{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:08:51Z","timestamp":1774548531212,"version":"3.50.1"},"reference-count":77,"publisher":"Copernicus GmbH","issue":"8","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECIND\/03799\/2018\/CP1563\/CT0003"],"award-info":[{"award-number":["CEECIND\/03799\/2018\/CP1563\/CT0003"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00239\/2020"],"award-info":[{"award-number":["UIDB\/00239\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PCIF\/SSI\/0102\/2017"],"award-info":[{"award-number":["PCIF\/SSI\/0102\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/ASP-SIL\/28771\/2017"],"award-info":[{"award-number":["PTDC\/ASP-SIL\/28771\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DL 57\/2016\/CP1382\/CT0003"],"award-info":[{"award-number":["DL 57\/2016\/CP1382\/CT0003"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/05183\/2020"],"award-info":[{"award-number":["UIDB\/05183\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Earth Syst. Sci. Data"],"abstract":"<jats:p>Abstract. Wildfire behaviour depends on complex interactions between fuels,\ntopography, and weather over a wide range of scales, being important for\nfire research and management applications. To allow for significant\nprogress towards better fire management, the operational and research\ncommunities require detailed open data on observed wildfire behaviour. Here,\nwe present the Portuguese Large Wildfire Spread database (PT-FireSprd) that\nincludes the reconstruction of the spread of 80 large wildfires that\noccurred in Portugal between 2015 and 2021. It includes a detailed set of\nfire behaviour descriptors, such as rate of spread (ROS), fire growth rate\n(FGR), and fire radiative energy (FRE). The wildfires were reconstructed by\nconverging evidence from complementary data sources, such as satellite\nimagery and products, airborne and ground data collected by fire personnel, and\nofficial fire data and information in external reports. We then implemented\na digraph-based algorithm to estimate the fire behaviour descriptors and\ncombined it with the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) fire radiative power estimates. A total of 1197\nROS and FGR estimates were calculated along with 609 FRE estimates. The\nextreme fires of 2017 were responsible for the maximum observed values of\nROS (8900\u2009m\u2009h\u22121) and FGR (4400\u2009ha\u2009h\u22121). Combining both descriptors, we describe\nthe fire behaviour distribution using six percentile intervals that can be\neasily communicated to both research and management communities. Analysis of\nthe database showed that burned extent is mostly determined by FGR rather\nthan by ROS. Finally, we explored a practical example to show how the\nPT-FireSprd database can be used to study the dynamics of individual\nwildfires and to build robust case studies for training and capacity\nbuilding. The PT-FireSprd is the first open-access fire progression and behaviour\ndatabase in Mediterranean Europe, dramatically expanding the extant\ninformation. Updating the PT-FireSprd database will require a continuous\njoint effort by researchers and fire personnel. PT-FireSprd data are\npublicly available through https:\/\/doi.org\/10.5281\/zenodo.7495506 (Benali\net al., 2022) and have large potential to improve current\nknowledge on wildfire behaviour and to support better decision making.<\/jats:p>","DOI":"10.5194\/essd-15-3791-2023","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T07:17:13Z","timestamp":1692775033000},"page":"3791-3818","source":"Crossref","is-referenced-by-count":23,"title":["The Portuguese Large Wildfire Spread database (PT-FireSprd)"],"prefix":"10.5194","volume":"15","author":[{"given":"Akli","family":"Benali","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4627-6128","authenticated-orcid":false,"given":"Nuno","family":"Guiomar","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Bernardo","family":"Mota","sequence":"additional","affiliation":[]},{"given":"F\u00e1bio","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0336-4398","authenticated-orcid":false,"given":"Paulo M.","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Mota","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Penha","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2583-3669","authenticated-orcid":false,"given":"Jos\u00e9 M. C.","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Ana C. L.","family":"S\u00e1","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref1","unstructured":"Albini, F. A.: Wildland Fires: Predicting the behavior of wildland\nfires \u2013 among nature's most potent forces \u2013 can save lives, money, and\nnatural resources, Am. Sci., 72, 590\u2013597, 1984."},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Alcasena, F., Ager, A., Le Page, Y., Bessa, P., Loureiro, C., and Oliveira,\nT.: Assessing wildfire exposure to communities and protected areas in\nPortugal, Fire, 4, 82, https:\/\/doi.org\/10.3390\/fire404008, 2021.","DOI":"10.3390\/fire4040082"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Alexander, M. and Cruz, M. G.: Are the applications of wildland fire\nbehaviour models getting ahead of their evaluation again?, Environ. 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M.: The fire environment concept, USDA Forest Service,\nPacific Southwest Range and Experiment Station, Berkeley, California, USA,\n1972."},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Crowley, M. A., Cardille, J. A., White, J. C., and Wulder, M. A.: Generating\nintra-year metrics of wildfire progression using multiple open-access\nsatellite data streams, Remote Sens. Environ., 232, 111295,\nhttps:\/\/doi.org\/10.1016\/j.rse.2019.111295, 2019.","DOI":"10.1016\/j.rse.2019.111295"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Cruz, M. G.: Monte Carlo-based ensemble method for prediction of grassland\nfire spread, Int. J. Wildland Fire, 19, 521\u2013530, https:\/\/doi.org\/10.1071\/WF08195,\n2010.","DOI":"10.1071\/WF08195"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Cruz, M. G. and Alexander, M. E.: Uncertainty associated with model\npredictions of surface and crown fire rates of spread, Environ. Modell.\nSoftw., 47, 16\u201328, https:\/\/doi.org\/10.1016\/j.envsoft.2013.04.004, 2013.","DOI":"10.1016\/j.envsoft.2013.04.004"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Cruz, M. G. and Alexander, M. E.: The 10\u2009% wind speed rule of thumb for\nestimating a wildfire's forward rate of spread in forests and shrublands,\nAnn. Forest Sci., 76, 1\u201311, https:\/\/doi.org\/10.1007\/s13595-019-0829-8, 2019.","DOI":"10.1007\/s13595-019-0829-8"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Cruz, M. G., Gould, J. S., Alexander, M. E., Sullivan, A. L., McCaw, W. L.,\nand Matthews, S.: Empirical-based models for predicting head-fire rate of\nspread in Australian fuel types, Aust. Forestry, 78, 118\u2013158,\nhttps:\/\/doi.org\/10.1080\/00049158.2015.1055063, 2015.","DOI":"10.1080\/00049158.2015.1055063"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Cruz, M. G., Alexander, M. E., Sullivan, A. L., Gould, J. S., and Kilinc,\nM.: Assessing improvements in models used to operationally predict wildland\nfire rate of spread, Environ. Modell. Softw., 105, 54\u201363,\nhttps:\/\/doi.org\/10.1016\/j.envsoft.2018.03.027, 2018.","DOI":"10.1016\/j.envsoft.2018.03.027"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Cruz, M. G., Alexander, M. E., and Kilinc, M.: Wildfire rates of spread in\ngrasslands under critical burning conditions, Fire, 5, 55,\nhttps:\/\/doi.org\/10.3390\/fire5020055, 2022.","DOI":"10.3390\/fire5020055"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Cruz, M. G., Cheney, N. P., Gould, J. S., McCaw, W. L., Kilinc, M., and\nSullivan, A. L.: An empirical-based model for predicting the forward spread\nrate of wildfires in eucalypt forests, Int. J. Wildland Fire, 31, 81\u201395,\nhttps:\/\/doi.org\/10.1071\/WF21068, 2021.","DOI":"10.1071\/WF21068"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Dale, M. R. T. and Fortin, M. J.: From graphs to spatial graphs, Annu. Rev.\nEcol. Evol. Systs., 41, 21\u201338, https:\/\/doi.org\/10.1146\/annurev-ecolsys-102209-144718, 2010.","DOI":"10.1146\/annurev-ecolsys-102209-144718"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Duff, T. J., Chong, D. M., and Tolhurst, K. G.: Quantifying spatio-temporal\ndifferences between fire shapes: Estimating fire travel paths for the\nimprovement of dynamic spread models, Environ. Modell. Softw, 46, 33\u201343,\nhttps:\/\/doi.org\/10.1016\/j.envsoft.2013.02.005, 2013.","DOI":"10.1016\/j.envsoft.2013.02.005"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Fernandes, P. M., Botelho, H. S., Rego, F. C., and Loureiro, C.: Empirical\nmodelling of surface fire behaviour in maritime pine stands, Int. J.\nWildland Fire, 18, 698\u2013710, https:\/\/doi.org\/10.1071\/WF08023, 2009.","DOI":"10.1071\/WF08023"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Fernandes, P. M., Barros, A. 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