摘要
传统洪涝管理方法多依赖工程措施,难以应对复杂多变的灾害情境,亟须引入系统性评估框架以提升城市应对能力。构建洪涝抵抗、经济恢复与功能恢复3个阶段评估体系,提出地理信息系统-贝叶斯网络(GIS-BN)耦合模型,通过条件概率计算与Birnbaum重要性测度(B_(I))量化关键因素贡献度,进而解析洪涝韧性驱动机制。以大湾区城市群案例进行城市洪涝韧性关键因素识别,研究发现:洪水管理水平(F_(M))和淹没风险(I_(R))为2个核心驱动因素;肇庆是城市洪涝抵抗力突出的城市,广州和深圳具有较强的经济恢复能力,同时广州的功能恢复能力优势明显;城市洪涝韧性在大湾区城市群呈现周边高、中心低的空间异质化特征。研究结果可为城市洪涝管理提供理论与技术支撑。
With rapid urbanization exacerbating flood risks in metropolitan regions,enhancing urban flood resilience has become a critical priority for disaster prevention and mitigation.Traditional flood management approaches,which predominantly relied on rigid engineering measures,are increasingly inadequate in addressing the complexity and dynamism of modern flood scenarios.This necessitated the development of a framework to evaluate and improve the adaptive capacity of urban systems.Focusing on urban agglomerations,this study aimed to identify key factors influencing flood resilience and unravel the underlying mechanisms driving spatial disparities in resilience levels.A three-stage assessment system was constructed,encompassing flood resistance,economic recovery capacity,and functional restoration capability.A novel Geographic Information System-Bayesian Network(GIS-BN)coupled model was proposed to quantify the contributions of critical factors through conditional probability analysis and Birnbaum Importance measures.The model integrated spatial data with probabilistic reasoning to evaluate both structural and non-structural factors,including drainage infrastructure,land use patterns,socioeconomic indicators,and emergency response mechanisms.The Guangdong-Hong Kong-Macao Greater Bay Area,a highly urbanized coastal region in China,was selected as the case study.Data were collected from hydrological records,satellite imagery,government reports,and field surveys,with variables discretized to align with Bayesian network requirements.Flood management capacity and inundation risk were the two most influential factors,accounting for 34.2%and 28.7%of the variability in resilience,respectively.Spatial analysis revealed significant heterogeneity:Zhaoqing exhibited superior flood resistance due to its low urbanization density and robust natural drainage systems,while Guangzhou and Shenzhen demonstrated strong economic recovery capabilities,attributed to their financial resources and infrastructure redundancy.Functional recovery was particularly effective in Guangzhou,where governance efficiency and public participation facilitated rapid post-disaster restoration.Conversely,core urban areas displayed lower resilience compared to peripheral zones,highlighting the vulnerability of densely populated centers to cascading failures.The GIS-BN model,validated through historical flood event comparisons,achieved 82.6%accuracy in predicting resilience levels.This study showed that urban flood resilience depends on interconnected physical,economic,and governance factors,rather than just infrastructure alone.The spatial resilience pattern in the Greater Bay Area emphasizes the need for region-specific strategies,focusing on nature-based solutions in peri-urban areas and adaptive governance in city centers.Additionally,the proposed GIS-BN framework offers policymakers a flexible tool to simulate intervention effects and better allocate resources.These findings suggest that future urban planning should incorporate resilience metrics into land-use zoning and promote cross-jurisdictional cooperation to address systemic vulnerabilities.Ultimately,these results enhance the understanding of resilience as a multi-dimensional concept and provide practical insights for flood-prone megacities worldwide.
作者
韩义超
王永阳
李昱
赵铜铁钢
彭勇
郝凌霄
HAN Yichao;WANG Yongyang;LI Yu;ZHAO Tongtiegang;PENG Yong;HAO Lingxiao(Liaoning Water Conservancy and Hydropower Survey and Design Research Institute Co.,Ltd.,Shenyang 110006,China;School of Hydraulic Engineering,Dalian University of Technology,Dalian 116000,China;Ningbo Institute of Dalian University of Technology,Ningbo 315000,China;School of Civil Engineering,Sun Yat-sen University,Zhuhai 519082,China;Hebei Water Group,Shijiazhuang 050011,China)
出处
《南水北调与水利科技(中英文)》
北大核心
2025年第6期1491-1500,共10页
South-to-North Water Transfers and Water Science & Technology
基金
国家重点研发计划青年项目(2022YFC3205100)
国家自然科学基金面上项目(52479005)。
关键词
城市洪涝韧性
GIS-BN模型
韧性理论
指标体系
大湾区
城市群
urban flood resilience
GIS-BN model
resilience theory
indicator system
Greater Bay Area
urban agglomeration
