Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-3917-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-3917-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coupling wildfire spread simulations and connectivity analysis for hazard assessment: a case study in Serra da Cabreira, Portugal
Ana C. L. Sá
CORRESPONDING AUTHOR
Centro de Estudos Florestais, Instituto Superior de Agronomia,
Universidade de Lisboa, Lisbon, Portugal
Bruno Aparicio
Centro de Estudos Florestais, Instituto Superior de Agronomia,
Universidade de Lisboa, Lisbon, Portugal
Akli Benali
Centro de Estudos Florestais, Instituto Superior de Agronomia,
Universidade de Lisboa, Lisbon, Portugal
Chiara Bruni
Centro de Estudos Florestais, Instituto Superior de Agronomia,
Universidade de Lisboa, Lisbon, Portugal
Michele Salis
Istituto per la BioEconomia, Consiglio Nazionale delle Ricerche, Sassari, Italy
Fábio Silva
Autoridade Nacional de Emergência e Proteção Civil,
Lisbon, Portugal
Martinho Marta-Almeida
Centro Oceanográfico de A Coruña, Instituto Español de
Oceanografía, A Coruña, Spain
Susana Pereira
Centro de Estudos do Ambiente e do Mar, Universidade de Aveiro, Aveiro, Portugal
Alfredo Rocha
Centro de Estudos do Ambiente e do Mar, Universidade de Aveiro, Aveiro, Portugal
José Pereira
Centro de Estudos Florestais, Instituto Superior de Agronomia,
Universidade de Lisboa, Lisbon, Portugal
Related authors
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
Short summary
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data, 17, 4957–4984, https://doi.org/10.5194/essd-17-4957-2025, https://doi.org/10.5194/essd-17-4957-2025, 2025
Short summary
Short summary
This data description paper provides details on the development of the first Portuguese Burn Severity Atlas for 1984 to 2022, derived from satellite imagery via the Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create a burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management but also within research areas related to air, water, and soil.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
Short summary
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Tomás Calheiros, Akli Benali, Mário Pereira, João Silva, and João Nunes
Nat. Hazards Earth Syst. Sci., 22, 4019–4037, https://doi.org/10.5194/nhess-22-4019-2022, https://doi.org/10.5194/nhess-22-4019-2022, 2022
Short summary
Short summary
Fire weather indices are used to assess the effect of weather on wildfires. Fire weather risk was computed and combined with large wildfires in Portugal. Results revealed the influence of vegetation cover: municipalities with a prevalence of shrublands, located in eastern parts, burnt under less extreme conditions than those with higher forested areas, situated in coastal regions. These findings are a novelty for fire science in Portugal and should be considered for fire management.
Carolina Viceto, Irina V. Gorodetskaya, Annette Rinke, Marion Maturilli, Alfredo Rocha, and Susanne Crewell
Atmos. Chem. Phys., 22, 441–463, https://doi.org/10.5194/acp-22-441-2022, https://doi.org/10.5194/acp-22-441-2022, 2022
Short summary
Short summary
We focus on anomalous moisture transport events known as atmospheric rivers (ARs). During ACLOUD and PASCAL, three AR events were identified: 30 May, 6 June, and 9 June 2017. We explore their spatio-temporal evolution and precipitation patterns using measurements, reanalyses, and a model. We show the importance of the following: Atlantic and Siberian pathways during spring–summer in the Arctic, AR-associated heat/moisture increase, precipitation phase transition, and high-resolution datasets.
Cited articles
Alcasena, F. J., Salis, M., and Vega-García, C.: A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain, Eur. J. Forest. Res., 135, 87–107, https://doi.org/10.1007/S10342-015-0919-6, 2016.
Alcasena, F., Ager, A., Le Page, Y., Bessa, P., Loureiro, C., and Oliveira,
T.: Assessing Wildfire Exposure to Communities and Protected Areas in Portugal, Fire, 4, 82, https://doi.org/10.3390/FIRE4040082, 2021.
Alexander, M. E. and Cruz, M. G.: Fireline Intensity, in: Encycl. Wildfires
Wildland-Urban Interface Fires, Springer, 1–8,
https://doi.org/10.1007/978-3-319-51727-8_52-1, 2019.
Anderson, H. E.: Aids to determining fuel models for estimating fire behavior, General Technical Report INT-122, USDA Forest Service, Intermountain Forest and Range Experiment Station, 28 pp., https://books.google.pt/books?hl=pt-PT&lr=&id=IeAhH-ovVKcC&oi=fnd&pg=PA1&ots=1h2dntjZ6q&sig=7jRzP15v_VqnVcyVFdGjf6Km44I&redir_esc=y#v=onepage&q&f=false (last access: 7 December 2022), 1982.
Anderson, W. R., Cruz, M. G., Fernandes, P. M., McCaw, L., Vega, J. A.,
Bradstock, R. A., Fogarty, L., Gould, J., McCarthy, G., and Marsden-Smedley,
J. B.: A generic, empirical-based model for predicting rate of fire spread
in shrublands, Int. J. Wildl. Fire, 24, 443–460, 2015.
Aparício, B. A., Pereira, J. M. C., Santos, F. C., Bruni, C., and Sá, A. C. L.: Combining wildfire behaviour simulations and network analysis to support wildfire management: A Mediterranean landscape case study, Ecol. Indic., 137, 108726, https://doi.org/10.1016/J.ECOLIND.2022.108726, 2022.
Banfield, J. D. and Raftery, A. E.: Model-Based Gaussian and Non-Gaussian
Clustering, Biometrics, 49, 803, https://doi.org/10.2307/2532201, 1993.
Barreiro, S., Benali, A., Rua, J. C. P., Tomé, M., Santos, J. L., and
Pereira, J. M. C.: Combining Landscape Fire Simulations with Stand-Level
Growth Simulations to Assist Landowners in Building Wildfire-Resilient
Landscapes, Forests, 12, 1498, https://doi.org/10.3390/F12111498, 2021.
Benali, A., Ervilha, A. R., Sá, A. C. L., Fernandes, P. M., Pinto, R. M.
S., Trigo, R. M., and Pereira, J. M. C.: Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations, Sci. Total Environ., 569, 73–85, https://doi.org/10.1016/j.scitotenv.2016.06.112, 2016a.
Benali, A., Russo, A., Sá, A. C. L., Pinto, R. M. S., Price, O., Koutsias, N., and Pereira, J. M. C.: Determining Fire Dates and Locating
Ignition Points With Satellite Data, Remote Sens., 8, 1–20,
https://doi.org/10.3390/rs8040326, 2016b.
Benali, A., Sá, A. C. L., Ervilha, A. R., Trigo, R. M., Fernandes, P. M.,
and Pereira, J. M. C.: Fire spread predictions: Sweeping uncertainty under
the rug, Sci. Total Environ., 592, 187–196, https://doi.org/10.1016/J.SCITOTENV.2017.03.106, 2017.
Benali, A., Sá, A. C. L., Pinho, J., Fernandes, P. M., and Pereira, J. M.
C.: Understanding the Impact of Different Landscape-Level Fuel Management
Strategies on Wildfire Hazard in Central Portugal, Forests, 12, 522, https://doi.org/10.3390/F12050522, 2021.
Bowman, D., Williamson, G., Abatzoglou, J., Kolden, C., Cochrane, M., and
Smith, A.: Human exposure and sensitivity to globally extreme wildfire events, Nat. Ecol. Evol., 1, 1–6, https://doi.org/10.1038/s41559-016-0058, 2017.
Butler, B. W., Anderson, W. R., and Catchpole, E. A.: Influence of slope on
fire spread rate, USDA For. Serv. Proc., 75–82, 2007.
Byram, G.: Combustion of forest fuels, For. fire Control use, 61–89
http://ci.nii.ac.jp/naid/10029316165/en/ (last access: 5 January 2022), 1959.
Calheiros, T., Nunes, J. P., and Pereira, M. G.: Recent evolution of spatial
and temporal patterns of burnt areas and fire weather risk in the Iberian
Peninsula, Agr. Forest Meteorol., 287, 107923, https://doi.org/10.1016/J.AGRFORMET.2020.107923, 2020.
Calkin, D. E., Cohen, J. D., Finney, M. A., and Thompson, M. P.: How risk
management can prevent future wildfire disasters in the wildland-urban
interface, P. Natl. Acad. Sci. USA, 111, 746–751, https://doi.org/10.1073/pnas.1315088111, 2014.
Castellnou, M., Guiomar, N., Rego, F., and Fernandes, P.: Fire growth patterns in the 2017 mega fire episode of October 15, central Portugal, Adv. Forest. Fire Res., 2018, 447–453, https://doi.org/10.14195/978-989-26-16-506_48, 2018.
Catry, F. X., Rego, F. C., Bação, F. L., and Moreira, F.: Modeling
and mapping wildfire ignition risk in Portugal, Int. J. Wildl. Fire, 18,
921–931, https://doi.org/10.1071/WF07123, 2009.
Costa, J., Aguiar, C., Capelo, J., Lousã, M., and Neto, C.: Biogeografia
de Portugal continental, https://bibliotecadigital.ipb.pt/handle/10198/714 (last access: 12 August 2021), 1998.
Curt, T. and Frejaville, T.: Wildfire Policy in Mediterranean France: How Far is it Efficient and Sustainable?, Risk Anal., 38, 472–488,
https://doi.org/10.1111/RISA.12855, 2018.
DGT – Direção-Geral do Território Dados abertos | DGT,
Port. L. Use L. Cover 2018, https://www.dgterritorio.gov.pt/dados-abertos, last access: 23 August 2021.
Duane, A., Miranda, M. D., and Brotons, L.: Forest connectivity percolation
thresholds for fire spread under different weather conditions, Forest Ecol.
Manage., 498, 119558, https://doi.org/10.1016/J.FORECO.2021.119558, 2021.
Duguy, B., Alloza, J. A., Röder, A., Vallejo, R., and Pastor, F.:
Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain), Int. J. Wildl. Fire,
16, 619–632, https://doi.org/10.1071/WF06101, 2007.
European Commission: Land-based wildfire prevention: principles and experiences on managing landscapes, forests and woodlands for safety and
resilience in Europe, edited by: Nuijten, D., Onida, M., and Lelouvier, R.,
Publications Office, https://op.europa.eu/en/publication-detail/-/publication/4e6cc1f1-8b8a-11eb-b85c-01aa75ed71a1 (last access: 7 December 2022), 2021.
EEA – European Environment Agency: Copernicus Land Monitoring Service User
Manual Consortium Partners, https://land.copernicus.eu/pan-european/high-resolution-layers/forests
(last access: 26 February 2022), 2018.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D. E.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fernandes, P., Gonçalves, H., Loureiro, C., Fernandes, M., Costa, T., Cruz, M., and Botelho, H.: Modelos de combustível florestal para Portugal, in: Actas Do 6o Congresso Florestal Nacional, SPCF – Sociedade Portuguesa de Ciências Florestais, Lisboa, Portugal, https://www.researchgate.net/publication/261708410_Modelos_de_Combustivel_Florestal_para_Portugal (last access: 7 December 2022), 2009.
Fernandes, P. M., Loureiro, C., Guiomar, N., Pezzatti, G. B., Manso, F. T.,
and Lopes, L.: The dynamics and drivers of fuel and fire in the Portuguese
public forest, J. Environ. Manage., 146, 373–382, 2014.
Fernandes, P. M., Monteiro-Henriques, T., Guiomar, N., Loureiro, C., and
Barros, A. M. G.: Bottom-Up Variables Govern Large-Fire Size in Portugal,
Ecosystems, 19, 1362–1375, https://doi.org/10.1007/S10021-016-0010-2/TABLES/3, 2016.
Fernandes, P. M., Guiomar, N., and Rossa, C. G.: Analysing eucalypt expansion
in Portugal as a fire-regime modifier, Sci. Total Environ., 666, 79–88,
https://doi.org/10.1016/J.SCITOTENV.2019.02.237, 2019.
Filippi, J.-B., Mallet, V., Nader, B., Filippi, J.-B., Mallet, V., and Nader, B.: Representation and evaluation of wildfire propagation simulations, Int. J. Wildl. Fire, 23, 46–57, https://doi.org/10.1071/WF12202, 2013.
Finney, M. A.: Fire growth using minimum travel time methods, Can. J. Forest Res., 32, 1420–1424, https://doi.org/10.1139/x02-068, 2002.
Finney, M. A.: An overview of FlamMap fire modeling capabilities, in Fuels
management – how to measure success: conference proceedings, March 28–30,
in: Proceedings RMRS-P-41, Department of Agriculture, Forest Service, Fort Collins, CO, US, Rocky Mountain Research Station, Portland,
Oregon, p. 13, https://www.fs.usda.gov/treesearch/pubs/25948 (last access: 23 August 2021), 2006.
Fletcher, R. J., Burrell, N. S., Reichert, B. E., Vasudev, D., and Austin, J.
D.: Divergent Perspectives on Landscape Connectivity Reveal Consistent
Effects from Genes to Communities, Curr. Landsc. Ecol. Rep., 1, 67–79, https://doi.org/10.1007/S40823-016-0009-6, 2016.
ICNF: Instituto da Conservação da Natureza e das Florestas; Projeto
áGIL – Dados LiDAR, https://geocatalogo.icnf.pt/geovisualizador/agil.html (last access:
25 March 2022), 2021a.
ICNF: Instituto da Conservação da Natureza e das Florestas, http://www.icnf.pt/portal/florestas/dfci/inc/estat-sgif (last access: 20 December 2021), 2021b.
Ingalsbee, T.: Whither the paradigm shift? Large wildland fires and the
wildfire paradox offer opportunities for a new paradigm of ecological fire
management, Int. J. Wildl. Fire, 26, 557–561, https://doi.org/10.1071/WF17062, 2017.
Keeley, A. T. H., Ackerly, D. D., Cameron, D. R., Heller, N. E., Huber, P.
R., Schloss, C. A., Thorne, J. H., and Merenlender, A. M.: New concepts, models, and assessments of climate-wise connectivity, Environ. Res. Lett., 13, 073002, https://doi.org/10.1088/1748-9326/AACB85, 2018.
Liberatore, F., León, J., Hearne, J., and Vitoriano, B.: Fuel management
operations planning in fire management: A bilevel optimisation approach,
Safe. Sci., 137, 105181, https://doi.org/10.1016/J.SSCI.2021.105181, 2021.
Lozano, O. M., Salis, M., Ager, A. A., Arca, B., Alcasena, F. J., Monteiro, A. T., Finney, M. A., Del Giudice, L., Scoccimarro, E., and Spano, D.: Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas, Risk Anal., 37, 1898–1916, https://doi.org/10.1111/RISA.12739, 2017.
Marta-Almeida, M., Teixeira, J. C., Carvalho, M. J., Melo-Gonçalves, P.,
and Rocha, A. M.: High resolution WRF climatic simulations for the Iberian
Peninsula: Model validation, Phys. Chem. Earth Pt. A/B/C, 94, 94–105,
https://doi.org/10.1016/J.PCE.2016.03.010, 2016.
Martín, A., Botequim, B., Oliveira, T. M., Ager, A., and Pirotti, F.:
Resource Communication. Temporal optimization of fuel treatment design in
blue gum (Eucalyptus globulus) plantations, Forest Syst., 25, eRC09–eRC09,
https://doi.org/10.5424/FS/2016252-09293, 2016.
Martín-Martín, C., Bunce, R. G. H., Saura, S., and Elena-Rosselló, R.: Changes and interactions between forest landscape connectivity and burnt area in Spain, Ecol. Indic., 33, 129–138,
https://doi.org/10.1016/J.ECOLIND.2013.01.018, 2013.
Moreira, F., Viedma, O., Arianoutsou, M., Curt, T., Koutsias, N., Rigolot,
E., Barbati, A., Corona, P., Vaz, P., and Xanthopoulos, G.: Landscape–wildfire interactions in southern Europe: implications for
landscape management, J. Environ. Manage., 92, 2389–2402, 2011.
Moreira, F., Ascoli, D., Safford, H., Adams, M. A., Moreno, J. M., Pereira,
J. M. C., Catry, F. X., Armesto, J., Bond, W., González, M. E., Curt, T., Koutsias, N., McCaw, L., Price, O., Pausas, J. G., Rigolot, E., Stephens, S., Tavsanoglu, C., Vallejo, V. R., Van Wilgen, B. W., Xanthopoulos, G., and Fernandes, P. M.: Wildfire management in Mediterranean-type regions: Paradigm change needed, Environ. Res. Lett., 15, 011001, https://doi.org/10.1088/1748-9326/AB541E, 2020.
Moudio, P. E., Pais, C., and Shen, Z.-J. M.: Quantifying the impact of ecosystem services for landscape management under wildfire hazard, Nat. Hazards, 106, 531–560, https://doi.org/10.1007/s11069-020-04474-y, 2021.
Nelson Jr., R. M.: Prediction of diurnal change in 10-h fuel stick moisture
content, Can. J. Forest. Res., 30, 1071–1087, 2000.
Oliveira, S., Gonçalves, A., Benali, A., Sá, A., Zêzere, J. L.,
and Pereira, J. M.: Assessing Risk and Prioritizing Safety Interventions in
Human Settlements Affected by Large Wildfires, Forests, 11, 859,
https://doi.org/10.3390/F11080859, 2020.
Oliveira, S. L. J., Pereira, J. M. C., and Carreiras, J. M. B.: Fire frequency analysis in Portugal (1975–2005), using Landsat-based burnt area
maps, Int. J. Wildl. Fire, 21, 48–60, https://doi.org/10.1071/WF10131, 2012.
Oliveira, T. M., Barros, A. M. G., Ager, A. A., and Fernandes, P. M.: Assessing the effect of a fuel break network to reduce burnt area and wildfire risk transmission, Int. J. Wildl. Fire, 25, 619–632,
https://doi.org/10.1071/WF15146, 2016.
Palaiologou, P., Ager, A. A., Nielsen-Pincus, M., Evers, C. R., and Kalabokidis, K.: Using transboundary wildfire exposure assessments to improve fire management programs: A case study in Greece, Int. J. Wildl. Fire, 27, 501–513, https://doi.org/10.1071/WF17119, 2018.
Palaiologou, P., Kalabokidis, K., Ager, A. A., and Day, M. A.: Development of
Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in Greece, Forests, 11, 789, https://doi.org/10.3390/F11080789, 2020.
Parisien, M.-A., Dawe, D. A., Miller, C., Stockdale, C. A., and Armitage, O.
B.: Applications of simulation-based burn probability modelling: a review,
Int. J. Wildl. Fire, 28, 913–926, https://doi.org/10.1071/WF19069, 2019.
Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980–2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343–3358, https://doi.org/10.5194/nhess-11-3343-2011, 2011.
Plucinski, M. P.: Fighting Flames and Forging Firelines: Wildfire Suppression Effectiveness at the Fire Edge, Curr. Forest. Rep., 5, 1–19, https://doi.org/10.1007/s40725-019-00084-5, 2019.
Rachmawati, R., Ozlen, M., Reinke, K. J., and Hearne, J. W.: An optimisation
approach for fuel treatment planning to break the connectivity of high-risk
regions, Forest Ecol. Manage., 368, 94–104, https://doi.org/10.1016/J.FORECO.2016.03.014, 2016.
RCM: Resolução do Conselho de Ministros 71-A/2021, 2021-06-08 – DRE,
Aprova o Programa Nacional de Ação do Plano Nacional de Gestão
Integrada de Fogos Rurais, https://dre.pt/home/-/dre/164798802/details/maximized, last access: 30 September 2021.
Ribeiro, L. M., Rodrigues, A., Lucas, D., and Viegas, D. X.: The Impact on
Structures of the Pedrógão Grande Fire Complex in June 2017 (Portugal), Fire, 3, 57, https://doi.org/10.3390/FIRE3040057, 2020.
Rodrigues, M., Alcasena, F., and Vega-García, C.: Modeling initial attack success of wildfire suppression in Catalonia, Spain, Sci. Total Environ., 666, 915–927, https://doi.org/10.1016/J.SCITOTENV.2019.02.323, 2019.
Rothermel, R. C.: A mathematical model for predicting fire spread in
wildland fuels, Res. Pap. INT-115, US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, p. 40, https://books.google.pt/books?hl=pt-PT&lr=&id=27n_RugVVK0C&oi=fnd&pg=PA1&ots=9V3MCEpv34&sig=0pz2rz81krPm3gPxhvKPCFa_GaY&redir_esc=y#v=onepage&q&f=false (last access: 6 December 2022), 1972.
Salis, M., Ager, A. A., Arca, B., Finney, M. A., Bacciu, V., Duce, P., and
Spano, D.: Assessing exposure of human and ecological values to wildfire in
Sardinia, Italy, Int. J. Wildl. Fire, 22, 549–565, https://doi.org/10.1071/WF11060, 2013.
Salis, M., Laconi, M., Ager, A. A., Alcasena, F. J., Arca, B., Lozano, O.,
Fernandes de Oliveira, A., and Spano, D.: Evaluating alternative fuel treatment strategies to reduce wildfire losses in a Mediterranean area, Forest Ecol. Manage., 368, 207–221, https://doi.org/10.1016/j.foreco.2016.03.009, 2016a.
Salis, M., Arca, B., Alcasena, F., Arianoutsou, M., Bacciu, V., Duce, P.,
Duguy, B., Koutsias, N., Mallinis, G., Mitsopoulos, I., Moreno, J. M., Pérez, J. R., Urbieta, I. R., Xystrakis, F., Zavala, G., and Spano, D.:
Predicting wildfire spread and behaviour in Mediterranean landscapes, Int.
J. Wildl. Fire, 25, 1015–1032, https://doi.org/10.1071/WF15081, 2016b.
Salis, M., Del Giudice, L., Arca, B., Ager, A. A., Alcasena-Urdiroz, F., Lozano, O., Bacciu, V., Spano, D., and Duce, P.: Modeling the effects of
different fuel treatment mosaics on wildfire spread and behavior in a
Mediterranean agro-pastoral area, J. Environ. Manage., 212, 490–505,
https://doi.org/10.1016/j.jenvman.2018.02.020, 2018.
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., and Duce, P.: Application of simulation modeling for wildfire
exposure and transmission assessment in Sardinia, Italy, Int. J. Disast. Risk Reduct., 58, 102189, https://doi.org/10.1016/J.IJDRR.2021.102189, 2021.
Scrucca, L., Fop, M., Murphy, T. B., and Raftery, A. E.: mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models, R J., 8, 289–317, https://doi.org/10.32614/RJ-2016-02, 2016.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., and Barker, D. M. A.: Description of the Advanced Research WRF Model Version 4, National Center for Atmospheric Research, Boulder, CO, USA, p. 145, https://doi.org/10.5065/D68S4MVH, 2019.
Sørensen, T.: 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, Biol. Skr., 5, 1–34, 1948.
Stahl, D. and Sallis, H.: Model-based cluster analysis, Wiley Interdisciplin.
Rev. Comput. Stat., 4, 341–358, https://doi.org/10.1002/WICS.1204, 2012.
Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, M. R., Delogu, G.
M., Fernandes, P. M., Ferreira, C., McCaffrey, S., McGee, T. K., Parente,
J., Paton, D., Pereira, M. G., Ribeiro, L. M., Viegas, D. X., and Xanthopoulos, G.: Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts, Fire, 1, 9, https://doi.org/10.3390/FIRE1010009, 2018.
Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V.,
Turubanova, S., Parker, D., Zalles, V., Lima, A., Kommareddy, I., Song,
X.-P., Wang, L., and Harris, N.: Global Trends of Forest Loss Due to Fire
From 2001 to 2019, Front. Remote Sens., 3, 9, https://doi.org/10.3389/FRSEN.2022.825190, 2022.
Wunder, S., Calkin, D. E., Charlton, V., Feder, S., Martínez de Arano,
I., Moore, P., Rodríguez y Silva, F., Tacconi, L., and Vega-García,
C.: Resilient landscapes to prevent catastrophic forest fires: Socioeconomic
insights towards a new paradigm, Forest Policy Econ., 128, 102458,
https://doi.org/10.1016/J.FORPOL.2021.102458, 2021.
Zeller, K. A., Lewison, R., Fletcher, R. J., Tulbure, M. G., and Jennings, M.
K.: Understanding the Importance of Dynamic Landscape Connectivity, Land, 9, 303, https://doi.org/10.3390/LAND9090303, 2020.
Short summary
Assessing landscape wildfire connectivity supported by wildfire spread simulations can improve fire hazard assessment and fuel management plans. Weather severity determines the degree of fuel patch connectivity and thus the potential to spread large and intense wildfires. Mapping highly connected patches in the landscape highlights patch candidates for prior fuel treatments, which ultimately will contribute to creating fire-resilient Mediterranean landscapes.
Assessing landscape wildfire connectivity supported by wildfire spread simulations can improve...
Altmetrics
Final-revised paper
Preprint