International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)

Hydrological Modeling of Vamsadhara River Basin, using SWAT

Manoj. Jain, and Survey Daman. Sharma

 as the lack of reliable long-term data in developing countries Abstract—The runoff generation and sediment outflow from a makes rigorous and accurate water resources assessments medium sized basin of Vamsadhara river in India is investigated challenging. using the Soil and Water Assessment Tool (SWAT). Sensitivity The developments in computing technology and recent analysis is performed on twenty-seven parameters of the SWAT advances in the availability of digital datasets and the use of model which revealed that initial SCS curve number for moisture geographic information systems (GIS) for water resources condition II (CN2) is the most sensitive parameter for both flow and sediment while saturated hydraulic conductivity (SOL_K) and management have revolutionized the study of hydrologic average slope length (SLSUBBSN) are the next most sensitive systems. Numerous hydrologic models ranging from model parameters to flow. Similarly, USLE support practices factor empirical to physically based distributed parameters have (USLE_P), and available water capacity of soil layer (SOL_AWC) been developed to estimate runoff and sediment yield during are the next most sensitive model parameters to sediment. the past three decades. The Soil and Water Assessment Tool Available data on runoff and sediment outflow is split into two (SWAT) developed by the United States Department of groups for calibrations and validation of the model parameters. Calibration and validation results for stream flow are good (R2 = Agriculture - Agricultural Research Services (USDA - ARS) 0.73, NSE = 0.73 for calibration period and R2 = 0.73, NSE = 0.72 [1] is one such model that integrates the spatial analysis for validation period). The calibration and validation results capabilities of GIS with the temporal analysis simulation obtained for sediment yield are also good on daily basis (R2 = 0.56, abilities of hydrologic models. SWAT is a small watershed 2 NSE = 0.55 for calibration period and R = 0.69, NSE = 0.69 for to river basin-scale model to simulate the quality and validation period). However on monthly time scale, the results quantity of surface and ground water and predict the could be categorized under very good category for stream flow (R2 = 0.90, NSE = 0.89 for calibration period and R2 = 0.91, NSE = environmental impact of land use, land management 0.91 for validation period) as well as for sediment (R2 = 0.82, NSE practices, and climate change. SWAT is widely used in = 0.81 for calibration period and R2 = 0.78, NSE = 0.77 for assessing soil erosion prevention and control, non-point validation period). Overall the study revealed that the SWAT source pollution control and regional management in model could be employed for simulation of runoff and sediment watersheds. SWAT uses the basic principles of hydrologic yield behavior of Vamsadhara river basin. cycle for simulating the behavior of a watershed. SWAT

divides a basin into sub-basins based on unique Keywords—hydrologic modeling, rainfall, runoff, sediment yield. combinations of topography, soil type and land use which helps in preserving the spatially distributed parameters of the I. INTRODUCTION entire watershed and the homogenous characteristics of the basin. SWAT has been extensively used for a variety of OIL and water are the two major natural resources, S which are responsible for the existence of life on earth purposes and its applications have expanded worldwide in by providing the life supporting system for all living beings. the last decade. About 1600 peer-reviewed journal articles They also significantly influence the hydro-geological and have been published in the SWAT literature database that biological systems of the Earth. Information on natural document various uses of SWAT. SWAT has been widely condition and form of soil and water resources is essential applied to evaluate the hydrologic and water quality impacts for the socio-economic development of any area. This of land management and agricultural practices [2], [3], [4]. information is collected by carrying out water resources The objective of this study is to model the stream flow assessments of the areas of interest. Water resources and sediment yield behavior using SWAT model in a mid- assessment involves developing a comprehensive size basin of India. This include setup, calibration and understanding of water inflows, storage, outflows, sediment validation of SWAT model to simulate stream flow and yield and their inter-relationship over time. Information on sediment yield in Vamsadhara basin, India and to determine water resources assessment could be utilized to estimate the the most sensitive model parameters affecting water and sustainable environmental flows and the measures that can sediment yield by performing sensitivity analysis of be taken to sustain these flows and prevent erosion of soil. parameters. Water resources management is more profound and complex in developing countries as compared to developed countries, II. THE STUDY AREA The Vamsadhara river basin is situated between the Manoj Kumar. Jain is with the Department of Hydrology, Indian and basins of south India. The Institute of Technology, Roorkee, Uttarakhand, India. Phone: +91-1332- 285845; fax:+91-1332-285236; e-mail: [email protected]). total catchment area of Vamsadhara river basin, upstream to 2 Survey Daman. Sharma, was with the Department of Hydrology, Indian the point where it joins the , is 10,830 km and Institute of Technology, Roorkee, Uttarakhand, India.

82 International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand) lies within the geographical co-ordinates of 18015' to 19055' ET viz. Hargreaves, Priestley-Taylor, and Penman-Monteith north latitudes and 83020' to 84020' east longitudes. [2]. The surface runoff hydrologic component uses However, the catchment upstream to the last gauging and Manning's formula to determine the watershed time of discharge measurement station on the river at Kashinagar, concentration and considers both overland and channel flow. comprises of 7,820 km2 is used for model setup. The basin is A full description of SWAT can be found in the SWAT influenced by the south-west monsoon during the months of theoretical documentation [1], which is available online on June to October, and by occasional cyclones due to the SWAT website. formation of depression in the Bay of Bengal. The temperature variation in the plains of the basin is between IV. INPUT DATA 100C to 430C. The mean annual rainfall of the three districts Phulabani, and Ganjam in which the basin lies is A. Digital Elevation Model (DEM) 1280 mm, 1700 mm and 1500 mm respectively. The soil of The DEM is the raster data consisting of sampled array of the area is classified as mixed red, black soils, red sandy pixels containing elevation values representing ground soils, yellow soils, coastal sands and forest soils. Map of the positions at regularly spaced intervals. It is used for study area is shown in Fig.1. watershed and stream network delineation and the computation of several geomorphological parameters of the III. THE SWAT MODEL catchment including slope for HRUs. The Shuttle Radar SWAT (Soil and Water Assessment Tool) developed by Topography Mission (SRTM) obtained elevation data on a USDA-ARS is a direct outgrowth of the SWRRB model [5], near-global scale to generate the most complete high- [6], which was designed to simulate management impacts on resolution digital topographic database of Earth. For the water and sediment movement for un-gauged rural basins. present analysis projected DEM to SWAT is a basin scale, continuous time, conceptual and WGS_1984_UTM_Zone_44N coordinate system is used in long term simulation model that operates on daily time step. ArcSWAT Watershed Delineator [11] for watershed SWAT contains several hydrologic components (surface delineation. runoff, ET, recharge, stream flow, snow cover and snow B. Landuse /Land Cover melt, interception storage, infiltration, pond and reservoir The land use / land cover data of the study area is required water balance, and shallow and deep aquifers) that have for HRU definition and subsequently for assigning the Curve been developed and validated at smaller scales within the Numbers (CN) to the land areas for runoff computations and EPIC, GLEAMS and SWRRB models. Characteristics of hydrological analysis. The land use of an area is one of the this flow model include non-empirical recharge estimates, most important factors that affect surface erosion, runoff, accounting of percolation, and applicability to basin-wide and evapotranspiration in a watershed during simulation. management assessments with a multi-component basin Land use/Land cover classified data on a scale of 1:50,000 water budget [12]. 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 Fig. 1. Location map of Vamsadhara river basin in India Division, South-Eastern Region, Bhubaneswar, Odissa is used. SWAT model has eight major modules viz. hydrology, climate, sedimentation, agricultural management, water V. MODEL SETUP quality, land cover, water bodies and main channel Watershed delineation tool is used to delineate sub- processes. The runoff simulation on daily basis can be watersheds based on an automatic procedure using the DEM obtained by using a modified curve number technique [7] of the area. The basin has to be delineated into an adequate and on hourly basis by Green and Ampt infiltration equation number of hydrologic response units which will take account [8]. The model offers three options for estimating potential

83 International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand) of changes in climate, land use and soil types. Accordingly, standardizes the Root Mean square error using observations the basin is divided into 27 sub-basins. The Hydrological standard deviation. RSR is calculated as the ratio of RMSE analysis in SWAT is carried out at Hydrologic Response and standard deviation of measured data as shown below. 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 RSR varies from the optimal value of 0 to large positive below the given respective threshold values are ignored value. 0 indicates zero residual variation and therefore while delineating the HRUs. In the present study, threshold perfect model. General performance rating for acceptable values of 1% for Land use class, 2% for Soil class and 2% statistics is given in Table I. for Slope class are considered, resulting in formation of 793 HRUs in the study area spread over 27 subbasins. TABLE I Location table of Weather Data and Daily precipitation GENERAL PERFORMANCE RATINGS FOR RECOMMENDED STATISTICS [10] data files, are link with the required files already created for Pbias (%) Performance RSR NSE the purpose. Data on Solar Radiation, Maximum and Stream rating Sediment Minimum Temperatures, Wind Speed and Relative flow 0.00 to 0.75 to Humidity are generated by model itself using weather Very good < ± 10 < ± 15 0.50 1.00 generator tool due to non-availability of observed values. 0.50 to 0.65 to ± 10 to ± Good ± 15 to ± 30 After loading all the input data and generating the required 0.60 0.75 15 0.60 to 0.50 to ± 15 to ± database files, SWAT model was initially run using default Satisfactory ± 30 to ± 55 0.70 0.65 25 parameter values. Available discharge data was divided into two parts; period from 1984 to 1989 was used for calibration Unsatisfactory > 0.70 < 0.50 > ± 25 > ± 55 purpose whereas data from 1990 to 1995 was used for validation of the calibrated model. VII. SENSITIVITY ANALYSIS SWAT model is a comprehensive conceptual model and VI. PERFORMANCE EVALUATION relies on several parameters varying widely in space and The performance of SWAT model is analyzed based on time while transforming input into output. Calibration graphical representation of observed and simulated total process becomes complex and computationally extensive flow and observed and simulated sediment yield as well as when the number of parameters in a model is substantial. on the basis of various statistical parameters such as Nash With the help of sensitivity analysis, we can reduce the Sutcliff Efficiency (NSE) [9], Percent bias (Pbias), and number of parameters by not considering non-sensitive RMSE-observations Standard deviation Ratio (RSR). parameters for calibration, which in turn can give results The NSE determines the relative magnitude of the relatively in short time. Sensitivity analysis is performed residual variance compared to the measured data variance. 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.

obs sim Where Y and Y are the observed and simulated values VIII. CALIBRATION AND VALIDATION in respective time steps i, Ymean is the mean of observed data Model calibration is the process of estimating model during the duration and n is the number of observations. parameters by comparing model predictions for a given set The value of NSE ranges between -∞ and 1, with NSE = 1 of input model parameters with observed data. In this study, being optimum value. Values between 0.6 and 1.0 are the model is calibrated for stream flow as well as sediment viewed as acceptable levels of performance whereas yield (at Kashinagar site i.e. sub-basin 22) on daily as well negative values or zero indicate that the mean observed as monthly basis. Auto calibration procedure is followed value is a better predictor than the simulated value indicating using SUFI-2 algorithm of SWAT-CUP program. Twenty- unacceptable performance. seven SWAT parameters influencing stream flow and Pbias or percentage of deviation measures the average sediment yield are considered for calibration. Calibration of tendency of the simulated values to be larger or smaller than flow and sediment is carried out using 3000 iterations. the observed values. IX. EVALUATION OF MODEL PERFORMANCE The goodness-of-fit of the calibrated model during calibration and validation is evaluated using visual and The optimal value of Pbias is 0 with low magnitude values statistical indicators described previously. The visual indicating accurate model simulation. Positive values comparison provides information about overall qualitative indicate model under estimation bias and negative values visual match such as matching of peaks, trends of recession indicate model over estimation bias. and general agreement in hydrograph characteristics. In this RMSE-observations Standard deviation Ratio (RSR) study, calibration and validation both for stream flow and

84 International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand) sediment yield at daily time step and monthly time step is biasness towards underestimation for sediment yield. Pbias carried out. Hence, the performance of the model under both for monthly simulation is found to be 10.1 and -4.7 for the conditions is evaluated. calibration and validation period respectively for stream flow, which can be classified as good. Similarly, Pbias for A. Statistical Evaluation sediment yield is found to be 13.0 and 3.8 respectively. The performance of SWAT model is evaluated statistically both for runoff and sediment yield based on TABLE II STATISTICAL EVALUATION OF MODEL PERFORMANCE (DAILY) various statistical parameters such as NSE, Percent bias Calibration Period (Pbias), and RMSE-observations Standard deviation Ratio NSE P-Bias RSR P-Factor R-Factor (RSR). Runoff 0.73 5.4 0.52 0.44 0.92 The NSE is perhaps one of the most used objective Sediment 0.55 24.6 0.67 0.33 0.77 function for evaluating model performance. NSE expresses Validation Period the fraction of the measured stream flow or sediment yield NSE P-Bias RSR P-Factor R-Factor variance that is reproduced by the model. As per NSE Runoff 0.72 18.9 0.53 0.50 0.00 criteria simulation results are considered very good for Sediment 0.69 23.9 0.56 0.51 0.00 values of NSE above 0.75, good for NSE values between TABLE III 0.65 to 0.75 and satisfactory for NSE values between 0.50 STATISTICAL EVALUATION OF MODEL PERFORMANCE (MONTHLY) and 0.65 (Table I). The NSE values less than 0.50 are Calibration Period considered as unsatisfactory in the present study. The NSE P-Bias RSR P-Factor R-Factor computed values of NSE on daily and monthly basis are Runoff 0.89 10.1 0.33 0.49 1.14 given in Table III and IV respectively. The values of NSE Sediment 0.81 13.0 0.43 0.99 2.91 on daily basis for calibration and validation period are 0.73 Validation Period and 0.72 respectively for stream flow indicating good model NSE P-Bias RSR P-Factor R-Factor performance. Similarly, the values for sediment yield are Runoff 0.91 -4.7 0.30 0.29 0.00 Sediment 0.77 3.8 0.48 0.60 0.00 0.55 and 0.69 for calibration and validation period respectively. The performance rating of the model has been B. Graphical Evaluation found to be even better for monthly time step. For monthly The graphical evaluation provides information about simulation, the NSE values obtained for stream flow are 0.89 overall qualitative visual match such as matching of peaks, and 0.91 for calibration and validation period respectively. trends of recession and general agreement in hydrograph For sediment yield simulation, the NSE values obtained are characteristics. To evaluate model performance based on 0.81 and 0.77 respectively indicating very good model graphical comparison, plots between observed and simulated performance. values of discharge and sediment yield are prepared and two The second evaluation criteria used is the percent bias such plots are given as Figs. 2 and 3 for illustration. Visual (Pbias), which is a measure of the average tendency of the inspection of these figures indicates close agreement simulated values to be larger or smaller than the observed between observed and simulated runoff values. However, the values. The optimal value of Pbias is zero; a positive value model seems to underestimate sediment yield on daily basis indicates model bias towards underestimation, whereas a for calibration as well as validation periods. In addition, negative value of Pbias indicates bias towards daily discharge is underestimated for validation period overestimation. The model performance is “very good” if the absolute percent error is less than 10% for stream flow and REFERENCES less than 15% for sediment, “good” if the error is between [1] S.L. Neitsch, J. G. Arnold, J. R. Kiniry, and J. R. Williams, Soil and 10 and 15% for stream flow and between 15 to 30% for Water Assessment Tool – Theoretical Documentation, Version 2009. sediment and “satisfactory” if the error is between 15 and Texas, USA, 2009. 25% for stream flow and between 30 and 55% for sediment. [2] J.G. Arnold, and N. Fohrer. “SWAT2000: current capabilities and research opportunities in applied watershed modeling,” Hydrological This standard was adopted for the Pbias evaluation criteria Processes, vol. 19, no. 3, pp. 563-572, 2005. in this study, with Pbias values >=25% for stream flow and [3] D.K. Borah, and M. Bera, “Watershed-scale hydrologic and >=55% for sediment unsatisfactory. nonpoint-source pollution models:Review of applications,” Trans. ASAE, vol. 47, no. 3, pp. 789-803, 2004. Computed values of Pbias for daily and monthly time step [4] USDA-ARS (U.S. Department of Agriculture, Agricultural Research are given in Table II and III respectively. The value of Pbias Service). The automated geospatial watershed assessment tool obtained for daily simulation during calibration is 5.4 for (AGWA). Available at: www.tucson.ars.ag.gov/agwa/. Accessed 23 August 2006. stream flow and 24.6 for sediment indicating good model [5] J.R. Williams, A.D. Nicks, and J.G. Arnold, “Simulator for water performance for stream flow. Positive value of Pbias for resources in rural basins,” J. Hydrol. Engr., vol. 111, no. 6, pp. 970- sediment yield indicates that the model underestimated 986, 1985. [6] J.G. Arnold, J.R. Williams, A.D. Nicks, and N.B. Sammons, sediment yield during calibration period. 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85 International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)

Available at: www.wcc.nrcs.usda.gov/hydro/hydro-techref-neh- quantification of accuracy in watershed simulations,” Trans. ASABE, 630.html. Accessed 14 February 2007, 2004. 2006. [8] W.H. Green, and G.A. Ampt, “Studies on soil physics: Part 1. The [11] SWAT, Soil and Water Assessment Tool: ArcSWAT, College Station, flow of air and water through soils,” Journal of Agricultural Texas: Texas A&M University. Available at: Sciences, vol. 4, pp. 11-24, 1911. www.brc.tamus.edu/swat/arcswat.html. Accessed 20 February 2007. [9] J.E. Nash, and J.V. Sutcliffe, “River flow forecasting through [12] P.W. Gassman, M.R. Reyes, C.H. Green, and J.G. Arnold, “The Soil conceptual models, Part I: A discussion of principles,”. J. Hydrol., and Water Assessment Tool: historical development, applications, vol. 10, no. 3, pp. 282-290, 1970. and future research directions,” Transactions of the ASABE, vol. 50, [10] D.N. Moriasi, J.G. Arnold, M.W. Van Liew, R.L. Binger, R.D. no. 4, pp. 1211-1250, 2007. Harmel, and T. Veith. “Model evaluation guidelines for systematic

Fig. 2. Daily observed and simulated discharge and sediment yield during validation period.

Rainfall Observed Discharge 900 0 800 100

/s) 700 200 3 600 300 500 400 500 400 600 300 700 200 800

100 900 (mm) Rainfall 0 1000 Monthly Discharge (m Monthly Discharge

Fig. 3. Monthly observed and simulated discharge and sediment yield during validation period.

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