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Bart Nijssen
Member of:
University of Washington
Public Documents
13
October 14, 2022
Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating mod...
Wouter Johannes Maria Knoben, Martyn P. Clark, Jerad Bales, et al.
June 11, 2021
Explainable AI uncovers how neural networks learn to regionalize in simulations of tu...
Andrew Bennett and Bart Nijssen
March 23, 2021
Deep learned process parameterizations provide better representations of turbulent he...
Andrew Bennett and Bart Nijssen
December 22, 2021
A process-conditioned and spatially consistent method for reducing systematic biases...
Andrew Bennett, Adi Stein, Yifan Cheng, et al.
June 15, 2022
Where and When Does Streamflow Regulation Significantly Affect Climate Change Outcome...
Jane Harrell, Bart Nijssen, Chris Frans, et al.
December 22, 2021
Learning from Observations: The Case for a New Generation of Land Surface Models
Bart Nijssen, Andrew Bennett, Grey Nearing, et al.
December 22, 2021
The Need to Account for Water Resources Management in Climate Change Impact and Adapt...
Bart Nijssen, Jane Harrell, Yifan Cheng, et al.
December 23, 2021
Using physics-based machine learning to estimate unobserved quantities: A case study...
Andrew Bennett, Maoya Bassiouni, Bart Nijssen, et al.
November 20, 2020
A coupled approach to incorporating deep learning into process-based hydrologic model...
Andrew Bennett and Bart Nijssen
January 14, 2020
Climate change impacts on thermal characteristics of freshwater fish habitats in a re...
Yifan Cheng, Bart Nijssen, Gordon Holtgrieve, et al.
January 14, 2020
Dynamic process connectivity for model diagnostics, evaluation, and intercomparison
Andrew Bennett, Bart Nijssen, Grey Nearing, et al.
January 14, 2020
Exploring the ability of reservoir infrastructure to mitigate climate change compound...
Yifan Cheng, John Yearsley, Voisin Nathalie, et al.
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