Hydrological modeling using SWAT model and geoinformatic techniques

ARTICLE IN PRESS The Egyptian Journal of Remote Sensing and Space Sciences (2014) xxx, xxx–xxx National Authority for Remote Sensing and Space Sciences The Egyptian Journal of Remote Sensing and Space Sciences www.elsevier.com/locate/ejrs www.sciencedirect.com RESEARCH PAPER Hydrological modeling using SWAT model and geoinformatic techniques S.S Panhalkar * Department of Geography, Shivaji University, Kolhapur, India Received 13 September 2013; revised 24 January 2014; accepted March 2014 KEYWORDS Hydrological modeling; Runoff; SWAT; Reservoir management Abstract In India the availability of accurate information on runoff is scarce However in view of the quickening watershed management programme for conservation and development of natural resources and its management, runoff information assumes great relevance Soil and Water Assessment Tool (SWAT) is a physically based distributed parameter model which has been developed to predict runoff, erosion, sediment and nutrient transport from agricultural watersheds under different management practices For the present study, Satluj basin up to the Bhakra dam has been selected as the study region The basic intent of the present study is to derive the parameters required for runoff modeling using the geospatial database and estimate the runoff of the Satluj basin During the basic data preparation stage of the study, the land use map and the digital elevation model covering the study area were derived with the help of remotely sensed information Weather data have been analysed for thirty years with the help of ENVI and ERDAS softwares to calculate the mean monthly values of each weather parameter Shuttle Radar Topographic Mission (SRTM) data have been imported in the SWAT project to start watershed delineation Six hundred and eight hydrological resource units are created by defining the land use, soil and slope conditions By providing all the inputs for model set up, SWAT model was simulated for the period of thirty years (year 1980–2010) After the successful execution of the model, it shows the sediment yield to be highest in April and May months with a total sediment loading of about 51.27 T/HA Result of stream flow is validated with observed data of Kasol with RMSE and r2 techniques The average annual surface runoff is about 79.67 mm Such type of runoff modeling is of immense importance for reservoir management of the Bhakra dam of the Satluj basin Further, this model can be utilized as a potential tool for water resource management of the Satluj basin Ó 2014 Production and hosting by Elsevier B.V on behalf of National Authority for Remote Sensing and Space Sciences * Address: Department of Geography, Shivaji University, Vidyanagari, Kolhapur 416004, Maharashtra, India Mobile: +91 9011774456 E-mail address: panhalkarsachin@gmail.com Peer review under responsibility of National Authority for Remote Sensing and Space Sciences Production and hosting by Elsevier Introduction Environmentally, socially and financially sound management of water resources requires long-term, reliable hydrologic information Poor availability of comprehensive and good quality hydrologic data leads to unsound planning and inadequate design and operation of water resources projects 1110-9823 Ó 2014 Production and hosting by Elsevier B.V on behalf of National Authority for Remote Sensing and Space Sciences http://dx.doi.org/10.1016/j.ejrs.2014.03.001 Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS The National water policy of Government of India, 2002 emphasis that a well developed information system, for water related data at the national/state level is a prime requisite for water resources planning All reservoirs formed by dams on natural water courses are subject to some degree of sediment inflow and deposition The deposition of sediment which takes place progressively in time reduces the active capacity of the reservoir which in turn affects the regulating capability of the reservoir to provide the outflows through the passage of time Accumulation of sediment at or near the dam may interfere with the future functioning of water intakes and hence affects decisions regarding location and height of various outlets It may also result in greater inflow of sediment into the canals/water problems of the rise in flood levels in the head reaches However, the modeling of runoff, soil erosion and sediment yield are essential for sustainable development Further, the reliable estimates of the various hydrological parameters including runoff and sediment yield for remote and inaccessible areas are tedious and time consuming by conventional methods So it is desirable that some suitable methods and techniques are used for quantifying the hydrological parameters from all parts of the watersheds Use of mathematical models for the hydrologic evaluation of watersheds is the current trend and extraction of watershed parameters using remote sensing and geographical information system (GIS) in high speed computers are the aiding tools and techniques for it Surface runoff is one of the major causes of erosion of the earth’s surface and the location of high runoff generating areas is very important for making better land management practices The location of runoff production in a watershed depends on the mechanism by which runoff is generated Infiltration excess occurs when the rainfall intensities exceed to the soil infiltration rate or any depression storage has been already filled Soil infiltration rates are controlled by soil characteristics, vegetation cover and land use practices Rainfall runoff models are classified as deterministic (physical), parametric (empirical) and mathematical models (Dawson and Wilby, 2001) Deterministic model is based on physical laws of mass and energy transfer and the empirical model represents simplified hydrological processes Mathematical models are much more popular for runoff assessment as these are less data driven, simpler and cheaper (Fontaine et al., 2002) Statistical methods such as multivariate regression models (Wang et al., 2008; Hundecha et al., 2008; McIntyre and Al-Qurashi, 2009), artificial neural networks (Kumar et al., 2005; Nayak et al., 2007; Machado et al., 2011) and multivariate time series models are generally used for rainfall runoff analysis Different types of models have been developed for the purpose of water-resource management and planning (Chen and Adams, 2006) Physically-based models such as ANSWERS (Beasley et al., 1980), WEPP (Nearing et al., 1989), GUEST (Misra and Rose, 1989), EUROSEM (Morgan et al., 1998) and LISEM (De Roo et al., 1996) are now widely accepted models for simulating soil erosion processes Storm Water Management Model (SWMM) is being used widely to simulate all aspects of urban hydrologic and quality cycles, including rainfall, snowmelt, overland flow, flow routing through a drainage network, and urban nonpoint pollution concentrations (Huber and Dickinson, 1992) The Soil and Water Assessment Tool (SWAT) was developed to predict the effects of different management S.S Panhalkar practices on water quality, sediment yield and pollution loading in watersheds (Chen and Adams, 2006) Arnold et al (1998) applied SWAT with the addition of a streamflow filter and recession methods for regional estimation of baseflow and groundwater recharge in the upper Mississippi River basin Tolson and Shoemaker (2004) have applied SWAT2000 model for the Cannonsville Reservoir of New York City water supply reservoir They found it useful to identify and quantitatively evaluate effects of various phosphorus management options for mitigating loading to the reservoir Abbaspour et al (2007) have used the SWAT model to simulate all related processes affecting water quantity, sediment and nutrient loads in the the Thur watershed in Switzerland Their study provided excellent results for discharge and sediment yield Rosenthal et al (1995) used the SWAT model to assess water yield of the lower Colorado river basin in Texas The review indicated that SWAT is capable of simulating hydrological processes with reasonable accuracy and can be applied to a large ungauged basin Therefore, to test the capability of the model in determining the runoff of the watershed, SWAT 2000 with ARCGIS 9.3 interface was selected for the present study The main objective of the present study is to derive the parameters required for runoff modeling using the geospatial database and estimate the runoff and sediment yield of the Satluj basin Study area For the present study, Satluj basin up to the Bhakra dam has been selected as a study region (Fig 1) The geographical limits of the Satluj basin right from start up to the Bhakra dam lie between Latitudes 31°N to 33°N and Longitudes 76°E to 80°E The Catchment area of the river Satluj upto the Bhakra dam is about 56,874 sq km The Satluj River flows through the Western Himalayan region Apart from the hilly topography, faulty cultivation practices and deforestation within the basin result in huge loss of productive soil and water as runoff Considering hydrological behavior of the basin and applicability of the existing models for the solutions of aforesaid problems, the current study was undertaken with the application of SWAT 2000 in integration with remote sensing and GIS to estimate the surface runoff and sediment yield of an intermediate watershed of the Satluj river (up to Kasol) Methodology SWAT model is data driven and it requires several types of data ranging from topography, land use, soil, climate, etc Data were collected from various sources as mentioned below and different processes have been carried out 3.1 Land use database Land use/land cover map for the study region has been downloaded from GLC (2000) database The study region falls in South Asia and China, after downloading both the datasets from GLC, 2000 Both the datasets are mosaiced and a subset has been created It was again re-projected in UTM projection by using ERDAS 9.1 software Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques Figure Location of the study area 3.2 Soil database 3.3 Weather database Soil dataset has been downloaded from FAO (1981) website and it was also reprojected in the same projection after creating the subset The necessary input information required by the SWAT model was extracted from the same database for each soil type, namely soil texture, Hydrological Soil Group (HSG), soil depth, rock fragments, and organic carbon content were obtained for each soil type SWAT requires daily values for precipitation, maximum and minimum temperature, solar radiation, precipitation, relative humidity and wind speed for modeling of various physical processes: soilnrainfall being the most important Weather data were collected from CISL, Prinston University Weather database of NETCDF (Network Common Data Form) format has been downloaded from the Princeton University It was converted in TIFF format in ENVI IDL Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS S.S Panhalkar the Satluj basin At first, setup for new SWAT project has been created SRTM data (90 m resolution) had a Geographic coordinate system so it was converted into the Projected coordinate system by using reproject tool of Erdas 9.1 After subsetting the SRTM data, it has been imported in the SWAT project to start watershed delineation 3.4.1 Stream definition Figure Flow chart of statistical calculation for weather data For this model, monthly data of each climatic variable are required Hence, the downloaded data of fifty-eight years range from 1948 to 2006 (708 layers) Flow chart (Fig 2) describes the methodology used for the generation of the weather database For the study purpose, thirty years data from 1977 to 2006 have been used for further analysis To calculate the mean monthly values all the layers are stacked month wise For this ENVI software has been used To calculate the standard deviation and skewness of rainfall data ERDAS model is used and the following functions (Fig 3) have been constructed At last by applying the zonal statistical tool, each subbasin’s statistics calculation has been carried out in ARCGIS 9.3 The processed data are in different units so by applying the raster calculator the said data are converted in SWAT input format 3.4 SWAT project SWAT model is physically based, computationally efficient, and capable of continuous simulation over long time periods However, the Swat model is being used to estimate runoff of Figure In this section, initial stream network and sub-basin outlets were defined It provides the option of defining streams based on a drainage area threshold or importing pre-defined watershed boundaries and streams After that flow direction and accumulation have been calculated 3.4.2 Outlet and inlet definition Watershed delineation was more defined in this section by defining the outlet point of discharge for the sub-basin and for the whole watershed Sub-watershed outlets are the points in the drainage network of a sub-watershed where the stream flow exits the sub-watershed area The Kasol point has been considered as the outlet point for the whole watershed where the rainfall station is located (Fig 4) It is useful for comparison of measured and predicted flows and concentrations Outlet for the whole watershed was defined manually It is convenient to select the most down-stream outlet of each target watershed to determine the whole basin The area of the sub-basin was cut short from previous defined sub-basin area after defining the outlet and those are stored in the ‘‘Monitoring Points’’ layer Final step in the delineation of the watershed was calculation of basin parameters such as geomorphic parameters The Calculation of Subbasin Parameters section contains functions for calculating geomorphic characteristics of the subbasins and reaches, as well as defining the locations of reservoirs within the watershed Topographic report was created which contained the summary and distribution of discrete land surface elevations in the sub-basins Statistical calculation in ERDAS Modeler Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques Figure Outlet and inlet definition 3.5 Defining land use/soil data The movement of water depends on the soil type and vegetation cover The amount of rain lost due to interception storage on the plants depends on the type of vegetation and has a significant effect on the infiltration capacity of the soil Dense vegetation covers the soil from raindrop impact and reduces the problem of erosion As vegetation cover decreases, the surface runoff increases resulting in increasing sediment transportation to the streams For each of the delineated sub-basins, land use and soil data were defined for modeling of various hydrological and other physical processes The prepared land-use from digital maps was given as input to the model The look up table containing various SWAT land use has been prepared 3.5.1 Land use The default land use of the SWAT model was linked to land use map through the look up table which was again linked to the land use map 3.5.2 Soil Soil physical attributes were initially stored to the SWAT’s soil database through an Edit database interlace and relevant information required for hydrological modeling and soil erosion modeling was provided to the model The database was linked to the soil map through the look up table which was again linked to the soil map (Fig 5) It was given as input to the SWAT model 3.6 Elevation zones Most snowmelt runoff models handle spatial and temporal variations due to elevation by incorporating elevation bands or zones allowing the model to discretize the snowmelt process based on basin topographic controls (Arnold et al., 2000) Slope map (Fig 7) is generated by using 3D analysis tool of ArcGIS The ability to represent up to elevation bands within each subbasin was added to SWAT Within the subbasin input files, the average elevation of each elevation band is entered, followed by the percentage of the subbasin area within that band Six elevation zones (Fig 6) were established for all the subbasins in the Satluj river basin 3.7 HRU distribution The load predictions will be good and accurate if each HRU is considered obtaining the total effect of different land cover/ crops and soils The total runoff depends on the actual hydrologic condition of each land cover/crops and soil present in the watershed Therefore, the impact of each type of land use is considered in this modeling to calculate runoff and sediment load in the basin After the overlay of the land-use, soil maps and slope, the distributions of the Hydrological Response Units (HRUs) were determined 3.8 Defining climate database One of the main sets of input for simulating the watershed in SWAT is climate data Climate inputs consist of precipitation, maximum and minimum temperature, solar radiation, wind speed and relative humidity The daily precipitation records for the period of 1935–2002 were used which were analysed to develop the climate-input files required for the model The remaining climate inputs were generated internally within SWAT using processed monthly climatic data of the Princeton University Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS S.S Panhalkar Figure Soil Map of Satluj Basin Figure Elevation zones 3.9 Model input set-up The Write Input tables menu contains items that allow building database files containing the information needed to generate default input for SWAT The Write commands become enabled after weather data were successfully loaded These commands were enabled in sequence and need to be processed only once for a project Before SWAT can be run, the initial watershed input values have been defined These values were set automatically based on the watershed delineation and landusensoilnslope characterization There are two ways to build the initial values: activate the Write All command or the individual Write commands on the Write Input Tables menu The first option has been selected Finally, the other key aspects of the SWAT simulation performed for the watershed are listed below:     Output time step: Monthly Simulation period: thirty years (1980–2010) Rainfall distribution: skewed normal Runoff generation: CN method Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques Figure 7 Slope map of Satluj basin 3.10 Model calibration Model calibration is necessary for preliminary testing of a model and observed data can be tuned with it Model calibration is necessary for the successful use of any hydrologic and water quality simulation Manual and automatic calibration methods can be applied For better estimation of sediment transport and runoff the model was calibrated in two phases The model was first automatically calibrated for hydrology After hydrologic calibration, the model was calibrated for sediment transport Model calibration was conducted for 30 years from 1980 to 2010 The first five years were used for priming the model The model needs at least five years for better estimation of results through priming (Gitau et al., 2003) 3.11 SWAT Simulation The SWAT Simulation menu allows us to finalize the setup of input for the SWAT model and to run the SWAT model after this sensitivity analysis and auto-calibration has been carried out Results and discussion 4.1 Final SWAT land use/soil classes The SWAT Land use classes are prepared by using GLC (2000) data set There are fifty land use/ land cover classes as per the said data base After defining land use classes as per SWAT, there are thirteen land use classes in the Satluj basin The classification result has been shown in the land use map (Fig 8) RNGB is the dominant class as per the spatial extent with 30.73% Soil classes are also defined as per SWAT, There are nine soil classes as per SWAT soil definition (Fig 9) out of that Satluj6 (I-Bh-U-c-3717) is the dominant class 4.2 Final HRU report Defining the number of HRUs was a two-step process, first land-uses were chosen and then the different soils for each land use were chosen In the first step, number of land use units were defined, which were to be considered for generating HRUs The number is controlled by a threshold value given for each sub-basin Suppose if the threshold value is given 10% then the model will exclude all the land cover classes from modeling that occupies less than 10% of area in a particular sub-basin When the area of the threshold was defined as small value (1%), the model included the residential and road network types in HRU creation which affected the model output in terms of the increase in runoff amounts But when the threshold area was increased then it excluded the residential and road area in HRU creation and thus the runoff decreased sharply Thereafter the threshold value was calibrated and adjusted appropriately to account for various land use types covering a significant area in the watershed while defining HRUs Second step control was not altered as soil types were defined on the basis of physiographic units, so they occupied more or less the same area as land use types A threshold of 10% for land use and 10% for soil was used, which deducted any land use that occupied less than 10% of the land in the sub-basin and any soil that represented less than Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS S.S Panhalkar Figure SWAT land use Figure SWAT soil class 10% of the land use in the sub-basin As per the final HRU report, 608 HRUs are being created within the Satluj basin and sub basin wise HRU report has been generated 4.3 Surface runoff prediction in SWAT model SWAT is not a parametric model requiring a formal calibration procedure to optimize parameter values using simulated vs observed results Instead, the model was designed as such the GRASS interface can characterize basin processes using readily available GIS databases and meteorological information, combined with internal model libraries Parameters have physical meanings in the field, allowing parameters to be set using these databases for land use and cover, soil type, topography, and climate statistics Several studies have demonstrated that the GRASS GIS interface can successfully select input parameter values for SWAT without calibration in a wide variety of hydrologic systems and geographic locations using the readily available GIS databases (Chen and Adams, 2006) The model simulation was executed for 30 years (1980–2010) The first years were not used for model evaluation because, during early time periods for the simulation, model parameters such as soil–water content and residue cover are initially not in equilibrium with actual physical conditions (Gitau et al., 2003) Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques Table Average monthly basin values Snow (mm) Water (mm) Sediment Month Rainfall Snowfall Surf Q Lat Q Yield Et (mm) Yield (T/HA) Pet (mm) Jan Feb Mar April May June July Aug Sep Oct Nov Dec 23.85 44.04 41.20 29.54 36.30 33.69 94.29 95.18 40.11 15.85 10.19 21.24 21.26 38.76 30.72 18.91 15.77 3.38 0.33 1.55 6.10 8.05 7.17 16.98 1.66 2.14 12.09 23.48 36.30 3.38 5.15 4.81 1.15 0.93 1.15 1.43 0.31 0.76 1.49 1.42 3.61 3.63 9.31 9.93 4.40 1.33 0.40 0.56 2.52 3.89 15.36 26.57 26.25 12.41 17.16 17.45 8.63 4.21 2.47 2.52 4.07 6.67 16.18 21.62 33.39 40.70 74.27 74.78 38.20 13.78 6.57 5.67 0.51 0.48 6.15 17.94 19.61 3.72 0.39 1.11 0.18 0.42 0.43 0.35 56.69 62.01 147.45 261.19 539.79 733.19 859.52 776.28 587.09 349.95 194.47 81.16 Stream flow is the most important element calibrated in this model After the successful run of SWAT model, average monthly basin values (Table and Fig 10) show that snow cover increases from December to February and again start decreasing from February Hydrograph (Fig 11) is also generated for the period 1980–2010 As the snow starts decreasing in March, the yield of the basin also increases because of snow melt The sediment yield records highest in April and May As per Table 2, average annual rainfall and snowfall are 485 mm and 168 mm respectively The total sediment loading is 51.27 T/HA The average annual surface runoff is 79.67 mm Average Basin Monthly Values 900 800 700 SNOW / RAINFALL 600 (In MM-T/HA) SNOW FALL 500 WATER(MM) SURF Q 400 WATER(MM) LAT Q 300 WATER(MM) YIELD SED ET (MM) 200 SED YIELD (T/HA) 100 SED PET(MM) JAN MAR MAY JULY SEP NOV Month Figure 10 Average monthly basin values Hydrograph 800 700 600 Discharge m3/s 500 400 300 200 1980 1980 1981 1982 1983 1983 1984 1985 1986 1986 1987 1988 1989 1989 1990 1991 1992 1992 1993 1994 1995 1995 1996 1997 1998 1998 1999 2000 2001 2001 2002 2003 2004 2004 2005 2006 2007 2007 2008 2009 2010 2010 100 Figure 11 Calculated Discharge Data Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS 10 Table S.S Panhalkar observed and simulated flow indicated that the SWAT model is capable of simulating the hydrology of the Satluj basin Legates and McCabe (1999) indicated that a hydrological model can be evaluated by coefficient of determination and Root mean square error (RMSE) Both measures have been calculated for the observed and simulated values for the Kasol river gauging station Root-Mean-Square Error (RMSE) is a frequently used measure of the differences between values actually observed and the values predicted by a model RMSE has been calculated to check the applicability of the model RMSE for the observed and simulated data for Kasol is about 0.71 The coefficient of determination is the percent of the variation that can be explained by the regression equation As per Fig 13, value of Coefficient of Determination is 0.88 Therefore, result shows quite appreciable validation of the SWAT model Average annual basin values Precipitation = 484.4 mm Snow fall = 167.98 mm Snow melt = 162.27 mm Sublimation = 4.74 mm Surface runoff Q = 79.67 mm Lateral soil Q = 37.12 mm Tile Q = 0.00 mm Groundwater (Shal Aq) Q = 23.40 mm Revap (Shal Aq => soil/plants) = 6.60 mm Deep Aq recharge = 1.58 mm Total Aq recharge = 31.58 mm Total water Yield = 139.35 mm Percolation out of soil = 30.82 mm Et = 335.7 mm Pet = 4647.2 mm Transmission losses = 0.84 mm Total sediment loading = 51.279 T/Ha Conclusion Model validation Process parameters were adjusted with the help of observed data of stream flow and meteorological data To validate the model, simulated and observed runoff hydrographs at the Kasol station were compared for ten years as shown in Fig 12 It shows that the calculated hydrographs reasonably match the observed discharge data The hydrograph of To develop a suitable model for the hydrological process for a river basin is the most important aspect for water resource management SWAT hydrological model was applied to the mountainous Satluj basin to assess runoff and sediment yield of the basin Input data generated through Geospatial techniques are quite applicable to run the SWAT model for the Satluj basin The performance and applicability of SWAT model was successfully evaluated through model calibration and validation Stream flow is the most important element simulated in this model Average annual prediction of stream flow is 79.67 mm The total average sediment loading ispredicted to OBSERVED AND SIMULATED DISCHARGE AT KASOL 7000 Discharge (m3/s) 6000 5000 4000 3000 2000 1000 Observed Discharge Simulated Discharge Year Figure 12 Observed and Simulated Discharge at Kasol Figure 13 Validation of Discharge Please cite this article in press as: Panhalkar, S.S., Hydrological modeling using SWAT model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques be about 51.279 T/Ha annually Result of stream flow is validated with the observed data of Kasol with RMSE and r2 techniques Values of Coefficient of Determination and RMSE are 0.88 and 0.71, respectively Therefore, this model can be utilized as a potential tool for water resource management of the 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http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT model and geoinformatic techniques Figure Outlet and inlet definition 3.5 Defining land use/soil data The movement... using SWAT model modeling using SWAT model and geoinformatic techniques Figure 7 Slope map of Satluj basin 3.10 Model calibration Model calibration is necessary for preliminary testing of a model. .. model and geoinformatic techniques, Egypt J Remote Sensing Space Sci (2014), http://dx.doi.org/10.1016/j.ejrs.2014.03.001 ARTICLE IN PRESS Hydrological modeling using SWAT model modeling using SWAT