Abstract
Live fuel moisture content (LFMC) strongly affects the behavior of wildland fire, resulting in its incorporation into wildfire spread models and danger ratings. In this study, over ten thousand LFMC observations are combined with predictor variables from Landsat imagery and the Weather Research and Forecasting model to train species-specific random forest models that predict the LFMC of four fuel types—chamise, old growth chamise, black sage, and bigpod ceanothus. These models are then utilized to create a historical, 32-year long, LFMC dataset in southern California chaparral. Additionally, the high spatial and temporal sampling frequency of chamise allowed for quantile mapping bias correction to be applied. The final chamise output, which is the most robust, has a mean absolute error of 9.68% and an R2 value of 0.76. The LFMC dataset successfully captures the variability in the annual cycle, the spatial heterogeneity, and the interspecies differences, which makes it applicable for better understanding varying fire season characteristics and landscape level flammability.
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Data availability
The LFMC dataset described in this work is available on Dryad (https://doi.org/10.5061/dryad.rjdfn2zkw). The LFMC observations used for training the models are also available on Dryad. The observations were downloaded from the US National Fuel Moisture Database (https://github.com/wmjolly/pyNFMD) and the Santa Barbara County Fire Department (https://sbcfire.com/wildfire-predictive-services/). The data used for LFMC model predictors is publicly available via the UCSB CLIVAC Lab (https://clivac.eri.ucsb.edu/clivac/SBCWRFD/index.html) and the NASA Landsat program (https://doi.org/10.1016/j.srs.2023.100103).
Code availability
The codebase for the creation of this dataset is publicly available as Jupyter Notebooks on GitHub (https://github.com/kcvarga7/sba_lfm_1987-2019). All code was written in Python, with the exception of the GEE script used for downloading Landsat imagery. That GEE script is referenced in the GitHub readme, as well as the applicable Jupyter Notebook.
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Acknowledgements
This research was supported by the NASA Future Investigators in NASA Earth and Space Science and Technology program (Award No. 80NSSC21K1630), the University of California Office of the President Laboratory Fees Program (Grant ID: LFR-20-652467), and The Nature Conservancy’s Jack and Laura Dangermond Preserve. We would also like to acknowledge high-performance computing support provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation Prediction of and Resilience against Extreme Events program (Award No. 1664173). Lastly, we thank Matt Jolly, United States Forest Service Ecologist, for providing essential live fuel moisture observations.
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Kevin Varga: Conceptualization, methodology, data curation, formal analysis, visualization, writing-original draft preparation, writing-review and editing. Charles Jones: Conceptualization, methodology, writing-review and editing, supervision.
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Varga, K., Jones, C. A 32-year species-specific live fuel moisture content dataset for southern California chaparral. Sci Data (2026). https://doi.org/10.1038/s41597-026-06794-3
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DOI: https://doi.org/10.1038/s41597-026-06794-3
