From Complexity to Clarity: An Integrative Framework for Enhancing Flood Forecasting in Urban Catchments

Compound flooding poses significant global socio-economic and infrastructure risks that are projected to intensify due to climate change and anthropogenic development. These compound floods, where multiple interacting drivers amplify flood extents and depths, are the most widespread and catastrophic natural hazards, in particular in urban catchments, inflicting billions of dollars in damages and jeopardizing livelihoods and critical resources. A primary challenge in addressing these events lies in the incomplete understanding of the nonlinear and complex climatic, hydrological, and hydrodynamic processes involved in compound flooding, which often leads to ineffective flood management strategies. This gap in knowledge also limits the development of suitable tools and methods for accurate flood characterization and modeling. Given the massive and escalating impacts of such events, there is a clear need for a more comprehensive understanding of the key drivers that shape flood dynamics, including uncertainties related to climate, human activity, and natural systems. Although significant advances have been made in developing physically-based dynamic models for flood simulation, these models often fall short in terms of accuracy and reliability, and remain computationally intensive for operational use. These challenges stem from an incomplete understanding of flood processes, uncertainties in predictability, and limitations in model assumptions. This presentation addresses these challenges by proposing an integrated framework that incorporates human activity, hydrological factors, topography, river morphology, and land use to enhance our understanding of riverine, coastal, and compound flood generation. It also highlights strategies for improving flood forecasting and inundation modeling through the integration of state-of-the-art process-based models, data assimilation, and machine learning, while considering cascading uncertainties in both model predictions and real-world applications.