Abstract

Complexities involved in flood risks over global coastal multi-hazard catchments are a severe concern for vulnerable communities, infrastructure, and the environment. Data scarcity in these regions often hinders our holistic understanding of flood risks, especially when socio-economic and physical vulnerabilities are involved. The extent to which Satellite Precipitation Products (SPPs), which are looked upon as alternatives to ground-based observations, can influence flood risk dynamics remains unexplored. In an attempt to answer the most riveted questions in flood management literature, this study, for the first time, explores the suitability of two competent SPPs, i.e., CHIRPS v2.0 and PERSIANN-CDR, in multi-hazard flood risk mapping. The proposed framework is demonstrated over the sensitive flood-prone deltaic stretches of the Lower Mahanadi River Basin (India). A computationally efficient MIKE+ 1D2D hydrodynamic model is developed to account for the wave propagation of concurrent flood drivers and generate high-resolution flood hazard maps for three disastrous historical flood events (July 2019, September 2020, and August 2022). To understand the hidden characteristics of vulnerability, a comprehensive set of 24 physical and socio-economic indicators is considered in a Shannon-entropy and TOPSIS framework. The variations in flood risk from both SPPs at the finest administrative scale are represented using the novel concept of Bivariate Choropleth, which portrays the marginal and compound contributions of hazard and vulnerability. A superlative performance of CHIRPS v2.0 over PERSIANN-CDR was observed in capturing hydro-climatological behaviors. CHIRPS v2.0-derived flood hazards were found analogous to the SAR-derived maps for all the three events. >70 % of villages display large disparities in flood risk, thereby affirming the role of appropriate SPPs towards efficient flood management. The observations from the study add vital information to the existing flood management policies, especially over resource-constrained regions in low and middle-income nations.

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