Scholarlink

<p>As the core element triggering urban waterlogging, disaster-causing rainfall is difficult to predict accurately due to its randomness and uncertainty. Identifying disaster-causing rainfall and clarifying its evolutionary characteristics and development patterns are of great significance for flood disaster reduction. The research takes the High-tech Zone of Zhengzhou City in China as the study area, constructs an SWMM model for urban waterlogging simulation, and identify the disaster-causing rainfall events based on waterlogging risk process. Then, the copula function was employed to construct multidimensional joint distributions of the key characteristics of disaster-causing rainfall to assess their behaviors. The results are as follows: (1) A total of 48 disaster-causing rainfall events were identified in the study area, with event-scale precipitation ranging from 32.5 to 86&#xa0;mm. (2) The joint probability tends to increase with the magnitude of rainfall characteristics, and the event return periods mainly concentrates between 1 and 10 years. Two-dimensional event return periods may overestimate disaster risk compared to three-dimensional ones. (3) The synchronous encounter probability among total rainfall, rainfall intensity, and peak rainfall was found to be substantially greater than the asynchronous probability, indicating a strong positive correlation among these variables.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of urban disaster-causing rainfall and analysis of its key characteristics based on urban waterlogging hazard process

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月13日

查看全文
  • Xuechun Li,
  • Jinping Zhang,
  • Zhiwei Li,
  • Derun Duan

摘要

As the core element triggering urban waterlogging, disaster-causing rainfall is difficult to predict accurately due to its randomness and uncertainty. Identifying disaster-causing rainfall and clarifying its evolutionary characteristics and development patterns are of great significance for flood disaster reduction. The research takes the High-tech Zone of Zhengzhou City in China as the study area, constructs an SWMM model for urban waterlogging simulation, and identify the disaster-causing rainfall events based on waterlogging risk process. Then, the copula function was employed to construct multidimensional joint distributions of the key characteristics of disaster-causing rainfall to assess their behaviors. The results are as follows: (1) A total of 48 disaster-causing rainfall events were identified in the study area, with event-scale precipitation ranging from 32.5 to 86 mm. (2) The joint probability tends to increase with the magnitude of rainfall characteristics, and the event return periods mainly concentrates between 1 and 10 years. Two-dimensional event return periods may overestimate disaster risk compared to three-dimensional ones. (3) The synchronous encounter probability among total rainfall, rainfall intensity, and peak rainfall was found to be substantially greater than the asynchronous probability, indicating a strong positive correlation among these variables.