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Abstract

Due to the confluence of multiple flood drivers (e.g., monsoonal rainfall, cyclone-induced rainfall, and storm tide), coastal regions are expediting into global flooding hotspots- a matter of grave concern for stakeholders and policy-makers. In recent times, Satellite Precipitation Products (SPPs) have gained immense popularity as an ideal alternative for ground-based observations. However, the sensitivity of these SPPs for the quantification of compound flood risk is not explored so far. The present study considers state-of-the-art five widely used SPPs, namely, CHIRPS v2.0, PERSIANN-CCS-CDR, GSMaP- Gauge v7, SM2RAINASCAT V1.3, and CMORPH. A set of design rainfalls for 50-yr, 100-yr, and 200-yrs is derived from each SPP, considering the Annual Maxima Series (AMS). The MIKE+ 1D-2D dynamically coupled model set-up is developed to derive flood hazard maps for various scenarios. The widely used non-parametric Data Envelopment Analysis (DEA) technique is adopted to quantify vulnerability, which comprises a list of sensitive socio-economic indicators. The flood hazard and vulnerability maps are agglomerated to derive bivariate flood risk information for each SPP. The comprehensive framework of understanding the efficacy of different SPPs in quantifying flood risk is demonstrated over Jagatsinghpur district (~ 1760 km²), a severely flood-prone urbanizing catchment in the Lower Mahanadi River Basin, India. It was found that CHIRPS v2.0 and PERSIANN-CCS-CDR performed better in capturing flood risk information at the inundation scale. The study highlights a careful selection of SPP for accurately identifying flood risk at the finest administrative scale, which is essential information to the stakeholders involved in flood protection and management.