A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people
A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people
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DOI：
10.5194/nhess-16-1323-2016
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发表时间：
2016-01-01
影响因子：
4.6
通讯作者：
Giupponi, Carlo
中科院分区：
文献类型：
--
作者：
Balbi, Stefano;Villa, Ferdinando;Giupponi, Carlo
本文提出了一种新的方法，通过整合人们的脆弱性以及通过应对和适应来缓冲灾害的能力，来评估洪水对人的风险。所提出的方法将传统的风险评估扩展到物质损失之外；用主观和当地知识补充定量和半定量数据，改进了常用信息的使用；并通过为其所有输出提供概率分布来估计模型的不确定性。对人的洪水风险是使用一个基于空间明确的贝叶斯网络模型进行建模的，该模型根据专家意见进行了校准。风险是根据以下方面进行评估的：（1）非致命身体伤害的可能性，（2）创伤后应激障碍的可能性，（3）死亡的可能性。研究区域涵盖锡尔河谷下游（瑞士），包括苏黎世市。该模型用于评估改进现有预警系统的效果，同时考虑到可靠性、提前期和范围（即预警所覆盖的人群）。模型结果表明，在重大洪水事件中，改进预警在避免人类影响方面的潜在效益尤为显著。
This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.