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: jain.mkj@gmail.com).

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

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[3] D.K. Borah, and M. Bera, “Watershed-scale hydrologic and

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August 2006.

[5] J.R. Williams, A.D. Nicks, and J.G. Arnold, “Simulator for water

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986, 1985.

[6] J.G. Arnold, J.R. Williams, A.D. Nicks, and N.B. Sammons,

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[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)

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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,

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[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.

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