Using satellite‐based rainfall estimates for streamflow modelling: Bagmati Basin

Abstract

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.

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

“…Yatagai and Kawamoto [18] found that the TRMM Precipitation Radar (PR) product underestimated summer precipitation by 28-38% over the Himalayas. Focusing specifically on Nepal, Shrestha et al [19] found that streamflow in the Bagmati river (in a region of Nepal with relatively dense station coverage) was simulated poorly when forced by basin-wide precipitation from a Climate Prediction Center (CPC) satellite-derived product compared to using interpolated rain gauge measurements, since the CPC product failed to capture some of the heaviest precipitation events. Similarly, Islam et al [20] found that Version 6 of the TRMM product generally underestimated daily precipitation over Nepal compared to available station data.…”

Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

“…Several precipitation estimates derived from satellite data or modeled through retrospective weather forecast model analysis (reanalysis) provide estimates that are independent from gauge networks. Both types of precipitation estimates have being increasingly used in hydrological applications [e.g., for reanalysis (Dedong et al 2007;Li et al 2009;Yan et al 2010;MiguezMacho and Fan 2012) and for satellite (Shrestha et al 2008;Behrangi et al 2011;Khan et al 2012; among many others)].…”

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

“…For the preparedness phases, establishing an early warning system is the most recognized application of EO. Applications for early flood and drought warnings were proposed [20,21,[27][28][29] and have been introduced as pilot operations [45].…”

“…Earth observations (EO) have served as an important information resource in DRM and has been applied to earthquakes [13][14][15], tsunami arrival predictions [16], volcanic hazards [17,18], floods [19][20][21][22][23], landslides [24,25], forest fires [26], and drought monitoring and prediction [27][28][29][30][31]. Earth observations can help to simultaneously capture the land condition of a broad area and are less expensive than field surveys.…”