A spatiotemporal analysis of hydro-meteorological factors driving floods

Study Region: Major river basins in India. Study Focus: Floods pose major risks to human lives and infrastructure, requiring short to long-term mitigation measures. Modeling floods under changing conditions needs a better understanding of hydro-meteorological drivers beyond extreme rainfall. The long-assumed link between annual maximum rainfall (AMR) and annual maximum floods (AMF) is not well understood, as AMF events are not always associated with the highest rainfall. To address this, we examine the relative role of rainfall, soil moisture, evapotranspiration, and baseflow in generating AMF across Indian river basins. We define three rainfall-streamflow cases: (1) AMR leading to AMF, (2) AMR producing flow lower than AMF, and (3) heavy rainfall (less than AMR) triggering AMF. Temporal composite analysis, Bayesian multi-linear regression, and Moran’s I spatial autocorrelation are employed to disentangle these dynamics. New Hydrological Insights for the Region: Our results demonstrate that AMF is frequently caused by rainfall events smaller than AMR, underscoring the inadequacy of relying solely on maximum rainfall for flood estimation. Temporal analysis reveals that rainfall and baseflow are dominant drivers of high-magnitude AMF events, while spatial clustering reveals that floods are more likely when heavy rainfall coincides with low evapotranspiration and high soil moisture. These findings provide a novel synthesis of temporal and spatial drivers of AMF, emphasizing the need for flood risk assessments that incorporate multiple hydro-meteorological factors, especially under climate change.