• Energy Research

AbstractThe strong El Niño 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 2015–2016 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 2015–2016 and the 2016–2017 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 (1997–1998), 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.

Extreme droughts have been recurrent in the Amazon over the past decades, causing socio-economic and environmental impacts. Here, we investigate the vulnerability of Amazonian forests, both undisturbed and human-modified, to repeated droughts. We defined vulnerability as a measure of (i) exposure, which is the degree to which these ecosystems were exposed to droughts, and (ii) its sensitivity, measured as the degree to which the drought has affected remote sensing-derived forest greenness. The exposure was calculated by assessing the meteorological drought, using the standardized precipitation index (SPI) and the maximum cumulative water deficit (MCWD), which is related to vegetation water stress, from 1981 to 2016. The sensitivity was assessed based on the enhanced vegetation index anomalies (AEVI), derived from the newly available Moderate Resolution Imaging Spectroradiometer (MODIS)/Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) product, from 2003 to 2016, which is indicative of forest's photosynthetic capacity. We estimated that 46% of the Brazilian Amazon biome was under severe to extreme drought in 2015/2016 as measured by the SPI, compared with 16% and 8% for the 2009/2010 and 2004/2005 droughts, respectively. The most recent drought (2015/2016) affected the largest area since the drought of 1981. Droughts tend to increase the variance of the photosynthetic capacity of Amazonian forests as based on the minimum and maximum AEVI analysis. However, the area showing a reduction in photosynthetic capacity prevails in the signal, reaching more than 400 000 km 2 of forests, four orders of magnitude larger than areas with AEVI enhancement. Moreover, the intensity of the negative AEVI steadily increased from 2005 to 2016. These results indicate that during the analysed period drought impacts were being exacerbated through time. Forests in the twenty-first century are becoming more vulnerable to droughts, with larger areas intensively and negatively responding to water shortage in the region. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Amazonia lost 947 million tons of carbon induced by forest edge effect between 2001 and 2015, about 30% of deforestation losses.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

AbstractThe joint and relative effects of future land‐use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land‐use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041–2070) and late (2071–2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071–2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best‐case land‐use scenario (“Sustainability”) alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse‐case land‐use scenario (“Fragmentation”) causes a 73.2% increase. The optimistic climate‐land‐use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071–2100 compared to the baseline period. In contrast, the most pessimistic climate‐land‐use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land‐use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land‐use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

AbstractFragmented tropical forest landscapes preserve much of the remaining biodiversity and carbon stocks. Climate change is expected to intensify droughts and increase fire hazard and fire intensities, thereby causing habitat deterioration, and losses of biodiversity and carbon stock losses. Understanding the trajectories that these landscapes may follow under increased climate pressure is imperative for establishing strategies for conservation of biodiversity and ecosystem services. Here, we used a quantitative predictive modelling approach to project the spatial distribution of the aboveground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest (AF) domain. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report. Our AGB models had a satisfactory performance (area under the curve > 0.75 and p value < .05). The models projected a significant increase of 8.5% in the total carbon stock. Overall, the projections indicated that 76.9% of the AF domain would have suitable climatic conditions for increasing biomass by 2100 considering the RCP 4.5 scenario, in the absence of deforestation. Of the existing forest fragments, 34.7% are projected to increase their AGB, while 2.6% are projected to have their AGB reduced by 2100. The regions likely to lose most AGB—up to 40% compared to the baseline—are found between latitudes 13° and 20° south. Overall, although climate change effects on AGB vary latitudinally for the 2071–2100 period under the RCP 4.5 scenario, our model indicates that AGB stocks can potentially increase across a large fraction of the AF. The patterns found here are recommended to be taken into consideration during the planning of restoration efforts, as part of climate change mitigation strategies in the AF and elsewhere in Brazil.