城市建筑小区内涝风险快速识别与驱动因素分析  

Rapid identification and driving factor analysis of waterlogging risk in urban building communities

作　　者：王璇 张伟[1,2,3] 刘方华 孔烨 孙慧超 WANG Xuan;ZHANG Wei;LIU Fanghua;KONG Ye;SUN Huichao(Beijing Engineering Research Center of Sustainable Urban Sewage System Construction and Risk Control,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Key Laboratory of Urban Stormwater System and Water Environment,Ministry of Education,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Energy Conservation&Sustainable Urban and Rural Development Provincial and Ministry Co-construction Collaboration Innovation Center,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Luzhou Housing and Urban-Rural Development Bureau,Luzhou 646000,China;CAUPD Beijing Planning&Design Consultants Co.,Ltd.,Beijing 100044,China)

机构地区：[1]北京建筑大学北京市可持续城市排水系统构建与风险控制工程技术研究中心,北京100044 [2]北京建筑大学城市雨水系统与水环境教育部重点实验室,北京100044 [3]北京建筑大学北京节能减排与城乡可持续发展省部共建协同创新中心,北京100044 [4]泸州市住房和城乡建设局,四川泸州646000 [5]中规院(北京)规划设计有限公司,北京100044

出　　处：《人民长江》2024年第5期23-32,共10页Yangtze River

基　　金：国家重点研发计划项目(2021YFC3001400);泸州市海绵城市科研课题研究项目(N5105012022000106);北京市属高等学校高水平科研创新团队建设支持计划项目(BPHR20220108);北京建筑大学培育项目专项资金资助项目(X23047)。

摘　　要：城市内涝风险快速识别及致涝因素初步分析是开展城市内涝治理的首要工作,传统的城市排水模型模拟方法需要高精度的基础数据支持和较长的计算周期,难以满足城市内涝快速识别需求。基于泸州市中心城区建筑小区2015~2022年实际内涝灾害数据,通过核密度估计和空间相关性分析对中心城区建筑小区内涝风险空间分布进行了快速识别,并采用Spearman相关分析和地理探测器法对内涝驱动因素进行了分析。结果表明:泸州市中心城区建筑小区内涝风险呈现从中心向四周逐渐降低的趋势,高风险区域主要位于城北片区、中心半岛老城片区和龙马潭老城片区;土壤地质、土地利用、社会因素和降雨因素是内涝风险的主要驱动因素,并表现为多因素协同发生的复杂形式。研究成果可为泸州市内涝风险精细化模拟分析提供基础,也可为西南丘陵城市建筑小区内涝风险快速识别及致涝因素初步分析提供方法支撑。Rapid identification and driving factor analysis of urban waterlogging risk have been the primary requirements to implement urban waterlogging management.However,the traditional urban drainage modelling method requires high-resolution basic data support and a large modeling cost,which is difficult to meet the demand of urban waterlogging rapid identification.Based on the actual urban waterlogging disaster data of building communities in Luzhou City from 2015 to 2022,the spatial distribution of waterlogging risk in building communities was rapidly identified using kernel density estimation and spatial correlation analysis.Spearman correlation analysis and geodetector approach were used to investigate the waterlogging risk driving factors.The results show that the waterlogging risk of building communities in Luzhou City tends to decrease gradually from the center to the perimeter,and the high-risk areas are mainly located in the Chengbei region,Zhongxinbandao region and Longmatan region.The primary driving factors of the waterlogging risk are soil texture,land use,social factors,and rainfall factors,and they exhibit complex forms of multifactorial synergies.The results can provide a basis for high-resolution modeling of the waterlogging risk in Luzhou City,and the methods can also provide methodological support for a rapid identification of the waterlogging risk and preliminary analysis on factors contributing to waterlogging in urban building communities in hilly cities of southwest China.

关 键 词：建筑小区 城市内涝风险 快速识别 驱动因素 核密度估计 空间相关性分析 Spearman相关分析 地理探测器 丘陵城市 

分 类 号：TU992[建筑科学—市政工程] TU998.4