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.

城市景观中的洪水加剧，强烈的降雨与高度不透水的土地覆盖相结合，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“雨洪”）。 ' '）对城市的恢复能力产生不利影响，并且是造成总体洪水损失和死亡的主要原因，尽管人们越来越认识到其重要性，但由于传统的降雨预测和洪水评估方法无法评估洪水的可能性，人们对洪水的了解仍然很少。其发生的严重性，以及更复杂模型的极端计算成本，这些模型本来可以帮助解决这一知识差距，通过关注人口稠密的城市地区的极端夏季降水和洪水，该抗灾研究补助金（DRRG）项目将解决这一问题。通过量化河流洪泛区之外洪水灾害的可能性，该研究将为工程实践规模的灾害管理规划提供信息。保留那个(i) 现代概率分析技术可以简化产生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动力学特征决定了河流地区以外洪水的形成； iii) 洪水“替代”建模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雨洪的抵御能力是必不可少的。这项研究的目的是 (1) 展示利用最先进的降雨和地表数据以及混合建模方法进行洪水预测的潜力；(2) 通过结合最先进的科学方法来解决项目假设；数据、建模和不确定性量化方法；(3) 通过开源软件包分发开发的工具 项目活动将重点关注被区域利益相关者确定为关键区域的底特律郊区的城市流域。了解雨水的形成和管理。该提案是共同资助的该奖项反映了 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

劳伦五大湖陆地水收支变化的控制

• DOI: 10.1029/2022wr033759
• 发表时间: 2023-09-25
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: S. Minallah;Allison L. Steiner;Valeriy Y. Ivanov;Andrew W. Wood
• 通讯作者: Andrew W. Wood

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

接近水文预测精度：结合高保真度和物理不可知模型的优势

• DOI: 10.1029/2023gl104464
• 发表时间: 2023-09-07
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: V. N. Tran;V. Ivanov;Donghui Xu;Jongho Kim
• 通讯作者: Jongho Kim

A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs

用于梯级水库流入流出预测的具有不确定性量化的深度学习建模框架

• DOI: 10.1016/j.jhydrol.2024.130608
• 发表时间: 2024-02
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Ngoc Tran, Vinh;Ivanov, Valeriy Y.;Tien Nguyen, Giang;Ngoc Anh, Tran;Huy Nguyen, Phuong;Kim, Dae;Kim, Jongho
• 通讯作者: Kim, Jongho

Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes

数据重组 – 一种新颖的数据处理技术，增强机器学习在预测水流极端情况方面的适用性

• DOI: 10.1016/j.advwatres.2023.104569
• 发表时间: 2023-12-01
• 期刊: Advances in Water Resources
• 影响因子: 4.7
• 作者: V. N. Tran;V. Ivanov;Jongho Kim
• 通讯作者: Jongho Kim