Wildfire risk under alternative fuel management strategies: spatial datasets of in situ and transmitted risk for populated areas in north-central New Mexico and Sierra Mountain Range within California

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Vogler, Kevin C.
Originator: Thompson, Matthew P.
Originator: Scott, Joe H.
Originator: Miller, Carol
Publication_Date: 2022
Title:
Wildfire risk under alternative fuel management strategies: spatial datasets of in situ and transmitted risk for populated areas in north-central New Mexico and Sierra Mountain Range within California
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2022-0026
Description:
Abstract:
Simulation modeling was used to examine tradeoffs and synergies between hypothetical post-treatment conditions generated according to distinct treatment prioritization schemes (Housing Protection, Federal Risk Transmission, Random) and variable treatment extents. We used stochastic wildfire simulation and computations of exposure to wildfire to compare strategy performance across two very large landscapes - the southern Sierra in California (approximately 28 million acres) and northern New Mexico (approximately 21 million acres). This data publication represents the model results for the two study areas analyzed as well as all input data required to reproduce our analysis. All input data and simulation model parameters were calibrated to represent conditions within the two study areas in 2015.
Purpose:
Despite the recent progress represented by advances in fire simulation, quantitative estimates of risk informing fuels management planning, and risk analysis being used to inform planning that supports operational fire management decisions, a need remains for guidance for designing and prospectively evaluating landscape-scale fuel treatments with protection objectives, resource management objectives, and wildfire response in mind. This project looks to illustrate an approach for examining whether, and how, fuels management can foster the expansion of beneficial wildfire.
Supplemental_Information:
For more information about this study and these data, see Thompson et al. (2022).

These data were published on 04/15/2022. On 10/24/2024, minor metadata updates were made.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2015
Currentness_Reference:
Publication date
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
This project involved two distinct study areas: southern Sierra Mountain Range in California and northern New Mexico, United States.
Bounding_Coordinates:
West_Bounding_Coordinate: -121.61627
East_Bounding_Coordinate: -104.22108
North_Bounding_Coordinate: 39.41426
South_Bounding_Coordinate: 34.26908
Bounding_Altitudes:
Altitude_Minimum: 0
Altitude_Maximum: 4421
Altitude_Distance_Units: meters
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: burn probability
Theme_Keyword: fire likelihood
Theme_Keyword: fire modeling
Theme_Keyword: fire planning
Theme_Keyword: FLPGen
Theme_Keyword: FSim
Theme_Keyword: fuels management
Theme_Keyword: hazard
Theme_Keyword: Landscape Treatment Designer
Theme_Keyword: risk assessment
Theme_Keyword: wildfire
Theme_Keyword: wildfire exposure
Theme_Keyword: wildfire transmission
Theme_Keyword: wildland-urban interface
Theme_Keyword: fire suppression
Theme_Keyword: forest management
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme_Keyword: environment
Theme_Keyword: planningCadastre
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Ecology, Ecosystems, & Environment
Theme_Keyword: Environment and People
Theme_Keyword: Fire
Theme_Keyword: Fire ecology
Theme_Keyword: Fire suppression, pre-suppression
Theme_Keyword: Wildland/urban interface
Theme_Keyword: Inventory, Monitoring, & Analysis
Theme_Keyword: Assessments
Theme_Keyword: Natural Resource Management & Use
Theme_Keyword: Landscape management
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: New Mexico
Place_Keyword: United States
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Vogler, Kevin C.; Thompson, Matthew P.; Scott, Joe H.; Miller, Carol. 2022. Wildfire risk under alternative fuel management strategies: spatial datasets of in situ and transmitted risk for populated areas in north-central New Mexico and Sierra Mountain Range within California. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2022-0026
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Matthew P. Thompson
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: 970-498-1302
Contact_Electronic_Mail_Address: matthew.p.thompson@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
Funding for this project provided by Joint Fire Science Program (JFSP # 17-1-01-4): https://www.firescience.gov. Funding also provided by the USDA Forest Service, Rocky Mountain Research Station (RMRS) and the Aldo Leopold Wilderness Research Institute.

Author Information:

Vogler, Kevin C.
Pyrologix, LLC
https://orcid.org/0000-0002-7080-2557

Thompson, Matthew P.
USDA Forest Service, Rocky Mountain Research Station

Scott, Joe H.
Pyrologix, LLC

Miller, Carol
USDA Forest Service, Rocky Mountain Research Station, Aldo Leopold Wilderness Research Institute
https://orcid.org/0000-0002-3091-5602
Cross_Reference:
Citation_Information:
Originator: Thompson, Matthew P.
Originator: Vogler, Kevin C.
Originator: Scott, Joe H.
Originator: Miller, Carol
Publication_Date: 2022
Title:
Comparing risk-based fuel treatment prioritization with alternative strategies for enhancing protection and resource management objectives
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Fire Ecology
Issue_Identification: 18(26)
Online_Linkage: https://doi.org/10.1186/s42408-022-00149-0
Online_Linkage: https://research.fs.usda.gov/treesearch/66231
Cross_Reference:
Citation_Information:
Originator: Miller, Carol
Originator: Vogler, Kevin C.
Originator: Scott, Joe H.
Originator: Thompson, Matthew P.
Publication_Date: 2021
Title:
Can landscape fuel treatments enhance both protection and resource management objectives?
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: Joint Fire Science Program Final Report
Issue_Identification: Project ID: 17-1-01-4
Other_Citation_Details:
(Available in data publication download: \Supplements\17-1-01-4_final_report.pdf)
Online_Linkage: https://www.firescience.gov/projects/17-1-01-4/project/17-1-01-4_final_report.pdf
Analytical_Tool:
Analytical_Tool_Description:
FSim is a geospatial Fire Simulation system developed by the USDA Forest Service, Missoula Fire Sciences Laboratory to estimate the burn probability and variability in fire behavior across large landscapes. FSim is used for national, regional, and local risk modeling in the United States.
Tool_Access_Information:
Online_Linkage: https://www.firelab.org/project/fsim-wildfire-risk-simulation-software
Tool_Access_Instructions:
See website for details.
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Model results are objectively evaluated through comparison with historical fire patterns and statistics, including the mean annual burn probability and fire size distribution, for each simulation unit. This evaluation is part of the FSim calibration process, whereby simulation inputs are adjusted until the slopes of the historical and modeled fire size distributions are similar and the modeled average burn probability falls within an acceptable range of the historical reference value (i.e., the 90% confidence interval for the mean). For more information on the calibration process see Thompson et al. (2016).

Thompson, Matthew P.; Bowden, Phil; Brough, April; Scott, Joe H.; Gilbertson-Day, Julie; Taylor, Alan; Anderson, Jennifer; Haas, Jessica R. 2016. Application of wildfire risk assessment results to wildfire response planning in the Southern Sierra Nevada, California, USA. Forests 7(3): 1-22. https://doi.org/10.3390/f7030064
Logical_Consistency_Report:
The data are logically consistent. The consistency was verified as part of the quality assurance that occurred during data analysis.
Completeness_Report:
Cells with a zero (“0”) value for burn probability were either (1) characterized as a non-burnable fuel type in the 120-meter LANDFIRE dataset, or (2) burnable, but not burned within the simulation limits (20,000 annual weather scenarios modeled). While, for the latter case, it should be possible to obtain nonzero values (i.e., extremely imprecise estimates of some value less than 0.00005) with additional years in simulation, such efforts were beyond the scope and interest of this work.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Short, Karen C.
Publication_Date: 2017
Title:
Spatial wildfire occurrence data for the United States, 1992-2015 [FPA_FOD_20170508]
Edition: 4th Edition
Geospatial_Data_Presentation_Form: vector digital data and database
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2013-0009.4
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1992
Ending_Date: 2015
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Short (2017)
Source_Contribution:
FSim simulation calibration data
Source_Information:
Source_Citation:
Citation_Information:
Originator: Scott, Joe H.
Originator: Brough, April M.
Originator: Gilbertson-Day, Julie W.
Originator: Dillon, Gregory K.
Originator: Moran, Christopher
Publication_Date: 2020
Title:
Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0060
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Scott et al. (2020)
Source_Contribution:
National Housing-unit density dataset
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Agriculture, Forest Service
Originator: U.S. Department of the Interior
Publication_Date: 2017
Title:
LANDFIRE 2014
Geospatial_Data_Presentation_Form: database
Online_Linkage: https://www.landfire.gov
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2014
Source_Currentness_Reference:
ground condition
Source_Citation_Abbreviation:
LANDFIRE 2014 - LF_1.4.0
Source_Contribution:
LANDFIRE Refresh 2014 (LF 2014 - LF_1.4.0) fuel and terrain data
Process_Step:
Process_Description:
FSim Model & Calibration to Current Conditions

FSim generates an event set, which is a set of simulated wildfire perimeters that collectively are integrated into a probabilistic result of wildfire likelihood. Individually, simulated perimeters represent a known probability of occurrence and can be analyzed to estimate asset exposure and risk transmission. The event set is exported in Esri shapefile format, representing the final perimeter of each simulated wildfire. An attribute table specifying certain characteristics of each simulated wildfire (its start location and date, duration, final size, and other characteristics) is included with the shapefile. FSim simulations were completed at 120-meter (m) resolution using the LANDFIRE 14 fuelscape (www.landfire.gov). FSim simulations were calibrated to historical measures of large fire occurrence (mean large fire size, and the mean number of large fires per million hectares [ha]) derived from the 1992 – 2016 USDA Forest Service Fire Occurrence Database (Short 2017). After calibrating FSim for the current condition, we ran FSim on each of the hypothetical fuelscapes.
Source_Used_Citation_Abbreviation:
Short (2017)
Process_Date: 2020
Process_Step:
Process_Description:
Deterministic Wildfire Modeling - FLEPgen

To estimate wildfire characteristics across the Analysis Area we used a scripted geospatial modeling process called the Flame-Length Exceedance Probability Generator (FLEPgen, Scott 2020). FLEPgen performs multiple deterministic FLAMMAP simulations (Finney 2006) under a range of weather types (wind speed, wind direction, and fuel moisture content), then integrates those simulations by weighting them according to their weather type probabilities, which weighs high-spread weather conditions that will be expressed to a greater degree across the landscape. The FLEPgen process was applied to both the Current Condition fuelscape and the Treated fuelscape at 120-m resolution. The Treated fuelscape was developed previously for a national-scale risk assessment. The dataset represents a modified version of the LANDFIRE 14 fuelscape where a set of hypothetical treatments were implemented across the United States. Forested fuels received a moderate severity ‘mechanical remove’ treatment. Shrub fuels received a moderate severity ‘prescribed fire’ treatment. Grass and sagebrush fuel types were excluded from treatment because treatments would be ineffective at meaningful time scales or ecologically inappropriate given the risk of invasive annual grass introduction. All treatments were modified to align in age with the LANDFIRE 5 years post-disturbance period. The national-scale Treated fuelscape was not specifically calibrated to the local fuels within the project Analysis Area. To prevent model effects where fuel reduction treatments inadvertently exacerbate fire behavior, we removed fuel treatments from the analysis that were ineffective from consideration in the prioritization themes. To be considered effective a fuel treatment had to reduce flame length by at least 0.15 m and not increase the rate of spread by more than 20%. FLEPgen was run with the same weather inputs as the FSim model. Utilizing FLEPgen allows for analysis of fire behavior at the pixel/stand-level without the influence of adjacent fuels. The FLEPgen-derived fire intensity results were used to model treatment effect and in the development of the priority fuelscapes described in further detail below. While the FLEPgen tool was used in the development of priority fuelscapes, the stochastic FSim tool was used to measure treatment effects across the landscape.

Scott, Joe H. 2020. A deterministic method for generating flame-length probabilities. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, [eds.]. Proceedings of the Fire Continuum-Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 195-205. https://research.fs.usda.gov/treesearch/62336

Finney, Mark A. 2006. An Overview of FlamMap Fire Modeling Capabilities. In: Andrews, Patricia L.; Butler, Bret W., comps. 2006. Fuels Management-How to Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 213-220. https://research.fs.usda.gov/treesearch/25948
Process_Date: 2020
Process_Step:
Process_Description:
Mapping Local and Transmitted Exposure

Housing units were mapped using the national Housing-unit density (HuDen) dataset (Scott et al. 2020). HuDen was generated using population and housing-unit count data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. Building footprints were assigned population and housing-unit counts based on the population estimates of the Census block unit, then smoothed to create raster data at 30-m resolution. We converted housing-unit density values to housing-unit count and summed those values to 120 m resolution using the ArcGIS Aggregate tool. Our measure of local exposure evaluates the likelihood that housing units would be impacted by wildfire. We measured housing exposure by overlaying the annualized burn probability results from the FSim model with raster maps of housing unit counts to produce estimates of the annual number of homes exposed by wildfire. To map transmitted wildfire exposure, we selected all FSim fire perimeters that originated on federal lands and calculated the number of Housing Units exposed from each by summing the total number of homes within each fire polygon shapefile with the ArcGIS Zonal Sum tool. Summarizing the number of homes exposed by simulation year provided an estimate of the annual number of homes exposed from fires that originate on federal lands. Maps of in situ and transmitted wildfire exposure were used in the generation of priority fuelscapes.
Source_Used_Citation_Abbreviation:
Scott et al. (2020)
Process_Date: 2020
Process_Step:
Process_Description:
Developing Priority Landscapes

To test the impact of fuel on local (in situ) and transmitted exposure we developed a series of 20 hypothetical post-treatment fuelscapes per study area. Each of the individual fuelscapes were generated from a combination of the current condition and the treated fuelscape where the entire landscape was treated with a hypothetical fuel reduction treatment. Treatments were implemented at the stand level. Each of the objective values was attributed to a hexcel grid that covered the Analysis Area. The hexcel grid was approximately 33.5 ha in size and mimics the operational scale of treatments within the Analysis Areas. Given the broad scale of this analysis, additional site-specific variables that may impact the feasibility of treatments such as road access, slope steepness, treatment cost, etc. were not considered. Individual stands were prioritized for management utilizing the Landscape Treatment Designer (LTD, Ager et al. 2012). Treatments were weighted by their ability to address each of the prioritization metrics. We modeled scenarios where 1% , 2.5% , 5% , 7.5% , 10% , 25% , and 100% of the analysis areas were treated. Note that the Federal Transmission scenario was limited to only treating on federal lands. The 1% treatment scenario is roughly equivalent to a 5-year plan of work for the federal agencies within the analysis area.

Reduce Housing Exposure – Protect Housing

Developing priority treatments to reduce housing exposure first required the level of housing exposure under the current condition scenario. We used the calibrated current condition wildfire simulation outputs generated from FSim to quantify housing exposure by overlaying the annualized burn probability results from the FSim model with raster maps of housing unit counts to produce estimates of the annual number of housing units exposed by wildfire. Treatments were weighted by their ability to reduce flame lengths as measured by the FLEPgen tool. A 2.5 kilometer (km) Kernel Smoothing was iteratively implemented on the weighted Housing Unit exposure values and summarized to the stand level. Priority stands maximized the reduction of fire intensity in densely developed locations.

Expand Opportunities for Managed Fire - Reduce Federal Transmitted Exposure

Developing priority treatments to reduce transmission of wildfire exposure from federal lands relied on first mapping the locations of fire transmission under the current condition scenario. We used the calibrated current condition wildfire simulation outputs generated from FSim to quantify housing exposure using a method similar to that previously used in Ager et al. (2017) & Ager et al. (2019). Ignitions were filtered for those occurring on federal lands and associated perimeters were intersected with the housing density to determine total home exposure per ignition. The resulting point data were smoothed using a kernel density tool with a 2.5 km fixed search radius at 120 m resolution for the entire Analysis Area. Treatments were weighted by the ability to reduce transmission calculated as the change in rate of spread value developed in the FLEPgen simulations. Priority stands maximized the reduction of rate of spread in locations with the highest level of risk transmission.

Ager, Alan A.; Vaillant, Nicole M.; Owens, David E.; Brittain, Stuart; Hamann, Jeff. 2012. Overview and example application of the Landscape Treatment Designer. Gen. Tech. Rep. PNW-GTR-859. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 11 p. https://doi.org/10.2737/pnw-gtr-859

Ager, Alan A.; Evers, Cody R.; Day, Michelle A.; Preisler, Haiganoush K.; Barros, Ana M.; Nielsen-Pincus, Max. 2017. Network analysis of wildfire transmission and implications for risk governance. PloS ONE 12(3): e0172867. https://doi.org/10.1371/journal.pone.0172867 and https://research.fs.usda.gov/treesearch/54923

Ager, Alan A.; Palaiologou, Palaiologos; Evers, Cody R.; Day, Michelle A.; Ringo, Chris; Short, Karen. 2019. Wildfire exposure to the Wildland Urban Interface in the western US. Applied Geography 111, 102059. https://doi.org/10.1016/j.apgeog.2019.102059 and https://research.fs.usda.gov/treesearch/58946
Process_Date: 2020
Process_Step:
Process_Description:
Random Treatments

A random treatment scenario was developed to serve as a benchmark to assess the relative effectiveness of the other prioritization scenarios. Each analysis area stand was assigned a random number and stands were selected for treatment until the treatment intensity targets were met.
Process_Date: 2020
Process_Step:
Process_Description:
Modeling Alternative Strategies

After calibrating FSim for the current condition landscape, FSim was rerun as a ‘record off’ run on the 19 additional fuelscape scenarios within each analysis area. Using the ‘record off’ functionality of FSim allows for the simulation of the same set of wildfire events where location, weather, and duration are held constant but the fuelscape is variable. This allowed us to attribute differences among the simulations to the fuelscapes that changed between simulations rather than to model stochasticity (see Scott et al. 2016). All simulations were run on 48-thread Windows machines using FSim version B1.22.

Scott, Joe H.; Thompson, Matthew P.; Gilbertson-Day, Julie W. 2016. Examining alternative fuel management strategies and the relative contribution of National Forest System land to wildfire risk to adjacent homes–a pilot assessment on the Sierra National Forest, California, USA. Forest Ecology and Management 362, 29-37. https://doi.org/10.1016/j.foreco.2015.11.038 and https://research.fs.usda.gov/treesearch/50667
Process_Date: 2020
Process_Step:
Process_Description:
Evaluation of Treatment Strategies

Each treatment strategy and treatment intensity level was evaluated on its ability to reduce landscape-level housing exposure and increase areas of opportunities for managed wildfire by reducing federally transmitted housing exposure. Treatments show the effect by altering the size of simulated wildfire perimeters (event set) that result in the exposure of housing units. There are two mechanisms within the FSim model for fuel treatments to alter the size of wildfire perimeters. First, treatments may alter the rate of spread within the minimum travel time growth algorithm (Finney, 2002). This would result both in a smaller overall fire size as well as a higher probability of the occurrence of simulated weather conditions that would extinguish a fire before it reaches housing units. Secondly, treatments may reduce simulated flame lengths which would lead to a smaller overall size as a result of the perimeter trimming function that mimics wildfire suppression actions. FSim uses a function to limit wildfire growth on the flanks of modeled perimeters under low flame length conditions.

Treatment Performance – Housing Exposure

To quantify housing exposure, we overlaid simulated fire perimeters on housing unit density layers. We report exposure in terms of the expected number of exposed housing units (HU) per year. Exposure was calculated for all simulated fires and for only those that originated on federal lands. It should be noted that suppression strategies such as point protection or positioning engines along roads that could reduce housing exposure are not specifically modeled here. Nor does the modeling consider potential home to home ignition in urban fuels, such that this measure is an estimated lower bound on exposure.

Treatment Performance – Expanded Opportunities for Managed Fire

To quantify the opportunity for resource benefit from managed fire, we quantify the area burned from fires that didn’t expose homes. To compute this, we summed the number of housing units exposed to each simulated fire and added that attribute to the location of its ignition. Ignition points whose perimeters did not encounter any nonzero housing unit pixel were assigned a value of zero (e.g., zero housing units exposed). Points were converted to a raster and smoothed using a 2.5k search radius point density smoothing. The exposure raster was divided by a 2.5k smoothed point density raster of large simulated wildfires. The results generated a raster-based quantitative fireshed. We mapped opportunities assuming a risk tolerance of 0 homes exposed with a 90% probability of success (i.e., 90% is the proportion of simulated ignitions with the smoothed area resulting in the corresponding level of exposure). We characterize this as an upper bound on area of opportunity, recognizing that the presence of other fire-sensitive resources or assets on the landscape wouldn’t necessarily support managed fire in all places, and furthermore recognizing that fires that burn onto non-federal jurisdictions may warrant a shift to more aggressive fire containment strategies.

Finney, Mark A. 2002. Fire growth using minimum travel time methods. Canadian Journal of Forest Research. 32, 1420-1424. https://doi.org/10.1139/x02-068
Process_Date: 2020
Process_Step:
Process_Description:
Risk Tolerance

We explored how risk tolerance could affect resource benefit opportunity, specifically how increasing tolerance for potential housing exposure could increase opportunity. To do so we varied thresholds for an acceptable amount of housing unit by varying the exposure term to allow for greater than 0 homes exposed. The analysis described above in calculating resource benefit was iteratively completed for all combinations of treatment strategy/extent and tolerable number of housing units exposed.
Process_Date: 2020
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:
Standard_Parallel: 34
Standard_Parallel: 40.5
Longitude_of_Central_Meridian: -120
Latitude_of_Projection_Origin: 0
False_Easting: 0
False_Northing: -4000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: Coordinate Pair
Coordinate_Representation:
Abscissa_Resolution: 120
Ordinate_Resolution: 120
Planar_Distance_Units: Meters
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name: Universal Transverse Mercator
Universal_Transverse_Mercator:
UTM_Zone_Number: 17
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.9996
Longitude_of_Central_Meridian: -105
Latitude_of_Projection_Origin: 0
False_Easting: 500000
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: Coordinate Pair
Coordinate_Representation:
Abscissa_Resolution: 120
Ordinate_Resolution: 120
Planar_Distance_Units: Meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.25722210
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find a list and description of the files included in this data publication.

USER GUIDE

\UserGuide_RDS-2022-0026.pdf: Portable Document Format (PDF) file containing a complete description of the geodatabases included in this data publication, which includes specific details about each of the attributes in the rasters and feature classes.


DATA FILES (10)

1. \Data\NCNM\NCNM_FLEPGen_Intensity.gdb: Esri ArcGIS 10.3 file geodatabase (GDB) containing 120 meters (m) wildfire intensity rasters generated with the FLPGen fire model on the current condition and treated fuelscapes for the north-central New Mexico study area.

2. \Data\NCNM\NCNM_FSim_BurnProbability.gdb: GDB containing 120 m wildfire likelihood (burn probability) rasters generated with the FSim model on each of the priority fuelscapes in the north-central New Mexico study area.

3. \Data\NCNM\NCNM_FSim_EventSet.gdb: GDB containing feature classes of all simulated wildfire perimeters and ignition locations generated with the FSim model on each of the priority fuelscapes in the north-central New Mexico study area.

4. \Data\NCNM\NCNM_Fueldata.gdb: GDB containing 120 m rasters of current condition and treated fuel conditions for the north-central New Mexico study area.

5. \Data\NCNM\NCNM_Priority_Fuelscapes.gdb: GDB containing optimally generated fuelscape masks to address either home protection, federal risk transmission, or random treatment priorities for the north-central New Mexico study area.


6. \Data\Sierra\Sierra_FLEPGen_Intensity.gdb: GDB containing 120 m wildfire intensity rasters generated with the FLPGen fire model on the current condition and treated fuelscapes for the California study area.

7. \Data\Sierra\Sierra_FSim_BurnProbability.gdb: GDB containing 120 m wildfire likelihood (burn probability) rasters generated with the FSim model on each of the priority fuelscapes in the California study area.

8. \Data\Sierra\Sierra_FSim_EventSet.gdb: GDB containing feature classes of all simulated wildfire perimeters and ignition locations generated with the FSim model on each of the priority fuelscapes in the California study area.

9. \Data\Sierra\Sierra_Fueldata.gdb: GDB containing 120 m rasters of current condition and treated fuel conditions for the California study area.

10. \Data\Sierra\Sierra_Priority_Fuelscapes.gdb: GDB containing optimally generated fuelscape masks to address either home protection, federal risk transmission, or random treatment priorities for the California study area.



SUPPLEMENTAL FILES (1)

1. \Supplements\17-1-01-4_final_report.pdf: PDF file containing the Joint Fire Science Program final report for project # 17-1-01-4: "Can landscape fuel treatments enhance both protection and resource management objectives?"
Entity_and_Attribute_Detail_Citation:
Scott, Joe H. 2020. A deterministic method for generating flame-length probabilities. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, [eds.]. Proceedings of the Fire Continuum-Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 195-205. https://research.fs.usda.gov/treesearch/62336

Finney, Mark A.; McHugh, Charles W.; Grenfell, Isaac C.; Riley, Karin L.; Short, Karen C. 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stochastic Environmental Research and Risk Assessment. 25: 973-1000. https://www.doi.org/10.1007/s00477-011-0462-z and https://research.fs.usda.gov/treesearch/39312
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of October 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2022-0026
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GDB
Format_Version_Number: see Format Specification
Format_Specification:
Esri ArcGIS file geodatabase (GDB)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0026
Digital_Form:
Digital_Transfer_Information:
Format_Name: PDF
Format_Version_Number: see Format Description
Format_Specification:
Portable Document Format (PDF)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0026
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20241024
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Matthew P. Thompson
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: 970-498-1302
Contact_Electronic_Mail_Address: matthew.p.thompson@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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