journal article·2023-02-04Open access

An Historical Review of the Simplified Physical Fire Spread Model PhyFire: Model and Numerical Methods

María Isabel Asensio, José Manuel Cascón, Diego Prieto-Herráez, Luis Ferragut

Applied Sciences· Volume 13· Issue 4

Abstract

A historical review is conducted of PhyFire, a simplified physical forest fire spread model developed by the research group on Numerical Simulation and Scientific Computation (SINUMCC) at the University of Salamanca. The review ranges from the first version of the model to the current one now integrated into GIS, considering all the mathematical problems and numerical methods involved throughout its development: finite differences, mixed, classical and adaptive finite elements, data assimilation, sensitivity analysis, parameter adjustment, and parallel computation, among others. The simulation of processes as complex as forest fires involves a multidisciplinary effort that is constantly being enhanced, while posing interesting challenges from a mathematical, numerical, and computational perspective, without losing sight of the overriding aim of developing an efficient, effective, and useful simulation tool.
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Research context

OpenAlex topics: Fire effects on ecosystems · Plant Water Relations and Carbon Dynamics · Landslides and related hazards

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Fundamental Physics and Mathematics, Universidad de Salamanca, Plaza de la Merced 4, 37008 Salamanca, Spain

Department of Applied Mathematics, Universidad de Salamanca, Calle del Parque 2, 37008 Salamanca, Spain

Department of Economy and Economic History, Universidad de Salamanca, Edificio FES, Campus Miguel de Unamuno, 37007 Salamanca, Spain

Sciences and Arts Faculty, Universidad Católica de Ávila (UCAV), Calle Canteros s/n, 05005 Ávila, Spain

Research Group on Numerical Simulation and Scientific Calculation, Universidad de Salamanca, 37008 Salamanca, Spain

Funding

European Union’s Horizon 2020-Research and Innovation Framework Programme · European Regional Development Fund (ERDF) · Spanish Ministry of Economy and Competitiveness (MINECO)

References (68)

Show source-provided references
1. (2022, September 28). EFFIS Estimates for EU Countries. Available online: https://effis.jrc.ec.europa.eu/apps/effis.statistics/estimates/EU/2022/2006/2021.
2. (2022, September 28). GFASv1.2 Wildfire Locations and Total Fire Radiative for June–August 2022 over Eurasia. Credit: Copernicus Atmosphere Monitoring Service. Available online: https://atmosphere.copernicus.eu/europes-summer-wildfire-emissions-highest-15-years.
3. The Global Fire Atlas of individual fire size, duration, speed and direction10.5194/essd-11-529-2019 ↗
4. Finney, M. (2004). FARSITE: Fire Area Simulator-Model Development and Evaluation, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Research Paper RMRS-RP-4 (Revised).
5. Andrews Patricia, L., and Butler, B.W. (2006). Fuels Management-How to Measure Success: Conference Proceedings, Proceedings RMRS-P-41 ed., U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
6. Tymstra, C., Bryce, R., Wotton, B., Taylor, S., and Armitage, O. (2010). Information Report NOR-X-417, Canadian Forest Service, Northern Forestry Centre.
7. A review of a new generation of wildfire-atmosphere modeling10.1139/cjfr-2018-0138 ↗
8. Numerical simulations of grass fires using a coupled atmosphere-fire model: Basic fire behavior and dependence on wind speed10.1029/2004JD005597 ↗
9. A physics-based approach to modeling grassland fires10.1071/WF06002 ↗
10. Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 201110.5194/gmd-4-591-2011 ↗
11. Simulation of coupled fire/atmosphere interaction with the MesoNH-ForeFire models10.1155/2011/540390 ↗
12. Towards predictive data-driven simulations of wildfire spread—Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread10.5194/nhess-15-1721-2015 ↗
13. (2022). Simulation of forest fire spread based on artificial intelligence. Ecol. Indic., 136, 108653.10.1016/j.ecolind.2022.108653 ↗
14. A convection model for fire spread simulation10.1016/j.aml.2004.04.011 ↗
15. High definition local adjustment model of 3D wind fields performing only 2D computations10.1002/cnm.1314 ↗
16. Local wind speed forecasting based on WRF-HDWind coupling10.1016/j.atmosres.2020.105219 ↗
17. Prieto, D., Asensio, M., Ferragut, L., Cascón, J., and Morillo, A. (2017). A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model. Int. J. Geogr. Inf. Sci., 1–22.
18. Asensio, M.I., Oliver, A., and Sarrate, J. PhyFire: An Online GIS-Integrated Wildfire Spread Simulation Tool Based on a Semiphysical Model. Proceedings of the Applied Mathematics for Environmental Problems.
19. Cox, G. (1995). Combustion Fundamentals of Fire, Academic Press.
20. Simulación de incendios forestales
21. Manzello, S.L. (2018). Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer International Publishing.10.1007/978-3-319-51727-8_261-1 ↗
22. Application of a nonlinear evolution model to fire propagation10.1016/S0362-546X(97)00341-6 ↗
23. On a wildland fire model with radiation10.1002/nme.420 ↗
24. Modelling fire spread through fuel beds10.1016/0360-1285(91)90003-6 ↗
25. Bebernes, J., and Eberly, D. (1987). Mathematical Problems from Combustion Theory, Springer. Applied Mathematical Sciences.
26. Roberts, J.E., and Thomas, J. (1991). Mixed and Hybrid Methods, Handbook of Numerical Analysis, North-Holland.10.1016/S1570-8659(05)80041-9 ↗
27. The stefan problem in several space variables10.1090/S0002-9947-1968-0227625-7 ↗
28. Scientific advances in fire modeling and its integration in a forest fire decision system
29. Duality methods for solving variational inequalities10.1016/0898-1221(81)90006-7 ↗
30. A numerical method for solving convection-reaction-diffusion multivalued equations in fire spread modeling10.1016/j.advengsoft.2006.09.007 ↗
31. Modelling radiation and moisture content in fire spread10.1002/cnm.927 ↗
32. A Wildland Fire Physical Model Well Suited to Data Assimilation10.1007/s00024-014-0893-9 ↗
33. Effects of wind velocity and slope on flame properties10.1139/x26-210 ↗
34. Arellano, S., Vega, J.A., Arellano, A.D.R.A., Álvarez, J.G., Vega, D.J., and Pérez, E. (2016). Foto-Guía de Combustibles Forestales de Galicia, Andavira Editora, S.L.. (In Spanish).
35. Global sensitivity analysis of fuel-type-dependent input variables of a simplified physical fire spread model10.1016/j.matcom.2020.01.001 ↗
36. A physical model for wildland fires10.1016/j.combustflame.2009.07.010 ↗
37. Fire-spotting generated fires. Part I: The role of atmospheric stability10.1016/j.apm.2019.02.010 ↗
38. Fire-spotting generated fires. Part II: The role of flame geometry and slope10.1016/j.apm.2021.11.010 ↗
39. RandomFront 2.3: A physical parameterisation of fire spotting for operational fire spread models-implementation in WRF-SFIRE and response analysis with LSFire+10.5194/gmd-12-69-2019 ↗
40. Numerical study of ground-level distribution of firebrands generated by line fires10.1016/j.combustflame.2008.05.006 ↗
41. Sensitivity analysis and parameter adjustment in a simplified physical wildland fire model10.1016/j.advengsoft.2015.08.001 ↗
42. Manzello, S.L. (2018). Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer International Publishing.10.1007/978-3-319-51727-8_261-1 ↗
43. Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel10.1071/WF14195 ↗
44. On the transport-diffusion algorithm and its applications to the Navier–Stokes equations10.1007/BF01396435 ↗
45. Chapman, B., Jost, G., and Van Der Pas, R. (2007). Using OpenMP: Portable Shared Memory Parallel Programming, MIT Press.
46. Cascón, J., Ferragut, L., Asensio, M., Prieto, D., and Álvarez, D. (2018). XVIII Spanish-French School Jacques-Louis Lions about Numerical Simulation in Physics and Engineering, Springer.
47. IGN (2023, January 30). Instituto Geográfico Nacional de España. Spanish Base Map. Available online: http://www.ign.es/wms-inspire/ign-base.
48. Esri World Topographic Map (2023, January 30). Living Atlas. Available online: https://www.arcgis.com/home/item.html?id=7dc6cea0b1764a1f9af2e679f642f0f5.
49. Brunner, F.K. (1998). Proceedings of the Advances in Positioning and Reference Frames, Springer.10.1007/978-3-662-03714-0 ↗
50. GDAL/OGR Contributors (2023, January 30). GDAL/OGR Geospatial Data Abstraction: Software Library; Open Source Geospatial Foundation. Available online: https://gdal.org.
51. AngularJS: A modern MVC framework in JavaScript
52. IGN (2023, January 30). Instituto Geográfico Nacional De España. WCS Inspire de Modelos Digitales del Terreno del IGN. Available online: https://servicios.idee.es/wcs-inspire/mdt.
53. CNIG (2023, January 30). Centro de Descargas del Centro Nacional de Información Geográfica. Modelo Digital del Terreno Con Paso de Malla de 2 m. Available online: https://centrodedescargas.cnig.es/CentroDescargas/index.jsp.
54. CNIG (2023, January 30). Centro de Descargas del Centro Nacional de Información Geográfica. Modelo Digital del Terreno Con Paso de Malla de 5 m. Available online: https://centrodedescargas.cnig.es/CentroDescargas/index.jsp.
55. IGN (2023, January 30). Instituto Geográfico Nacional De España. Spanish Land Cover Information System. Available online: https://www.siose.es/.
56. IGN (2023, January 30). Instituto Geográfico Nacional De España. High-resolution Spanish Land Cover Information System. Available online: https://www.siose.es/.
57. CNIG (2023, January 30). Centro de Descargas del Centro Nacional de Información Geográfica. Base Topográfica Nacional 1:25,000. Available online: http://centrodedescargas.cnig.es/CentroDescargas.
58. Google (2023, January 30). OpenStreetMap Contributors Planet Dump Retrieved from https://planet.osm.org. Available online: https://www.openstreetmap.org.
59. Red and photographic infrared linear combinations for monitoring vegetation10.1016/0034-4257(79)90013-0 ↗
60. Fire intensity, fire severity and burn severity: A brief review and suggested usage10.1071/WF07049 ↗
61. Anderson, H. (1982). Aids to Determining Fuel Models for Estimating fire Behavior, USDA Forest Service. Technical Report.10.2737/INT-GTR-122 ↗
62. Scott, J., and Burgan, R. (2005). Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. General Technical Report RMRS-GTR-153.10.2737/RMRS-GTR-153 ↗
63. Fire models and methods to map fuel types: The role of remote sensing10.1016/j.foreco.2008.06.048 ↗
64. (2023, January 30). Ministerio De Agricultura, Alimentación Y Medio Ambiente. Spanish Forestry Map 1:25,000. Available online: https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe25_informacion_disp.aspx.
65. Ministerio Para la Transición ecolóGica y el Reto Demográfico (2023, January 30). Spanish Forestry Map 1:50,000. Available online: https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.aspx.
66. High Resolution Fuel Type Mapping through Satellite Imagery and Neural Networks for Wildfire Simulations: A Case Study in Spain
67. Morillo, A. (2011). Master of Advances Studies Work, Higher Politechnical School of Lugo, University of Santiago de Compostela. (In Spanish).
68. ICONA (1987). Photographic Key to the Identification of Fuel Models, Ministerio de Agricultura Pesca y Alimentación, Instituto para la Conservación de la Naturaleza. Technical Report.