Watershed Models with Deep Learning

Unusual application of interest.

Deep learning improves stream discharge-based estimates of subsurface permeability, allowing scientists to create more accurate watershed models. Deep Learning Uses Stream Discharge to Estimate Watershed Subsurface Permeability

U.S. Department of Energy

September 26, 2022

Deep learning can calculate a watershed's subsurface permeability from stream discharge data more accurately than conventional methods. Scientists from the U.S. Department of Energy's Pacific Northwest National Laboratory, Oak Ridge National Laboratory, and Los Alamos National Laboratory taught deep neural networks (DNNs) to estimate subsurface permeability from stream discharge hydrographs. The researchers trained the DNNs to map relationships between soil and geologic layer permeabilities and simulated stream discharge acquired via an integrated surface-subsurface hydrologic watershed model; this returned more accurate permeability than inverse modeling. The networks then estimated the permeability of an actual watershed using observed stream discharge from the study site, accurately predicting stream flows. The enhanced parameter estimation promises to reduce uncertainty in predictive