Modeling thinning effects on fire behavior with STANDFIRE
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We describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model.
Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts.
The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models.
The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs.
The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management.
This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.
KeywordsFuel treatments Fire behavior Modeling Physics-based WFDS FIRETEC FuelManager
Authors PIMONT, WELLS, COHN, De COLIGNY, JOLLY, and PARSONS developed the modeling system. Authors PARSONS and PIMONT wrote most of the paper with contributions from all other authors. Authors MELL, LINN, de COLIGNY, DUPUY, and RIGOLOT provided key information used in the development of the project. Authors WELLS and COHN carried out simulations; WELLS, COHN, and PARSONS analyzed data. PARSONS, WELLS, COHN, and PIMONT produced figures.
- Adams HD, Guardiola-Claramonte M, Barron-Gafford GA, Villegas JC, Breshears DD, Zou CB, Troch PA, Huxman TE (2009) Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc Natl Acad Sci 106:7063–7066. https://doi.org/10.1073/pnas.0901438106 CrossRefPubMedPubMedCentralGoogle Scholar
- Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. Gen Tech Rep INT-122. U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden UTGoogle Scholar
- Covington WW, Moore MM (1994) Southwestern ponderosa forest structure: changes since Euro-American settlement. J For 92:39–47Google Scholar
- Forney GP, McGrattan KB (2004) User’s guide for smokeview version 4: a tool for visualizing fire dynamics simulation data, US Department of Commerce, National Institute of Standards and TechnologyGoogle Scholar
- Hoffman CM, Linn R, Parsons R, Sieg C, Winterkamp J (2015) Modeling spatial and temporal dynamics of wind flow and potential fire behavior following a mountain pine beetle outbreak in a lodgepole pine forest. Agric For Meteorol 204:79–93. https://doi.org/10.1016/j.agrformet.2015.01.018 CrossRefGoogle Scholar
- Jones KW, Cannon JB, Saavedra FA, Kampf SK, Addington RN, Cheng AS, MacDonald LH, Wilson C, Wolk B (2017) Return on investment from fuel treatments to reduce severe wildfire and erosion in a watershed investment program in Colorado. J Environ Manag 198:66–77. https://doi.org/10.1016/j.jenvman.2017.05.023 CrossRefGoogle Scholar
- Larson AJ, Churchill D (2012) Tree spatial patterns in fire-frequent forests of western North America, including mechanisms of pattern formation and implications for designing fuel reduction and restoration treatments. For Ecol Manag 267:74–92. https://doi.org/10.1016/j.foreco.2011.11.038 CrossRefGoogle Scholar
- Linn RR (1997) A transport model for prediction of wildfire behavior. Thesis No.# LA–13334-T, Los Alamos National Laboratory, Los Alamos, NMGoogle Scholar
- Linn RR, Sieg CH, Hoffman CM, Winterkamp JL, McMillin JD (2013) Modeling wind fields and fire propagation following bark beetle outbreaks in spatially-heterogeneous pinyon-juniper woodland fuel complexes. Agric For Meteorol 173:139–153. https://doi.org/10.1016/j.agrformet.2012.11.007 CrossRefGoogle Scholar
- McGaughey RJ (2004) Stand visualization system, Version 3.3. Seattle, WA: Pacific Northwest Research Station, Forest Service, US Department of AgricultureGoogle Scholar
- Mell W, Maranghides A, McDermott R, Manzello SL (2009) Numerical simulation and experiments of burning douglas fir trees. Combust Flame 156:2023–2041. https://doi.org/10.1016/j.combustflame.2009.06.015 CrossRefGoogle Scholar
- Noonan-Wright EK, Vaillant NM, Reiner AL (2014) The effectiveness and limitations of fuel modeling using the Fire and Fuels Extension to the Forest Vegetation Simulator. For Sci 60:231–240Google Scholar
- Omi PN, Martinson EJ (2010) Effectiveness of fuel treatments for mitigating wildfire severity: a manager-focused review and synthesisGoogle Scholar
- Rebain SA (2015) The Fire and Fuels Extension to the Forest Vegetation Simulator: updated model documentation. Internal Rep, Fort Collins, CO: U. S. Department of Agriculture, Forest Service, Forest Management Service Center, p 403Google Scholar
- Reinhardt E, Crookston NL (2003) The fire and fuels extension to the forest vegetation simulator. Gen Tech Rep RMRS-GTR-116. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, p 209Google Scholar
- Reinhardt ED, Keane RE, Brown JK (1997) First order fire effectsmodel: FOFEM 4.0, user’s guide. Gen Tech Rep INT-GTR-344. U.S. Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, p 65Google Scholar
- Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Res Pap INT-115. U.S. Department of Agriculture, Intermountain Forest and Range Experiment Station, Ogden, p 40Google Scholar
- Rothermel RC (1991) Predicting behavior and size of crown fires in the Northern Rocky Mountains. Res Pap INT-438. U.S. Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, p 46Google Scholar
- Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen Tech Rep RMRS-GTR-153. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research StationGoogle Scholar
- Scott JH, Reinhardt ED (2001) Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service Res Pap RMRS-RP-29. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, p 59Google Scholar
- Ziska L, Reeves J, Blank B (2005) The impact of recent increases in atmospheric CO2 on biomass production and vegetative retention of cheatgrass (Bromus tectorum): implications for fire disturbance. Glob Chang Biol 11:1325–1332. https://doi.org/10.1111/j.1365-2486.2005.00992.x CrossRefGoogle Scholar