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Abstract

Snow water equivalent (SWE) quantifies the amount of water present in a snowpack. Water in a snowpack is dependent on its depth, density, type of snow, previous freezing-thawing cycles, recent precipitation events, etc. This study deals with estimation of snow depth (HS) and snow density (RHO) from remotely sensed data using empirical relationships developed elsewhere and thereby generation of SWE maps and assessing the usability of such approach for non-instrumented eastern Himalayan watersheds. Remotely sensed data: digital elevation model (DEM), MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST), snow albedo, fractional snow cover and precipitation were used as inputs. Models were developed to find out the RHO, HS and SWE at pixel level. Finally, the SWE maps were generated for the dominating snow months of the year 2013. Results showed that RHO increased from January to May with its peak in the month of May having considerable amount of ripened snow. HS and SWE were found to follow the same trend as SCA (Snow Covered Area), which increased from January to peak in February and then decreased till May. The estimated RHO, HS and SWE were found acceptable in comparison to other published data collected from relevant research works on the Himalayan region and SWE from Global Land Data Assimilation Systems NOAH Land Surface Model (GLDAS NOAH LSM) simulation.