Harnessing WRF Numerical Weather Prediction for Enhanced Flood and Damage Forecasting in Urban Environments

Accurate flood and damage forecasting is critical for mitigating the devastating impacts of floods, which globally affect millions and cause substantial economic losses. This study evaluates the efficacy of the Weather Research and Forecasting (WRF) model in simulating precipitation for flood forecasting and direct damage estimation in the Poldokhtar basin, Iran. By testing 87 WRF configurations, optimal schemes (e.g., WSM3, Kessler, Grell 3D, and YSU) were identified for accurate precipitation and flood simulation. Coupled with the HEC-HMS and HEC-RAS models, WRF outputs facilitated robust flood hydrograph and hydraulic simulations, achieving Nash-Sutcliffe efficiency above 0.85 and peak discharge errors below 15%. Bias correction using quantile mapping significantly enhanced peak flood accuracy, reducing errors to as low as 1.25% for the 2018 event. A depth-damage curve was developed, showed strong correlations (CC > 0.92) with observed flood damages, and the average damage estimates ranging from 41% to 70% across residential areas for the 2016–2019 events. The results demonstrate that integrating WRF with hydrological and hydraulic models provides a reliable and practical framework for flood forecasting and damage assessment in urban environments, offering valuable insights for flood risk management.