Abstract

The dynamics of flood risk over Coastal Multi-hazard Catchments (CMC) exhibit bizarre characteristics. In these regions, flood hazards are governed by a complex interaction of multiple flood-inducing sources; varying in magnitudes, origin, and direction of propagation. Our conventional understanding of vulnerability may be obscure within these catchments. This can be attributable to the heterogeneous nature of various physical and socio-economic entities. The study proposes a comprehensive framework to quantify bivariate flood risks over a severely flood-prone region in India. The study considers flood hazards, along with vulnerabilities transpiring from (a) physical, (b) socio-economic, and (c) composite (combination of both) groups of indicators. To overcome data scarcity prevalent in CMCs, CHIRPS v2.0, a high-resolution Satellite Precipitation Product, along with other ancillary datasets, are forced to 1D2D coupled MIKE+ hydrodynamic model to simulate flood hazards. A set of 24 indicators are considered within the Shannon Entropy-cum-TOPSIS framework to derive three types of vulnerability. The marginal and compound contributions of hazard and each vulnerability type are represented through a novel concept of bivariate flood risk classifier at the village scale. We notice high and very-high flood hazards over the coastline and floodplains. An equitable influence of socio-economic vulnerability and hazards is reflected, as they cover 41 % of villages together under varied degrees of flood risks. The impacts of hazards are underscored in the presence of physical vulnerability, as the latter contributes to risks in about 72 % of villages. Composite vulnerability prevails its impact over 53 % of villages, dominating its influence on flood risks over hazards. The study delivers vital information to the global flood management community on the prudent selection of indicators, as their influence is markedly noticed on the overall flood risks. The diversified characteristics of flood risk inspire a rationalized implementation of structural and non-structural options in resource-constrained conditions.

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