Abstract—The runoff generation and sediment outflow from a
medium sized basin of Vamsadhara river in India is investigated
using the Soil and Water Assessment Tool (SWAT). Sensitivity
analysis is performed on twenty-seven parameters of the SWAT
model which revealed that initial SCS curve number for moisture
condition II (CN2) is the most sensitive parameter for both flow
and sediment while saturated hydraulic conductivity (SOL_K) and
average slope length (SLSUBBSN) are the next most sensitive
model parameters to flow. Similarly, USLE support practices factor
(USLE_P), and available water capacity of soil layer (SOL_AWC)
are the next most sensitive model parameters to sediment.
Available data on runoff and sediment outflow is split into two
groups for calibrations and validation of the model parameters.
Calibration and validation results for stream flow are good (R2 =
0.73, NSE = 0.73 for calibration period and R2 = 0.73, NSE = 0.72
for validation period). The calibration and validation results
obtained for sediment yield are also good on daily basis (R2 = 0.56,
NSE = 0.55 for calibration period and R2 = 0.69, NSE = 0.69 for
validation period). However on monthly time scale, the results
could be categorized under very good category for stream flow (R2
= 0.90, NSE = 0.89 for calibration period and R2 = 0.91, NSE =
0.91 for validation period) as well as for sediment (R2 = 0.82, NSE
= 0.81 for calibration period and R2 = 0.78, NSE = 0.77 for
validation period). Overall the study revealed that the SWAT
model could be employed for simulation of runoff and sediment
yield behavior of Vamsadhara river basin.
Keywords—hydrologic modeling, rainfall, runoff, sediment
yield.
I. INTRODUCTION
OIL and water are the two major natural resources,
which are responsible for the existence of life on earth
by providing the life supporting system for all living beings.
They also significantly influence the hydro-geological and
biological systems of the Earth. Information on natural
condition and form of soil and water resources is essential
for the socio-economic development of any area. This
information is collected by carrying out water resources
assessments of the areas of interest. Water resources
assessment involves developing a comprehensive
understanding of water inflows, storage, outflows, sediment
yield and their inter-relationship over time. Information on
water resources assessment could be utilized to estimate the
sustainable environmental flows and the measures that can
be taken to sustain these flows and prevent erosion of soil.
Water resources management is more profound and complex
in developing countries as compared to developed countries,
Manoj Kumar. Jain is with the Department of Hydrology, Indian
Institute of Technology, Roorkee, Uttarakhand, India. Phone: +91-1332-
285845; fax:+91-1332-285236; e-mail: [email protected]).
Survey Daman. Sharma, was with the Department of Hydrology, Indian
Institute of Technology, Roorkee, Uttarakhand, India.
as the lack of reliable long-term data in developing countries
makes rigorous and accurate water resources assessments
challenging.
The developments in computing technology and recent
advances in the availability of digital datasets and the use of
geographic information systems (GIS) for water resources
management have revolutionized the study of hydrologic
systems. Numerous hydrologic models ranging from
empirical to physically based distributed parameters have
been developed to estimate runoff and sediment yield during
the past three decades. The Soil and Water Assessment Tool
(SWAT) developed by the United States Department of
Agriculture - Agricultural Research Services (USDA - ARS)
[1] is one such model that integrates the spatial analysis
capabilities of GIS with the temporal analysis simulation
abilities of hydrologic models. SWAT is a small watershed
to river basin-scale model to simulate the quality and
quantity of surface and ground water and predict the
environmental impact of land use, land management
practices, and climate change. SWAT is widely used in
assessing soil erosion prevention and control, non-point
source pollution control and regional management in
watersheds. SWAT uses the basic principles of hydrologic
cycle for simulating the behavior of a watershed. SWAT
divides a basin into sub-basins based on unique
combinations of topography, soil type and land use which
helps in preserving the spatially distributed parameters of the
entire watershed and the homogenous characteristics of the
basin. SWAT has been extensively used for a variety of
purposes and its applications have expanded worldwide in
the last decade. About 1600 peer-reviewed journal articles
have been published in the SWAT literature database that
document various uses of SWAT. SWAT has been widely
applied to evaluate the hydrologic and water quality impacts
of land management and agricultural practices [2], [3], [4].
The objective of this study is to model the stream flow
and sediment yield behavior using SWAT model in a mid-
size basin of India. This include setup, calibration and
validation of SWAT model to simulate stream flow and
sediment yield in Vamsadhara basin, India and to determine
the most sensitive model parameters affecting water and
sediment yield by performing sensitivity analysis of
parameters.
II. THE STUDY AREA
The Vamsadhara river basin is situated between the
Mahanadi and Godavari river basins of south India. The
total catchment area of Vamsadhara river basin, upstream to
the point where it joins the Bay of Bengal, is 10,830 km2 and
Hydrological Modeling of Vamsadhara River
Basin, India using SWAT
Manoj. Jain, and Survey Daman. Sharma
S
International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)
82
lies within the geographical co-ordinates of 18015' to 19
055'
north latitudes and 83020' to 84
020' east longitudes.
However, the catchment upstream to the last gauging and
discharge measurement station on the river at Kashinagar,
comprises of 7,820 km2
is used for model setup. The basin is
influenced by the south-west monsoon during the months of
June to October, and by occasional cyclones due to the
formation of depression in the Bay of Bengal. The
temperature variation in the plains of the basin is between
100C to 43
0C. The mean annual rainfall of the three districts
Phulabani, Koraput and Ganjam in which the basin lies is
1280 mm, 1700 mm and 1500 mm respectively. The soil of
the area is classified as mixed red, black soils, red sandy
soils, yellow soils, coastal sands and forest soils. Map of the
study area is shown in Fig.1.
III. THE SWAT MODEL
SWAT (Soil and Water Assessment Tool) developed by
USDA-ARS is a direct outgrowth of the SWRRB model [5],
[6], which was designed to simulate management impacts on
water and sediment movement for un-gauged rural basins.
SWAT is a basin scale, continuous time, conceptual and
long term simulation model that operates on daily time step.
SWAT contains several hydrologic components (surface
runoff, ET, recharge, stream flow, snow cover and snow
melt, interception storage, infiltration, pond and reservoir
water balance, and shallow and deep aquifers) that have
been developed and validated at smaller scales within the
EPIC, GLEAMS and SWRRB models. Characteristics of
this flow model include non-empirical recharge estimates,
accounting of percolation, and applicability to basin-wide
management assessments with a multi-component basin
water budget [12].
Fig. 1. Location map of Vamsadhara river basin in India
SWAT model has eight major modules viz. hydrology,
climate, sedimentation, agricultural management, water
quality, land cover, water bodies and main channel
processes. The runoff simulation on daily basis can be
obtained by using a modified curve number technique [7]
and on hourly basis by Green and Ampt infiltration equation
[8]. The model offers three options for estimating potential
ET viz. Hargreaves, Priestley-Taylor, and Penman-Monteith
[2]. The surface runoff hydrologic component uses
Manning's formula to determine the watershed time of
concentration and considers both overland and channel flow.
A full description of SWAT can be found in the SWAT
theoretical documentation [1], which is available online on
SWAT website.
IV. INPUT DATA
A. Digital Elevation Model (DEM)
The DEM is the raster data consisting of sampled array of
pixels containing elevation values representing ground
positions at regularly spaced intervals. It is used for
watershed and stream network delineation and the
computation of several geomorphological parameters of the
catchment including slope for HRUs. The Shuttle Radar
Topography Mission (SRTM) obtained elevation data on a
near-global scale to generate the most complete high-
resolution digital topographic database of Earth. For the
present analysis projected DEM to
WGS_1984_UTM_Zone_44N coordinate system is used in
ArcSWAT Watershed Delineator [11] for watershed
delineation.
B. Landuse /Land Cover
The land use / land cover data of the study area is required
for HRU definition and subsequently for assigning the Curve
Numbers (CN) to the land areas for runoff computations and
hydrological analysis. The land use of an area is one of the
most important factors that affect surface erosion, runoff,
and evapotranspiration in a watershed during simulation.
Land use/Land cover classified data on a scale of 1:50,000
published under Bhuvan Thematic Services of National
Remote Sensing Center (NRSC), ISRO is used for this
study.
C. Soil Map
The soil map of the study area has been obtained from the
National Bureau of Soil Science & Land Use Planning
(NBSS&LUP). The soil is classified into different categories
on the basis of USDA taxonomy viz., Typic Rhodustalfs,
Aeric Endoaquepts, Vertic Endoaquepts, Ultic Paleustalfs,
Rhodic Paleustalfs, Typic Haplustalfs, Typic Haplustepts,
Typic Endoaquepts, Typic Argiustolls, Typic Paleustalfs,
Typic Ustipsamments and Ultic Haplustalfs.
D. Hydro-meteorological Data
The principal datasets within this category are
hydrological data, sediment data and weather data and
respective spatial information describing the location of
stations. The hydro-meteorological data of the area obtained
from India Meteorological Department (IMD), Central
Water Commission (CWC), Godavari Mahanadi Circle
Division, South-Eastern Region, Bhubaneswar, Odissa is
used.
V. MODEL SETUP
Watershed delineation tool is used to delineate sub-
watersheds based on an automatic procedure using the DEM
of the area. The basin has to be delineated into an adequate
number of hydrologic response units which will take account
International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)
83
of changes in climate, land use and soil types. Accordingly,
the basin is divided into 27 sub-basins. The Hydrological
analysis in SWAT is carried out at Hydrologic Response
Unit (HRU) level, on daily time step. HRUs are lumped land
areas within each subbasin with unique land cover, slope,
soil and management combinations. Runoff is calculated for
each HRU separately and routed to obtain the total runoff.
The landuse/landcover map, soil map and slope maps of the
study area have been overlaid to demarket HRUs. Area
below the given respective threshold values are ignored
while delineating the HRUs. In the present study, threshold
values of 1% for Land use class, 2% for Soil class and 2%
for Slope class are considered, resulting in formation of 793
HRUs in the study area spread over 27 subbasins.
Location table of Weather Data and Daily precipitation
data files, are link with the required files already created for
the purpose. Data on Solar Radiation, Maximum and
Minimum Temperatures, Wind Speed and Relative
Humidity are generated by model itself using weather
generator tool due to non-availability of observed values.
After loading all the input data and generating the required
database files, SWAT model was initially run using default
parameter values. Available discharge data was divided into
two parts; period from 1984 to 1989 was used for calibration
purpose whereas data from 1990 to 1995 was used for
validation of the calibrated model.
VI. PERFORMANCE EVALUATION
The performance of SWAT model is analyzed based on
graphical representation of observed and simulated total
flow and observed and simulated sediment yield as well as
on the basis of various statistical parameters such as Nash
Sutcliff Efficiency (NSE) [9], Percent bias (Pbias), and
RMSE-observations Standard deviation Ratio (RSR).
The NSE determines the relative magnitude of the
residual variance compared to the measured data variance.
Where Y
obs and Y
sim are the observed and simulated values
in respective time steps i, Ymean
is the mean of observed data
during the duration and n is the number of observations.
The value of NSE ranges between -∞ and 1, with NSE = 1
being optimum value. Values between 0.6 and 1.0 are
viewed as acceptable levels of performance whereas
negative values or zero indicate that the mean observed
value is a better predictor than the simulated value indicating
unacceptable performance.
Pbias or percentage of deviation measures the average
tendency of the simulated values to be larger or smaller than
the observed values.
The optimal value of Pbias is 0 with low magnitude values
indicating accurate model simulation. Positive values
indicate model under estimation bias and negative values
indicate model over estimation bias.
RMSE-observations Standard deviation Ratio (RSR)
standardizes the Root Mean square error using observations
standard deviation. RSR is calculated as the ratio of RMSE
and standard deviation of measured data as shown below.
RSR varies from the optimal value of 0 to large positive
value. 0 indicates zero residual variation and therefore
perfect model. General performance rating for acceptable
statistics is given in Table I.
TABLE I
GENERAL PERFORMANCE RATINGS FOR RECOMMENDED STATISTICS [10]
Performance
rating RSR NSE
Pbias (%)
Stream
flow Sediment
Very good 0.00 to
0.50
0.75 to
1.00 < ± 10 < ± 15
Good 0.50 to
0.60
0.65 to
0.75
± 10 to ±
15 ± 15 to ± 30
Satisfactory 0.60 to
0.70
0.50 to
0.65
± 15 to ±
25 ± 30 to ± 55
Unsatisfactory > 0.70 < 0.50 > ± 25 > ± 55
VII. SENSITIVITY ANALYSIS
SWAT model is a comprehensive conceptual model and
relies on several parameters varying widely in space and
time while transforming input into output. Calibration
process becomes complex and computationally extensive
when the number of parameters in a model is substantial.
With the help of sensitivity analysis, we can reduce the
number of parameters by not considering non-sensitive
parameters for calibration, which in turn can give results
relatively in short time. Sensitivity analysis is performed
using the SUFI-2 algorithm of SWAT-CUP. The parameter
producing the highest average percentage change in the
objective function value is ranked as most sensitive.
VIII. CALIBRATION AND VALIDATION
Model calibration is the process of estimating model
parameters by comparing model predictions for a given set
of input model parameters with observed data. In this study,
the model is calibrated for stream flow as well as sediment
yield (at Kashinagar site i.e. sub-basin 22) on daily as well
as monthly basis. Auto calibration procedure is followed
using SUFI-2 algorithm of SWAT-CUP program. Twenty-
seven SWAT parameters influencing stream flow and
sediment yield are considered for calibration. Calibration of
flow and sediment is carried out using 3000 iterations.
IX. EVALUATION OF MODEL PERFORMANCE
The goodness-of-fit of the calibrated model during
calibration and validation is evaluated using visual and
statistical indicators described previously. The visual
comparison provides information about overall qualitative
visual match such as matching of peaks, trends of recession
and general agreement in hydrograph characteristics. In this
study, calibration and validation both for stream flow and
International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)
84
sediment yield at daily time step and monthly time step is
carried out. Hence, the performance of the model under both
the conditions is evaluated.
A. Statistical Evaluation
The performance of SWAT model is evaluated
statistically both for runoff and sediment yield based on
various statistical parameters such as NSE, Percent bias
(Pbias), and RMSE-observations Standard deviation Ratio
(RSR).
The NSE is perhaps one of the most used objective
function for evaluating model performance. NSE expresses
the fraction of the measured stream flow or sediment yield
variance that is reproduced by the model. As per NSE
criteria simulation results are considered very good for
values of NSE above 0.75, good for NSE values between
0.65 to 0.75 and satisfactory for NSE values between 0.50
and 0.65 (Table I). The NSE values less than 0.50 are
considered as unsatisfactory in the present study. The
computed values of NSE on daily and monthly basis are
given in Table III and IV respectively. The values of NSE
on daily basis for calibration and validation period are 0.73
and 0.72 respectively for stream flow indicating good model
performance. Similarly, the values for sediment yield are
0.55 and 0.69 for calibration and validation period
respectively. The performance rating of the model has been
found to be even better for monthly time step. For monthly
simulation, the NSE values obtained for stream flow are 0.89
and 0.91 for calibration and validation period respectively.
For sediment yield simulation, the NSE values obtained are
0.81 and 0.77 respectively indicating very good model
performance.
The second evaluation criteria used is the percent bias
(Pbias), which is a measure of the average tendency of the
simulated values to be larger or smaller than the observed
values. The optimal value of Pbias is zero; a positive value
indicates model bias towards underestimation, whereas a
negative value of Pbias indicates bias towards
overestimation. The model performance is “very good” if the
absolute percent error is less than 10% for stream flow and
less than 15% for sediment, “good” if the error is between
10 and 15% for stream flow and between 15 to 30% for
sediment and “satisfactory” if the error is between 15 and
25% for stream flow and between 30 and 55% for sediment.
This standard was adopted for the Pbias evaluation criteria
in this study, with Pbias values >=25% for stream flow and
>=55% for sediment unsatisfactory.
Computed values of Pbias for daily and monthly time step
are given in Table II and III respectively. The value of Pbias
obtained for daily simulation during calibration is 5.4 for
stream flow and 24.6 for sediment indicating good model
performance for stream flow. Positive value of Pbias for
sediment yield indicates that the model underestimated
sediment yield during calibration period. For the validation
period, the value of Pbias is found to be 18.9 for stream flow
and 23.9 for sediment indicating good model performance.
However, positive value of Pbias for sediment indicates that
the model underestimated sediment yield for the validation
period too. Therefore, positive values of Pbias for sediment
during calibration and validation periods indicate the model
biasness towards underestimation for sediment yield. Pbias
for monthly simulation is found to be 10.1 and -4.7 for
calibration and validation period respectively for stream
flow, which can be classified as good. Similarly, Pbias for
sediment yield is found to be 13.0 and 3.8 respectively.
TABLE II
STATISTICAL EVALUATION OF MODEL PERFORMANCE (DAILY)
TABLE III
STATISTICAL EVALUATION OF MODEL PERFORMANCE (MONTHLY)
B. Graphical Evaluation
The graphical evaluation provides information about
overall qualitative visual match such as matching of peaks,
trends of recession and general agreement in hydrograph
characteristics. To evaluate model performance based on
graphical comparison, plots between observed and simulated
values of discharge and sediment yield are prepared and two
such plots are given as Figs. 2 and 3 for illustration. Visual
inspection of these figures indicates close agreement
between observed and simulated runoff values. However, the
model seems to underestimate sediment yield on daily basis
for calibration as well as validation periods. In addition,
daily discharge is underestimated for validation period
REFERENCES
[1] S.L. Neitsch, J. G. Arnold, J. R. Kiniry, and J. R. Williams, Soil and
Water Assessment Tool – Theoretical Documentation, Version 2009.
Texas, USA, 2009.
[2] J.G. Arnold, and N. Fohrer. “SWAT2000: current capabilities and
research opportunities in applied watershed modeling,” Hydrological
Processes, vol. 19, no. 3, pp. 563-572, 2005.
[3] D.K. Borah, and M. Bera, “Watershed-scale hydrologic and
nonpoint-source pollution models:Review of applications,” Trans.
ASAE, vol. 47, no. 3, pp. 789-803, 2004.
[4] USDA-ARS (U.S. Department of Agriculture, Agricultural Research
Service). The automated geospatial watershed assessment tool
(AGWA). Available at: www.tucson.ars.ag.gov/agwa/. Accessed 23
August 2006.
[5] J.R. Williams, A.D. Nicks, and J.G. Arnold, “Simulator for water
resources in rural basins,” J. Hydrol. Engr., vol. 111, no. 6, pp. 970-
986, 1985.
[6] J.G. Arnold, J.R. Williams, A.D. Nicks, and N.B. Sammons,
SWRRB: A basin scale simulation model for soil and water resources
management, College Station, Texas A&M University Press, pp-125,
1990.
[7] USDA-NRCS. (U.S. Department of Agriculture, Agricultural
Research Service). Chapter 10: Estimation of direct runoff from
storm rainfall: Hydraulics and hydrology – technical references,
NRCS national engineering handbook, part 630 hydrology.
Calibration Period
NSE P-Bias RSR P-Factor R-Factor
Runoff 0.73 5.4 0.52 0.44 0.92
Sediment 0.55 24.6 0.67 0.33 0.77
Validation Period
NSE P-Bias RSR P-Factor R-Factor
Runoff 0.72 18.9 0.53 0.50 0.00
Sediment 0.69 23.9 0.56 0.51 0.00
Calibration Period
NSE P-Bias RSR P-Factor R-Factor
Runoff 0.89 10.1 0.33 0.49 1.14
Sediment 0.81 13.0 0.43 0.99 2.91
Validation Period
NSE P-Bias RSR P-Factor R-Factor
Runoff 0.91 -4.7 0.30 0.29 0.00
Sediment 0.77 3.8 0.48 0.60 0.00
International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)
85
Available at: www.wcc.nrcs.usda.gov/hydro/hydro-techref-neh-
630.html. Accessed 14 February 2007, 2004.
[8] W.H. Green, and G.A. Ampt, “Studies on soil physics: Part 1. The
flow of air and water through soils,” Journal of Agricultural
Sciences, vol. 4, pp. 11-24, 1911.
[9] J.E. Nash, and J.V. Sutcliffe, “River flow forecasting through
conceptual models, Part I: A discussion of principles,”. J. Hydrol.,
vol. 10, no. 3, pp. 282-290, 1970.
[10] D.N. Moriasi, J.G. Arnold, M.W. Van Liew, R.L. Binger, R.D.
Harmel, and T. Veith. “Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations,” Trans. ASABE,
2006.
[11] SWAT, Soil and Water Assessment Tool: ArcSWAT, College Station,
Texas: Texas A&M University. Available at:
www.brc.tamus.edu/swat/arcswat.html. Accessed 20 February 2007.
[12] P.W. Gassman, M.R. Reyes, C.H. Green, and J.G. Arnold, “The Soil
and Water Assessment Tool: historical development, applications,
and future research directions,” Transactions of the ASABE, vol. 50,
no. 4, pp. 1211-1250, 2007.
Fig. 2. Daily observed and simulated discharge and sediment yield during validation period.
010020030040050060070080090010000
100200300400500600700800900
Rai
nfa
ll (m
m)
Mo
nth
ly D
isch
arge
(m3/s
)
Rainfall Observed Discharge
Fig. 3. Monthly observed and simulated discharge and sediment yield during validation period.
International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)
86