Priscila Esposte Coutinho

Brazil is increasingly affected by extreme weather events due to climate change, with pronounced regional differences in temperature and precipitation patterns. The southeast region is particularly vulnerable, frequently experiencing severe droughts and extreme heatwaves linked to atmospheric blocking events and intense rainfall episodes driven by the South Atlantic Convergence Zone (SACZ). These phenomena contribute to recurring climate-related disasters. The country’s heavy reliance on hydropower heightens its susceptibility to droughts, while growing evidence points to intensifying dry spells and wildfires across multiple regions, threatening agricultural output and food security. Urban areas, particularly, are experiencing more frequent and severe heatwaves, posing serious health risks to vulnerable populations. This study investigates the links between global teleconnection indices and synoptic-scale systems, specifically blocking events and SACZ activity, and their influence on Brazil’s extreme heat, drought conditions, and river flow variability over the past 30 to 40 years. By clarifying these interactions, the research aims to enhance understanding of how large-scale atmospheric dynamics shape climate extremes and to assess their broader implications for water resource management, energy production, and regional climate variability.

Renewable energy sources are inherently influenced by environmental variability. In Brazil, hydropower constitutes approximately 65% of the country’s electricity matrix, relying directly on river flow, which is strongly governed by precipitation on an operational timescale. This study investigates the influence of atmospheric blocking events and episodes of the South Atlantic Convergence Zone (SACZ) on the occurrence and magnitude of natural flow in several hydropower plants in river basins across Brazil. Atmospheric blocking and SACZ episodes were characterized using indices developed at LAMMOC/UFF, which effectively capture the behavior of these systems in different regions of the country. The SACZ index was calculated using NCEP reanalysis data, while the blocking index was derived from ERA5 reanalysis data. Natural flow data for the power plant areas were provided by the National Electric System Operator (ONS). To maximize the availability of records for this study, the time series was defined from 1960 to 2023. All data were normalized for statistical analyses, and methods such as Pearson’s correlation coefficient, Principal Component Analysis (PCA), K-means clustering, trend analysis, and the Mann-Kendall test were employed to identify and quantify trends. Results indicate that blocking events have shown a rising trend across all evaluated regions, whereas SACZ episodes do not display an increasing trend uniformly throughout the country. Regarding river flow trends, increases were observed in Southern Brazil, while decreases predominate in the Southeast, Central-West, Northeast, and North regions. SACZ episodes positively influence flow in hydropower plants in the Central-West, North, and Northeast regions, while inhibiting precipitation in the South as their effects shift northward, away from the basins. For instance, the Paranaíba River basin in the Northeast shows a correlation of 0.55 with SACZ episodes, and the Paraopeba River basin located between the Southeast and Central-West Regions, presents a correlation of 0.57. Notable SACZ-related correlations are also observed in the Grande, Paranaíba, and Baixo Paraná basins, with values exceeding 0.3 and increasing towards the South, reaching over 0.5 for Baixo Paraná and 0.6 for Grande and Paranaíba basins. Conversely, the Araguaia-Tocantins basin in the North exhibits one of the highest correlations, at 0.69. Atmospheric blocking events, in turn, are positively correlated with river flow in the South, particularly in the Uruguai and Jacuí basins, which exhibit the highest correlation values. However, they produce a negative correlation in basins of other regions, as their associated high-pressure systems inhibit atmospheric dynamics, reduce precipitation, and prevent the advance of cold fronts, concentrating precipitation in Southern areas. The results reveal a decline in river flow across most hydropower plant areas, posing risks to Brazil’s electricity production, with potential impacts on the country’s Gross Domestic Product (GPD). SACZ and atmospheric blocking events significantly influence river flow, especially in the power plants of the Central-West, North, and Northeast regions. Developing indices for these atmospheric phenomena offers valuable insights into regional water availability, supporting strategies to mitigate risks from shifting precipitation regimes across Brazil’s diverse climates and biomes.

Rising global average temperatures, as a consequence of climate change, have worsened the occurrences of extreme weather events, causing disruptions in rainfall patterns around the world. In Brazil, such effects are already observed with the increase of heat waves, floods, droughts, and wildfires. The correlation between disruptions in precipitation patterns and fires is complex, nevertheless, the intensity, frequency, and duration of drought events have significant impacts on fuel flammability and fire behavior. Drought monitoring is particularly relevant in Brazil, where the vast majority of forest fires have an anthropogenic ignition and prolonged dry periods favor such fires to spread out of control. The Standardized Precipitation Index (SPI) is one of the most important tools used to evaluate precipitation variability, offering simple yet robust statistical information on the distribution, duration, and frequency of rainfalls and, consequently, droughts. The SPI uses precipitation as input data to standardize the deviation of cumulated rainfall from the mean of historical precipitation, detecting water deficit (negative values) or water surplus (positive values) for a given location. In doing so, this index allows direct spatial comparability between arid and humid regions. This is an advantageous characteristic when drought analysis is applied to a country with different regional rainfall regimes, such as Brazil. The applicability of SPI as a source of drought prediction was investigated by observing its performance with historical climate simulations of the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and the fifth generation ECMWF atmospheric reanalysis of the global climate, ERA5. The direct comparison of the SPI data, employing the climatology extending from 1980-2014 in Brazil, derived both from the climate simulation model and the reanalysis data - which combines observations and models – has provided valuable insights. Preliminary results show an overall consistency in the calculated indexes from both sources, which are in line with seasonal regional rainfall patterns in Brazil. On average, the SPI indexes recognize water deficits for the North-east, north of the South-east and central regions of Brazil. During the months of winter, both indexes detect droughts in these regions, with ERA-5 SPI index registering severe droughts in central Brazil. These results suggest that the SPI index calculated using the reanalysis data seems to register droughts with greater severity and longer duration, identifying more precisely periods with little to no rainfall, whilst the SPI derived from the MIROC6 simulation data, although able to acceptably identify and delimitate droughts, records less severity for the same period. These findings are important to recognize the MIROC6-derived SPI index as a valuable tool in drought prediction. However, they also highlight the necessity of acknowledging the limitations of the model regarding the severity of droughts. The understanding and prediction of precipitation anomalies is fundamental to coping with the impacts of climate change on water resources, agriculture, and biodiversity, guiding mitigation and adaptation strategies in Brazil.

According to the Sixth Report of the Intergovernmental Panel on Climate Change (IPCC), published in 2023, between 2011 and 2020, global surface temperatures increased by 1.1°C compared to the period 1850-1900. This upward trend in temperature is related to changes in the observed climate patterns, which will potentially lead to a greater incidence of extreme weather events and ecosystem changes, as well as impacting the health of human populations. In Brazil, this scenario could also result in biodiversity losses, reduced agricultural productivity and changes in the availability of water resources, with consequences for the country's economy and energy security. Considering this context, this study aims to evaluate the ability of the EC-Earth3-Veg model, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), to reproduce the evolution of maximum and minimum air temperatures at 2 meters in Brazil during the historical period, at intervals between 1961 and 2014, comparing it with ERA5 reanalysis data. The CMIP6 data was interpolated to the ERA5 grid to carry out the desired analysis. Based on this, it was observed that EC-Earth3-Veg was able to reproduce the historical climatology for Brazil but showed climatological differences when compared to ERA5 in the four periods observed. It is common among the periods analyzed that the further north of the country is warmer, with maximum temperatures in summer and autumn. In winter and spring, the same happens in the eastern part of the North, the Center-West, and the northern part of the Southeast of Brazil. In the fall, the Northeast and Midwest show cooler highs. The period in which the model's results were closest to ERA5 was from 1961 to 1990, especially for minimum temperatures in summer and winter. Even so, in the fall and spring of this period, the model showed warming in relation to the minimums in the South, and, in all the quarterly cut-outs, it showed cooling in the minimums near the Northeast. In general, certain regional and seasonal patterns were observed in the results, which may indicate a limitation of the model in terms of horizontal resolution in considering a more characteristic atmosphere for Brazil. In the southern region, for example, the maximum and minimum temperatures in the model showed warming. This may indicate that the model is limited in its horizontal resolution and does not consider an atmosphere that is more characteristic of Brazil. Therefore, to improve the model's performance in simulating the climate in Brazil, it is necessary to correct the bias and use EC-Earth3-Veg in conjunction with other models to reduce systematic errors. This study aims to replicate this work for other models.

Wildfires represent a significant threat to natural ecosystems, biodiversity, and communities worldwide. Disruption in precipitation regimes and temperature rise caused by climate change are key factors that worsen and increase wildfire incidents. In Brazil, recent studies have shown the majority of fire incidents are initiated by anthropogenic action, as a consequence of agricultural expansion, deforestation and land disputes. Although the human use of fire as an illegal tool is difficult to predict, the occurrence of dry meteorological conditions, prone to uncontrolled spreading of fires, can be studied employing climate modeling, providing a useful instrument to aid authorities in preventive measures and improved responses to mitigate these impacts, contributing to more efficient and sustainable management of fire-related risks. The Standardized Precipitation Index (SPI) is a useful tool for assessing precipitation variability, allowing the analysis of drought period duration, distribution, and severity. The SPI uses precipitation data to standardize the deviation of accumulated precipitation from the historical average in each location. This process yields negative or positive values, which correspond to water deficits or surpluses, respectively. Aiming to identify areas in Brazil where predicted disruption in rainfall patterns, in face of climate change, may create drier conditions and increase vulnerability to fire incidents, we evaluated precipitation trends, comparing historical simulations from the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and future scenarios data from the Intergovernmental Panel on Climate Change (IPCC). We focused our analysis on 3 climate change scenarios, referred to as Shared Socioeconomic Pathways: SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios encompass anticipated global socioeconomic transformations up to the year 2100, based on different projections of greenhouse gas emissions, and offer an assessment of the climate outlook for current society. Thus, we calculated SPI indexes for the time spans 1960-1990 and 2020-2050, examining the variations in rainfall patterns across the country during both periods. Using SPI derived from MIROC6 climatological data, it is possible to identify past patterns that are the basis for understanding future changes' impact. The results from SPI climatological data are consistent with the climate and seasonal rainfall patterns historically observed in Brazil, where Northeast and Central Brazil exhibit greater water deficits. The scenarios employed suggested that the historical patterns of droughts would be worsened in severity in central Brazil and the areas of influence would be extrapolated, creating drier meteorological conditions to the Southern and East portions of Amazonia and the Southeast of Brazil. The SPI indexes calculated to the projected scenarios reinforce the understanding of the impacts of climate change, suggesting the pathway SSP55-8.5, with higher emission of CO2, implicates in increased occurrences of extreme events, particularly prolonged and severe droughts in regions that suffer from wildfires. Identifying regions with an increased likelihood of prolonged drought events in the projected future is a valuable instrument for examining fire hazard and mitigation plans within a country such as Brazil, which encompasses diverse climates and biomes across its territory with resources of significant conservation value.

Brazil has a large availability of natural resources, and its economy was historically built around their exploitation. Changes in climate trends are already causing several environmental impacts, which affect the economic and social organization of the country. Impacts linked to the hydrological cycle are particularly concerning since water resources are used for electricity production, representing approximately 65% of the Brazilian electricity matrix. This study, therefore, aims to evaluate the extreme hydroclimatic trends of river basins located in the Northeast and South regions of the country. For this purpose, we carried out a flow analysis from 2020 to 2100, considering the precipitation data from the BCC CSM1-1, CCSM4, MIROC5, and NorESM1-M models presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). We used the SMAP rainfall-runoff model to obtain future flow projections for the RCP4.5 and RCP8.5 scenarios. As a result, we observed a trend toward water loss and the intensification of extreme events, with an increase in variability in both scenarios. We also noted that these climate models have difficulty reproducing the natural variability of southern basins, as parameterization of small-scale atmospheric processes prevents them from correctly projecting the precipitation.

Brazil has a large availability of natural resources and its economy was historically built around their exploitation. Changes in climate trends are already causing several environmental impacts, which affect the economic and social organization of the country. Impacts linked to the hydrological cycle are particularly concerning since water resources are used for electricity production, representing approximately 65% of the Brazilian electricity matrix. This study, therefore, aims to evaluate the hydroclimatic extreme trends of river basins located in the Northeast and South regions of the country. For this purpose, we carried out a flow analysis from 2020 to 2100, considering the precipitation data from the BCC CSM1-1, CCSM4, MIROC5, and NorESM1-M models presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change - IPCC. We used the SMAP rainfall-runoff model to obtain future flow projections for the RCP4.5 and RCP8.5 scenarios. As a result, we observed a trend toward water loss and intensification of extreme events, with an increase in variability in both scenarios. We also noted that these climate models have difficulty reproducing the natural variability of southern basins, as the simplification of atmospheric phenomena parameterization prevents them from correctly projecting the precipitation.

Brazil has a significant offshore oil production, which dates back to the late 1960s and is currently focused on exploring pre-salt reservoirs. The drilling technology Petrobras uses is considered a world standard: in 2020, it allowed offshore production to reach 97% of the country’s total oil production. During the process, however, unwanted events, and even operational failures may occur, which are capable of significant damage. Thus, failure detection is extremely important to prevent production losses or delays, to reduce costs and to avoid accidents. This study uses a real, public database on offshore production, and proposes using wavelet transforms to detect production failures. With the technique, we pinpointed which time intervals between measurements showed relevant variability, and then clustered the data, according to mobile averages, to shrink the record number. Using wavelet transforms, we analyzed which variables could be used as predictors of production failures and identified the temperature read by the Temperature and Pressure Transducer sensor (T-TPT) and the pressure at the Production Choke sensor (P-PCK) as possible predictor variables. We also observed the creation of a filtered series, averaged from the original data series, which maintained its variability, showing the viability of record regrouping in shorter series.

In the last century, changes in climate trends have been observed around the planet, which have resulted in alterations in the hydrological cycle. Studies that take into account the impact of climate change on water availability are of great importance, especially in Brazil’s case, where water from rivers, beyond being destined for human consumption, animal watering and economic activities, has a great participation in electricity generation. This fact makes its energy matrix vulnerable to variations in the climate system. In this study, a flow analysis for the head of the São Francisco river basin was performed between 2010 and 2100, considering the precipitation data of the CCSM4 climate model presented in the Fifth Assessment Report (AR5) from the Intergovernmental Panel on Climate Change (IPCC). Projections of future flow were performed for the scenarios RCP4.5 and RCP8.5, based on the SMAP rain-flow model, followed by a comparative analysis with the present climate. In general, we can observe that the decades of 2010 to 2100 will be marked by the high levels of precipitation, interspersed by long droughts, in which the recorded flow will be lower than the Long Term Average (LTA) calculated for the basin. Therefore, new management strategies must be considered to maintain the multiple uses of the basin.