由于气候变化导致的极端降雨,城市洪水风险是近期重大风险之一,其发生频率和强度不断加快。系统评价城市洪涝灾害对社会经济的影响,本文提出了一种基于GIS的空间模糊综合评价(FCE)框架,用于地方政府有效地采取应急措施,特别是在紧急救援条件下。风险评估程序可以从四个方面进行研究:1)应用水动力模型来模拟淹没的深度和程度;2)用6种方法选择的有关运输衰减的评估指标来量化洪水的影响,住宅安全,根据深度损害函数和有形和无形的货币损失;3)实施FCE方法:利用模糊理论对城市洪水风险进行综合评价。4)在ArcGIS平台上直观地呈现单个和多个影响因素的风险图。在SA市进行了详细的案例研究,验证了所采用的多指标评估框架的有效性,这可以帮助检测运输效率低的高风险区域,高经济损失,社会影响大,和高无形损害。单因素分析的结果也可以为决策者和其他利益相关者提供可行性建议。理论上,由于可以通过水动力模型来模拟淹没分布,而不是使用危害因素进行主观预测,因此该方法倾向于提高评估精度。而洪水损失模型的影响量化也可以直接反映所涉及因素的脆弱性,而不是传统方法的经验权重分析。此外,结果表明,风险水平较高的地区合理地与严重的淹没情况和密集的危险体相吻合。这个系统的评估框架可以为进一步扩展到其他类似城市提供适用的参考。
Due to the climate change-induced extreme rainfall, urban flooding risk is one of the major concerning risks in the near future with accelerating occurrence frequency and intensity. To systematically evaluate the socioeconomic impacts induced by urban flooding, this paper proposed a GIS-based spatial fuzzy comprehensive evaluation (FCE) framework for local government to efficiently take contingency measures especially under urgent rescue conditions. The risk-assessing procedure could be investigated in 4 aspects: 1) application of the hydrodynamic model to simulate the depth and extent of inundation; 2) quantification of the impact of flooding with 6 methodically picked evaluation indexes concerning the transportation attenuation, residential security, and tangible and intangible monetary losses according to depth-damage functions; 3) implementing FCE method: comprehensive evaluation of urban flooding risk with the diverse socioeconomic indexes by fuzzy theory; and 4) presenting the risk maps of single and multiple impact factors intuitively in ArcGIS platform. The detailed case study in SA city validates the effectiveness of the adopted multiple index evaluation framework, which could help detect higher risk areas with low transport efficiency, high economic loss, high social impact, and high intangible damage. The results of single-factor analysis can also provide feasible suggestions for decision-makers and other stakeholders. Theoretically, the proposed method tends to improve the evaluation accuracy as the inundation distribution can be simulated by hydrodynamic model rather than subjective prediction with hazard factors, while the impact quantification with flood-loss models can also directly reflect the vulnerability of involved factors instead of empirical weight analysis of traditional methods. Besides, the results illustrate that the areas with higher risk levels reasonably coincide with severe inundation situations and dense hazard-bearing bodies. This systematic evaluation framework can support applicable references for further extension to other similar cities.