@Article{FonsecaAASXXMWA:2017:ClAnDr,
               author = "Fonseca, Marisa G. and Anderson, Liana O. and Arai, Egidio and 
                         Shimabukuro, Yosio Edemir and Xaud, Haron A. M. and Xaud, 
                         Maristela R. and Madani, Nima and Wagner, Fabien Hubert and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
          affiliation = "{Tropical Ecosystems and Environmental Sciences Laboratory 
                         (TREES)} and {Centro Nacional de Monitoramento e Alertas de 
                         Desastre Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and Embrapa and EMBRAPA and EMBRAPA and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Climatic and anthropogenic drivers of northern Amazon fires during 
                         the 2015–2016 El Ni~no event",
              journal = "Ecological Applications",
                 year = "2017",
               volume = "27",
               number = "8",
                pages = "2514--2527",
             keywords = "anthropogenic ignition, climate, fire modeling, hot pixels, 
                         machine learning, multivariate ENSO index, savannas, tropical 
                         forests.",
             abstract = "The strong El Nino Southern Oscillation (ENSO) event that occurred 
                         in 2015 ~ 2016 caused extreme drought in the northern Brazilian 
                         Amazon, especially in the state of Roraima, increasing fire 
                         occurrence. Here we map the extent of precipitation and fire 
                         anomalies and quantify the effects of climatic and anthropogenic 
                         drivers on fire occurrence during the 20152016 dry season (from 
                         December 2015 to March 2016) in the state of Roraima. To achieve 
                         these objectives we first estimated the spatial pattern of 
                         precipitation anomalies, based on long-term data from the TRMM 
                         (Tropical Rainfall Measuring Mission), and the fire anomaly, based 
                         on MODIS (Moderate Resolution Imaging Spectroradiometer) active 
                         fire detections during the referred period. Then, we integrated 
                         climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) 
                         model to quantify fire probability, assessing (1) the model 
                         accuracy during the 20152016 and the 20162017 dry seasons; (2) the 
                         relative importance of each predictor variable on the model 
                         predictive performance; and (3) the response curves, showing how 
                         each environmental variable affects the fire probability. 
                         Approximately 59% (132,900 km2 ) of the study area was exposed to 
                         precipitation anomalies \≤1 standard deviation (SD) in 
                         January and ~48% (~106,800 km2 ) in March. About 38% (86,200 km2 ) 
                         of the study area experienced fire anomalies \≥1 SD in at 
                         least one month between December 2015 and March 2016. The distance 
                         to roads and the direct ENSO effect on fire occurrence were the 
                         two most influential variables on model predictive performance. 
                         Despite the improvement of governmental actions of fire prevention 
                         and firefighting in Roraima since the last intense ENSO event 
                         (19971998), we show that fire still gets out of control in the 
                         state during extreme drought events. Our results indicate that if 
                         no prevention actions are undertaken, future road network 
                         expansion and a climate-induced increase in water stress will 
                         amplify fire occurrence in the northern Amazon, even in its humid 
                         dense forests. As an additional outcome of our analysis, we 
                         conclude that the model and the data we used may help to guide 
                         on-the-ground fire-prevention actions and firefighting planning 
                         and therefore minimize fire-related ecosystems degradation, 
                         economic losses and carbon emissions in Roraima.",
                 issn = "1051-0761",
             language = "en",
           targetfile = "Fonseca_et_al-2017-Ecological_Applications.pdf",
        urlaccessdate = "27 abr. 2024"
}