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了洪水，强烈的降雨与高水平的不透水土地覆盖层结合在一起，在不与河流及其传统定义的洪泛区相邻的地区造成洪水。全国最近的事件表明，这种类型的洪水（称为“ plobial”）对城市的韧性产生了不利影响，并且是造成整体洪水破坏和死亡的主要贡献者。尽管人们对其重要性的认识越来越多，但由于常规降雨预测和淹没评估方法的失败，羽毛洪水仍然鲜为人知，无法评估其发生的可能性和严重性，以及更复杂的模型的极端计算成本，否则可以帮助解决这一知识差距。通过关注炎热的城市地区的极端夏季降水和洪水，这项灾难恢复研究补助金（DRRG）项目将解决洪水危害现象的前体和发生的情况，以及与其预测相关的不确定性。通过量化河流洪泛区以外的洪水危害的可能性，这项研究将在工程实践的范围内为灾难管理计划提供信息。该项目假设（i）概率分析中的现代技术可以简化产生底层地板的极端降雨过程的表示； （ii）城市景观中的表面排水网络和流动动力学特征决定了河流地区以外的地板的形成； （iii）洪水“替代”建模与高保真性，第一原理建模对于洪水科学中的计算发现以及开发实用工具的开发是必不可少的，以增强乌尔任何河流环境的弹性来覆盖洪水。这项研究的具体目标是（1）证明使用科学降雨以及陆地表面数据以及混合建模方法的洪水预测的潜力； （2）通过最先进的数据，建模和不确定性量化方法的结合来解决项目假设； （3）通过开源软件包分发开发的工具。项目活动将重点关注一个案例研究在底特律郊区的案例研究，该案例被区域利益相关者确定为了解雨水的形成和管理的一个关键领域。该提案是由NSF-NIST灾难灾难恢复能力研究共同资助的，并由NSF的智力授予NSF的基础奖，并以nsf的基础来表示，这是由NSF的基础奖。和更广泛的影响审查标准。

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