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<p>Flash floods in ungauged and data-scarce catchments pose serious threats to human lives, infrastructure, and agricultural productivity, particularly in arid and semi-arid regions. Data Scarcity has exposed fundamental limitations in conventional flood hazard assessment approaches. This study presents a scalable framework for flash flood hazard assessment in such environments by integrating the HEC-HMS with hydraulic routing in HEC-RAS 2D, supported by remotely sensed data and geospatial analysis. Discharges against multiple return periods were simulated using the HEC-HMS, using rainfall, transposed from a hydrologically similar gauged catchment. The HEC-HMS model was calibrated using historical flood events of 2022. The model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.948, Root Mean Square Error (RMSE) of 0.2, and Percent Bias (PBIAS) of − 16.88%, indicating good agreement between simulated and observed flows. The Design hydrograph were used as boundary condition in HEC RAS to simulate the flood extent, flow depth and flow velocity. Model validation against Sentinel-1 SAR imagery from the 2022 flood event demonstrated high accuracy, with a Probability of Detection (POD) of 0.952 and a Critical Success Index (CSI) of 0.529. Hazard mapping based on the flow depth and velocity revealed a progressive increase in extreme hazard zones from 514&#xa0;ha (2%) to 2,498&#xa0;ha (7%) between the 10-year and 100-year return period scenarios. The proposed framework offers a practical and transferable methodology for flood hazard assessment in ungauged basins and supports climate-resilient planning and disaster mitigation strategies in vulnerable regions.</p>

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A scalable framework for flash flood hazard assessment in data-scarce catchments using coupled modeling

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Moiz Ahmed Khan,
  • Muhammad Masood,
  • Mudassar Iqbal,
  • Saliha Qamar,
  • Muhammad Awais Zafar,
  • Khalil Ahmad

摘要

Flash floods in ungauged and data-scarce catchments pose serious threats to human lives, infrastructure, and agricultural productivity, particularly in arid and semi-arid regions. Data Scarcity has exposed fundamental limitations in conventional flood hazard assessment approaches. This study presents a scalable framework for flash flood hazard assessment in such environments by integrating the HEC-HMS with hydraulic routing in HEC-RAS 2D, supported by remotely sensed data and geospatial analysis. Discharges against multiple return periods were simulated using the HEC-HMS, using rainfall, transposed from a hydrologically similar gauged catchment. The HEC-HMS model was calibrated using historical flood events of 2022. The model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.948, Root Mean Square Error (RMSE) of 0.2, and Percent Bias (PBIAS) of − 16.88%, indicating good agreement between simulated and observed flows. The Design hydrograph were used as boundary condition in HEC RAS to simulate the flood extent, flow depth and flow velocity. Model validation against Sentinel-1 SAR imagery from the 2022 flood event demonstrated high accuracy, with a Probability of Detection (POD) of 0.952 and a Critical Success Index (CSI) of 0.529. Hazard mapping based on the flow depth and velocity revealed a progressive increase in extreme hazard zones from 514 ha (2%) to 2,498 ha (7%) between the 10-year and 100-year return period scenarios. The proposed framework offers a practical and transferable methodology for flood hazard assessment in ungauged basins and supports climate-resilient planning and disaster mitigation strategies in vulnerable regions.