Pyros: a raster–vector spatial simulation model for predicting wildland surface fire spread and growth
Debora Voltolina
A B * , Giacomo Cappellini A , Tiziana Apuani B and Simone Sterlacchini A
A
B
Abstract
Euro–Mediterranean regions are expected to undergo a climate-induced exacerbation of fire activity in the upcoming decades. Reliable predictions of fire behaviour represent an essential instrument for planning and optimising fire management actions and strategies.
The aim of this study was to describe and analyse the performance of an agent-based spatial simulation model for predicting wildland surface fire spread and growth.
The model integrates Rothermel’s equations to obtain fire spread metrics and uses a hybrid raster–vector implementation to predict patterns of fire growth. The model performance is evaluated in quantitative terms of spatiotemporal agreement between predicted patterns of fire growth and reference patterns, under both ideal and real-world environmental conditions, using case studies in Sardinia, Italy.
Predicted patterns of fire growth demonstrate negligible distortions under ideal conditions when compared with circular or elliptical reference patterns. In real-world heterogeneous conditions, a substantial agreement between observed and predicted patterns is achieved, resulting in a similarity coefficient of up to 0.76.
Outcomes suggest that the model exhibits promising performance with low computational requirements.
Assuming that parametric uncertainty is effectively managed and a rigorous validation encompassing additional case studies from Euro–Mediterranean regions is conducted, the model has the potential to provide a valuable contribution to operational fire management applications.
Keywords: agent-based model, Euro–Mediterranean, fire behaviour, fire management, fire suppression, Italy, Rothermel model, Sardinia, spatial simulation model.
References
Albini FA (1976) Computer-based models of wildland fire behavior: a users’ manual. General Technical Report. 68 pp. (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station). Available at https://www.frames.gov/catalog/8178
Alessandri A, Bagnerini P, Gaggero M, Mantelli L (2021) Parameter estimation of fire propagation models using level set methods. Applied Mathematical Modelling 92, 731-747.
| Crossref | Google Scholar |
Alexander ME, Cruz MG (2013a) Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environmental Modelling and Software 41, 65-71.
| Crossref | Google Scholar |
Alexander ME, Cruz MG (2013b) Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview. The Forestry Chronicle 89, 370-381.
| Google Scholar |
Alexandridis A, Vakalis D, Siettos CI, Bafas G V (2008) A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990. Applied Mathematics and Computation 204, 191-201.
| Crossref | Google Scholar |
Allaire F, Mallet V, Filippi J-B (2021) Emulation of wildland fire spread simulation using deep learning. Neural Networks 141, 184-198.
| Crossref | Google Scholar | PubMed |
Anderson HE (1982) Aids to Determining Fuel Models for Estimating Fire Behavior. General Technical Report GTR-122. 22 pp. (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station). 10.2737/INT-GTR-122
Andrews PL (2012) Modeling Wind Adjustment Factor and Midflame Wind Speed for Rothermel’s Surface Fire Spread Model. General Technical Report GTR-266. 39 pp. (US Department of Agriculture, Forest Service, Rocky Mountain Research Station) 10.2737/RMRS-GTR-266
Andrews PL (2018) The Rothermel Surface Fire Spread Model and Associated Developments: A Comprehensive Explanation. General Technical Report GTR-371. 121 pp. (USUnited States Department of Agriculture, Forest Service, Rocky Mountain Research Station). 10.2737/RMRS-GTR-371
Arca B, Ghisu T, Casula M, Salis M, Duce P (2019) A web-based wildfire simulator for operational applications. International Journal of Wildland Fire 28, 99-112.
| Crossref | Google Scholar |
Autonomous Region of Sardinia (2008) Carta dell’Uso del Suolo in scala 1:25.000. Available at http://webgis2.regione.sardegna.it/
Autonomous Region of Sardinia (2010) Digital Terrain Model (10 m). Available at https://www.sardegnageoportale.it/webgis2/sardegnamappe/
Autonomous Region of Sardinia (2017a) Rapporto sugli incendi boschivi e rurali in Sardegna - Anno 2016. (Eds Regione Autonoma della Sardegna - Assessorato alla Difesa dell’Ambiente) [In Italian]. Available at https://www.regione.sardegna.it/documenti/1_274_20170525122956.pdf
Autonomous Region of Sardinia (2017b) Incendio di Arbus - Gonnosfanadiga: un indagato per incendio colposo [In Italian]. Available at https://www.sardegnaambiente.it/index.php?xsl=612&s=344949&v=2&c=4577&idsito=19
Autonomous Region of Sardinia (2020) Sardegna Geoportale - Perimetrazioni aree percorse dal fuoco. Available at http://www.sardegnageoportale.it/webgis2/sardegnamappe/
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.
| Crossref | Google Scholar |
Bowman DMJS, Kolden CA, Abatzoglou JT, Johnston FH, van der Werf GR, Flannigan MD (2020) Vegetation fires in the Anthropocene. Nature Reviews Earth & Environment 1, 500-515.
| Crossref | Google Scholar |
Canu S, Rosati L, Fiori M, Motroni A, Filigheddu R, Farris E (2015) Bioclimate map of Sardinia (Italy). Journal of Maps 11, 711-718.
| Crossref | Google Scholar |
Carmignani L, Oggiano G, Funedda A, Conti P, Pasci S (2016) The geological map of Sardinia (Italy) at 1:250,000 scale. Journal of Maps 12, 826-835.
| Crossref | Google Scholar |
Cattau ME, Wessman C, Mahood A, Balch JK (2020) Anthropogenic and lightning-started fires are becoming larger and more frequent over a longer season length in the U.S.A. Global Ecology and Biogeography 29, 668-681.
| Crossref | Google Scholar |
Chuvieco E, Cocero D, Riaño D, Martin P, Martı’nez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 92, 322-331.
| Crossref | Google Scholar |
Clarke KC (2014) Cellular Automata and Agent-Based Models. In ‘Handbook of Regional Science’. (Eds MM Fischer, P Nijkamp) pp. 1217–1233. 10.1007/978-3-642-23430-9
Coen J (2018) Some requirements for simulating wildland fire behavior using insight from coupled weather—wildland fire models. Fire 1, 6.
| Crossref | Google Scholar |
Collin A, Bernardin D, Séro-Guillaume O (2011) A physical-based cellular automaton model for forest-fire propagation. Combustion Science and Technology 183, 347-369.
| Crossref | Google Scholar |
Cruz MG, Alexander ME, Sullivan AL (2017) Mantras of wildland fire behaviour modelling: Facts or fallacies? International Journal of Wildland Fire 26, 973-981.
| Crossref | Google Scholar |
Cruz MG, Alexander ME, Sullivan AL, Gould JS, Kilinc M (2018) Assessing improvements in models used to operationally predict wildland fire rate of spread. Environmental Modelling & Software 105, 54-63.
| Crossref | Google Scholar |
de Sousa LM, Leitão JP (2017) Hex-utils: A tool set supporting HexASCII hexagonal rasters. In ‘Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM)’. pp. 177–183. 10.5220/0006275801770183
de Sousa LM, Leitão JP (2018) HexASCII: A file format for cartographical hexagonal rasters. Transactions in GIS 22, 217-232.
| Crossref | Google Scholar |
Duce P, Pellizzaro G, Arca B, Ventura A, Bacciu VM, Salis M, Spano D, Santoni P-A, Barboni T, Leroy V, Cancellieri D, Ferrat L, Perez Y (2012) Fuel types and potential fire behaviour in Sardinia and Corsica islands: a pilot study. In ‘Modelling fire behaviour and risk’. (Eds Spano D, Bacciu V, Salis M, Sirca C) pp. 2–8. Available at http://www.cmcc.it/wp-content/uploads/2013/04/P_Book_Modelling-Fire-Behaviour-and-Risk.pdf
Dupuy J-l, Fargeon H, Martin-StPaul N, Pimont F, Ruffault J, Guijarro M, Hernando C, Madrigal J, Fernandes P (2020) Climate change impact on future wildfire danger and activity in southern Europe: a review. Annals of Forest Science 77, 35.
| Crossref | Google Scholar |
Filippi JB (2018) ForeFire open source wildfire front propagation model solver and programming interface. CEUR Workshop Proceedings 2146, 87-91.
| Google Scholar |
Filippi JB, Morandini F, Balbi JH, Hill DR (2010) Discrete Event Front-tracking Simulation of a Physical Fire-spread Model. Simulation 86, 629-646.
| Crossref | Google Scholar |
Finney MA (1998) FARSITE: Fire Area Simulator - Model development and evaluation. Research Paper RMRS-RP-4. pp. 1–47. (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station). 10.2737/RMRS-RP-4
Finney MA (2002) Fire growth using minimum travel time methods. Canadian Journal of Forest Research 32, 1420-1424.
| Crossref | Google Scholar |
Finney MA, McHugh C (2019) FlamMap 6.0. Available at https://www.firelab.org/project/flammap
Finney MA, Cohen JD, McAllister SS, Jolly WM (2013) On the need for a theory of wildland fire spread. International Journal of Wildland Fire 22, 25-36.
| Crossref | Google Scholar |
Finney M, McAllister S, Grumstrup T, Forthofer J (2021) ‘Wildland Fire Behaviour - Dynamics, Principles and Processes.’ (CSIRO Publishing: Melbourne) 10.1071/9781486309092
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.
| Crossref | Google Scholar |
Flannigan MD, Wotton BM, Marshall GA, de Groot WJ, Johnston J, Jurko N, Cantin AS (2016) Fuel moisture sensitivity to temperature and precipitation : climate change implications. Climatic Change 134, 59-71.
| Crossref | Google Scholar |
Forkel M, Andela N, Harrison SP, Lasslop G, Van Marle M, Chuvieco E, Dorigo W, Forrest M, Hantson S, Heil A, Li F, Melton J, Sitch S, Yue C, Arneth A (2019) Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences 16, 57-76.
| Crossref | Google Scholar |
Forthofer JM, Shannon KS, Butler BW (2009) Simulating Diurnally Driven Slope Winds with WindNinja. In ‘Proceedings of 8th Symposium on Fire and Forest Meteorological Society’. p. 13. Available at https://www.fs.usda.gov/research/treesearch/61476
Forthofer JM, Butler BW, Wagenbrenner NS (2014) 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.
| Crossref | Google Scholar |
Freire JG, Castro DaCamara C (2018) Using cellular automata to simulate wildfire propagation and to assist in fire prevention and fighting. Natural Hazards and Earth System Sciences Discussions 19, 169-179.
| Crossref | Google Scholar |
Ganteaume A, Syphard A (2018) Ignition Sources. In ‘Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires’. (Ed. SL Manzello) pp. 1-17. (Springer International Publishing: Cham.) 10.1007/978-3-319-51727-8
García M, Chuvieco E, Nieto H, Aguado I (2008) Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sensing of Environment 112, 3618-3627.
| Crossref | Google Scholar |
Ghisu T, Arca B, Pellizzaro G, Duce P (2014) A level-set algorithm for simulating wildfire spread. CMES - Computer Modeling in Engineering and Sciences 102, 83-102.
| Crossref | Google Scholar |
Ghisu T, Arca B, Pellizzaro G, Duce P (2015) An optimal Cellular Automata algorithm for simulating wildfire spread. Environmental Modelling & Software 71, 1-14.
| Crossref | Google Scholar |
Giglio L, Boschetti L, Roy DP, Humber ML, Justice CO (2018) The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment 217, 72-85.
| Crossref | Google Scholar | PubMed |
Glasa J, Halada L (2011) A note on mathematical modelling of elliptical fire propagation. Computing and Informatics 30, 1303-1319.
| Google Scholar |
Hernández Encinas L, Hoya White S, Martín del Rey A, Rodríguez Sánchez G (2007) Modelling forest fire spread using hexagonal cellular automata. Applied Mathematical Modelling 31, 1213-1227.
| Crossref | Google Scholar |
Hodges JL, Lattimer BY (2019) Wildland Fire Spread Modeling Using Convolutional Neural Networks. Fire Technology 55, 2115-2142.
| Crossref | Google Scholar |
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environmental Reviews 28, 478-505.
| Crossref | Google Scholar |
Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DMJS (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications 6, 7537.
| Crossref | Google Scholar | PubMed |
Katan J, Perez L (2021) ABWiSE v1.0: toward an agent-based approach to simulating wildfire spread. Natural Hazards and Earth System Sciences 21, 3141-3160.
| Crossref | Google Scholar |
Kelley DI, Bistinas I, Whitley R, Burton C, Marthews TR, Dong N (2019) How contemporary bioclimatic and human controls change global fire regimes. Nature Climate Change 9, 690-696.
| Crossref | Google Scholar |
Mallet V, Keyes DE, Fendell FE (2009) Modeling wildland fire propagation with level set methods. Computers & Mathematics with Applications 57, 1089-1101.
| Crossref | Google Scholar |
Mantero G, Morresi D, Marzano R, Motta R, Mladenoff DJ, Garbarino M (2020) The influence of land abandonment on forest disturbance regimes: a global review. Landscape Ecology 35, 2723-2744.
| Crossref | Google Scholar |
Moreno G, Pulido FJ (2009) The Functioning, Management and Persistence of Dehesas. In ‘Agroforestry in Europe: Current Status and Future Prospects’. (Eds A Rigueiro-Rodróguez, J McAdam, MR Mosquera-Losada) pp. 127-160. (Springer: Netherlands, Dordrecht) 10.1007/978-1-4020-8272-6_7
Muñoz-Esparza D, Kosović B, Jiménez PA, Coen JL (2018) An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method. Journal of Advances in Modeling Earth Systems 10, 908-926.
| Crossref | Google Scholar |
Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez-Fernández NJ, Zsoter E, Buontempo C, Thépaut JN (2021) ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349-4383.
| Crossref | Google Scholar |
Nolan RH, Resco de Dios V, Boer MM, Caccamo G, Goulden ML, Bradstock RA (2016) Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data. Remote Sensing of Environment 174, 100-108.
| Crossref | Google Scholar |
Ntinas VG, Moutafis BE, Trunfio GA, Sirakoulis GC (2017) Parallel fuzzy cellular automata for data-driven simulation of wildfire spreading. Journal of Computational Science 21, 469-485.
| Crossref | Google Scholar |
Pais C, Carrasco J, Martell DL, Weintraub A, Woodruff DL (2021) Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning. Frontiers in Forests and Global Change 4, 692706.
| Crossref | Google Scholar |
Pausas JG, Keeley JE (2021) Wildfires and global change. Frontiers in Ecology and the Environment 19, 387-395.
| Crossref | Google Scholar |
Plucinski MP (2019a) Fighting Flames and Forging Firelines: Wildfire Suppression Effectiveness at the Fire Edge. Current Forestry Reports 5, 1-19.
| Crossref | Google Scholar |
Plucinski MP (2019b) Contain and Control: Wildfire Suppression Effectiveness at Incidents and Across Landscapes. Current Forestry Reports 5, 20-40.
| Crossref | Google Scholar |
Quill R, Sharples JJ, Wagenbrenner NS, Sidhu LA, Forthofer JM (2019) Modeling Wind Direction Distributions Using a Diagnostic Model in the Context of Probabilistic Fire Spread Prediction. Frontiers in Mechanical Engineering 5, 1-16.
| Crossref | Google Scholar |
Radke D, Hessler A, Ellsworth D (2019) Firecast: Leveraging deep learning to predict wildfire spread. In ‘Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence’, 10-16 August 2019, Macao, China. (Ed. International Joint Conference on Artificial Intelligence Organization) pp. 4575–4581. 10.24963/ijcai.2019/63610.24963/ijcai.2019/636
Radočaj D, Jurišić M, Gašparović M (2022) A wildfire growth prediction and evaluation approach using Landsat and MODIS data. Journal of Environmental Management 304, 114351.
| Crossref | Google Scholar | PubMed |
Rothermel RC (1972) A Mathematical Model for Predicting Fire Spread in Wildland Fuels. Research Paper INT-115. (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station) Available at https://www.fs.usda.gov/research/treesearch/32533
Rui X, Hui S, Yu X, Zhang G, Wu B (2018) Forest fire spread simulation algorithm based on cellular automata. Natural Hazards 91, 309-319.
| Crossref | Google Scholar |
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.
| Crossref | Google Scholar |
Salis M, Arca B, Alcasena F, Arianoutsou M, Bacciu V, Duce P, Duguy B, Koutsias N, Mallinis G, Mitsopoulos I, Moreno JM, Pérez JR, Urbieta IR, Xystrakis F, Zavala G, Spano D (2016) Predicting wildfire spread and behaviour in Mediterranean landscapes. International Journal of Wildland Fire 25, 1015-1032.
| Crossref | Google Scholar |
Salis M, Arca B, Alcasena-Urdiroz F, Massaiu A, Bacciu V, Bosseur F, Caramelle P, Dettori S, Fernandes de Oliveira AS, Molina-Terren D, Pellizzaro G, Santoni P-A, Spano D, Vega-Garcia C, Duce P (2019) Analyzing the recent dynamics of wildland fires in Quercus suber L. woodlands in Sardinia (Italy), Corsica (France) and Catalonia (Spain). European Journal of Forest Research 138, 415-431.
| Crossref | Google Scholar |
Salis M, Arca B, Del Giudice L, Palaiologou P, Alcasena-Urdiroz F, Ager A, Fiori M, Pellizzaro G, Scarpa C, Schirru M, Ventura A, Casula M, Duce P (2021) Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy. International Journal of Disaster Risk Reduction 58, 102189.
| Crossref | Google Scholar |
Sullivan AL (2009a) Wildland surface fire spread modelling, 1990-2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire 18, 349-368.
| Crossref | Google 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.
| Crossref | Google 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.
| Crossref | Google Scholar |
Tedim F, Leone V, Amraoui M, Bouillon C, Coughlan MR, Delogu GM, Fernandes PM, Ferreira C, McCaffrey S, McGee TK, Parente J, Paton D, Pereira MG, Ribeiro LM, Viegas DX, Xanthopoulos G (2018) Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts. Fire 1, 9.
| Crossref | Google Scholar |
Thompson MP, Calkin DE (2011) Uncertainty and risk in wildland fire management: A review. Journal of Environmental Management 92, 1895-1909.
| Crossref | Google Scholar | PubMed |
Thompson MP, Rodríguez y Silva F, Calkin DE, Hand MS (2017) A review of challenges to determining and demonstrating efficiency of large fire management. International Journal of Wildland Fire 26, 562-573.
| Crossref | Google Scholar |
Trucchia A, D’Andrea M, Baghino F, Fiorucci P, Ferraris L, Negro D, Gollini A, Severino M (2020) Propagator: An operational cellular-automata based wildfire simulator. Fire 3, 26.
| Crossref | Google Scholar |
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.
| Crossref | Google Scholar |
Turco M, Bedia J, Di Liberto F, Fiorucci P, Von Hardenberg J, Koutsias N, Llasat MC, Xystrakis F, Provenzale A (2016) Decreasing fires in mediterranean Europe. PLoS One 11, e0150663.
| Crossref | Google Scholar | PubMed |
Turco M, Rosa-Cánovas JJ, Bedia J, Jerez S, Montávez JP, Llasat MC, Provenzale A (2018) Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nature Communications 9, 3821.
| Crossref | Google Scholar | PubMed |
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, Alberta) Information Report NOR-X-417. 102 p. Available at https://cfs.nrcan.gc.ca/publications?id=31775
Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmospheric Chemistry and Physics 16, 5229-5241.
| Crossref | Google Scholar |
Williams AP, Abatzoglou JT (2016) Recent Advances and Remaining Uncertainties in Resolving Past and Future Climate Effects on Global Fire Activity. Current Climate Change Reports 2, 1-14.
| Crossref | Google Scholar |
Zhai C, Zhang S, Cao Z, Wang X (2020) Learning-based prediction of wildfire spread with real-time rate of spread measurement. Combustion and Flame 215, 333-341.
| Crossref | Google Scholar |
