Assessing the distribution patterns of wildfire sizes in Mississippi, USA
Changyou Sun A B and Branden Tolver AA Department of Forestry, Mississippi State University, Mississippi State, MS 39762, USA.
B Corresponding author. Email: csun@cfr.msstate.edu
International Journal of Wildland Fire 21(5) 510-520 https://doi.org/10.1071/WF10107
Submitted: 15 September 2010 Accepted: 26 October 2011 Published: 18 May 2012
Abstract
Wildland fires can produce dramatic ecological and economic impacts. The objective of this study was to analyse the temporal and spatial distribution patterns of wildland fires using 64 474 fire records in Mississippi, collected between 1991 and 2007. The methodology employed was descriptive statistics and extreme value statistics. The analyses were conducted for all the fires combined, and also by year, period, ecoregion and cause separately. Wildland fires occurred most frequently between February and May, with more than half of all the fires occurring in that period. The ecoregion of outer coastal plain mixed-forest province had more fire occurrences and the ecoregion of south-eastern mixed-forestry province had more catastrophic fires. By fire cause, debris and incendiary fires combined were responsible for 89.6% of the area burned. The top 10% of the largest fires burned 58.8% of the total area. The extreme value statistics revealed that wildfires in Mississippi displayed a generalised Pareto distribution. Based on predictions from the peaks-over-threshold models, the largest wildland fire in Mississippi within the next 10 years could burn 2171 ha. These outcomes can help landowners and government agencies make better decisions related to forest investments, fire suppression and budget planning.
Additional keywords: extremal index, generalised Pareto distribution, peaks over threshold, return level.
References
Alvarado E, Sandberg DV, Pickford SG (1998) Modeling large forest fires as extreme events. Northwest Science 72, 66–75.Bailey RG (1995) Description of the ecoregions of the United States. USDA Forest Service, Publication 1391. (Washington, DC)
Beirlant J, Goegebeur Y, Segers J, Teugels J (2004) ‘Statistics of Extremes: Theory and Applications.’ (Wiley: San Francisco, CA)
Beverly JL, Martell DL (2005) Characterizing extreme fire and weather events in the Boreal Shield ecozone of Ontario. Agricultural and Forest Meteorology 133, 5–16.
| Characterizing extreme fire and weather events in the Boreal Shield ecozone of Ontario.Crossref | GoogleScholarGoogle Scholar |
Bivand RS, Pebesma EJ, Gomez-Rubio V (2008) ‘Applied Spatial Data Analysis with R.’ (Springer: London)
Brewer S, Rogers C (2006) Relationships between prescribed burning and wildfire occurrence and intensity in pine–hardwood forests in north Mississippi, USA. International Journal of Wildland Fire 15, 203–211.
| Relationships between prescribed burning and wildfire occurrence and intensity in pine–hardwood forests in north Mississippi, USA.Crossref | GoogleScholarGoogle Scholar |
Coles S (2001) ‘An Introduction to Statistical Modeling of Extreme Values.’ (Springer-Verlag: London)
Cui W, Perera AH (2008) What do we know about forest fire size distribution, and why is this knowledge useful for forest management? International Journal of Wildland Fire 17, 234–244.
| What do we know about forest fire size distribution, and why is this knowledge useful for forest management?Crossref | GoogleScholarGoogle Scholar |
Dalgaard P (2008) ‘Introductory Statistics with R.’ (Springer: London)
de Zea Bermudez P, Mendes J, Pereira MMC, Turkman KF, Vasconcelos MJP (2009) Spatial and temporal extremes of wildfire sizes in Portugal (1984–2004). International Journal of Wildland Fire 18, 983–991.
| Spatial and temporal extremes of wildfire sizes in Portugal (1984–2004).Crossref | GoogleScholarGoogle Scholar |
Embrechts P, Kluppelberg C, Mikosch T (2003) ‘Modelling Extremal Events for Insurance and Finance.’ (Springer-Verlag: Berlin, Germany)
Fernandes PM, Botelho HS (2003) A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire 12, 117–128.
| A review of prescribed burning effectiveness in fire hazard reduction.Crossref | GoogleScholarGoogle Scholar |
Gilleland E, Katz RW (2005) ‘Extremes Toolkit (extRemes): Weather and Climate Applications of Extreme Value Statistics.’ (National Center for Atmospheric Research: Boulder, CO) Available at http://www.assessment.ucar.edu/toolkit/ [Verified 20 April 2012]
Grala K, Cooke WH (2010) Spatial and temporal characteristics of wildfires in Mississippi, USA. International Journal of Wildland Fire 19, 14–28.
| Spatial and temporal characteristics of wildfires in Mississippi, USA.Crossref | GoogleScholarGoogle Scholar |
Henderson JE, Munn IA, Perez-Verdin G, Grebner DL (2008). Forestry in Mississippi: the impact of the forest products industry on the post-Katrina Mississippi economy – an input–output analysis. Mississippi State University, Forest and Wildlife Research Center, Research Bulletin FO 374. (Mississippi State, MS)
Holmes TP, Huggett RJ, Westerling AJ (2008). Statistical analysis of large wildfires. In ‘The Economics of Forest Disturbances: Wildlife, Storms, and Invasive Species’. (Eds TP Holmes, JP Prestemon, KL Abt) pp. 59–77. (Springer: London)
Kotz S, Nadarajah S (2000) ‘Extreme Value Distributions: Theory and Applications.’ (Imperial College Press: London)
Li C, Corns I, Yang R (1999) Fire frequency and size distribution under natural conditions: a new hypothesis. Landscape Ecology 14, 533–542.
Malamud BD, Morein G, Turcotte DL (1998) Forest fires: an example of self-organized critical behavior. Science 281, 1840–1842.
| Forest fires: an example of self-organized critical behavior.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXmtVKhsLk%3D&md5=09493e0bc56308c8452b58f6f56eb475CAS |
Malamud BD, Millington JDA, Perry GLW (2005) Characterizing wildfire regimes in the United States. Proceedings of the National Academy of Sciences of the United States of America 102, 4694–4699.
| Characterizing wildfire regimes in the United States. Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXjt1Oiu74%3D&md5=ae57a8687543f158ff2ccec37595b687CAS |
Mississippi Forestry Association (2010) Mississippi Forest Facts. (Mississippi Forestry Association: Jackson, MS) Available at http://www.mfc.ms.gov/facts-n-data.php [Verified 1 August 2010]
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 | GoogleScholarGoogle Scholar |
R Development Core Team (2011) R: a language and environment for statistical computing. (R Foundation for Statistical Computing: Vienna, Austria) Available at http://www.r-project.org [Verified 15 April 2011]
Ramesh NI (2005) Semi-parametric analysis of extreme forest fires. Forest Biometry Modelling and Information Sciences 1, 1–10.
Reed WJ, McKelvey KS (2002) Power-law behaviour and parametric models for the size-distribution of forest fires. Ecological Modelling 150, 239–254.
| Power-law behaviour and parametric models for the size-distribution of forest fires.Crossref | GoogleScholarGoogle Scholar |
Schoenberg FP, Peng R, Woods J (2003) On the distributions of wildfire sizes. Environmetrics 14, 583–592.
| On the distributions of wildfire sizes.Crossref | GoogleScholarGoogle Scholar |
Song W, Wang J, Satoh K, Fan W (2006) Three types of power-law distribution of forest fires in Japan. Ecological Modelling 196, 527–532.
| Three types of power-law distribution of forest fires in Japan.Crossref | GoogleScholarGoogle Scholar |
Strauss D, Bednar L, Mees R (1989) Do one percent of forest fires cause ninety-nine percent of the damage? Forest Science 35, 319–328.
US Census Bureau (2010) Statistical abstracts. Available at http://www.census.gov/prod/www/abs/statab.html [Verified 27 August 2010]
US Department of the Interior (2010) Map layers. In ‘National Atlas of the United States’. (US Department of the Interior) Available at http://www.nationalatlas.gov [Verified 10 May 2010]
Wickham H (2009) ‘ggplot2: elegant graphics for data analysis.’ (Springer: London)