Bias‐adjusted satellite‐based rainfall estimates for predicting floods: Narayani Basin

Abstract

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32 000 km 2 ) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC_RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC_RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC_RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction.

An evaluation of high‐resolution gridded precipitation products over Bhutan (1998–2012)

“…Distribution-based bias correction approaches are frequently used in adjusting precipitation biases in satellite-based estimates (e.g. Shrestha et al, 2011;Müller and Thompson, 2013) and climate models (e.g. Piani et al, 2010).…”

Spatial and Temporal Variability of Rainfall in the Gandaki River Basin of Nepal Himalaya

“…The Gandaki River is partly snow-fed, originating from the southern edge of the Tibetan Plateau [22], flows through Nepal to India, and drains into the Ganges River. Elevations range from 60 m in the south to higher than 8000 m in the north where the watershed contains the Dhaulagiri (8167 m) and Annapurna (8091 m) peaks [23]. The area of the basin in Nepal is about 35,000 sq km and it covers all the agro-ecological zones in Nepal (Terai, Hill, and Mountain, including Trans-Himalaya), which have very contrasting climates.…”

Assessment of high‐resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Bagmati basin, Nepal

“…As a result, the streamflow ratio was 1.1. The good correlation and performance of the model using modified RFE data are consistent with the findings of previous studies in the Narayani river basin (Shrestha et al ., ). The excellent match at the peaks between simulated and observed flows indicates that the model using modified RFE is an effective tool for predicting floods and results of the analysis are similar to the findings of Thiemig et al .…”

“…In a current validation, modified National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center – Rainfall Estimates Version 2.0 (RFE) showed the best correspondence with observed rainfall and was the best product over the Brahmaputra basin, followed by TRMM, CMORPH, RFE, and GSMaP (Thiemig et al ., and Bajracharya et al ., ). The potential of modified RFE has been shown in a catchment (the Narayani river basin) where it was found to be suitable for use in hydrological applications (Shrestha et al ., ).…”