...
首页> 外文期刊>Risk analysis >Structured Coupling of Probability Loss Distributions: Assessing Joint Flood Risk in Multiple River Basins
【24h】

Structured Coupling of Probability Loss Distributions: Assessing Joint Flood Risk in Multiple River Basins

机译:概率损失分布的结构耦合:评估多个流域的联合洪水风险

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Losses due to natural hazard events can be extraordinarily high and difficult to cope with. Therefore, there is considerable interest to estimate the potential impact of current and future extreme events at all scales in as much detail as possible. As hazards typically spread over wider areas, risk assessment must take into account interrelations between regions. Neglecting such interdependencies can lead to a severe underestimation of potential losses, especially for extreme events. This underestimation of extreme risk can lead to the failure of risk-management strategies when they are most needed, namely, in times of unprecedented events. In this article, we suggest a methodology to incorporate such interdependencies in risk via the use of copulas. We demonstrate that by coupling losses, dependencies can be incorporated in risk analysis, avoiding the underestimation of risk. Based on maximum discharge data of river basins and stream networks, we present and discuss different ways to couple loss distributions of basins while explicitly incorporating tail dependencies. We distinguish between coupling methods that require river structure data for the analysis and those that do not. For the later approach we propose a minimax algorithm to choose coupled basin pairs so that the underestimation of risk is avoided and the use of river structure data is not needed. The proposed methodology is especially useful for large-scale analysis and we motivate and apply our method using the case of Romania. The approach can be easily extended to other countries and natural hazards.
机译:由于自然灾害事件造成的损失可能非常高,难以应对。因此,人们非常感兴趣的是尽可能详细地估计当前和未来极端事件在所有规模上的潜在影响。由于危害通常分布在更广的区域,因此风险评估必须考虑到区域之间的相互关系。忽视这种相互依存关系可能导致潜在损失的严重低估,尤其是在极端事件中。极端风险的这种低估会导致在最需要风险管理策略时(即在前所未有的事件发生时)失败。在本文中,我们提出了一种通过使用copulas来将这种相互依存性纳入风险的方法。我们证明,通过耦合损失,依赖关系可以纳入风险分析中,从而避免了风险的低估。基于流域和河流网络的最大流量数据,我们提出并讨论了在明确纳入尾部相关性的同时耦合流域损失分布的不同方法。我们区分需要分析河系结构数据的耦合方法和不需要分析方法的耦合方法。对于后一种方法,我们提出了一个极小极大算法来选择耦合流域对,从而避免了风险的低估并且不需要使用河流结构数据。所提出的方法对于大规模分析特别有用,我们以罗马尼亚为例来激励和应用我们的方法。该方法可以轻松扩展到其他国家和自然灾害。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号