Neglecting Spatiotemporal Rainfall Variability Underestimates Flood Hazard and Risk

This project investigates the effects of commonly held assumptions around rainfall and probability in flood hazard and risk modeling. Flood hazard and risk models are produced through computer simulations, in which natural systems are simplified to make the simulation computationally feasible. One such simplification is the "design storm" approach to simulating rainfall, in which a synthetic storm is constructed for a specified duration and return period (e.g., the 24-hour, 100-year storm), with precipitation represented as uniform in space, and highly idealized in time. The return period associated with the resulting flooding and damage are then assumed to be equal to that of the storm (e.g., a 100-year storm would produce 100-year flood and damage events). We investigate the effects of these assumptions on flood hazard maps and risk datasets by comparing those produced through design storm approaches against those produced via stochastic storm transposition (SST), an alternative, probabilistic approach that preserves realistic space-time structures in rainfall and allows for nonlinear relationships between the frequency of rainfall, hazard, and risk. This research provides insight into the validity of common flood hazard/risk modeling assumptions, with implications for the accuracy and utility of existing flood hazard and risk information. It is broadly directed toward audiences interested in flood hazard and risk, including hydrologists, modelers, engineers, floodplain managers, planners, and city managers. The datasets produced by this research, as provided herein, allow for validation of the results and conclusions drawn from this research, and enable future studies investigating e.g., the potential building-scale economic impacts of flooding, optimal flood risk reduction and management strategies, and other studies leveraging high-resolution flood hazard and risk data.