Spatiotemporal Analysis of Extreme Rainfall Frequency in the Northeast Region of Brazil

Morales, Fidel Ernesto Castro; Rodrigues, Daniele Tôrres; Marques, Thiago Valentim; Amorim, Ana Cleide Bezerra; Oliveira, Paula Regina de; Silva, Cláudio Moisés Santos e; Gonçalves, Weber Andrade; Lúcio, Paulo Sérgio

doi:10.3390/atmos14030531

Cited by 5 publications

(4 citation statements)

References 80 publications

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“…The prominent extreme indices in the lower São Francisco are consistent with a recent study conducted by (Morales et al 2023). In this study, the authors observed that the eastern coast of the Northeast region experiences a higher number of extreme precipitation events compared to the semiarid region.…”

Section: Precipitation Climate Extreme Indexsupporting

confidence: 91%

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Santos,

Justino,

Rodrigues

2024

Preprint

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The Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent on rainfall patterns. The São Francisco River Basin (SFRB) is a critical component of the BES, playing a key role in electricity generation. However, climate extremes have increasingly impacted energy production in recent decades, posing challenges for HPP management. This study, explores the relationship between extreme precipitation events in the SFRB and two crucial energy variables: Stored Energy (STE) and Affluent Natural Energy (ANE). We analyze the spatial distribution and trends of 11 extreme precipitation indices and investigate the seasonality, trends, and correlations between these energy variables and the extreme indices. Our findings reveal downward trends in both ANE and STE. Additionally, we identify a seasonal pattern influenced by extreme precipitation rates at various time scales. The results indicate that it is possible to estimate ANE and STE efficiently by employing three machine learning (ML) algorithms (Random Forest, Artificial Neural Networks and k-Nearest Neighbors) using extreme precipitation data. These results offer valuable insights for the strategic planning and management of the BES, aiding in decision-making and the development of energy security.

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Section: Precipitation Climate Extreme Indexsupporting

confidence: 91%

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Santos,

Justino,

Rodrigues

2024

Preprint

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“…On the other hand, R20mm, which measures the frequency of days with precipitation exceeding 20mm, indicates the occurrence of more than 11 days with values above this threshold. The prominent extreme indices in the lower São Francisco are consistent with a recent study conducted by Morales et al (2023). In this study, the authors observed that the eastern coast of the Northeast region experiences a higher number of extreme precipitation events compared to the semiarid region.…”

Section: Characterization Of Precipitationsupporting

confidence: 91%

Impact of precipitation extremes on energy production across the São Francisco river basin

Santos

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The Brazilian electrical system is predominantly hydrothermal, with hydroelectric power plants (HPP’s) dependent on rainfall variability. The São Francisco river basin plays a fundamental role in the country's electricity production with HPP’s in the Northeast and Southeast regions. However, climate extremes events have affected the energy production. To manage the use of HPP’s to avoid energy shortage during dry periods and the activation of thermal power plants, is a challenge as it increases production costs and may result in water wastage during rainy periods. The main variables influencing operational decisions are Stored Energy (STE) and Affluent Natural Energy (ANE), used to calculate the Marginal Cost of Operation (MCO) and the Settlement Price of Differences (SPD). The current study investigates the relationships between these variables and climate precipitation extremes events in the São Francisco river basin. Spatial distribution and trends of 11 extremes precipitation indices are analyzed. The seasonality, trends, and correlation between the energy variables and the extreme indices are also investigated. Three machine learning algorithms (Random Forest, Artificial Neural Networks, and k-Nearest Neighbors) were applied as regression models to estimate the energy variables (ANE, STE, MCO, and SPD). Correlations between energy variables show the impact of changes in ANE and STE availability in the São Francisco river basin on MCO and SPD, in the Northeast and Southeast/Midwest subsystems. ANE and STE showed downward trends, while MCO and SPD experienced an upward trend. Furthermore, the seasonal behavior throughout the year was demonstrated for STE and ANE, influenced by extreme precipitation rates in different time scales. Trends indicate a reduction in total precipitation (PRCTOT) and the number of wet days (CWD), as well as an increase in the number of dry days in the basin (CDD). Results based on machine learning algorithm indicate that it is reasonable to efficiently estimate ANE and STE using extremes precipitation data. These findings have significant implications for the planning and management of the Brazilian electricity sector, contributing to strategic decision-making and the formulation of public policies that ensure the country's energy security. Keywords: Affluent Natural Energy. Stored Energy. Prediction.

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“…As an alternative, numerous researchers have chosen to use data from remote sensing products in their studies [3][4][5][6]. The semiarid region of Brazil has a high interannual variability of rainfall, with an alternation of rainy and dry years, mainly due to the effects of the El Niño Southern Oscillation (ENSO) and the inter-hemispheric gradient of the North Atlantic [7,8]. In the context of climate change, extreme weather events have become increasingly frequent and intense [9,10], which can generate socioeconomic implications for the population, especially in the SAB region [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Through these investigations, it is possible to develop effective water resource management policies in the area, aiming to mitigate the adverse impacts of heavy rainfall or droughts. In this context, several studies indicated possible changes in the values of extreme precipitation and temperature indices over time in specific areas of the SAB region [2,8,[21][22][23]. However, conducting in-depth studies on this subject requires precipitation datasets with high temporal and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Space–Time Characterization of Extreme Precipitation Indices for the Semiarid Region of Brazil

dos Santos,

Gonçalves,

Andrade

et al. 2024

Climate

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Various indices of climate variability and extremes are extensively employed to characterize potential effects of climate change. Particularly, the semiarid region of Brazil is influenced by adverse effects of these changes, especially in terms of precipitation. In this context, the main objective of the present study was to characterize the regional trends of extreme precipitation indices in the semiarid region of Brazil (SAB), using daily precipitation data from the IMERG V06 product, spanning the period from 1 January 2001 to 31 December 2020. Twelve extreme precipitation indices were considered, which were estimated annually, and their spatial and temporal trends were subsequently analyzed using the nonparametric Mann–Kendall test and Sen’s slope. The analysis revealed that the peripheral areas of the SAB, especially in the northwest and extreme south regions, exhibited higher intensity and frequency of extreme precipitation events compared to the central portion of the area. However, a negative trend in event intensity was noted in the north, while positive trends were identified in the south. The frequency of extreme events showed a predominance of negative trends across most of the region, with an increase in consecutive dry days particularly throughout the western SAB. The average total precipitation index was above 1000 mm in the north of the SAB, whereas in the central region, the precipitation averages were predominantly below 600 mm, with rainfall intensity values ranging between 6 and 10 mm/day. Over the span of 20 years, the region underwent an average of 40 consecutive dry days in certain localities. A negative trend was observed in most of the indices, indicating a reduction in precipitation intensity in future decades, with variations in some indices. The dry years observed towards the end of the analyzed period likely contributed to the observed negative trends in the majority of extreme precipitation indices. Such trends directly impact the intensity and frequency of extreme weather events in the SAB. The study is important for highlighting and considering the impacts of changes in precipitation extremes in the semiarid region of Brazil. Based on the obtained results, we advocate the implementation of public policies to address future challenges, such as incorporating adaptations in water resource management, sustainable agricultural practices, and planning for urban and rural areas.

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Spatiotemporal Analysis of Extreme Rainfall Frequency in the Northeast Region of Brazil

Cited by 5 publications

References 80 publications

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Impact of precipitation extremes on energy production across the São Francisco river basin

Space–Time Characterization of Extreme Precipitation Indices for the Semiarid Region of Brazil

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