Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Northern Spotted owl Habitat; Topo-Climatic Fire refugia * Tier: 3 * Data Vintage: 2019; Time period(s) represented (i.e. modeled): 2020 * Unit Of Measure: An index of fire refugia; from no refugia to maximum refugia (0-100). Nulls are non-forest capable land areas. * Metric Definition and Relevance: Maps that represent the relative probability of fire refugia generated from an empirical model trained on the aggregate history (1985-2019) of wildfire severity, topographical features, and climate normal data with the Random Forest modeling algorithm. Areas identified as having a higher probability for fire refugia are those that burn less severely than the surrounding landscape. Map values at the other end of the spectrum represent areas that will likely burn at higher severity. Values in the future map represent output from application of the model with projections of large wildfire suitability based on future predictions for temperature and precipitation. These maps can be used to examine where forests, including old forests, are likely to survive a wildfire. Refugia models can be used to evaluate the outcomes of different types of past management and wildfire that influence the probability of fire refugia. Refugia products can be used to evaluate old forest dynamics by intersecting refugia probability maps with maps of old forest or spotted owl habitat, to evaluate the degree of overlap under different fire weather conditions and through time. Aligning forest/fuels/fire management with topography (conditioned on current or future climate) that relates to normal wildfire severity (low to high). The future model provides forest managers, fire protection agencies, and policy- makers empirical estimates of how much and where climate change might affect the landscape patterns. The modeled output covers the entire range of Northern Spotted Owls: Washington, Oregon, and northern California within the Northwest Forest Plan (NWFP) boundary. For RRK purposes, it has been clipped to the Northern California region. * Creation Method: This product was developed by Ray Davis and Zhiqiang Yang (USFS) and Andrew Yost (Oregon Department of Forestry). The goal of the work was to model the probability of potential fire refugia across the Northwest Forest Plan and Bioregional Assessment areas using four topographic variables and conditioned on climate (normal fire environment from Davis et al. (2017) as explanatory variables. The topo-climatic fire refugia model does not include variability in vegetation/fuels as a driver of fire refugia, instead using a simple mask of forest-capable sites to represent the intrinsic underlying condition. Accordingly, it should be overlaid with existing forest conditions to identify contemporary opportunities for supporting mature and older forest. In addition, this model provides the opportunity to identify where on the landscape might be good locations to recruit high quality mature and old late-successional closed canopy forest, even if such conditions do not currently occur. The climate change projections from the model may identify locations most likely to persist as fire refugia into the future. The models prioritize a focus on the closed canopy late-successional, complex older forest context. The topo-climatic fire refugia model is trained using multiple samples from contemporary fire severity data from the region (1985 to 2019), first building a model for probability of low-severity fire, next building a model for probability of high-severity fire, then aggregating the models together with the normal fire environment metric to generate fire refugia probability (Yang et al. in prep). Because the topo-climatic fire refugia models include the Davis et al. (2017) fire environment as an explanatory variable, the climate change projections from that work were fed in as scenarios for current period (2020), mid-century (2060), and late century (2100) climate change estimates. Spatial resolution of the models is 90 m (aggregated Landsat remote sensing data). Data sets are available on the USFS T:/drive and available in the Fire Refugia ToolBox (preferred) folder listed in section 8 (Additional Resources). Additional information will be provided in Yang et al. (in prep) and on the Fire Refugia in Mature and Old Forests website. Inputs were actual fire locations and fire environment triangle variables for topography and climate (based on 30-year averages, or a climate normal). Assumptions were that the current forest footprint used to train the model would remain static throughout this century and that the modeled relationship would remain temporally stable. As with all models there is always uncertainty. Since publication, this model continues to predict large wildfire occurrences well. The main uncertainty lies in whether the forest footprint will change as a result of climate change. * Credits: USDA Forest Service, Region 6 - Pacific Northwest Region Raymond Davis, Zhiqiang Yang, Andrew Yost, Cole Belongie, and Warren Cohen Davis, R., Z. Yang, A. Yost, C. Belongie, and W. Cohen. 2017. The normal fire environment—Modeling environmental suitability for large forest wildfires using past, present, and future climate normals. Forest Ecology and Management, 390, pp.173-186. Krawchuk, M.A., Hudec, J., Meigs, G.W. 2023. Manager’s brief: Integrating fire refugia concepts and data into vegetation management decisions. A case study on the Gifford Pinchot National Forest, Little White Salmon Project Area. 20 pages.