Water is a scarce resource of nature due to the increasing demand of fresh water over few decades. It’s a key element of natural resource management therefore researchers are finding the ways to manage this resource. This management includes the assessment of distribution and planning of surface and subsurface water. Hydrological Modeling using remote sensing and GIS provides a framework for this assessment. There are various models available under lump, semi distributed and distributed category. All of these model require spatial data inputs to model the hydrological regime. We have done it through Variable Infiltration Capacity (VIC) model in Beas river basin of Himachal Pradesh uptoThalot. VIC is a grid based distributed model which requires minimum four parameter files to simulate the model e.g. Vegetation Parameter File, Meteorological Forcing File, Soil Parameter File and Global Parameter File. Some optional parameter files can also be integrated including Elevation Band and Lake Parameter File. In Vegetation Parameter file the fraction of LULC classes within the grid are listed with their rooting depth. Meteorological Forcing File requires meteorological parameters such as minimum temperature, maximum temperature, wind speed and cloud cover etc. These all parameters has been used that are freely available by NCEP in netcdf format for year 2005. The fluxes files has been generated for each grid using netcdf files of each parameter with 24 hours timestep. All the parameters are aligned in one row for one day in an ascii format file. In Soil parameter file soil properties are stored for each grid in ascii format.In this paper we have discussed the preparation of hydrological modeling inputs using free and open source GIS tools. We have used R, Python and QGIS to prepare these inputs.These files were used as input for final hydrological simulation In VIC with elevation bands option at 1km grid size. The model is able to simulate snow cover area, snow depth, snowmelt for entire basin and values matched well with MODIS based SCA for study area. Validation of SWE and snow depth will be done with passive microwave data.


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