorporated as part of the automatic sensitivity/calibration package included in SWAT2005
(Gassman et al., 2007).
23
2.6.2. Calibration approach
Calibration is the process whereby model parameter are adjusted to make the model output
match with observed data. There are three calibration approaches widely used by the scientific
community. These are the manual calibration, automatic calibration and a combination of the
two. The manual calibration approach requires the user to compare measured and simulated
values, and then to use expert judgment to determine which variables to adjust, how much to
adjust them, and ultimately assess when reasonable results have been obtained (Gassman et
al., 2007).
Coffey et al. (2004) presented nearly 20 different statistical tests that can be used for
evaluating SWAT streamflow output during a manual calibration process. They recommended
using the Nash-Suttcliffe simulation efficiency NSE and regression coefficients R2 for
analyzing monthly output and median objective functions, sign test, autocorrelation, and crosscorrelation for assessing daily output, based on comparisons of SWAT streamflow results with
measured streamflows for the same watershed studied by (Spruill et al. 2000).
Eckhartd and Arnold (2001) outlined the strategy of imposing the constraints on the
parameters to limit the number of interdependently calibrated values of SWAT. Subsequently
an automatic calibration of the version SWAT-G of the SWAT model with a stochastic global
optimization algorithm and Shuffled Complex Evolution algorithm is presented for a
mesoscale catchment.
Automated techniques involve the use of Monte Carlo or other parameter estimation schemes
that determine automatically what the best choice of values are for a suite of parameters,
usually on the basis of a large set of simulations, for a calibration process (Gassman et al.,
2007). Automatic calibration involves the use of a search algorithm to determine best-fit
parameters. It is desirable as it is less subjective and due to extensive search of parameter
possibilities can give results better than if done manually.
24
2.6.3. Validation
In order to utilize any predictive watershed model for estimating the effectiveness of future
potential management practices the model must be first calibrated to measured data and should
then be tested (without further parameter adjustment) against an independent set of measured
data. This testing of a model on an independent data set is commonly referred to as model
validation. Model calibration determines the best or at least a reasonable, parameter set while
validation ensures that the calibrated parameters set performs reasonably well under an
independent data set. Provided the model predictive capability is demonstrated as being
reasonable in the calibration and validation phase, the model can be used with some
confidence for future predictions under somewhat different management scenarios (Dilnesaw,
2006).
2.7. Geographical Information System
Geographical information system (GIS) is a computer-based tool for handling spatial data in
digital form tasked. In such system, large quantities of data can be maintained and retrieved at
greater speeds and lower cost which is not the case with conventional method. A geographical
information system also performs manipulations and analysis of the available information
(Aronoff, 1991).
GIS is software tool for data base management that offers solution to many problems related
to watershed management which are spatially required to certain coordinate system
(Burrough,1990).The tool interacts with the remotely sensed data and the ground truth
information. Some of important applications of GIS are processing of digital elevation model,
mapping of the watershed, and established spatial variation of slope, land use, land cover and
soil Formation through the appropriate maps (Prinz et al., 1998). In addition it permits super
imposing one type over another type to give a composite picture of the various features of
watershed that may be useful in developing alternative plants for drainage system layout
(Gomer and Tauer, 1989).
25
2.8. Rainbow Software Package
RAINBOW software is designed to study meteorological or hydrologic records by means of a
frequency analysis and to test the homogeneity of the record. After the selection or creation of
a data set, an analysis on the data is performed. When opting for a frequency analysis, a menu
is opened which contains various folders where a probability distribution can be selected, the
data transformed, and results can be viewed or saved on disk. In RAINBOW the user can
select a Normal, Log-Normal, Weibul, Gamma, Gumbel, Exponential or Pareto distribution.
Apart from graphical methods (Probability plot and a Histogram of the data superimposed by
the selected probability function) for evaluating the goodness of fit, RAINBOW also offers
statistical tests for investigating whether data follow a certain distribution (Chi-square and the
Kolmogorov-Smirnov test). When the goodness-of-fit is inadequate, one can either select
another distribution or attempt to normalize the data by selecting a mathematical operator to
transform the data. RAINBOW also allows to analyze time-series with zero or near zero
events (the so called nil values) by separating temporarily the nil values from the non-nil
values. By calculating the global probability, the nil and no-nil rainfall are combined again.
When the probability distribution can be accepted, the user can view the calculated events that
can be expected for selected probabilities or return periods. Frequency analysis of data
requires that the data be homogeneous and independent. The restriction of homogeneity
assures that the observations are from the same population. Besides RAINBOW also offers a
test of homogeneity which is based on the cumulative deviations from the mean. By
evaluating the maximum and the range of the cumulative deviations from the mean, the
homogeneity of the data of a time series is tested.
Homogeneity test
Frequency analysis of rainfall data requires that the data be homogeneous and independent.
The restriction of homogeneity assures that the observations are from the same population.
One of the tests of homogeneity is based on the cumulative deviations from the mean:
𝑆𝑘 = ∑𝑘𝑖=1(𝑋𝑖 − 𝑋) k=1,2,……,n
(2.18)
26
where Xi are the records from the series X1, X2… Xn and X the mean. The initial value of S
(k=0) and last value S (k=n) are equal to zero. When
plotting
the Sk’s (sometimes called a
residual mass curve) changes in the mean are easily detected. For a record Xi above normal the
Sk increases, while for a record below normal, Sk decreases. For a homogenous record one
may expect that the Sk’s fluctuate around zero since there is no systematic pattern in the
deviations of the Xi’s from their average value.
To test the homogeneity of the data set, the cumulative deviation is plotted in the homogeneity
plot menu. The cumulative deviation is rescaled by dividing the Sk’s by the sample standard
deviation value (s). By evaluating the maximum (Q) or the range (R) of the rescaled
cumulative deviations from the mean, the homogeneity of the data of a time series is tested:
𝑆
𝑄 = 𝑚𝑎𝑥 ⌊ 𝑠𝑘 ⌋
𝑆
(2.19)
𝑆
𝑅 = 𝑚𝑎𝑥 ⌊ 𝑠𝑘 ⌋ − 𝑚𝑖𝑛 ⌊ 𝑠𝑘 ⌋
(2.20)
High values of Q or R are an indication that the data of the time series is not from the same
population and that the fluctuations are not purely random. Critical values for the test-statistic
which test the significance of the departures from homogeneity are plotted in the Homogeneity
plot menu as well (3 horizontal lines). If the cumulative deviation crosses one of the horizontal
lines the homogeneity of the data set is rejected with respectively 90, 95 and 99% probability.
The probability of rejecting the homogeneity of the data set is reported in the homogeneity
statistics menu. The menu is displayed by clicking on the ‘Statistics’ button in the
homogeneity plot menu. If as a result of a homogeneity test, the homogeneity of the data set is
rejected, the user can restrict the analysis to the fraction of the time series which is
homogenous.
27
2.9. Estimation of Baseflow
In general, base flow is estimated through hydrograph analysis by separating streamflow into
surface runoff and base flow. The separation is often estimated by using standard analytical
methodologies or tracer techniques or a mass balance approach (Pinder and Jones, 1968;
McCuen, 1989).
Several analytical methods have been developed to separate base flow from streamflow. Neff
et al. (2005), Scanlon et al. (2006) and Nolan et al. (2007) reviewed the relative merits of
several base flow separation methods including recursive digital filter methods. Although,
most of these methods are based on physical reasoning, exact separation of the streamflow
hydrograph into surface flow and ground water flow is difficult and time consuming,
especially, if there is a need to deal with regional scale studies. In addition, while such
separation methods are valuable in indicating regional trends in the base flow and surface
flow, they require long term continuous streamflow data without missing values.
Base flow filter are of two types; the United Kingdom (UK) smoothed minima method
(Institute of Hydrology, 1980; Wahl and Wahl, 1988) and the recursive digital filter method
(Nathan and McMahon, 1990).
2.9.1. Smoothed minima method or USGS BFI method
The US Geological Survey (USGS) has developed a base flow index raster data set for the
conterminous United States using base flow index (BFI) program (Wahl and Wahl, 1988,
1995; Wolock, 2003a). The BFI program implements a deterministic procedure proposed in
1980 by the Institute of Hydrology in the United Kingdom. The method combines a smoothed
minima approach with a recession slope test.
The BFI program uses a set of procedures in which the water year is divided into N-day period
(number of days, say 5 days or less) and the minimum flow during each N-day period (say, 5
day period) is identified. Each fixed period minimum is then compared to adjacent minima to
28
determine turning points on the base flow hydrograph. Straight lines drawn between turning
points on a semi- logarithmic paper define the base flow component of the stream hydrograph;
the area beneath the hydrograph is the estimate of the base flow volume for the period (Wahl
and Wahl, 1995). The ratio of this volume to the total streamflow volume for the period is
defined as the base flow index.
2.9.2. Recursive digital filter technique
The digital filter method used by Nathan and McMahon (1990) was originally used in signal
analysis and processing (Lyne and Hollick, 1979). Filtering surface runoff (high frequency
signals) from base flow (low flow signals) is similar to the filtering of high frequency signals
in signal processing.
The equation of the filter program is
qt = 𝛽𝑞𝑡−1 + (1+β)/2 ∗ (𝑄𝑡 − 𝑄𝑡−1 )
(2.21)
where qt is the filtered surface runoff at time step t, Qt is the original streamflow and 𝛽 is the
filter parameter. Baseflow bt is calculated using the equation
𝑏𝑡 = 𝑄𝑡 − 𝑞𝑡
(2.22)
In this technique, the filter can be passed over the streamflow data three times (forward,
backward, and forward). Passing the filter through the streamflow data multiple times
systematically lowers the percentage of base flow.
In general, each pass will result in less base flow as a percentage of total flow. This option
gives the user some flexibility in adjusting the separation more accurately to approximate site
conditions. Arnold et al. (1995) have provided a detailed description of this technique and
compared the digital filter results with results from manual separation techniques and with the
PART model (Rutledge, 1993; Rutledge and Daniel, 1994) for 11 watersheds in Pennsylvania,
29
Maryland, Georgia, and Virginia (White and Slotto, 1990). Annual base flow estimated from
the filter method was on an average within 11% of base flow estimated by manual techniques
and the PART model.
Another study by Mau and winter (1997) found that the filter method agreed reasonably well
with the graphical partitioning method. Neff et al. (2005) used six hydrograph separation
methods to estimate the base flow in the Great Lakes region of the United States. The six
methods were: the PART method (Rutledge, 1993; Rutledge and Daniel, 1994), digital filter
method (Arnold and Allen, 1999), three different methods of HYSEP (Sloto and Crouse,
1996), and UK Institute of Hydrology (UKIH)’s modified smoothed minima method (Piggott
et al., 2005). These authors concluded that the recursive digital filter method gave the same
range of separation but averaged the lowest total base flow index of the six methods.
As mentioned earlier, the base flow index value varies depending on the number of passes. It
was not stated how many passes of the filter were used in the filter method in their study so
direct comparison of the methods is difficult. However, the UKIH method is similar to the
USGS BFI method and the reported minimum BFI values were closer to the filter method.
When UKIH and digital filter methods were compared, the maximum BFI values estimated
were within 5% and the average was within 6% (Neff et al., 2005). Based on the studies
conducted in southern Australia, Nathan and McMahon (1990) have indicated that the
recursive filter method is found to be stable, reproducible, and objective method of continuous
base flow separation when compared to smoothed minima method.
30
3. MATERIALS AND METHODS
3.1. Description of the Study Area
The Bululo watershed falls into three Administrative district, Kersa, Kurfa Chele and Metta
districts of East Harerge Zone, Oromiya Regional States. It is located about 46km North West
of Harar town. Kersa and Kulibi are the nearest towns located within 5 km and 7km distance
respectively. Geographically, the Bululo Watershed area is situated at 9017′00″ - 9026′22″
North latitudes and 41042′00″- 41052′00″ East longitudes, with an elevation range of 1730–
3232 meter above sea level. The area covers 256.9Km2 of land. There is high elevation change
between the upstream and downstream of the watershed.
Figure 3.1. Location map of the study area.
31
The upper most part of the watershed topographically is the steepest slopes, while the lower
portion is having flat to gentle slope. As information obtained from Kersa Wereda Agricultural
office, the rainfall characteristic of the Watershed is tri modal pattern, which divide the year
into three distinct rain seasons: a main rainy season (summer) starts in June and extends up to
about late August. The two short rainy seasons are from mid September to late October and
from March to April. Based on the meteorological data of Kersa, Kulbi and Girawa the
watershed area has weighted mean annual precipitation of 905 mm with annual maximum and
minimum daily temperature of 10.89°C and 26.13°C, respectively. The land use of the study
area consists of 90.5% of agricultural land and 9.5% under shrubs. The major crops cultivated
are maize and sorghum. The major soil types in the area include Chromic Luvisols, Rendzic
Leptosols, Eutric Cambisols and Humic Nitisols.
3.2. Methods
3.2.1. SWAT input data and analysis
The most important spatial information needed for SWAT model were a Digital Elevation
Model (DEM), a land use or land cover map, stream network layers and a soil map.
Specifically, the daily meteorological and river discharge data was essential for prediction of
streamflow and calibration purposes.
3.2.1. Digital elevation model (DEM) of the study area
The 30m by 30m ASTER derived elevation grid DEM used in this study was obtained from
Haramaya University GIS department. Before the DEM data was loaded into Arc SWAT
interface, it was projected into projected coordinate system. The projection of the DEM data
was done using the Arc tool box operation in Arc GIS. The projected coordinate system
parameters of study area are: UTM— other GCS—Adindan UTM zone 38N. As the DEM
covered a larger area in which part of it was not required for the modeling work but reduced
the processing time of the GIS functions, a mask was created for the study area. Hence, only
the portion of the DEM covered by the mask was processed by the interface. This DEM was
32
used to delineate the watershed and analyze the drainage pattern of the land surface terrain.
Sub basin parameters such as slope gradient, slope length of the terrain and the stream network
characteristics such as canal slope length and width were derived from DEM.
Figure 3.2. DEM used for the study Area
33
3.2.2. Digitized stream networks.
The stream network data set was digitized from topographic map of scale 1:50000 of the study
area. The stream network dataset was superimposed onto the DEM to define the location of
the stream network.
Figure 3.3. Stream network map of the study area
3.2.3. Land use/Land cover
The land use is one of the most important inputs for the model. The land use map of the study
area was obtained as shape file from Ministry of Water Resources from the study of
Wabisheble river basin and field observation has been carried out. The reclassification of the
land use map was done to represent the land use according to the specific land cover types and
34
the respective crop parameter was selected from SWAT database. A look up table that
identifies the 4-letter SWAT code for the different categories of land cover/land use were
prepared so as to relate the grid values to SWAT land cover/land use classes. SWAT
calculated the area covered by each land use.
Figure 3.4. Land Use/Land Cover Map of the study area
Table 3 1. Land use/land cover of the study area and redefinition according of SWAT Code
Original Land Use
Area(Km2)
SWAT
SWAT
Redefined Land use
Code
Maize
Corn
CORN
121.41
47.26
Sorghum
Grain Sorghum
GRSG
83.00
32.31
Dense Shrub Land
Dense Shrub Land
SPLX
8.31
3.23
Wheat
Durum Wheat
DRWT
28.25
11.00
Open Shrub Land
Range Brush
RNGB
15.92
6.20
35
% of Area
Watershed
3.2.4. Soil data
The SWAT model requires soil data. The required soil data and digital soil map of the study
area were collected from Ministry of water resource. The major soil types in the area include
Chromic Luvisols, Rendzic Leptosols, Eutric Cambisols and Humic Nitisols.
3.2.5. Meteorological data
Meteorological data is needed by the SWAT model to simulate the hydrological conditions.
For this study the required meteorological data were collected from the Ethiopian National
Meteorological Services Agency (NMSA). The meteorological data collected were
precipitation, maximum and minimum temperature, relative humidity, and wind speed and
sunshine hours. Data from four stations, which is around the study area, were collected.
However, most of the stations have short length of record periods. Two of the stations (Kulbi
and Girawa) have records from 1990 to 2010 but most of them have missing data especially
during 1991-1992.
For this study Kulbi station was selected for weather generator station (station used for
infilling of missing data), due to the availability and quality of data. All the missing data were
filled with a missing data identifier of -99. SWAT has a built-in weather generator that is used
to fill these gaps. Moreover to identify the representative rainfall stations, which contributes
for the watershed we used Thiessen polygon method. The nearest meteorology stations such as
Kersa, Kulibi and Girawa were identified and used these stations as an input for rainfall and
Haramaya and Girawa for temperature. SWAT takes data of each climatic variable for every
sub-basin from the nearest weather station measured from the centroid of the sub watershed.
The weather generator input file contains the statistical data needed to generate representative
daily climate data for the subbasins. Climatic data was generated in two instances: when the
user specific that simulated weather could be used or when measured data was missing. All
weather generator input data, which are monthly daily averages, standard deviations,
probability of wet and dry days, skew coefficient were processed using Excel.
36
Figure 3.5. Location map of weather stations.
3.2.6. Hydrological data
The daily recorded river flow data was required for performing sensitivity analysis, calibration
and validation of the model. This data was collected from the Ethiopian MoWR hydrology
section. The hydrological data collected was daily flow for the Dawe gauge station feeding
into Wabishebele River. Using RAINBO software the homogeneity of average annual daily
flow data was tested. The restriction of homogeneity assures that the observations are from the
same population.
37
3.3. Arc SWAT Model Set Up
The SWAT hydrologic model set up process of the study area follows the following steps:
watershed delineation and determination of hydrologic parameters of sub-basins, land-use and
soil overlay for HRU definition, then specification of climatic data time series which would be
used for simulation. After that ArcSWAT build input files were used for simulation. Details of
the procedure are described below.
3.3.1. Watershed delineation
Watershed delineation was carried out using the watershed delineation operation tool and
expands ArcGIS and Spatial Analyst extension functions. The watershed delineation process
was required properly projected DEM. The first step in the watershed delineation was loading
the properly projected DEM. After the new project was created the automatic watershed
delineation tool was selected, which allows the user to set-up and preprocess the DEM for
modeling, define the threshold area for the sub-basins in the SWAT model, modify outlet and
inlet definitions for the watershed, and define the main watershed outlet. To reduce the
processing time of the GIS functions, a mask was selected and created over the DEM around
the study area this allowed only the portion of the DEM covered by the mask was processed
the interface. Other options used in this Watershed delineation includes burning
(superimposing) the digitized network of streams on to the DEM to ensure reaches continuity
and preprocessing the DEM to remove sinks. The initial stream network and sub-basin outlets
were defined based on streams drainage area threshold approach. The threshold area defines
the minimum drainage area required to form the origin of a stream. The interface lists a
minimum, maximum and suggested threshold area in hectares. The smaller the threshold area,
the more detailed the drainage network delineated by the interface, defining of the threshold
drainage area was done using the threshold value. Final step in the delineation of the
watershed was calculation of sub basins parameters such as geomorphic parameters and
relative stream reach.
38
3.3.2. Hydrologic response unit analysis
SWAT requires Land use and soil data to determine the area and the hydrologic parameters of
each land-soil category simulated within each sub-basin. Soil and land use information are
specified in SWAT as grid file format. Land use and soil data were used by hydrologic
models. These land-use and soil data were projected in the same projection as the DEM used
in the watershed delineation.
In Hydrologic response unit analysis first the soil and land use maps were classified and then
overlaid. In order to analyses the effect of different land use and soil type combinations to
runoff yield, the method of multiple HRUs for each sub-basin was adopted for HRU analysis
where number of HRUs could vary according to the requirement of user.
The land cover classes were defined using the look up table. A look-up table that identifies the
4-letter SWAT code for the different categories of land cover/land use was prepared so as to
relate the grid values to SWAT land cover/land use classes. After the land use SWAT code
assigned to all map categories, reclassification were done and a new SWAT Land use Classes
were displayed in the map.
Based on the DEM data used during the watershed delineation slope classification was done.
As the sub-basins have a wide range of slopes between them, the multiple slope discretization
operation was used for HRU delineation. Based on FAO 2006 slope classification, five slope
classes were applied and slope grids reclassified. And finally, land use, soil and slope grids
were overlaid.
After the land use, soil and slope grids were overlaid the last step in the HRU analysis was the
HRU definition. By using multiple HRU definition options to each sub-watershed the HRU
distribution was determined. In multiple HRU definition, a threshold level was used to
eliminate minor land uses, soils or slope classes in each sub-basin. The SWAT user’s manual
recommends the threshold levels for multiple HRUs to be set based on the project goal and the
amount of detail desired. For most applications, the default settings for land use threshold
39
values of 20%, soil threshold 10% and slope threshold 20% are recommended to be adequate
for most applications. However in this study, the threshold levels were set up at, 5% each for
land use, soil and slope classes so as to encompass most of spatial details.
3.3.3. Defining weather database
The climate of a watershed provides the moisture and energy inputs that control the water
balance and determine the relative importance of the different components of the water cycle.
Rainfall data for the three gauging stations and temperature data only for two of them were
provided in dBase file format, which contained location of the rain gauge site, linked with the
data table, containing the daily values of the rainfall. The location table was linked to the
SWAT weather database where the observed weather data was stored. Other daily climatic
values of maximum and minimum temperature, solar radiation, relative humidity and wind
speed were generated from the given data of daily rainfall and monthly average climatic data
by weather generator of the model.
3.3.4. Sensitivity analysis
Sensitivity analysis was conducted to quantify the impact of input parameters on the model
results/output and to limit the number of optimized parameters to obtain a good fit between the
simulated and measured data. Sensitivity analysis helps to determine the relative ranking of
which parameters most affect the output variance due to input variability (van Griensven et al.,
2002) which reduces uncertainty and provides parameter estimation guidance for the
calibration step of the model. SWAT model has an embedded tool to perform sensitivity
analysis and provides recommended ranges of parameter changes. SWAT2009 uses a
combination of Latine Hypercube Sampling and One-At-a-Time sensitivity analysis methods
(LH-OAT method) (van Griensven, 2005).
Generally, the sensitivity analysis was performed for the period of 1991-2003 and ten top
sensitive flow parameters were evaluated and identified.
40
3.3.5. Model calibration
Calibration is a process of standardizing predicted values, using deviations from observed
values for a particular area to derive correction factors that can be applied to generate
predicted values that are consistent with the observed values.
In order to improve model predictability the hydrologic component of the model was
calibrated with respect to local observational data of Dawe gauging station for calibration
period of 1991-1998 including one year of ‘warm-up’ period.
When model results match observed values from stream-flow measurement, users have greater
confidence in the reliability of the model. Before calibration the base flow and surface runoff
were separated using base-flow filter program. Calibration was achieved in two steps, first
surface runoff calibrated then the sensitive parameters affecting the base flow were calibrated.
SWAT has two built-in calibration tools: the manual calibration helper and the autocalibration. The manual calibration approach requires the user to compare measured and
simulated values, and then to use expert judgment to determine which variables to adjust, how
much to adjust them, and ultimately assess when reasonable results have been obtained.
In this study, both techniques were employed to get the best model parameters. First, the
manual calibration was performed and when the model evaluation parameters reached to an
unchanged level, the model was run automatically. Parameter changes in SWAT affecting
hydrology were done in a distributed way for selected sub-basins and HRU’s. They were
modified by replacement, by addition of an absolute change and by multiplication of a relative
change depending on the nature of the parameter.
Calibration for the water balance was done first for average annual conditions. Once the run
was calibrated for the average annual conditions, calibration for average monthly and daily
was performed to fine-tune the calibration.
41
The most sensitive model parameters governing surface water response were calibrated first,
which include the runoff curve number (CN2), the soil evaporation compensation factor
(ESCO), and the available soil water capacity (SOL_AWC) till the simulated values were
within ±15% of the observed. Then sensitive parameters affecting the base flow were
calibrated. Which include the baseflow alpha factor (ALPHA_BF), deep aquifer percolation
fraction which governed the fraction of percolation from the root zone to the deep aquifer
(RCHRG_DP) and threshold depth of water in the shallow aquifer required for return flow to
occur to the stream (GWQMN). After the base flow calibration was done, the amount of
surface runoff was also checked as adjustment of the base flow parameters might also affect
the surface runoff volume.
At each manual calibration processes, the coefficient of determination (R2), and the Nash and
Suttcliffe simulation efficiency (NSE) statistical tests were applied and evaluated in
accordance to Santhi et al. (2001) recommendation (R2>0.5 and NSE > 0.5).
After the water balance calibration was finalized, temporal flow calibration was performed at
each step by adjusting input parameters which affect the shape of the hydrograph. The
sensitive input parameters adjusted were Ch_K (effective hydraulic conductivity in main
channel alluvium), alpha_BF (baseflow alpha factor), Surlag (Surface runoff lag coefficient)
and GW-Delay (Groundwater delay time).
The final acceptable parameter values that were manually calibrated were used as the initial
values for the auto-calibration procedure. Maximum and minimum parameter value limits
were used to keep the output values within a reasonable value range. Finally, the auto
calibration tool was run to provide the best fit between the measured and simulated data. For
calibration of the model, the measured value recorded for eight years (1991-1998) were used.
42
Gauged Daily flow data
Base flow (BF) and
Surface Runoff (SR)
Separation.
Run
SWAT
If Simulated vs
Observed SR.
R2>0.5
NSE>0.5
Adjust
CN2
NO
SOL-AWC
ESCO
Check
YES
Run
SWAT
If Simulated vs
Observed BF.
R2>0.5
NSE>0.5
Adjust
NO
GWQMN
RCHRG-DP
ALPA-BF
YES
Go to automatic
Calibration.
Figure 3.6. Flow calibration procedure used in this study (Snathi et al., 2001)
43
3.3.6. Model validation
Once the model parameters were calibrated, the model was validated. Validation of the model
was conducted to see how the model would respond to the data set beyond the calibration data
set. In this study, data for a period of five years (1999-2003) which not used for calibration
was used at Dawe gauged station to validate and evaluate the model accuracy. The statistical
measures criteria used during the calibration procedure were also used for model validation.
3.3.7. Model evaluation
To support the results found from the model, the performance of SWAT was evaluated using
statistical measures. Coefficient of determination (R2) and Nash-Sutcliffe simulation
efficiency (NSE) were used as measure of the goodness of fit to evaluate model prediction.
The R2 value is an indicator of strength of relationship between the observed and simulated
values. The Nash-Sutcliffe simulation efficiency (NSE) indicates how well the plot of
observed versus simulated value fits the 1:1 line. If the measured value is the same as all
predictions, NSE is 1. If the NSE is between 0 and 1, it indicates deviations between measured
and predicted values. If NSE is negative, predictions are very poor, and the average value of
output is a better estimate than the model prediction (Nash and Sutcliffe, 1970). The R2 and
NSE values are explained in equations 3.1 and 3.2 respectively.
2
R2 = ∑n
[∑n
i=1(Qmi −Qavm )(Qs −Qavs )]
(3.1)
2 n
2
i=1(Qmi −Qavm ) ∑i=1(Qmi −Qavs )
∑n (Qs −Qavs )2
NSE =1 − ∑ni=1(Q
i=1
(3.2)
2
mi −Qavm )
where Qmi is the measured discharge, Qs, is the simulated discharge, Qavm is the average
measured discharge and Qavs is the average simulated discharge.
44
4. RESULTS AND DISCUSSION
4.1. Watershed and Channel Delineation
Before the watershed delineation was processed, the threshold area for the sub-watershed was
defined as having at least a 1000 hectare contributing area. Based on this streams drainage
area threshold approach the watershed delineation operation tool 13 sub-watershed were
identified and delineated (Figure 4.1). The watershed delineation resulted into a total area of
256.9 Km2. Area coverage results obtained for each sub-watershed is illustrated in Table 4.1.
Figure 4.1. Bululo Sub Watershed map.
45
Table 4.1. Area covered by sub-watershed
Sub-watershed
Area(Km2)
Sub-watershed
Area(Km2)
1
39.44
8
18.74
2
12.76
9
28.14
3
38.77
10
19.57
4
29.07
11
25.8
5
0.87
12
11.26
6
14.82
13
8.8
7
8.86
total
256.9
4.2. Hydrologic Response Units
As illustrated in Figure 4.2 and Figure 4.3.The land use and soil maps were classified and then
overlaid in order to make combinations and distributions of types of soil and their
corresponding land use for each sub-basin in the watershed. After land use, soil and slope
maps overlaid threshold values of 5% for each land use, soil type and slope classes were used
and resulted in 150 HRUs for the whole watershed. The area coverage by each land use type
after HRUs definition is illustrated in Figure 4.2.
As presented in Figure 4.2, most portion of the Bululo watershed was covered with Maize,
which accounted for 47.26% of the watershed area.
46
Figure 4.2. Land use /Land cover map of the study area.
The soil types were also identified in the Bululo watershed. As shown in Figure 4.3, the
Chromic Luvisols and Rendzic Leptosols were the major soil types covering 37.15% and
32.94% of the watershed area, respectively. The smallest portion of the area was covered by
Humic Nitisols (1.3%).
47
Figure 4.3. Soil map of study area.
The watershed was divided based on FAO 2006, slope classification into five slope classes: As
shown in Figure 4.4, after the definition of HRU, 70% of the area in the Bululo watershed had
a slope greater than 10% which was dominated by hilly rolling to mountain terrain land forms.
Such slopes indicate the importance of runoff harvesting compared to deep percolation.
48
Figure 4.4. Slope map of study area.
4.3. Contribution of Rainfall Stations for the Catchment
The rainfall stations that contributed for the watershed and the most influential station on the
catchment were identified using by Thiessen polygon method. The result of Thiessen polygon
showed in Figure 4.3 indicates Kulbi, Kersa and Girawa stations were contribute 52.2%,
43.6% and 4.2% of the catchment Area, respectively.
49
Figure 4.5. Contribution of Rainfall Stations for the Catchment (Thiessen Polygon)
4.4. Homogeneity Test of Annual Rainfall Data
Rainfall and statistical precipitation data of Kulubi, Kersa and Girawa stations are presented in
Appendix Table 1 to 6.
The homogeneity of the annual rainfall data was tested for all stations using RAINBO
software. The result of homogeneity test for the weather generator data shows that the
collected data was homogeneous. The test result for Kulbi station is presented in Table 4.2,
Figure 4.6 and Figure 4.7. Details of other stations (Kersa and Girawa) are presented in
Appendix Table 13, 14 and Appendix Figures 1 to 4.
50
Kulbi Weather Station: As presented in Figure 4.6. The rescaled cumulative deviations from
the mean for Kulbi total annual rainfall would not crossed one of the horizontal 90, 95 and
99% probabilities lines. The probability plot of annual rainfall was resulted in the least square
fit with R2 value as 0.96 (Figure 4.7).
The range of cumulative deviation and maximum cumulative deviation could not be rejected
on 90%, 95% and 99% probability levels (Table 4.2).
Figure 4.6. Rescaled cumulative deviation of annual rainfall at Kulbi station
51
Figure 4.7. Probability plot of annual rainfall at Kulbi Station
Table 4.2. Probability of rejecting homogeneity of annual rainfall (Kulbi station)
Statistics 90 %
Range of Cumulative Deviation
Maximum Cumulative Deviation
90 %
NO
NO
Rejected
95%
NO
NO
99%
NO
NO
4.5. Base Flow Separation for Dawe Gauge station
Hydrological data for Dawe gauging station are presented in Appendix Table 13. Using the
base flow filter program the base flow of the daily data measured at Dawe gauged station was
separated. The result showed that 34% of the flow was contributed by the base flow and the
rest by the surface runoff. This shows that the total stream flow was largely supplied by
surface runoff.
4.6. Sensitivity Analysis
Results of sensitivity analysis with observed data showed that most sensitive parameters for
the SWAT model in Dawe gauging station were curve number (CN2), base flow alpha factor
(ALPHA_BF), soil evaporation compensation factor (ESCO), effective hydraulic conductivity
of main channel (CH-K2), available water capacity (SOL_AWC), maximum potential leaf area
index (BLAI), maximum canopy storage (CANMX), deep aquifer percolation fraction
(RCHRG_DP ), saturated hydraulic conductivity of soil (SOL_K ) and plant uptake
composition factor (EPCO) evaluated.
Wondu (2011) had reported during application of SWAT model in Erer watershed, East
Harerghe, which had found 11 top sensitive parameter and CN2 was the most sensitive.
The sensitivity analysis results of this study confirmed the findings of Eyob (2010) in the
study of Ija Gelma Waqo Watershed, East Harerghe Zone, were 10 top sensitive parameters
had identified.
52
The top ten sensitive parameters which had effects on the runoff along with their ranking are
presented in Table 4.3.
Table 4.3. Sensitive parameter ranking and final auto-calibration result
Bound
Parameters
Rank
Auto-calibration result
Upper
Lower
Upper
Fitting value
Method
25%
1.085
multiply
Cn2
1
-25%
Alpha_Bf
2
0
1
0.372
replace
Esco
3
0
1
0.014
replace
Ch_K2
4
0
150
0.420
replace
Sol_Awc
5
-25
25
1.050
multiply
Blai
6
0
1
0.620
replace
Canmx
7
0
10
0.480
replace
Rchrg_Dp
8
0
1
0.010
replace
Sol_K
9
-25
25
1.015
multiply
Epco
10
0
1
0.754
replace
4.7. Model Calibration
Using the fitting values, which got from auto calibration result (Table 4.3), the model
parameters were calibrated in a distributed way for the study area. The statistical results of the
performance test of the calibrated model values are shown in Table 4.4.
The Nash efficiency and coefficient of determination gave high values for calibration period
indicating the predictive ability of the model on annual and monthly values of river discharge.
Scatter plots of the observed versus predicted annual and monthly stream flows at Dawe
station showed comparatively good agreement with the 1:1 line (Figure 4.8).
53
1.20
y = 1.15x-0.023
R² = O.956
NSE = 0.853
Simulated Flow(m³/s)
1.00
0.80
0.60
0.40
comparsion
Linear (comparsion)
0.20
0.00
0.00
0.20
0.40
0.60
0.80
1.00
1.20
Observed Flow(m³/s)
Figure 4.8. Scatter plots for annual model calibration period 1992-1998.
1.20
Flow (m³/s)
1.00
0.80
Observed
0.60
Simulated
0.40
0.20
0.00
1992
1993
1994
1995
1996
1997
1998
Year
Figure 4.9. Mean annual observed and simulated flow of calibration period 1992-1998.
54
Table 4.4. Calibrated average annual stream flow and model evaluation statistics for
calibration period 1992-1998.
Year
Observed
Flow (m3/s)
Simulated
Flow(m3/s)
1992
0.347
0.285
1993
0.359
0.421
1994
0.483
0.562
1995
0.439
0.519
1996
0.964
1.078
1997
0.420
0.488
1998
0.321
0.366
Mean
0.476
0.521
Statistics
Value
R2
0.956
NSE
0.853
Error
0.079
The scatter plots of monthly time step given in Figure 4.10 and a time-series plot of the
observed and simulated monthly stream flows (Figure 4.11) shows that a good agreement
between simulated and observed stream flow values and most of the time trend in the
simulated monthly flows closely follow the measured data. However peak values couldn’t be
caught. This may be due to inaccurate representation of the spatial distribution of precipitation
within the watershed by the available rain gauges used as model input.
4.00
Simulated Flow(m³/s)
3.50
y = 1.16x-0.049
R² = 0.873
NSE = 0.779
3.00
2.50
2.00
1.50
1.00
comparsion
0.50
Linear (comparsion)
0.00
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
Observed Flow(m³/s)
Figure 4.10. Scatter plots for monthly model calibration period (1992-1998).
55
1/1/1992
4/1/1992
7/1/1992
10/1/1992
1/1/1993
4/1/1993
7/1/1993
10/1/1993
1/1/1994
4/1/1994
7/1/1994
10/1/1994
1/1/1995
4/1/1995
7/1/1995
10/1/1995
1/1/1996
4/1/1996
7/1/1996
10/1/1996
1/1/1997
4/1/1997
7/1/1997
10/1/1997
1/1/1998
4/1/1998
7/1/1998
10/1/1998
Monthly Preciptation(mm)
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
Monthly Discharge(mm)
100
90
80
70
60
50
40
30
20
10
0
precipitation
observed
simulated
Figure 4.11. Observed and simulated flow hydrograph for calibration period (1992-1998)
Table 4.5. Calibrated monthly stream flow model evaluation statistics for (1992-1998)
Statistics
Observed Flow(m3/s)
Simulated Flow(m3/s)
0.472
0.498
Mean
Error
0.267
R2
0.873
NSE
0.779
4.8. Model Validation
Validation of the model was conducted to see how the model would respond to the data set
beyond the calibration data set. The model with the calibrated parameters was run again using
five years period from January 1, 1999 to December 31, 2003 for Bululo watershed which was
not used for calibration. Like the result found in calibration the validation results are also
shows good agreement between the observed and predicted discharge.
56
Simulated Flow(m³/s)
1.20
y = 1.24x-0.07
R² = 0.971
NSE = 0.788
1.00
0.80
0.60
comparsion
0.40
Linear (comparsion)
0.20
0.00
0.00
0.20
0.40
0.60
0.80
1.00
Observed Flow(m³/s)
Figure 4.12. Scatter plots for mean annual flow during model validation period (1999-2003)
Flow (m³/s)
1.20
1.00
0.80
Observed
0.60
Simulated
0.40
0.20
0.00
1999
2000
2001
2002
2003
Year
Figure 4.13. Mean annual observed and simulated stream flow of validation period (19992003)
57
Table 4.6. Validated annual stream flow and model evaluation statistics for (1999-2003).
Year
Observed
Simulated
Flow (m3/s)
Flow(m3/s)
1999
0.940
1.106
2000
0.591
0.567
2001
0.771
0.911
2002
0.337
0.363
2003
0.420
0.482
Mean
0.612
0.686
Statistics
Value
R2
0.971
NSE
0.788
Error
0.102
The scatter plots of monthly time step given in Figure 4.14 and a time-series plot of the
observed and simulated monthly stream flows as Figure 4.15 shows that a good agreement
between simulated and observed stream flow values and most of the time trend in the
simulated monthly flows closely follow the measured data. However peak values couldn’t be
caught. This may be due to inaccurate representation of the spatial distribution of precipitation
within the watershed by the available rain gauges used as model input.
Simulated Flow(m³/s)
5.00
y= 1.27x-0.1
R²= 0.802
NSE=0.512
4.00
3.00
comparsion
2.00
Linear (comparsion)
1.00
0.00
0.00
1.00
2.00
3.00
4.00
5.00
Observed Flow(m³/s)
Figure 4.14. Scatter plots for monthly model validation period (1999-2003)
58
Monthly Precipitation(mm)
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
1/1/1999
4/1/1999
7/1/1999
10/1/1999
1/1/2000
4/1/2000
7/1/2000
10/1/2000
1/1/2001
4/1/2001
7/1/2001
10/1/2001
1/1/2002
4/1/2002
7/1/2002
10/1/2002
1/1/2003
4/1/2003
7/1/2003
10/1/2003
Monthly Discharge(mm)
100
90
80
70
60
50
40
30
20
10
0
precipitation
observed
simulated
Figure 4.15. Comparison between observed and simulated monthly stream flow for validation
period (1999-2003).
Table 4.7. Validated monthly stream flow model evaluation statistics for (1999-2003)
Statistics
Observed Flow(m3/s)
Simulated Flow(m3/s)
Mean
0.613
0.682
Error
0.513
R2
0.802
NSE
0.514
4.9. Model Evaluation
To support the results found from the simulation, both the calibration and validation model
results were evaluated and the summary of evaluation parameters are shown in Table 4.8 the
graphs of observed discharge versus simulated discharge for the calibration period are shown
in Figure 4.8 and Figure 4.10. Coefficient of determination values of 0.956 and 0.873 were
found for the yearly and monthly time steps respectively. This indicates us both annual and
monthly time steps show a very good agreement between modeled and observed stream flow
59
values at Bululo Watershed. Also Scatter plots of simulated and observed stream flow values
show a very close trend to 1:1 line.
Table 4.8. Summary of the model performance evaluation statistical result for calibration and
validation.
Time step
Nash and
Sutcliff
(NSE)
Error
Annually
Coefficient of
determination
(R2)
Calibration
0.956
0.853
0.08
Monthly
0.873
0.779
0.27
Validation
Annually
0.971
0.788
0.10
Monthly
0.802
0.512
0.51
Nash and Sutcliff model evaluation resulted in values of 0.853 and 0.779 for the yearly and
monthly time step respectively. The statistical evaluation yielded high value of Nash
efficiency and coefficient of determination (Table 4.8), indicating a strong correlation between
the measured and predicted flows for both time steps.
The graphs of observed discharge and simulated discharge for the validation result are shown
in the Figure 4.12 and Figure 4.14.The Coefficient of determination for the yearly and
monthly time steps were found values of 0.971 and 0.802 respectively. Scatter plots of the
observed versus predicted annual and monthly stream flows at Dawe station showed
comparatively good agreement with the 1:1 line.
It was found that the model has strong predictive capability with NSE value of 0.788 and
0.512 for annual and monthly time steps respectively.
60
4.10. Water Yield Simulation of Bulullo Watershed
After calibration of sensitive parameters for flow obtained during the auto-calibration of Dawe
gauging station, the water yield was simulated for the year 1990 to 2010 on monthly time step.
The monthly water yield (Mm3) simulated for the watershed is summarized in Tables 4.9.
Table 4.9. Simulated monthly water yield (Mm3).
Year
Jan.
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
mean
STD
0.18
2.37
0.03
0.35
0.66
0.06
0.17
0.09
0.34
0.14
0.08
0.04
1.62
0.29
0.10
0.03
0.18
1.24
0.39
0.27
0.04
0.41
0.61
Feb. Mar Apr
0.53
0.15
0.05
0.16
0.30
0.03
0.10
0.03
0.13
0.06
0.04
0.03
0.04
0.04
0.05
0.03
0.03
0.11
0.18
0.14
2.15
0.21
0.46
1.03
2.04
0.12
0.28
0.04
0.05
1.68
0.27
0.04
1.85
0.06
0.10
0.08
0.04
0.13
0.92
0.13
0.16
0.07
0.03
2.82
0.57
0.83
2.34
3.91
0.69
3.20
0.86
0.62
2.80
1.60
0.06
0.67
2.73
6.63
0.35
6.04
7.07
0.79
4.28
3.00
1.22
0.03
3.61
2.50
2.15
Month
May. Jun. Jul.
Aug.
Sep
Oct.
Nov Dec
Total
0.21
0.67
2.40
3.59
1.79
1.49
14.62
1.83
2.30
0.12
2.49
3.16
0.14
0.62
0.52
2.11
1.33
0.44
0.50
0.11
2.43
2.04
3.07
0.93
3.44
3.08
1.20
7.89
8.33
6.81
5.94
4.00
8.69
2.26
13.09
4.00
2.71
2.30
1.71
2.21
2.52
5.11
1.72
5.72
4.46
3.10
7.30
0.29
0.48
1.07
0.91
0.69
1.29
1.55
1.41
9.03
5.23
2.54
2.10
2.18
1.14
1.51
1.65
1.39
0.59
1.37
6.02
2.37
2.40
1.33
0.32
0.46
0.86
0.75
0.27
1.22
0.42
0.92
10.38
4.16
0.23
0.72
0.41
0.99
2.45
1.38
0.49
0.56
1.81
1.15
1.49
2.23
0.19
0.08
0.06
0.36
0.29
0.36
0.41
0.42
0.23
0.71
0.60
0.05
0.04
0.04
0.06
0.25
0.53
0.04
0.17
0.18
0.08
0.25
0.20
14.37
13.55
8.16
13.28
17.73
13.82
34.07
15.37
11.56
34.88
17.92
28.72
11.45
15.20
12.63
10.47
12.52
11.94
10.00
6.20
25.09
16.24
7.94
0.12
0.05
0.15
0.82
0.11
1.37
2.28
0.99
0.07
2.47
0.08
0.12
0.22
0.17
0.04
0.19
0.17
0.06
0.65
0.08
0.20
0.49
0.72
0.02
0.20
0.49
1.19
3.95
0.27
2.59
1.97
1.97
0.66
0.12
2.67
0.18
1.46
0.19
0.35
0.44
0.35
0.19
0.37
0.53
0.96
1.08
0.19
0.04
0.15
0.21
0.19
0.27
0.08
0.27
0.09
0.11
0.07
0.06
1.97
1.22
0.04
0.13
0.19
2.13
0.37
0.10
0.36
0.39
0.60
The mean annual water yield of the watershed was obtained 16.24Mm3. The detailed result of
the frequency analysis and estimation of probabilities of exceedance is shown in Table 4.10.
61
Table 4.10. Water yield in Mm3 for different probabilities of Exceedance.
Probability of
Return period
Annual
Probability of
Return Period
Annual
Exceedance (%)
(years)
(Mm3)
exceedance (%)
(years)
(Mm3)
5
10
15
20
25
30
35
40
45
50
20
10
6.67
5
4
3.33
2.86
2.5
2.22
2
26.88
26.07
24.17
22.66
21.36
20.20
19.12
18.10
17.11
16.14
55
60
65
70
75
80
85
90
95
1.82
1.67
1.54
1.43
1.33
1.25
1.18
1.11
1.05
15.14
14.18
13.16
12.08
10.92
9.62
8.11
6.21
3.40
4.11. Average Annual Water Yield of sub watersheds
The average annual water yield for each sub watershed was calculated by summing surface
runoff, lateral flow, base flow from which subtracting losses and abstraction. Table 4.11 gives
the water yield of every sub watershed.
Table 4.11. Water yield in Mm3 for each sub watershed
Sub
Watershed
1
2
3
4
5
6
7
8
9
10
11
12
13
total
Area
(Km2)
39.44
12.76
38.77
29.07
0.87
14.82
8.86
18.74
28.14
19.57
25.8
11.26
8.8
256.9
Average annual
precipitation (mm)
764.8
764.8
1016.6
1016.6
1016.6
1016.6
764.8
764.8
1016.6
1016.6
1016.6
977.8
977.8
62
Water yield
(Mm3)
1.189
0.309
2.243
1.516
0.050
1.619
0.555
0.495
2.926
0.952
2.940
0.860
0.607
16.240
Drainage Ratio
(Mm3/km2)
0.030
0.024
0.058
0.052
0.057
0.109
0.063
0.026
0.104
0.049
0.114
0.076
0.069
0.063
Figure 4.16. Average annual water yield generated from each subbasins
The average annual water yield for each sub watershed was shown in Figure 4.16. As
presented in the figure 4.16 and the drainage ratio presented in table 4.11, there has a spatial
difference of water yield among sub watersheds. The output of model showed that sub
watersheds 6, 9 and 11 generate a maximum annual average water yield or has maximum
drainage ratio (Table 4.11), this was attributed due to western part of the watershade were
receiving high amount of precipitation (Figure 4.5). In addition, sub watershed 6, 9 and 11
were covered 82% of agricultural land with slope >15% and 18% of it was covered by open
shrub land. However, sub watersheds 3, 4 and 5 were received the same amount of average
annual precipitation that received as sub watersheds 6, 9 and 11, the generated average annual
water yields and the drainage ratio was indicates smaller when compared to subbasin 6, 9 and
63
11 (Table 4.11), this was attributed due to the upper part of sub watershed 3 and sub watershed
4 were covered by dense shrub land and sub watershed 5 was agricultural land with gentle
slope. This is indicated that apart from rainfall characteristics, i.e., intensity, duration and
distribution; the topography, land use and vegetation cover have a direct contribution for the
occurrence and generation of water yields from watersheds (Yitebitu, 2004).
Relatively small amount of average annual water yield generated from the Eastern part of the
watershed were observed, this was attributed due to Eastern part of the watershade were
receiving small amount of precipitation (Figure 4.5), also the topography of the land was flat
to gentle flat slopes. However, sub watershed 7 contributed large amount of average annual
water yield when compared to sub watershed 1, 2, 8 and 10. It was cultivated agricultural
cropland with steep slope. On steep slopes, water tends to move down the slope more rapidly
but, on gentle slopes, water may be temporarily pond and later soaked in. It is indicate that
steep slopes of drainage basins tend to generate more runoff than gentle slopes (Sharma et al.,
1986).
64
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS.
5.1. Summary
A proper investigation of the runoff yield of the catchment is essential for management and
utilization of water resource. Due to the spatial and temporal heterogeneity in soil properties,
vegetation and land use practices a hydrological cycle is a complex system. So it is desirable
that some suitable methods and techniques are used for quantifying the hydrological
parameters from all parts of the watersheds. Soil and Water Assessment Tool (SWAT) model
is one of the semi physically based and computationally efficient model.
The objective of this study was to estimate the runoff yield of Bululo Watershed as a whole
and individual sub-watershed. The study was carried out in East Harerge, Kersa Wereda,
Bulullo Watershed. For this study, the SCS runoff curve number option was used to estimate
surface runoff from precipitation. The potential evapotranspiration was estimated using
Hargreaves method and the variable storage method was used for channel water routing.
Twenty one year’s meteorological, twelve years hydrological; land use, soil and 30m by 30m
grid DEM data were used for the study. Seven years from 1992 to 1998 and five years from
1999 to 2003 observed stream flow data were used to calibrate and validate the model,
respectively.
In order to determine the model predictive capabilities, the performance of the model was
evaluated using two different techniques: coefficient of determination and Nash and Sutcliff.
Model calibration and validation indicated a good fit between the observed and simulated
discharge values. Values of coefficient of determination for calibration were obtained to be
0.956 and 0.873 for the annually and monthly time steps, respectively. Similarly, Nash and
Sutcliff values of 0.853 and 0.779 were obtained respectively.
Validation of the model was also done with independent observed stream flow data from 1999
to 2003. The performance evaluation statistics for validation showed the values of coefficient
65
of determination were 0.971 for annual and 0.802 for monthly time steps, which indicated the
presence of high linear association between measured and predicted values. The corresponding
NSE values were 0.788 for annual and 0.512 for monthly time steps.
After calibration the mean annual simulated water yield of the watershed was 16.24Mm3 and
also the most water yielding part of the watershed was identified. Those sub watersheds,
which have steep slope generated substantially runoff yield. The output of model showed that
Sub watersheds 6, 9 and 11 generate a maximum annual average water yield, this was
attributed due to the Sub watersheds 6, 9 and 11 were receiving high amount of precipitation.
In addition, sub watersheds 6, 9 and 11 were covered 82% of agricultural land with slope
>15% and 18% of it was covered by open shrub land. And the minimum water yield which has
less than 48mm was observed on sub watersheds 1, 2, 8 and 10, this was attributed due to
eastern part of the watershade were receiving small amount of precipitation, also the
topography of the land was flat to gentle flat slopes.
5.2. Conclusion
In order to overcome the food security and related problem, Sub-watershed based
development and management strategy is widely used and becoming important topic in our
country. However, lacks of systematic and proper investigation of hydrological process in the
watershed the general achievement were not satisfactory. As a result shown in this study, a
properly calibrated and validated SWAT model is promising model for strategic decisionmaking on water resource and watershed as well as sub-watershed related development
projects. However, a careful calibration and uncertainty analysis and proper application of
modeling results should be exercised.
As the study results showed that most of the water yield was generated from cultivated land,
hence above 90% of the watershed is covered by agricultural land. However, there has a
spatial difference of water yield and drainage ratio among sub watersheds was observed. For
instant, sub watershed 1, 2, 7, 8 and 10 were receiving the same amount of precipitation and
the same land use. However, sub watershed 7 contributed large amount of average annual
66
water yield or has maximum drainage ratio when compared to sub watershed 1, 2, 8 and 10
(Table 4.11) this was attributed due to the topography of sub watershed 7 was steep slope
(Figure 4.4). The hydrological investigation of this study has shown that the rates and amounts
of runoff which generated from Bululo watershade are dominantly influenced by
topographical conditions of the watershed.
5.3. Recommendations
Despite the data uncertainty, SWAT model efficiently analyzed and investigated the runoff
yield that generated from Bululo watershed. The study is limited with time and space,
therefore further investigation and studies must be carried out for diversion mechanism of
water to the field and for recommendation of the appropriate water harvesting structure.
However, most of the water generated from agricultural lands, therefore, the community can
retain the water on the soil surface using by on farm water harvesting technique and by
constructing household farm ponds. Most parts of sub watersheds 3, 4, 5, 6, 9 and 11 are
covered by agricultural lands with steep slope, therefore it is recommended to harvest the
water in the farms by constructing terraces and contour bunds, moreover, some upper portion
of sub watershed 6, 9 and 11 are covered by open shrub land, at this area the water can be
harvested by constructing trench and can use the water for fodder and fruit production.
However, the land use of sub watershed 1, 2, 8 and 10 is cultivated land which has flat to
gentle flat slope; therefore it is recommended that the community harvest or store the water in
the soil profile by constructing contour bund and tied ridge, in addition it can be harvested by
constructing individual farm ponds and use the water for homestead development.
67
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7. APPENDICES
77
7.1. Appendix Tables
7.1.1. Meteorological data
Appendix Table 1. Statistical Precipitation Data of Kulbi Station (1990-2010)
Item
Month
Jan. Feb. Mar. Apr
83.95. 124.3
6.47 79.75
3.87 3.51
0.18 0.29
PCP_MM
PCPSTD
PCPSKW
PR_W1
28.11
3.44
12.12
0.03
35.4
3.72
7.77
0.06
PR_W2
0.85
0.76 0.70
PCPD
7.00
6.33 12.33 15.1
0.67
May. Jun. Jul.
Aug.
Sep.
Oct. Nov Dec.
71.65
6.32
5.01
0.25
62.9
5.68
0
7.05
0.19
161.67
8.18
3.48
0.37
207.5
10.16
3
2.92
0.44
126.4
7.13
3.59
0.35
52.3
4.80
5.40
0.09
.30.4
2.92
6
6.24
0.04
31.79
3.31
9.78
0.05
0.64
0.68
0.65
0.66
0.76
0.83
0.88
0.87
13.17
12.0
16.43
18.33
18.6
12.2
9.67
10.2
7
7
where:
PCP_MM: average monthly precipitation (mm)
PCPSTD: standard deviation for daily precipitation in month (mm)
PCPSKW: skew coefficient
PR_W1: probability of a wet day following a dry day in month
PR_W2: probability of a wet day following a wet day in month
PCPD: average number of days of precipitation in month
78
Appendix Table 2. Kulbi total monthly precipitation (mm).
Year
Month
Jan.
Apr.
May.
Jun.
1990 57.66 148.25 178.88 164.76
39.54
67.27
1991
0.00
63.36 167.97 170.37
84.78
0.00 180.98 188.69
1992
0.00
61.80
95.26
55.14
1993 125.1 120.96
85.11 102.73
1994 17.49
57.06
33.03
1995
0.00
40.34
1996
9.82
10.01 194.69
1997 17.62
0.00 111.31
1998 101.6
Feb.
Mar.
Sep.
Oct.
Nov.
Dec.
76.18 260.46 299.20
44.71
9.21
3.20 1349.31
81.31
20.95
22.34
96.82 295.00 146.50
72.76
29.41
37.80
80.96 1064.46
98.66
64.81 152.51
83.26
88.98 140.67
18.64 1157.78
89.49
86.19
41.24 376.28 160.36 116.72
67.47 242.14
54.35
39.84 158.36 240.24
93.59 263.16
72.07 187.09 252.95
93.01
76.33
67.57 124.02 194.29 167.87
56.36
17.12
0.00
61.76
0.90
46.25
0.00 1192.29
87.29 180.98
55.76
7.51 1110.71
0.00
40.24
62.46
24.42
24.52
66.97
31.03 142.04 156.36 335.23 187.89 180.78
1.20
79.78
2000
0.00
0.00
35.94 123.52
2001
9.02
3.60 101.30
7.21
71.47
73.87
980.75
56.36
17.12
0.90
0.00
Total
5.11
23.12
0.00
2003
Aug.
96.50
1999
2002 105.1
Jul.
53.45 125.23 140.64 220.22
3.10 1042.41
16.42
932.63
833.03
4.20 1186.38
30.33 113.01 223.82 219.12
62.46
35.94
14.21
932.23
73.77 119.72
75.38 222.32 330.03
91.29
39.14
0.00
68.07
25.73
55.96 101.80 253.95
89.99
29.93
0.00 115.12
924.32
11.61 1077.18
1.70
12.41
40.24 221.72
19.22
67.67 177.98 165.47 148.55
1.80
0.50
66.27
923.52
2004 43.24
0.00
69.37 227.33
4.81
70.67 106.31 176.38 127.13
68.07
12.71
21.82
927.83
2005 18.22
6.61 118.72 121.52 115.42
67.37 139.44 159.06 104.50
0.30
49.85
0.00
901.00
96.79
36.05
0.10 1033.79
96.29
43.49
18.94 106.78
923.80
18.75
65.55
54.51
819.34
73.87 110.71
26.03
24.52
746.34
2006 10.61
15.42 100.53 242.04
86.59
18.77
2007 69.57
35.28
19.48
30.79 146.63 233.84
22.20 100.50
73.10 225.03 128.76
2008
3.50
0.00
0.20
41.74
71.67 106.91 121.36 217.05 118.11
2009
0.00
0.90
12.11
59.56
29.03
52.35 144.24 213.01
2010
0.00 136.34 162.56 169.97
96.20
43.14 146.55 191.29 190.51
34.84
71.65
62.90 161.67 207.53 126.42
52.36
mean 28.11
35.41
83.95 124.37
79
0.00 118.69 1290.08
30.46
31.79 1016.63
Appendix Table 3. Statistical Precipitation Data of Kersa Station (1995-2010)
Item
Month
PCP_MM
PCPSTD
PCPSKW
PR_W1
PR_W2
PCPD
Jan.
Feb.
Mar.
Apr.
21.4
4
4.51
5.21
2.48
16.0
40.03
0.84
6.19
51.6
1
5.02
109.64
10.46
9.08
0.16
0.73
11.81
14.9
00.04
0.85
7.63
4.57
0.10
0.75
10.1
May
.
66.8
7
6.77
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
51.2
2
4.72
145.4
9
9.28
106.5
6
6.66
5.12
0.12
0.76
11.0
4.22
0.32
0.64
15.38
3.48
0.30
0.67
14.75
67.32
6.60
6.78
0.10
0.88
16.69
25.4
2
2.62
6.48
0.11
0.63
7.38
95.96
7.21
5.18
0.21
0.67
12.88
6.67
0.04
0.87
9.88
18.09
2.57
7.63
0.03
0.91
10.88
Sep.
Oct.
Nov.
Dec.
Total
Appendix Table 4. Kersa total monthly precipitation (mm).
Month
Year
1995
Jan.
Feb
Mar.
Apr.
May.
Jun.
.
0.00 0.40
42.71 315.41 71.37 57.16
Jul.
6.91
Aug.
11.51
94.69 211.81 52.95 27.63 892.56
1996 98.70 2.50 32.03 188.69 170.1 25.53 90.29 221.22
34.43
0.00 35.44
0.00 898.90
1997 20.42 0.00 90.19
89.89 81.68 58.20 73.77 104.70
63.56 170.87 46.75
0.00 800.04
1998 32.13 0.00 31.43
23.72 101.4 10.41 231.2 165.47 137.94
1999
0.00 0.70 149.2
2000
6.01 0.00 20.12 135.44 125.7 29.63 53.45 191.89 131.63
2001
0.00 0.00 69.07 175.08 88.99 15.82 84.38 148.55
2002 57.06 0.00 61.56
2003
57.46 20.72
4.91 13.91 105.5 99.00 189.69 135.14 205.61
42.44
0.00 6.31 47.35 128.53
2004 45.35 0.00 38.94 137.34
2.60 10.61 60.76 125.13
0.00
86.49
2.60 92.19 149.2 105.41 145.65
0.00 16.92 63.84
68.07 133.03
0.00 811.91
5.51
9.51 918.72
84.48 41.14
2.60 822.12
0.00
0.00
0.00 581.88
78.18 21.32 52.45 598.60
1.40
0.20 63.46 742.34
62.56
0.00 10.81 576.86
2005
0.00 0.00 91.69
35.24 82.68 20.32 47.25 167.37 157.66 112.75 32.63
0.00 747.58
2006
9.41 4.20 21.02
81.18
0.00 664.25
0.00 59.43 176.4 116.40 112.73
2007 61.89 0.00 51.42 159.85 14.72 65.26 113.4 102.97 153.85
2008 12.11 0.00 15.64
0.00
99.80 79.08 149.3 70.74 205.71
41.94
0.00 55.66 45.35 37.64 122.32 144.32
58.88 24.58
0.00
25.19
0.00
2.83 726.21
0.00 45.84 745.39
2009
0.00 3.00
2010
0.00 66.2 63.36 136.74 179.5 57.85 176.9 281.38 131.83
0.00 59.52 68.35 1221.7
mean 21.44 5.21 51.61 109.64 66.87 51.22 95.96 145.49 106.56
67.32 25.42 18.09 764.83
80
7.98 65.93
5.96 488.15
Appendix Table 5. Statistical Precipitation Data of Girawa Station (1990-2010)
Item
Month
Jan.
PCP_MM
Feb.
Mar.
Apr.
May Jun.
Jul.
Aug.
Sep.
Oct. Nov.
.
44.21 29.55 77.22 142.28 91.87 67.91 103.14 149.76 110.74 67.18 47.94
Dec.
45.95
PCPSTD
3.20
2.83
6.48
10.92
6.35
3.87
5.48
7.43
5.98
3.48
2.34
2.49
PCPSKW
7.68
4.03
5.94
3.74
4.63
5.35
4.45
3.79
3.77
6.07
4.58
6.45
PR_W1
0.03
0.09
0.13
0.30
0.26
0.20
0.32
0.39
0.37
0.11
0.05
0.05
PR_W2
0.94
0.88
0.85
0.77
0.79
0.85
0.78
0.78
0.81
0.90
0.93
0.93
PCPD
16.91 14.61 16.09
18.26 18.43 18.70
19.35
20.96
21.22 19.22 16.70
17.22
TMPMX
21.09 21.72 22.13
21.25 21.36 20.48
20.05
20.27
20.53 20.91 20.71
20.80
1.84
2.25
2.05
1.70
1.64
1.85
1.99
10.50 10.82 10.50
10.19
10.17
10.40
9.69
8.52
8.28
1.26
1.08
0.91
1.45
1.78
2.14
TMPSTDMX
1.52
1.53
TMPMN
8.80 10.03 10.50
TMPSTDMN
2.15
1.43
2.23
1.26
1.89
1.40
1.45
1.09
0.97
where:
PCP_MM: average monthly precipitation (mm)
PCPSTD: standard deviation for daily precipitation in month (mm)
PCPSKW: skew coefficient
PR_W1: probability of a wet day following a dry day in month
PR_W2: probability of a wet day following a wet day in month
PCPD: average number of days of precipitation in month
TMPMAX: Average or mean daily maximum air temperature for month °C). TMPSTDMX:
Standard deviation for daily maximum air temperature in month (°C). TMPMN: Average or
mean daily minimum air temperature for month (°C). TMPSTDMN: Standard deviation for
daily minimum air temperature in month (°C).
81
Appendix Table 6. Girawa total monthly precipitation (mm).
Year
Month
Jan.
Feb.
Mar.
Apr.
Aug.
Sep.
Oct.
Nov.
Dec.
Total
1990 12.01 68.17 62.26 183.88 32.63 47.05 39.54 193.89
61.86
7.01
25.43
0.00
733.73
1991 31.03 35.14 218.9 129.13 123.22 42.84 119.72 176.18
76.10
66.34
34.74
17.57 1070.92
1992 0.00 14.13 83.67 130.59 71.52 106.7 131.22 158.86
87.28
35.80
23.36 113.81 956.99
1993 31.78 102.0 131.3 155.16 53.62 70.05 136.35 231.16
44.16
75.07 104.50 110.53 1245.71
1994 53.95 34.96 59.79 221.86 121.81 23.63 70.42
146.75
92.39
59.46
44.52 1025.98
77.39 46.34 151.57 121.98 133.51
62.46
91.24
84.07
86.88
16.50 1109.55
1995 26.00 16.14 78.81 59.33
May.
Jun.
Jul.
96.43
948.82
1996 69.46 61.78 85.22 231.62 190.45 19.01 65.61 104.44
83.73
94.86
1997 58.11 22.13 33.20 80.08 138.16 64.25 70.70
99.33
90.18 115.54
0.00
855.10
75.01
82.51
66.02
902.66
31.03 142.0 156.36 335.23 187.89 180.78
1.20
4.20
1186.38
23.12
54.15
12.51
812.23
80.78
21.22
36.04
33.63
948.16
2002 111.9 2.00 72.07 126.63 90.09 100.7 166.77 166.67 142.84
78.98
0.00
38.54 1097.19
2003 34.53 0.00 16.72 163.56 27.53 73.07 94.29 205.91
89.19
19.22
2.00
46.55
772.57
2004 21.42 0.00 24.32 226.33
134.53
54.45
36.44
0.00
791.19
2005 22.97 52.29 44.40 180.17 139.38 20.56 76.07 154.30 134.49
43.16
24.96
96.10
988.83
2006 68.12 5.84 82.15 174.23 133.66 82.09 93.73 56.33 102.43
2007 129.2 23.95 150.1 204.75 116.27 70.61 112.92 109.93 99.63
98.57 133.96 50.91 1082.00
36.07 0.00
0.00 1053.46
2008 163.5 82.71 73.38 228.93 66.90 76.36 50.81 177.89
79.51
139.39 48.57 115.57 1303.49
2009 94.69 52.84 60.67 25.17
122.60
63.67
25.59
33.84
796.32
2010 0.00 10.35 100.2 114.21 89.82 81.13 96.08 113.40 94.32
Mean 44.22 29.57 77.22 142.28 91.88 67.91 103.14 149.76 110.75
53.17
67.19
20.21
47.94
80.29
45.96
853.14
977.83
83.43
1998 0.00 14.04 41.67 62.07 105.92 42.83 138.63 106.32 167.64
1999 0.00
0.90 79.78 66.97
2000 0.00
0.00 23.02 163.26 102.80 85.01 58.56 132.63 157.16
2001 0.00 21.54 100.1 59.86 171.77 78.58 95.60 249.05
9.11
71.67 127.33 85.59
36.48 81.64 113.72 85.42
82
7.1.2. Soil data parameters
Appendix Table 7. Soil Parameter Value for Chromic Luvisols
Soil Name: Chromic Luvisols
Soil Hydrologic Group:
Maximum rooting depth(mm)
Texture 1
Layers
Depth (mm)
Bulk Density Moist [g/cc]
Ave. AW Incl. Rock Frag
Ksat. (est.) [mm/hr]
Organic Carbon [weight %]
Clay [weight %]
Silt [weight %]
Sand [weight %]
Rock Fragments [vol. %]
Soil Albedo (Moist)
Erodibilty factor K
Salinity (EC)
Porosity fraction from which anions are excluded:
Crack volume potential of soil
D
1800.00
C
Layer-1
300.00
1.31
0.09
0.28
0.40
59.00
15.00
26.00
0.00
0.25
0.12
0.30
0.50
0.100
Layer-2
700.00
1.35
0.10
0.35
0.20
54.00
15.00
31.00
0.00
0.25
0.13
0.80
-
Layer-3
1100.00
1.40
0.16
23.61
0.20
18.00
55.00
27.00
0.00
0.25
0.20
0.30
-
Appendix Table 8. Soil Parameter Value for Rendzic Leptosols
Soil Name: Rendzic Leptosols
Soil Hydrologic Group:
D
Maximum rooting depth(mm)
300
Texture 1
C
Layers
Layer-1
Depth (mm)
300
Bulk Density Moist [g/cc]
1.19
Ave. AW Incl. Rock Frag
0.09
Ksat. (est.) [mm/hr]
6.08
Organic Carbon [weight %]
3.5
Clay [weight %]
51
Silt [weight %]
38
Sand [weight %]
11
Rock Fragments [vol. %]
0
Soil Albedo (Moist)
0.04
Erodibilty factor K
0.15
Salinity (EC)
0.1
Porosity fraction from which anions are excluded:
0.50
Crack volume potential of soil
0.10
83
Layer-4
1800.00
1.30
0.10
0.83
0.10
55.00
23.00
22.00
0.00
0.25
0.14
0.30
-
Appendix Table 9. Soil Parameter Value for Humic Nitosols
Soil Name: Humic Nitosols
Soil Hydrologic Group:
Maximum rooting depth(mm)
Texture 1
Layers
Depth (mm)
Bulk Density Moist [g/cc]
Ave. AW Incl. Rock Frag
Ksat. (est.) [mm/hr]
Organic Carbon [weight %]
Clay [weight %]
Silt [weight %]
Sand [weight %]
Rock Fragments [vol. %]
Soil Albedo (Moist)
Erodibilty factor K
Salinity (EC)
Porosity fraction from which anions are excluded:
D
2000
SC
Layer-1
200
1.23
0.09
1.65
2.4
61
26
13
0
0.09
0.12
0.1
0.50
Layer-2
700
1.13
0.05
1.13
1.4
77
15
8
0
0.17
0.12
0.2
-
Layer-3
2000
1.24
0.11
3.84
0.6
55
36
9
0
0.25
0.2
0.2
-
Crack volume potential of soil
0.10
-
-
Appendix Table 10. Soil Parameter Value for Eutric Cambisols
Soil Name: Eutric Cambisols
Soil Hydrologic Group:
Maximum rooting depth(mm)
Texture 1
Layers
Depth (mm)
Bulk Density Moist [g/cc]
Ave. AW Incl. Rock Frag
Ksat. (est.) [mm/hr]
Organic Carbon [weight %]
Clay [weight %]
Silt [weight %]
Sand [weight %]
Rock Fragments [vol. %]
Soil Albedo (Moist)
Erodibilty factor K
Salinity (EC)
Porosity fraction from which anions are excluded:
Crack volume potential of soil
D
1800.00
SC
Layer-1
300
1.38
0.11
1.33
1.5
45
22
33
0
0.16
0.12
0.2
0.50
0.10
84
Layer-2
800
1.48
0.12
1.88
0.9
46
18
36
0
0.25
0.13
0.7
-
Layer-3
1200
1.49
0.11
1.69
0.8
38
16
46
0
0.25
0.14
1.9
-
Layer-4
1800
1.29
0.11
0.88
1.5
55
24
21
0
0.16
0.12
-
7.1.3. Hydrological data and SWAT out put
Appendix Table 11. Hydrological data for Dawe gauging station (m3/s)
Year
Month
Jan.
Feb.
Mar.
Apr.
May.
1992
0.049
0.054
0.121
0.605
1993
0.129
0.088
0.120
1994
0.104
0.113
1995
0.021
1996
Jun.
Jul.
Aug.
0.982 0.067
0.437
0.870
0.834 0.299
0.063
0.626
0.055
0.021
0.062
0.055
1997
0.032
1998
Sep.
Oct.
Nov.
Dec.
Average
1.114 0.684
0.164
0.047
0.140
0.372
0.463
0.729 0.338
0.205
0.162
0.078
0.360
0.796 0.101
1.327
1.752 0.367
0.297
0.101
0.047
0.475
0.205
0.808 0.484
0.927
1.900 0.330
0.177
0.125
0.101
0.429
0.963
1.057
3.287 0.598
0.760
2.380 1.211
0.558
0.356
0.078
0.947
0.024
0.071
0.755
0.882 0.247
0.579
1.581 0.380
0.127
0.175
0.101
0.413
0.083
0.090
0.073
0.488
0.478 0.125
0.475
1.356 0.310
0.161
0.051
0.042
0.311
1999
0.066
0.070
0.361
0.605
0.285 0.907
1.359
2.727 2.707
1.551
0.486
0.145
0.939
2000
0.106
0.176
0.056
1.377
0.577 0.153
0.612
0.704 2.257
0.828
0.197
0.072
0.593
2001
0.083
0.087
0.093
2.057
1.456 0.386
0.979
2.690 0.968
0.148
0.147
0.142
0.769
2002
0.345
0.045
0.049
0.462
0.225 0.128
0.499
1.204 0.631
0.234
0.010
0.212
0.337
2003
0.023
0.020
0.017
2.091
0.168 0.297
0.429
1.474 0.534
0.017
0.042
0.012
0.427
men
std
0.092
0.087
0.073
0.042
0.167
0.266
0.933
0.611
0.898 0.316
0.836 0.248
0.737
0.338
1.634 0.893
0.687 0.798
0.372
0.431
0.158
0.139
0.098
0.055
0.531
0.229
Appendix Table 12. SWAT Monthly Stream Flow out Put for the Calibration and
Validation Period (m3/s)
Year
Month
Jan.
Feb.
Mar.
Apr.
May.
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec. Average
1992 0.010 0.019 0.044 0.265 0.898 0.059 0.184 1.151 0.185 0.172 0.023 0.055
0.255
1993 0.129 0.067 0.104 1.234 1.339 0.315 0.444 0.448 0.413 0.321 0.141 0.079
0.419
1994 0.246 0.122 0.015 0.332 0.667 0.041 1.475 2.947 0.350 0.280 0.110 0.071
0.555
1995 0.022 0.014 0.018 0.239 0.556 0.530 1.054 3.108 0.268 0.102 0.141 0.100
0.513
1996 0.065 0.041 0.628 1.081 3.457 0.881 0.966 2.542 0.500 0.457 0.160 0.031
0.901
1997 0.034 0.011 0.101 0.615 0.685 0.381 0.737 2.217 0.599 0.157 0.162 0.101
0.483
1998 0.126 0.055 0.016 0.024 0.859 0.026 0.735 1.494 0.543 0.342 0.088 0.034
0.362
1999 0.051 0.023 0.692 0.257 0.046 0.952 0.246 3.243 3.483 3.876 0.274 0.041
1.099
2000 0.032 0.014 0.023 1.055 0.929 0.030 0.046 0.842 2.018 1.553 0.232 0.027
0.567
2001 0.014 0.013 0.035 2.556 1.182 0.046 0.998 4.887 0.981 0.085 0.020 0.022
0.903
2002 0.605 0.018 0.031 0.136 0.053 0.083 0.066 1.493 0.810 0.268 0.015 0.735
0.359
2003 0.107 0.014 0.013 2.329 0.232 0.064 0.546 1.010 0.842 0.152 0.015 0.454
0.482
mean 0.120 0.034 0.143 0.843 0.909 0.284 0.625 2.115 0.916 0.647 0.115 0.146
0.575
std
0.167 0.033 0.244 0.849 0.901 0.339 0.448 1.284 0.942 1.091 0.087 0.220
85
0.257
Appendix Table 13. Probability of rejecting homogeneity of annual rainfall (Girawa station)
Statistics
90%
Range of Cumulative Deviation
Maximum Cumulative Deviation
NO
NO
Rejected?
95%
NO
NO
99%
NO
NO
Appendix Table 14. Probability of rejecting homogeneity of annual rainfall (Kersa station)
Statistics
90%
Range of Cumulative Deviation
Maximum Cumulative Deviation
NO
NO
Rejected?
95%
NO
NO
99%
NO
NO
7.2. Appendix Figure.
Appendix Figure 1. Rescaled cumulative deviation of annual rainfall at Girawa station
86
Appendix Figure 2. Probability plot for annual rainfall (1990-2010) at Girawa station
Appendix Figure 3. Rescaled cumulative deviation of annual rainfall at Kersa station
87
Appendix Figure 4. Probability plot of annual rainfall at Kersa station
88