Enhanced efficiency of pluvial flood risk estimation in urban areas using spatial-temporal rainfall simulations
Enhanced efficiency of pluvial flood risk estimation in urban areas using spatial-temporal rainfall simulations
复制标题
利用时空降雨模拟提高城市地区雨洪风险评估的效率
DOI：
10.1111/j.1753-318x.2012.01135.x
复制
发表时间：
2012
影响因子：
4.1
通讯作者：
Blanc J
中科院分区：
文献类型：
--
作者：
Blanc J
城市地区是洪水风险的集中地，因为开发密度高，而且往往建在低洼地区。它们可能会受到来自河流或海洋的洪水的影响，但也容易受到强烈的直接降雨的影响，这可能会使城市排水系统不堪重负，并导致复杂的、往往是局部的暴雨洪水模式。洪涝灾害的风险尤其难以评估，因为它对降雨的时空特征、当地径流和地表径流过程、城市排水系统的性能以及建筑物的确切位置都很敏感。对所有这些过程中的变异性或不确定性进行采样，以便快速生成准确的洪水风险估计，这在计算上是困难的，特别是对于大城市地区。在本文中，我们评估了在城市洪水风险分析中使用高分辨率时空降雨模拟的替代方法。洪水深度用下水道和地表洪水耦合模型计算，洪水损失用标准的深度-损害标准估计。对降雨事件的有效抽样和将降雨事件特性与洪水数量和损失联系起来的响应面的合理使用进行了评估，并表明可将风险分析的计算费用减少70%以上。风险分析方法在英国两个不同的城市地点得到了成功的演示。
Urban areas are concentrations of flood risk because of the density of development and because they tend to be constructed in low‐lying areas. They may be subject to flooding from rivers or the sea but are also vulnerable to the effects of intense direct rainfall, which can overwhelm urban drainage systems, and cause complex and often localised patterns of pluvial flooding. The risk from pluvial flooding is particularly difficult to assess because it is sensitive to the spatial–temporal characteristics of rainfall, local run‐off and surface flow processes, the performance of urban drainage systems, and the exact location of buildings. Sampling the variability or uncertainty in all of these processes in order to generate accurate flood risk estimates quickly becomes computationally prohibitive, especially for large urban areas. In this paper, we evaluate alternative approaches for making use of high‐resolution spatial–temporal rainfall simulations in urban flood risk analysis. Flood depths are computed with a coupled sewer and surface flood model, and flood damage is estimated using standard depth‐damage criteria. Efficient sampling of rainfall events and judicious use of response surfaces that relate rainfall event properties to flood volumes and damages are evaluated and shown to reduce the computational expense of risk analysis by more than 70%. The risk analysis methodology is successfully demonstrated for two contrasting urban locations in the UK.