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

获得清洁的水是21世纪的决定性挑战。即使在水资源丰富的地方，清洁水的获取也存在差距。创造工具来确定这些不平等现象发生在哪里，它们如何随时间变化，以及它们如何对政策和管理作出反应，对于机构、公用事业公司、非政府组织和社区在寻求改善获得清洁水的努力方面至关重要。然而，美国现有的水质监测数据在空间上是稀疏的，决策者难以获取。因此，在获取清洁地表水用于捕鱼、娱乐和作为饮用水源方面，测量空间差异是具有挑战性的。遥感提供了一种强大的替代方案，可以避开水质数据中的这些限制和差异。这项研究的总体目标是利用卫星图像改变评估清洁水获取方面的差距的方式，并为清洁水获取的决策提供信息。这一努力汇集了一个跨部门和多学科的合作伙伴团队，共同创建和试验一个新的高空间分辨率、开放源码的决策支持系统“EQUATE”，该系统将整合各组织、社区和机构的意见和反馈。EQUATE将在俄亥俄河上游流域进行试点，但可以翻译到任何美国河流流域。最终，EQUATE将提供一个公开可用的可视化、分析和交流界面，提高公众对水量、水质和不平等的认识，并为利益相关者、研究人员、教育工作者、社区成员和领导人提供一个工具来解释水信息。匹兹堡大学的匹兹堡水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一个等值原型。EQUATE将利用重合的历史现场采样将陆地卫星卫星图像转换为空间连续的长期水质观测，以训练和验证机器学习算法，这些算法包括：叶绿素a浓度(叶绿素a，藻类大量繁殖的指标)；总悬浮沉积物浓度(TSS，生境适宜性、污染物负荷和养分供应的关键指标)；以及表面温度(敏感物种的关键生境指标)。这些数据将与称为GeoConneX的水文特征和可发现的跨部门水数据的地理空间结构相关联。最初的等值可视化将用于(1)为河流规划、管理和大坝运行提供信息，以及(2)确定水质差的地方与社会脆弱性和公共卫生方面的差异指标相吻合。最后，该项目为未来以用户界面、参与和联合设计流程为中心的遥感应用奠定了基础，这些应用将为俄亥俄河流域和其他地区的公平水资源结果提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.