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

合作研究：使用降阶模型了解城市对洪涝灾害的抵御能力

• 批准号: 2053429
• 负责人: Valeriy Ivanov
• 金额: $ 28.27万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053429&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Urban; Resilience

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.

洪水在城市景观中加剧，那里的强降雨与高水平的不透水土地覆盖相结合，在不直接毗邻河流及其传统定义的泛滥平原的地区产生洪水。最近全国各地发生的事件表明，这种类型的洪水(被称为“暴雨”)对城市的抗洪能力产生了不利影响，是造成整体洪水破坏和死亡的主要因素。尽管人们日益认识到洪涝灾害的重要性，但由于传统的降雨预测和洪水淹没评估方法无法评估其发生的可能性和严重性，以及本来可以帮助解决这一知识差距的更复杂的模型的极端计算成本，人们对洪涝灾害仍然知之甚少。通过关注夏季极端降水和人口稠密城市地区的洪灾，这一抗灾研究补助金(DRRG)项目将解决洪灾危险现象的前兆和发生，以及与其预测相关的不确定性。通过量化河流泛滥平原以外发生洪水危险的可能性，这项研究将在工程实践的规模上为灾害管理规划提供信息。该项目假设：(I)概率分析中的现代技术可以简化产生暴雨洪水的极端降雨过程的表示；(Ii)城市景观内的地表排水网络和水流流体动力学特征决定了河流地区以外的洪水的形成；以及(Iii)洪水“代理”模型与高保真第一原理建模相结合，对于洪水科学的计算发现和开发实用工具以增强城市环境对暴雨洪水的适应能力是必不可少的。这项研究的具体目标是(1)展示使用最先进的降雨量和陆地表面数据以及混合建模方法进行洪水预测的潜力；(2)通过结合最先进的数据、建模和不确定性量化方法来解决项目假设；以及(3)通过开放源码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城市分水岭的案例研究上，该分水岭被地区利益相关者确定为了解暴雨形成和管理的关键区域。该提议由NSF-NIST灾难恢复研究补助金和NSF的水文科学计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Controls of Variability in the Laurentian Great Lakes Terrestrial Water Budget

• DOI: 10.1029/2022wr033759
• 发表时间: 2023-09
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: S. Minallah;Allison L. Steiner;Valeriy Y. Ivanov;Andrew W. Wood

A Deep Learning Modeling Framework with Uncertainty Quantification for Inflow-Outflow Predictions for Cascade Reservoirs

• DOI: 10.1016/j.jhydrol.2024.130608
• 发表时间: 2024-01
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Vinh Ngoc Tran;V. Ivanov;Giang Tien Nguyen;Anh Ngoc Tran;Phuong Huy Nguyen;Dae-Hong Kim;Jongho Kim

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

• DOI: 10.1029/2023gl104464
• 发表时间: 2023-09
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: V. N. Tran;V. Ivanov;Donghui Xu;Jongho Kim