Publications

  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). doi: 10.3389/frwa.2020.000
  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.

Presentations

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. Guillem Sole-Mari (Berkeley Lab): Hybrid physics-based/statistical modeling of seasonal flow and reactive transport through heterogeneous hillslopes
  7. Carl Steefel (Berkeley Lab): Machine Learning-Assisted Multifidelity Biogeochemical Modeling for Watersheds and River Basins
  8. Sudershan Gangrade (ORNL): Evaluation of Machine Learning Assisted Reservoir Operation Models for Long-Term Water Management Simulation
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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
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