Potential Control Location Suitability (PCL) is developed through statistical associations between fire containment successes (fire perimeters) and failures (fire interiors) from the 2002-2021 period with physical landscape conditions related to topography, fuels, accessibility, suppression difficulty, and potential fire behavior. PCL is scaled from zero to one hundred, corresponding to conditions with low to high probability of containing a fire. The PCL model is not developed with detailed weather for past incidents and the prepositioned products on the RMA dashboard are representative of 90th percentile fire weather, so PCL will not provide a precise probability of containment for a given fire environment. PCL provides a reasonable assessment of where containment is most likely to be successful based on where fires stopped in the past. A key assumption of PCL is that current controls on fire containment success (both physical landscape and fire management factors) are consistent with those during the 2002-2021 period. Model diagnostics can be used examine the relative importance of predictor variables and how each variable influences containment potential.

PCL is displayed using five containment likelihood classes ranging from very low in red (PCL <10), to orange (11-25), green (26-50), light blue (51-75) and high in dark blue (PCL > 75). Red zones represent areas where large fire containment is unlikely—potentially suggesting the need for an indirect suppression strategy based on higher probability of success features. Blue zones (PCL > 0.5) in the PCL surface are areas where fires tend to stall or stop due to some combination of site conditions and suppression efficacy.

PCL incorporates rate of spread from basic FlamMap runs (Finney 2006; Stratton 2004), using 90th percentile fire weather and fuel moisture conditions. The pre-season products use the wind blowing uphill option to represent a consistent worst-case scenario. The fuel inputs are either the “Wild West” regionally-calibrated fuelscape (11 Western States + Black Hills) or LANDFIRE 2022 (v2.3.0) updated with fuel treatments and disturbances through the end of calendar year 2023 to make the data 2024 ready. The model uses gradient boosting machine learning to quantify statistical relationships between past fire containment successes and failures with landscape conditions; for example, for the 2024 fires season, fires from 2002-2021 were used to train the model and the model was then projected onto the 2024 ready fuelscape. Predictor variables including road distance, travel cost distance, distance to barriers, and distance to topographic features (ridges, valleys, and flats) are calculated from HERE 2020 Roads, USFS, and DOI road and trails databases, LANDFIRE digital elevation models, and the National hydrography dataset.

For incident support applications, PCL is supplied to the fire operations and planning section chiefs, LTAN, FBAN, and SOPL positions. PCL PDF maps or digital overlays in Google Earth are also used as a communications tool to discuss suppression opportunities, operational challenges. and potential strategies with line officers. PCL has been multiplied by 100 and rounded to the closest integer to reduce GIS file sizes. PCL now ranges between 0 and 100 instead of 0 and 1.0. Previous breakpoints still apply – just multiply them by 100 to fit the new scale.

In pre-fire planning, local fire managers can leverage the PCL surface to assess the quality of potential holding features. When used in combination with the SDI, PCL can help to inform potential operational delineation (POD) boundaries for landscape-scale fire response planning.

In large fire management, PCL can help with assessing indirect strategies for containment, potential fire duration, and PACE Model options, as well as with visualizing and communicating strategies and operational challenges or opportunities.

As with all modeled products, PCL should be vetted by local personnel and ground-truthed prior to its use on incident-level decision support. The West-wide PCL product on the RMA dashboard is modeled at ecoregion scale to highlight common control features for broad regions. Control potential may not be accurately estimated for rare biophysical settings within an ecoregion or along ecoregion boundaries. Alternative PCL models are available for many areas that may better reflect local control potential.

References

Finney, M. A. 2006. An overview of FlamMap fire modeling capabilities. In: Fuels management—how to measure success: conference proceedings. 2006 March 28-30; Portland, Oregon. Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: 213-220. (647 KB; 13 pages)

O'Connor Christopher D., Calkin David E., Thompson Matthew P. 2017. An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. International Journal of Wildland Fire 26, 587-597.

Reeves, M.C.; Lankston, R. 2020. Fuelcast: weekly fuel and rangeland production forecasts. [Website]. Available at https://www.fuelcast.net/

Stratton, R. D. 2004. Assessing the Effectiveness of Landscape Fuel Treatments on Fire Growth and Behavior. Journal of Forestry. Pp 32-40 October 2004

Potential Control Location Suitability (PCL) is developed through statistical associations between fire containment successes (fire perimeters) and failures (fire interiors) from the 2002-2021 period with physical landscape conditions related to topography, fuels, accessibility, suppression difficulty, and potential fire behavior. PCL is scaled from zero to one hundred, corresponding to conditions with low to high probability of containing a fire. The PCL model is not developed with detailed weather for past incidents and the prepositioned products on the RMA dashboard are representative of 90th percentile fire weather, so PCL will not provide a precise probability of containment for a given fire environment. PCL provides a reasonable assessment of where containment is most likely to be successful based on where fires stopped in the past. A key assumption of PCL is that current controls on fire containment success (both physical landscape and fire management factors) are consistent with those during the 2002-2021 period. Model diagnostics can be used examine the relative importance of predictor variables and how each variable influences containment potential.

PCL is displayed using five containment likelihood classes ranging from very low in red (PCL <10), to orange (11-25), green (26-50), light blue (51-75) and high in dark blue (PCL > 75). Red zones represent areas where large fire containment is unlikely—potentially suggesting the need for an indirect suppression strategy based on higher probability of success features. Blue zones (PCL > 0.5) in the PCL surface are areas where fires tend to stall or stop due to some combination of site conditions and suppression efficacy.

PCL incorporates rate of spread from basic FlamMap runs (Finney 2006; Stratton 2004), using 90th percentile fire weather and fuel moisture conditions. The pre-season products use the wind blowing uphill option to represent a consistent worst-case scenario. The fuel inputs are either the “Wild West” regionally-calibrated fuelscape (11 Western States + Black Hills) or LANDFIRE 2022 (v2.3.0) updated with fuel treatments and disturbances through the end of calendar year 2023 to make the data 2024 ready. The model uses gradient boosting machine learning to quantify statistical relationships between past fire containment successes and failures with landscape conditions; for example, for the 2024 fires season, fires from 2002-2021 were used to train the model and the model was then projected onto the 2024 ready fuelscape. Predictor variables including road distance, travel cost distance, distance to barriers, and distance to topographic features (ridges, valleys, and flats) are calculated from HERE 2020 Roads, USFS, and DOI road and trails databases, LANDFIRE digital elevation models, and the National hydrography dataset.

For incident support applications, PCL is supplied to the fire operations and planning section chiefs, LTAN, FBAN, and SOPL positions. PCL PDF maps or digital overlays in Google Earth are also used as a communications tool to discuss suppression opportunities, operational challenges. and potential strategies with line officers. PCL has been multiplied by 100 and rounded to the closest integer to reduce GIS file sizes. PCL now ranges between 0 and 100 instead of 0 and 1.0. Previous breakpoints still apply – just multiply them by 100 to fit the new scale.

In pre-fire planning, local fire managers can leverage the PCL surface to assess the quality of potential holding features. When used in combination with the SDI, PCL can help to inform potential operational delineation (POD) boundaries for landscape-scale fire response planning.

In large fire management, PCL can help with assessing indirect strategies for containment, potential fire duration, and PACE Model options, as well as with visualizing and communicating strategies and operational challenges or opportunities.

As with all modeled products, PCL should be vetted by local personnel and ground-truthed prior to its use on incident-level decision support. The West-wide PCL product on the RMA dashboard is modeled at ecoregion scale to highlight common control features for broad regions. Control potential may not be accurately estimated for rare biophysical settings within an ecoregion or along ecoregion boundaries. Alternative PCL models are available for many areas that may better reflect local control potential.

References

Finney, M. A. 2006. An overview of FlamMap fire modeling capabilities. In: Fuels management—how to measure success: conference proceedings. 2006 March 28-30; Portland, Oregon. Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: 213-220. (647 KB; 13 pages)

O'Connor Christopher D., Calkin David E., Thompson Matthew P. 2017. An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. International Journal of Wildland Fire 26, 587-597.

Reeves, M.C.; Lankston, R. 2020. Fuelcast: weekly fuel and rangeland production forecasts. [Website]. Available at https://www.fuelcast.net/

Stratton, R. D. 2004. Assessing the Effectiveness of Landscape Fuel Treatments on Fire Growth and Behavior. Journal of Forestry. Pp 32-40 October 2004