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<p>Accurate flood and damage forecasting is essential for mitigating the severe impacts of floods, which affect millions of people worldwide and cause substantial economic losses each year. 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. The WRF outputs were coupled with the HEC-HMS and HEC-RAS models to simulate flood hydrographs and hydraulic characteristics. The integrated modeling framework achieved Nash-Sutcliffe efficiency values above 0.85 and peak discharge errors below 15%. Bias correction using quantile mapping significantly improved peak flood estimates, reducing errors to as low as 1.25% for the 2018 event. An empirical depth-damage curve was developed based on observed data and showed strong correlations (CC &gt; 0.92) with reported flood damages. The average estimated damage to residential areas ranged from 41–70% for the flood events between 2016 and 2019. Overall, the results demonstrate that integrating WRF with hydrological and hydraulic models provides a robust and practical framework for flood forecasting and damage assessment in urban environments, offering valuable insights for flood risk management.</p>

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Improving flood and damage forecasting through WRF-based hydrological and hydraulic models in urban environments

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Sakine Koohi,
  • Asghar Azizian,
  • Mohammad Saeed Najafi

摘要

Accurate flood and damage forecasting is essential for mitigating the severe impacts of floods, which affect millions of people worldwide and cause substantial economic losses each year. 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. The WRF outputs were coupled with the HEC-HMS and HEC-RAS models to simulate flood hydrographs and hydraulic characteristics. The integrated modeling framework achieved Nash-Sutcliffe efficiency values above 0.85 and peak discharge errors below 15%. Bias correction using quantile mapping significantly improved peak flood estimates, reducing errors to as low as 1.25% for the 2018 event. An empirical depth-damage curve was developed based on observed data and showed strong correlations (CC > 0.92) with reported flood damages. The average estimated damage to residential areas ranged from 41–70% for the flood events between 2016 and 2019. Overall, the results demonstrate that integrating WRF with hydrological and hydraulic models provides a robust and practical framework for flood forecasting and damage assessment in urban environments, offering valuable insights for flood risk management.