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70%的饮用水供应来自地表水，包括潮汐河流、河口的潮汐淡水区域。干旱和海平面上升导致海水入侵，以及土地利用的变化导致水资源短缺。淡水盐碱化正在使这一宝贵的水资源面临风险，但迄今为止，只有最大和最富有的用水者能够做好准备。许多水用户，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。该项目建议通过联合生产开发和原型化盐度管理决策支持和监测工具。拟议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，因为美国国会最近提出了这一项目。通过了两党基础设施​​投资和就业法案，其中 10% 专门用于更新水基础设施一个新的流域-河口耦合模型，利用水文和河口建模的最新进展，以切萨皮克湾及其潮汐河流作为试点研究地点，模拟主要盐离子的迁移和归宿。然后与人工智能算法结合使用，在规划工具中确定管理策略并量化淡水资源竞争需求之间的权衡。这种方法还将用于以适应路径的形式寻找长期规划策略。将使用完整的软件生命周期方法与利益相关者共同开发网络和移动决策支持工具，重点关注农村社区和小型供水系统的需求，盐监测工具的原型将侧重于开发电导率和各种盐离子之间的关系。为了评估盐的相关影响该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。