1. Mital U, Dwivedi D, Brown JB, Faybishenko B, Painter SL and Steefel CI (2020). Sequential Imputation of Missing Spatio-Temporal Precipitation Data Using Random Forests. Frontiers in Water, 2(20).
  2. 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.
  3. 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.
  4. 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).
  5. 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.
  6. Bhanja, S. N., Coon, E. T., Lu, D., & Painter, S. L. (2023). The evaluation of distributed process-based hydrologic model performance uses only a priori information to define model inputs. Journal of Hydrology, 618, 129176.
  7. Coon, E. T., & Shuai, P. (2022). Watershed Workflow: A toolset for parameterizing data-intensive, integrated hydrologic models. Environmental Modelling & Software, 105502.
  8. Gangrade, S., Lu, D., Kao, S.-C., & Painter, S. L. (2022). Machine Learning Assisted Reservoir Operation Model for Long-Term Water Management Simulation. JAWRA Journal of the American Water Resources Association, 58(6), 1592-1603.
  9. Topp, Simon N., Janet R. Barclay, Jeremy A. Diaz, Alexander Y. Sun, Xiaowei Jia, Dan Lu, Jeffrey M. Sadler, and Alison P. Appling. 2023. “Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture.” Water Resources Research 59 (e2022WR033880). 
  10. Sun, A. Y., Jiang, P., Yang, Z. L., Xie, Y., & Chen, X. (2022). A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion. Hydrology and Earth System
  11. Mudunuru, M. K., Cromwell, E. L., Wang, H., & Chen, X. (2022). Deep learning to estimate permeability using geophysical data.Advances in Water Resources, 167, 104272. Sciences, 26(19), 5163-5184.
  12. Steefel, C. I., & Hu, M. (2022). Reactive transport modeling of mineral precipitation and carbon trapping in discrete fracture networks. Water Resources Research58(9), e2022WR032321.
  13. Mital, U., Dwivedi, D., Özgen-Xian, I., Brown, J. B., & Steefel, C. I. (2022). Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps. Artificial Intelligence for the Earth Systems1(4), e220010.
  14. Dwivedi, D., Steefel, C. I., Arora, B., Banfield, J., Bargar, J., Boyanov, M. I., … & Zavarin, M. (2022). From legacy contamination to watershed systems science: a review of scientific insights and technologies developed through DOE-supported research in water and energy security. Environmental Research Letters17(4), 043004.
  15. Dwivedi, D., Mital, U., Faybishenko, B., Dafflon, B., Varadharajan, C., Agarwal, D., … & Hubbard, S. S. (2022). Imputation of contiguous gaps and extremes of sub-hourly groundwater time series using random forests. Journal of Machine Learning for Modeling and Computing3(2).
  16. 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 Hydrometeorology22(6), 1421-1438.
  17. Sun, A. Y., Jiang, P., Mudunuru, M. K., & Chen, X. (2021). Exploring Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks. Water Resources Research57(12), e2021WR030394.
  18. Mital, U., Dwivedi, D., Brown, J. B., & Steefel, C. I. (2022). Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States). Earth System Science Data14(11), 4949-4966.


AGU 2021

  1. Utkarsh Mital (Berkeley Lab): A methodology for generating and validating downscaled estimates of meteorological data at the basin scale
  2. Simon Topp (US Geological Survey): Understanding limitations in generalizability and performance across two process guided deep learning architectures for predicting stream temperature
  3. Pin Shuai (Pacific Northwest Lab): The Effects of Spatial and Temporal Resolution of Meteorological Forcing on Watershed Hydrological Responses
  4. Alex Sun (UT Austin): Toward Spatiotemporal Learning of Large Sample Hydrology Using Graph Neural Networks
  5. Dipankar Dwivedi (Berkeley Lab): A scale-adaptive framework to predict river corridor and watershed hydrobiogeochemistry across scales
  6. Carl Steefel (Berkeley Lab): Machine Learning-Assisted Multifidelity Biogeochemical Modeling for Watersheds and River Basins
  7. Sudershan Gangrade (ORNL): Evaluation of Machine Learning Assisted Reservoir Operation Models for Long-Term Water Management Simulation
  8. Charuleka Varadharajan, Scott L Painter, Jitendra Kumar, Dan Lu, Chaopeng Shen, Xingyuan Chen, John D Moulton, Soumendra Nath Bhanja, Wen-Ping Tsai, Dapeng Feng, Mohammed Ombadi, Helen Weierbach, Jared Willard, Wei Zhi and Alexander Y Sun. 2022. Opportunities for using Artificial Intelligence and Machine Learning to Address Hydrological Grand Challenges

  9. Dan Lu, Siyan Lu, Scott L. Painter, Natalie Griffiths, Eric M. Pierce. 2022.  Uncertainty Quantification of Machine Learning Models to Improve Streamflow Prediction in Changing Climate and Environmental Conditions

  10. Bilal Iftikhar, Sudershan Gangrade, Dan Lu, Shih-Chieh Kao, Scott L Painter and Ethan Coon. 2022. Simulating Operation Behaviors of Cascade Reservoirs Using Physics-Based Machine Learning Models: A Case Study for Gunnison River Basin

  11. Soumendra Nath Bhanja, Dan Lu, Ethan Coon and Scott L Painter. 2022. Exploring Interpretability and Performance of an Attention-based Long Short-Term Memory (LSTM) Network for Rainfall-runoff Modeling

AGU 2020

  1. Steefel et al. H103-05 ExaSheds: Advancing Watershed System Science using Machine Learning and Data-Intensive Simulation
  2. Painter et al. H103-06 Combining Data-Driven Machine Learning and Process-Based Models for Streamflow Simulation: Preliminary Results from the ExaSheds project
  3. Lu et al. H166-0001 Streamflow Predictions in Data-Scarce Basins using Bayesian and Physics-Informed Machine Learning Models
  4. Mital et al. H174-06 Generating High-Resolution Estimates of Precipitation at the Watershed Scale using Machine Learning
  5. Cromwell et al. H049-07 Subsurface Permeability Estimation using Deep Neural Networks
  6. Coon et al. H196-0001 Hyperresolution Hydrology on Full River Basins: Addressing Challenges in Machine Architectures and Data Integration
  7. Appling et al. H224-02 Process-Guided Deep Learning for Water Temperature Prediction