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Kendall´s tau values for SPI and SPEI values calculated from gridded data of Xavier et al. (2022) for the period 1980–2019 and for different timescales. Colored pixels indicate statistically significant values (p < 0.05)
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Drought indices are a numerical representation of drought conditions aimed to provide quantitative assessments of the magnitude, spatial extent, timing, and duration of drought events. Since the adverse effects of droughts vary according to the characteristics of the event, the socioeconomic vulnerabilities, exposed communities or envi...
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... the SPEI derived from the gridded data of Xavier et al. (2016) was validated with other sources of information, we analyzed trends of SPI and SPEI for the period 1980-2019. Figure 4 shows Kendall´s tau values for the SPI and SPEI values derived from the gridded data of Xavier et al. (2022) for different timescales. Colored pixels indicate statistical significance for a confidence level below 0.05. ...Context 2
... ( Hastenrath and Heller 1977). On the other hand, Northern Amazonia shows significant positive trends, which are consistent with the intensification of the hydrological cycle in northern Amazonia ( Gloor et al. 2013). Positive trends were also detected in a region close to the coastal area of the Brazilian Northeast and South. More importantly, Fig. 4 clearly illustrates that the trends are much more significant and spatially homogeneous in the case SPEI compared to SPI. Consequently, it is clear that the magnitude of the trends are enhanced for the index that include reference ...Context 3
... with several previous studies (for instance, Salviano et al. 2016;Penereiro et al. 2018). On the other hand, the bottom row shows that significant trends for monthly precipitation are present only in a few areas in Brazil, mostly concentrated in the northern region. Besides, it shows a spatial distribution similar to SPI on the shorter time scale (Fig. 4). In contrast, the difference in P-ETo shows more significant decrease trends (dryness trends) than P, mainly in central Brazil. However, the significant areas are also smaller than those identified by SPEI on longer time scales (Fig. 4). Although the magnitude of trends in P-ETo is not more pronounced such as in the case of ...Context 4
... in the northern region. Besides, it shows a spatial distribution similar to SPI on the shorter time scale (Fig. 4). In contrast, the difference in P-ETo shows more significant decrease trends (dryness trends) than P, mainly in central Brazil. However, the significant areas are also smaller than those identified by SPEI on longer time scales (Fig. 4). Although the magnitude of trends in P-ETo is not more pronounced such as in the case of temperature, it is clear that those trends become more significant for increasing time-steps, as indicated by Fig. 4. Based on this analysis, it is possible to conclude that there are statistically significant negative trends in both SPI and SPEI ...Context 5
... than P, mainly in central Brazil. However, the significant areas are also smaller than those identified by SPEI on longer time scales (Fig. 4). Although the magnitude of trends in P-ETo is not more pronounced such as in the case of temperature, it is clear that those trends become more significant for increasing time-steps, as indicated by Fig. 4. Based on this analysis, it is possible to conclude that there are statistically significant negative trends in both SPI and SPEI over the period 1980-2019 in most of Central Brazil, which becomes more significant for SPEI mostly related to the increase in evaporation associated with higher temperatures. Those trends become pronounced ...Context 6
... area most affected by the negative trends of Fig. 4 includes regions heavily populated to the east, and almost the whole region of the country known as the new agricultural frontier in central Brazil within the Cerrado Biome, which is an area undergoing intense land use dynamics in recent decades and is responsible for the increase in grain production in Brazil ( Vieira et al. 2021a, ...Context 7
... addition, the areas affected by negative trends in Fig. 4 correspond to the headwaters of the Paraná, Paraguay, São Francisco, and Tocantins rivers. Besides the importance of these basins for navigation and water supply, they host more than 90% of the country's hydropower installed capacity (ONS 2021). In this context, the blue line of Fig. 5 shows the stored energy in hydropower plants of ...Context 8
... a decrease in the number of consecutive dry days. This result indicates that intense precipitation is concentrated in fewer days separated by longer dry spells, which can be partly explained by natural climate variability, global warming and/ or urbanization ( Marengo et al. 2020) and provides an additional explanation for the results shown in Fig. ...Context 9
... it is worth mentioning that the projections of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021) show the same spatial patterns observed in Fig. 4, i.e., wetter conditions in the south of Brazil and drier conditions in the rest of the country. IPCC scenarios indicate the same pattern even in the case of the milder climate change scenario (Paris agreement), though changes are exacerbated under higher ...Similar publications
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... In this context, there is a marked increase in the maximum number of consecutive dry days from the coast (~30 days) to the interior of the state (~50 days) (Luiz- Silva & Oscar-Júnior, 2022). Additionally, recent studies report a significant trend in drought severity for cumulative water 440 imbalances on time scales of 12 months and longer (Tomasella et al., 2022). In addition, Cordeiro has registered the largest precipitation reduction in the State of Rio de Janeiro over the period 1979-2009(Sobral et al., 2019. ...
During the 2023/24 season, the State of Rio de Janeiro experienced unprecedented maximum temperatures, resulting in a substantial increase in human mortality. This study aims to analyze the contribution of global warming to changes in the distribution of annual maximum temperatures and their subsequent impact on mortality rates. Our analysis of extreme temperatures reveals that a non-stationary model, in which the location parameter shifts linearly as a function of El Niño-Southern Oscillation (ENSO) and/or climate change, provides a significantly better fit to the data than its stationary counterpart. The northern region of the State exhibited the strongest response to climate change, while ENSO effects were most pronounced in the eastern region. Events as likely as the 2023 heatwave were estimated about 2 °C colder in pre-industrial times. Under a 2 °C global warming scenario, the probability of experiencing 2023-like daytime temperatures increases by at least a factor of three. These findings highlight climate change as the primary driver of extreme temperature intensification, with ENSO acting as a secondary but significant factor in the eastern region. As global warming approaches 2 °C, Rio de Janeiro is projected to experience heatwaves of that magnitude every four years approximately. Climate change has contributed to one in three heat-related deaths recorded during the peak of the event. Without adaptation measures, global warming would further increase the death toll during extreme events of the same frequency as the 2023/24 heat wave.
... Aggregated data eliminates the need to interpolate values between different gauge stations, considering that all gaps are filled with observed data, ensuring higher reliability in the results (Gao et al. 2022). Precipitation distribution and trends can be analysed using different indices, allowing for the examination of longer historical data from various perspectives (Teixeira and Satyamurty 2011;Hänsel, Schucknecht, and Matschullat 2016), reducing uncertainty regarding conclusions about changes in precipitation patterns (Salviano, Groppo, and Pellegrino 2016;Tomasella et al. 2023). ...
... The 12-and 24-month SPI showed a reduction in precipitation occurring with greater intensity, suggesting an increase in hydrological droughts (Gonçalves et al. 2021), which led to the depletion of surface and groundwater reservoirs (Hobeichi et al. 2022). Tomasella et al. (2023) observed similar results, with a significant negative trend for most of Goiás State and the Federal District using SPI for 3, 6, 12 and 24 months from 1980 to 2019, in association with an increase in evapotranspiration due to higher temperatures. Such events cause water scarcity, and the results provide a foundation for the development of waterscarcity mitigation strategies to ensure water supply for different sectors. ...
Climate change is reshaping the global climate patterns with varying effects, necessitating a thorough analysis of historical precipitation trends. This study aimed to assess precipitation trends over 60 years in Goiás State and the Federal District of Brazil using data from 21 precipitation gauges maintained by the National Water and Basic Sanitation Agency of Brazil. The Mann–Kendall test was applied to evaluate the precipitation trend significance (α = 5%) from 1960 to 2021, and Sen's slope was applied for trend magnitude. The Standardised Anomaly Index (SAI), Rainfall Anomaly Index (RAI) and Standard Precipitation Index (SPI) were used. The region had a mean (max‐min) annual precipitation of 1462 (2007–915) mm, concentrated between October and April. The Mann–Kendall test showed that 76% of sites had a significant reduction in rainfall, with Sen's slope reaching −60.42 mm decade⁻¹. Only Goiânia showed a positive trend of 1.15 mm decade⁻¹, but with no significant trend at 5% by the Mann–Kendall test. The results indicate that precipitation decreases in September, October and spring. The RAI and SAI highlighted rainfall reduction in October, moving to drier classes, whereas the other months remained stable. The SPI showed a decreasing trend at 3, 6, 12 and 24 months, whereas no significant trend was observed at 1 month. The SPI for 12 and 24 months showed intensification of drier classes in the recent period (2011–2021). The results indicated consistent rainfall reduction over the region, mainly in September, October and summer. Furthermore, the SPI indicated meteorological droughts of low intensity and increased hydrological droughts. This has wide‐ranging implications, particularly for groundwater reservoirs, affecting urban areas, hydroelectric power generation and irrigation systems, increasing conflict and requiring an urgent plan for water use.
... On the other hand, the variations in NDVI, air temperature, and LST show a tendency to increase over time. The increase in NDVI values can be associated with an assumed intensification of agricultural practices, mainly in the replacement of natural vegetation areas by pastures [102]. In addition, rising temperatures, both atmospheric and surface, indicate an ongoing warming process in the Brazilian Pantanal [103]. ...
Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−069 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development.
... BR-DWGD's spatial coverage encompasses the entire Brazilian territory and includes a range of climatic variables: precipitation, minimum and maximum temperature, solar radiation, relative air humidity, wind speed at 2 m and evapotranspiration. This dataset has found applications in various fields within Brazil, such as drought event analysis (Tomasella et al. 2022), model evaluations (Herdies et al. 2023;Rasera et al. 2023;Ramalho et al. 2023) and assessment of precipitation products (Andrade et al. 2022). Interested parties can access the data at https:// sites. ...
This paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year⁻¹ for PRCPTOT, 0.7 day year⁻¹ for R10mm, 0.2 day year⁻¹ for R20mm, 0.01 mm year⁻¹ for Rx1day, 0.03 mm year⁻¹ for Rx5day, 0.4 day year⁻¹ for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year⁻¹. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.
... The characteristics of droughts at the local and regional levels may undergo changes in the future. While much of the semi-arid region typically experiences drought conditions with low deficits and intensity, recent research has pointed to a drying trend in these areas over the past few decades (Chagas et al., 2022;Cunha et al., 2018;Marengo et al., 2017;Tomasella et al., 2022). Chagas et al. (2022) have linked this drying trend to reduced rainfall and increased water consumption in Central and Northeastern Brazil's agricultural zones. ...
Spatially compounding drought events affect multiple locations simultaneously, severely affecting food, water, energy, human health, and infrastructure sectors. Despite the cascading impacts and challenges compound droughts impose on society, we still lack an in‐depth understanding of spatially connected drought occurrences. Given the complexity and costs of droughts in Brazil, identifying regions prone to co‐experiencing droughts is critical for developing effective adaptation measures. Here, we develop a novel framework to assess the spatial co‐occurrence of hydrological drought events, which can be adapted for global applications to evaluate spatially compounding drought. This framework involves extracting drought data from individual catchments and calculating the co‐occurrence of droughts across all catchments. We apply this method to investigate the spatial connectedness of droughts in 511 Brazilian catchments over 39 years (1983–2022). Additionally, we classify catchments based on drought duration, intensity, deficit, number of events, and spatial connectedness to identify regions with similar drought behavior. Our findings reveal significant variability in drought characteristics and connectedness across Brazil, with the Central‐Northeast and Northwest Amazon regions being most affected by multiple and widespread droughts. We identify five distinct regions in Brazil that exhibit common drought behaviors, sharing attributes such as aridity, catchment area, and precipitation seasonality. These regions hold the potential to guide future adaptation plans for managing hydrological compound extremes at both the catchment and regional scales, including the development of risk pool networks. Our results underscore the importance of considering the interactions of spatially compounding hydrological droughts in risk assessments and adaptation strategies.
... As a result, drought assessment is crucial for understanding, managing water resources, and disaster preparedness, particularly in agriculturally significant regions. Tomasella et al. 2023). ...
Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.
... More basins with significant drought-rich periods were recorded in the second dry cycle than in the first dry cycle, with frequencies of up to 67% of the gauges in the northeast region, 60% in the southeast, and 51% in the center ( Figure 6). Streamflow in the 2010s often reached record lows (Cuartas et al., 2022;Getirana et al., 2021), resulting in severe water crises like those in São Paulo in 2014-2015 (Braga & Kelman, 2016) and partial disruption of the national hydropower generation (Tomasella et al., 2022). However, rainfall-poor periods were not as frequent as drought-rich periods after 2000. ...
... In general, Brazil experienced a dry period from the 1940s to the 1960s, a wet period in the 1980s and 1990s with the largest floods on record (Fleischmann et al., 2020), and another dry period from the 2000s onward with record low flows. The current dry period has been linked with water abstractions (Chagas et al., 2022a) and to a partial disruption of the national hydropower generation (Tomasella et al., 2022). These multidecadal cycles are associated with 241-month anomalies in the AMO and PDO, characterized by extended periods of opposite phases with rapid transitions between them (Newman et al., 2016). ...
Streamflow exhibits persistent decadal variability; however, it is unclear if the magnitude and spatial extent of these variabilities are symmetric for droughts and floods. Here, we examine drought‐rich and flood‐rich periods in 319 streamflow gauges in Brazil from 1940 to 2020. Drought‐ and flood‐rich periods are detected by computing annual streamflow minima and maxima time series and using scan statistics to verify if events exceeding a threshold follow a Bernoulli process. We contrast streamflow time clustering with rainfall, evaporation, water abstraction, the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). We detected a higher spatial frequency of drought‐ than flood‐rich periods. For 5‐year return period thresholds, drought‐rich periods are observed in 81% of the basins, 16.7 times the false positive rate (4.8%) and 4.7 times flood‐rich periods (17%). This asymmetry is linked with sharp increases in water abstractions since the 1990s and a higher prevalence of rainfall‐poor periods (41% of gauges) compared to rainfall‐rich (22% of gauges), which we interpret as being further amplified into drought‐rich periods due to an interannual persistence of water storage deficits. Brazil experienced a dry period until the 1960s, extensive flooding in the 1980s, and record low flows from the 2000s onward. Drought and flood‐rich periods are well aligned with rainfall clustering, water abstractions, the AMO and PDO. Droughts‐rich periods are more frequent in shorter time scales (several years to one decade) and flood‐rich periods in longer time scales (a few decades). Our findings highlight the nonlinearity and asymmetry of drought and flood change at decadal scales.
... The spatial-temporal patterns of SPI and SPEI trends had the same sign, i.e., they tended to decrease in Mato Grosso. However, the SPEI series had a greater amplitude and lower variability than SPI due to the inclusion of potential evapotranspiration (PET), which suggests that SPEI is more appropriate for drought monitoring than other indices that use only precipitation (Tomasella et al. 2023). The increase in drought events in Mato Grosso is related to the decrease in precipitation and the increase in air temperature and the dry season in the state dos Santos et al. 2023). ...
Precipitation and air temperature have decreased and increased, respectively, in the three biomes of the state of Mato Grosso (Amazon - AMZ, Cerrado - CER, and Pantanal - PAN), making them susceptible to increased drought. The objective of this study was to analyze the trends of severity, intensity and duration of drought events between 1970 and 2022, and their relationships with sea surface temperature anomalies (SSTA) and the Southern Oscillation Index (SOI). The Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) were calculated using precipitation and air temperature data from the ERA5-land product. The trends of the SPI and SPEI were obtained using the Mann-Kendall (MK) test, and the number of occurrences, severity, intensity, and duration were obtained using the Run-Theory method. The results showed that the SPI and SPEI tended to decrease in the three biomes between 1970 and 2022. Moderate, severe and extreme drought events occurred in all biomes, with emphasis on the highest drought severity in the Pantanal between 2019 and 2022. The highest number of occurrences of extreme droughts by the SPI occurred in AMZ and CER between 2020 and 2022, and the highest values of severity, duration and intensity occurred in CER and PAN in the same period. However, the values of severity, duration and intensity by the SPEI occurred in AMZ and CER. The SPI and SPEI had a similar correlation pattern with the SSTA and SOI indices. The SPI had weak negative correlation with the Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNAI) and Tropical Southern Atlantic Index (TSAI), while the SPEI had moderate negative correlation with AMO and TSAI. In conclusion, the results of this study showed that the severity of drought in Mato Grosso increased between 1970 and 2022. The severity of drought in the Pantanal was particularly high between 2019 and 2022. SSTA anomalies and the SOI had some influence on the seasonal and interannual dynamics of drought in Mato Grosso, but not all SSTA anomalies had influence.
... Thus, between the years of 2011 and 2015 this region was affected by several droughts (Cunha et al. 2019;Tomasella et al. 2023), this could have influenced the capacity of the models to estimate the long-term runoff. In addition, giving the exceptional characteristic of the drought, observed discharge values had to be extrapolated since water stage values were beyond the measurements (ratting curve). ...
Runoff projections are subject to uncertainties related to model structure and parameters. This study aims to analyze uncertainties in long-term runoff estimations from an empirical (Budyko framework) and a conceptual hydrological model (MHD-INPE). Results indicate that both MHD-INPE and Budyko estimations tend to overestimate long-term runoff during years of recurring droughts. Pareto front solutions in MHD-INPE exhibited small uncertainties in long-term runoff estimations regarding parameter calibration (bias between 5 and 7%); differences were observed in low (bellow 5% variation) and high (bellow 10% variation) daily runoff. Related to model structure uncertainties, both models follow similar patterns and performance for a qualitative analysis. Budyko's future projections tend to exceed MHD-INPE's during high precipitation estimates, where at 2000 mm yearly precipitation the estimated runoff from Budyko tends to be 100 mm greater than the hydrological model. As opposed, under arid conditions Budyko tends to estimate smaller runoff than MHD-INPE due to variations in soil moisture and water storage not properly represented in Budyko's parameter. Although uncertainties were identified related to model complexity and calibrated parameters, higher uncertainties were identified as related to the climate models. Therefore, the Budyko method is a viable alternative for first-order analysis of long-term impacts.
... Although the Brazilian electrical matrix is predominantly renewable, the National Interconnected System (SIN) presents instabilities, especially during periods of drought and variation in the rainfall regime [6]. This vulnerability amplifies thermal dependence, and there is a need to complement the lack of hydroelectric generation with nonrenewable sources, mainly coal and fossil fuels [7,8]. A typical example of dependence on thermoelectric generation occurred in 2023 during the historic drought in the Amazon, resulting in increased operating costs [9,10]. ...
The need to replace nonrenewable sources is a global challenge, especially for emerging countries with financial constraints to invest in energy transition. In Brazil, electrical generation is predominantly renewable, since the hydraulic source is responsible for more than half of the domestic electricity supply. Other alternative sources, such as biomass, solar, and wind, complement the renewable portion of the Brazilian electrical matrix. However, periods of drought and climate disturbances threaten the stability of the electrical system and increase dependence on thermal generation. Based on the global movement to intensify the use of renewable sources, which are volatile and influenced by climate variations, the objective of this research is to predict electricity generation using the Heston model. This model, originally applied to predict option prices, assumes nonconstant volatility and has the ability to estimate the average value including the stochastic volatility process. The database is made up of monthly values of the volume of electrical energy generation from biomass, coal, fuel oil, hydraulic, industrial waste, natural gas, nuclear, solar, and wind sources. Compared to the Black-Scholes model, the chosen method proved to be superior in predicting the generation from coal, hydropower, and solar energy, according to the MAE, MAPE, and RMSE statistics. We conclude that a financial model can be effectively applied to the energy sector and the Heston model can be a useful instrument to assist in the planning and management of the national electrical system. One suggestion for the Brazilian case is to encourage the use of alternative energies and low-carbon sources, such as solar, wind, and natural gas, providing greater energy security and a more reliable electrical system.
