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Optimizing Green Infrastructure Investment to Improve Urban Storm Water System Resilience under Environmental Uncertainty

优化绿色基础设施投资，提高城市雨水系统在环境不确定性下的抵御能力

基本信息

批准号：
1634975
负责人：
Jon Hathaway
金额：
$ 34.95万
依托单位：
University of Tennessee Knoxville
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634975&HistoricalAwards=false
关键词：
Optimizing Green Infrastructure Investment Improve

项目摘要

This research will develop an approach to optimize investment decisions to improve urban storm water system resilience to forecasted extreme weather events. Many cities already struggle with storm water pipes that are in poor condition and are undersized relative to changing weather patterns. Green infrastructure is recommended as a "low regret" strategy to reduce flooding while also providing co-benefits to freshwater provision, ecological processes, and freshwater fish populations. The question remains, however, as to how to allocate investments under environmental uncertainty. This research will formulate approaches that allow decision-makers to evaluate trade-offs in storm water infrastructure investment. The research team aims to increase gender and ethnic diversity in STEM by collaborating with various campus-wide student chapters of minority organizations, such as the Tennessee Louis Stokes Alliance for Minority Participation, to recruit underrepresented students to pursue degrees in engineering. Although more frequent and intense weather events are projected for many locations, the exact timing and effects of such events are uncertain. This research will provide a scientifically informed approach to evaluate the consequences of making infrastructure planning decisions under this uncertainty, and showcases the power of this approach to obtain and evaluate optimal green infrastructure placement. Here, the team will: (1) develop a novel framework that can incorporate predictions from a variety of weather prediction models, (2) use a semi-distributed, well calibrated, hydrologic model to evaluate the response of a storm water system to these scenarios by identifying the vulnerability of the system to flooding, (3) adopt sophisticated mathematical methodologies, including stochastic programming and robust optimization, to account for the uncertainties in weather patterns over a long planning horizon, and (4) optimize green infrastructure placement within the storm water system to maximize flood resiliency under climate uncertainties, subject to various economic, physical, and social constraints.

这项研究将开发一种优化投资决策的方法，以提高城市雨水系统对预测的极端天气事件的适应能力。许多城市已经在与雨水管道作斗争，这些管道状况很差，而且与不断变化的天气模式相比尺寸偏小。建议将绿色基础设施作为减少洪灾的“低遗憾”战略，同时也为淡水供应、生态过程和淡水鱼种群提供共同利益。然而，问题仍然是如何在环境不确定的情况下分配投资。这项研究将制定方法，使决策者能够评估雨水基础设施投资的权衡。该研究小组旨在通过与田纳西州路易斯·斯托克斯少数族裔参与联盟等少数族裔组织在校园范围内的各个学生分会合作，招募代表性不足的学生攻读工程学学位，从而增加STEM的性别和种族多样性。尽管预计许多地区会出现更频繁和更强烈的天气事件，但此类事件的确切时间和影响尚不确定。这项研究将提供一种科学的知情方法来评估在这种不确定性下做出基础设施规划决策的后果，并展示这种方法在获得和评估最优绿色基础设施布置方面的力量。在这里，该团队将：(1)开发一个新的框架，可以结合各种天气预测模型的预测，(2)使用半分布式、校准良好的水文模型，通过确定系统对洪水的脆弱性来评估暴雨系统对这些情况的响应，(3)采用复杂的数学方法，包括随机规划和稳健优化，以考虑长期规划周期内天气模式的不确定性，以及(4)优化雨水系统内的绿色基础设施布置，以最大限度地提高受各种经济、自然和社会约束制约的气候不确定性下的洪水弹性。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An agent based model for joint placement of PV panels and green roofs

基于代理的光伏电池板和绿色屋顶联合放置模型

DOI：
10.1109/wsc.2017.8247861
发表时间：
2017
期刊：
Proceedings of the 2017 Winter Simulation Conference
影响因子：
0
作者：
Li, Xueping;Ramshani, Mohammad;Khojandi, Anahita;Omitaomu, Olufemi;Hathaway, Jon Michael
通讯作者：
Hathaway, Jon Michael

An agent-based approach to study the diffusion rate and the effect of policies on joint placement of photovoltaic panels and green roof under climate change uncertainty

基于主体的方法研究气候变化不确定性下光伏板和绿色屋顶联合放置的扩散率和政策效应