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Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

A web-based wildfire simulator for operational applications

Bachisio Arca A D , Tiziano Ghisu B , Marcello Casula A , Michele Salis A C and Pierpaolo Duce A
+ Author Affiliations
- Author Affiliations

A Institute of Biometeorology, National Research Council, Sassari, 07100, Italy.

B Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, 09123, Italy.

C Euro-Mediterranean Centre on Climate Change, Impacts on Agriculture Forests and Ecosystem Services (IAFES) Division, Sassari, 07100, Italy.

D Corresponding author: b.arca@ibimet.cnr.it

International Journal of Wildland Fire 28(2) 99-112 https://doi.org/10.1071/WF18078
Submitted: 22 May 2018  Accepted: 5 December 2018   Published: 5 February 2019

Abstract

Wildfire simulators and decision support systems can assist the incident command teams in charge of tactical wildfire suppression. This paper presents a web-based wildfire simulator developed to provide real-time support for wildfire management. The paper describes the overall software architecture, the modelling chain characteristics and the results produced by the simulator considering a set of actual wildfires that occurred in the island of Sardinia, Italy. The simulator consists of a graphical user interface that deals with data input–output management, a mass-consistent model devoted to the downscaling of wind fields, and a module that provides a spatially explicit representation of wildfire propagation. The simulator is a client‐server application that is operated through a web-based graphical user interface that leaves the computational work to a dedicated server; most of the code is parallelised in order to minimise computational run-time. The validation phase demonstrated the capabilities of the simulator in providing wildfire predictions with a substantial agreement with actual wildfires, and a computational cost suitable for faster than real-time applications. The simulator is proposed as a tool to provide assistance to civil protection and fire management agencies during the incident response phase. The simulator is also appropriate for the training of personnel.

Additional keywords: on-line simulation, parallelisation, wildfire management, wildfire risk.


References

Ager AA, Finney MA, Kerns BK, Maffei H (2007) Modeling wildfire risk to northern spotted owl (Strix occidentalis caurina) habitat in central Oregon, USA. Forest Ecology and Management 246, 45–56.
Modeling wildfire risk to northern spotted owl (Strix occidentalis caurina) habitat in central Oregon, USA.Crossref | GoogleScholarGoogle Scholar |

Agresti A (1996) ‘An introduction to categorical data analysis.’ (Wiley: New York, NY, USA).

Alcasena FJ, Salis M, Ager AA, Arca B, Molina D, Spano D (2015) Assessing landscape-scale wildfire exposure for highly valued resources in a Mediterranean area. Environmental Management 55, 1200–1216.
Assessing landscape-scale wildfire exposure for highly valued resources in a Mediterranean area.Crossref | GoogleScholarGoogle Scholar | 25613434PubMed |

Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-GTR-122. (Ogden, UT, USA)

Arca B, Duce P, Laconi M, Pellizzaro G, Salis M, Spano D (2007) Evaluation of FARSITE simulator in Mediterranean maquis. International Journal of Wildland Fire 16, 563–572.
Evaluation of FARSITE simulator in Mediterranean maquis.Crossref | GoogleScholarGoogle Scholar |

Arca B, Bacciu V, Pellizzaro G, Salis M, Ventura A, Brundu G, Duce P, Spano D (2009) Fuel model mapping by Ikonos imagery to support spatially explicit fire simulators. In ‘Proceedings of the VII international EARSeL workshop’, 2–5 September 2009, Matera, Italy. (Eds Emilio Chuvieco, Rosa Lasaponara) pp. 75–78. (Il Segno – Arti grafiche: Potenza, Italy).

Augustijn-Beckers EW, Flacke J, Retsios B (2010) Investigating the effect of different pre-evacuation behavior and exit choice strategies using agent-based modeling. Procedia Engineering 3, 23–35.
Investigating the effect of different pre-evacuation behavior and exit choice strategies using agent-based modeling.Crossref | GoogleScholarGoogle Scholar |

Beaucage P, Brower MC, Tensen J (2014) Evaluation of four numerical wind flow models for wind resource mapping. Wind Energy 17, 197–208.
Evaluation of four numerical wind flow models for wind resource mapping.Crossref | GoogleScholarGoogle Scholar |

Bogdos N, Manolakos ES (2013) A tool for simulation and geo-animation of wildfires with fuel editing and hotspot monitoring capabilities. Environmental Modelling & Software 46, 182–195.
A tool for simulation and geo-animation of wildfires with fuel editing and hotspot monitoring capabilities.Crossref | GoogleScholarGoogle Scholar |

Bova AS, Mell WE, Hoffman CM (2016) A comparison of level set and marker methods for the simulation of wildland fire front propagation. International Journal of Wildland Fire 25, 229–241.
A comparison of level set and marker methods for the simulation of wildland fire front propagation.Crossref | GoogleScholarGoogle Scholar |

Calkin DC, Finney MA, Ager AA, Thompson MP, Gebert KM (2011) Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making. Forest Policy and Economics 13, 378–389.
Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making.Crossref | GoogleScholarGoogle Scholar |

Cochrane MA, Moran CJ, Wimberly MC, Baer AD, Finney MA, Beckendorf KL, Eidenshink J, Zhu Z (2012) Estimation of wildfire size and risk changes due to fuels treatments. International Journal of Wildland Fire 21, 357–367.
Estimation of wildfire size and risk changes due to fuels treatments.Crossref | GoogleScholarGoogle Scholar |

Coleman J, Sullivan A (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 |

Donovan GH, Noordijk P (2005) Assessing the accuracy of wildland fire situation analysis (WFSA) fire size and suppression cost estimates. Journal of Forestry 103, 2003–2006.

Eklund P (2001) A distributed spatial architecture for bush fire simulation. International Journal of Geographical Information Science 15, 363–378.
A distributed spatial architecture for bush fire simulation.Crossref | GoogleScholarGoogle Scholar |

Ferragut L, Asensio M, Simon J (2011) High definition local adjustment model of 3D wind fields performing only 2D computations. International Journal for Numerical Methods in Biomedical Engineering 27, 510–523.
High definition local adjustment model of 3D wind fields performing only 2D computations.Crossref | GoogleScholarGoogle Scholar |

Ferragut L, Asensio MI, Cascón JM, Prieto D (2014) A wildland fire physical model well suited to data assimilation. Pure and Applied Geophysics 172, 121–139.
A wildland fire physical model well suited to data assimilation.Crossref | GoogleScholarGoogle Scholar |

Filippi J-B, Morandini F, Balbi JH, Hill DR (2010) Discrete event front-tracking simulation of a physical fire-spread model. Simulation 86, 629–646.
Discrete event front-tracking simulation of a physical fire-spread model.Crossref | GoogleScholarGoogle Scholar |

Filippi J-B, Bosseur F, Pialat X, Santoni P-A, Strada S, Mari C (2011) Simulation of coupled fire/atmosphere interaction with the MesoNH–ForeFire models. Journal of Combustion 2011, 1–13.
Simulation of coupled fire/atmosphere interaction with the MesoNH–ForeFire models.Crossref | GoogleScholarGoogle Scholar |

Filippi JB, Mallet V, Nader B (2014a) Representation and evaluation of wildfire propagation simulations. International Journal of Wildland Fire 23, 46–57.
Representation and evaluation of wildfire propagation simulations.Crossref | GoogleScholarGoogle Scholar |

Filippi J-B, Mallet V, Nader B (2014b) Evaluation of forest fire models on a large observation database. Natural Hazards and Earth System Sciences 14, 3077–3091.
Evaluation of forest fire models on a large observation database.Crossref | GoogleScholarGoogle Scholar |

Finney MA (2002) Fire growth using minimum travel time methods. Canadian Journal of Forest Research 32, 1420–1424.
Fire growth using minimum travel time methods.Crossref | GoogleScholarGoogle Scholar |

Finney MA (2004) FARSITE: Fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Fort Collins, CO, USA)

Finney MA (2005) The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management 211, 97–108.
The challenge of quantitative risk analysis for wildland fire.Crossref | GoogleScholarGoogle Scholar |

Finney MA, Grenfell IC, McHugh CW, Seli RC, Trethewey D, Stratton RD, Brittain S (2011) A method for ensemble wildland fire simulation. Environmental Modeling and Assessment 16, 153–167.
A method for ensemble wildland fire simulation.Crossref | GoogleScholarGoogle Scholar |

Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing 70, 627–633.
Thematic map comparison: evaluating the statistical significance of differences in classification accuracy.Crossref | GoogleScholarGoogle Scholar |

Forthofer JM, Butler BW, McHugh CW, Finney MA, Bradshaw LS, Stratton RD, Shannon KS, Wagenbrenner NS (2014a) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part ii. An exploratory study of the effect of simulated winds on fire growth simulations. International Journal of Wildland Fire 23, 982–994.
A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part ii. An exploratory study of the effect of simulated winds on fire growth simulations.Crossref | GoogleScholarGoogle Scholar |

Forthofer JM, Butler BW, Wagenbrenner NS (2014b) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. International Journal of Wildland Fire 23, 969–981.
A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements.Crossref | GoogleScholarGoogle Scholar |

Gebert KM, Calkin DE, Huggett RJ, Abt KL (2008) Economic analysis of Federal wildfire management programs. In ‘The economics of forest disturbances’. (Eds Thomas P. Holmes, Jeffrey P. Prestemon, Karen L. Abt) pp. 295–322. (Springer: Dordrecht, Netherlands)

Ghisu T, Arca B, Pellizzaro G, Duce P (2014) A level-set algorithm for simulating wildfire spread. CMES – Computer Modeling in Engineering & Sciences 102, 83–102.

Ghisu T, Arca B, Pellizzaro G, Duce P (2015) An optimal cellular automata algorithm for simulating wildfire spread. Environmental Modelling & Software 71, 1–14.
An optimal cellular automata algorithm for simulating wildfire spread.Crossref | GoogleScholarGoogle Scholar |

GrADS (2008). Grid Analysis and Display System. Available at http://cola.gmu.edu/grads/ [Verified 14 September 2018]

Hand MS, Gebert KM, Liang J, Calkin DE, Thompson MP, Zhou M (2014) ‘Economics of wildfire management: the development and application of suppression expenditure models.’ (Springer-Verlag: New York, USA)

Ingalsbee T, Raja U (2015) The rising costs of wildfire suppression and the case for ecological fire use. In ‘The Ecological Importance of Mixed-Severity Fires’. (Eds DellaSala, Dominick A and Hanson, Chad T) pp. 348-371 (Elsevier: Amsterdam, Netherlands)

Jahdi R, Salis M, Darvishsefat AA, Alcasena F, Mostafavi MA, Etemad V, Lozano OM, Spano D (2016) Evaluating fire modelling systems in recent wildfires of the Golestan national park, Iran. Forestry 89, 136–149.
Evaluating fire modelling systems in recent wildfires of the Golestan national park, Iran.Crossref | GoogleScholarGoogle Scholar |

Kalabokidis K, Xanthopoulos G, Moore P, Caballero D, Kallos G, Llorens J, Roussou O, Vasilakos C (2012) Decision support system for forest fire protection in the Euro-Mediterranean region. European Journal of Forest Research 131, 597–608.
Decision support system for forest fire protection in the Euro-Mediterranean region.Crossref | GoogleScholarGoogle Scholar |

Kalabokidis K, Athanasis N, Gagliardi F, Karayiannis F, Palaiologou P, Parastatidis S, Vasilakos C (2013) Virtual Fire: a web-based GIS platform for forest fire control. Ecological Informatics 16, 62–69.
Virtual Fire: a web-based GIS platform for forest fire control.Crossref | GoogleScholarGoogle Scholar |

Kochanski AK, Jenkins MA, Mandel J, Beezley JD, Krueger SK (2013) Real time simulation of 2007 Santa Ana fires. Forest Ecology and Management 294, 136–149.
Real time simulation of 2007 Santa Ana fires.Crossref | GoogleScholarGoogle Scholar |

Lautenberger C (2013) Wildland fire modeling with an Eulerian level set method and automated calibration. Fire Safety Journal 62, 289–298.
Wildland fire modeling with an Eulerian level set method and automated calibration.Crossref | GoogleScholarGoogle Scholar |

López AS, San-Miguel-Ayanz J, Burgan RE (2002) Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing 23, 2713–2719.
Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale.Crossref | GoogleScholarGoogle Scholar |

Mandel J, Beezley JD, Kochanski AK (2011) Coupled atmosphere–wildland fire modeling with WRF 3.3 and SFIRE 2011. Geoscientific Model Development 4, 591–610.
Coupled atmosphere–wildland fire modeling with WRF 3.3 and SFIRE 2011.Crossref | GoogleScholarGoogle Scholar |

Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16, 1–22.
A physics-based approach to modelling grassland fires.Crossref | GoogleScholarGoogle Scholar |

National Interagency Fire Center (2017) National Interagency Fire Center: Statistics 2011(10/19). Available at https://www.predictiveservices.nifc.gov/intelligence/2017_statssumm/2017Stats&Summ.html [Verified 14 September 2018]

NCO (2017). NetCDF Operator. Available at http://nco.sourceforge.net/ [Verified 14 September 2018]

Noble IR, Gill AM, Bary GAV (1980) McArthur’s fire danger meters expressed as equations. Australian Journal of Ecology 5, 201–203.
McArthur’s fire danger meters expressed as equations.Crossref | GoogleScholarGoogle Scholar |

Nudda G, Botti P, Tola F, Chessa M, Diana G, Cocco S, Masnata C, Congiu F, Delogu G, Giannasi M, Mavuli S, Muntoni G, Pirisi A, Sattanino A, Pulina G, Casula A, Patteri G, Bianco G, Fiori M, Capece P, Salis M, Sirca C, Del Giudice L, Scarpa C, Lozano O, Duce P, Arca B, Pellizzaro G, Bacciu V (2015) ‘Rapporto sugli incendi boschivi e rurali in Sardegna.’ (Regione Autonoma della Sardegna, Assessorato alla Difesa dell’Ambiente: Cagliari, Italy).

Pacheco AP, Claro J, Fernandes PM, de Neufville R, Oliveira TM, Borges JG, Rodrigues JC (2015) Cohesive fire management within an uncertain environment: a review of risk handling and decision support systems. Forest Ecology and Management 347, 1–17.
Cohesive fire management within an uncertain environment: a review of risk handling and decision support systems.Crossref | GoogleScholarGoogle Scholar |

Palaiologou P, Ager AA, Max N-P, Evers CR, Kostas K (2018) Using transboundary wildfire exposure assessments to improve fire management programs: a case study in Greece. International Journal of Wildland Fire 27, 501–513.
Using transboundary wildfire exposure assessments to improve fire management programs: a case study in Greece.Crossref | GoogleScholarGoogle Scholar |

Papadopoulos GD, Pavlidou F-N (2011) A comparative review on wildfire simulators. IEEE Systems Journal 5, 233–243.
A comparative review on wildfire simulators.Crossref | GoogleScholarGoogle Scholar |

Prieto Herráez D, Sevilla MIA, Canals LF, Barbero JMC, Rodríguez AM (2017) A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model. International Journal of Geographical Information Science 31, 2142–2163.
A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model.Crossref | GoogleScholarGoogle Scholar |

Ramírez J, Monedero S, Buckley D (2011) New approaches in fire simulations analysis with wildfire analyst. In ‘The 5th international wildland fire conference’, 9–13 May 2011, Sun City, South Africa. Available at http://dx.doi.org/10.13140/2.1.2045.7766 [Verified 14 September 2018]

Ratto C, Festa R, Romeo C, Frumento O, Galluzzi M (1994) Mass-consistent models for wind fields over complex terrain: the state of the art. Environmental Software 9, 247–268.
Mass-consistent models for wind fields over complex terrain: the state of the art.Crossref | GoogleScholarGoogle Scholar |

Regione Autonoma della Sardegna (2008) Sardinian Land Use Map. Available at http://www.sardegnageoportale.it [Verified 14 September 2018].

Rodriguez-Aseretto D, de Rigo D, Di Leo M, Cortés A, San-Miguel-Ayanz J (2013) A data-driven model for large wildfire behaviour prediction in Europe. Procedia Computer Science 18, 1861–1870.
A data-driven model for large wildfire behaviour prediction in Europe.Crossref | GoogleScholarGoogle Scholar |

Ross DG, Smith IN, Manins PC, Fox DG (1988) Diagnostic wind field modeling for complex terrain: model development and testing. Journal of Applied Meteorology 27, 785–796.
Diagnostic wind field modeling for complex terrain: model development and testing.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, Arca B, Alcasena F, Arianoutsou M, Bacciu V, Duce P, Duguy B, Koutsias N, Mallinis G, Mitsopoulos I, Moreno JM, Pérez JR, Rodriguez I, Xystrakis F, Zavala G, Spano D (2016a) Predicting wildfire spread and behaviour in Mediterranean landscapes. International Journal of Wildland Fire 25, 1015–1032.
Predicting wildfire spread and behaviour in Mediterranean landscapes.Crossref | GoogleScholarGoogle Scholar |

Salis M, Laconi M, Ager AA, Alcasena FJ, Arca B, Lozano O, de Oliveira AF, Spano D (2016b) Evaluating alternative fuel treatment strategies to reduce wildfire losses in a Mediterranean area. Forest Ecology and Management 368, 207–221.
Evaluating alternative fuel treatment strategies to reduce wildfire losses in a Mediterranean area.Crossref | GoogleScholarGoogle Scholar |

Salis M, Del Giudice L, Arca B, Ager AA, Alcasena-Urdiroz F, Lozano O, Bacciu V, Spano D, Duce P (2018) Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area. Journal of Environmental Management 212, 490–505.
Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area.Crossref | GoogleScholarGoogle Scholar | 29475158PubMed |

San-Miguel-Ayanz J, Pereira JM, Boca R, Strobl P, Kucera J, Pekkarinen A (2009) Forest fires in the European Mediterranean region: mapping and analysis of burned areas. In ‘Earth observation of wildland fires in Mediterranean ecosystems’. (Ed. E Chuvieco) pp. 189–203. (Springer: Berlin/Heidelberg, Germany)

San-Miguel-Ayanz J, Schulte E, Schmuck G, Camia A (2013) The European forest fire information system in the context of environmental policies of the European Union. Forest Policy and Economics 29, 19–25.
The European forest fire information system in the context of environmental policies of the European Union.Crossref | GoogleScholarGoogle Scholar |

Sasaki Y (1958) An objective analysis based on the variational method. Journal of the Meteorological Society of Japan 36, 77–88.
An objective analysis based on the variational method.Crossref | GoogleScholarGoogle Scholar |

Scott J, Burgan R (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, RockyMountain Research Station, General Technical Report RMRS-GTR-153. (Fort Collins, CO, USA)

Scott JH, Thompson MP, Calkin DE (2013) A wildfire risk assessment framework for land and resource management. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-315. (Fort Collins, CO, USA)

Skamarock WC, Klemp JB (2008) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics 227, 3465–3485.
A time-split non-hydrostatic atmospheric model for weather research and forecasting applications.Crossref | GoogleScholarGoogle Scholar |

Sorensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter 5, 1–34.

Sullivan AL (2009a) Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models.Crossref | GoogleScholarGoogle Scholar |

Sullivan AL (2009b) Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |

Sullivan AL (2009c) 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 |

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.

Trunfio GA, D’Ambrosio D, Rongo R, Spataro W, Di Gregorio S (2011) A new algorithm for simulating wildfire spread through cellular automata. ACM Transactions on Modeling and Computer Simulation 22, 1–26.
A new algorithm for simulating wildfire spread through cellular automata.Crossref | GoogleScholarGoogle Scholar |

Tymstra C, Bryce RW, Wotton BM, Taylor SW, Armitage OB (2010) Development and structure of Prometheus: the Canadian wildland fire growth simulation model. Canadian Forest Service, Northern Forestry Centre. (Edmonton, AB, Canada)

US General Accounting Office (2009) Wildland fire management: Federal Agencies have taken important steps forward, but additional action is needed to address remaining challenges. Report GAO-09–906T. (Washington, D.C., USA). Available at http://www.gao.gov/products/GAO-09-906T [Verified 14 September 2018]

Van Wagner CE, Stocks BJ, Lawson BD, Alexander ME, Lynham TJ, McAlpine RS (1992) Development and structure of the Canadian forest fire behavior prediction system. Fire Danger Group, Forestry Canada, Information Report ST-X-3. (Ottawa, ON, USA)