NSF Convergence Accelerator Track K: Remote Sensing Tools for Catalyzing Equitable Water Outcomes

NSF 融合加速器轨道 K：促进公平水成果的遥感工具

• 批准号: 2344337
• 负责人: Emily Elliott
• 金额: $ 65万
• 依托单位: University of Pittsburgh
• 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=2344337&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Remote

Access to clean water is a defining challenge of the 21st century. Even in places with abundant water, there are disparities in clean water access. Creating tools to identify where these inequities occur, how they change over time, and how they respond to policy and management is essential to inform the efforts of agencies, utilities, non-governmental organizations, and communities as they seek to improve access to clean water. Yet, existing water quality monitoring data in the U.S. is spatially sparse and difficult for decision-makers to access. As a result, it is challenging to gage spatial disparities in access to clean surface water for fishing, recreation, and as source water for drinking. Remote sensing offers a powerful alternative that sidesteps these constraints and disparities in water quality data. The overarching goal of this research is to use satellite imagery to transform how disparities in clean water access are evaluated and to inform decision making about clean water access. This effort brings together a cross-sectoral and multi-disciplinary team of partners to co-create and pilot a new high-spatial resolution, open-source decision support system called “EQUATE” that will integrate input and feedback from organizations, communities, and agencies. EQUATE will be piloted in the Upper Ohio River basin but could be translatable to any U.S. river basin. In its final form, EQUATE will provide a publicly available visualization, analysis, and communication interface that will enhance public awareness of water quantity, quality, and inequities, and provide a tool for stakeholders, researchers, educators, community members, and leaders to interpret water information. The Pittsburgh Water Collaboratory at the University of Pittsburgh will lead a team of government, community, non-profit, and private sector partners to co-design an EQUATE prototype. EQUATE will transform Landsat satellite imagery into spatially continuous, long-term water quality observations using coincident historical field sampling to train and validate machine learning algorithms including chlorophyll-a concentrations (chl-a, an indicator of algal blooms); total suspended sediment concentrations (TSS, key for habitat suitability, contaminant burden, and nutrient availability); and surface temperature (critical habitat indicator for sensitive species). These data will be linked to a geospatial fabric of hydrologic features and discoverable cross-sector water data called GeoConnex. Initial EQUATE visualizations will be used to (1) inform river planning, management, and dam operation and (2) identify where poor water quality coincides with indicators of disparities in social vulnerability and public health. Finally, this project builds a foundation for future remote sensing applications centered on user interfaces, engagement and co-design processes that will inform equitable water outcomes in the Ohio River Basin and beyond.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.

获得清洁水是 21 世纪的一项决定性挑战，即使在水资源丰富的地方，创建工具来确定这些不平等现象的发生地点、它们如何随时间变化以及它们对政策和政策的反应如何。管理对于机构、公用事业、非政府组织和社区寻求改善清洁水的获取至关重要。然而，美国现有的水质监测数据空间稀疏，决策者难以获取。作为一个因此，测量用于捕鱼、娱乐和饮用的清洁地表水的空间差异是一项挑战，遥感提供了一个强大的替代方案，可以避开水质数据的这些限制和差异。旨在利用图像卫星改变清洁水获取方面差异的评估方式，并为有关清洁水获取的决策提供信息。这项工作汇集了跨部门和多学科的合作伙伴团队，共同创建和试点一个新的高水平水源。空间分辨率，名为“EQUATE”的开源决策支持系统将整合来自组织、社区和机构的输入和反馈，该系统将在俄亥俄河流域上游进行试点，但其最终形式 EQUATE 可以移植到美国任何河流流域。将提供公开的可视化、分析和通信界面，提高公众对水量、质量和不平等的认识，并为利益相关者、研究人员、教育工作者、社区成员和领导者提供解释水信息的工具。匹兹堡大学的合作实验室将领导一个由政府、社区、非营利组织和私营部门合作伙伴组成的团队共同设计 EQUATE 原型，EQUATE 将利用历史数据将 Landsat 卫星图像转换为空间连续的长期水质观测结果。现场采样以训练和验证机器学习算法，包括叶绿素-a 浓度（chl-a，藻华的指标）（TSS，栖息地适宜性、污染物负荷和营养物的关键）；和地表温度（敏感物种的关键栖息地指标）。这些数据将与水文特征的地理空间结构和可发现的跨部门水数据（称为 GeoConnex）相关联，初始 EQUATE 可视化将用于 (1) 为河流规划、管理、 (2) 确定水质差与社会脆弱性和公共卫生差异指标相一致的地方。 最后，该项目为未来以用户界面、参与度和公众健康为中心的遥感应用奠定了基础。共同设计流程，将为俄亥俄河流域及其他地区的公平水资源成果提供信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。