Collaborative Research: Understanding Urban Resilience to Pluvial Floods Using Reduced-Order Modeling

洪水在城市景观中加剧，强降雨与高水平的不透水土地覆盖相结合，在不紧邻河流及其传统定义的洪泛区的地区产生洪水。全国各地最近发生的事件表明，这种类型的洪水（称为“雨”）对城市的复原力产生不利影响，是造成整体洪水破坏和死亡的主要因素。尽管人们越来越认识到洪积洪水的重要性，但对洪积洪水的了解仍然很少，因为传统的降雨预测和洪水评估方法无法评估其发生的可能性和严重程度，而且更复杂的模型的计算成本极高，而这些模型本来可以帮助弥补这一知识空白。通过关注人口密集的城市地区的极端夏季降水和洪水，这个抗灾能力研究赠款（DRRG）项目将解决洪水灾害现象的前兆和发生，以及与其预测相关的不确定性。通过量化河流洪泛区以外洪水灾害的可能性，这项研究将为工程实践规模的灾害管理规划提供信息。该项目假设：（一）概率分析的现代技术可以简化产生洪积洪水的极端降雨过程的表示;（二）城市景观内的地表排水网络和水流动力学特征决定了河流地区以外洪水的形成;以及（iii）结合高保真度的洪水“替代”建模，第一性原理建模对于洪水科学的计算发现和开发实用工具以提高城市环境对洪水的复原力是不可或缺的。本研究的具体目标是：（1）使用最新的降雨和地表数据以及混合建模方法来展示洪水预测的潜力;（2）通过结合最新的科学数据、建模和不确定性量化方法来解决项目假设;以及（3）通过开源软件包分发开发的工具。项目活动将集中在底特律郊区的一个案例研究城市流域，该流域被区域利益相关者确定为了解雨水形成和管理的一个关键领域。NIST灾难恢复研究赠款和NSF的水文科学计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

Flooding is exacerbated in urban landscapes, where intense rainfall combines with high levels of impervious land cover to produce flooding in areas not immediately adjacent to rivers and their traditionally defined floodplains. Recent events across the nation have demonstrated that this type of flooding (termed ‘pluvial’) adversely affects urban resilience and is a major contributor to overall flood damage and fatalities. Despite the increasing recognition of its importance, pluvial flooding remains poorly understood because of the failure of conventional rainfall predictions and inundation assessment methods to assess the likelihood and severity of its occurrence, and the extreme computational cost of more sophisticated models that could otherwise help address this knowledge gap. By focusing on extreme summer precipitation and flooding in densely populated urban areas, this Disaster Resilience Research Grants (DRRG) project will address the precursors to and the occurrence of flooding hazard phenomena, as well as the uncertainty associated with its prediction. By enabling quantification of the likelihood of a flood hazard outside of riverine floodplains, this research will inform disaster management planning at scales of engineering practice. This project hypothesizes that (i) modern techniques in probabilistic analysis can simplify the representation of extreme rainfall processes that produce pluvial floods; that (ii) both surface drainage network and flow hydrodynamic features within an urban landscape determine the formation of floods outside of riverine areas; and that (iii) flood “surrogate” modeling combined with high-fidelity, first-principles modeling is indispensable for computational discovery in flood science and the development of practical tools to enhance the resilience of urban environments to pluvial flooding. The specific objectives of this research are (1) to demonstrate the potential for flood prediction using state-of-the-science rainfall and land surface data and hybrid modeling approaches; (2) to address the project hypotheses through a combination of state-of-the-science data, modeling, and uncertainty quantification methods; and (3) to distribute developed tools through open-source software packages. Project activities will focus on a case study urban watershed in a suburban area of Detroit identified by regional stakeholders as one key area to understand the formation and management of stormwater.This proposal is co-funded by NSF-NIST Disaster Resilience Research Grants and NSF's Hydrologic Sciences Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.