Abstract：
Risk assessment and adaptation have become key focuses in the examination of urban flooding risk. In recent decades, global climate change has resulted in a high incidence of extreme weather events, notably flooding. This study introduces a spatial multi-indicator model developed for assessing flood risk at the urban agglomeration scale. A crucial addition to the model is the incorporation of an adaptive capacity within the IPCC risk framework. The model systematically considers various flood risk indicators related to the economic, social, and geographic environments of the central and southern Liaoning urban agglomeration (CSLN). It generates a spatial distribution map of integrated flood risk for multiple scenario combinations. Furthermore, the intricate relationship between different risk indicators and flood risk was analyzed using correlation analysis and the Light Gradient Boosting Machine model (Light GBM). The findings reveal notable variations in flood risk under different scenarios. The inclusion of vulnerability indicators increased flood risk by 33 %, while the subsequent inclusion of adaptive indicators decreased flood risk by 45 %. Dense populations and assets contribute to high flood risk, while adaptive capacity significantly mitigates urban flood risk. The framework adopted in this paper can be applied to other areas where urban agglomeration-scale flood risk assessment is needed, and can contribute to advancing scientific research on flood forecasting and mitigation.
摘要：
风险评估和适应已成为研究城市洪水风险的重点。近几十年来,全球气候变化导致了极端天气事件的高发，尤其是洪水。本研究引入了一种用于评估城市群规模洪水风险的空间多指标模型。该模型的一个重要补充是将适应能力纳入IPCC风险框架。该模型系统地考虑了各种与经济、社会,辽宁中南部城市群（CSLN）的地理环境。它为多个场景组合生成综合洪水风险的空间分布图。此外，使用相关分析和光梯度增压机模型（LightGBM）分析了不同风险指标与洪水风险之间的复杂关系。研究结果揭示了不同情景下洪水风险的显着变化。脆弱性指标的加入使洪水风险增加了33%,而随后纳入适应性指标将洪水风险降低了45%。密集的人口和资产导致高洪水风险，同时适应能力显著缓解了城市洪水风险。本文采用的框架可以应用于其他需要城市群规模洪水风险评估的地区,并有助于推进洪水预报和减灾的科学研究。
