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

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

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 disparities 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 gauge 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 disparities, 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 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.