ABSTRACT

Streamflow predictions are essential for forecasting floods and managing water resources under intensifying pressures on water use. To make reliable streamflow predictions for all rivers, including those with no flow gauges, we need computer models that accurately simulate watershed processes and how they vary across the U.S. landscape. For example, how do surface flows, recharge, groundwater storage and flow patterns change from watershed to watershed? The latest hydrologic models are flexible enough to simulate spatially variable processes, but we currently lack the knowledge of how those processes vary by watershed. This project will fill this knowledge gap by developing a new framework to predict how watershed processes vary across the U.S.. The approach is novel in leveraging small-scale field hydrology knowledge within a continental-scale, machine learning application. The research will discover new relationships between landscape features, streamflow dynamics and watershed processes. Project scientists will work with NOAA?s National Water Center to apply the results in the design of the Next-Generation National Water Model that provides streamflow predictions for every river in the U.S.. The project will provide research experiences for under-represented minority students, and will develop online learning materials.

The goals of the project are to (1) Identify a suite of landscape metrics that quantify landscape characteristics most likely to activate specific runoff generation processes. (2) Identify dominant hydrologic processes across a large database of gauged U.S. watersheds, by relating streamflow dynamics to the upstream processes that drive them. (3) Develop a data-driven model that predicts dominant hydrologic processes based on landscape metrics. (4) Evaluate the data-driven model by testing it for a range of locations and case studies. The framework developed in this project will improve on previous methods of identifying and predicting landscape and hydrologic metrics, by redesigning the metrics to target specific hydrologic processes. Further, the project will apply new machine learning developments to identify and interpret predictive relationships between landscapes and processes. Deliverables will include GIS (geographic information system) maps of hydrologic processes across the contiguous U.S., and open-source code to estimate hydrologic processes from landscape characteristics. Overall, the project aspires to transform how continental-scale hydrology models represent water fluxes in diverse climates and landscapes.

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.

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