NSF Convergence Accelerator Track D America's Water Risk: Water System Data Pooling for Climate Vulnerability Assessment and Warning System

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。这个关于气候脆弱性评估和预警系统的水系统数据汇集的项目解决了美国供水弹性的一个主要差距，即，应对气候多变性和变化的能力，特别是关注美国数千家小型公用事业公司的脆弱性，这些公司可能缺乏必要的资金和技术能力来分析这些风险并评估其对运营的影响。该项目通过汇集水系统，气候科学，人工智能技术，仿真模型和软件开发方面的专家，建立了一个融合研究议程，用于人工智能（AI）和机器学习（ML）模型的概念设计，开发和共享，以量化美国在水务公司及其监管国家和联邦机构层面的供水风险。气候和水风险分析的汇总数据源和可扩展模型将提供给所有对此信息感兴趣的社区。该项目采用基于人工智能的技术来促进气候观测、气候模型模拟和相应的水系统响应的探索，以帮助我们在对供水风险的理解方面取得突破。在气候变化和基础设施老化的条件下，洪水和干旱的频率越来越高，这些因素对国家的安全供水构成了重大风险，开发的模型将有助于水系统的战略规划和运营。将开发一个基于云的多尺度人工智能建模、模型和数据共享平台，以支持供水行业以用户为中心的分析。该平台为特征识别、时空建模和预测、函数依赖、逆问题和迁移学习提供了多尺度建模。基于物理的模型以及人工智能模型将在此背景下进行探索。将使用各种数据来源，包括沿着的全国范围的水数据以及公用事业收集的数据。这些产出将响应已确定的用户需求，并将成为社区数据和建模资源。通过哥伦比亚水中心的美国水倡议和通过马萨诸塞州大学的可持续小型系统水创新网络与行业合作伙伴的深入合作指导了这一过程。将通过网络研讨会、现场培训和关于该平台的演示，让广泛的组织及其成员参与进来。这项工作的一个特别重点是代表性不足的社区的组织和代表，这些社区特别容易受到气候驱动的破坏。教育材料将针对这些社区的用户，目标是在当地培养更多受过培训的个人，他们可以支持在当地系统环境中使用和解释ML工具进行水风险分析。推广活动将特别针对那些资源有限的小型水务公司，因此，他们可以从将要创建的共享平台中受益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project on Water System Data Pooling for Climate Vulnerability Assessment and Warning System addresses a major gap in the resiliency of America's Water Supply, viz., resiliency to climate variability and change, especially focusing on the vulnerability of thousands of smaller utilities in the United States that may lack the financial wherewithal and technical capacity to analyze these risks and assess their impact on operations. This project establishes a convergence research agenda by bringing together experts in water systems, climate science, AI technologies, emulation models and software development for the conceptual design, development, and sharing of Artificial Intelligence (AI) and Machine Learning (ML) models to quantify America's water supply risk at the level of water utilities and their regulatory state and federal agencies. The aggregated data sources and scalable models for climate and water risk analyses will be made available and accessible to all communities interested in this information. The project employs AI-based techniques to facilitate the exploration of climate observations, climate model simulations and corresponding water system response to help create breakthroughs in our understanding of water supply risk. The models developed will assist in the strategic planning and operations of water systems in the face of an increasing frequency of floods and droughts under climate change and aging infrastructure conditions—factors that constitute significant risks to the nation’s safe supply of water. A cloud-based, multi-scale AI-enabled modeling, and model and data sharing, platform will be developed to support user-centric analyses for the water supply industry. The platform provides multiscale modeling for feature identification, spatiotemporal modeling and forecasting, functional dependence, inverse problems and transfer learning. Physics-based models as well as AI models will be explored in this context. A diverse set of data sources will be used, including national-scale water data will along with utility-collected data. The outputs will be responsive to identified user needs and will become a community data and modeling resource. A deep collaboration with industry partners via the Columbia Water Center’s America’s Water initiative and through the University of Massachusetts’s Water Innovation Network for Sustainable Small Systems guides this process. A broad range of organizations and their constituents will be engaged via webinars and on-site training and demonstrations about the platform. A particular focus of this effort is on organizations and representatives of underrepresented communities that are especially vulnerable to climate driven disruption. Educational materials will be targeted towards users from such communities with the goal of developing additional trained individuals locally who could support the use and interpretation of ML tools for water risk analysis in a local system context. Outreach activities will be especially targeted to the smaller water utilities who may be resource constrained and can, thus, benefit from the shared platform that will be created.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.