Collaborative Research: ULTRA-Data: Developing global riverine solute regime and synchrony frameworks for understanding watershed-scale controls on river biogeochemical signals

About This Grant

Rivers are vital to life, providing drinking water and supporting agriculture, recreation, transportation, and fisheries. However, changes on land can alter runoff in ways that negatively impact river ecosystems and water quality. The goal of this research is to better understand and predict changes in river chemistry at the global scale. The investigators will (re)use publicly available water chemistry and flow data from more than 450 rivers across all seven continents. Novel machine learning and other data analysis techniques will be used to determine how and why substances in rivers, including nutrients, metals, salts, and trace minerals, vary throughout the year and across the landscape. The project includes training workshops for early career researchers and public outreach through the Science Museum of Minnesota. Project outcomes will provide insights into how we can better manage rivers and protect water resources for the future. This data-intensive project will examine the similarities and differences in the seasonal distribution of chemicals across rivers with varying flow regimes and watershed characteristics. Researchers will harmonize large datasets from Long-Term Ecological Research, National Ecological Observatory Network, and other federal science investments. They will use deep learning and statistical approaches to identify different drivers and seasonal regimes for a broad range of chemical solutes. The investigators hypothesize less synchrony amongst biologically active substances and across disturbed watersheds. The resulting dataset and analyses will enable prediction of water quality in unmonitored and remote river systems that are difficult and expensive to monitor. The project also supports workforce development in water resources and “big data” analysis. The outcome of this research will be new global frameworks for understanding and predicting how river chemistry responds to environmental changes over long timeframes and broad spatial scales. 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.