Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk
Data files
Feb 28, 2024 version files 7.86 MB
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README.md
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SF-fireshed-supporting.zip
Abstract
Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.
README
These data are outputs of LADIS-II simulations using the photosynthesis and evapotranspiration (PnET) succession, Dynamic Fuels and Fire, and Biomass Harvest extensions to simulation forest growth and disturbance using a 100m resolution.
We ran 25 simulations for each of 5 different climate models (CCSM, CNRM, FGOALS, GFDL and MIROC5) for a total of 125 replicates,both for the management and no management scenarios. Each simulation lasted 50 years. The objective of the study was to examine the effects of additional plot data on the initial communites layer and the subsequent model outputs. We compared results from this study and Krofcheck et al., 2019 in the no management scenario, and results from this study between the management and no management scenarios.
Files with the word 'new' refer to files from this study derived from an initial communities layer made using Forest Inventory and Analysis (FIA) + Common Stand Exam (CSE) plots. Files with the word 'old' refer to files from Krofcheck et al., 2019, derived from an initial communities layer made using FIA plots only.
In all cases biomass is represented by aboveground carbon.
The files included by directory are:
1) BioDifMgmt
Files regarding a comparisson between biomass at the end of the simulations for management and no magement scenarios in this study.
Biomass maps the mean biomass of all 125 simulations for the respective situations.
'NewYear50MeanBiomassMgmt.tif' A raster of biomass at year 50 for the management scenario in this study (in Mg C per hectare).
'NewYear50MeanBiomassNoMan.tif' A raster of biomass at year 50 for the no managmenet scenario in this study (in Mg C per hectare).
2) BioDifOldNew
Files regarding a comparison between biomass derived from initial communities made using FIA plots versus FIS + CSE plots.
Biomass maps the mean biomass of all 125 simulations for the respective situations.
'NewYear1MeanBiomassNoMan.tif' A raster of biomass at year 1 for the no management scenario in this study (in Mg C per hectare).
'NewYear50MeanBiomassNoMan.tif' A raster of biomass at year 50 for the no management scenario in this study (in Mg C per hectare).
'OldYear1MeanBiomassNoMan.tif' A raster of biomass at year 1 for the no management scenario from Krofcheck et al., 2019 (in Mg C per hectare).
'OldYear50MeanBiomassNoMan.tif' A raster of biomass at year 50 for the no managmenet scenario from Krofcheck et al., 2019 (in Mg C per hectare).
'allttestbyclimate.csv' A csv file of percent of sites with significanlty similar ("percent" comlun) and different ("different" column)
biomass between this study and Krofcheck et al., 2019 by climate model.
3) NECB
Files regarding the Net Ecosystem Carbon Balance in this study.
'NewMgmtNetPsn.csv' Net photosynthesis summed for the whole fireshed in the management scenario in megagrams C
per hectare. This is pulled from a monthly raster output and stored in a csv file for all replicates.
First column is time in months. Further columns are Net photosynthesis as described for each replicate where the first number in column title indicates the climate scenario (1-CCSM, 2-CNRM, 3-fgoals, 4-GFDL, 5-MIROC5) and the next number is replicate name (ex. 401, 402)
'NewMgmtTreatmentLoss.csv' Mg Biomass removed from the whole fireshed by treatment, both thinning and prescribed burns, in the management scenario. This is extracted from a csv summary log output, summed by year and stored in a csv file for all replicates.
First column is time in year. Further columns are Mg Biomass removed as described for each replicate where the first part in column title includes the climate name and the next part is replicate name (ex. 401, 402)
'NewMgmtWildfireEmissions.csv' Grams biomass lost due to wildfire in the management scenario. Calculated as the difference in biomass over burn scars between the year of the burn and the year prior to the burn, stores in a csv for all replicates.
First column is time in year. Further columns are grams biomass as described for each replicate where the first part in column title includes the climate name and the next part is replicate name (ex. 401, 402)
'NewNoManNetPsn.csv'Net photosynthesis summed for the whole fireshed in the no management scenario in megagrams C per hectare. This is pulled from a monthly raster output and stored in a csv file for all replicates.
First column is time in months. Further columns are Net photosynthesis as described for each replicate where the first number in column title indicates the climate scenario (1-CCSM, 2-CNRM, 3-fgoals, 4-GFDL, 5-MIROC5)and the next number is replicate name (ex. 1, 2)
'NewNoManWildfireEmissions.csv' Biomass lost due to wildfire in grams in the no management scenario. Calculated as the difference in biomass over burn scars between the year of the burn and the year prior to the burn, stores in a csv for all replicates.First column is time in year. Further columns are grams biomass as described for each replicate
where the first part in column title includes the climate name and the next part is replicate name (ex. 1, 2)
4) pHS
Files regarding probability of high severity fire analysis.
'NewfireTotalsMgmt.tif' A raster of total number of fires in the management scenario of this study summed across all 125 replicates and all simulation years.
'NewfireTotalsNoMan.tif' A raster of total number of fires in the no management scenario of this study summed across all 125 replicates and all simulation years.
'NewHSTotalMgmt.tif'A raster of total number of high severity fires in the management scenario of this study
summed across all 125 replicates and all simulation years.
'NewHSTotalNoMan.tif' A raster of total number of high severity fires in the no management scenario of this study summed across all 125 replicates and all simulation years.
'OldfireTotalsNoMan.tif' A raster of total number of fires in the no management scenario from Krofcheck et al., 2019,summed across all 125 replicates and all simulation years.
'OldHSTotalNoMan.tif' A raster of total number of high severity fires in the no management scenario from Krofcheck et al., 2019, summed across all 125 replicates and all simulation years.
5) random plot selection
Files regarding analysis of the effects of the lack of plot location of the CSE plots. 31 Initial communities layer were produced
using data from randomly selected plots of the same stand. simulations were run using each of these layers and each of the 5 climate models for a total of 155 replicates.
'NewYear1MeanBiomassNoMan.tif' A raster of biomass at year 1 for the no managmenet scenario in this study (Mg C per hectare).
'randICbioMean.tif' A raster of mean biomass of year 1 for all 155 replicates (Mg C per hectare).
'randomPlotSelectionBiomass.csv' Total aboveground biomass summed across the whole fireshed for each of the replicates.
column names: climate- climate name; rep- replicate name;
totBiomass_MgC- total biomass in Mg C; MgC_ha- biomass in Mg C per hectare; TgC- biomass in teragrams C.
6) sampling intensity
'PlotSampleIntensity' A csv of sampling intensity for each plot. This is how much of the landscape per forest type is represented by each plot. columns are: PLOT_ID; Area - area represented by this plot in hectares; plot_num - number of CSE plots used to create the initial communities layer in addition to all FIA plots; sf_fortyp - forest type code corresponds to fortyp column; totForType - total area for thisforest type in hectares; sampleIntensity- Area divided by totForType (unitless ratio); fortype- forest type.
7) treatment
Files regarding treatment maps.
'NewMgmtMap.tif' A raster of the treatment map in this study.
'NewTretmentReclass.csv' A file for converting treatment codes in this study to a simplified version. Unitless
'OldMgmtMap.tif' A raster of the treatment map from Krofcheck et al., 2019.
'OldTretmentReclass.csv' A file for converting treatment codes from Krofcheck et al., 2019 to a simplified version.NAs in this file correspond to sites outside the study area. Unitless
'TreatmentDifReclass.csv' A file for reclassifying the difference between treatments to a simplified version. Unitless
8) varying number of plots
Files regarding analysis of the effect of additional plot data. We created new initial communities layers using a varying number
of plots available for the interpolation. We then ran one simulation for each climate model for a total of 5 replicates for each
new initial communities layer. The biomass presented is a mean of all 5 replicates. All initial communities layers were produced
using all FIA plots and the number in the file name represents the number of added CSE plots. For example:
'biomassMean135.tif' A raster of biomass at year 1 derived from an initial communities layer produced using all FIA plots with an additional 135 available CSE plots.
'biomassPerCellPerNumPlots.csv' A csv file sumarizing data from all these rasters.
column names: x y- coordinates of site (pixel); layer- biomass in Mg C per hectare; plot_num: number of CSE plots used to produce the initial comuunities later in addition to FIA plots;fortyp- forest type.
Methods
Initial Communities Data
The initial communities layer is the base vegetation layer that sets the starting conditions for the exchange of carbon, water, energy, species interactions, disturbance effects, and other landscape processes. The initial treatment optimization study in this landscape (Krofcheck et al., 2019) used 68 Forest Inventory and Analysis (FIA) plots from within the Santa Fe National Forest that had been inventoried in 2010 or later and had not burned since 2005. Forest types represented by the FIA plots were piñon-juniper, ponderosa pine, Douglas-fir (Pseudotsuga menziesii), Engelman spruce (Picea engelmannii) and limber pine (Pinus flexilis). The latter three were grouped into a general mixed-conifer forest type. The authors then used elevation, transformed aspect using Topographic Radiation Aspect Index, TRASP (Roberts & Cooper, 1989), and a tasseled cap transformation of spectral data from Landsat 8 (available at https://www.usgs.gov/landsat-missions/landsat-8) as predictors for Random Forest models and used the rfUtilities library (Evans & Murphy, 2018) to select the most parsimonious model for each forest category separately. Existing vegetation classification from the Southwest Regional Gap Analysis (SWReGap, https://swregap.org/) using the ‘yaImpute’ library (Crookston & Finley, 2008) was used to stratify the measured plots for the imputation. We determined plot sampling intensity by calculating the relative area of land each plot represents within its forest type.
To evaluate the influence of additional plot data on the initial communities layer and its effects on model behavior, we used data collected as part of the planning process by the US Forest Service. These data were located entirely within the study area and included 1072 plots from 111 stands inventoried in 2011, where each stand included between 3 and 31 plots. Plot data were collected using a common stand exam protocol using variable radius plots. The specific Basal Area Factor (BAF) prism chosen for each stand was a function of stand density and they ranged from 10 to 30 BAF. We had coordinates for the centroid of each stand, but not for each individual plot, which effects the imputation process. We used the tree data from each plot to determine a specific forest type and generalized category (e.g. piñon-juniper, ponderosa pine, mixed-conifer), corresponding to the FIA classification, and added an aspen forest type, resulting in a total of four generalized forest types. We also defined non-forested areas and included two generalized species parameterizations to represent shrubs that resprout and shrubs that do not resprout following fire. We used all FIA and common stand exam (CSE) plot data (n=1140) to generate a new initial communities layer following the same method as Krofcheck et al. (2019) in R v4.1.2 (R Core Team, 2021).
Simulation Analysis
To estimate the uncertainty in the initial communities layer that is due to not having coordinates for all CSE plots, we re-ran interpolations randomly selecting one plot for each set of stand coordinates. This led to 31 initial communities layers, which we used to initialize the model with the five climate projections, for a total of 155 simulations. We compared the aboveground carbon following model initialization of these initial communities layers with the initial communities layer that we created using all plot data and that we used for our management simulations. We calculated the difference in aboveground carbon between each layer and the one we used in our simulations to determine how much the initial communities layer is influenced by this source of uncertainty.
To determine the influence of the number of plots used in the development of the initial communities layer, we produced five additional initial communities layers with differing numbers of underlying plot data. For four of the five layers, we halved the number of CSE plots used in the interpolation each time (e.g. 536, 268, 134, 67) and combined those with the FIA data. For the fifth initial communities layer, we only used the 68 FIA plots. For each of the layers, we randomly selected plots from each forest type proportional to the prevalence of each forest type on the landscape. We then initialized the model using each of these initial communities layers using the five climate projections and compared the aboveground carbon following model initialization to that of the initial communities layer that we used for our simulations.
We quantified differences between our primary initial communities layer and that of Krofcheck et al., (2019) by comparing the difference in quantity and distribution of aboveground carbon at the beginning and end of the simulations. We ran an independent t-test to assess the difference in carbon between the two studies at each site every 10 years for each of the climate models, and computed the percent of area with a significant difference (p < 0.01) in aboveground carbon. We compared treatment location as determined by the probability of high-severity fire between our initial communities layer and that of Krofcheck et al. (2019). We calculated Net Ecosystem Carbon Balance (NECB) by subtracting carbon lost from the system (treatment and wildfires) from carbon gained (photosynthesis) and then relativized the treatment scenario NECB values to the no-management scenario for both our simulations and those of Krofcheck et al. (2019). Data processing and analysis was conducted using R v4.1.2 (R Core Team, 2021).
Treatment scenarios
To develop the optimized treatment placement scenario, we first ran simulations that included no management to identify locations where landscape conditions were such that there was a high probability of high-severity wildfire. We ran the no-management simulations using the same five projected climate data sets and fire weather data described above. We ran 25 replicate simulations using each of five projected climate data sets, for a total of 6250 simulation years. We used fire severity raster data from these model outputs to quantify the probability of high-severity by dividing the number of years with high-severity fires by the total number of fire years per site. We then identified sites with a probability of high-severity fire greater than 0.3 and targeted those locations in the treatment scenario simulations, assigning treatment to those areas first.
To determine the type of treatment we used the probability of high-severity fire in combination with slope and forest type. We limited our management simulations to the ponderosa pine and dry mixed-conifer forest where the combined ponderosa pine and Douglas-fir aboveground carbon was at least 65% of the total. We used the same thinning and prescribed burning treatments as Krofcheck et al. (2019), which were designed to approximate common treatments for the region. Thinning treatments simulated thinning from below by removing approximately 30% of the biomass, preferentially removing the youngest cohorts (Hurteau et al., 2011, 2016) and was only applied to ponderosa pine forest and confined to slopes <30%, to account for a common limitation on mechanical thinning. We simulated prescribed burning based on historic mean fire return intervals, with all ponderosa pine burned using a 10-year return interval and forests co-dominated by ponderosa pine and Douglas-fir burned using a 15-year return interval. The forest type and probability of high-severity fire are highly dependent on the initial communities layer which defines the initial forest conditions. As a result, our treatment placement map differed substantially from the one in Krofcheck et al. (2019).
To examine the effects of the treatment on the landscape we produced a new probability of high-severity fires raster and calculated the difference in aboveground carbon between the management and no management scenarios of this study at the end the simulations.
We ran simulations over a 50-year period, using climate model projections for years 2000-2050. We ran 25 replicates for each of the five climate projections, totaling 125 simulations each for the no-management and management scenarios. Fire weather distributions tracked projected climate and were updated each decade to account for changes in temperature and precipitation.
Usage notes
R