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Flood risk management in data-scarce and tropical cyclone-influenced regions can be improved by assessing multi-scale hydrological and hydraulic variability, derived from changes in anthropogenic, climatological and moisture conditions. This study proposes a methodology to analyse multi-scale variability response of a basin and floodplain to annual maximum events, enabling variability-informed flood management measures. The approach integrates distributed hydrological modelling with two-dimensional hydraulic simulation at sub-basin, basin and floodplain scale, to determine key flood characteristics across multiple events. The framework was applied to the Acaponeta River basin and floodplain, on Mexico’s Pacific Coast, which is flood-prone. Results indicated moderate variability. Climatological conditions exhibit greater variation (Coefficient of Variation CoV = 0.36) than land use changes (CoV = 0.22), transferring to high hydrological variability in peak discharges (CoV = 1.06) and moderate hydraulic variability in flood extent (CoV = 0.50). Flood variability is associated with long-term climatic trends (+0.28 mm/year for precipitation; +0.016°C/year for temperature) and interannual variability (Spearman correlation with ENSO indices ρ ≥ 0.63). By analysing flood-driving factors across spatial scales, this study provides a transferable framework for targeted multi-scale flood management, enhancing flood resilience in data-scarce and cyclone-prone regions.
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Bonilla-López et al. (2026) studied this question.
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