Towards a Dual-Scale Flood Risk Assessment in India: Copula-Based Urban Extremes, Basin-Scale Design Flood Simulation, and Socio-Hydrological Vulnerability

Flooding remains one of India’s most persistent hydroclimatic threats, driven by monsoon variability, rapid urbanisation, and chronic gaps in observational hydrometric data. These limitations affect both fast-growing cities and large river basins, where ungauged tributaries and limited socio-economic preparedness compound the overall risk. To address this challenge, we develop a holistic, multi-scale flood-risk assessment framework that integrates reanalysis-driven flood hazards with socio-hydrological vulnerability patterns across India. First, four leading Hydrological Reanalysis Datasets, ERA5, IMDAA, CFSR/CFSv2, and MERRA-2, are evaluated using three decades of rainfall. ERA5 emerges as the most reliable dataset across diverse hydro-climatic zones. Validation using CC, KGE, NSE, POD, FAR, EB, and CSI shows strong agreement with gauged data.  At the city scale, compound flood behaviour is quantified using bivariate and trivariate copulas linking long-duration rainfall (Rx1day), short cloudbursts (N25), and annual peak discharge (Q). River-connected cities exhibit pronounced upper-tail dependence under Gumbel–Hougaard structures, revealing synchronised extremes where intense rainfall and river overflow co-occur. Non-river cities show distinct rainfall-only compound signatures. These joint-probability structures provide realistic estimates of compound flood likelihoods in complex urban environments. At the basin scale, an extreme-value workflow using rolling annual maxima, KS-based distribution selection, and event-shape normalisation is applied to generate gridded 3-day, 5-day, and 7-day RP100 rainfall and discharge inputs. These time series force LISFLOOD-FP to produce representative design-flood hazard layers—depth, velocity, and depth×velocity—across India’s major basins. To capture the human dimension of flood impacts, a socio-hydrological module integrates 54 indicators of exposure, sensitivity, and adaptive capacity using a multi-criteria decision-making approach. District-level vulnerability scores and rankings reveal high- and very-high-vulnerability clusters across eastern, central, and northeastern India. These socio-economic patterns are analysed alongside hazard outputs, identifying critical “hotspot” regions where high exposure (population and area), weak coping capacity, and severe hydrodynamic hazards converge.