Hydrological Modeling of Vamsadhara River Basin, India using SWAT

Transcription

1 Hydrological Modeling of Vamsadhara River Basin, India using SWAT Manoj. Jain, and Survey Daman. Sharma 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 (R 2 = 0.73, NSE = 0.73 for calibration period and R 2 = 0.73, NSE = 0.72 for validation period). The calibration and validation results obtained for sediment yield are also good on daily basis (R 2 = 0.56, NSE = 0.55 for calibration period and R 2 = 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 (R 2 = 0.90, NSE = 0.89 for calibration period and R 2 = 0.91, NSE = 0.91 for validation period) as well as for sediment (R2 = 0.82, NSE = 0.81 for calibration period and R 2 = 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. S 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: ; fax: ; 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 midsize 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 km 2 and 82

2 lies within the geographical co-ordinates of ' to ' north latitudes and ' to ' east longitudes. However, the catchment upstream to the last gauging and discharge measurement station on the river at Kashinagar, comprises of 7,820 km 2 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 10 0 C to 43 0 C. 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]. 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 highresolution 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. 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 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 subwatersheds 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 83

3 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, Y mean 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 Very good Good Satisfactory RSR 0.00 to to to 0.70 NSE 0.75 to to to 0.65 Stream flow Pbias (%) Sediment < ± 10 < ± 15 ± 10 to ± 15 ± 15 to ± 25 ± 15 to ± 30 ± 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. Twentyseven 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 84

4 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) Calibration Period Runoff Sediment Validation Period Runoff Sediment TABLE III STATISTICAL EVALUATION OF MODEL PERFORMANCE (MONTHLY) Calibration Period Runoff Sediment Validation Period Runoff Sediment 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 Texas, USA, [2] J.G. Arnold, and N. Fohrer. SWAT2000: current capabilities and research opportunities in applied watershed modeling, Hydrological Processes, vol. 19, no. 3, pp , [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 , [4] USDA-ARS (U.S. Department of Agriculture, Agricultural Research Service). The automated geospatial watershed assessment tool (AGWA). Available at: Accessed 23 August [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 , [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, [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. 85

5 Available at: html. Accessed 14 February 2007, [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 , [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 , [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, [11] SWAT, Soil and Water Assessment Tool: ArcSWAT, College Station, Texas: Texas A&M University. Available at: Accessed 20 February [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 , Fig. 2. Daily observed and simulated discharge and sediment yield during validation period. Monthly Discharge (m 3 /s) Rainfall Observed Discharge Rainfall (mm) Fig. 3. Monthly observed and simulated discharge and sediment yield during validation period. 86