Spatially explicit forecasts of large wildland fire probability and suppression costs for CaliforniaHaiganoush K. Preisler A G , Anthony L. Westerling B , Krista M. Gebert C F , Francisco Munoz-Arriola D and Thomas P. Holmes E
A USDA Forest Service, Pacific Southwest Research Station, 800 Buchanan Street, West Annex Building, Albany, CA 94710, USA.
B University of California – Merced, 5200 N. Lake Road, Merced, CA 95343, USA.
C USDA Forest Service, Rocky Mountain Research Station, PO Box 7669, Missoula, MT 59807, USA.
D University of California – San Diego, 9500 Gilman Drive, San Diego, CA 92093-5004, USA.
E USDA Forest Service, Southern Research Station, Forestry Sciences Lab, PO Box 12254, Research Triangle Park, NC 27709, USA.
F Present address: USDA Forest Service, Northern Region, 200 East Broadway, PO Box 7669, Missoula, MT 59807, USA.
G Corresponding author. Email: firstname.lastname@example.org
International Journal of Wildland Fire 20(4) 508-517 https://doi.org/10.1071/WF09087
Submitted: 8 August 2009 Accepted: 13 August 2010 Published: 20 June 2011
In the last decade, increases in fire activity and suppression expenditures have caused budgetary problems for federal land management agencies. Spatial forecasts of upcoming fire activity and costs have the potential to help reduce expenditures, and increase the efficiency of suppression efforts, by enabling them to focus resources where they have the greatest effect. In this paper, we present statistical models for estimating 1–6 months ahead spatially explicit forecasts of expected numbers, locations and costs of large fires on a 0.125° grid with vegetation, topography and hydroclimate data used as predictors. As an example, forecasts for California Federal and State protection responsibility are produced for historic dates and compared with recorded fire occurrence and cost data. The results seem promising in that the spatially explicit forecasts of large fire probabilities seem to match the actual occurrence of large fires, with the exception of years with widespread lightning events, which remain elusive. Forecasts of suppression expenditures did seem to differentiate between low- and high-cost fire years. Maps of forecast levels of expenditures provide managers with a spatial representation of where costly fires are most likely to occur. Additionally, the statistical models provide scientists with a tool for evaluating the skill of spatially explicit fire risk products.
Additional keywords: fire simulations, generalised Pareto distribution, hydroclimate, logistic regression, moisture deficit, spline functions.
ReferencesAbt KL, Prestemon JP, Gebert KM (2008) Forecasting wildfire suppression expenditures for the United States Forest Service. In ‘The Economics of Forest Disturbances: Wildfires, Storms and Invasive Species’. (Eds TP Holmes, JP Prestemon, KL Abt) pp. 341–360. (Springer Publishing: New York)
Abt KL, Prestemon JP, Gebert KM (2009) Wildfire suppression cost forecasts for the US Forest Service. Journal of Forestry 107, 173–178.
Bachelet D, Daly C, Lenihan JM, Neilson R, Parton W, Ojima D (2000) Interactions between fire, grazing, and climate change at Wind Cave National Park, SD. Ecological Modelling 134, 229–244.
| Interactions between fire, grazing, and climate change at Wind Cave National Park, SD.CrossRef | 1:CAS:528:DC%2BD3cXntFyis70%3D&md5=74d007e86e51c837b932544347597d19CAS |
Brillinger DR (1993) Earthquake risk and insurance. Environmetrics 4, 1–21.
| Earthquake risk and insurance.CrossRef |
Canton-Thompson J, Thompson B, Gebert KM, Calkin DE, Donovan GH, Jones G (2006) Factors affecting fire suppression costs as identified by incident management teams. USDA Forest Service, Rocky Mountain Research Station, Research Note RMRS-RN-30. (Fort Collins, CO)
Canton-Thompson J, Gebert KM, Thompson B, Jones JG, Calkin DE, Donovan G (2008) External human factors in incident management team decision-making and their effect on large fire suppression expenditures. Journal of Forestry 106, 416–424.
Cumming SG (2001) A parametric model of the fire-size distribution. Canadian Journal of Forest Research 31, 1297–1303.
| A parametric model of the fire-size distribution.CrossRef |
Davison AC (2003) ‘Statistical Models.’ (Cambridge University Press: Cambridge, UK)
Efron B, Tibshirani R (1993) ‘An Introduction to the Bootstrap.’ (Chapman & Hall: New York)
Gebert KM, Schuster EG (1999) Predicting national fire suppression expenditures. In ‘Proceedings of Symposium on Fire Economics, Planning, and Policy: Bottom Lines’. (Eds A González-Cabán, PN Omi) USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW-GTR-173, pp. 21–30. (Albany, CA)
Gebert KM, Calkin DE, Yoder J (2007) Estimating suppression expenditures for individual large wildland fires. Western Journal of Applied Forestry 22, 188–196.
Gesch DB, Larson DS (1996) Techniques for development of global 1-km digital elevation models. In ‘Pecora Thirteen, Human Interactions with the Environment–Perspectives from Space’, 20–22 August 1996, Sioux Falls, SD. (CD-ROM) (American Society for Photogrammetry & Remote Sensing: Bethesda, MD)
Hamlet AF, Lettenmaier DP (2005) Production of temporally consistent gridded precipitation and temperature fields for the continental US. Journal of Hydrometeorology 6, 330–336.
| Production of temporally consistent gridded precipitation and temperature fields for the continental US.CrossRef |
Hansen MC, DeFries RS, Townshend JRG, Sohlberg R (2000) Global land cover classification at 1km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21, 1331–1364.
Hastie TJ, Tibshirani R, Friedman J (2001) ‘The Elements of Statistical Learning: Data Mining, Inference, and Prediction.’ (Springer: New York)
Hastings NAJ, Peacock JB (1975) ‘Statistical Distributions.’ (Butterworth & Co: London)
Holmes TP, Hugget RJ, Westerling AL (2008) Statistical Analysis of Large Wildfires. In ‘Economics of Forest Disturbance: Wildfires, Storms, and Invasive Species’, Forestry Sciences series, Vol. 79. (Eds TP Holmes, JP Prestemon, KL Abt) pp. 59–77. (Springer: Dordrecht, the Netherlands)
Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research 99, 14 415–14 428.
| A simple hydrologically based model of land surface water and energy fluxes for general circulation models.CrossRef |
Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based data set of land surface fluxes and states for the conterminous United States. Journal of Climate 15, 3237–3251.
| A long-term hydrologically based data set of land surface fluxes and states for the conterminous United States.CrossRef |
Mitchell KE, Lohmann D, Houser PR, Wood EF, Schaake JC, Robock A, Cosgrove BA, Sheffield J, Duan Q, Luo L, Higgins RW, Pinker RT, Tarpley JD, Lettenmaier DP, Marshall CH, Entin JK, Pan M, Shi W, Koren V, Meng J, Ramsay BH, Bailey AA (2004) The multiinstitution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. Journal of Geophysical Research 109, D07S90
| The multiinstitution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system.CrossRef |
Monteith JL (1965) Evaporation and environment. In ‘Symposium of the Society for Experimental Biology, The State and Movement of Water in Living Organisms’, Vol. 19. (Ed. GE Fogg) pp. 205–234. (Academic Press, Inc.: New York)
Moritz MA (1997) Analyzing extreme disturbance events: fire in Los Padres National Forest. Ecological Applications 7, 1252–1262.
| Analyzing extreme disturbance events: fire in Los Padres National Forest.CrossRef |
Penman HL (1948) Natural evaporation from open water, bare soil, and grass. Proceedings of the Royal Society of London. Series A 193, 120–146.
Preisler HK, Westerling AL (2007) Statistical model for forecasting monthly large wildfire events in western United States. Journal of Applied Meteorology and Climatology 46, 1020–1030.
| Statistical model for forecasting monthly large wildfire events in western United States.CrossRef |
Preisler HK, Chen SC, Fujioka F, Benoit JW, Westerling AL (2008) Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire 17, 305–316.
| Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices.CrossRef |
Prestemon JP, Abt KL, Gebert KM (2008) Suppression cost forecasts in advance of wildfire seasons. Forest Science 54, 381–396.
R Development Core Team (2008) R: a language and environment for statistical computing. (R Foundation for Statistical Computing: Vienna, Austria) Available at http://www.R-project.org [Verified 5 April 2011]
Ramesh NI (2005) Semi-parametric analysis of extreme forest fires. Forest Biometry, Modeling and Information Sciences 1, 1–10. Available at http://cms1.gre.ac.uk/conferences/iufro/fbmis/A/5_1_RameshNI_1.pdf [Verified 5 April 2011]
Schoenberg FP, Peng R, Woods J (2003) On the distribution of wildfire sizes. Environmetrics 14, 583–592.
| On the distribution of wildfire sizes.CrossRef |
United States Senate (1998) The Congressional Budget Process: An Explanation. Committee Print. S. Prt. 105–67. (Government Printing Office: Washington, DC)
Verdin KL, Greenlee SK (1996) Development of continental scale digital elevation models and extraction of hydrographic features. In ‘Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling’, 21–26 January 1996, Santa Fe, NM. (CD-ROM) (NCGIA Publications: Santa Barbara, CA)
Westerling AL, Bryant BP (2008) Climate change and wildfire in California. Climatic Change 87, 231–249.
| Climate change and wildfire in California.CrossRef |
Westerling AL, Gershunov A, Cayan DR, Barnett TP (2002) Long lead statistical forecasts of western US wildfire area burned. International Journal of Wildland Fire 11, 257–266.
| Long lead statistical forecasts of western US wildfire area burned.CrossRef |
Westerling AL, Brown TJ, Gershunov A, Cayan DR, Dettinger MD (2003) Climate and wildfire in the western United States. Bulletin of the American Meteorological Society 84, 595–604.
| Climate and wildfire in the western United States.CrossRef |
Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increases western US forest wildfire activity. Science 313, 940–943.
| Warming and earlier spring increases western US forest wildfire activity.CrossRef | 1:CAS:528:DC%2BD28XotFCitbo%3D&md5=0c50ad3f867d0006079217644781016cCAS | 16825536PubMed |
Westerling AL, Bryant BP, Preisler HK, Hidalgo HG, Das T (2009) Climate change, growth and California wildfire. Public Interest Energy Research CEC-500-2009-046-F. (California Energy Commission: Sacramento, CA) Available at http://www.energy.ca.gov/2009publications/CEC-500-2009-046/CEC-500-2009-046-F.PDF [Verified 19 April 2011]
Wood AW, Lettenmaier DP (2006) A testbed for new seasonal hydrologic forecasting approaches in the western US. Bulletin of the American Meteorological Society 87, 1699–1712.
| A testbed for new seasonal hydrologic forecasting approaches in the western US.CrossRef |