Zarei et al. (2025) Integrative modeling for enhanced flood risk forecasting and management in Semi-Arid area of Iran

Identification

• Journal: Applied Water Science
• Year: 2025
• Date: 2025-12-02
• Authors: Mahdi Zarei, Rasoul Sarvestan, Seyedhassan Alavinia, Leila Rahimi
• DOI: 10.1007/s13201-025-02699-5

Research Groups

• Research Center of Social Studies and Geographical Sciences, Hakim Sabzevari University, Sabzevar, Iran
• Faculty of Natural Resources and Earth Sciences, Department of Nature Engineering, University of Kashan, Kashan, Iran
• Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, USA
• Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL, USA

Short Summary

This study developed an integrated multi-model framework (WRF-HC-HMS-HEC-RAS) for enhanced pre- and post-flood risk forecasting and management in a semi-arid Iranian basin, successfully predicting 48-hour floods and mapping post-event damage.

Objective

• To develop and apply a comprehensive integrated framework combining meteorological, hydrological, and hydraulic models with remote sensing to forecast floods 48 hours in advance and assess post-event flood damage in the Kashafrud River basin, a semi-arid region in Iran.

Study Configuration

• Spatial Scale: Kashafrud River basin, northeastern Razavi Khorasan province, Iran, with a total area of approximately 16,750 square kilometers. WRF model utilized a nested domain setup with an outer resolution of 9 kilometers and an inner resolution of 5 kilometers.
• Temporal Scale: 48-hour precipitation and runoff forecasting. The study analyzed five rainfall events from 2019 to 2022. Observational rainfall data spanned from 1982 to 2022. Satellite imagery was collected for periods before, during, and after flood events.

Methodology and Data

• Models used:
  • Weather Research and Forecasting (WRF) model (version WRF 4.1.5, ARW core) for rainfall prediction, with various microphysics schemes tested (Lin, Kessler, Ferrier, WSM3, WSM5).
  • Hydrologic Component-Hydrologic Modeling System (HC-HMS) model for rainfall-runoff simulation, employing the Soil Conservation Service (SCS) curve number method.
  • Hydraulic Engineering Center-River Analysis System (HEC-RAS) model (version 0.6) for two-dimensional unsteady flow simulation and flood inundation mapping.
  • Google Earth Engine (GEE) platform for post-flood damage area estimation.
• Data sources:
  • Global Forecast System (GFS) model data (25 km resolution) for WRF initial and lateral boundary conditions.
  • Daily rainfall data from the Mashhad Administration rain gauge station (1982–2022), provided by the Iran Meteorological Organization.
  • Sentinel-1 satellite imagery (from Copernicus website) for pre- and post-flood damage assessment.
  • Digital Elevation Model (DEM) for hydraulic modeling.
  • Field surveys and photographic evidence for Manning's roughness coefficients.

Main Results

• The WRF model, specifically the Lin microphysics scheme, demonstrated the highest accuracy in forecasting 48-hour precipitation, achieving a True Skill Score (TS) of 0.93. The variance between observed and predicted rainfall was less than 5 millimeters for most events.
• The coupled WRF-HC-HMS model showed a Nash-Sutcliffe Efficiency (NSE) ranging from 0.33 to 0.93 for 48-hour runoff simulation.
• The integration of HC-HMS outputs into the HEC-RAS hydraulic model produced two-dimensional flood inundation maps with simulation accuracies (NSE) between 0.60 and 0.83.
• Flood inundation velocity in the Kashafrud River exceeded 15 meters per second in the middle and most parts of the river during high flow events.
• The maximum inundation depth reached 1.5 meters during the highest rainfall event.
• Post-flood damage assessment using Google Earth Engine and Sentinel-1 imagery estimated the maximum affected area at 2327.8 hectares and the minimum at 328.5 hectares for the studied events.
• The integrated framework successfully provides 48-hour runoff/precipitation forecasts and flood hazard maps, enabling early flood warnings and estimation of flood-affected zones.

Contributions

• This study introduces a novel integrated framework that combines meteorological, hydrological, and hydraulic modeling with pre- and post-event remote sensing data, offering a comprehensive approach to both 48-hour flood forecasting and post-event damage mapping, which is a significant advancement over studies focusing solely on prediction.
• It provides an effective, affordable, and simple methodology for complete pre- and post-flood operations within watersheds, from rainfall forecasting to flood damage area simulation, particularly valuable for semi-arid regions.
• The research enhances the capability for timely decision-making and preparedness for flood disasters, improving public awareness and response strategies.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Citation

@article{Zarei2025Integrative,
  author = {Zarei, Mahdi and Sarvestan, Rasoul and Alavinia, Seyedhassan and Rahimi, Leila},
  title = {Integrative modeling for enhanced flood risk forecasting and management in Semi-Arid area of Iran},
  journal = {Applied Water Science},
  year = {2025},
  doi = {10.1007/s13201-025-02699-5},
  url = {https://doi.org/10.1007/s13201-025-02699-5}
}