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.

美国大约70%的饮用水供应来自地表水，包括潮汐河，河口的潮汐淡水区。干旱和海平面上升导致海水从海洋入侵，土地利用的变化导致淡水盐碱化，这些都使这一宝贵的水资源处于危险之中。这种风险延伸到用于热电发电、灌溉和工业生产的水。然而，到目前为止，只有最大和最富有的用水户能够为盐度的增加做好准备。许多用水者，如小型或农村饮用水供应商，没有必要的规划和技术能力来为这些变化做好准备。该项目建议通过与水资源管理者、资源不足的农村社区和水供应商合作生产，开发和制作盐度管理决策支持和监测工具的原型。提出的创新流域-河口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相一致。这个项目是及时的，因为美国国会最近通过了两党支持的《基础设施投资和就业法案》，其中10%用于更新水基础设施。一个更好的盐度管理和沿海社区决策支持系统对于加强水基础设施的复原力和保护公众健康将是有价值的。研究人员将开发一种新的流域-河口耦合模型，利用水文和河口模型的最新进展，模拟主要盐离子的运输和命运，将切萨皮克湾及其潮汐河流作为试点研究地点。然后，耦合模型将与人工智能算法结合使用，用于规划工具，以确定管理策略并量化淡水资源竞争需求之间的权衡。这种方法还将用于寻找适应途径形式的长期规划战略。网络和移动决策支持工具将与利益攸关方共同开发，采用完整的软件生命周期方法，重点关注农村社区和小型供水系统的需求。盐监测工具的原型设计将侧重于发展电导率与各种盐离子之间的关系，以便评估盐污染对水质、水基础设施和公共卫生的相关影响。该奖项反映了美国国家科学基金会的法定使命，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。