"A Comprehensive Assessment of Suitability of Global Precipitation Products for Hydro- meteorological Applications in a Data-Sparse Himalayan Region"

Author:

Kumar Sonu1,Babel Mukand S2,Agarwal Anshul2,Khadka Dibesh2,Baghel Triambak2

Affiliation:

1. National Institute of Education (NIE) and Earth Observatory of Singapore (EOS), Nanyang Technological University (NTU)

2. Asian Institute of Technology

Abstract

Abstract Observation rainfall networks in developing countries like Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to Gauge-based Observed Rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions still needs to be discovered and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak river basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. The performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.78) and the least percentage bias (PBIAS < 15%). At a daily timescale, IMERG_Final, ERA5, and PERSIANN_CDR stand out for probability of detection (POD) of rainfall, while all GPPs perform poorly in False alarm ratio (FAR). Although all GPPs have relatively high RMSE (6–14 mm/day), correlation (CC) with observed rainfall was high for IMERG_Final, ERA5, and MERRA_2 in most of the sub-basins. With elevation, the performance of all GPPs is reduced, as evidenced by higher RMSE, PBIAS, and lower CC. Although there is plenty of room for improvements in rainfall estimation by GPPs, among the existing dataset, IMERG_Final scored best in the majority of the performance indicators and ranked first in five out of six sub-basins. It would be relatively the better choice in the data-sparse Himalayan region when daily rainfall data is required. For applications that require monthly/annual rainfall, both CHIRPS and IMERG_Final are equally suitable. The method proposed in the study for assessing GPPs can be readily applied in other river basins and at sub-daily timescales.

Publisher

Research Square Platform LLC

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