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<p>Flood risk assessment is essential for minimizing the adverse impacts of flooding on communities, particularly in regions experiencing more frequent and intense precipitation events. While existing flood modeling and mapping tools are widely used, workflows often require extensive manual processing especially when dealing with multi-basin and multi-scenario analyses which can affect efficiency and consistency. This study presents a semi-automated workflow designed to streamline flood risk modeling, mapping, and impact assessment using Python scripting within the ArcGIS Pro environment. The approach automates key steps including geoprocessing, hydrologic input preparation, map generation, and impact analysis, significantly reducing processing time by approximately 85% while ensuring uniformity across scenarios. The workflow was applied to generate 48 distinct flood scenarios incorporating rainfall, sea level rise, and tidal conditions. It includes probabilistic flood risk mapping using z-score calculations to account for modeling and elevation uncertainties. A case study from North Miami; Florida demonstrates how this semi-automated method improves efficiency and reproducibility in support of planning and decision-making. The proposed framework offers a flexible, scalable solution for local governments and water resource managers seeking timely, data-driven strategies for flood mitigation. </p>

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Semi-automated workflow for multi-basin, multi-scenario flood risk modeling, mapping, and impact assessment

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • A. K. Mandal,
  • Madan Thapa Chhetri,
  • Fred Bloetscher,
  • Yan Yong,
  • Hongbo Su

摘要

Flood risk assessment is essential for minimizing the adverse impacts of flooding on communities, particularly in regions experiencing more frequent and intense precipitation events. While existing flood modeling and mapping tools are widely used, workflows often require extensive manual processing especially when dealing with multi-basin and multi-scenario analyses which can affect efficiency and consistency. This study presents a semi-automated workflow designed to streamline flood risk modeling, mapping, and impact assessment using Python scripting within the ArcGIS Pro environment. The approach automates key steps including geoprocessing, hydrologic input preparation, map generation, and impact analysis, significantly reducing processing time by approximately 85% while ensuring uniformity across scenarios. The workflow was applied to generate 48 distinct flood scenarios incorporating rainfall, sea level rise, and tidal conditions. It includes probabilistic flood risk mapping using z-score calculations to account for modeling and elevation uncertainties. A case study from North Miami; Florida demonstrates how this semi-automated method improves efficiency and reproducibility in support of planning and decision-making. The proposed framework offers a flexible, scalable solution for local governments and water resource managers seeking timely, data-driven strategies for flood mitigation.