In this study, we have described a hydrologic modelling system that uses satellite‐based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.
Snow governs interaction between atmospheric and land surface processes in high mountains, and is also source of fresh water. It is thus important to both climate scientists and local communities. However, our understanding of snow cover dynamics in terms of space and time is limited across the Hindu Kush Himalaya (HKH) region, which is known to be a climatically sensitive region. We used MODIS snow cover area (SCA) data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012), APHRODITE temperature data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007), and monthly long term in-situ river discharge data of the Gandaki (1968Gandaki ( -2010, Koshi (1977Koshi ( -2010 and Manas (1987Manas ( -2004 basins to analyse variations among four basins. We gained insights into short term SCA and temperature, long term discharge trends, and regional variability thereby. Strong correlations were observed among SCA, temperature and discharge thereby highlighting the strong nexus between them. Temporal and spatial snow cover variability across the basins is strongly coupled with the variability of two weather systems: Western Disturbances (WD) and Indian Monsoon System (IMS), and strongly influenced by topography. Manifestation of these variability in terms if downstream discharge can have repercussion to water based sectors: hydropower and agriculture, as low flow seasons is seen affected. This study adds to our knowledge of snow fall and melt dynamics in the HKH region, and intra-annual snow melt contributions to downstream discharges. The study is limited by short span of data and it is desirable to perform a similar study using data representing a much longer time span.
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