November 24, 2023
Spatio-temporal machine learning for continental scale terrestrial hydrology
Andrew Bennett, Hoang Tran, Luis De la Fuente, et al.
February 27, 2023
Quantifying changes in global snow at high spatial resolutions under future warming s...
Andrew Bennett and Oriana Chegwidden
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.
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 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
Dynamic process connectivity for model diagnostics, evaluation, and intercomparison
Andrew Bennett, Bart Nijssen, Grey Nearing, et al.
January 14, 2020
A Spatially Consistent Bias Correction Technique for Distributed Streamflow Modeling
Bart Nijssen, Andrew Bennett, Marketa McGuire, et al.