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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Wisconsin-Madison |
| Country | United States |
| Start Date | Aug 01, 2022 |
| End Date | Jul 31, 2027 |
| Duration | 1,825 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2144750 |
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
Freshwater is a critical resource needed for drinking water, agriculture, and industrial use. In some regions of the United States, freshwater resources are threatened by increasing salt levels. The use of water softeners, spreading salt on roads in winter, and some agricultural activities add salts to lakes, rivers, and groundwater.
This project will study where the salt is coming from, how it moves through rivers and lakes, and what impacts it has on freshwater habitats in Wisconsin. The overarching goal of the project is to protect our freshwater resources from further salt pollution through improved scientific understanding, education, and public engagement. Researchers will work with community-based organizations whose missions are to protect water quality and promote awareness of salt impacts through community activities and social media.
Resources for teaching and analyzing data will be made publicly available to encourage greater awareness and further study of salt impacts on freshwater resources.
The research themes of this project focus on understanding salt cycling in freshwater environments at a range of spatial scales. At the local scale, this project will establish a long-term salt observatory in the Yahara Watershed, Wisconsin. There, scientists will track salt pollution from source to outflow, studying the impacts on freshwater ecosystems dynamics, and modeling the role of river networks on salinity regimes.
Findings will be expanded to the regional scale to investigate the role of lakes in larger river networks and machine learning approaches will be used to transfer knowledge of well-monitored systems to unmonitored systems for better spatial and temporal prediction of salinity dynamics. This work aims to advance the use of machine learning approaches for conducting data-driven ecological science.
Globally, the risk of lake salinization exists for a broad range of lakes beyond the United States. Project members will collaborate with the international limnological community to establish a global database of lake salinity and ion concentration data. This resource will be used to advance our global understanding of salinization trends, drivers, adaptation, and management strategies.
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.
University of Wisconsin-Madison
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