Skip to main content
U.S. flag

An official website of the United States government

Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks Active

By Community for Data Integration (CDI) April 5, 2023

Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.

Knowing when and how much streamflow occurs is essential for evaluating water use, water quality, and habitat for many species. However, streamflow is not measured for most streams in the United States. This makes it difficult to estimate when streams will flow, if they will flow, and how much they will flow and limits information about water availability and wildlife habitat for most streams in the United States. By combining other indicators of streamflow, like observations of when a stream is wet or dry, with streamflow measurements we hope to make better streamflow estimates on streams that are not measured. This new modeling method will help provide more information about water availability, water quality, and wildlife habitat for understudied streams. This method will also help advance efforts to model drought and improve land management decision making.

Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.

Knowing when and how much streamflow occurs is essential for evaluating water use, water quality, and habitat for many species. However, streamflow is not measured for most streams in the United States. This makes it difficult to estimate when streams will flow, if they will flow, and how much they will flow and limits information about water availability and wildlife habitat for most streams in the United States. By combining other indicators of streamflow, like observations of when a stream is wet or dry, with streamflow measurements we hope to make better streamflow estimates on streams that are not measured. This new modeling method will help provide more information about water availability, water quality, and wildlife habitat for understudied streams. This method will also help advance efforts to model drought and improve land management decision making.