This study analyzes the spatiotemporal variability of rainfall and temperature (minimum, maximum and average) trends at 47 stations throughout the Brazilian Legal Amazon for the period 1973–2013. Annual, wet season and dry season trends were quantified by Sen's slope for each station and the entire region. The Mann–Kendall test was used to determine the statistical significance of the trends. For the whole region, minimum, maximum and average annual temperatures showed increasing trend of approximately 0.04 °C per year. The rainfall showed an insignificant trend for most stations for annual and seasonal series. Nevertheless, some stations showed significant increasing trends in the annual and wet season rainfalls while a few stations showed decreasing trends for the dry season rainfall. A positive trend of the annual range between wet and dry season rainfall was found in some stations, caused mainly by an increasing trend in wet season rainfall.

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha−1 yr−1) compared to those in the east (1.3 ± 0.3 Mg C ha−1 yr−1). Disturbances reduce regrowth rates by 8–55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr−1 until 2030, contributing ~5.5% to Brazil’s 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution.

Amazonia is home to more than half of the world's remaining tropical forests, playing a key role as reservoirs of carbon and biodiversity. However, whether at a slower or faster pace, continued deforestation causes forest fragmentation in this region. Thus, understanding the relationship between forest fragmentation and fire incidence and intensity in this region is critical. Here, we use MODIS Active Fire Product (MCD14ML, Collection 6) as a proxy of forest fire incidence and intensity (measured as Fire Radiative Power-FRP), and the Brazilian official Land-use and Land-cover Map to understand the relationship among deforestation, fragmentation, and forest fire on a deforestation frontier in the Brazilian Amazonia. Our results showed that forest fire incidence and intensity vary with levels of habitat loss and forest fragmentation. About 95% of active fires and the most intense ones (FRP > 500 megawatts) were found in the first kilometre from the edges in forest areas. Changes made in 2012 in the Brazilian main law regulating the conservation of forests within private properties reduced the obligation to recover illegally deforested areas, thus allowing for the maintenance of fragmented areas in the Brazilian Amazonia. Our results reinforce the need to guarantee low levels of fragmentation in the Brazilian Amazonia in order to avoid the degradation of its forests by fire and the related carbon emissions.Forests 2018, 9, 305 2 of 16 on access to credit, expansion of protected areas, and civil society interventions in the soy and beef supply chains [16]. Nonetheless, the deforestation rate increased markedly in 2015 and 2016 [15] (24% and 27% in relation to the previous year, respectively), raising concerns that the recent weakening of environmental-protection policies could be already reversing the Brazilian progress in reducing the Amazonian forest destruction.Whether at a slower or faster pace, continued deforestation cumulatively causes forest habitat loss, altering habitat configuration, such as the change in spatial arrangement of the remaining habitat through forest fragmentation. Metrics of habitat configuration, such as the number and mean size of forest patches and edge length covary with habitat amount. Understanding these relationships is important to correctly interpret the effects of habitat fragmentation on tropical forests [17]. Following Farhig (2003) [18], the mean patch size of remaining forests is expected to linearly decrease with the reduction in habitat amount, while both the number of patches and the total edge are expected to rise up to a certain threshold of habitat loss and then decrease with increasing deforestation.Forest edges resulting from landscape fragmentation are highly fire-prone due to increased dryness, higher fuel load compared to forest interior and proximity to ignition sources from adjacent management areas [19][20][21][22][23][24]. Fragmentation and its resulting edge effects may act synergistically with the ongoing large-scale changes in climate and fire regimes, threate...

Highlights Pandemics can become a new indirect driver of tropical deforestation. Halting illegal deforestation should be considered an essential activity during the pandemic. Forest fires could aggravate the health risks of COVID-19. Tropical deforestation will increase the risks of emerging zoonotic diseases. Indigenous people should be especially protected during the current pandemic.

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Extreme droughts in Amazonia cause anomalous increase in fire occurrence, disrupting the stability of environmental, social, and economic systems. Thus, understanding how droughts affect fire patterns in this region is essential for anticipating and planning actions for remediation of possible impacts. Focused on the Brazilian Amazon biome, we investigated fire responses to the 2010 and 2015/2016 Amazonian droughts using remote sensing data. Our results revealed that the 2015/2016 drought surpassed the 2010 drought in intensity and extent. During the 2010 drought, we found a maximum area of 846,800 km 2 (24% of the Brazilian Amazon biome) with significant (p ≤ 0.05) rainfall decrease in the first trimester, while during the 2015/2016 the maximum area reached 1,702,800 km 2 (47% of the Brazilian Amazon biome) in the last trimester of 2015. On the other hand, the 2010 drought had a maximum area of 840,400 km 2 (23% of the Brazilian Amazon biome) with significant (p ≤ 0.05) land surface temperature increase in the first trimester, while during the 2015/2016 drought the maximum area was 2,188,800 km 2 (61% of the Brazilian Amazon biome) in the last trimester of 2015. Unlike the 2010 drought, during the 2015/2016 drought, significant positive anomalies of active fire and CO 2 emissions occurred mainly during the wet season, between October 2015 and March 2016. During the 2010 drought, positive active fire anomalies resulted from the simultaneous increase of burned forest, non-forest vegetation and productive lands. During the 2015/2016 drought, however, this increase was dominated by burned forests. The two analyzed droughts emitted together 0.47 Pg CO 2 , with 0.23 Pg CO 2 in 2010, 0.15 Pg CO 2 in 2015 and 0.09 Pg CO 2 in 2016, which represented, respectively, 209%, 136%, 82% of annual Brazil's national target for reducing carbon emissions from deforestation by 2017 (approximately 0.11 Pg CO 2 year −1 from 2006 to 2017). Finally, we anticipate that the increase of fires during the droughts showed here may intensify and can become more frequent in Amazonia due to changes in climatic variability if no regulations on fire use are implemented.

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RESUMOPara avaliar os dados de precipitação pluvial via satélite no estado do Amazonas, compararam-se as estimativas do produto 3B43 do satélite TRMM (2004TRMM ( -2008 Assessment of Rainfall Estimates from the TRMM-3B43 Product in the State of Amazonas ABSTRACTThe aim of this study was to evaluate the rainfall data via satellites in Amazonas state, Brazil. To this end, the estimates from the TRMM-3B43 product (2004)(2005)(2006)(2007)(2008) were compared with data from seven Conventional Weather Stations (CWS). The comparison was based on the following statistical parameters: Average Error (AE), Root Mean Square Error (RMSE), linear correlation coefficient (r), and Wilmott's index of agreement (d). The TRMM-3B43 estimates were similar to the surface data and represent well the seasonal variability of rainfall. The data showed high linear correlation (r = 0.83), high index of agreement (d = 0.85), and satisfactory RMSE (66.6 mm/month). Therefore, rainfall estimates from the TRMM-3B43 product can be used as an alternative source of quality data.