Unraveling Hydrometeorological Drivers of Floods: A Data-Driven Analysis Across India's River Catchments

The risk associated with river floods has escalated significantly due to the increasing frequency and intensity of extreme precipitation events, compounded by the complex interplay of flood-generating mechanisms under a changing climate. Accurately estimating flood risks poses a formidable challenge, as these mechanisms often interact and exhibit varying influences across spatial and temporal scales. An in-depth understanding of flood-generating processes is critical for improving hydrological modeling, flood frequency analysis, and risk management strategies in diverse climatic regions. Despite India being one of the most flood-prone countries globally, a systematic classification of hydrometeorological flood-generating processes remains largely absent. Understanding the role of catchment and climate attributes in flood generation is crucial for advancing our ability to predict and manage flood risks. This study proposes a robust framework to classify flood-generating processes into three primary categories: long rainfall floods, short rainfall floods, and excess rain floods. The analysis focuses on major river basins across India, offering insights into region-specific flood dynamics. To achieve this, we leverage the CAMELS-IND dataset, a comprehensive repository of hydrological and meteorological data, covering 471 catchments across India from 1980 to 2020. Using a peaks-over-threshold (POT) approach, we identify significant flood events and assess their characteristics. We then employ a Light Gradient Boosting Machine (LightGBM) model, an advanced machine learning algorithm known for its efficiency and accuracy, to evaluate the contribution of various climate and catchment attributes in triggering these floods. To enhance interpretability, we integrate Shapley additive explanations (SHAP), which provide a localized and global understanding of the model's predictions, highlighting the relative importance of each attribute. Our findings underscore the dominant role of climate attributes, such as precipitation intensity, antecedent soil moisture, and temperature, in determining the spatial distribution of flood-generating processes across diverse climatic zones. Catchment attributes, including soil type, slope, and land use, also contribute but to a lesser extent. These insights have significant implications for flood risk management, particularly in ungauged catchments, and can enhance the accuracy of hydrological and hydrodynamic models under changing climatic conditions.