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<p>Floods are among the most devastating natural hazards, causing significant damage to communities, infrastructure, and local economies. This study integrates hydraulic modelling with geographic information systems and remote sensing techniques to simulate flood dynamics and assess housing exposure in the Vu Gia-Thu Bon River Basin, Quang Nam Province, Vietnam. The major historical 2020 flood event was analyzed using the MIKE FLOOD model to evaluate flood depth, duration, and velocity. Input data included topographic maps, river cross-sections, meteorological and hydrological records, and field-verified flood traces. Building footprints were extracted from high-resolution satellite imagery, and residential zones were mapped using recent land-use datasets. Flood inundation maps were overlaid with housing data to quantify exposure and identify spatial patterns of flood impact across key districts. The October 2020 flood event inundated an area of 7452.5 hectares and an estimated 800,994 flooded houses. In order of the districts affected by the 2020 flood event in terms of flood depth, duration, and velocity, as follows: Dien Ban &gt; Dai Loc &gt; Duy Xuyen &gt; Hoi An. The findings highlight the importance of integrating modelling tools with geospatial analysis to support risk-informed decision-making and enhance disaster preparedness in flood-prone regions.</p>

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Assessing flood hazard and housing exposure using hydraulic modelling and spatial analysis

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Quang Dong Pham,
  • Phan Thi Luong Ha,
  • Hang Ha,
  • Quynh Duy Bui,
  • Quoc-Hung Vu,
  • Chinh Luu

摘要

Floods are among the most devastating natural hazards, causing significant damage to communities, infrastructure, and local economies. This study integrates hydraulic modelling with geographic information systems and remote sensing techniques to simulate flood dynamics and assess housing exposure in the Vu Gia-Thu Bon River Basin, Quang Nam Province, Vietnam. The major historical 2020 flood event was analyzed using the MIKE FLOOD model to evaluate flood depth, duration, and velocity. Input data included topographic maps, river cross-sections, meteorological and hydrological records, and field-verified flood traces. Building footprints were extracted from high-resolution satellite imagery, and residential zones were mapped using recent land-use datasets. Flood inundation maps were overlaid with housing data to quantify exposure and identify spatial patterns of flood impact across key districts. The October 2020 flood event inundated an area of 7452.5 hectares and an estimated 800,994 flooded houses. In order of the districts affected by the 2020 flood event in terms of flood depth, duration, and velocity, as follows: Dien Ban > Dai Loc > Duy Xuyen > Hoi An. The findings highlight the importance of integrating modelling tools with geospatial analysis to support risk-informed decision-making and enhance disaster preparedness in flood-prone regions.