Publications

1. Willard, J. D., & Varadharajan, C. (2025). Machine learning ensembles can enhance hydrologic predictions and uncertainty quantification. Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000732. https://doi.org/10.1029/2025JH000732 

2. (In Review) Willard, J. D., Ciulla, F., Weierbach, H., Kumar, V., & Varadharajan, C. (2024). Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models. arXiv preprint arXiv:2410.19865.

3. Willard JD, Varadharajan C, Jia X, Kumar V. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources. Environmental Data Science. 2025;4:e7. doi:10.1017/eds.2024.14

4. Ciulla, F. and Varadharajan, C.: A network approach for multiscale catchment classification using traits, Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, 2024. (Selected as a highlight paper)

5. Ombadi, M., Risser, M.D., Rhoades, A.M. et al. A warming-induced reduction in snow fraction amplifies rainfall extremes. Nature (2023). https://doi.org/10.1038/s41586-023-06092-7 

6. Willard J.,  Machine Learning Techniques for Time Series Regression in Unmonitored Environmental Systems. Univ. of Minnesota, May 2023

7. Varadharajan, C., Appling, A. P., Arora, B., Christianson, D. S., Hendrix, V. C., Kumar, V., Lima, A. R., Müller, J., Oliver, S., Ombadi, M., Perciano, T., Sadler, J. M., Weierbach, H., Willard, J. D., Xu, Z., & Zwart, J. (2022). Can machine learning accelerate process understanding and decision-relevant predictions of river water quality? Hydrological Processes, 36(4), e14565. https://doi.org/10.1002/hyp.14565

8. Weierbach, H.; Lima, A.R.; Willard, J.D.; Hendrix, V.C.; Christianson, D.S.; Lubich, M.; Varadharajan, C. Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning. Water 2022, 14, 1032. https://doi.org/10.3390/w14071032  

9. Varadharajan, C., Hendrix V.C., Christianson D.S., Burrus M., Wong C., Hubbard S S., Agarwal D. (2022). BASIN-3D: A brokering framework to integrate diverse environmental data, Computers & Geosciences, Volume 159, 105024, https://doi.org/10.1016/j.cageo.2021.105024  

10. Ombadi, M., & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States. Water Research, 118664. https://doi.org/10.1016/j.watres.2022.118664 

11. Hubbard, SS, Varadharajan, C, Wu, Y, Wainwright, H, Dwivedi, D. Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry. Hydrological Processes. 2020; 1– 8. https://doi.org/10.1002/hyp.13807 (Invited Commentary)

Reports

1. U.S. DOE. 2024. Artificial Intelligence for the Methane Cycle, DOE/SC-0213. U.S. Department of Energy Office of Science.  https://doi.org/10.2172/2204972.

2. Hickmon, Nicki L., Varadharajan, Charuleka, Hoffman, Forrest M., Wainwright, Haruko M., and Collis, Scott. Artificial Intelligence for Earth System Predictability (AI4ESP), Hydrology chapter (2022). doi:10.2172/1888810. Available at http://ai4esp.org.

3. Community Coordinating Group on Integrated Hydro-Terrestrial Modeling (2020), “Integrated Hydro-Terrestrial Modeling: Development of a National Capability,” report of an interagency workshop held September 4-6, 2019 with support from the National Science Foundation, the U.S. Department of Energy, and the U.S. Geological Survey, https://doi.org/10.25584/09102020/1659275

Data

1. Nagamoto E ; Ciulla F ; Ombadi M ; Willard J ; Carroll R ; Varadharajan C (2025): Dataset: "Factors influencing Regional Declines in Streamflows and Water quality in the Upper Colorado River Basin during Droughts (1998-2022)". iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/2551894 

2. Willard J ; Varadharajan C (2025): Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025). iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/2527393  

3. Willard J ; Ciulla F ; Weierbach H ; Kumar V ; Varadharajan C (2024): Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024). iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/2448016 

4. Ciulla F ; Varadharajan C (2023): Classification of River Catchments in the Contiguous United States: Processed Dataset, Similarity Patterns, and Resulting Classes. iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/1987555 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1987555 

5. Ombadi M ; Risser M ; Rhoades A ; Varadharajan C (2023): Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'. iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/1987525 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1987525.

6. Weierbach H ; Lima A R ; Willard J D ; Hendrix V C ; Christianson D S ; Lubich M ; Varadharajan C (2022): Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022. iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/1854257 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1854257 

7. Ombadi M ; Varadharajan C (2022): Dataset for 'Ombadi, M. & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States, Water Research'. iNAIADS, ESS-DIVE repository. Dataset. doi:10.15485/1870708 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1870708

Software

1. Hendrix, V.C. et al. “Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets (basin3d). (2019) [Computer software]. http://github.com/BASIN-3D/basin3d. https://doi.org/10.11578/dc.20210429.4.

Invited/Plenary Talks

1. (Plenary) Varadharajan C. et al., “Unraveling Watershed Responses to Drought from Catchment to Continental Scales using Machine Learning and Data Science”, Gordon Research Conference on Catchment Science: Interactions of Hydrology, Biology and Geochemistry, Andover, NH, June 2025

2. Varadharajan C. et al. “The case for using low-complexity models and ensembling approaches for hydrological predictions”, Society for Freshwater Science Annual Meeting, Puerto Rico, May 2025

3. Varadharajan C. et al. “Evaluating the Impacts of Climate Disturbances and Extreme Events on Water Resources using Machine Learning”, AI for Science Symposium, San Francisco, May 2025

4. Varadharajan, C., et al. "The importance of" data" in data-driven modeling for predictions in unmonitored basins." AGU Fall Meeting, Washington DC, December 2024

5. Varadharajan C., et al. “Using big data to decipher functional traits affecting watershed response to drought in the Upper Colorado River Basin”, AGU Fall Meeting, Washington DC, December 2024

6. Varadharajan C. et al. “Spatiotemporal predictions using deep learning for water resources management”, 1st Berkeley Lab AI for Science Summit, Berkeley, Oct 2024

7. (Plenary Panel Discussion) Varadharajan C., "AI for natural resources", Science Day organized by California Council of Science and Technology, California Natural Resources Agency

8. (Keynote) Varadharajan, C., Willard J., Ciulla F., Weierbach H., Kumar V., Mahoney M., Nakata R. “Machine Learning for Predictions in Unmonitored Basins”, Computational Methods in Water Resources conference, October 2024 

9. (Invited) Varadharajan C., Willard J., Ciulla F., Weierbach H., Lima A.R., Kumar V.  “Improving predictability and reducing model complexity with knowledge-guided machine learning", Knowledge Guided Machine Learning Workshop, Aug 2024.

10. Varadharajan C., Willard J., Ciulla F., Weierbach H., Lima A.R., Kumar V.  “Comparing top-down and bottom-up modeling approaches for regionalization of stream temperature predictions”, HydroML Symposium, May 2024.

11. (Plenary Panel Discussion), Varadharajan C., “Responsible use of AI for Earth/Geoscience”, Hydro ML, May 2023.

12. (Plenary Panel Discussion) Varadharajan C., "Inclusive and Equitable Research: Best Practices and Lessons Learned Roundtable, ESS PI Meeting, April 2024

13. Varadharajan C., Willard J., Ciulla F., Weierbach H., Lima A.R., Bouskill, N., Brodie E., Kumar V. An approach towards including watershed traits in machine learning models for predictions in unmonitored basins. 104th American Meteorological Society Fall Meeting, Jan 2024. 

14. Varadharajan C., et al. Similarity versus Diversity: MultiScale Machine Learning Models for Predictions of River Hydrology and Water Quality. American Geophysical Union Fall Meeting, Dec 2023. 

15. Ombadi M., Risser M., Rhoades A., and Varadharajan C., Extreme events in a warmer climate: more rain, less snow, and implications for adaptation. American Geophysical Union Fall Meeting, Dec 2023. 

16. Varadharajan C. et al., Learnings from continental water quality studies across heterogenous watersheds in the United States and Australia using data-driven approaches, Women Advancing River Research 2023 Series, Nov 16, 2023

17. Varadharajan C. et al. "Investigating the impacts of climate disturbances on water resources using data-driven approaches". NASEM Webinar: Paving the Way for Continental Scale Biology: Technology, Techniques, and Teamwork for Connecting Research Across Scales, Aug 2023.

18. Varadharajan C. et al. “Understanding and predicting watershed response to disturbance using open data and cyberinfrastructure”. USGS Community for Data Integration Workshop (Session: Earth Science in the AI/ML age), May 2023. https://2023cdiworkshop.sched.com/ 

19. (Plenary) Varadharajan C. et al. “Using machine learning and other data-driven approaches to understand watershed response to disturbance”, HydroML Symposium, May 2023. https://sites.google.com/lbl.gov/2023hydromlsymposium/home 

20. (Plenary) Varadharajan C. et al. Early Career Panel, ESS PI Meeting, May 2023

21. Ombadi et al. Quantifying Hydrologic Resistance, ESS PI Meeting - Disturbance and Resilience breakout, May 2023

22. Varadharajan C. AI and Earth Sciences, NERSC AI Strategy Meeting, June 2023

23. Varadharajan C. et al. “Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Hydrologic Perturbations on River Water Quality”. NSF Virtual Workshop on Knowledge-Guided Machine Learning, August 2021. https://bbe.umn.edu/events/workshop-kgml2021 

24. Varadharajan C. et al. “Multi-scale machine learning models to predict impacts of extreme events on stream temperature”, symposium on science-guided AI at the AAAI Fall Symposium series, November 2021. https://sites.google.com/vt.edu/sgai-aaai-21 

25. Varadharajan, C., Agarwal, D., Arora, B., & Others. Data-Model Integration and Machine Learning Approaches for Hydrobiogeochemical Modeling Applications. AGU Fall Meeting, December 2021. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/797688 

26. Varadharajan, C., Weierbach, H., Ombadi, M., Lubich, M., Lima, A., Hendrix, V. C., Christianson, D. S., Wong, C., & Willard, J. Investigating the Impacts of Climate-driven Disturbances on River Water Quality using Machine Learning and Statistical Modeling Approaches. AGU Fall Meeting, December 2021. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/797675 

27. (Plenary Panel Discussion) Varadharajan, C. 2021 Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research

28. Varadharajan C., Mueller J., Sahu R., Park J., Arora B., Faybishenko B., Barr-Ramsey M., Hendrix V.C., Christianson D.S., Weierbach H., Agarwal D.A. Data-driven approaches to building efficient machine learning models for aquatic science and hydrology. NSF Virtual Workshop on Knowledge-Guided Machine Learning, August 2020 (https://sites.google.com/umn.edu/kgml/workshop)

29. Varadharajan C., Arora B., Cholia S., Christianson D.S., Damerow J.E., Dwivedi D., Elbashandy H., Faybishenko B., Henderson M., Hendrix V.C., Hubbard S.S., Kakalia Z., Mueller J., O'Brien F., Park J., Robles E., Sahu R., Snavely C., Versteeg R., Whitenack K., Agarwal D.A. Utilizing Diverse Data in Scientific Analysis and Modeling for Water Resource Management. American Geophysical Union Fall Meeting, San Francisco, December 2019

30. (Plenary) Varadharajan C., Open Science by Design – Data, Codes, and Interoperability. Integrated HydroTerrestrial Modeling (IHTM) Workshop, Washington DC, Sep 2019.

31. (Plenary) Varadharajan C., Open Science By Design- Making it Happen: People and Resources. Synthesis presentation of workshop breakouts. IHTM Workshop, Sep 2019.

Other Conference and Workshop Presentations

1. (Oral) Nagamoto, E., Varadharajan, C., Ombadi, M., Ciulla, F., Willard, J., & Carroll, R. W. Widespread Declines in Streamflow and Water Quality during Drought Episodes in the Upper Colorado River Basin from 2000 to 2021. In AGU Fall Meeting , Dec 2024 

2. (Oral) Wang, Y. H., Yang, Y., Ciulla, F., Willard, J., Gupta, H., & Varadharajan, C. ML-Enabled Physically-Interpretable Modeling of Catchment-Scale Precipitation-Runoff Dynamics Using the Mass-Conserving-Perceptron: Large-Sample Investigation. AGU Fall Meeting, Dec 2024 

3. (Oral) Ciulla, F., Nagamoto, E., Willard, J., Wang, Y. H., Weierbach, H., Lima, A., & Varadharajan, C. A Network Approach to Determine Factors Affecting the Functional Behavior of Watershed Systems. In AGU Fall Meeting, Dec 2024 

4. (Oral) Willard, J., Weierbach, H., Ciulla, F., Wang, Y. H., & Varadharajan, C. Ensemble Deep Learning Strategies for Improved Prediction and Uncertainty Quantification of Stream Flows and Temperature in Unmonitored River Sites. AGU Fall Meeting, Dec 2024

5. (Oral) Willard J., Weierbach H., Ciulla F., Lima A.R., Kumar V., Varadharajan C.,  “Machine Learning Predictions in Unmonitored Sites: Exploring Scalability, Input Customization, and Architectures of Stream Temperature Prediction Models”, WaterSciCon, June 2024.

6. (Oral) Willard J., Weierbach H., Ciulla F., Lima A.R., Kumar V., Varadharajan C.,  “Exploring Ensemble Deep Learning Strategies for Improved Stream Temperature Predictions in Ungauged Basins”, HydroML Symposium, May 2024.

7. (Oral) Ciulla and Varadharajan,  “A network approach for multiscale catchment classification using traits”, HydroML Symposium, May 2024.

8. (Oral) Willard J., “Deep Learning Applications on Perlmutter for a Changing Planet: Weather, Climate, and Hydrology”, National Energy Research Scientific Computing Center (NERSC) Talk Series. May 2024.

9. (Poster) Varadharajan, C., Willard J., Ciulla F., Weierbach H., Kumar V., Lima A., “Data-driven modeling strategies for predicting stream flow and temperature at watershed to continental scales”, 2024 ESS PI Meeting.

10. (Oral) Willard, J; Weierbach, H; Lima, A; Kumar, V; Varadharajan, C; Improving Daily Stream Temperature Predictions in Unmonitored Basins: Broad-Scale Deep Learning, Transfer Learning, and Open Questions, AGU Fall Meeting, Dec 2023

11. (Poster) Ciulla, F; Weierbach, H; Willard, J; Lima, A; Varadharajan, C; Connecting Catchment Traits to Hydrological Functions using Data-driven Approaches, AGU Fall Meeting, Dec 2023

12. (Poster) Ombadi, M; Ajami, N K; Brodie, E; Nico, P S; Risser, M D; Varadharajan, C; Flowing against the Odds: Measuring Hydrological Resilience to Drought Events, AGU Fall Meeting, Dec 2023

13. (Poster) Weierbach, H., Willard, J., Ciulla, F. Lima, A.R., & Varadharajan, C. (2023, May). Continental-scale Stream Temperature Modeling across Temporal Scales, and the Applicability of Trait-Based Training. HydroML Symposium, May 2023.

14. (Poster) Varadharajan, C., Ombadi, M., Nagamoto, E., Ciulla, F., Weierbach, H., Willard, J., Lima, A.R. Examining the Impacts of Disturbances on River Hydrology at Regional to Continental Scales. ESS PI Meeting, May 2023

15. (Lightning poster) Ciulla F. and Varadharajan C. Interpretable Unsupervised Classification of River Catchments with Network Science, EGU, April 2023

16. (Oral) Ombadi, M. and Varadharajan, C., The use of Artificial Intelligence for Hypothesis Formulation and Assessment of Extreme Hydrologic Events Impact on Water Quality. American Meteorological Society’s 37th Conference on Hydrology. 103rd American Meteorological Society Annual Meeting. January, 2023. 

17. (Oral) Weierbach, H., Willard, J., Lima, A.R., Hendrix, V.C., Christianson, D.S.,  Lubich, M., and Varadharajan, C. Classical Machine Learning for Widespread Stream Temperature Predictions: Demonstrations in the Pacific Northwest and Mid Atlantic Regions. American Geophysical Union Fall Meeting, December 2022.

18. (Oral) Nagamoto E., Ombadi M., and Varadharajan C. Investigating the Impacts of Drought on Water Quality in the Upper Colorado River Basin using Data-Driven Methods. American Geophysical Union Fall Meeting, December 2022.

19. (Poster) Willard, J., Varadharajan, C., Weierbach, H., Lima, A.R., Ciulla, F., Kumar, V. Transfer Learning and Meta-learning Approaches for Stream Temperature Prediction in Unmonitored Basins. American Geophysical Union Fall Meeting, December 2022.

20. (Oral) Varadharajan, C., Painter S., Kumar, J., Shen C., Lu D., Moulton D., Chen X., Ombadi M., Feng D., Bhanja S., Weierbach H., Tsai W., Willard J., Zhi W., Sun A., Opportunities for using Artificial Intelligence and Machine Learning to Address Hydrological Grand Challenges. American Geophysical Union Fall Meeting, December 2022.

21. (Poster) Ombadi M. and Varadharajan C.,. The Interplay between Urbanization and Aridity in Determining The Response of Salinity to Floods: A Continental-scale Analysis. AGU Fall Meeting, December 2022.

22. (Poster) Ciulla F., Willard J., Weierbach H., Varadharajan C. Interpretable Classification of the Contiguous United States River Catchments using Network Science Methods. American Geophysical Union Fall Meeting, December 2022.

23. (Online Discussion Session) Ombadi, M., Jiang, P., Rodriguez, L. C. H., & Risser, M. D. Causal Inference in Hydrology: Inter-comparisons, Applications and Future Avenues. In Fall Meeting 2022. AGU.

24.  (Poster Session) Ombadi, M., Jiang, P., Rodriguez, L. C. H., & Risser, M. D. Causal Inference in Hydrology: Inter-comparisons, Applications and Future Avenues. In Fall Meeting 2022. AGU.

25. (Oral) ‪Varadharajan C, Ombadi M, Weierbach H, Willard J, Lima AR, Hendrix VC, Christianson DC, Lubich M, Wong C,  O'Ryan D , Multiscale Effects of Climate-driven Disturbances on River Water Quality, ‪Frontiers in Hydrology Meeting, June 2022

26. (Oral) ‪Varadharajan, C.*, Appling, A. P., Arora, B., Christianson, D. S., Hendrix, V. C., Kumar, V., Lima, A. R., Müller, J., Oliver, S., Ombadi, M., Perciano, T., Sadler, J. M., Weierbach, H., Willard, J. D., Xu, Z., & Zwart, J., Using Machine Learning to Advance Decision-Relevant Predictions of River Water Quality, ‪Frontiers in Hydrology Meeting, June 2022

27. (Poster) Ombadi M., Varadharajan C., Weierbach H., and Hendrix V.C. Impact of Floods on Salinity Levels in Freshwater Ecosystems. AGU Fall Meeting, December 2021. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/907168 

28. (Poster) Weierbach H., Lima A.R., Lubich M., Ombadi M., Christianson, D.S., Hendrix, V.C., and Varadharajan, C. Predicting Stream Temperature Across Spatial Scales With Low Complexity ML. AGU Fall Meeting, December 2021. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/948097 

29. (Oral) Weierbach, H., Lima, A. R., Willard, J., Christianson, D.S., Hendrix, V.C., and Varadharajan, C. Using Classical ML for Widespread Monthly Stream Temperature Predictions Lightning presentation at the HydroML Symposium. Penn State University. May 2022.

30. (Oral) Varadharajan, C., Painter S., Kumar J., Lu D., and Shen C. “State-of-the-art of machine learning in hydrology”, AI4ESP workshop hydrology session, November 2022.

31. (Oral) Varadharajan C., Weierbach H., Lima A.R., Hendrix V.C., Christianson D.S., Water Quality Predictions in the Delaware River Basin Using Machine Learning Methods, National Monitoring Conference, April 2021

32. (Oral) Varadharajan C., Agarwal D.A., Burrus M., Christianson D.S., Faybishenko B., Hendrix V.C., Hubbard S.S., Weierbach H., Wong C., Integration of Diverse, Regional-Scale Water Data for Water Quality Analysis and Predictions, National Monitoring Conference, April 2021

33. (Poster/Paper) Weierbach H., Lima A.R., Christianson D.S., Faybishenko B, Hendrix V., Varadharajan C., A Comparison of Data-Driven Models for Predicting Stream Water Temperature. Workshops on AI for Earth Sciences and Tackling Climate Change using Machine Learning at NeurIPS 2020

34. (Oral) Varadharajan C., Weierbach H., Lima A.R., Hendrix V.C., Christianson D.S., Mueller J., Park J., Faybishenko B. A data-driven approach to predicting the impacts of streamflow disturbance on water quality in river corridors. American Geophysical Union Fall Meeting, December 2020.

35. (Oral) Hendrix V.C., Christianson D.S., Varadharajan C., Burrus M., Cholia S., Cheah Y., Chu H., Crystal-Ornelas R., Damerow J. E., Kakalia Z., O’Brien F., Pastorello G., Robles E., Agarwal D.A. Tackling the Challenges of Earth Science Data Synthesis: Insights from (meta)data standards approaches. American Geophysical Union Fall Meeting, December 2020.

36. (Poster) Christianson D.S., Hendrix V.C., Varadharajan C., Agarwal D.A. BASIN-3D v2.0: A Researcher-focused Data Synthesizer. American Geophysical Union Fall Meeting, December 2020.

37. (Oral) Varadharajan C., Christianson D.S., Hendrix V.C., Agarwal D.A., Burrus M., Dwivedi D., Faybisheno B., Hubbard S.S., Kakalia Z., Versteeg R.. Data-Driven Approaches for Water Resource Science and Management. Virtual Aquatic Data/Open Science Summit, July 2020