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<p>Climate change is intensifying extreme rainfall events, increasing flood risks to urban populations, infrastructure, and public health. In rapidly urbanizing monsoon regions such as the Erqi District of Zhengzhou, China, there is an urgent need for scalable methods to assess urban flood vulnerability and support adaptive planning. This study identifies urban flood risk hotspots using a multi-scale GIS-based hydrological modeling framework that captures spatiotemporal runoff dynamics under real world extreme precipitation. Integrating land-use, digital elevation, and precipitation datasets, we simulated surface runoff across three spatial scales (built-up areas, administrative units, and 300&#xa0;m grids) and three temporal scales (daily, monthly, and seasonal precipitation). Short-duration rainfall events produced sharp, localized runoff peaks, whereas long-duration events generated broader but less concentrated flooding. Built-up areas consistently produced more than twice the runoff volume of natural surfaces. At the administrative scale, Houzhaixiang recorded the highest total runoff (2.7 × 10⁵ m³ under monthly precipitation), while high-density areas such as Changjianglu exhibited the greatest runoff intensity (15.6&#xa0;mm across 3.9&#xa0;km²). High-resolution grid analysis delineated specific flood-prone zones within the urban core. Additional simulations at 200&#xa0;m and 400&#xa0;m grid resolutions confirmed the spatial consistency of the results, supporting the robustness of the hotspot identification method. This study provides a transferable, multi-scale framework for urban flood-risk mapping that can inform climate adaptation planning. The findings support targeted mitigation strategies, infrastructure upgrades, and nature-based solutions to reduce flood exposure in vulnerable areas. By aligning with global objectives such as disaster risk reduction (SDG 11.5), climate resilience (SDG 13.1), and integrated water management (SDG 6.5), this study contributes to strengthening urban resilience and protecting human well-being amid environmental change.</p>

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Multi-scale spatiotemporal gis mapping of urban flood risk hotspots in Zhengzhou: implications for adaptation strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

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  • Xiaoyan Zhang,
  • Xinyu Wang,
  • Zsolt Győző Török

摘要

Climate change is intensifying extreme rainfall events, increasing flood risks to urban populations, infrastructure, and public health. In rapidly urbanizing monsoon regions such as the Erqi District of Zhengzhou, China, there is an urgent need for scalable methods to assess urban flood vulnerability and support adaptive planning. This study identifies urban flood risk hotspots using a multi-scale GIS-based hydrological modeling framework that captures spatiotemporal runoff dynamics under real world extreme precipitation. Integrating land-use, digital elevation, and precipitation datasets, we simulated surface runoff across three spatial scales (built-up areas, administrative units, and 300 m grids) and three temporal scales (daily, monthly, and seasonal precipitation). Short-duration rainfall events produced sharp, localized runoff peaks, whereas long-duration events generated broader but less concentrated flooding. Built-up areas consistently produced more than twice the runoff volume of natural surfaces. At the administrative scale, Houzhaixiang recorded the highest total runoff (2.7 × 10⁵ m³ under monthly precipitation), while high-density areas such as Changjianglu exhibited the greatest runoff intensity (15.6 mm across 3.9 km²). High-resolution grid analysis delineated specific flood-prone zones within the urban core. Additional simulations at 200 m and 400 m grid resolutions confirmed the spatial consistency of the results, supporting the robustness of the hotspot identification method. This study provides a transferable, multi-scale framework for urban flood-risk mapping that can inform climate adaptation planning. The findings support targeted mitigation strategies, infrastructure upgrades, and nature-based solutions to reduce flood exposure in vulnerable areas. By aligning with global objectives such as disaster risk reduction (SDG 11.5), climate resilience (SDG 13.1), and integrated water management (SDG 6.5), this study contributes to strengthening urban resilience and protecting human well-being amid environmental change.