DOI: 10.5061/dryad.rjdfn2zkw
title: A 32-year species-specific live fuel moisture content dataset for southern California chaparral

description: 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 dataset, over ten thousand LFMC observations
 were 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.
description: LFMC observations were acquired as the model predictands. Then,
 predictors were calculated, including long-term lag variables, such as
 90-day precipitation, 90-day mean temperature, and 150-day mean
 insolation, as well as short-term lag or instantaneous predictors, such as
 day length, 7-day mean soil moisture, and near-infrared reflectance of
 vegetation (NIRv). Species-specific random forest models were then trained
 and tested with two techniques: 5-fold cross validation using all
 observations and observation site specific cross validation, where each
 site was individually withheld as the test data. After testing, models
 were trained on all predictors/predictands and used to create the
 species-specific LFMC datasets. Lastly, quantile mapping bias correction
 was applied to the modeled chamise LFMC dataset, which was possible due to
 the high spatial frequency of observation sites.
description: # A 32-year species-specific live fuel moisture content dataset for
 southern California chaparral Dataset DOI:
 [10.5061/dryad.rjdfn2zkw](https://doi.org/10.5061/dryad.rjdfn2zkw) ##
 Description of the data and file structure The lfmc_observations.csv file
 contains all of the live fuel moisture content (LFMC) observations that
 were used to train species-specific, LFMC predictive random forest models.
 The studied species include new and old growth of chamise (*Adenostoma
 fasciculatum*), new growth of black sage (*Salvia mellifera*), and new
 growth of bigpod ceanothus (*Ceanothus megacarpus*). The
 sba_lfmc_1987-2019.nc file contains the LFMC predictions created with the
 four random forest models. The predictions cover a spatial domain that
 stretches from San Luis Obispo, CA, to Los Angeles, CA, with 1km,
 semi-monthly (1st and 15th) LFMC outputs from December 1987 through June
 2019. The netCDF file variables include each fuel type, as well as
 uncertainty calculations for each fuel type. The chamise predictions also
 underwent quantile mapping bias correction. The chamise outputs without
 the bias correction are also included. ### Files and variables #### File:
 lfmc_observations.csv **Description:** LFMC observations used for training
 random forest models. ##### Variables * site: Observation sampling site *
 date: Date sample collected * latitude: Degrees north * longitude: Degrees
 east * fuel: Fuel type * percent: Live fuel moisture content percent
 (water content / dry content * 100) #### File: sba_lfmc_1987-2019.nc
 **Description:** LFMC predictions of four fuel types: new growth chamise,
 old growth chamise, new growth black sage, new growth bigpod ceanothus,
 and their corresponding uncertainty * chamise: LFMC predictions of new
 growth chamise with quantile mapping bias correction applied (percent) *
 chamise_uncertainty: uncertainty calculated from the variance of the
 decision trees (percent) * chamise_old_growth: LFMC predictions of old
 growth chamise (percent) * chamise_old_growth_uncertainty: uncertainty
 calculated from the variance of the decision trees (percent) * sage_black:
 LFMC predictions of new growth black sage (percent) *
 sage_black_uncertainty: uncertainty calculated from the variance of the
 decision trees (percent) * ceanothus_bigpod: LFMC predictions of new
 growth bigpod ceanothus (percent) * ceanothus_bigpod_uncertainty:
 uncertainty calculated from the variance of the decision trees (percent) *
 chamise_no_bias_correction: LFMC predictions of new growth chamise without
 quantile mapping bias correction applied (percent) *NaN values correspond
 to grid cells where model predictor data was not available. ##
 Code/software No software is needed to view the data. The codebase used to
 create the data is found here:
 [https://github.com/kcvarga7/sba_lfm_1987-2019](https://github.com/kcvarga7/sba_lfm_1987-2019) ## Access information Data was derived from the following sources: * National Fuel Moisture Database ([https://github.com/wmjolly/pyNFMD](https://github.com/wmjolly/pyNFMD)) * Santa Barbara County Fire Department ([https://sbcfire.com/wildfire-predictive-services/](https://sbcfire.com/wildfire-predictive-services/)) * Landsat (NASA) * Santa Barbara Area WRF Climatology ([https://clivac.eri.ucsb.edu/clivac/SBCWRFD/index.html](https://clivac.eri.ucsb.edu/clivac/SBCWRFD/index.html))