{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:45:59Z","timestamp":1772261159102,"version":"3.50.1"},"posted":{"date-parts":[[2018,8,14]]},"group-title":"Biogeosciences","reference-count":0,"publisher":"Copernicus GmbH","license":[{"start":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T00:00:00Z","timestamp":1534204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Abstract. Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to Dynamic Global Vegetation Models (DGVMs) and Earth System Models (ESMs). We present SEVER-FIRE (Socio-Economic and natural Vegetation ExpeRimental global fire model which is incorporated into the SEVER-DGVM. One of the major focuses of SEVER-FIRE model is an implementation of pyrogenic behaviour of humans (timing of their activities and their willingness\/necessity to ignite or supress fire), related to socio-economic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires are compared with regional historical fire statistics. We focus both on the model output accuracy and on its assumptions regarding fire drivers, and perform: 1\u2013 An evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and inter-annual variability. 2\u2013 Analyses to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate driven inter-annual fire variability at a regional scale, such as the large fires associated with the 1997\u201398 El Ni\u00f1o event in Indonesia, Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM, and of assumed proxies of human fire practices. We further suggest possible development directions, to enable such models to better project future fire activity.<\/jats:p>","DOI":"10.5194\/gmd-2018-178","type":"posted-content","created":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T03:01:01Z","timestamp":1534215661000},"source":"Crossref","is-referenced-by-count":0,"title":["Analysis fire patterns and drivers with a global SEVER-FIRE model\nincorporated into Dynamic Global Vegetation Model and satellite\nand on-ground observations"],"prefix":"10.5194","author":[{"given":"Sergey","family":"Venevsky","sequence":"first","affiliation":[]},{"given":"Yannick","family":"Le Page","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2583-3669","authenticated-orcid":false,"given":"Jos\u00e9 M. C.","family":"Pereira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-856X","authenticated-orcid":false,"given":"Chao","family":"Wu","sequence":"additional","affiliation":[]}],"member":"3145","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.geosci-model-dev-discuss.net\/gmd-2018-178\/gmd-2018-178.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T08:46:11Z","timestamp":1738399571000},"score":1,"resource":{"primary":{"URL":"https:\/\/gmd.copernicus.org\/articles\/12\/89\/2019\/gmd-12-89-2019-discussion.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/gmd-2018-178","relation":{"has-comment":[{"id-type":"doi","id":"10.5194\/gmd-2018-178-SC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-SC4","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-SC2","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-SC3","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-SC5","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-AC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-AC2","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5194\/gmd-2018-178-RC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-2018-178-RC2","asserted-by":"subject"}],"is-preprint-of":[{"id-type":"doi","id":"10.5194\/gmd-12-89-2019","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/gmd-12-89-2019","asserted-by":"object"}]},"subject":[],"published":{"date-parts":[[2018,8,14]]},"subtype":"preprint"}}