NSF Convergence Accelerator Track K: Prototyping decision support and monitoring tools for equitable management of salt contamination of water supplies in tidal rivers

NSF 融合加速器轨道 K：为潮汐河供水盐污染的公平管理制定决策支持和监测工具原型

• 批准号: 2344042
• 负责人: Ming Li
• 金额: $ 65万
• 依托单位: University of Maryland Center for Environmental Sciences
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344042&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Prototyping

About 70% of the U.S. drinking water supply comes from surface waters, including tidal rivers, the tidal fresh region of estuaries. Drought and sea level rise, which lead to saltwater intrusion from the ocean, and changes in land-use, which lead to freshwater salinization, are putting this precious water resource at risk. This risk extends to water uses for thermoelectric power, irrigation, and industrial production. As yet, however, only the largest and wealthiest water users are able to prepare for increasing salinity. Many water users, like small or rural drinking water suppliers, do not have the necessary planning and technical capacity to prepare for these changes. This project proposes to develop and prototype decision support and monitoring tools for salinity management through co-production with water resource managers, under-resourced rural communities, and water suppliers. The proposed convergence research of innovative watershed–estuary models and decision support tools aligns with the grand challenge of ensuring access to clean water. This project is timely as the U.S. Congress recently passed the bipartisan Infrastructure Investment & Jobs Act, 10% of which is dedicated to renewing water infrastructure. A better decision support system for salinity management and coastal communities will be valuable for bolstering the resilience of water infrastructure and protecting public health.The researchers will develop a new coupled watershed–estuary model that simulates the transport and fate of major salt ions by leveraging recent advances in hydrological and estuarine modeling, using Chesapeake Bay and its tidal rivers as a pilot study site. The coupled model will then be used in combination with artificial intelligence algorithms, in a planning tool to identify management strategies and quantify the tradeoffs between competing needs for freshwater resources. This approach will also be used to search for long-term planning strategies in the form of adaptation pathways. Web and mobile decision support tools will be co-developed with stakeholders using a full software life cycle approach, focusing on the needs of rural communities and smaller water supplies. Prototyping of the salt monitoring tool will focus on developing relationships between conductivity and various salt ions in order to assess the associated impacts of salt contamination on water quality, water infrastructure, and public health.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.

美国约有70％的饮用水供应来自地表水，包括潮汐河，潮汐新鲜地区的潮汐河。干旱和海平面上升，导致海洋侵入盐水，而土地利用的变化导致淡水盐化，这使这种宝贵的水资源处于危险之中。这种风险扩展到热电，灌溉和工业生产的水用途。然而，到目前为止，只有最大，最富有的用水用户才能为增加盐度做准备。许多用水用户，例如小型或粗糙的饮用水供应商，没有必要的计划和技术能力来为这些变化做准备。这项项目提案通过与水资源经理，资源不足的农村社区和水供应商共同生产来开发和原型决策支持和监测盐度管理工具。拟议的融合研究对创新的分水岭模型和决策支持工具的融合研究与确保获得清洁水的巨大挑战保持一致。该项目是及时的，因为美国国会最近通过了两党基础设施​​投资和就业法案，其中10％致力于更新水基础设施。一个更好的盐度管理和沿海社区决策支持系统对于增强水基础设施的弹性并保护公共卫生将是有价值的。研究人员将开发一种新的耦合的水域 - 季节模型，该模型通过使用水文和河口群模型来模拟主要盐离子的运输和命运。然后，耦合模型将与人工智能算法结合使用，以识别管理策略并量化淡水资源竞争需求之间的权衡。这种方法还将用于以适应途径的形式搜索长期计划策略。网络和移动决策支持工具将使用完整的软件生命周期方法与利益相关者共同开发，重点关注农村社区和较小的供水的需求。盐监测工具的原型制作将集中在电导率与各种盐离子之间建立关系，以评估盐污染对水质，水基础设施和公共卫生的相关影响。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来评估诚实地通过评估来诚实地支持。