DOI: 10.25349/d9hs51
title: Forecasting live fuel moisture of Adenostema fasciculatum and its relationship to regional wildfire dynamics across southern California shrublands

description:  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.
description: All live fuel
 moisture data used in the model calibration were drawn from the National
 Fuel Moisture Database (NFMD,
 https://www.wfas.net/nfmd/public/index.php (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<em> </em>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
 &lt;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 &lt;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<em> </em>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.  
description: Software presented here was developed using python v3.7, and
 requires installation the following packages:
 Rasterio Pandas
 Numpy Geopandas
 Jupyter Copy
 Matplotlib