Improving the uncertainty assessment of economic losses from large destructive wildfires
Bruno Guillaume A , Bernard Porterie B , Antonio Batista C , Phil Cottle D and Armand Albergel A E
+ Author Affiliations
- Author Affiliations
A ARIA Technologies, 8–10 rue de la Ferme, F-92100 Boulogne-Billancourt, France.
B Institut Universitaire des Systèmes Thermiques Industriels (IUSTI), UMR CNRS 7343, Université Aix-Marseille, 5 Rue Enrico Fermi, F-13453 Marseille Cedex 13, France.
C Federal University of Paraná, Department of Forestry Science, Curitiba, Paraná, Brazil.
D ForestRe Limited, 27 Wheel House, Burrell’s Wharf, London E14 3TA, UK.
E Corresponding author. Email: aalbergel@aria.fr
International Journal of Wildland Fire 28(6) 420-430 https://doi.org/10.1071/WF18104
Submitted: 10 July 2018 Accepted: 26 March 2019 Published: 28 May 2019
Abstract
Currently, as fire risk is considered a high-frequency and low-severity risk, actuarial and underwriting pricing and risk management methods have stuck to methods based purely on historical loss data. In the global context of both increasing fire severity with climate change and increasing wildland–urban interface area, the use of environmental-based dynamical modelling tools offers a good alternative to better evaluate fire risk. A new method is presented here that combines the raster-based fire spread model SWIFFT and a stochastic approach for generating the spatial and temporal distribution of ignition points. Monte Carlo simulations are conducted, and the uncertainties of hazard and losses are evaluated. Adapted and applied to the landscape conditions of a Brazilian plantation, it is shown to be well suited for a precise reconstruction of the fire burnt area. Finally, the uncertainty assessment of losses for this study zone is presented. We conclude by discussing this new method, which has a high level of traceable uncertainty and how fire risk insurance can deal with it, as well as the progress of future research that will benefit from this method.
Additional keywords: Brazilian forestry, fire risk insurance, fire spread, loss uncertainty calculation, raster-based model.
References
Albini FA (1981) A model for the wind-blown flame from a line fire. Combustion and Flame 43, 155–174.| A model for the wind-blown flame from a line fire.Crossref | GoogleScholarGoogle Scholar |
Batista AC (2009) Estudos sobre o comportamentodo fogo na Universidaga Federal do Paraná. In ‘Incêndios florestais no Brasil: o estado da arte’. (Eds RV Soares, AC Batista, JRS Nunes) pp. 35–52. (CMPQ: Curitiba, Brazil)
Berjak S, Hearne J (2002) An improved cellular automaton model for simulating fire in a spatially heterogeneous savanna system. Ecological Modelling 148, 133–151.
| An improved cellular automaton model for simulating fire in a spatially heterogeneous savanna system.Crossref | GoogleScholarGoogle Scholar |
Beutling A (2009) Combustiveis florestais. In ‘Incêndios florestais no Brasil: o estado da arte’. (Eds RV Soares, AC Batista, JRS Nunes) pp. 21–34. (CMPQ: Curitiba, Brasil)
Brotak EA, Reifsnyder WE (1977) An investigation of the synoptic situations associated with major wildland fires. Journal of Applied Meteorology 16, 867–870.
| An investigation of the synoptic situations associated with major wildland fires.Crossref | GoogleScholarGoogle Scholar |
Byram GM (1954) Atmospheric conditions related to blow-up fires. USDA Forest Service, Southeastern Forest and Range Experiment Station, Paper 35. (Asheville, NC, USA)
Carmel Y, Paz S, Jahashan F, Shoshany M (2009) Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management 257, 370–377.
| Assessing fire risk using Monte Carlo simulations of fire spread.Crossref | GoogleScholarGoogle Scholar |
Charney JJ, Bian X, Potter BE, Heilman WE (2003) Mesoscale simulations during the Double Trouble State Park wildfire in east-central New Jersey on June 2, 2002. In ‘Preprints of the 10th conference on mesoscale processes’, 23–27 June 2003, Portland, OR, USA. (American Meteorological Society: Boston, MA, USA)
Coleman JR, Sullivan AL (1996) A real-time computer application for the prediction of fire spread across the Australian landscape. Simulation 67, 230–240.
| A real-time computer application for the prediction of fire spread across the Australian landscape.Crossref | GoogleScholarGoogle Scholar |
Cox RM, Sontowski J, Dougherty CM (2005) An evaluation of three diagnostic wind models (CALMET, MCSCIPUF and SWIFT) with wind data from the Dipole Pride 26 field experiments. Meteorological Applications 12, 329–341.
| An evaluation of three diagnostic wind models (CALMET, MCSCIPUF and SWIFT) with wind data from the Dipole Pride 26 field experiments.Crossref | GoogleScholarGoogle Scholar |
de Gennaro M, Billaud Y, Pizzo Y, Garivait S, Loraud J-C, El Hajj M, Porterie B (2017) Real-time wildland fire spread modeling using tabulated flame properties. Fire Safety Journal 91, 872–881.
| Real-time wildland fire spread modeling using tabulated flame properties.Crossref | GoogleScholarGoogle Scholar |
Filippi JB, Bosseur F, Mari C, Lac C, Le Moigne P, Cuenot B, Veynante D, Cariolle D, Balbi J-H (2009) Coupled atmosphere–wildland fire modelling. Journal of Advances in Modeling Earth Systems 1, 210–226.
| Coupled atmosphere–wildland fire modelling.Crossref | GoogleScholarGoogle Scholar |
Filippi JB, Pialat X, Clements CB (2013) Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the Fire Flux experiment. Proceedings of the Combustion Institute 34, 2633–2640.
| Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the Fire Flux experiment.Crossref | GoogleScholarGoogle Scholar |
Finney MA (1998) FARSITE: fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Ogden, UT, USA)
Flannigan MD, Krawchuk MA, De Groot WJ, Wotton BM, Gowman LM (2009) Implications of changing climate for global wildland fire. International Journal of Wildland Fire 18, 483–507.
| Implications of changing climate for global wildland fire.Crossref | GoogleScholarGoogle Scholar |
Friedman DG (1984) Natural hazard risk assessment for an insurance program. The Geneva Papers on Risk and Insurance, 9, No. 30, from the ‘Proceedings of the First Meeting of the International Working Group on Natural Disasters and Insurance (I)’ pp. 57–128. (Geneva Association: Geneva, Switzerland)
Gellie N, Gibos K, Mattingley G, Wells T, Salkin O (2012) Reconstructing the spread and behaviour of the February 2009 Victorian Fires. In ‘Proceedings of the 3rd fire behaviour and fuels conference’, 25–29 October 2010, Spokane, WA, USA. (International Association of Wildland Fire: Missoula, MN, USA)
Green D, Tridgell A, Gill A (1990) Interactive simulation of bushfires in heterogeneous fuels. Mathematical and Computer Modelling 13, 57–66.
| Interactive simulation of bushfires in heterogeneous fuels.Crossref | GoogleScholarGoogle Scholar |
Hargrove WW, Gardner RH, Turner MG, Romme WH, Despain DG (2000) Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135, 243–263.
| Simulating fire patterns in heterogeneous landscapes.Crossref | GoogleScholarGoogle Scholar |
Hellmuth ME, Moorhead A, Thomson MC, Williams J (Eds) (2007) Climate risk management in Africa: learning from practice. International Research Institute for Climate and Society (IRI), Columbia University. (New York, NY, USA)
Kourtz P, O’Regan W (1971) A model for a small forest fire to simulate burned and burning areas for use in a detection model. Forest Science 17, 163–169.
Linnerooth-Bayer J, Mechler R (2006) Insurance for assisting adaptation to climate change in developing countries: a proposed strategy. Climate Policy 6, 621–636.
| Insurance for assisting adaptation to climate change in developing countries: a proposed strategy.Crossref | GoogleScholarGoogle Scholar |
Major JA (1999a) Uncertainty in catastrophe models part I: what is it and where does it come from? (February) In ‘Financing Risk & Reinsurance’. (International Risk Management Institute, Inc.: Dallas, TX, USA)
Major JA (1999b) Uncertainty in catastrophe models part II: how bad is it? (March) In ‘Financing Risk & Reinsurance’. (International Risk Management Institute, Inc.: Dallas, TX, USA)
Malamud B, Morein GL, Turcotte D (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 | 9743494PubMed |
Moritz MA, Parisien M-A, Batllori E, Krawchuk MA, Van Dorn J, Ganz DJ, Hayhoe K (2012) Climate change and disruptions to global fire activity. Ecosphere 3, 1–22.
| Climate change and disruptions to global fire activity.Crossref | GoogleScholarGoogle Scholar |
Oliveira DS, Batista AC, Soares V, Slutter CR (2002) Fire risk mapping for pine and eucalyptus stands in Três Barras, State of Santa Catarina, Brazil. In ‘Forest fire research & wildland fire safety: proceedings of the IV International Conference on Forest Fire Research [and] 2002 Wildland Fire Safety Summit, 18–23 November 2002, Luso, Coimbra, Portugal’. (Ed. DX Viegas) (Millpress: Rotterdam, the Netherlands)
Osgood DE, Suarez P, Hansen J, Carriquiry M, Mishra A (2008) Integrating seasonal forecast and insurance for adaptation among subsistence farmers: the case of Malawi. World Bank Policy Research Working Paper no. WPS 4651. (Washington, DC, USA)
Pausas JG, Keeley JE (2009) A burning story: the role of fire in the history of life. Bioscience 59, 593–601.
| A burning story: the role of fire in the history of life.Crossref | GoogleScholarGoogle Scholar |
Peterson SH, Morais ME, Carlson JM, Dennison PE, Roberts DA, Moritz MA, Weise DR (2009) Using HFire for spatial modeling of fire in shrublands. USDA Forest Service, Pacific Southwest Research Station, Research Paper PSW-RP-259. (Albany, CA, USA)
Riitters KH, Wickham JD (2003) How far to the nearest road? Frontiers in Ecology and the Environment 1, 125–129.
| How far to the nearest road?Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station Research Paper INT-115. (Ogden, UT, USA)
Salis M, Ager AA, Arca B, Finney MA, Bacciu V, Duce P, Spano D (2013) Assessing exposure of human and ecological values to wildfire in Sardinia, Italy. International Journal of Wildland Fire 22, 549–565.
| Assessing exposure of human and ecological values to wildfire in Sardinia, Italy.Crossref | GoogleScholarGoogle Scholar |
Scott JH, Burgan HRE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-153. (Fort Collins, CO, USA)
Scott JH, Reinhardt ED (2001) Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-29. (Fort Collins, CO, USA)
Silva J, Ribeiro C, Guedes R (2007) Roughness length classification of CORINE Land Cover classes. In ‘Proceedings of the European Wind energy conference’, 7–10 May 2007, Milan, Italy. Available at https://www.researchgate.net/publication/228474930_Roughness_length_classification_of_Corine_Land_Cover_classes [Verified 21 May 2019]
Simpson CC, Sharples JJ, Evans JP (2014) Resolving vorticity-driven lateral fire spread using the WRF-Fire coupled atmosphere–fire numerical model. Natural Hazards and Earth System Sciences 14, 2359–2371.
| Resolving vorticity-driven lateral fire spread using the WRF-Fire coupled atmosphere–fire numerical model.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models. International Journal of Wildland Fire 18, 387–403.
| Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models.Crossref | GoogleScholarGoogle Scholar |
Tetto AF, Soares RV, Batista AC, Wendling WT (2012) Fire suppression efficiency in the Fazenda Monte Alegre, Paraná, from 1965 to 2009. Scientia Forestalis 40, 483–489.
Tolhurst K, Shields B, Chong D (2008) Phoenix: development and application of a bushfire risk management tool. Australian Journal of Emergency Management 23, 47–54.
Tymstra C, Bryce RW, Wotton BM, Armitage OB (2009) Development and Structure of Prometheus: the Canadian Wildland Fire Growth Simulation Model. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Information Report NOR-X-417. (Edmonton, AB, Canada)
Van Wagner CE (1977) Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7, 23–34.
| Conditions for the start and spread of crown fire.Crossref | GoogleScholarGoogle Scholar |
Vasconcelos M, Guertin D (1992) FIREMAP – simulation of fire growth with a geographic information system. International Journal of Wildland Fire 2, 87–96.
| FIREMAP – simulation of fire growth with a geographic information system.Crossref | GoogleScholarGoogle Scholar |
Weick KE, Sutcliffe KM (2001) ‘Managing the unexpected: assuring high performance in an age of complexity.’ (Jossey-Bass: San Francisco, CA, USA).
Weisheimer A, Palmer TN (2014) On the reliability of seasonal climate forecasts. Journal of the Royal Society, Interface 11, 20131162
| On the reliability of seasonal climate forecasts.Crossref | GoogleScholarGoogle Scholar | 24789559PubMed |
Werth PA, Potter BE, Clements CB, Finney MA, Goodrick SL, Alexander ME, Cruz MG, Forthofer JM, McAllister SS (2011) Synthesis of knowledge of extreme fire behavior: Vol. 1 for fire managers. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-854. (Portland, OR, USA)
Woo H, Chung W, Graham JM, Lee B (2017) Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation. International Journal of Wildland Fire 26, 789–805.
| Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation.Crossref | GoogleScholarGoogle Scholar |
Yassemi S, Dragićević S, Schmidt M (2008) Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecological Modelling 210, 71–84.
| Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour.Crossref | GoogleScholarGoogle Scholar |
