CAS-Climate/Collaborative Research: Prediction and Uncertainty Quantification of Non-Gaussian Spatial Processes with Applications to Large-scale Flooding in Urban Areas

CAS-气候/合作研究：非高斯空间过程的预测和不确定性量化及其在城市地区大规模洪水中的应用

• 批准号: 2210840
• 负责人: Soutir Bandyopadhyay
• 金额: $ 36.46万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210840&HistoricalAwards=false
• 关键词: CAS; Climate; Collaborative; Research; Prediction

This Critical Aspects of Sustainability - Climate (CAS-Climate) project develops an accurate and fast statistical representation (also known as emulation) of large-scale flooding events as an alternate to running complex physical models in every instance. This is achieved by training the statistical formulation on representative flood simulations but adding flexibility to infer results for situations that are not in the training set. Large floods are expected to occur more frequently due to climate change and so motivate increased attention for robust, efficient, and real-time flood modeling. For most river engineering problems, including flood mitigation, there is a need for quantification of water depth and surface elevation under different scenarios of rainfall and storm surges. While many hydraulic models (e.g., the TELEMAC model) can represent these features, large-scale hydraulic simulations of flooding using high-resolution topographic and infrastructure data, are computationally expensive to complete. Thus, despite these modeling resources, simulating possible flood scenarios routinely or in a rapid manner is limited. A novel aspect of this work is to focus on statistical distributions for extreme events, which are appropriate for flooding, rather than average quantities that are less useful for assessing risks and hazards. Also, the statistical basis of this approach lends itself to measures of uncertainty in the predictions and so support decision making and other practical uses.There is little existing methodology to emulate computer models built on spatial extreme distributions to represent flooding in a complex domain and an important feature of this work is matching the flooding response with the appropriate statistical processes. This is in contrast with a machine learning, purely data-driven approach where extreme events may not be as well represented. The modeling framework is transformative including: a Markov random field formulation using generalized extreme value distributions for site-specific conditional distributions, selecting neighborhood size using a penalized likelihood method for spatial extremes, a theoretical investigation into the validity and large sample properties of emulators, and developing computational algorithms to handle very large data volumes. This work will be validated through proof-of-concept studies with TELEMAC model simulations, forced by realistic patterns of extreme rainfall events and storm surges and located in an urban area of the US. Open-source tools will be generated based on the results from this project and shared with the engineering communities, especially civil infrastructure community that frequently faces challenges in coping with spatially-dependent data and uncertainties.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.

可持续性 - 气候（CAS气候）项目的这一关键方面开发了大规模洪水事件的准确，快速的统计表示（也称为仿真），作为每种情况下运行复杂物理模型的替代方法。这是通过训练代表性洪水模拟的统计公式来实现的，但增加了灵活性，以推断不在训练集中的情况下的结果。由于气候变化，预计大洪水会更频繁地发生，因此激励人们增加对稳健，高效和实时洪水建模的关注。对于大多数河流工程问题，包括减轻洪水，需要在不同的降雨和暴风雨中量化水深和表面高度。尽管许多液压模型（例如，Telemac模型）可以代表这些功能，但使用高分辨率地形图和基础设施数据对洪水进行大规模的液压模拟在计算上的昂贵。因此，尽管有这些建模资源，但通常会迅速模拟可能的洪水场景是有限的。 这项工作的一个新方面是专注于极端事件的统计分布，这些分布适合洪水，而不是平均数量，这些数量对于评估风险和危害而言不太有用。 同样，这种方法的统计基础为预测中的不确定性衡量，因此支持决策和其他实际用途。几乎没有现有的方法模仿基于空间极端分布的计算机模型以表示复杂领域中的洪水，而这项工作的重要特征是将洪水响应与适当的统计过程相匹配。这与机器学习，纯粹是数据驱动的方法形成鲜明对比，在该方法中，极端事件可能没有很好地代表。该建模框架具有变革性，包括：马尔可夫随机场公式，使用广义的极值分布进行特定地点的条件分布，使用惩罚的可能性方法来选择邻里大小，用于空间极端，对模拟器的有效性和大型样本属性进行了理论研究，并开发了计算算法来处理非常大的数据弱点。这项工作将通过Telemac模型模拟的概念验证研究来验证，这是由极端降雨事件和风暴潮的现实模式强迫，并位于美国市区。开源工具将根据该项目的结果生成，并与工程社区共享，尤其是民事基础设施社区，这些社区经常在应对空间依赖数据和不确定性时面临挑战。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务。