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4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan Application and Test of the SWAT Model in the Upper Cauvery River Basin, Karnataka, India Kumar Raju B C and Lakshman Nandagiri Department of Applied Mechanics & Hydraulics, National Institute of Technology Karnataka, Surathkal, Karnataka, India, e-mail: kumarrajubc@gmail.com ABSTRACT: With increased availability of spatial data-sets of catchment characteristics and hydrometeorological variables, distributed hydrological models are being applied to solve a variety of problems related to catchment hydrology and water resources management In this study, we explore the applicability of the distributed Soil and Water Assessment Tool (SWAT) hydrological model to map the spatial distribution of hydrological processes in the Upper Cauvery basin (36682 km 2) located in Karnataka State, India SWAT was applied to the basin using input data of daily rainfall from 33 influencing rain gauges, climatic variables from climate stations, land use-land cover and topography information derived from satellite imagery and soil characteristics from a map Daily streamflow records of the Biligundulu gauging site was used for model calibration and validation Model sensitivity analysis, to identify the most critical parameters was performed using the Latin Hypercube (LH) and One-factor-At-a-Time (OAT) sampling approach SWAT performance in simulating streamflow at the Billigundulu gauge site was good as indicated by coefficient of determination (R 2) of 0.81 and Nash-Sutcliffe efficiency (ENS) of 80% between simulated and observed daily flows Results of this study prove that SWAT is an effective modeling tool for hydrologic analyses in large heterogeneous tropical catchments Key words: SWAT, Hydrological modeling, Sensitivity analysis, Surface runoff, Cauvery basin, India INTRODUCTION Hydrological models are used to represent temporal and spatial processes of the hydrological cycle at a basin scale These models are also useful in identifying sensitive parameters for basin response for the sub hydrological process A variety of distributed hydrological models are available of which some are integrated within a Geographical Information System (GIS) environment for predicting watershed water balance components (Arnold et al., 1998; Beasley and Huggins, 1981; Beven and Kirkby, 1979; Young et al., 1989) Among these, Soil Water Assessment Tool (SWAT) has been widely used in various regions of the world and in different climatic zones at daily, monthly and annual time steps and also it has been successfully implemented at spatial scales ranging from small watersheds to large river basins (Arnold et al., 1998) SWAT is also suitable in the quantification of relative impact using alternative input data on water balance components, land use changes and water quality assessment (Tripathi et al., 2003; Xu et al., 2011) For Indian climatic condition SWAT has been effectively implemented to predict streamflow, sediment yield and water quality of a catchment (Dhar and Mazumdar, 2009; Gosain et al., 2006; Immerzeel and Droogers, 2008; Wagner et al., 2013) In this study, it is intended to test a SWAT model for the upper Cauvery basin, Karnataka, India Also, this study aims at identifying sensitive parameters which govern water yield of the basin 4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan SWAT MODEL DESCRIPTION STUDY AREA The SWAT model has been used for investigating the impact assessment of water availability, erosion, sediment and nutrient transport at the basin scale (Arnold et al., 1998) SWAT requires rainfall, climate, land use, soil data and elevation data for delineating the watershed boundaries, streams and flow routing In SWAT the watershed is subdivided into a number of sub watersheds These sub watersheds are further divided into Hydrologic Response Units (HRUs), which are units of unique intersections of land use and soils and it simulates water balance components for each HRU by using a water balance equation The hydrologic cycle is simulated by the water balance equation: The Cauvery River, also known as the Dakshin Ganga, is one of the major interstate peninsular rivers of South India The Cauvery River rises in the Western Ghats and flows in an eastwardly direction passing through the states of Karnataka, Tamil Nadu, Kerala and Pondicherry before it drains into the Bay of Bengal The present study was taken up to the Billigundulu gauge site of the Cauvery basin to simulate the water yield The basin up to the Billigundulu gauge site has an area of 36682 km2 with many tributaries, including the Shimsha, Hemavathi, Harangi, Arkavati, Lakshmanathirtha and Kabini (Table 1) The south-west monsoon covers most of the annual precipitation The recorded maximum and minimum temperatures are 39.1°C and 4.8°C respectively There are many major and medium projects in the upper Cauvery basin located in Karnataka state from which water is utilized for irrigated areas in many districts A few of the selected major water resources projects in the upper Cauvery basin are KRS dam, Harangi dam, Hemavathi dam, and Kabini dam The larger part of the upper Cauvery basin is comprised of irrigated agriculture More than 70, 000 of land is irrigated from canals, groundwater wells and tanks Irrigated agriculture in this basin provides livelihood to a large population and contributes significantly to the food production of Karnataka State An assessment of the basin is important because it provides a statistic on the water yield of the basin, especially under the impacts of significant human-induced land use/cover and climate changes The topography map of the upper Cauvery basin is shown in Fig (1) t SW t  SW0   R day  Q surf  E a  w seep  Q gw i 1 where SW is the soil water content, i is time in days for the simulation period t, and Rday, Qsurf, Ea, Wseep, and Qgw respectively are the daily precipitation, surface runoff, evapotranspiration, percolation and return flow Surface runoff (Qsurf) for each HRU is calculated by using SCS-CN method The general form of the SCS-CN method (USDA, 1972), is given by the following equation: Q surf  Pe2 Pe  S (2) Where Pe (mm) is the depth of effective precipitation (precipitation minus initial abstraction), S (mm) is the amount of water storage available in the soil profile or the retention parameter and it is defined as:  1000  S  25.4    10   CN  (3) The CN is a function of the soil’s permeability, land use and antecedent soil water conditions Table Sub basins in the Upper Cauvery Basin Sl Drainage Area Sub basin Name No (km2) Hemavathy 5548.32 Harangi 3209.20 Lakshmanathirtha 1912.45 Kabini 7021.33 Shimsha 8646.89 Arkavathy 4123.84 Upper Cauvery 36682.00 4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan Fig Topography of Upper Cauvery basin and the location of the rain gauge stations The urban areas, industrial area and INPUT DATA villages cover 2.95% of the basin The Basic input data required for the SWAT map and distribution of the land use hydrological model includes topography, obtained from KSRSAC are shown in Fig weather, rainfall, land use and soil data Topographic data was obtained in the form of DEM (Digital Elevation Model) at 90 m resolution from the SRTM (Shuttle Radar Topography Mission) and it was used to delineate a basin into multiple sub basins and calculate topographic related parameters such as slope class, stream length and locate monitoring points 1:50,000 scale land use data was collected from Karnataka State Remote Sensing Application Centre (KSRSAC) and soil map and its physical properties database were obtained from the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP) Daily precipitation data was collected from Karnataka Irrigation Investigation Division (KIID) for 33 rain gauge stations located in and around the basin Daily climate data were collected from the Indian Meteorological Department (IMD) climate stations 4.1 LAND USE AND SOIL The predominant land use in the basin is agriculture Agricultural fields and forest areas cover more than 64.17% and 24.47% of the basin respectively The water bodies include reservoirs and tanks cover 3.9% of the basin The barren rocky and scrub land cover 4.51% of the basin SENSITIVITY ANALYSIS Sensitivity analysis can give a better understanding of the impact of change in an individual input parameter of the model response and can be performed using various methods The method in the ArcSWAT interface combines the Latin Hypercube (LH) and One-factor-At-a-Time (OAT) sampling Van Griensven et al (2006), characterized Global rank as “very important”, rank to as ‘important”, rank to 19 as “slightly important” and rank 28 as “not important” Sensitivity analysis was performed on 16 different SWAT model parameters for the upper Cauvery basin Parameters and parameter ranges used in the sensitivity analysis are shown in Table By using default upper and lower boundary parameter values, the parameters were tested for sensitivity using without observed streamflow data Results of sensitivity analysis for the upper Cauvery basin are presented in Fig with parameters ranked according to their magnitude of response The most sensitive factor governing the streamflow for the upper Cauvery basin was base flow alpha factor (Alpha_Bf) This indicates that base 4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan flow is very significant in the water yield The SCS runoff curve number for moisture condition II (CN2), Effective hydraulic conductivity in main channel alluvium (Ch_K2), threshold depth of water in the shallow aquifer required for return flow to occur (Gwqmn), available water capacity (Sol_Awc) and soil evaporation compensation factor (Esco) are also important parameters Alpha_Bf and Sol_Awc were correlated to base flow and that could be the reason for their higher ranking in the sensitivity analysis Sol_Z, Canmx, Blai, Gw_delay and Sol_K were the next sensitive parameters Ch_N2, Gw_revap, Surlag, Epco and Revapmn parameters had very less influence on the streamflow Fig Land use/Land cover map of the upper Cauvery basin Table Parameters and their ranges considered for the sensitivity analysis (Gw = groundwater, Evap = evaporation, Geom = geomorphology) Name Min Max Definition Process Alpha_Bf Base flow alpha factor (days) Gw Blai -20 20 Leaf area index for crop * Crop Canmx 10 Maximum canopy index Effective hydraulic conductivity channel alluvium (mm/hr) Runoff in main Ch_K2 150 Ch_N2 -20 20 CN2 -20 20 Manning coefficient for channel SCS runoff curve number for condition II * Epco -20 20 Plant evaporation compensation factor * Evap Esco Soil evaporation compensation factor Evap Gw_delay 100 Groundwater delay (days) Gw Gw_revap 0.02 0.2 Gw Gwqmn 1000 Revapmn 500 Groundwater ‘‘revap’’ coefficient Threshold depth of water in the shallow aquifer required for return flow to occur (mm) Threshold depth of water in the shallow aquifer for ‘‘revap’’ to occur (mm) Sol_Awc -20 20 Available water capacity (mm/mm soil)* Soil Sol_K -20 20 Soil conductivity (mm/h) * Soil Sol_Z -20 20 Soil depth * Soil Surlag 10 Surface runoff lag coefficient Runoff *Relative percent change Channel Channel moisture Runoff Soil Gw 4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan Surlag Sol_Z Sol_K Sol_Awc Revapmn Gwqmn Gw_Revap Gw_Delay Esco Epco CN2 Ch_N2 Ch_K2 Canmx Blai Alpha_Bf 14 11 16 13 10 15 12 10 12 14 16 Fig Sensitivity ranking for hydrology over the upper Cauvery basin for SWAT model RESULTS AND DISCUSSION CALIBRATION OF THE SWAT PARAMETERS Model calibration involves the adjustment of parameter values and comparison of simulated streamflow, to observe data until a defined objective function is attained In this study, automatic calibration was carried out using a dataset of daily streamflow records The parameters obtained from the sensitivity analysis using LH-OAT were chosen for automatic calibration To calibrate the SWAT model, auto-calibration tool in the ArcSWAT interface was used with Parasol mode The model predictions are evaluated for the calibration periods using three statistical methods: Nash–Sutcliffe efficiency coefficient (ENS), Percent BIAS (PBIAS) and coefficient of determination (R2) Streamflow was estimated based on calibrated model parameters The SWAT model was calibrated using the daily observed streamflow data on Billigundulu gauge site The calibration period was chosen between 1/6/2005 to 31/12/2005 Fig shows the time series and a scatter plot of simulated and observed flows (m3/s) for the SWAT model at the gauging site of the basin The model was also capable of simulating the pattern of flow during dry and wet period reliable with the climate data inputs From these Figure it is noticed that observed streamflow are simulated well, which implies that the performance of SWAT model is good The ENS, R2 and PBAIS values of the SWAT model for the basin was 0.80, 0.81 and -2.73 respectively 4000 3600 Observed SWAT 3000 3000 2400 2000 1800 1200 R2 = 0.81 1000 600 6/1/2005 8/1/2005 10/1/2005 12/1/2005 1000 2000 3000 4000 Fig Time Series and scatter plot of observed and simulated daily streamflow in the upper Cauvery basin 4th International Engineering Symposium - IES 2015 March 4-6, 2015, Kumamoto University, Japan CONCLUSION The present study was able to interpret the physically based SWAT model for agricultural dominated basin like upper Cauvery The behavior of the basin in terms of response to streamflow at the gauge site was successfully assessed by identifying the sensitive parameters The sensitivity analysis of the model showed that the base flow alpha factor was the most sensitive parameter The model was successfully calibrated using observed daily flow data on the Billigundulu gauge site using auto-calibration tool The results showed that the major part of flow was from the aquifer zone nearer to the gauge site that reaches up to 40% of the streamflow contribution The statistics of model performance in simulating temporal variations in the Billigundulu gauge site are good In view of the results obtained in this study, it may be concluded that SWAT is an effective modeling tool for hydrologic analyses and water resources management in the upper Cauvery basin ACKNOWLEDGEMENTS The authors are grateful for the financial assistance provided by the Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, India We would also like to thank all the Government Departments and Organizations for providing required data REFERENCES [1] Arnold, J G., et al., (1998), Large area hydrologic modeling and assessment part i: model development, JAWRA Journal of the American Water Resources Association, vol 34, pp 73-89 [2] Beasley, D B and Huggins, L F., (1981), ANSWERS users manual, Chicago: United States Environmental Protection Agency, [3] Beven, K J and Kirkby, M J., (1979), A physically based, variable contributing area model of basin hydrology, Hydrological Sciences Bulletin, vol 24, pp 43-69 [4] Dhar, S and Mazumdar, A., (2009), Hydrological modelling of the Kangsabati river under changed climate scenario: case study in India, Hydrological Processes, vol 23, pp 2394-2406 [5] Gosain, A., et al., (2006), Climate change impact assessment on hydrology of Indian river basins, Current Science, vol 90, pp 346-353 [6] Immerzeel, W W and Droogers, P., (2008), Calibration of a distributed hydrological model based on satellite evapotranspiration, Journal of Hydrology, vol 349, pp 411-424 [7] Tripathi, M P., et al., (2003), Identification and prioritisation of critical sub-watersheds for soil conservation management using the SWAT Model, Biosystems Engineering, vol 85, pp 365-379 [8] USDA, S., (1972), National Engineering Handbook, Section 4: Hydrology, Washington, DC, [9] Wagner, P D., et al., (2013), An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India, Natural Hazards and Earth System Sciences, vol 17, pp 2233-2246 [10] Xu, H., et al., (2011), Quantifying uncertainty in the impacts of climate change on river discharge in subcatchments of the Yangtze and Yellow River Basins, China, Natural Hazards and Earth System Sciences, vol 15, pp 333-344 [11] Young, R A., et al., (1989), AGNPS: A nonpointsource pollution model for evaluating agricultural watersheds, Journal of Soil and Water Conservation, vol 44, pp 168-173