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

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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. Affluent Natural Energy Stored Energy Prediction Machine Learning Artificial Intelligence Regression Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The Brazilian electrical system (BES) has a continental extension. The system is dominated by hydroelectric power plants (HPP’s), which use large reservoirs to regulate seasonal river flows (Mendes 2019 ). The operational planning is sensitive to rainfall variability due to its dependence on hydroelectric generation. While water is a low-cost resource, prolonged rainfall scarcity can lead to energy deficits and the need to apply more expensive sources, such as thermal power plants(Luiz Silva et al. 2019 ). The centralized operations of HPP reservoirs are carried out by the National Electric System Operator (ONS), which enables maximization of natural flows utilization, reducing water waste, and minimizing costs. These operations aim to achieve an optimized balance between minimum cost and maximum operational security, ensuring full demand supply (Mendes 2019 ). The BES is characterized by the interconnection between the stages of power generation and transmission (Hidalgo et al. 2020 ). One prominent watershed is the São Francisco river basin. The São Francisco river plays an important role in supplying electrical energy to the Northeast region of the country ([CSL STYLE ERROR: reference with no printed form.]). The total installed capacity in the National Interconnected System (NIS) was 161,526 MW as of 12/31/2018, distributed as follows: 63.7% in HPP’s, 14.2% in conventional and nuclear thermal power plants, and 22.1% in small hydroelectric power plants (SHPs), biomass, wind, and solar power plants (Daher and Martinez 2019 ). The dilemma is to balance the intensive use of hydroelectric power to avoid energy scarcity during dry periods, and the frequent activation of thermal power plants, which increases production costs and lead to the waste of water during intense rainy periods (Zambom 2008 ). Success of the BES operation depends on climatic and hydrological factors. The ONS focuses on the most economical energy generation and system security, considering the storage level and water flow in HPP’s as the main variables, represented by Stored Energy (STE) and Affluent Natural Energy (ANE), respectively (Mendes 2019 ). The ANE represents the producible energy by the power plant from the natural inflows into the reservoirs ([CSL STYLE ERROR: reference with no printed form.]). The corresponding values are presented in average MW or as a percentage of the long-term historical average (LTA). The monitoring and forecasting of ANE volumes are carried out in relation to its historical average verified since 1931 ([CSL STYLE ERROR: reference with no printed form.]). The STE represents the energy associated with the amount of water available in the reservoir that can be converted into electrical energy by all power plants. In the Northeast, it has been observed a significant reduction in precipitation extremes (Regoto et al. 2021 ), resulting in a drier climate and prolonged periods of drought, which is more pronounced during the rainy seasons, intensifying water scarcity-related problems. Although, the water flows can be regulated and an increase in the use of alternative sources is possible, Brazil has faced a series of crises in the water and energy sectors in recent years (Hidalgo et al. 2020 ). During the period from 2012 to 2017, the contribution of HPP’s to meet the total electricity demand in the Northeast was, on average, only 31%. Furthermore, in November 2015 and 2017, the reservoirs of the São Francisco river reached the lowest level since the construction of the dams in 1994, with only 5% of STE. This situation is directly associated with the occurrence of extreme weather events that result in dry periods in the region (de Jong et al. 2018 ). Prolonged drought periods have contributed to intensification of events, such as the water crisis of 2014–2015 in the Southeast region, and recurrent drought episodes in the Northeast (Avila-Diaz et al. 2020 ). Analyses by (Oliveira et al. 2021 ) revealed sub-regions vulnerable to intensified hydrological and geological processes due to human activity and the recurrence of hydrometeorological phenomena in the São Francisco river basin. According to (Silveira et al. 2016 ), studies aiming to identify climate variability in the region contribute to the reformulation of water resource management policies and increase the system resilience in the face of climate change challenges. Additionally, (Lucas et al. 2021 ) emphasize that the assessment of climate extremes in a specific area is a fundamental tool for water resource management, decision-making in planning, and formulation of environmental protection policies. Thus, analyses of precipitation extremes can be useful in guiding the formulation of strategies for mitigating climate disasters in areas with high vulnerability (Silva et al. 2022 ). Certainly, deep investigation on how these precipitation extremes events are connected with the energy system in terms of the different quantities related such as STE ANE is crucial to characterize the temporal impact of climate on the Brazilian electrical system. Therefore, this study takes the opportunity to analyze whether extreme rainfall climatic events occurring in the São Francisco river basin may impact on energy variables from 2000 to 2019. Moreover, based on machine learning algorithms it is verified the potential to deliver a conceptual model which can link climate indices and energy variable. 2. Materials and Methods 2.1 Study Area The São Francisco river basin (Fig. 1 ) covers 8% of the Brazilian territory with a length of 2,863 km and a drainage area of over 639,219 km². Originating in the state of Minas Gerais (Southeastern Brazil), the São Francisco river source at the Serra da Canastra and flows into the Atlantic Ocean, along the borders of the states of Alagoas and Sergipe (Northeastern Brazil). The Upper São Francisco region comprises the area from the source of the São Francisco river to the city of Pirapora-MG (110,696 km², corresponding to 17% of the basin's surface area). The middle São Francisco extends from Pirapora-MG to Remanso-BA (322,140 km²; 50% of the basin). The Sub-Middle São Francisco hydrographic region covers the stretch from Remanso-BA to Paulo Afonso-BA (168,528 km²; 26% of the basin). Finally, the lower São Francisco encompasses the stretch from Paulo Afonso-BA to the mouth of the São Francisco river (36,959 km²; 6% of the basin). Among the main reservoirs in the São Francisco river basin, that are used for flow control and/or hydroelectric power generation, the following stand out: Três Marias, located in the state of Minas Gerais, Sobradinho, Paulo Afonso, and Luiz Gonzaga (Itaparica) in Bahia, and Xingó, situated between the states of Alagoas and Sergipe ([CSL STYLE ERROR: reference with no printed form.]). The Paulo Afonso hydroelectric Complex, composed of Paulo Afonso I, II, III, IV, and Apolônio Sales (Moxotó), along with the Xingó hydropower plant, uses run-of-river reservoirs for power generation. In contrast, the other HPP in the São Francisco river basin (Retiro Baixo, Queimado, Luiz Gonzaga, Três Marias, and Sobradinho) operate with flow control reservoirs, which have different characteristics including larger useful volumes than run-of-river reservoirs ([CSL STYLE ERROR: reference with no printed form.]). 2.2 Data description 2.2.1 Energy Data Regarding the production of electrical energy, data on Affluent Natural Energy (ANE) and Stored Energy (STE) were used. Data on ANE and STE for Brazilian hydro energy basins are available on a daily basis through the database of the ONS, with information from 2000 to the present day. These data can serve as input for energy studies. However, the data are part of a recurrent consistency process and may undergo updates. 4.2.2 Climate Data Two precipitation data sets are used. The first data set is the Brazilian Daily Weather Gridded Data (BR-DWGD) (Xavier et al. 2022 ), which consists of daily and monthly meteorological data in grid format, covering the period from January 1, 1961, to July 31, 2020, with a spatial resolution of 0.1º × 0.1º. These data were generated from six distinct interpolation methods using meteorological and rainfall station data (Xavier et al. 2016 ) and have been widely utilized in climate studies (de Andrade et al. 2022 ; Tomasella et al. 2022 ). The second data set used is the ERA5-Land, which provides hourly reanalysis data for the land surface on a global scale, covering the period from January 1, 1950, to the present, with a spatial resolution of 0.1º × 0.1º (Muñoz-Sabater et al. 2021 ). Reanalysis combines model data with observations into a comprehensive and consistent data set, using the laws of physics (Muñoz Sabater 2019 ). The ERA5-Land dataset has also been used by the scientific community in climate studies in Brazil and worldwide (Araújo et al. 2022 ; de Andrade et al. 2022 ). 2.3 Data pre-processing 2.3.1 Energy Data For the study of the ANE and STE variables, raw and relative values from the available series were selected. For ANE, the raw value in MW mean (Gross ANE) and the percentage value relative to the long-term average (ANE (%)) were used. For STE, the value verified on the day in MW month (Gross STE) and the same value in percentage terms (STE (%)) were obtained, considering that the maximum storage capacity is reached when all reservoirs in the basin are full. The daily ANE and STE series were transformed into monthly series for the period from 2000 to 2022. 2.3.2 Climate Data The daily precipitation series from the BR-DWGD dataset were used. And the hourly data from ERA5-Land were aggregated to generate the daily precipitation series. Both datasets were clipped to the area of the São Francisco river basin for the period from 1990 to 2019, representing the most recent 30 common years for both datasets. 2.3.3 Precipitation Climate Extremes In order to analyze the magnitudes and seasonality of rainfall in the basin, maps of annually and monthly average precipitation were generated across the study area. The Expert Team on Climate Change Detection and Indices (ETCCDI) of the World Meteorological Organization (WMO) has developed 27 indicators based on daily data of maximum and minimum temperature, as well as precipitation (Karl et al. 1999 ; Frich et al. 2002 ). In this study, the indicators listed in Table 1 were selected for the analysis of annual and/or monthly frequency of precipitation extremes. These indicators were calculated using the xclim library (Logan et al. 2023 ). Days with daily precipitation equal to or greater than 1 mm were considered wet days, while dry days were defined as those with daily precipitation lower than 1 mm. For the 11 indices, the annual averages of the corresponding precipitation extremes were calculated, allowing for the analysis of spatial distribution and trends. Table 1 Precipitation climate extreme index. Index Description Frequency PRCPTOT Total Precipitation (mm) Annual and Monthly RX1DAY Maximum precipitation in one day (mm) Annual and Monthly RX5DAYS Maximum precipitation in five consecutive days (mm) Annual and Monthly SDII Simple Daily Intensity Index (mm/day) Annual and Monthly R20mm Number of days with very heavy precipitation (days) Annual and Monthly CWD Consecutive wet days (days) Annual and Monthly CDD Consecutive dry days (days) Annual and Monthly WD Wet days (days) Annual and Monthly DD Dry days (days) Annual and Monthly PRCWQ Precipitation in the wettest quartile (mm) Annual PRCDQ Precipitation in the driest quartile (mm) Annual For each of the nine indices calculated on a monthly basis, four additional series were generated, consisting of the sum of extreme values from the previous 3, 6, 12, and 24 months. This provided monthly, quarterly, semi-annual, annual, and biennial accumulated values for each precipitation extreme index. 2.4 Trends The Mann-Kendall test (Mann 1945 ; Kendall 1948 ) and Sen's slope estimation (Sen 1968 ; Theil 1992 ) were employed to examine the trends in the monthly series of energy variables and the annual series of extreme climate precipitation indices. The non-parametric Mann-Kendall test was used with a 95% confidence level. For conducting the Mann-Kendall and Sen's slope tests, the PyMannKendall library available in the Python language was used (Hussain and Mahmud 2019 ). 2.5 Correlations The Pearson correlation coefficient (r) was employed to investigate the relationships between energy variables, as well as to identify the association between monthly series of ANE and STE with precipitation extremes indices at different time scales, including monthly, quarterly, semi-annually, annually, and biennially. The value of r can be interpreted as shown in Table 2 . Table 2 Interpretation of Pearson's correlation coefficient value (r). r (+ or -) Interpretation 0,00 a 0,19 Very Weak Correlation 0,20 a 0,39 Weak Correlation 0,40 a 0,69 Moderate Correlation 0,70 a 0,89 Strong Correlation 0,90 a 1,00 Very Strong Correlation 2.6 Use of Artificial Intelligence tool for generating predictive regression models The potential prediction of energy variables based on precipitation extremes may be crucial to the understanding of the relationships between these variables. For this purpose, three machine learning regression methods were used, taking as input the data of precipitation climate extremes with Pearson correlation (r), identified as moderate or strong, in order to estimate the respective energy variables on a monthly basis. The methods used are Random Forest (RF), which is an estimator that consists of a set of decision trees (Breiman 2001 ; Hastie et al. 2009 ); Artificial Neural Networks (ANN), which is a method of data processing inspired by the transfer of data in biological neural systems (Lee et al. 2008 ); and k-Nearest Neighbors (kNN), which is a non-parametric method that estimates the relationship between inputs and outputs without any predetermined assumptions (Anaraki et al. 2021 ). The appropriate selection of regression model parameters is crucial for obtaining accurate results. To determine the best parameter values for each regression method, the GridSearchCV algorithm was used. This algorithm performs an exhaustive search over a predefined grid of hyperparameters, evaluating the model's performance for each parameter combination (Ranjan et al. 2019 ). 2.6.1 Cross-validation Cross-validation is a statistical method used to evaluate the performance of machine learning models, in which the main goal is to assess the model's ability to generalize to unknown data (Chen et al. 2021 ). This technique was applied by dividing the data into five subsets, keeping one subset as the test set and the others as the training set. This process is repeated five times, ensuring that each subset is used as the test set at least once. Evaluation metrics are calculated for each round, and the average is taken across the five results. This procedure is repeated 30 times, totaling 150 tests. Results are presented in box plots, showing the average of the evaluation metrics obtained in the 30 repetitions. 2.6.2 Assessment Metrics To assess the quality of predictions obtained by the regression models five metrics have been used. The Mean Absolute Error (MAE) (Eq. 1) is calculated as the average of the absolute differences between the predicted values and the observed values. It provides a direct measure of the average error of the predictions. The Mean Absolute Percentage Error (MAPE) (Eq. 2) calculates the average of the absolute percentage errors, providing a relative measure of the average error relative to the true values. The Root Mean Squared Error (RMSE) (Eq. 3) is another widely used metric that calculates the square root of the average of the squared errors between the predictions and the true values. This metric penalizes larger errors more heavily and is sensitive to outliers. The Kling-Gupta Efficiency (KGE) coefficient (Eq. 4), proposed by (Gupta et al. 2009 ) and modified by (Kling et al. 2012 ), is a measure of efficiency that compares the variability, trends, and correlation of predictions with the true values. This coefficient ranges from -∞ to 1, where values close to 1 indicate a good fit of predictions to the observed data. Lastly, the Willmott's concordance coefficient (d) (Eq. 7) is used to evaluate the comparative accuracy of estimation values by calculating the discrepancy between the values of one estimation relative to another (Willmott et al. 1985 ). The values of this index range from 0 to 1, representing no agreement and perfect agreement, respectively. \(MAE=\frac{1}{n}\sum _{i=1}^{n}\left|{P}_{i}-{O}_{i}\right|\) (1) \(MAPE=\frac{1}{n}\sum _{i=1}^{n}\frac{\left|{P}_{i}-{O}_{i}\right|}{{O}_{i}}\) (2) \(RMSE=\sqrt{\frac{1}{n}\sum _{i=1}^{n}{\left({P}_{i}-{O}_{i}\right)}^{2}}\) (3) \(KGE=1-\sqrt{{(r-1)}^{2}+{(\beta -1)}^{2}+{(\gamma -1)}^{2}}\) (4) \(\beta = \frac{{\mu }_{P}}{{\mu }_{O}}\) (5) \(\gamma = \frac{{CV}_{P}}{{CV}_{O}}\) (6) \(d=1-\left[\frac{\sum _{i=1}^{n}{\left({P}_{i}-{O}_{i}\right)}^{2}}{\sum _{i=1}^{n}{\left(\left|{P}_{i}-{\mu }_{O}\right|+\left|{O}_{i}-{\mu }_{O}\right|\right)}^{2}}\right]\) (7) In the equations shown above, n is the number of data points, µ is the mean value, CV is the coefficient of variation, P is the prediction value, and O is the true value. 3. Results and Discussion This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. 3.1. Precipitation Climate Extremes 3.1.1. Precipitation Climate Extreme Index Figure 2 presents the results of two datasets, BR-DWGD and ERA5-Land (Figs. 4 a and 4 b, respectively), which are consistent regarding the spatial distribution of annual mean precipitation extremes in the São Francisco river basin. Among the analyzed variables, the indicators PRCPTOT (Total Precipitation) and PRCWQ (Wettest Quartile Precipitation) stand out. They have the highest values in the upper and middle São Francisco meso-region, indicating that about 60% of the total precipitation, in the rainiest areas occurs in the wettest quarter. The indicators of wet days (WD) and consecutive wet days (CWD) show higher values in the upper São Francisco meso-region, the western part of the middle São Francisco, and the eastern area of the lower São Francisco. On average, there are more than 85 rainy days per year, with consecutive rainfall events lasting between 16 and 24 days in these regions. On the other hand, a low value of these indices is observed in the sub-middle São Francisco meso-region. It is noticed an average of 30 to 60 rainy days per year, with only 8 to 12 consecutive rainy days in most of this meso-region. The indices RX5DAYS and R20mm reveal the occurrence of extreme precipitation events in the upper and middle São Francisco meso-regions, as they also have the highest values for these indicators. RX5DAYS measures the maximum intensity of precipitation over five consecutive days and records values above 120mm in these same meso-regions. 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 variable RX1DAY (Maximum Precipitation in a Single Day) is distributed throughout the basin area, with values exceeding 50mm observed in all meso-regions, with values exceeding 60mm in the upper and middle São Francisco. On the other hand, the SDII indicator (Mean Daily Intensity) shows differences between the datasets, as for BR-DWGD, higher magnitudes (above 10 mm/day) are observed in the upper and middle São Francisco meso-regions, with values between 8 and 9 millimeters per day in the sub-middle São Francisco meso-region. For the ERA5-Land, the highest SDII values also occur in the upper and middle São Francisco (between 8 and 9 mm/day), with minimum values in the other areas across the basin. 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. Regarding the meso-region most affected by precipitation extremes, there is agreement with the study conducted by (Jeferson de Medeiros et al. 2022 ), in which the extreme precipitation events (PRCPTOT, RX1day, RX5day, SDII, R20mm, CWD) are more intense, frequent, and prolonged in the northern, southern, and southeastern regions of Brazil. During the rainy season, the South Atlantic Convergence Zone (SACZ) has a significant influence on the rainfall regime in the southeastern region of Brazil (Marengo et al. 2015 ; Nielsen et al. 2019 ; Rosa et al. 2020 ). The study conducted by (da Fonseca Aguiar and Cataldi 2021 ) demonstrates that the frequent occurrence of these extreme events is caused by the influence of the SACZ. Their results reveal that the average conditional probability of the occurrence of the SACZ, when there are disasters due to intense or persistent rainfall events in the Southeast, is 48%, and in Minas Gerais, this probability is even higher, reaching 50%. Lastly, it is worth noting analyses of the indicators DD (Dry Days), CDD (Consecutive Dry Days), and PRCDQ (Driest Quartile Precipitation). The highest number of dry days (from 280 to 320 days) is observed throughout the sub-middle São Francisco meso-region and the eastern part of the middle São Francisco, which also records the maximum number of CDD (from 145 to 190 days). These results suggest that, on average, this area goes without precipitation for more than six months, resulting in high vulnerability to drought events. Predominantly in the lower São Francisco meso-region, precipitation in the driest quarter is higher compared to other meso-regions. These analyses corroborate the study by (Jeferson de Medeiros et al. 2022 ), which found lower intensity and frequency of extreme precipitation events in Northeast Brazil. In this region, there is a high number of Consecutive Dry Days (CDD) in the central portion of the Northeast, which has a semiarid climate. However, this does not mean that there are no occurrences of extreme rainfall in the Northeast, as the RX1DAY and SDII indices stand out. According to the conclusions of (Monteiro 2022 ), in the semiarid area of the Northeast, when atmospheric conditions result from the interaction of two or more meteorological systems, such as UTCV’s, EWD, and ITCZ, it is frequent to result in significant rainfall characterizing true occurrences of extreme indices. These results highlight the spatial heterogeneity of extreme precipitation events in the São Francisco river basin, which requires the adoption of specific strategies for each hydrographic meso-region in order to promote efficient water resources management. It is also important to understand the seasonal behavior of the extreme indices (Fig. 3 ). Analyses of the distribution of average PRCPTOT values (Fig. 3 a) throughout the years reveal the presence of a rainy period between November and March, with monthly averages above 100mm which are represented by both datasets. On the other hand, a dry period occurs in the basin from May to September, with monthly total precipitation below 30mm. October and April are transitional months between the rainy and dry periods, with an average of approximately 60mm. The extreme indices related to heavy rainfall (WD, CWD, SDII, RX1DAY, RX5DAYS, and R20mm) (Figs. 5 a to 5 g), show their maximum during the rainy period, between November and March. This indicates a strong influence of these extreme events on the precipitation regime of the São Francisco river basin. The presence of these extreme events during the rainy season, highlights the influence identified by (da Fonseca Aguiar and Cataldi 2021 ), as shown by a significant association between natural disasters and the SACZ, in the months of November, December and January. According to the authors, more than half of all SACZ-related events occurred during this period, with respect to others in the Southeast region. On the other hand, the extreme indices related to drought reach their monthly maximum averages in the months between June and September. During this period, on average, 25 days of each month are characterized as dry (Fig. 3 h), with at least 20 of these days being consecutively dry (Fig. 3 i) indicating the dry period in the basin. 3.1.2. Trends in Precipitation Climate Extreme Indexes Significant trends calculated for the annual extreme precipitation indices over the study period are shown in Fig. 4 . It is worth noting that only the significant values are highlighted (p ≤ 0.05), thus, the entire white area within the basin does not exhibits trends or, if they do, they are not significant. Trends of extreme precipitation indices differ in terms of spatial distribution and magnitude in the two analyzed datasets. This behavior is justified by (Regoto et al. 2021 ), which states that comparing different datasets and methods can lead to large variations and uncertainties in climate extreme trends. A negative trend is observed throughout the basin for the PRCPTOT, WD, CWD, PRCWQ, and R20mm indices, with larger magnitudes exhibited by the ERA5-Land dataset. It is important to mention that both datasets in most cases deliver similar spatial pattern but with different magnitudes. These results are similar to those detected by (Avila-Diaz et al. 2020 ), that found predominantly negative trends in the São Francisco basin for the PRCPTOT, R20mm, and CWD indices, although only CWD showed a significant trend. Downward trends are observed in the sub-middle São Francisco meso-region and the northern portion of the middle São Francisco for the SDII, RX1DAY, and RX5DAYS indices, especially in the ERA5-Land dataset, diverging from the results obtained by BR-DWGD (Fig. 4 a), which generally do not indicate significant trends in these areas, and show positive trends in the middle São Francisco for the mentioned indicators. An increasing trend in DD index is observed throughout the entire area of the São Francisco river basin, with larger magnitudes found in the ERA5-Land, which also revealed positive trends in CDD in all hydrographic meso-region of the basin. On the other hand, the BR-DWGD dataset suggests a relatively milder increasing trend in CDD in the middle São Francisco. Observations of reduction (increase) in intense rainfall events (drought) in the semiarid region of the basin are consistent with the findings by (Assis et al. 2022 ). They emphasize the decrease in total precipitation, daily rainfall, maximum volumes in 1 and 5 consecutive days, rainy days, and the number of days with moderate, heavy, and intense rainfall. Positive trends were only observed in consecutive days without rain, reinforcing the negative trend of rainfall and recurrent droughts in northeastern Brazil. The PRCDQ index shows downward trends throughout the basin, with the greatest magnitude in the lower São Francisco. In the BR-DWGD dataset, these trends are characterized by minimal magnitudes. Climate analyses are essential to understand how climate extremes affect water resource availability for energy generation. Changes in extreme precipitation event trends can directly impact hydroelectric power production. Therefore, efficient energy production, water resource management and sustainable energy supply may benefit for a clear understand of short-scale weather events. 3.2. Relationship between Climate Extremes and Energy Variables 3.2.1. Correlations between Climate Extremes and Energy Variables The Pearson linear correlation coefficients (r) between energy variables and accumulated climate extremes are presented in Table 3 . Empty fields in the table indicate that the corresponding correlations did not reach the desired significance level (p ≤ 0.05). Both datasets used showed r values > |0.70| between Gross Affluent Natural Energy (ANE) and all accumulated extreme indices for six months, except for CDD 6, where the ERA5-Land dataset indicates a moderate correlation. In fact, 1, 3, 6, 12 and 24 indicate that the indices are summed throughout these specific months interval. Moderate correlations are also observed with indices accumulated over three months. As for ANE (%), strong correlations are observed with CWD 24 from ERA5-Land and R20mm 24 from BR-DWGD, with consensus only for PRCPTOT 24, while correlations are moderate for the other indices accumulated over 24 months, as well as for all indices accumulated over 12 months. The correlations between Gross Stored Energy (STE) (both gross and percentage) and extreme indices exhibit strong values, particularly highlighting the correlations with RX1DAY 24 and RX5DAYS 24 from the BR-DWGD dataset, as well as with PRCPTOT 24, WD 24, CWD 24, R20mm 24, and DD 24, with agreement between the two data sources. For the other extremes accumulated over 24 months and all accumulated over 12 months, the correlations with stored energy are moderate. Table 3 Correlations (r) between Energy Variables and Precipitation Extremes BR-DWGD (a) and ERA5-Land (b). Extremes Gross ANE ANE (%) Gross STE STE (%) a b a b a b a b PRCPTOT 1 0.17 0.16 -0.22 -0.22 -0.22 -0.22 PRCPTOT 3 0.66 0.66 0.31 0.31 PRCPTOT 6 0.78 0.77 0.29 0.30 0.29 0.30 0.29 0.30 PRCPTOT 12 0.38 0.37 0.69 0.68 0.69 0.68 0.69 0.68 PRCPTOT 24 0.38 0.37 0.72 0.70 0.79 0.78 0.79 0.78 WD 1 0.25 0.28 -0.22 -0.18 -0.22 -0.18 WD 3 0.67 0.69 0.29 0.29 WD 6 0.75 0.74 0.29 0.30 0.33 0.37 0.33 0.37 WD 12 0.35 0.35 0.66 0.65 0.66 0.65 0.66 0.65 WD 24 0.36 0.35 0.68 0.66 0.76 0.74 0.76 0.74 CWD 1 0.20 0.23 -0.25 -0.21 -0.25 -0.21 CWD 3 0.65 0.67 0.26 0.27 CWD 6 0.74 0.73 0.24 0.26 0.25 0.31 0.25 0.31 CWD 12 0.33 0.36 0.61 0.67 0.56 0.62 0.56 0.62 CWD 24 0.35 0.37 0.66 0.70 0.71 0.74 0.71 0.74 SDII 1 0.19 -0.26 -0.29 -0.26 -0.29 SDII 3 0.62 0.58 0.23 0.22 -0.16 -0.16 SDII 6 0.73 0.74 0.22 0.22 0.26 0.20 0.26 0.20 SDII 12 0.24 0.25 0.51 0.51 0.54 0.50 0.54 0.50 SDII 24 0.35 0.35 0.64 0.66 0.66 0.67 0.66 0.66 RX1DAY 1 0.24 0.19 -0.24 -0.26 -0.24 -0.26 RX1DAY 3 0.64 0.61 0.26 0.24 RX1DAY 6 0.74 0.73 0.25 0.22 0.28 0.22 0.28 0.22 RX1DAY 12 0.31 0.25 0.60 0.47 0.61 0.50 0.61 0.50 RX1DAY 24 0.36 0.30 0.68 0.57 0.73 0.65 0.73 0.65 RX5DAYS 1 0.23 0.21 -0.24 -0.24 -0.24 -0.24 RX5DAYS 3 0.66 0.65 0.29 0.27 RX5DAYS 6 0.75 0.74 0.26 0.24 0.29 0.27 0.29 0.27 RX5DAYS 12 0.31 0.28 0.59 0.53 0.62 0.56 0.62 0.56 RX5DAYS 24 0.36 0.33 0.68 0.61 0.72 0.68 0.72 0.68 R20mm 1 -0.22 -0.23 -0.22 -0.23 R20mm 3 0.64 0.58 0.33 0.31 -0.17 -0.17 R20mm 6 0.78 0.76 0.29 0.26 0.27 0.19 0.27 0.19 R20mm 12 0.37 0.35 0.65 0.60 0.67 0.59 0.68 0.59 R20mm 24 0.38 0.36 0.71 0.68 0.78 0.74 0.78 0.74 DD 1 -0.27 -0.30 0.21 0.18 0.21 0.18 DD 3 -0.68 -0.69 -0.27 -0.28 DD 6 -0.75 -0.73 -0.28 -0.29 -0.33 -0.38 -0.33 -0.38 DD 12 -0.35 -0.34 -0.65 -0.65 -0.66 -0.65 -0.66 -0.65 DD 24 -0.36 -0.34 -0.67 -0.66 -0.75 -0.74 -0.75 -0.74 CDD 1 -0.37 -0.39 0.22 0.21 0.22 0.21 CDD 3 -0.67 -0.68 -0.23 -0.23 CDD 6 -0.71 -0.69 -0.23 -0.22 -0.32 -0.33 -0.32 -0.33 CDD 12 -0.26 -0.23 -0.52 -0.45 -0.54 -0.45 -0.54 -0.45 CDD 24 -0.29 -0.23 -0.55 -0.45 -0.60 -0.51 -0.60 -0.51 Based on results presented in Table 3 , the correlations indicate that energy variables can be affected by climate extreme indices related to precipitation at various time scales. These previous analyses demonstrated that a gain can be obtained in the interpretation of those correlation. Such as, the more the extremes (PRCPTOT, RX1DAY, RX5DAYS) act in the basin, greater the water availability. On the contrary, the smaller the performance of the extremes, the more thermal plants will be activated in the system. 3.2.2. Affluent Natural Energy (ANE) In Fig. 5 , the historical time series and monthly averages of Gross ANE and Percentage of ANE are presented from January 2000 to December 2022. Both time series curves (Figs. 7 a and 7 b) show a cyclic oscillation, which can be explained by the monthly averages of the historical series (Figs. 7 c and 7 d) and their relationships with extreme climate precipitation indices. The influence of extremes in the São Francisco river basin is observed through the short-term response of Gross Affluent Natural Energy, with stronger correlations occurring in periods of three to six months (Table 3 ). This relationship reveals a well-defined seasonal behavior, with minimum values recorded in September, the last month of the driest quarter, at approximately 2000 MW.mean. The values of Gross ANE increase in the following months and peak in February, the fourth month of the rainy season, at over 10000 MW.mean, before decreasing in the subsequent months. This can be explained by the impact of extremes in the basin, which cause changes in the natural flow of the main river channel in short periods. On the other hand, percentage of ANE, which incorporates long-term averages in its determination, shows long-term responses, ranging from 12 to 24 months. Seasonality was also observed by (Vilar et al. 2020 ), according to the authors, the time series shows a decrease in observed ANE values due to the dry season. Percentage of ANE also exhibits seasonality, but with changes throughout the year. The lowest monthly average value, around 62%, is recorded in October, a transitional month between the dry and rainy periods in the basin. From there, the values gradually increase to reach a maximum of approximately 78% in January, the third month of the rainy season. Subsequently, the values decrease, with increases observed in April and June compared to the previous month. These increases may be related to the extreme R20mm, which is well correlated with the percentage of ANE, when it is accumulated over 24 months (Table 3 ). In May, on average, up to two events of intense precipitation can occur in the basin (Fig. 3 g), influencing the natural river flow in the subsequent month. The slight increase observed in June is likely associated with the total precipitation in the lower São Francisco hydrographic meso-region and the eastern extreme of the sub-middle São Francisco from May to August (Fig. 3 ). Despite being predominantly dry months in other meso-region of the basin, the Paulo Afonso hydroelectric complex and the Xingó HPP are located in these regions (Fig. 1 ), which use run-of-the-river reservoirs for energy generation, contributing significantly to ANE (%) in the basin. It is also worth noting that ANE (%) is based on a long-term mean (LTA) that has been verified since 1931 and, therefore, may contain climatological patterns not identified in the temporal scope of this study. 3.2.3. Stored Energy (STE) In Fig. 6 , the historical time series of Gross Energy Generation (STE) and Percentage of STE, as well as their monthly averages are presented for the January 2000 to December 2022 interval. It is notable that both time series (Figs. 8 a and 8 b) and the monthly average curves (Figs. 8 c and 8 d) exhibit identical behavior. This is because, throughout the analyzed period, the maximum STE of the São Francisco river basin remained steady at 52,727 MW.month, according to information from [20]. The cyclic oscillation of time series can be explained based on their respective monthly averages and their association with extreme precipitation climate indices. The response of STE (gross and percentage) to extremes shows a long-term relationship with precipitation extremes, as stronger correlations occurred in 24-month periods (Table 3 ). The Gross STE and Percentage of STE (Figs. 8 c and 8 d, respectively) patterns exhibit a clear seasonal cycle. The minimum value is observed in November, at the beginning of the rainy season, at approximately 17,500 MW.month, corresponding to 35% of the maximum energy production capacity of reservoir-based power plant. Over the following months, this value gradually increases, reaching its maximum in May, during the dry period of the basin, at around 35,000 MW.month, representing approximately 66% of the percentage of STE. This behavior can be attributed to the control of water levels in the reservoirs, which have a considerable storage volume. This provides the system with resilience to variations in water availability over short periods of time. Analyses of the average monthly values of ANE (Fig. 5 ) and STE (Fig. 6 ) reveal a significant difference in magnitude between the two quantities. This difference is mainly due to the fact that, in addition to differences in working volumes, HPP with flow control reservoirs have the ability to store water and therefore generate electrical energy during periods of high demand (STE), while run-of-the-river HPP have their electricity production limited by the available river flow (ANE). 3.2.4. Trends of Energy Variables Results obtained based on the Mann-Kendall trend test for the energy variables are presented in Table 4 . Table 4 Non-parametric tests for the energy variables. Variable Trend p-value Sens's Slope Gross ANE decreasing 0.0403 -238.63 MW.month/year ANE (%) decreasing 0.0403 -0.45%/year Gross STE decreasing 0.0001 -82.02 MW.month/year STE (%) decreasing 0.0001 -1.69%/year The ANE and STE deliver decreasing trends over the study period, indicating a reduction in water and electrical energy availability in the São Francisco river basin. This suggests the need to invest in management strategies to ensure the long-term sustainability of the electrical system. Similarly, (Da Silva et al. 2021 ) observed negative trends when analyzing time series of natural inflows into the Itaparica and Sobradinho reservoirs. The authors attributed these trends to the occurrence of droughts recorded in recent years in the São Francisco river basin. 3.3. Use of Artificial Intelligence tool for generating predictive regression models In the following Artificial Intelligence tools are used for generating predictive regression models. Results presented in this section provide a comparative analysis of evaluation metrics for the predictive regression models applied to two datasets: BR-DWGD, represented in blue, and ERA5-Land, represented in green. Figure 7 . Regression Model Performance for Gross ANE Prediction ( a ) and ANE (%) ( b ). By analyzing Fig. 7 a, the predictive models for Gross ANE yielded close and satisfactory results for both datasets. The KNN model demonstrated the best performance in all metrics for the ERA5-Land. For the BR-DWGD dataset, the KNN model also delivers the best performance in terms of KGE, d, and MAPE, but with relatively large dispersion. Regarding the predictions of ANE (%) (Fig. 7 b), it can be observed that the results were similar and acceptable for all three models in both datasets. The RF model showed the best performance with the BR-DWGD data in all metrics. On the other hand, for the ERA5-Land dataset, the performance of the RF model was comparable to that of the KNN model. The estimations of stored energy in both gross form (Fig. 8 a) and percentage form (Fig. 8 b), also exhibited satisfactory results for the BR-DWGD and ERA5-Land datasets. It can be observed that, except for ANN in STE (%), the dispersion of predicted values is relatively low compared to the ANE estimations. The RF model delivers the best performance for STE predictions in all metrics for both datasets. It is noteworthy that, for both gross STE and STE in percentage, the values of d and KGE are close to optimal values, with a low MAPE ranging between 25% and 30%. The results presented in this section indicate that extreme precipitation events are directly related to both the STE in reservoirs and the ANE in the São Francisco river Basin. Furthermore, it is observed that it is feasible to efficiently estimate STE and ANE using only information on extreme precipitation indices in the basin. This finding has significant relevance for the management of the National Interconnected System (SIN), as (Vilar et al. 2020 ) identified considerable differences between the projected values of ANE and STE by the models used by ONS (National Electric System Operator) and the observed values. According to (Vilar et al. 2020 ), this indicates the necessity for adjustments in the ONS methodology, especially in severe drought situations. In this context, the estimation methodology proposed in the present study emerges as a promising alternative. Understanding the relationships between extreme climate events and energy variables, especially the ability to efficiently estimate ANE and STE, based on information on extreme precipitation events, presents significant potential for future applications in energy production planning in Brazil. These findings are particularly relevant when considering extreme events predicted for the future, such as prolonged droughts or heavy rainfall, which can have substantial impacts on energy supply. The use of such information can contribute to more resilient and adaptable energy planning, that takes into account challenges arising from extreme climate events. Although the evaluated models in this study have demonstrated efficiency in estimating STE and ANE based on information on extreme precipitation, it is important to note that the inclusion of other climate variables can further enhance the accuracy of predictive models. In addition to precipitation, factors such as temperature and regional climate patterns play crucial roles in energy production. Therefore, future research exploring the integration of other climate variables into the models, can improve the ability to accurately predict energy availability in situations of extreme climate events. 4. Conclusions Analysis of extreme precipitation events in the São Francisco river basin reveals distinct patterns in different regions. The Upper São Francisco meso-region and the western portion of the middle São Francisco are the most impacted by extreme rainfall events, which is related to the rainy season in the basin. On the other hand, the sub-middle São Francisco and the eastern region of the middle São Francisco are more susceptible to extreme drought events, recording the highest magnitudes throughout the year. Trend tests indicate a reduction in total precipitation and the number of rainy days, as well as an increase in the number of dry days. Seasonal analysis of Energy Stored (STE) and Affluent Natural Energy (ANE) reveals a behavior influenced by precipitation extremes on different time scales. The short-term responses of Gross ANE levels and the long-term responses of ANE (%) and STE (both gross and percentage), are evident in response to extreme climatic events. The regression models based on artificial intelligence methods demonstrated efficient performance in estimating ANE and STE based on extreme precipitation events in the basin. These results pave the way for further research and practical applications, including forecasts of future energy conditions based on climate extremes, and the consideration of additional weather variables to enhance the efficiency of the models. These findings are relevant to the planning and management of the electricity sector, especially in relation to strategic decision-making and the development of public policies aimed at ensuring the energy security of the country. Declarations CRediT authorship contribution statement: Josielton Santos : Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Flávio Justino : Validation, Supervision, Project administration, Investigation, Writing - Review & Editing. Jackson Rodrigues : Validation, Supervision, Investigation, Writing - Review & Editing. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This research was funded by National Council for Scientific and Technological—CNPq, grant number 131148/2021-4. Author Contribution J. S. Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. F. J. Validation, Supervision, Project administration, In-vestigation, Writing - Review & Editing. J. R. Validation, Supervision, Investigation, Writ-ing - Review & Editing. Acknowledgments: We also thank the Federal University of Viçosa and National Council for Scientific and Technological—CNPq. Data Availability Statement: Data will be made available on request. 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Cite Share Download PDF Status: Published Journal Publication published 08 Jun, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 28 Apr, 2024 Reviews received at journal 05 Apr, 2024 Reviewers agreed at journal 17 Mar, 2024 Reviewers agreed at journal 16 Mar, 2024 Reviewers invited by journal 16 Mar, 2024 Submission checks completed at journal 12 Mar, 2024 Editor assigned by journal 12 Mar, 2024 First submitted to journal 12 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4086856","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278958270,"identity":"a600dbe2-aa12-42e4-910b-f9548076c489","order_by":0,"name":"Josielton Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3PsYrCMBjA8S8UdAm4KkLvFb4jUBELfZWUQl0OvMPFsZNZ+gB5DCdxjGRw6QMIgrT0BXq4COfgWRW6NK4O+U/JF34kAbDZ3jCSACj4bkx6QPJ6bCaIjy0HGCQOGsm9JkH1gjhCFKrCizuCbnH6OfsB20edPtkc2x+WZriViGycUDaUPA7XNcnm7UR+gaaI4UpRGFKuubefaSRLbiDTXF9q0i3//knAZOS8IBw11AS82y1k1Y9IbiS3v6TIGGrqTWgchzIrSR5m7eRTiLI6L1wXd6I8UN8PeiIC9bsxkOS5cprjdgDwYTiz2Ww2270rTpdT+nMT/9AAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Viçosa (UFV)","correspondingAuthor":true,"prefix":"","firstName":"Josielton","middleName":"","lastName":"Santos","suffix":""},{"id":278958271,"identity":"1340da2e-d0ff-494d-971f-f89987a39f02","order_by":1,"name":"Flávio Justino","email":"","orcid":"","institution":"Federal University of Viçosa (UFV)","correspondingAuthor":false,"prefix":"","firstName":"Flávio","middleName":"","lastName":"Justino","suffix":""},{"id":278958277,"identity":"7f48373d-c3cb-4fcd-9e90-60db0de41964","order_by":2,"name":"Jackson Rodrigues","email":"","orcid":"","institution":"Federal Fluminense University (UFF), Angra dos Reis – RJ","correspondingAuthor":false,"prefix":"","firstName":"Jackson","middleName":"","lastName":"Rodrigues","suffix":""}],"badges":[],"createdAt":"2024-03-12 18:16:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4086856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4086856/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05051-0","type":"published","date":"2024-06-08T14:49:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52855654,"identity":"4a8393fc-d177-4e3d-b73b-060e0e90d270","added_by":"auto","created_at":"2024-03-18 02:24:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142207,"visible":true,"origin":"","legend":"\u003cp\u003eHydrographic Regions of the São Francisco river Basin.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/148fd8eb49fdd8f00ca2fe29.png"},{"id":52855552,"identity":"d859a3dc-6d34-4751-b1ac-34d958508b94","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":719760,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Average of Precipitation Extremes (1990 – 2019) BR-DWGD (\u003cstrong\u003ea\u003c/strong\u003e) and ERA5-Land (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/56909655d2ac2e05b2eff051.jpeg"},{"id":52855554,"identity":"3d38192d-a29a-4bb5-a379-9d85f5e3496d","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109458,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Average of Extreme Precipitation (1990 – 2019).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/398d208fd0b9f9840341bb56.png"},{"id":52855553,"identity":"76481a1e-561b-4f2f-b758-981cbafe71cb","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":794372,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant Trends (95%) of Precipitation Climate Extremes (1990 – 2019) BR-DWGD (\u003cstrong\u003ea\u003c/strong\u003e) and ERA5-Land (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/da110b93b7d25b70be7bc2c5.jpeg"},{"id":52855558,"identity":"f7fe9b29-2383-406d-a73c-bf1d61294221","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99873,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Time Series of Gross ANE (\u003cstrong\u003ea\u003c/strong\u003e); Monthly Time Series of ANE (%) (\u003cstrong\u003eb\u003c/strong\u003e); Monthly Average of Gross ANE (\u003cstrong\u003ec\u003c/strong\u003e); Monthly Average of ANE (%) (\u003cstrong\u003ed\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/68a798b00a2ef1e59ce6e710.png"},{"id":52855556,"identity":"60e6a626-af46-45e9-8580-c9c395ad62c3","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":98178,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Time Series of Gross STE (\u003cstrong\u003ea\u003c/strong\u003e); Monthly Time Series of STE(%) (\u003cstrong\u003eb\u003c/strong\u003e); Monthly Average of Gross STE (\u003cstrong\u003ec\u003c/strong\u003e); Monthly Average of STE (%) (\u003cstrong\u003ed\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/9c185a1384e4cd75be992db0.png"},{"id":52855559,"identity":"58c88a08-5e13-4847-a10b-ae7f284cf085","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":262594,"visible":true,"origin":"","legend":"\u003cp\u003eRegression Model Performance for Gross ANE Prediction (\u003cstrong\u003ea\u003c/strong\u003e) and ANE (%) (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/2204257e4f90f1fab44889c6.jpeg"},{"id":52855557,"identity":"132d5096-d849-45c3-b49e-feba67b95152","added_by":"auto","created_at":"2024-03-18 02:16:49","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":219277,"visible":true,"origin":"","legend":"\u003cp\u003eRegression Model Performance for Gross STE Prediction (\u003cstrong\u003ea\u003c/strong\u003e) and STE (%) (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/1d0b5575226bacca7af007b2.jpeg"},{"id":58822158,"identity":"5fbeae39-4135-407d-8ed2-f3f0e6f00ecb","added_by":"auto","created_at":"2024-06-21 16:34:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3441283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4086856/v1/ccd59e6b-3cef-419d-b1e7-2c86202cb7b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Brazilian electrical system (BES) has a continental extension. The system is dominated by hydroelectric power plants (HPP\u0026rsquo;s), which use large reservoirs to regulate seasonal river flows (Mendes \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The operational planning is sensitive to rainfall variability due to its dependence on hydroelectric generation. While water is a low-cost resource, prolonged rainfall scarcity can lead to energy deficits and the need to apply more expensive sources, such as thermal power plants(Luiz Silva et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe centralized operations of HPP reservoirs are carried out by the National Electric System Operator (ONS), which enables maximization of natural flows utilization, reducing water waste, and minimizing costs. These operations aim to achieve an optimized balance between minimum cost and maximum operational security, ensuring full demand supply (Mendes \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe BES is characterized by the interconnection between the stages of power generation and transmission (Hidalgo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One prominent watershed is the S\u0026atilde;o Francisco river basin. The S\u0026atilde;o Francisco river plays an important role in supplying electrical energy to the Northeast region of the country ([CSL STYLE ERROR: reference with no printed form.]). The total installed capacity in the National Interconnected System (NIS) was 161,526 MW as of 12/31/2018, distributed as follows: 63.7% in HPP\u0026rsquo;s, 14.2% in conventional and nuclear thermal power plants, and 22.1% in small hydroelectric power plants (SHPs), biomass, wind, and solar power plants (Daher and Martinez \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dilemma is to balance the intensive use of hydroelectric power to avoid energy scarcity during dry periods, and the frequent activation of thermal power plants, which increases production costs and lead to the waste of water during intense rainy periods (Zambom \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuccess of the BES operation depends on climatic and hydrological factors. The ONS focuses on the most economical energy generation and system security, considering the storage level and water flow in HPP\u0026rsquo;s as the main variables, represented by Stored Energy (STE) and Affluent Natural Energy (ANE), respectively (Mendes \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ANE represents the producible energy by the power plant from the natural inflows into the reservoirs ([CSL STYLE ERROR: reference with no printed form.]). The corresponding values are presented in average MW or as a percentage of the long-term historical average (LTA). The monitoring and forecasting of ANE volumes are carried out in relation to its historical average verified since 1931 ([CSL STYLE ERROR: reference with no printed form.]). The STE represents the energy associated with the amount of water available in the reservoir that can be converted into electrical energy by all power plants.\u003c/p\u003e \u003cp\u003eIn the Northeast, it has been observed a significant reduction in precipitation extremes (Regoto et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), resulting in a drier climate and prolonged periods of drought, which is more pronounced during the rainy seasons, intensifying water scarcity-related problems. Although, the water flows can be regulated and an increase in the use of alternative sources is possible, Brazil has faced a series of crises in the water and energy sectors in recent years (Hidalgo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the period from 2012 to 2017, the contribution of HPP\u0026rsquo;s to meet the total electricity demand in the Northeast was, on average, only 31%. Furthermore, in November 2015 and 2017, the reservoirs of the S\u0026atilde;o Francisco river reached the lowest level since the construction of the dams in 1994, with only 5% of STE. This situation is directly associated with the occurrence of extreme weather events that result in dry periods in the region (de Jong et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProlonged drought periods have contributed to intensification of events, such as the water crisis of 2014\u0026ndash;2015 in the Southeast region, and recurrent drought episodes in the Northeast (Avila-Diaz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Analyses by (Oliveira et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed sub-regions vulnerable to intensified hydrological and geological processes due to human activity and the recurrence of hydrometeorological phenomena in the S\u0026atilde;o Francisco river basin.\u003c/p\u003e \u003cp\u003eAccording to (Silveira et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), studies aiming to identify climate variability in the region contribute to the reformulation of water resource management policies and increase the system resilience in the face of climate change challenges. Additionally, (Lucas et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasize that the assessment of climate extremes in a specific area is a fundamental tool for water resource management, decision-making in planning, and formulation of environmental protection policies.\u003c/p\u003e \u003cp\u003eThus, analyses of precipitation extremes can be useful in guiding the formulation of strategies for mitigating climate disasters in areas with high vulnerability (Silva et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Certainly, deep investigation on how these precipitation extremes events are connected with the energy system in terms of the different quantities related such as STE ANE is crucial to characterize the temporal impact of climate on the Brazilian electrical system.\u003c/p\u003e \u003cp\u003eTherefore, this study takes the opportunity to analyze whether extreme rainfall climatic events occurring in the S\u0026atilde;o Francisco river basin may impact on energy variables from 2000 to 2019. Moreover, based on machine learning algorithms it is verified the potential to deliver a conceptual model which can link climate indices and energy variable.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe S\u0026atilde;o Francisco river basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) covers 8% of the Brazilian territory with a length of 2,863 km and a drainage area of over 639,219 km\u0026sup2;. Originating in the state of Minas Gerais (Southeastern Brazil), the S\u0026atilde;o Francisco river source at the Serra da Canastra and flows into the Atlantic Ocean, along the borders of the states of Alagoas and Sergipe (Northeastern Brazil).\u003c/p\u003e \u003cp\u003eThe Upper S\u0026atilde;o Francisco region comprises the area from the source of the S\u0026atilde;o Francisco river to the city of Pirapora-MG (110,696 km\u0026sup2;, corresponding to 17% of the basin's surface area). The middle S\u0026atilde;o Francisco extends from Pirapora-MG to Remanso-BA (322,140 km\u0026sup2;; 50% of the basin). The Sub-Middle S\u0026atilde;o Francisco hydrographic region covers the stretch from Remanso-BA to Paulo Afonso-BA (168,528 km\u0026sup2;; 26% of the basin). Finally, the lower S\u0026atilde;o Francisco encompasses the stretch from Paulo Afonso-BA to the mouth of the S\u0026atilde;o Francisco river (36,959 km\u0026sup2;; 6% of the basin).\u003c/p\u003e \u003cp\u003eAmong the main reservoirs in the S\u0026atilde;o Francisco river basin, that are used for flow control and/or hydroelectric power generation, the following stand out: Tr\u0026ecirc;s Marias, located in the state of Minas Gerais, Sobradinho, Paulo Afonso, and Luiz Gonzaga (Itaparica) in Bahia, and Xing\u0026oacute;, situated between the states of Alagoas and Sergipe ([CSL STYLE ERROR: reference with no printed form.]). The Paulo Afonso hydroelectric Complex, composed of Paulo Afonso I, II, III, IV, and Apol\u0026ocirc;nio Sales (Moxot\u0026oacute;), along with the Xing\u0026oacute; hydropower plant, uses run-of-river reservoirs for power generation. In contrast, the other HPP in the S\u0026atilde;o Francisco river basin (Retiro Baixo, Queimado, Luiz Gonzaga, Tr\u0026ecirc;s Marias, and Sobradinho) operate with flow control reservoirs, which have different characteristics including larger useful volumes than run-of-river reservoirs ([CSL STYLE ERROR: reference with no printed form.]).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data description\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Energy Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegarding the production of electrical energy, data on Affluent Natural Energy (ANE) and Stored Energy (STE) were used. Data on ANE and STE for Brazilian hydro energy basins are available on a daily basis through the database of the ONS, with information from 2000 to the present day. These data can serve as input for energy studies. However, the data are part of a recurrent consistency process and may undergo updates.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Climate Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTwo precipitation data sets are used. The first data set is the Brazilian Daily Weather Gridded Data (BR-DWGD) (Xavier et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which consists of daily and monthly meteorological data in grid format, covering the period from January 1, 1961, to July 31, 2020, with a spatial resolution of 0.1\u0026ordm; \u0026times; 0.1\u0026ordm;. These data were generated from six distinct interpolation methods using meteorological and rainfall station data (Xavier et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and have been widely utilized in climate studies (de Andrade et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tomasella et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second data set used is the ERA5-Land, which provides hourly reanalysis data for the land surface on a global scale, covering the period from January 1, 1950, to the present, with a spatial resolution of 0.1\u0026ordm; \u0026times; 0.1\u0026ordm; (Mu\u0026ntilde;oz-Sabater et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Reanalysis combines model data with observations into a comprehensive and consistent data set, using the laws of physics (Mu\u0026ntilde;oz Sabater \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ERA5-Land dataset has also been used by the scientific community in climate studies in Brazil and worldwide (Ara\u0026uacute;jo et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; de Andrade et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data pre-processing\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Energy Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the study of the ANE and STE variables, raw and relative values from the available series were selected. For ANE, the raw value in MW mean (Gross ANE) and the percentage value relative to the long-term average (ANE (%)) were used. For STE, the value verified on the day in MW month (Gross STE) and the same value in percentage terms (STE (%)) were obtained, considering that the maximum storage capacity is reached when all reservoirs in the basin are full.\u003c/p\u003e \u003cp\u003eThe daily ANE and STE series were transformed into monthly series for the period from 2000 to 2022.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Climate Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe daily precipitation series from the BR-DWGD dataset were used. And the hourly data from ERA5-Land were aggregated to generate the daily precipitation series. Both datasets were clipped to the area of the S\u0026atilde;o Francisco river basin for the period from 1990 to 2019, representing the most recent 30 common years for both datasets.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Precipitation Climate Extremes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to analyze the magnitudes and seasonality of rainfall in the basin, maps of annually and monthly average precipitation were generated across the study area. The Expert Team on Climate Change Detection and Indices (ETCCDI) of the World Meteorological Organization (WMO) has developed 27 indicators based on daily data of maximum and minimum temperature, as well as precipitation (Karl et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Frich et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this study, the indicators listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were selected for the analysis of annual and/or monthly frequency of precipitation extremes. These indicators were calculated using the xclim library (Logan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDays with daily precipitation equal to or greater than 1 mm were considered wet days, while dry days were defined as those with daily precipitation lower than 1 mm. For the 11 indices, the annual averages of the corresponding precipitation extremes were calculated, allowing for the analysis of spatial distribution and trends.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrecipitation climate extreme index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum precipitation in one day (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum precipitation in five consecutive days (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimple Daily Intensity Index (mm/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of days with very heavy precipitation (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsecutive wet days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsecutive dry days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual and Monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCWQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation in the wettest quartile (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation in the driest quartile (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor each of the nine indices calculated on a monthly basis, four additional series were generated, consisting of the sum of extreme values from the previous 3, 6, 12, and 24 months. This provided monthly, quarterly, semi-annual, annual, and biennial accumulated values for each precipitation extreme index.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Trends\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Mann-Kendall test (Mann \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1948\u003c/span\u003e) and Sen's slope estimation (Sen \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; Theil \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) were employed to examine the trends in the monthly series of energy variables and the annual series of extreme climate precipitation indices. The non-parametric Mann-Kendall test was used with a 95% confidence level. For conducting the Mann-Kendall and Sen's slope tests, the PyMannKendall library available in the Python language was used (Hussain and Mahmud \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Correlations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Pearson correlation coefficient (r) was employed to investigate the relationships between energy variables, as well as to identify the association between monthly series of ANE and STE with precipitation extremes indices at different time scales, including monthly, quarterly, semi-annually, annually, and biennially. The value of r can be interpreted as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterpretation of Pearson's correlation coefficient value (r).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003er (+\u0026thinsp;or -)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,00 a 0,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Weak Correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,20 a 0,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeak Correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,40 a 0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,70 a 0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong Correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,90 a 1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Strong Correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Use of Artificial Intelligence tool for generating predictive regression models\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe potential prediction of energy variables based on precipitation extremes may be crucial to the understanding of the relationships between these variables. For this purpose, three machine learning regression methods were used, taking as input the data of precipitation climate extremes with Pearson correlation (r), identified as moderate or strong, in order to estimate the respective energy variables on a monthly basis.\u003c/p\u003e \u003cp\u003eThe methods used are Random Forest (RF), which is an estimator that consists of a set of decision trees (Breiman \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hastie et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); Artificial Neural Networks (ANN), which is a method of data processing inspired by the transfer of data in biological neural systems (Lee et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); and k-Nearest Neighbors (kNN), which is a non-parametric method that estimates the relationship between inputs and outputs without any predetermined assumptions (Anaraki et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe appropriate selection of regression model parameters is crucial for obtaining accurate results. To determine the best parameter values for each regression method, the GridSearchCV algorithm was used. This algorithm performs an exhaustive search over a predefined grid of hyperparameters, evaluating the model's performance for each parameter combination (Ranjan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Cross-validation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCross-validation is a statistical method used to evaluate the performance of machine learning models, in which the main goal is to assess the model's ability to generalize to unknown data (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This technique was applied by dividing the data into five subsets, keeping one subset as the test set and the others as the training set. This process is repeated five times, ensuring that each subset is used as the test set at least once. Evaluation metrics are calculated for each round, and the average is taken across the five results. This procedure is repeated 30 times, totaling 150 tests. Results are presented in box plots, showing the average of the evaluation metrics obtained in the 30 repetitions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Assessment Metrics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo assess the quality of predictions obtained by the regression models five metrics have been used. The Mean Absolute Error (MAE) (Eq.\u0026nbsp;1) is calculated as the average of the absolute differences between the predicted values and the observed values. It provides a direct measure of the average error of the predictions. The Mean Absolute Percentage Error (MAPE) (Eq.\u0026nbsp;2) calculates the average of the absolute percentage errors, providing a relative measure of the average error relative to the true values.\u003c/p\u003e \u003cp\u003eThe Root Mean Squared Error (RMSE) (Eq.\u0026nbsp;3) is another widely used metric that calculates the square root of the average of the squared errors between the predictions and the true values. This metric penalizes larger errors more heavily and is sensitive to outliers.\u003c/p\u003e \u003cp\u003eThe Kling-Gupta Efficiency (KGE) coefficient (Eq.\u0026nbsp;4), proposed by (Gupta et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and modified by (Kling et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), is a measure of efficiency that compares the variability, trends, and correlation of predictions with the true values. This coefficient ranges from -\u0026infin; to 1, where values close to 1 indicate a good fit of predictions to the observed data.\u003c/p\u003e \u003cp\u003eLastly, the Willmott's concordance coefficient (d) (Eq.\u0026nbsp;7) is used to evaluate the comparative accuracy of estimation values by calculating the discrepancy between the values of one estimation relative to another (Willmott et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The values of this index range from 0 to 1, representing no agreement and perfect agreement, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(MAE=\\frac{1}{n}\\sum _{i=1}^{n}\\left|{P}_{i}-{O}_{i}\\right|\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(MAPE=\\frac{1}{n}\\sum _{i=1}^{n}\\frac{\\left|{P}_{i}-{O}_{i}\\right|}{{O}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(RMSE=\\sqrt{\\frac{1}{n}\\sum _{i=1}^{n}{\\left({P}_{i}-{O}_{i}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(KGE=1-\\sqrt{{(r-1)}^{2}+{(\\beta -1)}^{2}+{(\\gamma -1)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta = \\frac{{\\mu }_{P}}{{\\mu }_{O}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma = \\frac{{CV}_{P}}{{CV}_{O}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(d=1-\\left[\\frac{\\sum _{i=1}^{n}{\\left({P}_{i}-{O}_{i}\\right)}^{2}}{\\sum _{i=1}^{n}{\\left(\\left|{P}_{i}-{\\mu }_{O}\\right|+\\left|{O}_{i}-{\\mu }_{O}\\right|\\right)}^{2}}\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the equations shown above, n is the number of data points, \u0026micro; is the mean value, CV is the coefficient of variation, \u003cem\u003eP\u003c/em\u003e is the prediction value, and \u003cem\u003eO\u003c/em\u003e is the true value.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Precipitation Climate Extremes\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Precipitation Climate Extreme Index\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of two datasets, BR-DWGD and ERA5-Land (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, respectively), which are consistent regarding the spatial distribution of annual mean precipitation extremes in the S\u0026atilde;o Francisco river basin. Among the analyzed variables, the indicators PRCPTOT (Total Precipitation) and PRCWQ (Wettest Quartile Precipitation) stand out. They have the highest values in the upper and middle S\u0026atilde;o Francisco meso-region, indicating that about 60% of the total precipitation, in the rainiest areas occurs in the wettest quarter.\u003c/p\u003e \u003cp\u003eThe indicators of wet days (WD) and consecutive wet days (CWD) show higher values in the upper S\u0026atilde;o Francisco meso-region, the western part of the middle S\u0026atilde;o Francisco, and the eastern area of the lower S\u0026atilde;o Francisco. On average, there are more than 85 rainy days per year, with consecutive rainfall events lasting between 16 and 24 days in these regions. On the other hand, a low value of these indices is observed in the sub-middle S\u0026atilde;o Francisco meso-region. It is noticed an average of 30 to 60 rainy days per year, with only 8 to 12 consecutive rainy days in most of this meso-region.\u003c/p\u003e \u003cp\u003eThe indices RX5DAYS and R20mm reveal the occurrence of extreme precipitation events in the upper and middle S\u0026atilde;o Francisco meso-regions, as they also have the highest values for these indicators. RX5DAYS measures the maximum intensity of precipitation over five consecutive days and records values above 120mm in these same meso-regions. 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.\u003c/p\u003e \u003cp\u003eThe variable RX1DAY (Maximum Precipitation in a Single Day) is distributed throughout the basin area, with values exceeding 50mm observed in all meso-regions, with values exceeding 60mm in the upper and middle S\u0026atilde;o Francisco. On the other hand, the SDII indicator (Mean Daily Intensity) shows differences between the datasets, as for BR-DWGD, higher magnitudes (above 10 mm/day) are observed in the upper and middle S\u0026atilde;o Francisco meso-regions, with values between 8 and 9 millimeters per day in the sub-middle S\u0026atilde;o Francisco meso-region. For the ERA5-Land, the highest SDII values also occur in the upper and middle S\u0026atilde;o Francisco (between 8 and 9 mm/day), with minimum values in the other areas across the basin.\u003c/p\u003e \u003cp\u003eThe prominent extreme indices in the lower S\u0026atilde;o Francisco are consistent with a recent study conducted by (Morales et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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. Regarding the meso-region most affected by precipitation extremes, there is agreement with the study conducted by (Jeferson de Medeiros et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in which the extreme precipitation events (PRCPTOT, RX1day, RX5day, SDII, R20mm, CWD) are more intense, frequent, and prolonged in the northern, southern, and southeastern regions of Brazil. During the rainy season, the South Atlantic Convergence Zone (SACZ) has a significant influence on the rainfall regime in the southeastern region of Brazil (Marengo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nielsen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rosa et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study conducted by (da Fonseca Aguiar and Cataldi \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrates that the frequent occurrence of these extreme events is caused by the influence of the SACZ. Their results reveal that the average conditional probability of the occurrence of the SACZ, when there are disasters due to intense or persistent rainfall events in the Southeast, is 48%, and in Minas Gerais, this probability is even higher, reaching 50%.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLastly, it is worth noting analyses of the indicators DD (Dry Days), CDD (Consecutive Dry Days), and PRCDQ (Driest Quartile Precipitation). The highest number of dry days (from 280 to 320 days) is observed throughout the sub-middle S\u0026atilde;o Francisco meso-region and the eastern part of the middle S\u0026atilde;o Francisco, which also records the maximum number of CDD (from 145 to 190 days). These results suggest that, on average, this area goes without precipitation for more than six months, resulting in high vulnerability to drought events.\u003c/p\u003e \u003cp\u003ePredominantly in the lower S\u0026atilde;o Francisco meso-region, precipitation in the driest quarter is higher compared to other meso-regions. These analyses corroborate the study by (Jeferson de Medeiros et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which found lower intensity and frequency of extreme precipitation events in Northeast Brazil. In this region, there is a high number of Consecutive Dry Days (CDD) in the central portion of the Northeast, which has a semiarid climate.\u003c/p\u003e \u003cp\u003eHowever, this does not mean that there are no occurrences of extreme rainfall in the Northeast, as the RX1DAY and SDII indices stand out. According to the conclusions of (Monteiro \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in the semiarid area of the Northeast, when atmospheric conditions result from the interaction of two or more meteorological systems, such as UTCV\u0026rsquo;s, EWD, and ITCZ, it is frequent to result in significant rainfall characterizing true occurrences of extreme indices.\u003c/p\u003e \u003cp\u003eThese results highlight the spatial heterogeneity of extreme precipitation events in the S\u0026atilde;o Francisco river basin, which requires the adoption of specific strategies for each hydrographic meso-region in order to promote efficient water resources management. It is also important to understand the seasonal behavior of the extreme indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAnalyses of the distribution of average PRCPTOT values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) throughout the years reveal the presence of a rainy period between November and March, with monthly averages above 100mm which are represented by both datasets. On the other hand, a dry period occurs in the basin from May to September, with monthly total precipitation below 30mm. October and April are transitional months between the rainy and dry periods, with an average of approximately 60mm.\u003c/p\u003e \u003cp\u003eThe extreme indices related to heavy rainfall (WD, CWD, SDII, RX1DAY, RX5DAYS, and R20mm) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea to \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg), show their maximum during the rainy period, between November and March. This indicates a strong influence of these extreme events on the precipitation regime of the S\u0026atilde;o Francisco river basin. The presence of these extreme events during the rainy season, highlights the influence identified by (da Fonseca Aguiar and Cataldi \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as shown by a significant association between natural disasters and the SACZ, in the months of November, December and January. According to the authors, more than half of all SACZ-related events occurred during this period, with respect to others in the Southeast region.\u003c/p\u003e \u003cp\u003eOn the other hand, the extreme indices related to drought reach their monthly maximum averages in the months between June and September. During this period, on average, 25 days of each month are characterized as dry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), with at least 20 of these days being consecutively dry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei) indicating the dry period in the basin.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Trends in Precipitation Climate Extreme Indexes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSignificant trends calculated for the annual extreme precipitation indices over the study period are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It is worth noting that only the significant values are highlighted (p\u0026thinsp;\u0026le;\u0026thinsp;0.05), thus, the entire white area within the basin does not exhibits trends or, if they do, they are not significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTrends of extreme precipitation indices differ in terms of spatial distribution and magnitude in the two analyzed datasets. This behavior is justified by (Regoto et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which states that comparing different datasets and methods can lead to large variations and uncertainties in climate extreme trends. A negative trend is observed throughout the basin for the PRCPTOT, WD, CWD, PRCWQ, and R20mm indices, with larger magnitudes exhibited by the ERA5-Land dataset. It is important to mention that both datasets in most cases deliver similar spatial pattern but with different magnitudes.\u003c/p\u003e \u003cp\u003eThese results are similar to those detected by (Avila-Diaz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), that found predominantly negative trends in the S\u0026atilde;o Francisco basin for the PRCPTOT, R20mm, and CWD indices, although only CWD showed a significant trend. Downward trends are observed in the sub-middle S\u0026atilde;o Francisco meso-region and the northern portion of the middle S\u0026atilde;o Francisco for the SDII, RX1DAY, and RX5DAYS indices, especially in the ERA5-Land dataset, diverging from the results obtained by BR-DWGD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), which generally do not indicate significant trends in these areas, and show positive trends in the middle S\u0026atilde;o Francisco for the mentioned indicators.\u003c/p\u003e \u003cp\u003eAn increasing trend in DD index is observed throughout the entire area of the S\u0026atilde;o Francisco river basin, with larger magnitudes found in the ERA5-Land, which also revealed positive trends in CDD in all hydrographic meso-region of the basin. On the other hand, the BR-DWGD dataset suggests a relatively milder increasing trend in CDD in the middle S\u0026atilde;o Francisco. Observations of reduction (increase) in intense rainfall events (drought) in the semiarid region of the basin are consistent with the findings by (Assis et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They emphasize the decrease in total precipitation, daily rainfall, maximum volumes in 1 and 5 consecutive days, rainy days, and the number of days with moderate, heavy, and intense rainfall. Positive trends were only observed in consecutive days without rain, reinforcing the negative trend of rainfall and recurrent droughts in northeastern Brazil.\u003c/p\u003e \u003cp\u003eThe PRCDQ index shows downward trends throughout the basin, with the greatest magnitude in the lower S\u0026atilde;o Francisco. In the BR-DWGD dataset, these trends are characterized by minimal magnitudes. Climate analyses are essential to understand how climate extremes affect water resource availability for energy generation. Changes in extreme precipitation event trends can directly impact hydroelectric power production. Therefore, efficient energy production, water resource management and sustainable energy supply may benefit for a clear understand of short-scale weather events.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Relationship between Climate Extremes and Energy Variables\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Correlations between Climate Extremes and Energy Variables\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Pearson linear correlation coefficients (r) between energy variables and accumulated climate extremes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Empty fields in the table indicate that the corresponding correlations did not reach the desired significance level (p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Both datasets used showed r values \u0026gt; |0.70| between Gross Affluent Natural Energy (ANE) and all accumulated extreme indices for six months, except for CDD 6, where the ERA5-Land dataset indicates a moderate correlation. In fact, 1, 3, 6, 12 and 24 indicate that the indices are summed throughout these specific months interval. Moderate correlations are also observed with indices accumulated over three months. As for ANE (%), strong correlations are observed with CWD 24 from ERA5-Land and R20mm 24 from BR-DWGD, with consensus only for PRCPTOT 24, while correlations are moderate for the other indices accumulated over 24 months, as well as for all indices accumulated over 12 months.\u003c/p\u003e \u003cp\u003eThe correlations between Gross Stored Energy (STE) (both gross and percentage) and extreme indices exhibit strong values, particularly highlighting the correlations with RX1DAY 24 and RX5DAYS 24 from the BR-DWGD dataset, as well as with PRCPTOT 24, WD 24, CWD 24, R20mm 24, and DD 24, with agreement between the two data sources. For the other extremes accumulated over 24 months and all accumulated over 12 months, the correlations with stored energy are moderate.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations (r) between Energy Variables and Precipitation Extremes BR-DWGD (a) and ERA5-Land (b).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExtremes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGross ANE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eANE (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGross STE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSTE (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRCPTOT 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWD 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDII 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX1DAY 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRX5DAYS 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR20mm 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDD 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the correlations indicate that energy variables can be affected by climate extreme indices related to precipitation at various time scales. These previous analyses demonstrated that a gain can be obtained in the interpretation of those correlation. Such as, the more the extremes (PRCPTOT, RX1DAY, RX5DAYS) act in the basin, greater the water availability. On the contrary, the smaller the performance of the extremes, the more thermal plants will be activated in the system.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Affluent Natural Energy (ANE)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the historical time series and monthly averages of Gross ANE and Percentage of ANE are presented from January 2000 to December 2022. Both time series curves (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) show a cyclic oscillation, which can be explained by the monthly averages of the historical series (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed) and their relationships with extreme climate precipitation indices.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe influence of extremes in the S\u0026atilde;o Francisco river basin is observed through the short-term response of Gross Affluent Natural Energy, with stronger correlations occurring in periods of three to six months (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This relationship reveals a well-defined seasonal behavior, with minimum values recorded in September, the last month of the driest quarter, at approximately 2000 MW.mean. The values of Gross ANE increase in the following months and peak in February, the fourth month of the rainy season, at over 10000 MW.mean, before decreasing in the subsequent months.\u003c/p\u003e \u003cp\u003eThis can be explained by the impact of extremes in the basin, which cause changes in the natural flow of the main river channel in short periods. On the other hand, percentage of ANE, which incorporates long-term averages in its determination, shows long-term responses, ranging from 12 to 24 months.\u003c/p\u003e \u003cp\u003eSeasonality was also observed by (Vilar et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), according to the authors, the time series shows a decrease in observed ANE values due to the dry season. Percentage of ANE also exhibits seasonality, but with changes throughout the year. The lowest monthly average value, around 62%, is recorded in October, a transitional month between the dry and rainy periods in the basin. From there, the values gradually increase to reach a maximum of approximately 78% in January, the third month of the rainy season. Subsequently, the values decrease, with increases observed in April and June compared to the previous month.\u003c/p\u003e \u003cp\u003eThese increases may be related to the extreme R20mm, which is well correlated with the percentage of ANE, when it is accumulated over 24 months (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In May, on average, up to two events of intense precipitation can occur in the basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), influencing the natural river flow in the subsequent month.\u003c/p\u003e \u003cp\u003eThe slight increase observed in June is likely associated with the total precipitation in the lower S\u0026atilde;o Francisco hydrographic meso-region and the eastern extreme of the sub-middle S\u0026atilde;o Francisco from May to August (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Despite being predominantly dry months in other meso-region of the basin, the Paulo Afonso hydroelectric complex and the Xing\u0026oacute; HPP are located in these regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which use run-of-the-river reservoirs for energy generation, contributing significantly to ANE (%) in the basin.\u003c/p\u003e \u003cp\u003eIt is also worth noting that ANE (%) is based on a long-term mean (LTA) that has been verified since 1931 and, therefore, may contain climatological patterns not identified in the temporal scope of this study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Stored Energy (STE)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the historical time series of Gross Energy Generation (STE) and Percentage of STE, as well as their monthly averages are presented for the January 2000 to December 2022 interval.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIt is notable that both time series (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) and the monthly average curves (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed) exhibit identical behavior. This is because, throughout the analyzed period, the maximum STE of the S\u0026atilde;o Francisco river basin remained steady at 52,727 MW.month, according to information from [20]. The cyclic oscillation of time series can be explained based on their respective monthly averages and their association with extreme precipitation climate indices.\u003c/p\u003e \u003cp\u003eThe response of STE (gross and percentage) to extremes shows a long-term relationship with precipitation extremes, as stronger correlations occurred in 24-month periods (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Gross STE and Percentage of STE (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, respectively) patterns exhibit a clear seasonal cycle. The minimum value is observed in November, at the beginning of the rainy season, at approximately 17,500 MW.month, corresponding to 35% of the maximum energy production capacity of reservoir-based power plant.\u003c/p\u003e \u003cp\u003eOver the following months, this value gradually increases, reaching its maximum in May, during the dry period of the basin, at around 35,000 MW.month, representing approximately 66% of the percentage of STE. This behavior can be attributed to the control of water levels in the reservoirs, which have a considerable storage volume. This provides the system with resilience to variations in water availability over short periods of time.\u003c/p\u003e \u003cp\u003eAnalyses of the average monthly values of ANE (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and STE (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) reveal a significant difference in magnitude between the two quantities. This difference is mainly due to the fact that, in addition to differences in working volumes, HPP with flow control reservoirs have the ability to store water and therefore generate electrical energy during periods of high demand (STE), while run-of-the-river HPP have their electricity production limited by the available river flow (ANE).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Trends of Energy Variables\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResults obtained based on the Mann-Kendall trend test for the energy variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon-parametric tests for the energy variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSens's Slope\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross ANE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edecreasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-238.63 MW.month/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edecreasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.45%/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross STE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edecreasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-82.02 MW.month/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edecreasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.69%/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe ANE and STE deliver decreasing trends over the study period, indicating a reduction in water and electrical energy availability in the S\u0026atilde;o Francisco river basin. This suggests the need to invest in management strategies to ensure the long-term sustainability of the electrical system.\u003c/p\u003e \u003cp\u003eSimilarly, (Da Silva et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed negative trends when analyzing time series of natural inflows into the Itaparica and Sobradinho reservoirs. The authors attributed these trends to the occurrence of droughts recorded in recent years in the S\u0026atilde;o Francisco river basin.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Use of Artificial Intelligence tool for generating predictive regression models\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the following Artificial Intelligence tools are used for generating predictive regression models. Results presented in this section provide a comparative analysis of evaluation metrics for the predictive regression models applied to two datasets: BR-DWGD, represented in blue, and ERA5-Land, represented in green.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Regression Model Performance for Gross ANE Prediction (\u003cb\u003ea\u003c/b\u003e) and ANE (%) (\u003cb\u003eb\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBy analyzing Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, the predictive models for Gross ANE yielded close and satisfactory results for both datasets. The KNN model demonstrated the best performance in all metrics for the ERA5-Land. For the BR-DWGD dataset, the KNN model also delivers the best performance in terms of KGE, d, and MAPE, but with relatively large dispersion.\u003c/p\u003e \u003cp\u003eRegarding the predictions of ANE (%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), it can be observed that the results were similar and acceptable for all three models in both datasets. The RF model showed the best performance with the BR-DWGD data in all metrics. On the other hand, for the ERA5-Land dataset, the performance of the RF model was comparable to that of the KNN model.\u003c/p\u003e \u003cp\u003eThe estimations of stored energy in both gross form (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) and percentage form (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), also exhibited satisfactory results for the BR-DWGD and ERA5-Land datasets. It can be observed that, except for ANN in STE (%), the dispersion of predicted values is relatively low compared to the ANE estimations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe RF model delivers the best performance for STE predictions in all metrics for both datasets. It is noteworthy that, for both gross STE and STE in percentage, the values of d and KGE are close to optimal values, with a low MAPE ranging between 25% and 30%.\u003c/p\u003e \u003cp\u003eThe results presented in this section indicate that extreme precipitation events are directly related to both the STE in reservoirs and the ANE in the S\u0026atilde;o Francisco river Basin. Furthermore, it is observed that it is feasible to efficiently estimate STE and ANE using only information on extreme precipitation indices in the basin.\u003c/p\u003e \u003cp\u003eThis finding has significant relevance for the management of the National Interconnected System (SIN), as (Vilar et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified considerable differences between the projected values of ANE and STE by the models used by ONS (National Electric System Operator) and the observed values. According to (Vilar et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this indicates the necessity for adjustments in the ONS methodology, especially in severe drought situations. In this context, the estimation methodology proposed in the present study emerges as a promising alternative.\u003c/p\u003e \u003cp\u003eUnderstanding the relationships between extreme climate events and energy variables, especially the ability to efficiently estimate ANE and STE, based on information on extreme precipitation events, presents significant potential for future applications in energy production planning in Brazil.\u003c/p\u003e \u003cp\u003eThese findings are particularly relevant when considering extreme events predicted for the future, such as prolonged droughts or heavy rainfall, which can have substantial impacts on energy supply. The use of such information can contribute to more resilient and adaptable energy planning, that takes into account challenges arising from extreme climate events.\u003c/p\u003e \u003cp\u003eAlthough the evaluated models in this study have demonstrated efficiency in estimating STE and ANE based on information on extreme precipitation, it is important to note that the inclusion of other climate variables can further enhance the accuracy of predictive models. In addition to precipitation, factors such as temperature and regional climate patterns play crucial roles in energy production. Therefore, future research exploring the integration of other climate variables into the models, can improve the ability to accurately predict energy availability in situations of extreme climate events.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAnalysis of extreme precipitation events in the S\u0026atilde;o Francisco river basin reveals distinct patterns in different regions. The Upper S\u0026atilde;o Francisco meso-region and the western portion of the middle S\u0026atilde;o Francisco are the most impacted by extreme rainfall events, which is related to the rainy season in the basin.\u003c/p\u003e \u003cp\u003eOn the other hand, the sub-middle S\u0026atilde;o Francisco and the eastern region of the middle S\u0026atilde;o Francisco are more susceptible to extreme drought events, recording the highest magnitudes throughout the year. Trend tests indicate a reduction in total precipitation and the number of rainy days, as well as an increase in the number of dry days. Seasonal analysis of Energy Stored (STE) and Affluent Natural Energy (ANE) reveals a behavior influenced by precipitation extremes on different time scales. The short-term responses of Gross ANE levels and the long-term responses of ANE (%) and STE (both gross and percentage), are evident in response to extreme climatic events.\u003c/p\u003e \u003cp\u003eThe regression models based on artificial intelligence methods demonstrated efficient performance in estimating ANE and STE based on extreme precipitation events in the basin. These results pave the way for further research and practical applications, including forecasts of future energy conditions based on climate extremes, and the consideration of additional weather variables to enhance the efficiency of the models.\u003c/p\u003e \u003cp\u003eThese findings are relevant to the planning and management of the electricity sector, especially in relation to strategic decision-making and the development of public policies aimed at ensuring the energy security of the country.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eCRediT authorship contribution statement: Josielton Santos\u003c/b\u003e: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. \u003cb\u003eFl\u0026aacute;vio Justino\u003c/b\u003e: Validation, Supervision, Project administration, Investigation, Writing - Review \u0026amp; Editing. \u003cb\u003eJackson Rodrigues\u003c/b\u003e: Validation, Supervision, Investigation, Writing - Review \u0026amp; Editing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by National Council for Scientific and Technological\u0026mdash;CNPq, grant number 131148/2021-4.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ. S. Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. F. J. Validation, Supervision, Project administration, In-vestigation, Writing - Review \u0026amp; Editing. J. R. Validation, Supervision, Investigation, Writ-ing - Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe also thank the Federal University of Vi\u0026ccedil;osa and National Council for Scientific and Technological\u0026mdash;CNPq.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnaraki MV, Farzin S, Mousavi S-F, Karami H (2021) Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods. 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Accessed 28 Jun 2023d\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Affluent Natural Energy, Stored Energy, Prediction, Machine Learning, Artificial Intelligence, Regression Model","lastPublishedDoi":"10.21203/rs.3.rs-4086856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4086856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent on rainfall patterns. The S\u0026atilde;o 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.\u003c/p\u003e","manuscriptTitle":"Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-18 02:16:44","doi":"10.21203/rs.3.rs-4086856/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-28T14:03:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-05T16:49:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2f0946cf-12d8-461d-8d09-5688b37c16cf_SNPRID","date":"2024-03-17T05:42:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5e7fd897-a97c-40d7-9021-41d84c4a8024","date":"2024-03-17T02:27:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-16T21:14:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-13T01:58:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-13T01:58:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-03-12T17:57:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b04db45-fc46-471e-ad61-f5201b7801c4","owner":[],"postedDate":"March 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:49:17+00:00","versionOfRecord":{"articleIdentity":"rs-4086856","link":"https://doi.org/10.1007/s00704-024-05051-0","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2024-06-08 14:49:17","publishedOnDateReadable":"June 8th, 2024"},"versionCreatedAt":"2024-03-18 02:16:44","video":"","vorDoi":"10.1007/s00704-024-05051-0","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05051-0","workflowStages":[]},"version":"v1","identity":"rs-4086856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4086856","identity":"rs-4086856","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}