Publicações

Severe floods and landslides in Eastern Northeast Brazil in May 2022 led to severe impacts with human losses and material damage. These disasters were a direct consequence of extremely heavy rainfall days. A rapid attribution study was performed to assess the role of anthropogenic climate change in 7 and 15-day mean rainfall over this region. A dense network of 389 weather stations was analysed resulting in 79 selected stations containing consistent and spatially well-distributed data over the study region with records starting in the 1970s. Daily rainfall from a multi-model ensemble of climate simulations were also examined to investigate the role of climate change in modifying the likelihood of such extreme events over the studied region. However, such an analysis was hindered by the fact that most investigated models have deficiencies in representing convection associated with warm rains, which are key for the manifestation of such extreme events associated with Easterly Wave Disturbances. From the observational analysis, both 7 and 15-day mean events in 2022 were found to be exceptionally rare, with an approximately 1-in-500 and 1-in-1000 chance of happening in any year in today’s climate, respectively. Even though both events were located far outside the previously observed records, because of the short observational record and associated uncertainties it was not possible to quantify how much climate change made these events more likely to happen. The performed analysis also revealed that global warming increased the intensity of such extreme rainfall: rainfall events as rare as those investigated here occurring in a 1.2 °C cooler climate would have been approximately a fifth less intense. Combining observations with the physical understanding of the climate system, this study showed that human-induced climate change is, at least in part, responsible for the increase in likelihood and intensity of heavy rainfall events as observed in May 2022. Besides, the extreme nature, as a result of such events, made it so extraordinary that population exposure and vulnerability was identified as the main driver for the observed impacts, although long-term impacts and recovery will likely be mediated by socio-economic, demographic and governance factors.

The enormous soil carbon pool stored by mangroves depends upon microbial respiratory activities and enzymes associated with carbon breakdown in soils. Our hypothesis is that increased microbial activity leads to elevated carbon breakdown. To prove this, we measured microbial respiratory and enzyme activity for nitrogen, phosphorus, and carbon cycle in soil from three coastal ecosystems: mangroves, restinga, and Atlantic forest. The multivariate ordination analysis showed that each treatment had distinct soil microbial activity according to the soil layer and seasons. Our results suggest that the Atlantic forest ecosystem had the highest respiration activity (i.e., CO2-C release), dehydrogenase, phosphatase, protease, and urease activities, while mangroves and sandy coastal plains (called restinga) had lower respiration and enzyme activity. Therefore, soil microbial respiratory activity variation was influenced by soil microbial activity in the most superficial layer in coastal soil ecosystems. The agreement between dehydrogenase activity and CO2-C respiration measurements suggests that microbial activity is an efficient indicator of carbon breakdown. Our findings also indicate that the vegetation type in the different ecosystems contribute to stimulate the soil microbiota increasing both its microbial activity and carbon storage. These issues should be considered for the conservation policies to promote effective protection of vital coastal ecosystems in Brazil. Understanding the coastal ecosystem-induced nutrient shifts in microbial communities is important because they can lead to lagged and multiplicative effects on carbon storage. Taken together, our results illustrate the identification of keystone (organic carbon and microbial activity) as an indicator to offer evidence-providing tools to achieve more ecologically efficient managing practices.

Deforestation rates in the Amazon have markedly increased in the last few years, affecting non-protected and protected areas (PAs). Brazil is a hotspot of Protected Area Downgrading, Downsizing, and Degazettement (PADDD) events, with most events associated with infrastructure projects. Despite the threats dams impose on PAs, there is a knowledge gap in assessing deforestation in PAs around large dams in the Amazon. This study investigates how deforestation affects Biodiversity Protection Areas (BioPAs) and Indigenous Lands around the Jirau and Santo Antônio (JSA) dams (Madeira River, Rondônia) and Belo Monte dam (Xingu River, Pará) in the Brazilian Amazon. We compared clear-cutting between PAs and control areas and the annual rates of forest change between pre-dam and post-dam periods. We discussed deforestation-related factors (e.g., PADDD events and the presence of management plans or councils). Our results show an increase in deforestation after the operation of the dams when environmental control from licensing agencies decreases and other political and economic factors are in practice. Indigenous Lands experienced a significant increase in deforestation around the Belo Monte dam, which is associated with the demarcation process and land conflicts. Surrounding the JSA dams, sustainable use BioPAs showed high deforestation rates, and 27 PADDD events were reported, four directly related to dams. In addition to dams, deforestation was associated with the crisis of Brazilian democracy and the weakening of environmental policies. In conclusion, the weak environmental control from environmental licensing agencies during dam operation and PADDD events have contributed to increased deforestation rates and additional stresses in the Amazon.

The lack of a solid methodology defining urban and non-urban areas has hindered accurately estimating the Surface Urban Heat Island (SUHI). This study addresses this issue by using the official national urban areas limit together with a surrounding areas classification to define three different reference classes: the urban adjacent (Ua), the future urban adjacent (FUa), and the peri-urban (PUa), consequently providing a more accurate SUHI estimation on the nine northeastern Brazilian capitals. The land surface temperature was obtained in this study using the Sentinel-3 satellite data for 2019 and 2020. Subsequently, the maximum and average SUHI and the complementary indexes, specifically the Urban Thermal Field Variation Index (UTFVI) and the Thermal Discomfort Index (TDI), were calculated. The UTFVI expresses how harmful the eco-environmental spaces are, with a very strong SUHI for three capitals. In addition, the TDI, with values between 24.6-28.8 degrees C, expresses the population’s thermal comfort, with six capitals showing a very hot TDI. These findings highlight the need for strategies to mitigate the effects of the SUHI and ensure the population’s thermal comfort. Therefore, this study provides a better SUHI understanding and comparison for the Brazilian northeastern region, which has diverse areas, populations, and demographic variations.

The Fluxpart partitioning model was employed to assess transpiration, photosynthesis and evaporation, respiration based exclusively through high-frequency eddy covariance (EC) data. The model was implemented across three sites with two vegetation cover conditions (dense and sparse) during the drought episode between 2012 and 2015 in the Caatinga biome in Northeast Brazil. Fluxpart, an open-source Python software, partitions data by analyzing the flux variance similarity (FVS) relationship and conducting correlation analyses of EC data. The main contribution to the evapotranspiration (ET) process was transpiration (T), representing about 64 and 67% of the amount ET in areas with dense and sparse vegetation cover, respectively. When the Caatinga biome is in a good state of conservation, it behaves as a carbon sink; the net ecosystem exchange average was -6 g C m – 2 day – 1 (gram of carbon per square meter daily) for dense vegetation and 3 g C m – 2 day – 1 for sparse vegetation. Through ET, vapor pressure deficit (VPD), and gross primary production (GPP) via Fluxpart, an assessment was made concerning the underlying water use efficiency (uWUE) to understand the nonlinear effects of VPD on the carbon-water coupling at the ecosystem scale. The overvalues of 60 g C hPa 0.5 kg -1 H 2 O -1 (grams of carbon times square root of hectopascals per kilogram of water) prevailing in the rainy season and below 20 g C hPa 0.5 kg – 1 H 2 O -1 were mostly in the dry season. uWUE was an important indicator of the ability of the Caatinga biome to optimize water loss and carbon gain according to the water available in the soil-plant-atmosphere system to withstand water stress, especially at dense vegetation covers.