Chapter 7: Projected Fire Regime Changes for the Western United States
Dataset Overview
This dataset includes future projections of fire regime change in the western U.S., specifically changes in fire frequency (represented as fire return interval, FRI, figure 7.1) and fire severity (represented as percent replacement severity, PRS), as well as vegetation classes (see chapter glossary for definitions of terms). These data layers were produced using a climate analog approach, in which the projected future climate conditions of each pixel (i.e., each target location) were compared to other pixels’ historical climate conditions. The underlying principle is that, for a given location with projected future climate conditions, that location's future vegetation and fire regime can be inferred by examining the current vegetation and fire regime of climate analogs (locations with current climate similar to the future climate of the target location).
Data Access: The dataset may be obtained by contacting the corresponding author.
Conservation Applications
Potential conservation applications of this dataset could include the following:
- Projections for fire regime changes represented in this dataset indicate that, rather than universal increase or decrease in fire frequency or severity, changes in these fire regime characteristics will vary based on bioclimatic domain. In relatively cool, moist forests of the Pacific Northwest, such as in the northern Cascades and the Olympic peninsula, fires are projected to become more frequent (lower FRI) and less intense (lower PRI). Conversely, in drier forests of the eastern Cascades, fires are projected to become less frequent (higher FRI). These projected changes can be used to help inform fire management policies, especially in protected areas where managers strive to restore or mimic natural fire patterns. In some cases, projected transitions from one vegetation type to another (such as from dry forest to non-forest) could be used to anticipate ecosystem-level changes with implications for ecosystem services, wildlife habitat, and other important components of ecosystem function.
Applicable scales for detailed spatial assessments:
- For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
- At the scale of: a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g. the North Cascades), a single state (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest or in western North America).
Applicable scales for assessing general patterns:
- Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
- At the scale of: a small (<1 km2) nature preserve, a state park or state wildlife area, a local watershed (12-digit hydrologic unit code [HUC-12]).
Use of the dataset in conservation applications may be limited by the following considerations:
- The vegetation, fire, and climate relationships represented in this dataset implicitly rely on an assumption of equilibrium. However, natural and human-caused disequilibrium will likely result in a lagged and nuanced response of fire regimes and vegetation to climate change. Instead of strictly interpreting the timing and magnitude of projected changes, users of these datasets are instead urged to consider the general direction of change. That is, users are urged to recognize that a warming climate is likely pushing the systems toward the fire regimes and vegetation depicted in these products. Nevertheless, results from the study that produced this dataset suggest a potential tipping point (at intermediate values of climatic moisture deficit generally corresponding to dry forests) at which small shifts in climate could result in forest conversion to non-forest.
Past or current conservation applications:
- The dataset has not yet been used in any on-the-ground conservation applications to the knowledge of the authors of this chapter.
Dataset citation:
Parks, S. A., L. M. Holsinger, C. Miller, and M. Parisien. 2018. Analog-based fire regime and vegetation shifts in mountainous regions of the western US. Ecography 41:910–921.
Dataset documentation link: https://www.fs.usda.gov/treesearch/pubs/55029 (open access)
Data access:
The dataset may be obtained by contacting the corresponding author.
The dataset is not available for interactive online map viewing.
Metadata access: Formal metadata is not available for this dataset.
Dataset corresponding author:
Sean Parks
Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, USDA Forest Service
[email protected]
Data type category (as defined in the Introduction to this guidebook): Fire, vegetation
Species or ecosystems represented: This dataset does not represent any individual species or ecosystems.
Units of mapped values:
Fire return interval: years
Percent replacement severity: percent
Vegetation classes
Range of mapped values: Ranges of mapped values depend on time period under consideration. Vegetation classes are coded as follows: 1=barren; 2=mesic forest; 3=cold forest; 4=dry forest; 5=shrubland; 6=grassland.
Spatial data type: a raster dataset (grid)
Data file format(s): GeoTiff (.tif)
Spatial resolution: 1 km
Geographic coordinate system: North American Datum of 1983
Projected coordinate system: North American Datum of 1983 Equidistant Conic
Spatial extent: Western United States (11 western states in the continental United States)
Dataset truncation: The dataset is truncated along the United States borders with Canada and Mexico and along the eastern borders of Montana, Wyoming, Colorado, and New Mexico.
Time period represented: Future
Future time period(s) represented: Mid-century (2041–2070), end-of-century (2071–2100)
Baseline time period (against which future conditions were compared): 1961–1990.
Methods overview:
Estimates of reference vegetation and fire regime variables were used to represent the period from approximately 1700 to 1900. For each pixel, climate data (climatic moisture deficit and evapotranspiration) were used to identify climate analogs, i.e., pixels with historical climate conditions similar to (analogous to) the projected future climate of the target pixel. Thus, climate analogs can be thought of as "incoming climates" because they represent the climate conditions that are anticipated to occupy a target pixel in the future (Parks et al. 2018). For the target pixel, the three geographically nearest climate analog pixels were identified and fire regime variables (FRI and PRS) were averaged, to enable comparison of reference fire variables to those variables from the averaged analogs. A similar analog-based approach was also used to examine changes in vegetation, represented as broad vegetation classes. For more information, please consult the dataset citation listed in section 2 of this chapter.
This dataset relied on the following general types of models: Climate-analog modeling.
Major input data sources for this dataset included: Historical climate observations or models, future climate projections, variables representing fire regime characteristics (FRI and PRS) and vegetation types from LANDFIRE (Rollins 2009).
This dataset used the following general circulation models (GCMs): CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO-Mk3.6.0, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R
An ensemble across GCMs is provided; individual files for individual GCMs are not available. More information about climate models is available in appendix 1. Detailed information about climate models, including model evaluation and comparison among models, is available from Randall et al. (2007) and Rupp et al. (2013).
This dataset used the following greenhouse-gas scenarios: RCP 8.5
More information about greenhouse-gas scenarios is available in appendix 2 and from Knutti and Sedláček (2013).
Creation of this dataset involved the following methods to change the spatial resolution of climate models (e.g. to downscale or resample climate models): Climate data were downscaled using ClimateNA software version 5.10 (Wang et al., 2016)
The mapped values of the dataset may be interpreted as follows:
Higher values of FRI indicate longer average time periods between fires, i.e., lower fire frequency. Higher values of PRS indicate greater fire intensity or severity. Projected changes in these variables can be assessed by examining differences between a given variable in the reference time period and a future time period.
Representations of key concepts in climate-change ecology:
The dataset integrates components of climate-change exposure (in that spatial differences across the landscape in climatic moisture deficit and evapotranspiration were used to drive the analysis) and sensitivity to change (in that climate analogs were used to infer potential changes to vegetation and fire regime characteristics). These changes can inform an overall assessment of climate vulnerability for a given location, related to the expected magnitude and direction of change for fire regime characteristics.
As described in chapter 6, projected climate-driven changes to fire regimes have a range of potential consequences for species, ecosystems, and watershed processes, as well as for human society. Changes in fire frequency and/or intensity may affect different species in different ways (e.g., some species may benefit from more frequent fires while others are negatively affected). In addition, projected changes to fire dynamics may interact with other climate-driven changes to habitats, such as changes in watershed hydrology, changes to seasonal timing, and invasive species and forest pest dynamics.
This dataset involves the following assumptions, simplifications, and caveats:
The analog-based approach used to produce this study implicitly assumes that climate, fire regimes, and vegetation are in equilibrium, and that there are no lags between changing climate and changing vegetation and fire patterns. Specifically, this analysis does not account for natural and human-caused disequilibrium between climate, vegetation, and fire. Natural disequilibrium arises, for example, when long-lived organisms such as trees can survive and persist under a warming climate even though seedlings of the same species cannot. Human-caused disequilibrium results from human actions such as fire suppression, prescribed burns, agricultural and grazing operations, and landscape fragmentation. The analog-based approach used to produce this dataset is not a process-based model and so does not explicitly represent processes of interest, such as carbon dioxide fertilization and vegetation feedbacks to fire dynamics. Notably, only two climate variables were used in the analysis to produce the dataset, although a parallel analysis with 26 climate variables (some of which represented climate extremes and seasonality) produced similar results. In addition, the reference-period fire regime and vegetation data used in the analysis have inaccuracies that are not quantified due to missing information on fire histories in some land cover types. Lastly, invasive species (some of which are known to substantially alter fire regimes) are not incorporated into this analysis.
Quantification of uncertainty:
Results from this analysis were potentially sensitive to a number of choices in the analysis, so a sensitivity analysis was conducted for several of these parameters, including bin size (the breadth of climate conditions used to define analogs), the number of analogs used for averaging of fire regime characteristics, and the number and type of climate variables used to define climate analogs.
Field verification:
Field verification of the mapped values in this dataset was not possible because the dataset represents a future condition. Historical fire regime and vegetation variables also could not be field-verified due to limited historical data.
Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).
Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.
Parks, S. A., L. M. Holsinger, C. Miller, and M. Parisien. 2018. Analog-based fire regime and vegetation shifts in mountainous regions of the western US. Ecography 41:910–921.
Randall, D., R. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, and et al. 2007. Cilmate models and their evaluation. in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. Miller, editors. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, NY.
Rollins, M. G. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18:235–249.
Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research 118: 884–907.
Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11:e0156720.