20220930 raster digital data The data layers included in this North Coast Regional Resource Kit were assembled/developed by a partnership that includes the Pacific Southwest Research Station of the U.S. Forest Service, the Fire and Resource Assessment Program (FRAP) of CALFIRE, the Climate and Wildfire Institute, and faculty from the University of California Berkeley and Irvine. This science team is working together at the behest of the California Wildfire and Forest Resilience Task Force. As we continue to develop geospatial data for landscape assessment and planning throughout the state, this partnership has now taken the lead in the creation of the Regional Resource Kits for the four regions of California. The RRK has adopted the Framework for Resilience to provide a structure for assessing landscape conditions, setting objectives, designing projects, and measuring progress towards social-ecological resilience. There are ten pillars that represent the desired outcomes of landscape resilience. Each of the pillars provides a series of elements and under the elements, metrics (the data layers) for assessing landscape conditions and verifying that actions meet resilience objectives. The metrics are organized by the ten pillars of resilience in the Framework for Resilience. The Metrics describe the characteristics of the elements (key characteristics) of each pillar in quantitative or, in a few cases, qualitative terms. Metrics are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. The metrics are also divided into three “tiers.” Among all these metrics, some are created and relevant statewide. Other metrics are more suited to conditions within a given region. The “Tiers” for metrics included in each RRK: Tier 1 – metrics that are a single, consistent data layer, developed statewide; they can also be clipped to the boundary of the region so values within that region are the only ones included for calculations or regional statistics. Example: Annual Burn Probability. Tier 2 – metrics relevant to a single region or relevant to multiple regions but data layers differ among regions because of varied data availability (sources) across regions. Example: California gnatcatcher habitat suitability. Tier 3 - metrics are those that would be of interest to some land managers for specific applications but not included as a core metric in an RRK. Example: Distribution of the Quino checkerspot butterfly. Each RRK will contain all Tier 1 and Tier 2 data together to comprise the kit. Tier 3 data will be pointed to for reference and use, as needed. Within each Tier, the data layers are available in two forms: 1) data values native to the metric (raw), and 2) translated data values. The raw data values are in the units of the metric, so for example the species richness map will show an estimated number of terrestrial vertebrate species per acre that can range from 0 to any number for each 30-m pixel, and the departure from historical fire return interval (FRID) map will have values that range from -100% to +100% departure. The translated data values represent each metric using a common unit of measure with the same range of values from -1 to +1 that represent values that are generally considered favorable (+1) and unfavorable (-1). In the case of species richness, higher species counts are considered more favorable and lower species counts are considered less favorable. In the case of FRID, values within the historical fire return interval are considered favorable, and high departure from the historical fire return interval is considered less favorable. In both cases, more and less favorable conditions for each metric are represented by values that range from +1 to -1 (respectively) so that multiple metrics can be evaluated together, including summarizing overall conditions at element and pillar levels to characterize socio-ecological resilience. Some data layers within this kit contain null values. We point this out here so users of the data will be aware and take whatever measures appropriate as they use and analyze the data. For some raster datasets in the RRK, areas have been masked (blanked) out and have a cell value of NoData (also referred to as null, NaN or missing). We, as producers and users of the data, cannot ignore NoData or fill them with zeros, since zero is often a valid value for some datasets. Removing NoData cells is not an option, a raster is a continuous grid. For users of the data performing further analyses and combining or "stacking" rasters, these NoData cells will mask out all values in that location in the output. To avoid this issue, the user must create values for the cells before combining them (i.e. 999 or any numeric value that is not real and clearly out of the range of the other values). Reasons for masking (blanking) out cells in RRK data: · Cells are lakes or reservoirs · Cells are urban or agriculture · Cells contain no information relevant to the dataset (i.e. streams, habitat) · Area (cells) subject to fire or other disturbance but the post disturbance condition or value is unknown RRK project: Reducing the risk of large, high intensity fire (and other mega-disturbances) through forest treatments has become a management imperative in California. A Strategy for Shared Stewardship (2018) (https://www.fs.usda.gov:443/sites/default/files/toward-shared-stewardship.pdf) and the USFS Wildfire Crisis Implementation Plan (2022) (https://www.fs.usda.gov:443/sites/default/files/Wildfire-Crisis-Implementation-Plan.pdf) reinforce specific goals for pace and scale of strategic forest treatments over the next decade. Concurrently, the State of California has issued a new Wildfire and Forest Resilience Action Plan (2022) (https://wildfiretaskforce.org:443/wp-content/uploads/2022/04/californiawildfireandforestresilienceactionplan.pdf), designed to strategically accelerate efforts to restore the health and resilience of California forests through a joint State of California - Forest Service framework to improve and enhance forest stewardship in California. The social incentives and the scientific knowledge to pursue meaningful restoration of forested landscapes in California are firmly established. High quality geospatial data are an essential ingredient to address restoration/conservation of the broad suite of core socio-ecological values across landscapes, and to drive analytic tools for planning management investments. To support these initiatives an interagency team of scientists from the Forest Service/Pacific Southwest Research Station, California Natural Resources Agency/CALFIRE, and the University of California at Berkeley and University of California at Irvine collaborated on development of a comprehensive set of mapped data layers needed to accomplish large-scale landscape planning and restoration. Landscape level assessment using high quality data developed from ecological modeling techniques, informative analytical approaches and the resulting credible scientific outputs will be fundamental to inform and support large landscape restoration planning and execution. The data layers included in this kit are meant to assist land managers in assessing their current landscape and plan for treatments to enhance resilience to human and natural disturbances. Thus each layer represents what the interagency team believes are the most relevant and reliable geospatial data available at this time. Each layer has been examined by the team and is supported by published data and/or was developed using standard methods. The methods for developing each layer are documented in the metric dictionary; however, the accuracy of each layer has not been quantified. It is anticipated that all data layers will be updated and refined as methods and source data evolve and improve. RRK Components: The authors and their partners are committed to increasing the “pace and scale” of forest treatments in California. Multiple federal and state initiatives in the last few years detail this commitment. Land managers need support to plan and implement treatments that will address restoration at a landscape scale. An essential component of these initiatives is the spatial data representing landscape conditions combined with new analytical tools for planning management investments. The authors joined forces to develop and/or collect and assemble existing sources of spatial data. This project, referred to as RRK (Regional Resource Kit), combines the expertise and experience of research and management to build this library of data on landscape conditions. These data reflect landscape conditions across ten “Pillars of Resilience” which address the full array of landscape management objectives. • Pillars are the desired long-term, landscape-scale outcomes of restoring resilience. They include ecological values, such as biodiversity, as well as societal benefits to communities, such as water security. • Elements represent the primary processes and functions that altogether make up a pillar, such as focal species, water quality, or economic health. • Metrics describe the characteristics of elements in quantitative or qualitative terms. Users can use metrics to assess, plan for, measure, and monitor progress towards desired outcomes and greater resilience. While pillars and elements are consistent across all of California, the metrics that a group uses may vary from region to region based on ecological and social differences (for example forest types, economy), available data, and user preferences. The ten pillars are Forest and Shrubland Resilience, Water Security, Carbon Sequestration, Air Quality, Fire Dynamics, Fire Adapted Communities, Economic Diversity, Social & Cultural Well-Being, Wetland Integrity, Biodiversity Conservation. The individual “Metrics” are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. Landscape level assessment using these high-quality data, combined with decision support tools that can help evaluate alternative treatment strategies, are fundamental to inform and support large landscape restoration planning. These data are assembled in one place to provide comprehensive access for land managers. They represent conditions as of 2022 (or otherwise, as noted) and intentions are to refresh these data on an annual basis. Each metric includes the following information to help users of the data (and for use with any decision support tools) to understand the details of the metric. This information is also included in a "metric dictionary" that comes with this set of metrics. • Tier group the metric is in (1, 2, or 3) • The vintage of the data • The definition meant by a given metric • The expected use(s) of the metric • The resolution of the developed data • The data sources used to derive the metric • The intended method of metric derivation • Where reasonable, a desired management target References have been included in the metric dictionary to help the reader understand methods used for deriving metrics and will be updated periodically, as necessary. This information will help people make better use of all the assembled information and how it can best be used with various decision support tools. Complete Unknown -123.046475 -119.297108 38.392294 34.819494 ISO 19115 Topic Categories environment, geoscientific Information, planning Cadastre, land management, landscape restoration None Appropriate use includes regional to statewide assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. Further use includes applying these data to assess management opportunities for treatments to restore landscape resiliency. The authors make no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. For all data layers you are free to share, copy, and redistribute the material in any medium or format AND adapt, remix, transform, and build upon the material for any purpose, even commercially under the following terms:Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No commercial use – the user is responsible for acknowledging those data layers within this RRK (as determined by the source of the data) that are not permitted for commercial use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything this license permits. Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org Climate and Wildfire Institute Carol Clark Senior Geospatial Data Analyst cclark@climateandwildfire.org Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org Esri ArcGIS 12.9.2.32739 Raster Pixel 3300 2550 20230902 Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time Peter A. Stine Climate and Wildfire Institute Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org 20230921 ArcGIS Metadata 1.0 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Appropriate use includes regional to statewide assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. Further use includes applying these data to assess management opportunities for treatments to restore landscape resiliency. The authors make no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. For all data layers you are free to share, copy, and redistribute the material in any medium or format AND adapt, remix, transform, and build upon the material for any purpose, even commercially under the following terms:</SPAN></P><P><SPAN>Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. </SPAN></P><P><SPAN>ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. </SPAN></P><P><SPAN>No commercial use – the user is responsible for acknowledging those data layers within this RRK (as determined by the source of the data) that are not permitted for commercial use. </SPAN></P><P><SPAN>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything this license permits.</SPAN></P></DIV></DIV></DIV> 38.392294 -123.046475 34.819494 -119.297108 Esri ArcGIS 12.9.2.32739 Project Manager, Regional Resource Kit Project Climate and Wildfire Institute pstine@climateandwildfire.org Peter A. Stine Senior Geospatial Data Analyst Climate and Wildfire Institute cclark@climateandwildfire.org Carol Clark Pyrologix, LLC --James Newman (California State BLM Office) jnewman@blm.gov WUI, Carlson et al, 2022 --Carlson, Amanda R., David P. Helmers, Todd J. Hawbaker, Miranda H. Mockrin, and Volker C. Radeloff RRK project: Reducing the risk of large, high intensity fire (and other mega-disturbances) through forest treatments has become a management imperative in California. A Strategy for Shared Stewardship (2018) (https://www.fs.usda.gov:443/sites/default/files/toward-shared-stewardship.pdf) and the USFS Wildfire Crisis Implementation Plan (2022) (https://www.fs.usda.gov:443/sites/default/files/Wildfire-Crisis-Implementation-Plan.pdf) reinforce specific goals for pace and scale of strategic forest treatments over the next decade. Concurrently, the State of California has issued a new Wildfire and Forest Resilience Action Plan (2022) (https://wildfiretaskforce.org:443/wp-content/uploads/2022/04/californiawildfireandforestresilienceactionplan.pdf), designed to strategically accelerate efforts to restore the health and resilience of California forests through a joint State of California - Forest Service framework to improve and enhance forest stewardship in California. The social incentives and the scientific knowledge to pursue meaningful restoration of forested landscapes in California are firmly established. High quality geospatial data are an essential ingredient to address restoration/conservation of the broad suite of core socio-ecological values across landscapes, and to drive analytic tools for planning management investments. To support these initiatives an interagency team of scientists from the Forest Service/Pacific Southwest Research Station, California Natural Resources Agency/CALFIRE, and the University of California at Berkeley and University of California at Irvine collaborated on development of a comprehensive set of mapped data layers needed to accomplish large-scale landscape planning and restoration. Landscape level assessment using high quality data developed from ecological modeling techniques, informative analytical approaches and the resulting credible scientific outputs will be fundamental to inform and support large landscape restoration planning and execution. The data layers included in this kit are meant to assist land managers in assessing their current landscape and plan for treatments to enhance resilience to human and natural disturbances. Thus each layer represents what the interagency team believes are the most relevant and reliable geospatial data available at this time. Each layer has been examined by the team and is supported by published data and/or was developed using standard methods. The methods for developing each layer are documented in the metric dictionary; however, the accuracy of each layer has not been quantified. It is anticipated that all data layers will be updated and refined as methods and source data evolve and improve. RRK Components: The authors and their partners are committed to increasing the “pace and scale” of forest treatments in California. Multiple federal and state initiatives in the last few years detail this commitment. Land managers need support to plan and implement treatments that will address restoration at a landscape scale. An essential component of these initiatives is the spatial data representing landscape conditions combined with new analytical tools for planning management investments. The authors joined forces to develop and/or collect and assemble existing sources of spatial data. This project, referred to as RRK (Regional Resource Kit), combines the expertise and experience of research and management to build this library of data on landscape conditions. These data reflect landscape conditions across ten “Pillars of Resilience” which address the full array of landscape management objectives. • Pillars are the desired long-term, landscape-scale outcomes of restoring resilience. They include ecological values, such as biodiversity, as well as societal benefits to communities, such as water security. • Elements represent the primary processes and functions that altogether make up a pillar, such as focal species, water quality, or economic health. • Metrics describe the characteristics of elements in quantitative or qualitative terms. Users can use metrics to assess, plan for, measure, and monitor progress towards desired outcomes and greater resilience. While pillars and elements are consistent across all of California, the metrics that a group uses may vary from region to region based on ecological and social differences (for example forest types, economy), available data, and user preferences. The ten pillars are Forest and Shrubland Resilience, Water Security, Carbon Sequestration, Air Quality, Fire Dynamics, Fire Adapted Communities, Economic Diversity, Social & Cultural Well-Being, Wetland Integrity, Biodiversity Conservation. The individual “Metrics” are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. Landscape level assessment using these high-quality data, combined with decision support tools that can help evaluate alternative treatment strategies, are fundamental to inform and support large landscape restoration planning. These data are assembled in one place to provide comprehensive access for land managers. They represent conditions as of 2022 (or otherwise, as noted) and intentions are to refresh these data on an annual basis. Each metric includes the following information to help users of the data (and for use with any decision support tools) to understand the details of the metric. This information is also included in a "metric dictionary" that comes with this set of metrics. • Tier group the metric is in (1, 2, or 3) • The vintage of the data • The definition meant by a given metric • The expected use(s) of the metric • The resolution of the developed data • The data sources used to derive the metric • The intended method of metric derivation • Where reasonable, a desired management target References have been included in the metric dictionary to help the reader understand methods used for deriving metrics and will be updated periodically, as necessary. This information will help people make better use of all the assembled information and how it can best be used with various decision support tools. The data layers included in this Northern California Regional Resource Kit were assembled/developed by a partnership that includes the Pacific Southwest Research Station of the U.S. Forest Service, the Fire and Resource Assessment Program (FRAP) of CALFIRE, the Climate and Wildfire Institute, and faculty from the University of California Berkeley and Irvine. This science team is working together at the behest of the California Wildfire and Forest Resilience Task Force. As we continue to develop geospatial data for landscape assessment and planning throughout the state, this partnership has now taken the lead in the creation of the Regional Resource Kits for the four regions of California. The RRK has adopted the Framework for Resilience to provide a structure for assessing landscape conditions, setting objectives, designing projects, and measuring progress towards social-ecological resilience. There are ten pillars that represent the desired outcomes of landscape resilience. Each of the pillars provides a series of elements and under the elements, metrics (the data layers) for assessing landscape conditions and verifying that actions meet resilience objectives. The metrics are organized by the ten pillars of resilience in the Framework for Resilience. The Metrics describe the characteristics of the elements (key characteristics) of each pillar in quantitative or, in a few cases, qualitative terms. Metrics are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. The metrics are also divided into three “tiers.” Among all these metrics, some are created and relevant statewide. Other metrics are more suited to conditions within a given region. The “Tiers” for metrics included in each RRK: Tier 1 – metrics that are a single, consistent data layer, developed statewide; they can also be clipped to the boundary of the region so values within that region are the only ones included for calculations or regional statistics. Example: Annual Burn Probability. Tier 2 – metrics relevant to a single region or relevant to multiple regions but data layers differ among regions because of varied data availability (sources) across regions. Example: California gnatcatcher habitat suitability. Tier 3 - metrics are those that would be of interest to some land managers for specific applications but not included as a core metric in an RRK. Example: Distribution of the Quino checkerspot butterfly. Each RRK will contain all Tier 1 and Tier 2 data together to comprise the kit. Tier 3 data will be pointed to for reference and use, as needed. Within each Tier, the data layers are available in two forms: 1) data values native to the metric (raw), and 2) translated data values. The raw data values are in the units of the metric, so for example the species richness map will show an estimated number of terrestrial vertebrate species per acre that can range from 0 to any number for each 30-m pixel, and the departure from historical fire return interval (FRID) map will have values that range from -100% to +100% departure. The translated data values represent each metric using a common unit of measure with the same range of values from -1 to +1 that represent values that are generally considered favorable (+1) and unfavorable (-1). In the case of species richness, higher species counts are considered more favorable and lower species counts are considered less favorable. In the case of FRID, values within the historical fire return interval are considered favorable, and high departure from the historical fire return interval is considered less favorable. In both cases, more and less favorable conditions for each metric are represented by values that range from +1 to -1 (respectively) so that multiple metrics can be evaluated together, including summarizing overall conditions at element and pillar levels to characterize socio-ecological resilience. Some data layers within this kit contain null values. We point this out here so users of the data will be aware and take whatever measures appropriate as they use and analyze the data. For some raster datasets in the RRK, areas have been masked (blanked) out and have a cell value of NoData (also referred to as null, NaN or missing). We, as producers and users of the data, cannot ignore NoData or fill them with zeros, since zero is often a valid value for some datasets. Removing NoData cells is not an option, a raster is a continuous grid. For users of the data performing further analyses and combining or "stacking" rasters, these NoData cells will mask out all values in that location in the output. To avoid this issue, the user must create values for the cells before combining them (i.e. 999 or any numeric value that is not real and clearly out of the range of the other values). Reasons for masking (blanking) out cells in RRK data: · Cells are lakes or reservoirs · Cells are urban or agriculture · Cells contain no information relevant to the dataset (i.e. streams, habitat) · Area (cells) subject to fire or other disturbance but the post disturbance condition or value is unknown - Metric Name: Damage Potential - Tier: 1 - Data Vintage: 08/2023. Includes disturbances through the end of 2022. - Unit Of Measure: Relative index, low to high - Metric Definition and Relevance: This metric combines two data layers; one is the Wildland Urban Interface (WUI) as defined by Carlson et al. 2022 (see WUI definition section for more information), and a second data layer, Damage Potential (DP), developed by Pyrologix LLC. The WUI includes the intermix and interface zones which collectively identify areas where structures occur. The distance selected for the interface definition is based on research from the California Fire Alliance suggesting that this is the average distance firebrands can travel from an active wildfire front.

The composite Damage Potential (DP) dataset represents a relative measure of wildfire’s potential to damage a home or other structure if one were present at a given pixel, and if a wildfire were to occur (conditional exposure). It is a function of ember load to a given pixel, and fire intensity at that pixel, and considers the generalized consequences to a home from fires of a given intensity (flame length). This index does not incorporate a measure of annual wildfire likelihood.

- Creation Method: This metric represents DP for WUI areas only. DP values were binned based on the following ranges into 6 classes and assigned class names.

0 (None): Values = 0
1 (Very Low): Values 0.01 to 20
2 (Low): Values 20 to 35
3 (Moderate): Values 35 to 50
4 (High): Values 50 to 80
5 (Very High): Values 80+

The current delineation of the WUI (Carlson et al. 2022) uses a mapping algorithm with definitions of the WUI; two classes of WUI were identified:

the intermix, where there is at least 50% vegetation cover surrounding buildings
the interface, where buildings are within 2.4 km (1.5 miles) of a patch of vegetation at least 5 km ² in size that contains at least 75% vegetation.

Both classes required a minimum building density of 6.17 buildings per km ² (using a range of circular neighborhood sizes).

Damage Potential (DP) data was produced by Pyrologix LLC, a wildfire threat assessment research firm, as part of a spatial wildfire hazard assessment across all land ownerships for the state of California. The ongoing work generally follows the framework outlined in Scott and Thompson (2013), with custom methods and significant improvements developed by Pyrologix. The project generally consists of three components: fuelscape calibration and updates, wildfire hazard assessment, and risk assessment. It utilizes a combination of wildfire models and custom tools, including WildEST (Wildfire Exposure Simulation Tool), a custom modeling tool developed by Pyrologix (Scott, 2020). To date, this work has resulted in a wide variety of spatial data layers related to wildfire hazard and risk, including Damage Potential (DP), representing conditions prior to the 2020, 2021, 2022 and 2023 fire seasons. Work to date has been funded by the USDA Forest Service Region 5, the California Energy Commission, and the USDI Bureau of Land Management with data contributions from CAL FIRE. Please reference the Pyrologix 2021 project report (Volger et al., 2021) for more information about the project or WildEST analysis.

Damage Potential (DP) is a proprietary index developed by Pyrologix LLC representing wildfire’s potential to damage a home or other structure if a wildfire were to occur (conditional exposure). It is a function of ember load to a given pixel and fire intensity at that pixel, and it considers the generalized consequences to a home from fires of a given intensity (flame length). DP is calculated based on two other datasets developed by Pyrologix: conditional risk to potential structures (cRPS) and conditional ember load index (cELI).

cRPS represents the potential consequences of fire to a home at a given location if a fire occurs there and if a home were located there. It is a measure that integrates wildfire intensity with generalized consequences to a home on every pixel. Wildfire intensity (flame length) is calculated using Pyrologix’ WildEST tool. WildEST is a scripted geospatial process used to perform multiple deterministic simulations under a range of weather types (wind speed, wind direction, fuel moisture content). Rather than weighting results solely according to the temporal relative frequencies of the weather scenarios, the WildEST process integrates results by weighting them according to their weather type probabilities (WTP), which appropriately weights high-spread conditions into the calculations. For fire-effects calculations, WildEST generates flame-length probability rasters that incorporate non-heading spread directions, for which fire intensity is considerably lower than at the head of the fire.

The response function characterizing potential consequences to an exposed structure is applied to fire effects flame lengths from WildEST for all burnable fuel types on the landscape regardless of whether an actual structure is present or not. The response function does not consider building materials of structures and is meant as a measure of the effect of fire intensity on structure exposure. The response function is provided below:

Flame length probability of 0-2 ft: -25
Flame length probability of 2-4 ft: -40
Flame length probability of 4-6 ft: -55
Flame length probability of 6-8 ft: -70
Flame length probability of 8-12 ft: -85
Flame length probability of >12 ft: -100

These results were calculated using 30m fire-effects flame-length probabilities from the WildEST wildfire behavior results and then further smoothed.

cELI is also calculated in WildEST, and represents the relative ember load per pixel, given that a fire occurs, based on surface and canopy fuel characteristics, climate, and topography within the pixel. Units are the relative number of embers. cELI is based on heading-only fire behavior.

Damage Potential is then calculated as the arithmetic mean of cELI and cRPS for each pixel across the landscape as follows:

𝐷𝑃 = 𝑐𝑅𝑃𝑆 + 𝑐𝐸𝐿𝐼/2

Although flame length and its potential impact to structures is a function of the fire environment at the subject location only, ember load is a function of ember production and transport in the area surrounding the subject location. A location with light fuel (and therefore low flame length) could still have significant Damage Potential if surrounded by a fire environment that produces copious embers.

DamagePotential_WUI_202308.tif 2022-09-30 raster digital data environment geoscientific Information planning Cadastre land management landscape restoration ISO 19115 Topic Categories environment geoscientific Information planning Cadastre land management landscape restoration 2 3300 2550 1.0 150000000 5000 FGDC