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Forecasting live fuel moisture of Adenostema fasciculatum and its relationship to regional wildfire dynamics across southern California shrublands

Cite this dataset

Park, Isaac; Fauss, Kristina; Moritz, Max (2022). Forecasting live fuel moisture of Adenostema fasciculatum and its relationship to regional wildfire dynamics across southern California shrublands [Dataset]. Dryad.


 In seasonally dry environments, the amount of water held in living plant tissue—live fuel moisture (LFM)—is central to vegetation flammability. LFM-driven changes in wildfire size and frequency are particularly important throughout southern California shrublands, which typically produce intense, rapidly spreading wildfires. However, the relationship between spatiotemporal variation in LFM and resulting long-term regional patterns in wildfire size and frequency within these shrublands is less understood. In this study, we demonstrated a novel method for forecasting the LFM of a critical fuel component throughout southern California chaparral, Adenostema fasciculatum (chamise) using gridded climate data. We then leveraged these forecasts to evaluate the historical relationships of LFM to wildfire size and frequency across chamise-dominant California shrublands. We determined that chamise LFM is strongly associated with fire extent, size, and frequency throughout southern California shrublands, and that LFM–wildfire relationships exhibit different thresholds across three distinct LFM domains. Additionally, the cumulative burned area and number of fires increased dramatically when LFM fell below 62%. These results demonstrate that LFM mediates multiple aspects of regional wildfire dynamics, and can be predicted with sufficient accuracy to capture these dynamics. Furthermore, we identified three distinct LFM ‘domains’ that were characterized by different frequencies of ignition and spread. These domains are broadly consistent with the management thresholds currently used in identifying periods of fire danger.


All live fuel moisture data used in the model calibration were drawn from the National Fuel Moisture Database (NFMD, (accessed on 9/1/2020)), and these consisted of 19,639 individual observations of chamise fuel moisture across 61 sites throughout California, spanning the years 1977 through 2017. Climate data used in this study were drawn from the California Basin Characterization Model v8 [28], and consisted of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) measurements through the years 1951–2016. This dataset represents a 270 m grid-based model of water balance calculations that incorporates not only climate inputs (through PRISM climate data [29]) but also solar radiation, topographic shading, and cloudiness, along with soil properties to estimate evapotranspiration [30]. Using these monthly values, we calculated the mean maximum temperature (TMX), mean actual evapotranspiration (AET), mean climatic water deficit (CWD), mean precipitation (PPT), and mean soil moisture storage (STR) at 1, 6, and 12 month periods with lags of 1, 2, 3, 4, 5, and 6 months. Fire history data were drawn from FRAP fire perimeter data [31], which incorporate the perimeters of all known fires from 1878 through 2017. Vegetation data used to identify chamise vegetation in this study were drawn from both CALFIRE FRAP FVEG data [32] and the LANDFIRE 2016 Existing Vegetation Type (EVT) dataset [33].

Data Preparation

Observations of LFM were merged with data recording the latitude and longitude of each site and then filtered to exclude those observations not pertaining to chamise. As an LFM below 50 can represent dead material on the sampled shrubs, observed in situ estimates of LFM below 50% (which were exceedingly rare) were also excluded. Because LFM within each site was often recorded at inconsistent intervals that did not align with the monthly climate data used in this study, and many sites incorporated observations from multiple individual plants (the number of which also varied over time), we then calculated a single mean LFM within each month and site in which observations were present. In order to reduce the computational load to a manageable scale, all climate data were rescaled to 1 km pixels through spatial averaging, conducted using Rasterio in Python v3.7 [34]. Six-month and twelve-month mean TMX and total PPT, AET, CWD, and STR were then extracted at monthly timesteps using python v3.7.

Predicting Live Fuel Moisture across California

The most relevant climate parameters, lags, and window durations were identified by regressing each LFM observation against the corresponding monthly climate parameters (including TMX, PPT, AET, CWD, and STR) with lags of 1 to 6 months, as well as against the six-month means of each parameter over the six months preceding each observation. Overall relationships between chamise LFM and local climate at monthly timescales were then modeled using a generalized additive model (GAM) framework. To minimize computational time while allowing for nonlinear relationships between local climate and LFM, a maximum of five smoothing terms was allowed for each climate parameter.

In order to determine the ability of this modeling technique to predict LFM in both (a) novel locations and (b) months not present in the training data, model performance was assessed using multidimensional k-fold cross-validation. All data were divided by month and year into to one of five randomly assigned temporal groups of equal size, and all were similarly divided into five randomly assigned spatial groups. GAM models were then constructed iteratively, while holding out one temporal and one spatial group as a testing data set within each iteration. The ability of these models to successfully predict LFM at monthly timescales was evaluated by calculating the mean Pearson correlation coefficient between the predicted LFM at training sites and months not used in model development, and the observed mean monthly LFM recorded at those sites and months across all model iterations. In order to avoid unnecessary complexity within these models and to limit the computational requirements, only parameters of which the inclusion increased the mean Pearson correlation coefficient by 0.02 or more were excluded from the selected model. In order to incorporate as long a wildfire series as possible, LFM was predicted monthly from 1952 through 2017.

Identifying Fires of Interest

First, we identified those fires in which chamise was likely to represent a major component of the overall fuel by eliminating those fires in which <50% of the burned area was predicted to consist of either Southern California coastal scrub or dry mesic chaparral according to the FVEG land cover dataset produced by CALFIRE-FRAP. Similarly, we eliminated all fires in which <50% of the burned area was predicted to consist of either mixed chaparral, chamise-redshanks chaparral, or coastal scrub according to EVT vegetation maps. Because of concerns surrounding mismatches among vegetation types between FVEG land cover data and EVT vegetation maps, only those fire scars which met both of these sets of criteria were selected for further analysis. It should be noted that these vegetation maps were static over time and did not attempt to incorporate variation in vegetation cover that may have occurrred across the study period or immediately after disturbance events. However, annual assessments of vegetation cover throughout the study period were not available. Thus, although land cover may have fluctuated somewhat throughout the study period and immediately after fires or other disturbance events, these data nevertheless represented the best available data pertaining to the spatial distribution of chamise-dominated vegetation across California.

To evaluate the relationship of chamise LFM to the mean fire size, frequency, and cumulative area burned across southern Californian forests, it was first necessary to measure the predicted (and observed) LFM within the area burned during each fire. In order to summarize the predicted LFM within each fire at the time of ignition based on the gridded LFM estimates produced in this study, the mean predicted LFM in the month and year in which the initial ignition occurred was calculated across the entirety of each fire scar. The resulting data included 1818 individual fires from the year 1952 through 2017.

Identifying Critical Thresholds in LFM and Relationship to Burned Area

To evaluate the relationship between LFM and fire, and to identify critical LFM thresholds associated with shifts in fire behavior, we first calculated the cumulative area burned with decreasing (simulated) LFM for all selected fire scars. As previous studies have shown that observed thresholds in LFM–wildfire relationships may be biased due to differences in the freequency with which different values of LFM occur over space and time [27], we converted these LFM values into percentile ranks based on the distribution of simulated LFM across the duration and spatial extent of this study. By carrying out this step, we corrected for any differences in the spatial or temporal frequency of LFM across the study area, which might otherwise bias the apparent relationships to cumulative area burnt. Using these percentile LFM values, we then conducted piecewise or ‘broken stick’ regression [35] in order to identify transition points in LFM that were associated with an increasing burned area. After identifying thresholds in LFM–wildfire relationships using LFM percentiles, these percentile ranks could then be converted back into actual LFM values in order to identify the transition points in LFM–cumulative burned area relationships.

Identifying Critical Thresholds in LFM and Relationship to Mean Fire Size

In order to determine whether the mean size of wildfires varied significantly with LFM, we similarly conducted piecewise analyses of the relationship between LFM and the mean size of all wildfires in which the predicted LFM (based on the mean LFM value across the burned area of each wildfire event) fell within a 5 percentile span (e.g., all fires in which LFM fell within the 5th to the 9.99th percentile). By evaluating mean fire size within set percentile ranges of LFM, this analysis eliminated any effects of differential fire frequency across the range of LFM, and enabled us to evaluate only the relationship of LFM to wildfire size. As with analyses of cumulative burned area, the identified percentile thresholds in LFM–wildfire relationships could then be converted back into actual LFM values in order to identify the actual transition points in LFM–mean-fire-size relationships.

Identifying Critical Thresholds in LFM and Relationship to Cumulative Number of Fires

Finally, in order to determine the degree to which low LFM was associated with a higher number of fires, and to identify critical thresholds of LFM below which fires occurred more frequently, we similarly conducted piecewise analyses of the relationship between LFM percentile ranks and the cumulative number of fire events that had occurred. As with our analyses of the cumulative area burned, these analyses were conducted using LFM percentiles rather than raw LFM in order to compensate for potential differences in the spatial and temporal frequency of different ranges of LFM across the study area, and then converted post hoc data into actual LFM data in order to identify the actual threshold values. As percentile ranks of LFM inherently compensate for variable frequencies of different ranges of LFM values over space and time, the rates at which fires accumulate may be considered to be a measure of mean fire frequency within each range of LFM.


Usage notes

Software presented here was developed using python v3.7, and requires installation the following packages:









University of California's National Laboratories (UCNL) Laboratory Fees grant program, Award: LFR-18-542511

the California Department of Forestry and Fire Protection, Fire and Resource Assessment Pro-gram (CAL FIRE – FRAP, Award: 8CA03698

the California Department of Forestry and Fire Protection, Fire and Resource Assessment Pro-gram (CAL FIRE – FRAP, Award: 8GG20803