Large-sample characterization of flooding events in India

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Effective flood management requires a robust understanding of past floods. In India, such understanding is largely limited to case studies due to the absence of a standardized observed flood dataset. We address this gap by presenting a national dataset of 7500 flooding events, developed by merging observed streamflow records with official flooding thresholds and augmenting it with multiple catchment-scale variables. Spatial analysis reveals high normalized flood magnitudes along the southwest coast—an area with intense rainfall and mountainous terrain. Temporally, 86% of floods occur during the southwest monsoon. Using a random forest model combined with the game-theoretic SHAP approach, we find that precipitation of the wettest month is the most influential predictor of flood magnitude. Grouped feature importance shows climatology contributes 61% to model performance, while geomorphology accounts for 39%. This comprehensive large-sample study surpasses conventional case studies, providing a more robust understanding of flooding patterns and drivers across India.

Article activity feed