Advancing Multivariate Flood Frequency Analysis Under Nonstationarity: Implications for Flood Forecasting Systems

Flood frequency analysis is crucial for understanding and mitigating the risks of extreme flood events. However, traditional methods often assume stationarity and fail to account for the complex physical phenomena driving changes in flood behaviour. This study addresses the challenge of nonstationary multivariate flood frequency analysis by incorporating multiple covariates that represent key physical processes influencing flood variables, such as peak discharge, volume, and duration. By leveraging advanced statistical methods, including copula-based modelling and covariate selection techniques, we provide a robust framework for analysing the dependencies and dynamics of flood variables under changing climatic and hydrological conditions. Applied to the Barakar River Basin, our framework identifies significant nonstationary trends influenced by covariates such as precipitation intensity, land-use changes, and soil moisture. Results reveal that the 100-year joint return period of extreme flood events has decreased significantly from (219,247m³  8264.58m³/s) in the stationary case to (175,881m³   7,241.52m³/s) in the nonstationary case for the volume-peak pair. Similarly, for the duration-volume pair, the stationary 100-year return period supersedes the nonstationary 100-year return period from (21.41days  149,776m³)  to (21.44days  159,189m³). Furthermore, the 100-year return level under stationary conditions (8150.44m³/s   30.23 days) is notably higher than the nonstationary equivalent (6995.5m³/s   25.09 days). The proposed methodology enhances the reliability of flood risk assessments by addressing the temporal evolution of key flood variables. Although not directly focused on early warning systems, the insights from this study can inform the development of probabilistic flood forecasting models and improve decision-making processes for disaster preparedness. By integrating physical drivers into multivariate flood frequency analysis, this work contributes to a deeper understanding of nonstationarity in flood regimes. The findings provide valuable implications for designing more adaptive and region-specific flood forecasting and warning systems, ultimately supporting global efforts to mitigate the impacts of extreme hydrometeorological events.