Publications
You can also find a list of publications on Google Scholar.
Submitted
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Keeney, N. J. and F. V. Davenport.
A spatially and temporally disaggregated inland flood dataset with flood metrics (2000-2024),
(submitted to Earth System Science Datasets)
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Choi, E., F. V. Davenport, Z. Lin, A. Ortiz-Bobea, K. Reed, E. Kebreab, N. D. Mueller.
Weather-driven US milk yield losses and economic damages revealed by 9 million cows,
(submitted to Nature Food)
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Hasan, M. F., R. G. Smith, F. V. Davenport, & S. Majumdar.
Groundwater Pumping and Irrigation in the Western United States Estimated at High Resolution with Machine Learning and Satellite Products,
(submitted to Journal of Hydrology: Regional Studies)
2026
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Davenport, F. V. and D. Touma
(2026).
Discrepancies in Extreme Daily and Monthly Precipitation Trends between Observations and Earth System Models,
Geophysical Research Letters,
https://doi.org/10.1029/2026GL124747
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Trok, J. T., E. A. Barnes, E. M. Gordon, F. V. Davenport, & N. S. Diffenbaugh
(2026).
Global emissions since the Paris agreement have intensified Europe's recent heatwaves,
Geophysical Research Letters,
https://doi.org/10.1029/2026GL125079
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Shaffer, M., N. Awad, F. V. Davenport, and S. Fakhreddine
(2026).
Evaluating the Resilience of Drinking Water Systems to Severe Weather Events Using Boil Water Notices and Bottled Water Sales,
Environmental Science & Technology,
https://doi.org/10.1021/acs.est.5c18579
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2025
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Orihuela-Pinto, B., P. W. Keys, F. V. Davenport, and E. A. Barnes
(2025).
Uncovering patterns of converging human-induced pressure on global lands,
Machine Learning: Earth,
https://doi.org/10.1088/3049-4753/ae2278
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Kalashnikov, D. A., J. T. Abatzoglou, F. V. Davenport, Z. M. Labe, P. C. Loikith, D. Touma, and D. Singh
(2025).
Projections of lightning-ignited wildfire risk in the western United States,
Earth's Future,
https://doi.org/10.1029/2025EF006108
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2024
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Kalashnikov, D. A., F. V. Davenport, Z. M. Labe, P. C. Loikith, J. T. Abatzoglou, and D. Singh
(2024).
Predicting Cloud‐To‐Ground Lightning in the Western United States From the Large‐Scale Environment Using Explainable Neural Networks,
Journal of Geophysical Research: Atmospheres,
https://doi.org/10.1029/2024JD042147
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Trok, J. T., E. A. Barnes, F. V. Davenport, and N. S. Diffenbaugh
(2024).
Machine learning–based extreme event attribution,
Science Advances,
https://doi.org/10.1126/sciadv.adl3242
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Davenport, F. V., E. A. Barnes, and E. M. Gordon
(2024).
Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability,
Geophysical Research Letters,
https://doi.org/10.1029/2023GL108099
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2023
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Gordon, E. M., E. A. Barnes, and F. V. Davenport
(2023).
Separating internal and forced contributions to near term SST predictability in the CESM2-LE,
Environmental Research Letters,
https://doi.org/10.1088/1748-9326/acfdbc
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Trok, J. T., F. V. Davenport, E. A. Barnes and N. S. Diffenbaugh
(2023).
Using Machine Learning with Partial Dependence Analysis to Investigate Coupling Between Soil Moisture and Near-surface Temperature,
Journal of Geophysical Research: Atmospheres,
https://doi.org/10.1029/2022JD038365
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Ly, A., F. V. Davenport, and N. S. Diffenbaugh
(2023).
Exploring the Influence of Summer Temperature on Human Mobility During the COVID-19 Pandemic in the San Francisco Bay Area,
GeoHealth,
https://doi.org/10.1029/2022GH000772
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2022
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Yu, G., D. B. Wright, and F. V. Davenport
(2022).
Diverse Physical Processes Drive Upper-Tail Flood Quantiles in the US Mountain West,
Geophysical Research Letters,
https://doi.org/10.1029/2022GL098855
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See media coverage in: Desert Research Institute, CSU News, NSF
2021
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Diffenbaugh, N. S., and F. V. Davenport
(2021).
On the impossibility of extreme event thresholds in the absence of global warming,
Environmental Research Letters,
https://doi.org/10.1088/1748-9326/ac2f1a
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Johnston, E. C., F. V. Davenport, L. Wang, J. K. Caers, S. Muthukrishnan, M. Burke, and N. S. Diffenbaugh
(2021).
Quantifying the effect of precipitation on landslide hazard in urbanized and non-urbanized areas,
Geophysical Research Letters,
https://doi.org/10.1029/2021GL094038
See media coverage in: AGU News -
Davenport, F. V., and N. S. Diffenbaugh
(2021).
Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation,
Geophysical Research Letters,
https://doi.org/10.1029/2021GL093787
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See media coverage in: Stanford News, Stanford Woods Institute (video) -
Diffenbaugh, N. S., F. V. Davenport, and M. Burke
(2021).
Historical warming has increased U.S. crop insurance losses,
Environmental Research Letters,
https://doi.org/10.1088/1748-9326/ac1223
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See media coverage in: Stanford News, Scientific American -
Simpson, I. R., K. A. McKinnon, F. V. Davenport, M. Tingley, F. Lehner, A. Al Fahad, and D. Chen
(2021).
Emergent constraints on the large scale atmospheric circulation and regional hydroclimate: do they still work in CMIP6 and how much can they actually constrain the future?,
Journal of Climate,
https://doi.org/10.1175/JCLI-D-21-0055.1
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Davenport, F. V., M. Burke, and N. S. Diffenbaugh
(2021).
Contribution of historical precipitation change to US flood damages,
Proceedings of the National Academy of Sciences,
https://doi.org/10.1073/pnas.2017524118
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See media coverage in: Washington Post, SF Chronicle, Bloomberg, CBS News, Reuters, CNBC, Earther, Stanford News
2020
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Davenport, F. V., J. E. Herrera-Estrada, M. Burke, and N. S. Diffenbaugh
(2020).
Flood size increases nonlinearly across the western United States in response to lower snow‐precipitation ratios,
Water Resources Research,
https://doi.org/10.1029/2019WR025571
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See media coverage in: Stanford News