地理信息系统模型、方法与应用分析

流域性暴雨洪涝灾害风险预警模型与应用分析

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  • 1. 民政部国家减灾中心, 北京 100124;
    2. 中国科学院地理科学与资源研究所, 北京 100101;
    3. 中国科学院研究生院, 北京 100049
廖永丰(1974-), 男, 甘肃人, 博士,副研究员。主要从事灾害风险评估、灾害管理研究。 E-mail: liaoyongfeng@ndrcc.gov.cn

收稿日期: 2011-01-24

  修回日期: 2011-05-27

  网络出版日期: 2011-06-15

基金资助

国家科技支撑计划课题"综合风险防范救助保障与保险体系示范"(2006BAD20B04);国家科技支撑计划课题"承险过程分析与承险脆弱性综合评估技术"(2008BAK50B05);中国科学院知识创新工程重要方向项目"重大自然灾害风险评估技术"(KZCX2-YW-Q03-01)。

A Disaster Risk Warning Model for the Basin-wide Rainstorm Flood Management

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  • 1. National Disaster Reduction Center of China, Beijing 100124,China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. Graduate University of CAS, Beijing 100049, China

Received date: 2011-01-24

  Revised date: 2011-05-27

  Online published: 2011-06-15

摘要

洪涝灾害是我国影响范围最广、持续时间最长、造成损失最大的一类自然灾害。暴雨是洪涝灾害的最主要致灾因素,所以,暴雨洪涝灾害风险预警研究是洪涝灾害防灾减灾的重要基础。本文立足洪涝灾害应急管理工作要求,对洪涝灾害系统空间仿真模拟研究模式进行改进,采用流域地表径流模拟代替控制断面水位模拟,彻底解决流域地表产流及河道水位模拟限制,建立"地表径流—流域淹没分析—灾害损失预评估"风险预警模型,提高模型适应性。该模型具有较高的准确性、时效性、可操作性,能够为防灾避难提供科学依据。

本文引用格式

廖永丰, 聂承静 . 流域性暴雨洪涝灾害风险预警模型与应用分析[J]. 地球信息科学学报, 2011 , 13(3) : 354 -360 . DOI: 10.3724/SP.J.1047.2011.00354

Abstract

Along with global warming, rainstorm flood disaster is increasing, and the loss is more and more serious, so the warning of flood disaster risk is an important foundation for flood disaster prevention and mitigation. In order to meet the demands of flood risk warning and flood emergency management, we improved the spatial simulation of flood system in this study. Based on the combination of numerical simulation technology and spatial technology, such as remote sensing and geographic information systems, we established a risk warning model to improve the model adaptation, which is "surface runoff-basin submerged analysis-disaster risk assessment", using watershed surface runoff simulation instead of water level simulation in controlling section, that completely broke the constraints of watershed surface runoff and river water level simulation. Through the case study, this model shows higher accuracy and better timeliness. It would supply reliable and efficient method for flood disaster prevention and reduction.

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