A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula
<p>Study area: the SW corner of Galicia (northwestern Spain).</p> "> Figure 2
<p>Map of the distribution of Rothermel fuel models in the study area (<b>left</b>) and a detailed view of a portion of the study area (<b>right</b>).</p> "> Figure 3
<p>Workflow followed to perform the study.</p> "> Figure 4
<p>Distribution of ignition points, represented as gray dots, in the study area.</p> "> Figure 5
<p>Output simulation data obtained for a sample ignition point.</p> "> Figure 6
<p>Graphical representation of wildfire size, FS (in hectares), for each ignition point considered.</p> "> Figure 7
<p>Wildfire behavior aggregated maps of the study area: (<b>A</b>) ROS in typical conditions; (<b>B</b>) FLI in typical conditions; (<b>C</b>) ROS in atypical conditions; (<b>D</b>) FLI in atypical conditions.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Study
2.2. Materials
2.2.1. Source Data
2.2.2. Software
2.3. Methodology
- -
- Territory modeling: The topographic parameters and the fuel model map were generated and introduced into the simulation software.
- -
- Definition of weather scenarios: Different scenarios were built to model the habitual weather conditions in the study area as well as conditions that can occur in extreme weather scenarios.
- -
- Fire modeling: Ignition points were defined as well as the duration of simulations and the absence of fire extinction.
- -
- Geospatial analysis of wildfire behavior.
2.3.1. Territory Modelling
2.3.2. Definition of Weather Scenarios
- -
- Typical conditions: The daily values for maximum temperature, minimum relative humidity, and prevailing wind speed were averaged to obtain four monthly values. These values were used for the scenarios that might correspond to the typical weather conditions in the study area.
- -
- Atypical conditions: The most adverse single record from the daily values was selected for maximum temperature, minimum relative humidity, and maximum wind speed. These values were used to build four additional scenarios that might correspond to anomalous or extreme conditions in the study area.
2.3.3. Fire Modeling
2.3.4. Geospatial Analysis of Wildfire Behavior
3. Results
3.1. Weather Scenarios
3.2. Fire Modelling
3.3. Geospatial Analysis of Wildfire Behavior
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Lannom, K.O.; Tinkham, W.; Smith, A.; Abatzoglou, J.; Nwingham, B.; Hall, T.; Morgan, P.; Strand, E.; Paveglio, T.; Anderson, J.; et al. Defining extreme wildland fires using geospatial and ancillary metrics. Int. J. Wildland Fire 2014, 23, 322–337. [Google Scholar] [CrossRef]
- Food and Agriculture Organization of the United Nations (FAO). Evaluación de los recursos forestales mundiales 2020. In Informe Principal; FAO: Roma, Italy, 2021. [Google Scholar] [CrossRef]
- Abatzoglou, J.T.; Williams, A.P.; Barbero, R. Global Emergence of Anthropogenic Climate Change in Fire Weather Indices. Geophys. Res. Lett. 2019, 46, 326–336. [Google Scholar] [CrossRef]
- Turco, M.; Rosa-Cánovas, J.J.; Bedia, J.; Jerez, S.; Montávez, J.P.; Llasat, M.C.; Provenzale, A. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun. 2018, 9, 3821. [Google Scholar] [CrossRef] [PubMed]
- Dowdy, A.J. Climatological Variability of Fire Weather in Australia. J. Appl. Meteor. Clim. 2018, 57, 221–234. [Google Scholar] [CrossRef]
- Couto, F.T.; Santos, F.L.M.; Campos, C.; Andrade, N.; Purificação, C.; Salgado, R. Is Portugal Starting to Burn All Year Long? The Transboundary Fire in January 2022. Atmosphere 2022, 13, 1677. [Google Scholar] [CrossRef]
- Wang, X.; Thompson, D.K.; Marshall, G.A.; Tymstra, C.; Carr, R.; Flannigan, M.D. Increasing frequency of extreme fire weather in Canada with climate change. Clim. Chang. 2015, 130, 573–586. [Google Scholar] [CrossRef]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 2021. Available online: https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SummaryVolume.pdf (accessed on 13 February 2023).
- Modugno, S.; Balzter, H.; Cole, B.; Borrelli, P. Mapping regional patterns of large forest fires in wildland–urban interface areas in Europe. J. Environ. Manag. 2016, 172, 112–126. [Google Scholar] [CrossRef] [PubMed]
- Ortega, M.; Silva, F.R.; Molina, J.R. Modeling fuel break effectiveness in southern Spain wildfires. Fire Ecol. 2024, 20, 40. [Google Scholar] [CrossRef]
- San-Miguel-Ayanz, J.; Moreno, J.M.; Camia, A. Analysis of Large Fires in European Mediterranean Landscapes: Lessons Learned and Perspectives. For. Ecol. Manag. 2013, 294, 11–22. [Google Scholar] [CrossRef]
- Molina-Terrén, D.M.; Xanthopoulos, G.; Diakakis, M.; Ribeiro, L.; Caballero, D.; Delogu, G.M.; Viegas, D.X.; Silva, C.A.; Cardil, A. Analysis of Forest Fire Fatalities in Southern Europe: Spain, Portugal, Greece and Sardinia (Italy). Int. J. Wildl. Fire 2019, 28, 85–98. [Google Scholar] [CrossRef]
- Palaiologou, P.; Kalabokidis, K.; Ager, A.A.; Day, M.A. Development of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in Greece. Forests 2020, 11, 789. [Google Scholar] [CrossRef]
- Hirsch, K.G.; Martell, D.L. A review of initial attack fire crew productivity and effectiveness. Int. J. Wildl. Fire 1996, 6, 199–215. Available online: https://www.publish.csiro.au/wf/WF9960199 (accessed on 12 July 2024). [CrossRef]
- Finney, M.; Grenfell, I.; McHugh, C. Modeling containment of large wildfires using generalized linear mixed-model analysis. For. Sci. 2009, 55, 249–255. Available online: https://www.fs.usda.gov/research/treesearch/33778 (accessed on 12 July 2024). [CrossRef]
- Fernandes, P.M. Empirical Support for the Use of Prescribed Burning as a Fuel Treatment. Curr. For. Rep. 2015, 1, 118–127. [Google Scholar] [CrossRef]
- Urza, A.K.; Hanberry, B.B.; Jain, T.B. Landscape-scale fuel treatment effectiveness: Lessons learned from wildland fire case studies in forests of the western United States and Great Lakes region. Fire Ecol. 2023, 19, 1. [Google Scholar] [CrossRef]
- Ott, J.E.; Kilkenny, F.F.; Jain, T.B. Fuel treatment effectiveness at the landscape scale: A systematic review of simulation studies comparing treatment scenarios in North America. Fire Ecol. 2023, 19, 10. [Google Scholar] [CrossRef]
- Beverly, J.; McLoughlin, N. Burn probability simulation and subsequent wildland fire activity in Alberta, Canada Implications for risk assessment and strategic planning. For. Ecol. Manag. 2019, 451, 117490. [Google Scholar] [CrossRef]
- Salis, M.; Del Giudice, L.; Arca, B.; Ager, A.A.; Alcasena-Urdiroz, F.; Lozano, O.; Bacciu, V.; Spano, D.; Duce, P. Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area. J. Environ. Manag. 2018, 212, 490–505. Available online: https://www.sciencedirect.com/science/article/abs/pii/S0301479718301191?via%3Dihub (accessed on 12 July 2024). [CrossRef] [PubMed]
- Lozano, O.; Salis, M.; Ager, A.; Arca, B.; Alcasena, F.; Monteiro, A.; Finney, M.; Del Giudice, L.; Scoccimarro, E.; Spano, D. Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas. Risk Anal. 2017, 37, 1898–1916. Available online: https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12739 (accessed on 12 July 2024). [CrossRef]
- Alcasena, F.; Ager, A.; Salis, M.; Day, M.; Vega-Garcia, C. Optimizing prescribed fire allocation for managing fire risk in central Catalonia. Sci. Total Environ. 2018, 621, 872–885. Available online: https://www.sciencedirect.com/science/article/abs/pii/S0048969717333545?via%3Dihub (accessed on 12 July 2024). [CrossRef]
- Miller, C.; Ager, A. A review of recent advances in risk analysis for wildfire management. Int. J. Wildl. Fire 2013, 22, 1–14. Available online: https://www.researchgate.net/publication/273741060_A_review_of_recent_advances_in_risk_analysis_for_wildfire_management (accessed on 12 July 2024). [CrossRef]
- Alcasena, F.; Salis, M.; Ager, A.; Castell, R.; Vega-García, C. Assessing wildland fire risk transmission to communities in Northern Spain. Forests 2017, 8, 30. Available online: https://www.mdpi.com/1999-4907/8/2/30 (accessed on 12 July 2024). [CrossRef]
- Ager, A.; Vaillant, N.; Finney, M. Integrating Fire Behavior Models and Geospatial Analysis for Wildland Fire Risk Assessment and Fuel Management Planning. J. Combust. 2011, 2011, 572452. [Google Scholar] [CrossRef]
- Wotton, W.; Flannigan, M.; Marshall, G. Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada. Environ. Res. Lett. 2017, 12, 095003. [Google Scholar] [CrossRef]
- Jahdi, R.; Del Giudice, L.; Melis, M.; Lovreglio, R.; Salis, M.; Arca, B.; Duce, P. Assessing the effects of alternative fuel treatments to reduce wildfire exposure. J. For. Res. 2023, 34, 373–386. [Google Scholar] [CrossRef]
- Thompson, M.P.; Vogler, K.C.; Scott, J.H.; Miller, C. Comparing risk-based fuel treatment prioritization with alternative strategies for enhancing protection and resource management objectives. Fire Ecol. 2022, 18, 26. [Google Scholar] [CrossRef]
- Alcasena, F.J.; Salis, M.; Vega-García, C. A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain. Eur J. For. Res 2016, 135, 87–107. [Google Scholar] [CrossRef]
- Bertomeu, M.; Pineda, J.; Pulido, F. Managing Wildfire Risk in Mosaic Landscapes: A Case Study of the Upper Gata River Catchment in Sierra de Gata, Spain. Land 2022, 11, 465. [Google Scholar] [CrossRef]
- Sakellariou, S.; Sfougaris, S.; Christopoulou, O.; Tampekis, S. Integrated wildfire risk assessment of natural and anthropogenic ecosystems based on simulation modeling and remotely sensed data fusion. Int. J. Disaster Risk Reduct. 2022, 78, 103129. [Google Scholar] [CrossRef]
- Rodrigues, M.; Cunill Camprubí, A.; Balaguer-Romano, R.; Coco Megía, C.J.; Castañares, F.; Ruffault, J.; Fernandes, P.M.; de Dios, V.R. Drivers and implications of the extreme 2022 wildfire season in Southwest Europe. Sci. Total Environ. 2023, 859, 160320. [Google Scholar] [CrossRef]
- Bar-Massada, A.; Syphard, A.D.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C. Effects of Ignition Location Models on the Burn Patterns of Simulated Wildfires. Environ. Model. Softw. 2011, 26, 583–592. [Google Scholar] [CrossRef]
- Quílez, R.; Valbuena, L.; Vendrell, J.; Uytewaal, K.; Ramirez, J. Establishing Propagation Nodes as a Basis for Preventing Large Wildfires: The Proposed Methodology. Front. For. Glob. Chan. 2020, 3, 548799. [Google Scholar] [CrossRef]
- Ager, A.; Day, M.; Finney, M.; Vance-Borland, K.; Vaillant, N. Analyzing the transmission of wildfire exposure on a fire-prone landscape in Oregon, USA. For. Ecol. Manag. 2014, 334, 377–390. [Google Scholar] [CrossRef]
- Duane, A.; Brotons, L. Synoptic weather conditions and changing fire regimes in a Mediterranean environment. Agric. For. Meteorol. 2018, 253, 190–202. [Google Scholar] [CrossRef]
- Liu, Y.; Stanturf, J.; Goodrick, S. Trends in global wildfire potential in a changing climate. Int. J. Wildl. Fire 2010, 259, 685–697. [Google Scholar] [CrossRef]
- Botequim, B.; Fernandes, P.; Borges, J.; González-Ferreiro, E.; Guerra, J. Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics. Int. J. Wildl Fire 2019, 28, 823–839. [Google Scholar] [CrossRef]
- Moreira, F.; Ascoli, D.; Safford, H.; Adams, M.A.; Moreno, J.M.; Pereira, J.; Catry, F.X.; Armesto, J.; Bond, W.; González, M.E.; et al. Wildfire management in Mediterranean-type regions: Paradigm change needed. Environ. Res. Lett. 2020, 15, 011001. [Google Scholar] [CrossRef]
- Montiel, M.C.; Karlsson, M.O.; Galiana, M.L. Regional fire scenarios in Spain: Linking landscape dynamics and fire regime for wildfire risk management. J. Environ. Manag. 2019, 233, 427–439. [Google Scholar] [CrossRef] [PubMed]
- Rothermel, R. A Mathematical Model for Predicting Fire Spread in Wildland Fuels; INT-115; Department of Agriculture, Intermountain Forest and Range Experiment Station: Ogden, UT, USA, 1972; 40p. [Google Scholar]
- Simons, N.E. Improving Decision Making during Wildland Fire Events (Order No. 3602215). Ph.D. Thesis, University of California, Santa Barbara, Berkeley, CA, USA, 2013. Available online: https://www.proquest.com/dissertations-theses/improving-decision-making-during-wildland-fire/docview/1468440376/se-2 (accessed on 12 July 2024).
- Ascoli, D.; Vacchiano, G.; Motta, R.; Bovio, G. Building Rothermel fire behavior fuel models by genetic algorithm optimization. Int. J. Wildl. Fire 2015, 24, 317–328. [Google Scholar] [CrossRef]
- Vacchiano, G.; Ascoli, D. An Implementation of the Rothermel Fire Spread Model in the R Programming Language. Fire Technol. 2015, 51, 523–535. [Google Scholar] [CrossRef]
- Anderson, H. Aids to Determining Fuel Models for Estimating Fire Behavior; INT-GTR-122; U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT, USA, 1982; 22p. [Google Scholar]
- Arroyo, L.; Pascual, I.; Manzanera, J. Fire models and methods to map fuel types: The role of remote sensing. For. Ecol. Manag. 2008, 256, 1239–1252. [Google Scholar] [CrossRef]
- Matthew, G.; Geoffrey, J.; Van Dijk, A.; Yebra, M. Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behavior. Remote Sens. Environ. 2021, 255, 112282. [Google Scholar] [CrossRef]
- Szpakowski, D.M.; Jensen, J.L.R. A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sens. Environ. 2019, 11, 2638. [Google Scholar] [CrossRef]
- Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO). Anuario de Estadística Forestal. 2020. Available online: https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/estadisticas/anuario_ef2020_tcm30-559705.pdf (accessed on 12 July 2024).
- de Galicia, X. Plan de Prevención y Defensa Contra Incendios Forestales de Galicia (PLADIGA). 2023. Available online: https://mediorural.xunta.gal/sites/default/files/temas/forestal/pladiga/2023/01_Memoria_Pladiga_Castellano_2023.pdf (accessed on 12 July 2024).
- Regos, A. Cartografía de áreas queimadas en Galicia no século XXI: Presentación do produto e aplicación web mapping. NACC 2018, 25, 45–53. Available online: https://revistas.usc.gal/index.php/nacc/article/view/5150 (accessed on 12 July 2024).
- METEOGALICIA. Red Meteorological. Available online: https://www.meteogalicia.gal/observacion/estacionshistorico/historico.action?idEst=10091# (accessed on 10 September 2022).
- Ministerio Para la Transición Ecológica y el Reto Demográfico (MITECO). Mapa Forestal de España (MFE) de Máxima Actualidad. 2011. Available online: https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/biodiversidad/mfe.aspx (accessed on 12 July 2024).
- Instituto Galego de Estadística. Censo de Población y Vivienda. Available online: https://www.ige.gal/web/mostrar_actividade_estatistica.jsp?codigo=0201001001 (accessed on 9 December 2022).
- Organismo Autónomo Centro Nacional de Información Geográfica (CNIG). Modelo Digital del Terreno (MDT05); Centro de descargas, Instituto Geografico Nacional. España. Available online: https://centrodedescargas.cnig.es/CentroDescargas/index.jsp (accessed on 5 November 2022).
- Solares-Canal, A.; Alonso, L.; Rincón, T.; Picos, J.; Molina-Terrén, D.M.; Becerra, C.; Armesto, J. Operational fuel model map for Atlantic landscapes using ALS and Sentinel-2 images. Fire Ecol. 2023, 19, 61. [Google Scholar] [CrossRef]
- Arellano, S.; Vega, J.A.; Ruiz, A.D.; Arellano, A.; Álvarez, J.G.; Vega, D.J.; Pérez, E. Foto-Guía de Combustibles Forestales de Galicia y Comportamiento del Fuego Asociado; Andavira Editora, S.L.: Santiago de Compostela, Spain, 2017; Available online: https://lourizan.xunta.gal/es/transferencias/foto-guia-de-combustibles-forestales-de-galicia (accessed on 8 June 2023).
- Technosylva. Wildfire Analyst Software, 2.9; Technosylva: La Jolla, CA, USA, 2014.
- Alan, A.; Finney, M.; Mark, A. Application of wildfire simulation models for risk analysis. Geophys. Res. Abstr. 2009, 11, EGU2009–EGU5489. Available online: https://www.fs.usda.gov/research/treesearch/42278 (accessed on 12 July 2024).
- ESRI. ArcGIS Desktop, version Release 10; Environmental Systems Research Institute: Redlands, CA, USA, 2011.
- Alcasena, F.; Ager, A.; Bailey, J.; Pineda, N.; Vega-García, C. Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain. J. Environ. Manag. 2019, 231, 303–320. [Google Scholar] [CrossRef]
- Mitsopoulos, I.; Mallinis, G.; Zibtsev, S.; Yavuz, M.; Saglam, B.; Kucuk, O.; Bogomolov, V.; Borsuk, A.; Zaimes, G. An integrated approach for mapping fire suppression difficulty in three different ecosystems of Eastern Europe. J. Spat. Sci. 2017, 62, 139–155. [Google Scholar] [CrossRef]
- Dillon, G.; Menakis, J.; Fay, F. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In Proceedings of the Large Wildland fires Conference, Missoula, MT, USA, 19–23 May 2014; Proc. RMRS-P-73. Keane, R.E., Jolly, M., Parsons, R., Riley, K., Eds.; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA; pp. 60–76. Available online: https://www.fs.usda.gov/research/treesearch/49429 (accessed on 12 July 2024).
- Alexander, M.E.; Lanoville, R.A. Predicting Fire Behavior in the Black Spruce-Lichen Woodland Fuel Type of Western and Northern Canada—Poster; Forestry Canada, Northern Forestry Center: Edmonton, AB, Canada; Government of the Northwest Territories, Department of Renewable Resources, Territorial Forest Fire Center: Fort Smith, NT, Canada, 1989; 16p. [Google Scholar]
- Bar Massada, A.; Radeloff, V.; Stewart, S.; Hawbaker, T. Wildfire risk in the wildland-urban interface: A simulation study in northwestern Wisconsin. For. Ecol. Manag. 2009, 258, 1990–1999. [Google Scholar] [CrossRef]
- Zigner, K.; Carvalho, L.M.V.; Jones, C.; Benoit, J.; Duine, G.-J.; Roberts, D.; Fujioka, F.; Moritz, M.; Elmquist, N.; Hazard, R. Wildfire Risk in the Complex Terrain of the Santa Barbara Wildland–Urban Interface during Extreme Winds. Fire 2022, 5, 138. [Google Scholar] [CrossRef]
- Fernández-Alonso, J.M.; Vega, J.A.; Jiménez, E.; Ruiz-González, A.D.; Álvarez-González, J.G. Spatially modeling wildland fire severity in pine forests of Galicia, Spain. Eur. J. For. Res 2016, 136, 105–121. [Google Scholar] [CrossRef]
- Fernandes, P.; Loureiro, C. Modelos de Combustível Florestal para Portugal—Documento de Referência, Versão de. 2021. Available online: https://www.researchgate.net/publication/357812218_Modelos_de_combustivel_florestal_para_Portugal_-_Documento_de_referencia_versao_de_2021 (accessed on 24 February 2024).
- Molina-Martinez, J.R.; Rodriguez y Silva, F. Modelos de Combustible Forestales UCO40. In Herramientas de Nueva Generación en Defensa contra Incendios Forestales. In Proceedings of the 5th Spanish Forestry Congress, Ávila, Spain, 21–25 September 2009. [Google Scholar]
- Madrigal, J.; Romero-Vivó, M.; Rodríguez y Silva, F. Definición y Recomendaciones Técnicas en el Diseño de Puntos Estratégicos de Gestión. Generalitat Valenciana; Conselleria d’Agricultura, Medi Ambient, Canvi Climatic i Desenvolupament Rural: Valencia, Spain, 2019; ISBN 978-84-941695-4-0. [Google Scholar]
- Krsnik, G.; Busquets Olivé, E.; Piqué Nicolau, M.; Larrañaga, A.; Cardil, A.; García-Gonzalo, J.; González Olabarría, J.R. Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning. Remote Sens. 2020, 12, 4124. [Google Scholar] [CrossRef]
- Gómez-González, J.L.; Cantizano, A.; Caro-Carretero, R.; Castro, M. Leveraging national forestry data repositories to advocate wildfire modeling towards simulation-driven risk assessment. Ecol. Ind. 2024, 158, 111306. [Google Scholar] [CrossRef]
- Cardil, A.; Monedero, S.; SeLegue, P.; Navarrete, M.Á.; de-Miguel, S.; Purdy, S.; Marshall, G.; Chavez, T.; Allison, K.; Quilez, R.; et al. Performance of operational fire spread models in California. Int. J. Wildl. Fire 2023, 32, 1492–1502. [Google Scholar] [CrossRef]
- Real Decreto-Ley 15/2022, de 1 de Agosto, por el que se Adoptan Medidas Urgentes en Materia de Incendios Forestales, Ministerio de la Presidencia, Justicia y Relaciones con Las Cortes, Gobierno de España. Available online: https://www.boe.es/eli/es/rdl/2022/08/01/15/con (accessed on 12 July 2024).
Type of Data | Source | Format | Resolution (Spatial/Temporal) | Source Date |
---|---|---|---|---|
Digital Terrain Model (DTM) | IGN [55] | Raster | Spatial: 5 × 5 m | 2009 |
Fuel model map | Solares-Canal et al. [56] | Raster | Spatial: 5 × 5 m | 2019 |
Weather records | Observation and Prediction Unit of Galicia [52] | Ascii | Temporal: Daily | 2019 |
Fuel Model | Description | Area (ha) | Percentage (%) |
---|---|---|---|
Model 0 | Anthropogenic areas, bodies of water | 1708.86 | 5.6 |
Model 1 | Rocky areas | 3525.33 | 11.6 |
Model 2 | Crops and pastures | 9240.84 | 30.4 |
Model 4 | High shrubs | 2818.49 | 9.3 |
Model 5 | Low shrubs | 1140.1 | 3.8 |
Model 7 | Tree-covered areas with continuous vertical structure | 7179.21 | 23.6 |
Model 10 | Tree-covered areas with discontinuous vertical structure | 4322.2 | 14.2 |
Model 12 | Harvested areas | 431.59 | 1.4 |
TOTAL | 30,366.62 | 100.0 |
Condition | ID Scenario | WD (°) | WS (km/h) | T (°C) | RH (%) |
---|---|---|---|---|---|
Typical | I | 180 (S) | 12.5 | 25 | 64.9 |
II | 45 (NE) | 11.7 | 28.0 | 53.1 | |
III | 315 (NW) | 12.4 | 31.0 | 53.1 | |
IV | 225 (SW) | 12.4 | 28.0 | 63.0 | |
Atypical | V | 180 (S) | 58.8 | 35.7 | 24.0 |
VI | 45 (NE) | 46.0 | 38.9 | 21.0 | |
VII | 315 (NW) | 54.7 | 41.5 | 14.0 | |
VIII | 225 (SW) | 35.4 | 34.7 | 62.0 |
FS (ha) | Typical Conditions | Atypical Conditions | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
I | II | III | IV | Aver. | V | VI | VII | VIII | Aver. | |
No fuel | 21.1 | 20.6 | 20.6 | 20.8 | 20.77 | 21.1 | 21.1 | 22.3 | 20.3 | 21.2 |
<100 | 3.7 | 2.3 | 2.5 | 2.5 | 2.7 | 0.0 | 0.3 | 1.4 | 0.6 | 0.6 |
100–500 | 34.9 | 45.4 | 35.8 | 36.3 | 38.1 | 6.2 | 5.6 | 9.9 | 5.4 | 6.8 |
500–1000 | 30.1 | 24.8 | 33.0 | 29.9 | 29.4 | 7.3 | 6.5 | 8.2 | 7.9 | 7.5 |
1000–1500 | 10.1 | 7.0 | 8.2 | 10.4 | 8.9 | 3.9 | 6.8 | 10.4 | 7.9 | 7.3 |
1500–2000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.8 | 4.8 | 6.5 | 5.6 | 5.4 |
>2000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 56.6 | 54.9 | 41.4 | 52.4 | 51.3 |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Variable | Values | I | II | III | IV | Typical | V | VI | VII | VIII | Atypical |
---|---|---|---|---|---|---|---|---|---|---|---|
ROS | <0.1 | 2.6 | 2.8 | 2.7 | 2.8 | 2.7 | 2.8 | 2.7 | 2.6 | 2.7 | 2.7 |
(km/h) | 0.1–0.6 | 43.0 | 45.5 | 39.3 | 41.3 | 42.3 | 0.0 | 0.1 | 0.0 | 0.4 | 0.1 |
0.6–2 | 46.4 | 47.1 | 47.3 | 45.5 | 46.5 | 0.6 | 1.2 | 0.5 | 27.1 | 7.3 | |
2–5 | 7.5 | 4.2 | 10.2 | 10.0 | 8.0 | 34.2 | 39.7 | 35.9 | 20.1 | 32.5 | |
>5 | 0.5 | 0.5 | 0.5 | 0.4 | 0.5 | 62.4 | 56.3 | 61.0 | 49.8 | 57.4 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
FLI | <500 | 12.6 | 17.0 | 12.6 | 11.8 | 13.5 | 2.8 | 2.7 | 2.7 | 2.7 | 2.7 |
(kW/m) | 500–2000 | 52.1 | 47.1 | 50.5 | 52.4 | 50.5 | 0.0 | 0.3 | 0.1 | 1.4 | 0.5 |
2000–4000 | 0.6 | 0.7 | 1.6 | 1.2 | 1.0 | 3.4 | 8.2 | 3.2 | 43.8 | 14.6 | |
4000–10,000 | 0.4 | 0.9 | 0.2 | 0.5 | 0.5 | 52.8 | 53.6 | 54.1 | 17.3 | 44.4 | |
10,000–30,000 | 34.0 | 33.9 | 34.6 | 33.7 | 34.0 | 7.0 | 0.1 | 4.7 | 0.1 | 3.0 | |
30,000–100,000 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 1.0 | 20.1 | 4.3 | 34.4 | 15.0 | |
>100,000 | 0.2 | 0.3 | 0.2 | 0.2 | 0.2 | 32.9 | 15.0 | 31.0 | 0.4 | 19.8 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Fuel Model | ||||||||
---|---|---|---|---|---|---|---|---|
Parameter | Value | 1 | 2 | 4 | 5 | 7 | 10 | 12 |
ROS (km/h) | <0.1 | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.2 |
0.1–0.6 | 1.2 | 36.0 | 0.7 | 6.3 | 39.1 | 38.9 | 25.3 | |
0.6–2 | 73.1 | 53.2 | 46.1 | 52.5 | 44.7 | 45.6 | 54.2 | |
2–5 | 24.3 | 10.5 | 52.8 | 40.7 | 15.9 | 15.0 | 19.5 | |
>5 | 1.2 | 0.3 | 0.6 | 0.3 | 0.4 | 0.5 | 0.8 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
FLI (kW/m) | <500 | 10.6 | 5.6 | 0.0 | 0.2 | 0.0 | 0.1 | 0.5 |
500–2000 | 48.5 | 62.0 | 1.0 | 13.5 | 53.3 | 49.9 | 28.3 | |
2000–4000 | 1.4 | 1.3 | 0.1 | 0.1 | 3.7 | 4.1 | 11.0 | |
4000–10,000 | 0.2 | 0.2 | 0.0 | 0.0 | 0.2 | 0.2 | 0.3 | |
10,000–30,000 | 38.0 | 30.5 | 97.9 | 85.8 | 42.3 | 45.2 | 59.0 | |
30,000–100,000 | 0.7 | 0.2 | 0.5 | 0.0 | 0.3 | 0.2 | 0.3 | |
>100,000 | 0.6 | 0.2 | 0.5 | 0.2 | 0.2 | 0.3 | 0.6 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Fuel Model | ||||||||
---|---|---|---|---|---|---|---|---|
Parameter | Value | 1 | 2 | 4 | 5 | 7 | 10 | 12 |
ROS (km/h) | <0.1 | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.2 |
0.1–0.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
0.6–2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8 | 0.5 | |
2–5 | 0.6 | 19.2 | 0.1 | 4.3 | 48.3 | 45.7 | 21.6 | |
>5 | 99.3 | 80.8 | 99.9 | 95.5 | 51.7 | 53.5 | 77.7 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
FLI (kW/m) | <500 | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.2 |
500–2000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
2000–4000 | 11.8 | 0.1 | 0.0 | 0.1 | 0.0 | 0.4 | 0.1 | |
4000–10,000 | 35.1 | 61.8 | 1.1 | 11.1 | 54.9 | 51.7 | 33.2 | |
10,000–30,000 | 14.5 | 17.4 | 0.3 | 3.4 | 2.9 | 2.5 | 6.6 | |
30,000–100,000 | 3.4 | 6.2 | 2.5 | 3.8 | 5.4 | 5.4 | 6.7 | |
>100,000 | 35.1 | 14.5 | 96.0 | 81.6 | 36.8 | 39.9 | 53.2 | |
Total | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Rincón, T.; Alonso, L.; Picos, J.; Molina-Terrén, D.M.; Armesto, J. A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula. Fire 2024, 7, 249. https://doi.org/10.3390/fire7070249
Rincón T, Alonso L, Picos J, Molina-Terrén DM, Armesto J. A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula. Fire. 2024; 7(7):249. https://doi.org/10.3390/fire7070249
Chicago/Turabian StyleRincón, Thais, Laura Alonso, Juan Picos, Domingo M. Molina-Terrén, and Julia Armesto. 2024. "A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula" Fire 7, no. 7: 249. https://doi.org/10.3390/fire7070249