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- Konapala, G., Kao, S. C., Painter, S. L., & Lu, D. (2020). Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US. Environmental Research Letters, 15(10), 104022.
- Lu, D., Konapala, G., Painter, S. L., Kao, S. C., & Gangrade, S. (2021). Streamflow Simulation in Data-Scarce Basins Using Bayesian and Physics-Informed Machine Learning Models. Journal of Hydrometeorology, 22(6), 1421-1438.
- Cromwell, E. L., Shuai, P., Jiang, P., Coon, E., Painter, S. L., Moulton, D., … & Chen, X. (2021). Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks. Frontiers in Earth Science, 9(PNNL-SA-156876).
- Shuai, P., Chen, X., Mital, U., Coon, E.T. and Dwivedi, D., 2021. The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses. Hydrology and Earth System Sciences Discussions, pp.1-43.
- Utkarsh Mital (Berkeley Lab): A methodology for generating and validating downscaled estimates of meteorological data at the basin scale
- Simon Topp (US Geological Survey): Understanding limitations in generalizability and performance across two process guided deep learning architectures for predicting stream temperature
- Pin Shuai (Pacific Northwest Lab): The Effects of Spatial and Temporal Resolution of Meteorological Forcing on Watershed Hydrological Responses
- Alex Sun (UT Austin): Toward Spatiotemporal Learning of Large Sample Hydrology Using Graph Neural Networks
- Dipankar Dwivedi (Berkeley Lab): A scale-adaptive framework to predict river corridor and watershed hydrobiogeochemistry across scales
- Guillem Sole-Mari (Berkeley Lab): Hybrid physics-based/statistical modeling of seasonal flow and reactive transport through heterogeneous hillslopes
- Carl Steefel (Berkeley Lab): Machine Learning-Assisted Multifidelity Biogeochemical Modeling for Watersheds and River Basins
- Sudershan Gangrade (ORNL): Evaluation of Machine Learning Assisted Reservoir Operation Models for Long-Term Water Management Simulation
- Steefel et al. H103-05 ExaSheds: Advancing Watershed System Science using Machine Learning and Data-Intensive Simulation
- Painter et al. H103-06 Combining Data-Driven Machine Learning and Process-Based Models for Streamflow Simulation: Preliminary Results from the ExaSheds project
- Lu et al. H166-0001 Streamflow Predictions in Data-Scarce Basins using Bayesian and Physics-Informed Machine Learning Models
- Mital et al. H174-06 Generating High-Resolution Estimates of Precipitation at the Watershed Scale using Machine Learning
- Cromwell et al. H049-07 Subsurface Permeability Estimation using Deep Neural Networks
- Coon et al. H196-0001 Hyperresolution Hydrology on Full River Basins: Addressing Challenges in Machine Architectures and Data Integration
- Appling et al. H224-02 Process-Guided Deep Learning for Water Temperature Prediction