Project Grant 2139649

This $403,680 project grant from the National Science Foundation's Geosciences program (CFDA 47.050) supports research to improve streamflow monitoring and forecasting methods. The University of Iowa will enhance protocols for continuously monitoring streamflows to more accurately capture effects of flood wave propagation and seasonal riparian vegetation changes. Researchers will integrate direct measurements with numerical models to enable real-time estimation and forecasting of streamflow responses to cyclical processes. They will develop rapid artificial intelligence tools to supplement existing forecasting capabilities. By combining experimental, data-driven, and physics-based investigations, the University will enable adoption of a reach-scale monitoring method and real-time tracking of unsteady flow and vegetation growth impacts. A heterogeneous routing approach and extensive data analysis will extract interdependencies among flow variables due to subtle hysteretic effects. Generalizing the inferences for diverse conditions will cost-effectively improve predictive streamflow relationships using only in-situ data without modeling. This supports the NSF program's goal of expanding fundamental earth science knowledge and understanding integrated earth systems through basic research.