Hydroclimatic variability and trends suggest improvements in water resource management in the cascade reservoirs of the Tocantins River

Hydroclimatic variability and trends suggest improvements in water resource management in the cascade reservoirs of the Tocantins River | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hydroclimatic variability and trends suggest improvements in water resource management in the cascade reservoirs of the Tocantins River Idelina Gomes da Silva, José Luiz Cabral da Silva Júnior, Bárbara Dunck This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4849979/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Research on hydroclimatic variations explains the relationships between water masses and global climate factors. Climate change causes changes in river flow regimes and impacts ecosystems, the economy, and society. In this study, we characterized the hydroclimatology of the seven reservoirs of the Tocantins River, along 1,500 km of river and during more than 12 years of sampling, where we analyzed climatic variables such as precipitation, global solar radiation, net evaporation, and air temperature, in addition to hydrological variables such as discharge and net evaporation of the reservoirs. We identified that the discharge of the reservoirs recovered more slowly after the dry period and that these discharges decreased at a rate of 575 m3/s between 1995 and 2023, followed by a negative and significant downward trend. As with discharge, precipitation showed a downward trend. The water deficit caused by prolonged droughts between 2015 and 2017 resulted in lower flows and higher air temperatures. In addition to climatic factors, the socioeconomics of the reservoir areas demand high water withdrawals, associated with population growth and agricultural production. We conclude that the reservoirs have a hydroclimatic gradient with latitudinal variations. These gradients are mainly due to differences in precipitation and flows, but are highly dependent on temperature conditions, solar radiation, evaporation, and water withdrawal. These factors are important and should be discussed in order to mitigate the ecological and socioeconomic impacts on the Tocantins River basin. Evaporation Precipitation Solar radiation Flow rate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction The study of hydroclimatic variations sheds light on the relationships between water masses and climate factors. Global water masses have undergone major human impacts and climate changes (Yao et al. 2023 ). Droughts, heat waves, floods, and storms are extreme meteorological phenomena that pose challenges to continental and oceanic waters, which in turn respond to these phenomena in various ways (Oliveira et al. 2021 ; Rodell and Li 2023 ; Winter et al. 2020 ). For example, with higher temperatures, the evaporation rate of water bodies increases, with a consequent reduction in water availability. On the other hand, human activities, such as the construction of dams, alter the river continuum and flows (Tiwari et al. 2023 ; Ward and Stanford 1995 ; Zhao et al. 2012 ). Many studies have shown consistent patterns of climate effects on river geomorphology (Larkin et al. 2020 ; Q. Wu et al. 2023 ), hydrological processes in river basins (Näschen et al. 2018 ), society and public policies (Sigalla et al. 2023 ), water storage in river basins (Hong et al. 2023 ; Moshir Panahi et al. 2020 ) and the seasonal regime of rivers (Liang et al. 2020 ). These studies demonstrate robust results of climate interference in fluviometric patterns and their dependence relationships between climate factors and the maintenance of aquatic systems. Understanding how climate change is affecting our planet’s water bodies, especially available freshwater, has become vital. Extreme weather events can reduce the quality and quantity of water available to living beings (Larkin et al. 2020 ; Mazacotte et al. 2024 ; van Vliet et al. 2023 ). Rivers are essential for human survival and biodiversity maintenance; in addition to supporting economic development and cultural enrichment (Larned et al. 2010 ; Sinclair et al. 2024 ), they play a crucial role in environmental regulation, which allows the persistence of high biodiversity. However, most of the world's major rivers have been dammed, with the formation of large artificial reservoirs (Grill et al. 2019 ), which has reduced the variety of services provided to society and threatened the maintenance of ecosystems (Forsberg et al. 2017 ; Oliveira et al. 2021 ; Swanson et al. 2021 ). Dams regulate river flow, increase water residence time, and reduce seasonal flow variability (Chong et al. 2021 ). They are recognized worldwide for the major hydrological changes they cause in river channels (Q. Wu et al. 2023 ). Although reservoir flows are less subject to seasonal variations when compared to free-flowing rivers, they still respond to climatic factors and can have their flows modified seasonally (Junqueira et al. 2020 ; Zaniolo et al. 2021 ; Zhao et al. 2012 ). The Tocantins River is an important Brazilian river that presents a water-energy-food connection. It flows through four Brazilian states with high agricultural production, Goiás, Tocantins, Maranhão and Pará. Its uses include power generation, irrigation, fishing, agriculture, navigation, recreation and supplying cities (Tundisi and Matsumura-Tundisi 2003 ), in addition to supporting several aquatic ecosystems. However, this river is undergoing transformation due to anthropogenic and climatic stressors (Costa et al. 2003 ; Pelicice et al. 2021 ; Swanson and Bohlman 2021 ). More recently, climatic factors have become a concern due to the increasing influence on its hydrological cycles (Junqueira et al. 2020 ; Von Randow et al. 2019 ). The Tocantins River’s flow rates are second only to those of the Amazon River (ANA 2009 ), but the installation of seven large hydroelectric dams installed in cascade has regulated and reduced these flows. These large hydroelectric reservoirs were installed in cascade over a stretch of more than 1,500 km on this river. Even with the flow regulated by dams, the main driving force for hydrological change, as in all tropical rivers, is climate variations (Chou et al. 2009 ; Dai 2021 ; Foley et al. 2002 ). Climate variations shape flows and regulate environmental systems, such as nutrient cycles and aquatic biota (Costa et al. 2003 ; Swanson et al. 2021 ; Von Randow et al. 2019 ). Given this, there is an urgent need to understand how climate change affects river flow and ecological systems, in order to develop better conservation strategies. To date, the analysis of temporal and spatial hydroclimatic trends in hydropower reservoirs has not been considered systematically. However, there is a knowledge gap on how these changes may affect hydropower reservoirs. There are few studies that have analyzed hydroclimatic effects globally (Wu et al., 2023 ; Dai 2020), some in river basins in China (Hong et al., 2023 ; Tiwari et al., 2023 ), Iran (Panahi et al., 2020), the Rufiji and Kilombero Rivers in Tanzania (Sigalla et al. 2023 ; Näschen et al. 2018 ), the Amazon River basin (Liang et al., 2020 ) and Australian rivers (Larkin et al., 2020 ). In the Tocantins River basin, some studies have analyzed the effects of precipitation and flows on floodplain areas (Swanson et al., 2021 ); the effects of meteorological and hydrological droughts on the river basin (Junqueira et al., 2020 ); and the hydrological impacts of climate and land use and cover on hydroelectric productivity (Costa et al., 2003 ; Von Randow et al., 2019 ). However, the shortcoming of these studies is that they have not analyzed hydroclimatic trends covering cascade reservoir systems, in order to identify which climate variables are affecting these systems and possible scenarios of climate impact on socioeconomic water demand. However, as highlighted, there is a lack of efforts that attempt to directly investigate the effects of precipitation, air temperature, net evaporation, and solar radiation on hydrological responses in cascade reservoir systems. An understanding of the long-term trend of hydroclimatic variables is useful in planning strategies, in conflict mitigation and in understanding how impounded freshwater ecosystems respond to climate change. To refine these concepts and identify temporal and spatial hydroclimatic trends of the reservoirs and identify important variables in hydrological maintenance, we sought to understand how the main hydroclimatic variables correlate and how each reservoir responds to these variations. Given the above, the objectives of this article were (I) to identify hydroclimatic differences between the reservoirs installed in cascades on the Tocantins River and seasonal patterns, (II) to quantify and analyze temporal trends of hydroclimatic parameters, (III) to identify critical variables for maintenance and those that influence changes in flow rates in the reservoirs of the Tocantins River, based on historical trends, the correlation between hydroclimatic variables, and socioeconomics. Methods Study area The Tocantins-Araguaia river basin is located between the southern parallels 0º 30’ and 18º 05’ and the western longitude meridians 45º 45’ and 56º 20’. The climate varies longitudinally from south to north, humid and hot in the south and humid in the north. This climate favors the existence of two biomes: Cerrado (Savanna) in the south, which occupies 65% of the basin area, and Amazon (Dense Ombrophilous Forest) in the north, which occupies the remaining 35%. The climate of the river basin is classified according to the Köppen methodology as “Cwa” with an annual rainfall index of around 1,500 mm; “Aw” with an annual rainfall index of 1,700 mm; “Am” with an annual rainfall index of 2,000 mm; and “Af” in the extreme north of the basin, with annual rainfall totals above 3,000 mm (ANA 2009 ). Altitudes follow the regional geomorphology, decreasing from the south (600 m) to the north (0 to 100 m). The Tocantins and Araguaia Rivers are the two main rivers that form this hydrographic basin. The Araguaia River is one of the few rivers in this basin with free flow, without the implementation of dams, but it concentrates a large part of the water withdrawal for irrigation. In contrast, the Tocantins River is highly fragmented by seven large dams (Fig. 1 ), which have led to a decline in its flows (Swanson et al. 2021 ), in addition to another 50 small hydroelectric plants (PCHs and CGHs) installed in the tributaries of its sub-basin (ANA 2023b ). The first reservoir of the cascade installed on the Tocantins River (Fig. 1 ) is the Serra da Mesa HPP – SM. Its accumulated water volume is approximately 54 billion m3, which remains in the reservoir for 750 days; this volume of water flooded an area of ​​1,784 km2. The second reservoir of the cascade is the Cana Brava HPP – CB (13º24'9.44'' S; 48º8'36.89'' W), which is not of the accumulation type like the first, but it flooded an area of ​​139 km2 and formed a reservoir of 2.3 bi/m3; its waters remain for 28 days before entering the next reservoir. The São Salvador-SS dam (12º48’29.91’’ S; 48º14’16.36’’ W), forms the smallest reservoir of the cascade, with an area of ​​104 km2 and 0.043 bi/m3; it is a run-of-river dam with a short water residence time, approximately 12 days. Peixe Angical – PA (12º14’15.25’’ S; 48º23’10.38’’ W), the fourth reservoir, is an accumulation-type reservoir, but its water residence time is short compared to the others, at 18 days, but this is enough to maintain a reservoir of 2.7 bi/m3 and flooded an area of ​​294 km2. The fifth reservoir is Lajeado – LA (9º45’34.14” S; 48º22’16.02” W), a run-of-river dam with a retention time of 24 days, a water volume of 5.7 bi/m3 and a flooded area of ​​630 km2. The most recent dam installed on this river and the sixth in the cascade is the Estreito Plant dam -ES (6º35’22.92” S; 47º27’52.54” W), a run-of-river dam with a reservoir of 5.4 bi/m3, which flooded an area of ​​434 km2 with a residence time of 16 days. At the end of the cascade is the Tucuruí reservoir – TU (3º49’56.95” S; 49º38’59.94” W), the largest among them and the largest in Brazil in terms of flooded area, 2,850 km2, with a water volume of 43 bi/m3. Although its area is larger than the Serra da Mesa reservoir, its water residence time is shorter, approximately 50 days. Observational dataset Precipitation data were obtained from seven rainfall stations available on the website of the National Water and Sanitation Agency (ANA) ( https://www.snirh.gov.br/hidroweb ). Natural flows and monthly net evaporation were also provided by ANA and calculated for each reservoir ( https://www.ana.gov.br/sar/sin/b_tocantins and https://metadados.snirh.gov.br/geonetwork ). The historical series of air temperature and global solar radiation were obtained from seven meteorological stations, available on the website of the National Institute of Meteorology – INMET ( https://portal.inmet.gov.br/dadoshistoricos ). To analyze spatial and seasonal differences, we used time series with monthly averages of hydroclimatic data for 12 years, between 2006 and 2018. In order to evaluate the largest possible number of years in the analyses of temporal trends in precipitation and flow, the periods analyzed were expanded. For flow, data from 28 years were used, referring to the periods from 1995 to 2023, and for precipitation, the interval was 53 years, referring to the years from 1970 to 2023. To calculate monthly averages of global solar radiation, daily values ​​were summed and then calculated as daily averages for each month. The temperature used was the daily maximum recorded at 3:00 p.m., from which we also calculated the monthly average. Precipitation was recorded as a monthly total, and annual totals were obtained by summing these values ​​for each year studied. Monthly averages of flow rates were calculated from daily values ​​(ANA 2024 ). Socioeconomic data, such as the Human Development Index (HDI), Gross Domestic Product (GDP), and population by States where the reservoirs are located, were provided by the Brazilian Institute of Geography and Statistics (IBGE) (IBGE 2024 ), and the amount of water withdrawn was calculated as the annual average of water withdrawn (m3/s) per State from each reservoir (ANA 2023a ). Data analysis To identify statistical hydroclimatic differences between the reservoirs, we first performed the nonparametric Kruskal-Wallis test applied to each hydroclimatic variable separately and without standardization. In this test, the reservoirs were used as response variables and the hydroclimatic variables as predictor variables. Subsequently, the Dunn test was performed to determine which reservoirs were significantly different from each other in terms of hydroclimate (p > 0.05). To assess whether the total set of hydroclimatic variables (flow rate – m3/s, precipitation – mm, solar radiation – MJ/m2/day, temperature – º C and evaporation – m3/s) was capable of differentiating the reservoirs and seasonal periods, we performed a similarity analysis (ANOSIM) on a similarity matrix based on Euclidean distances, using 9999 permutations. From the dissimilarity matrix, we constructed two nonparametric multidimensional scaling (nMDS) graphs to visually represent the differences between the reservoirs and seasonal periods. Each point on the graph represents a reservoir, and closer points indicate greater similarity between them in relation to the predictor variables used (flow rate – m3/s, precipitation – mm, radiation – MJ/m2/day, temperature – º C and evaporation – m3/s). To quantify and analyze temporal trends in hydroclimatic parameters, we used the nonparametric Mann–Kendall test, calculated from the monthly mean values ​​of flow, temperatures, solar radiation and accumulated monthly precipitation totals. In this test, “tau” values ​​can be positive, indicating increasing trends, or negative, indicating decreasing trends. The p-values ​​indicate the statistical significance of the test and are considered significant at a 95% confidence level. To identify the critical variables that influence and maintain flows in the Tocantins River reservoirs, we analyzed negative and significant trends using the Mann-Kendall test. We then performed Pearson correlations between hydroclimatic variables to determine which climatic factors are influencing these changes. All data analyses were performed in RStudio 4.3.1 (R Core Team 2020 ). For the Kruskal-Wallis test, we used the ‘kruskal.teste’ function and for the calculation of trends (Tau) and p-value, we used the ‘Kendall’ package (McLeod 2022 ). The graphs were prepared using the functions of the ‘ggplot2’ package (Wickham 2016 ). Results Spatial hydroclimatic variability The results showed statistically significant variations in hydroclimatic variables between reservoirs. The annual spatiotemporal average of maximum air temperature over all reservoirs, during the study period, 2006–2018, was 30.8°C. In two of the seven reservoirs studied (SS and SM), they were below this average, with average annual temperatures of 29.4 and 25.9°C respectively, which statistically differentiated them from the others (Kruskal-Wallis test: p < 0.001 - Fig. 2 b). The difference between the lowest average temperature (25.9°C in SS) and the highest (33°C in PA, LA and ES) was 8°C (Fig. 2 b). The reservoirs follow an increasing trend of precipitation and discharge, i.e., from upstream to downstream (Fig. 4 ). The average annual precipitation during the studied period for all reservoirs was 1584 mm. The lowest precipitation recorded was in PA (1,048 mm) and the highest in TU (2,511 mm), resulting in a difference of 1461 mm (Fig. 2 c). The ES and TU reservoirs were significantly different from the others (Kruskal-Wallis test: p < 0.001). In three of the seven reservoirs (SM, CB and PA) the average annual precipitation was less than 1584 mm. These spatial differences of 8°C in temperature and 1461 mm/year in precipitation highlight the spatial diversity of hydroclimatic conditions in the reservoirs of the Tocantins River. The average flow rate of the reservoirs was 1,536 m 3 /s, but it was less than 700 m3/s in three reservoirs (SM, CB and SS), which made them statistically similar (Kruskal-Wallis test: p = 0.432; p = 0.418 and p = 0.453). The ES and TU reservoirs were similar to each other (Kruskal-Wallis test: p > 0.001), but different from the others (Kruskal-Wallis test: p > 0.052) and recorded an average flow rate higher than 2,500 m 3 /s (Fig. 2 a). The average monthly evaporation, calculated from the area of ​​each reservoir, was 8,320 m 3 /s (Fig. 2 d). The highest evaporations occurred in the two largest reservoirs, SM (13.73 m3/s) and TU (24.85 m 3 /s), but SM had similar net vaporization to CB, SS, PA and LA TU (Kruskal-Wallis test: p < 0.001), and TU differed significantly from the others (Kruskal-Wallis test: p = 0.001 - Fig. 2 f, g). The spatial patterns for solar radiation and temperatures showed differences. Although temperatures in PA, LA and ES were similar, radiation varied. Statistically, solar radiation was similar in CB and ES (Kruskal-Wallis test: p < 0.001), in SM, CB and PA (Kruskal-Wallis test: p < 0.012), and in LA and TU (Kruskal-Wallis test: p < 0.001 - Fig. 2 e). The monthly average solar radiation was 22 MJ/m 2 , with only the TU reservoir above this average, at 40 MJ/m 2 . The results indicated that the reservoirs presented significant differences in their hydroclimatic variability. The dissimilarity analysis and the comparison in the nMDS plots applied to the hydrological variables revealed statistically significant differences between the reservoirs (ANOSIM R = 0.023; p = 0.001). The TU reservoir stood out as the most distinct, exhibiting high hydroclimatic variability, mainly due to the higher flows and precipitation (Fig. 3 a, blue dots). The spatial pattern also varied according to seasonality, showing a clear dissimilarity between the dry and rainy periods (Fig. 3 b). Greater similarity between the reservoirs was observed in the dry period. The greatest dissimilarities occurred in the rainy period (Fig. 3 b), suggesting that the climatic variations of the dry and rainy periods significantly influence the hydrodynamic variations (ANOSIM R = 0.256; p = 0.001). Temporal Seasonality The average monthly precipitation is highest in the months of January-February-March, which together accounted for 47% of the annual total, while the lowest values ​​were found in the months of June and August, representing only 1% and 0.6% of the annual total (Fig. 5 a). At the peak of precipitation in January, the reservoirs reached extreme flow values ​​(Fig. 5 b). Likewise, the lower precipitation resulted in lower flow levels in August. We observed a two-month delay in the response of flow to increased precipitation and a one-month delay in the response to reduced rainfall. In October and November, the increase in precipitation did not increase flow, which began to increase only in December. In contrast, the response in the reduction of rainfall was faster; at the beginning of the dry season, in May, reductions in flow were already observed. Evaporation and solar radiation follow a pattern that is inverse to precipitation, with an increase from June onwards and a decrease in October (Fig. 5 d,e). The maximum temperature values ​​(38º C) occurred in the driest months, August and September, recording the largest temperature ranges of 12º C. In January and March, we recorded the smallest temperature ranges of 8º C (Fig. 5 e). Trends The Mann-Kendall analysis revealed a clear trend of dry conditions and hot periods. All reservoirs showed a trend of decreasing precipitation and streamflows, together with increasing temperatures (Table 1 ; Fig. 6 b, c, d, f, g). The Kruskal-Wallis analysis followed by Dunn's post-hoc analysis indicated significant statistical differences in precipitation between the years 1970 and 2023. Specifically, the precipitation pattern in 2016 was different from the years 1973, 1977 and 1989 (Fig. 6 h). Table 1 Mann-Kendall trend test applied to time series of precipitation (1970 to 2023), flow (1995 to 2023), temperature and radiation (2006 to 2018). P-value indicates the significance of the trend of the time series with a 95% confidence level (α = 0.05). Reservoir Hydroclimatic Parameter Kendal tau P-value Serra Mesa -SM Precipitation (mm/year) -0.085 0.047 Cana Brava – CB -0.051 0.012 Peixe Angical – PA -0.119 0.007 São Salvador – SS -0.058 0.015 Lajeado – LA -0.121 < 0.001 Estreito – ES -0.395 < 0.001 Tucuruí – TU -0.395 < 0.001 Serra Mesa -SM Flow rate (m 3 /s) -0.024 0.012 Cana Brava – CB -0.038 < 0.001 Peixe Angical – PA -0.069 < 0.001 São Salvador – SS -0.074 < 0.001 Lajeado – LA -0.075 < 0.001 Estreito – ES -0.063 < 0.001 Tucuruí – TU -0.048 < 0.001 Serra Mesa -SM Temperature (º C) 0.174 0.002 Cana Brava – CB 0.195 < 0.001 Peixe Angical – PA 0.049 0.393 São Salvador – SS 0.092 0.168 Lajeado – LA 0.105 0.065 Estreito – ES 0.158 0.006 Tucuruí – TU 0.106 0.072 According to the analysis of long-term flow trends (Fig. 7 ), the reservoirs show a reduction rate of 575 m3/s. The natural flow data showed that the largest reductions were in the filling years. The largest reductions, of up to 1,382 m3/s (Fig. 8 b), occurred during the filling of the SM (1998) and PA (2006) and CB (2004) reservoirs, and they were more pronounced in the filling of SM and PA, due to the fact that these reservoirs are of the accumulation type that store water. Other reductions occurred in the LA filling in 2002 and ES filling in 2011 (Fig. 8 b). However, the reductions observed in the years 2017, 2018, 2020 and 2022, in which there was no implementation of new dams, reflected the hydroclimatic effects. These reductions were statistically significant, as indicated by the Kruskal-Wallis test followed by the Dunn test, applied to the period from 1995 to 2023. The years 2015 and 2016 were statistically different within the time series, with 2016 the year with the lowest flow in the entire series studied (Fig. 8 a). The Mann-Kendall trend test revealed an increasing trend in temperature in all reservoirs during the years 2006 to 2018 (Fig. 9 ). However, for four reservoirs (PA, SS, LA and TU) this trend between years was not statistically significant (Table 1 ). In 2016, we recorded the highest temperatures in the time series, but without statistically significant differences compared to the years 2006, 2010, 2012 and 2013 (Fig. 10 ). Factors influencing hydrological changes The results of the correlation analyses for the individual reservoirs (Fig. 11 ) indicated that precipitation, evaporation and solar radiation are explanatory variables for the variability of flows in the reservoirs. The positive and significant correlations between flow and precipitation (R = 0.618, p < 0.001) confirm this relationship, while negative and significant correlations between flow and evaporation (R = -0.369, p < 0.001) and between flow and solar radiation (R = -0.473, p < 0.001) reinforce the influence of these factors (Fig. 11 a, b, c). Furthermore, other climatic variables of the reservoir area also showed significant correlations. We detected positive correlations between evaporation and solar radiation (R = 0.310, p < 0.001) and between evaporation and air temperature (R = 0.365, p < 0.001) (Fig. 11 d, f). On the other hand, negative correlations were found between precipitation and solar radiation (R = -0.472, p < 0.001) and between precipitation and air temperature (R = -0.539, p < 0.001) (Fig. 11 e, g). These results indicate that increasing air temperatures and radiation are strongly correlated with evaporation, while high levels of precipitation are associated with lower solar radiation and evaporation. Hydroclimatic changes and regional development The socioeconomic data involving the states where the reservoirs are located showed that the state with the highest HDI and GDP is also the one that withdraws the most water (Fig. 12 d, c, d). In 2021, the total water withdrawn in the states from the reservoirs totaled 249 m3/s, with Goiás, where the Serra da Mesa -SM reservoir is located, accounting for 43% of this total (Fig. 12 d). In addition, we found a high positive correlation between GDP and water withdrawal (R = 0.851; p = 0.005) and a high negative correlation between rainfall and water withdrawal (R = -0.624; p = 0.0321). On the other hand, the reservoirs of Estreito (ES), in the state of Maranhão, and Tucuruí (TU), in the state of Pará, which have the largest populations and lowest HDI, withdraw 19% and 24% of the total water, respectively. Discussion We carried out a hydroclimatic assessment of the hydroelectric reservoirs installed in cascades on the Tocantins River, based on historical series of precipitation (between the years 1970 to 2023), flow (years 1995 to 2023), temperature and radiation (years 2006 to 2018). Our results indicated that the reservoirs are different in their hydroclimatic conditions, both spatially and temporally, forming a hydroclimatic gradient. We observed trends of reduction in precipitation and flow, as well as an increase in temperatures. Flow rates increased from upstream to downstream, following the rhythm of precipitation defined by seasonality (dry and rainy periods). It was evident that, due to seasonality, the reservoirs suffered from the increase in thermal amplitudes, evaporation and radiation, with evaporation being an important indicator of drought and increasing as temperatures increase (Han and Singh 2023 ). We also revealed drier scenarios, with low flow and high temperatures. Climatic conditions conditioned by the seasonality of precipitation, evaporation and radiation showed a strong correlation with reservoir flow. In addition, socioeconomic factors exerted strong anthropogenic pressure on the river basin. These implications suggest that the functioning of these ecosystems is being governed by hydroclimatic and anthropogenic changes, capable of influencing energy production, reducing ecosystem resilience and hindering the survival of aquatic organisms (Costa et al. 2003 ; Domingues and da Rocha 2022 ; Kåresdotter et al. 2023 ; Von Randow et al. 2019 ). The spatial differences in hydroclimatic variables demonstrate the geographic diversity of hydroclimatic conditions. This was corroborated by the climate mapping carried out by the National Water Agency (ANA, 2009 ), which classified the climate of the reservoirs into three categories, according to Köeppen, in Am, Aw and Cwa. From upstream to downstream, we observed an increasing gradient of precipitation, discharge and net evaporation. A group of four reservoirs, located at the beginning of the cascade (SM, CB, SS and PA), presented high hydroclimatic similarity, with little variability in discharge, precipitation and evaporation. In contrast, the last three reservoirs, which are further away (LA, ES and TU), showed significant differences between themselves and in relation to the others. The hydroclimatic similarity found in the first four reservoirs generates hydrological interdependence controlled by the first reservoir (SM), which was designed to regulate the discharges of the others. This indicates that climate impacts affect these reservoirs equally. Hydroclimatic and geomorphological differences, such as discharge, precipitation, and altitude, are the main drivers of river forces (Larkin 2020). As latitudes and altitudes decrease, the greater the climatic differences found. Nutrient and sediment transport is also reduced in these similar reservoirs, especially because the first, SM, is of the accumulation type with a high retention time (750 days). Excess sediment implies a reduction in the useful life of the reservoir, and sedimentation increased by the effect of the cascade installation of dams leads to a situation of oligotrophication downstream (Maavara et al. 2015 , 2020 ; Wei 2020). Our results suggest that hydroclimatic variations in reservoirs are reflections of seasonal cycle characteristics, with greater hydroclimatic dissimilarity during the rainy season. During this period, flow rates and temperatures have greater standard deviations and the reservoirs present greater hydroclimatological dynamics. Unlike the dry season, especially in July and early August, flow rates are stabilized by hydroelectric operators to meet the regional beach season. Reservoirs are influenced by the regional climate, which reveals consistent patterns of dry and rainy weather, with flow rates drastically decreasing during dry periods and slowly increasing during rainy periods. This generates a water deficit that is slowly replenished, generating a low water period of more than 6 months. Seasonality is a critical factor for these reservoirs, whose purpose is to generate electricity. A hydroelectric reservoir in the Tapajós River sub-basin in the Amazon River basin revealed a 27% loss in installed capacity during the dry season (Arias et al. 2020 ; Hofmann et al. 2023 ). The Tocantins River reservoirs experienced alarming reductions in their flows during the dry season. Serra da Mesa-SM, the largest reservoir in Brazil and one of the largest in the world in terms of water volume, reached the end of the 2020 drought with only 9% of its useful volume (ONS, 2021). Other Amazonian reservoirs, such as Belo Monte (Xingu River), Girau and Santo Antônio (Madeira River), produced below-projected targets due to strong regional seasonality and climate change (Hofmann et al. 2023 ). Our findings are consistent with recent long-term analyses for Brazil, which have observed streamflows being influenced by precipitation seasonality (Junqueira et al. 2020 ; Swanson et al. 2021 ). Ecologically, longer periods of drought have widespread implications for freshwater ecosystems. Droughts reduce habitat areas, increase water residence time, alter biogeochemical cycles, and increase solute concentrations in the water. This impacts aquatic food chains (Gómez-Gener et al. 2020 ) and species population densities, excluding sensitive species and increasing species more adapted to drought (Aspin et al. 2019 ). Our trend analysis applied to hydroclimatic variables showed negative trends for precipitation and flow, and positive trends for temperatures in the studied reservoirs. Recent studies in tropical regions agree with our results, indicating negative trends in precipitation in Brazil in the Cerrado and Amazon biomes (Dai 2021 ; Liang et al. 2020 ; Liu and Wang 2022 ). These studies pointed to the occurrence of droughts caused by reduced rainfall, increased temperature, high evaporation, and changes in vegetation cover. From the high correlation between precipitation and flow and their negative trends presented in our study, we expect that years with lower precipitation also present the lowest flows in the Tocantins River reservoirs. Other studies corroborate our findings, revealing climate trends for the Cerrado biome, where six of the seven reservoirs analyzed here are located, indicated a reduction of up to 50% in the total rainfall recorded in the dry period (Hofmann et al. 2023 ) with a consequent reduction in flow (Jong et al. 2021 ). For the Amazon, the biome of the last reservoir of the cascade analyzed, there are trends for dry periods to become even more severe (Liang et al. 2020 ). The National Water Agency confirmed that 2015, 2016, and 2017 were the driest years with the lowest flows in the last 87 years. The National Electric System Operator (ONS) reported that, in TU, the last reservoir of the cascade and with the highest precipitation index, 2016 was the year with the lowest annual flow in 80 years (ONS 2024 ). These events are attributed to the El Niño meteorological phenomenon which, in 2015 and 2016, led to an increase in drought risks (Dai 2021 ) influenced by global warming (Shin et al. 2022 ). Given the magnitude of the hydroclimatic changes that already occurred in 2015, 2016 and 2017, which resulted in water deficits in the reservoirs analyzed here, it is clear that any hydrological and management study must take hydroclimatic trends into account in its planning. The results presented here support the idea that climate is a key driver of the contrasting patterns in reservoir flows. It is evident that the observed climate patterns are consistent with streamflows, and the high linear correlation between streamflows and precipitation, as well as between streamflow and evaporation, clearly indicate that streamflows were dependent on regional climate. In addition to dams themselves, precipitation is the main driver of hydrological processes (Tang et al. 2009 ) and has the potential to influence streamflows in hydropower reservoirs (X. Wu et al. 2018 ; Yan et al. 2021 ). Studies have warned about the significant influence of climate on hydropower systems (Mekonnen et al. 2022 ; Moran et al. 2018 ; Sun et al. 2023 ), with predictions of a reduction in safe water levels for electricity generation. Under these hydroclimatic conditions, with reduced precipitation, increased temperatures and reduced flows, improving water consumption management and reinforcing the monitoring of licenses are suggested, to maintain water flow downstream (Sigalla et al. 2023 ). In addition to climate change, it is important to highlight the clear human influences on the reservoirs. The greatest demand for water withdrawal in the Tocantins-Araguaia basin is for irrigation (44%), with areas exceeding 30,000 hectares (ANA 2023a ). We observed a high demand for water, especially in the State of Goiás, where the Serra da Mesa reservoir (SM) is located. Paradoxically, we found a high negative correlation between precipitation and water withdrawal. This contrast suggests that lower precipitation led to increased water withdrawal in the portion of the river with the lowest average annual precipitation and flow. In a scenario of a trend of reduced precipitation and increasing demand for water, the reductions in flow rates may be aggravated. In addition to the withdrawal of water directly from the reservoirs, there is a large extraction concentrated in the main tributaries that supply water to these reservoirs, such as large irrigation projects that use water from the Araguaia River, the main tributary to the TU Reservoir (ANA 2023a ). The high withdrawal of water from the tributaries of the micro-basins is a strong indication that the loss of flow and the increase in water deficits may be exacerbated. This represents a major challenge to maintaining the balance between the growing demand for water and the conservation of ecological functions in the basin. The significant increase in population in all states where the reservoirs are located, together with the increased withdrawal of water, raises major concerns not only in terms of electricity production, but also ecological ones (Arias et al. 2014 ; Jong et al. 2021 ; Tornés et al. 2022 ). The hydrographic region of the Tocantins River includes the six largest states of the federation, in addition to the federal capital (Brasília), in terms of economic development, with GDPs above the national average. The sectors of the economy revolve around animal production, irrigated agriculture, industry, mining and thermoelectricity (ANA, 2024 ). This could lead to water shortages if there is no efficient management of water resources throughout the river basin. We note that the basin’s strategic water resources plan, prepared in 2006 to 2009 and not yet implemented, foresees the growth of agricultural, hydroelectric and mining ventures, but does not consider the possible impacts of global climate change on a regional scale. This scenario is common in developing countries, where there is little or no water resources legislation that addresses climate change (Moran et al. 2018 ). However, concerns about hydroclimatic changes have grown in recent years (Hong et al. 2023 ; Liang et al. 2020 ; Moshir Panahi et al. 2020 ; Sigalla et al. 2023 ). Studies have shown concerns about the supply of water to humanity (Drenkhan et al. 2015 ; Jongman et al. 2015 ), demonstrating that changes in precipitation and evaporation have changed population density and increased human conflicts (Kåresdotter et al. 2023 ). In addition, some authors claim that the reduction in available water can cause food and water insecurity (Shin et al. 2022 ; Tiwari et al. 2023 ; Trisurat et al. 2018 ). Therefore, it is important to include in the basin's strategic plan the mitigation of conflicts resulting from increased demand for water and hydroclimatic changes. It should also include the identification of where there is the greatest withdrawal of water, what the implications are for river flow and ecosystems, and how future increases in water withdrawal may affect the sustainable use of water in the Tocantins-Araguaia river basin. Conclusion We conclude that the hydroelectric reservoirs of the Tocantins River presented heterogeneous hydrological and climatological characteristics, both spatially and temporally. The flows of these reservoirs were significantly influenced by precipitation, high rates of liquid evaporation and high solar radiation. These combined factors reduce not only the quantity of water, but also the water quality of these reservoirs. In relation to historical trends, analyses showed a reduction in precipitation and flows, accompanied by an increase in air temperature. Given this context, and considering scenarios of constant climate change, with projections of increasingly severe droughts, it is essential to think about the hydrological and ecosystem resilience of the reservoirs of the Tocantins River. This resilience is essential for maintaining biodiversity, food production and electricity generation. In addition, future ecological research should address the resilience of species to these hydroclimatic variations. Another relevant question is how extreme droughts can influence the water quality of these reservoirs. In drought scenarios, water temperatures increase and oxygen concentrations decrease, which can lead to critical levels of anoxia and biota mortality. Therefore, it is essential to investigate and understand these dynamics to ensure the sustainability and functionality of the Tocantins River reservoirs in the face of hydroclimatic changes. Declarations Conflicts of interest Declarations of interest: none. Funding The author received no funding for this work. Author Contribution I.G.S., J.L.C.S., and B.D. devised the study. Silva, I.G. designed the project, collected the data and led the writing of the manuscript. J.L.C.S. and B.D. reviewed the data analysis, revised and edited the manuscript. All authors contributed to the writing and proofreading of the paper. Acknowledgement We would like to thank the Postgraduate Program in Ecology of the Federal University of Pará and the Laboratory of Ecology of Primary Producers (ECOPRO) and the Laboratory of Aquatic Ecology and Tropical Aquaculture of the Federal Rural University of the Amazon for the infrastructure and support. The Foundation for the Improvement of Higher Education Personnel (CNPq) granted a scholarship to Idelina Gomes da Silva. We would also like to thank ANA, INMET and IBGE, as well as the State Center for Meteorology and Water Resources of the State University of Tocantins-Unitins. 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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-4849979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343356103,"identity":"06a3a5ce-7b93-4bf8-a8fe-d11e6fbd0ddb","order_by":0,"name":"Idelina Gomes da Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNoYExgMJMN4HkAg7YS0McC2MM0AizATtAWqBMZl5wCQBDXzsyQcOPNxhZ9cvdvjwZ5tf2+T5mBkYP3zMweMwnmcJBxLPJCfPnJ2WJp3bd9uwjZmBWXLmNjxaJHIMDiS2MScb3M4xY87tuc0I1MLGzItXS/4HoJZ6oJb8z58te27bE6ElhwGo5bAd0BYGaYYftxMJa+F5BnLY8QTJ2Wlmkr0Nt5PbmBmb8fpFvj354cOfbdX2/NLJjz/8+HPbdn5788EPH/FogYHEBhDJ2AYmGwirBwJ7CPWHKMWjYBSMglEwwgAAHqNS0d7rJLwAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Pará","correspondingAuthor":true,"prefix":"","firstName":"Idelina","middleName":"Gomes da","lastName":"Silva","suffix":""},{"id":343356104,"identity":"70cebb96-bee5-44b6-a86d-eb8bde5d9178","order_by":1,"name":"José Luiz Cabral da Silva Júnior","email":"","orcid":"","institution":"State University of Tocantins – Unitins. State Center for Meteorology and Water Resources (Nemet/RH). Palmas – TO","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Luiz Cabral da Silva","lastName":"Júnior","suffix":""},{"id":343356105,"identity":"70436348-4035-4618-8e56-8b139c760dd2","order_by":2,"name":"Bárbara Dunck","email":"","orcid":"","institution":"Federal University of Pará","correspondingAuthor":false,"prefix":"","firstName":"Bárbara","middleName":"","lastName":"Dunck","suffix":""}],"badges":[],"createdAt":"2024-08-02 17:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4849979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4849979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63095646,"identity":"1beda47a-7f30-4a61-95ac-51bb6c2e8924","added_by":"auto","created_at":"2024-08-23 05:34:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":534140,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Tocantins-Araguaia river basin, including the geographic location of the reservoirs installed in cascades on the Tocantins River and the volume of rainfall (mm/year).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/b1a1813be21be465ca0924a2.png"},{"id":63095143,"identity":"bad328a2-6bd2-43ea-8ba3-5daa7780ff28","added_by":"auto","created_at":"2024-08-23 05:26:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115440,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plots of comparisons of the seven reservoirs of the Tocantins River (Serra da Mesa-SM, Cana Brava-CB, São Salvador-SS, Peixe Angical-PA, Lajeado-LA, Estreito-ES and Tucuruí-TU). Bars represent the median. Nonparametric Kruskal-Wallis tests were used to identify differences between reservoirs for each variable. Points sharing the same letter are not significantly different from each other, according to Dunn's test. (a) Reservoirs at the end of the cascade have higher discharges when compared to those at the beginning, as well as temperatures (b), precipitation (c) and evaporation (d), with different levels of radiation (e) longitudinally. Accumulation-type reservoirs have larger areas (f).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/e5fa3a06cf66b47583963ef3.png"},{"id":63095146,"identity":"201bfe42-f718-4fd3-aa9c-7a5fc17b2cda","added_by":"auto","created_at":"2024-08-23 05:26:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104867,"visible":true,"origin":"","legend":"\u003cp\u003eNonparametric multidimensional scaling (nMDS) similarity matrix visualizing the level of similarity between each reservoir with data on (a) precipitation, (b) streamflow, (c) temperatures, (d) evaporation, and (e) radiation. Pairwise ANOSIM analysis shows that reservoirs are statistically different from each other (p \u0026lt; 0.05). Each color in the nMDS plot represents a different reservoir and the distinction between dry and wet seasons. Closer points represent reservoirs that are more similar to each other based on the hydroclimatic variables evaluated, and points that are further apart represent more distinct reservoirs.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/359dacd498f385f2c37c34ad.png"},{"id":63095150,"identity":"7b9fe1cd-7a95-4156-a0b9-cac7afb23999","added_by":"auto","created_at":"2024-08-23 05:26:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195882,"visible":true,"origin":"","legend":"\u003cp\u003eHydroclimatic gradients in the study area. (a) Total annual precipitation (mm/year) and (b) mean annual discharge (m3/s) increase from south to north, i.e. from upstream to downstream. Small circles show precipitation or discharge in reservoirs.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/981d777b49e252704af2e390.png"},{"id":63096195,"identity":"d4b47d5a-5aac-48d5-bfa7-e41252fc0786","added_by":"auto","created_at":"2024-08-23 05:42:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38870,"visible":true,"origin":"","legend":"\u003cp\u003eHydroclimatic seasonality during the period 2006 to 2018 for flow (a), precipitation (b), EVP (c), solar radiation (d) and temperature (e) data in the Tocantins River reservoirs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/fe4a02c5aa875415f95ab8e6.png"},{"id":63095144,"identity":"a77cec17-f20b-47b6-8f03-51378caccfa1","added_by":"auto","created_at":"2024-08-23 05:26:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135725,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal patterns of the Mann-Kendall trend test applied to time series of mean annual precipitation values (1970 to 2023), in each reservoir of the Tocantins River (a-h). Serra da Mesa (SM), Cana Brava (CB), São Salvador (SS), Peixe Angical (PA), Lajeado (LA), Estreito (ES) and Tucuruí (TU). P-value indicates the significance of the trend of the time series with a 95% confidence level (α = 0.05). Red lines represent long-term trends. Boxplot of annual precipitation (h) shows only years with statistically significant differences (different letters p\u0026lt;0.005) by the Kruskal-Wallis test.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/bd9ea954d52f818130bb2f91.png"},{"id":63095648,"identity":"6e5c228a-15c1-46ad-b300-805b40bfbabc","added_by":"auto","created_at":"2024-08-23 05:34:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76955,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal patterns of the Mann-Kendall trend test applied to time series of mean annual flow values (1995 to 2023), in each reservoir on the Tocantins River (a-g). The red lines are the trend lines projected by the Mann-Kendall analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/b42993ae320bc1505245c670.png"},{"id":63095155,"identity":"9b86eddd-2549-47ce-9b82-5b907b33e0ac","added_by":"auto","created_at":"2024-08-23 05:26:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":321130,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of annual flows during the period 1995 to 2023 (a). Different letters represent statistical differences by the Kruskal-Wallis test. The boxes in different colors represent the year of filling of the reservoirs. Decreases and increases mean the differences in relation to the previous year (b).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/8abe4b7cd31f86416df0b285.png"},{"id":63095645,"identity":"3db44144-4b29-4d10-98b7-4b3b1bbd241c","added_by":"auto","created_at":"2024-08-23 05:34:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":132758,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal patterns of the Mann-Kendall trend test applied to time series of annual mean temperature values - Temp (2006 to 2018), in each reservoir of the Tocantins River (a-g). The red lines are the trend lines projected by the Mann-Kendall analysis.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/1f5c152357e9a5bbf16ee40d.png"},{"id":63095151,"identity":"23cb58fd-233e-44ae-be71-df9b9fafb063","added_by":"auto","created_at":"2024-08-23 05:26:40","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":19109,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of annual temperatures during the period 2006 to 2018 (a). Different letters represent statistical differences by Kruskal-Wallis and Dunn's posthoc test.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/533387dc242119415834415d.png"},{"id":63095154,"identity":"62f458e9-238e-4b5d-98d4-7ef170fc779d","added_by":"auto","created_at":"2024-08-23 05:26:40","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":182544,"visible":true,"origin":"","legend":"\u003cp\u003eHydroclimatic variables plotted according to Perason’s correlations. R (R) indicates the strength of the linear correlation between the variables. P values (P) indicate the statistical significance of the correlation. Mean annual flows were correlated with precipitation (a), evaporation - EVP (b) and solar radiation (c). Solar radiation correlated with EVP and precipitation (d, e), EVP with precipitation (g) and air temperature correlated with EVP (g).\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/04ab1fa28acf76299d734da7.png"},{"id":63095153,"identity":"136331a0-07b2-4657-9332-4bc62e2c9cfb","added_by":"auto","created_at":"2024-08-23 05:26:40","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":109589,"visible":true,"origin":"","legend":"\u003cp\u003eSocioeconomic data of the states where the reservoirs are located. Population (a), Human Development Index – HDI (b), Gross Domestic Product – GDP (c) and water withdrawal (d).\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/898476744616f50715058e6d.png"},{"id":74636584,"identity":"2e78ff3a-5ae6-478a-9939-ce5b05a8a179","added_by":"auto","created_at":"2025-01-24 08:32:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2621904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4849979/v1/8276f887-46ce-4bb8-8c40-3d017191ab91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydroclimatic variability and trends suggest improvements in water resource management in the cascade reservoirs of the Tocantins River","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe study of hydroclimatic variations sheds light on the relationships between water masses and climate factors. Global water masses have undergone major human impacts and climate changes (Yao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Droughts, heat waves, floods, and storms are extreme meteorological phenomena that pose challenges to continental and oceanic waters, which in turn respond to these phenomena in various ways (Oliveira et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rodell and Li \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Winter et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, with higher temperatures, the evaporation rate of water bodies increases, with a consequent reduction in water availability. On the other hand, human activities, such as the construction of dams, alter the river continuum and flows (Tiwari et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ward and Stanford \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Many studies have shown consistent patterns of climate effects on river geomorphology (Larkin et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Q. Wu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), hydrological processes in river basins (N\u0026auml;schen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), society and public policies (Sigalla et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), water storage in river basins (Hong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moshir Panahi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the seasonal regime of rivers (Liang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies demonstrate robust results of climate interference in fluviometric patterns and their dependence relationships between climate factors and the maintenance of aquatic systems.\u003c/p\u003e \u003cp\u003eUnderstanding how climate change is affecting our planet\u0026rsquo;s water bodies, especially available freshwater, has become vital. Extreme weather events can reduce the quality and quantity of water available to living beings (Larkin et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mazacotte et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; van Vliet et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rivers are essential for human survival and biodiversity maintenance; in addition to supporting economic development and cultural enrichment (Larned et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sinclair et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), they play a crucial role in environmental regulation, which allows the persistence of high biodiversity. However, most of the world's major rivers have been dammed, with the formation of large artificial reservoirs (Grill et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which has reduced the variety of services provided to society and threatened the maintenance of ecosystems (Forsberg et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Swanson et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dams regulate river flow, increase water residence time, and reduce seasonal flow variability (Chong et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They are recognized worldwide for the major hydrological changes they cause in river channels (Q. Wu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although reservoir flows are less subject to seasonal variations when compared to free-flowing rivers, they still respond to climatic factors and can have their flows modified seasonally (Junqueira et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zaniolo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Tocantins River is an important Brazilian river that presents a water-energy-food connection. It flows through four Brazilian states with high agricultural production, Goi\u0026aacute;s, Tocantins, Maranh\u0026atilde;o and Par\u0026aacute;. Its uses include power generation, irrigation, fishing, agriculture, navigation, recreation and supplying cities (Tundisi and Matsumura-Tundisi \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), in addition to supporting several aquatic ecosystems. However, this river is undergoing transformation due to anthropogenic and climatic stressors (Costa et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Pelicice et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Swanson and Bohlman \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recently, climatic factors have become a concern due to the increasing influence on its hydrological cycles (Junqueira et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Von Randow et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Tocantins River\u0026rsquo;s flow rates are second only to those of the Amazon River (ANA \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but the installation of seven large hydroelectric dams installed in cascade has regulated and reduced these flows. These large hydroelectric reservoirs were installed in cascade over a stretch of more than 1,500 km on this river. Even with the flow regulated by dams, the main driving force for hydrological change, as in all tropical rivers, is climate variations (Chou et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dai \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Foley et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Climate variations shape flows and regulate environmental systems, such as nutrient cycles and aquatic biota (Costa et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Swanson et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Von Randow et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given this, there is an urgent need to understand how climate change affects river flow and ecological systems, in order to develop better conservation strategies.\u003c/p\u003e \u003cp\u003eTo date, the analysis of temporal and spatial hydroclimatic trends in hydropower reservoirs has not been considered systematically. However, there is a knowledge gap on how these changes may affect hydropower reservoirs. There are few studies that have analyzed hydroclimatic effects globally (Wu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dai 2020), some in river basins in China (Hong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tiwari et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Iran (Panahi et al., 2020), the Rufiji and Kilombero Rivers in Tanzania (Sigalla et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; N\u0026auml;schen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the Amazon River basin (Liang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Australian rivers (Larkin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Tocantins River basin, some studies have analyzed the effects of precipitation and flows on floodplain areas (Swanson et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); the effects of meteorological and hydrological droughts on the river basin (Junqueira et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); and the hydrological impacts of climate and land use and cover on hydroelectric productivity (Costa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Von Randow et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the shortcoming of these studies is that they have not analyzed hydroclimatic trends covering cascade reservoir systems, in order to identify which climate variables are affecting these systems and possible scenarios of climate impact on socioeconomic water demand. However, as highlighted, there is a lack of efforts that attempt to directly investigate the effects of precipitation, air temperature, net evaporation, and solar radiation on hydrological responses in cascade reservoir systems. An understanding of the long-term trend of hydroclimatic variables is useful in planning strategies, in conflict mitigation and in understanding how impounded freshwater ecosystems respond to climate change.\u003c/p\u003e \u003cp\u003eTo refine these concepts and identify temporal and spatial hydroclimatic trends of the reservoirs and identify important variables in hydrological maintenance, we sought to understand how the main hydroclimatic variables correlate and how each reservoir responds to these variations. Given the above, the objectives of this article were (I) to identify hydroclimatic differences between the reservoirs installed in cascades on the Tocantins River and seasonal patterns, (II) to quantify and analyze temporal trends of hydroclimatic parameters, (III) to identify critical variables for maintenance and those that influence changes in flow rates in the reservoirs of the Tocantins River, based on historical trends, the correlation between hydroclimatic variables, and socioeconomics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Tocantins-Araguaia river basin is located between the southern parallels 0\u0026ordm; 30\u0026rsquo; and 18\u0026ordm; 05\u0026rsquo; and the western longitude meridians 45\u0026ordm; 45\u0026rsquo; and 56\u0026ordm; 20\u0026rsquo;. The climate varies longitudinally from south to north, humid and hot in the south and humid in the north. This climate favors the existence of two biomes: Cerrado (Savanna) in the south, which occupies 65% of the basin area, and Amazon (Dense Ombrophilous Forest) in the north, which occupies the remaining 35%. The climate of the river basin is classified according to the K\u0026ouml;ppen methodology as \u0026ldquo;Cwa\u0026rdquo; with an annual rainfall index of around 1,500 mm; \u0026ldquo;Aw\u0026rdquo; with an annual rainfall index of 1,700 mm; \u0026ldquo;Am\u0026rdquo; with an annual rainfall index of 2,000 mm; and \u0026ldquo;Af\u0026rdquo; in the extreme north of the basin, with annual rainfall totals above 3,000 mm (ANA \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Altitudes follow the regional geomorphology, decreasing from the south (600 m) to the north (0 to 100 m). The Tocantins and Araguaia Rivers are the two main rivers that form this hydrographic basin. The Araguaia River is one of the few rivers in this basin with free flow, without the implementation of dams, but it concentrates a large part of the water withdrawal for irrigation. In contrast, the Tocantins River is highly fragmented by seven large dams (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which have led to a decline in its flows (Swanson et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in addition to another 50 small hydroelectric plants (PCHs and CGHs) installed in the tributaries of its sub-basin (ANA \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first reservoir of the cascade installed on the Tocantins River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is the Serra da Mesa HPP \u0026ndash; SM. Its accumulated water volume is approximately 54\u0026nbsp;billion m3, which remains in the reservoir for 750 days; this volume of water flooded an area of ​​1,784 km2. The second reservoir of the cascade is the Cana Brava HPP \u0026ndash; CB (13\u0026ordm;24'9.44'' S; 48\u0026ordm;8'36.89'' W), which is not of the accumulation type like the first, but it flooded an area of ​​139 km2 and formed a reservoir of 2.3 bi/m3; its waters remain for 28 days before entering the next reservoir. The S\u0026atilde;o Salvador-SS dam (12\u0026ordm;48\u0026rsquo;29.91\u0026rsquo;\u0026rsquo; S; 48\u0026ordm;14\u0026rsquo;16.36\u0026rsquo;\u0026rsquo; W), forms the smallest reservoir of the cascade, with an area of ​​104 km2 and 0.043 bi/m3; it is a run-of-river dam with a short water residence time, approximately 12 days. Peixe Angical \u0026ndash; PA (12\u0026ordm;14\u0026rsquo;15.25\u0026rsquo;\u0026rsquo; S; 48\u0026ordm;23\u0026rsquo;10.38\u0026rsquo;\u0026rsquo; W), the fourth reservoir, is an accumulation-type reservoir, but its water residence time is short compared to the others, at 18 days, but this is enough to maintain a reservoir of 2.7 bi/m3 and flooded an area of ​​294 km2. The fifth reservoir is Lajeado \u0026ndash; LA (9\u0026ordm;45\u0026rsquo;34.14\u0026rdquo; S; 48\u0026ordm;22\u0026rsquo;16.02\u0026rdquo; W), a run-of-river dam with a retention time of 24 days, a water volume of 5.7 bi/m3 and a flooded area of ​​630 km2. The most recent dam installed on this river and the sixth in the cascade is the Estreito Plant dam -ES (6\u0026ordm;35\u0026rsquo;22.92\u0026rdquo; S; 47\u0026ordm;27\u0026rsquo;52.54\u0026rdquo; W), a run-of-river dam with a reservoir of 5.4 bi/m3, which flooded an area of ​​434 km2 with a residence time of 16 days. At the end of the cascade is the Tucuru\u0026iacute; reservoir \u0026ndash; TU (3\u0026ordm;49\u0026rsquo;56.95\u0026rdquo; S; 49\u0026ordm;38\u0026rsquo;59.94\u0026rdquo; W), the largest among them and the largest in Brazil in terms of flooded area, 2,850 km2, with a water volume of 43 bi/m3. Although its area is larger than the Serra da Mesa reservoir, its water residence time is shorter, approximately 50 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eObservational dataset\u003c/h2\u003e \u003cp\u003ePrecipitation data were obtained from seven rainfall stations available on the website of the National Water and Sanitation Agency (ANA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.snirh.gov.br/hidroweb\u003c/span\u003e\u003cspan address=\"https://www.snirh.gov.br/hidroweb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Natural flows and monthly net evaporation were also provided by ANA and calculated for each reservoir (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ana.gov.br/sar/sin/b_tocantins\u003c/span\u003e\u003cspan address=\"https://www.ana.gov.br/sar/sin/b_tocantins\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metadados.snirh.gov.br/geonetwork\u003c/span\u003e\u003cspan address=\"https://metadados.snirh.gov.br/geonetwork\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The historical series of air temperature and global solar radiation were obtained from seven meteorological stations, available on the website of the National Institute of Meteorology \u0026ndash; INMET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.inmet.gov.br/dadoshistoricos\u003c/span\u003e\u003cspan address=\"https://portal.inmet.gov.br/dadoshistoricos\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo analyze spatial and seasonal differences, we used time series with monthly averages of hydroclimatic data for 12 years, between 2006 and 2018. In order to evaluate the largest possible number of years in the analyses of temporal trends in precipitation and flow, the periods analyzed were expanded. For flow, data from 28 years were used, referring to the periods from 1995 to 2023, and for precipitation, the interval was 53 years, referring to the years from 1970 to 2023.\u003c/p\u003e \u003cp\u003eTo calculate monthly averages of global solar radiation, daily values ​​were summed and then calculated as daily averages for each month. The temperature used was the daily maximum recorded at 3:00 p.m., from which we also calculated the monthly average. Precipitation was recorded as a monthly total, and annual totals were obtained by summing these values ​​for each year studied. Monthly averages of flow rates were calculated from daily values ​​(ANA \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocioeconomic data, such as the Human Development Index (HDI), Gross Domestic Product (GDP), and population by States where the reservoirs are located, were provided by the Brazilian Institute of Geography and Statistics (IBGE) (IBGE \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the amount of water withdrawn was calculated as the annual average of water withdrawn (m3/s) per State from each reservoir (ANA \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eTo identify statistical hydroclimatic differences between the reservoirs, we first performed the nonparametric Kruskal-Wallis test applied to each hydroclimatic variable separately and without standardization. In this test, the reservoirs were used as response variables and the hydroclimatic variables as predictor variables. Subsequently, the Dunn test was performed to determine which reservoirs were significantly different from each other in terms of hydroclimate (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTo assess whether the total set of hydroclimatic variables (flow rate \u0026ndash; m3/s, precipitation \u0026ndash; mm, solar radiation \u0026ndash; MJ/m2/day, temperature \u0026ndash; \u0026ordm; C and evaporation \u0026ndash; m3/s) was capable of differentiating the reservoirs and seasonal periods, we performed a similarity analysis (ANOSIM) on a similarity matrix based on Euclidean distances, using 9999 permutations. From the dissimilarity matrix, we constructed two nonparametric multidimensional scaling (nMDS) graphs to visually represent the differences between the reservoirs and seasonal periods. Each point on the graph represents a reservoir, and closer points indicate greater similarity between them in relation to the predictor variables used (flow rate \u0026ndash; m3/s, precipitation \u0026ndash; mm, radiation \u0026ndash; MJ/m2/day, temperature \u0026ndash; \u0026ordm; C and evaporation \u0026ndash; m3/s).\u003c/p\u003e \u003cp\u003eTo quantify and analyze temporal trends in hydroclimatic parameters, we used the nonparametric Mann\u0026ndash;Kendall test, calculated from the monthly mean values ​​of flow, temperatures, solar radiation and accumulated monthly precipitation totals. In this test, \u0026ldquo;tau\u0026rdquo; values ​​can be positive, indicating increasing trends, or negative, indicating decreasing trends. The p-values ​​indicate the statistical significance of the test and are considered significant at a 95% confidence level.\u003c/p\u003e \u003cp\u003eTo identify the critical variables that influence and maintain flows in the Tocantins River reservoirs, we analyzed negative and significant trends using the Mann-Kendall test. We then performed Pearson correlations between hydroclimatic variables to determine which climatic factors are influencing these changes.\u003c/p\u003e \u003cp\u003eAll data analyses were performed in RStudio 4.3.1 (R Core Team \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the Kruskal-Wallis test, we used the \u0026lsquo;kruskal.teste\u0026rsquo; function and for the calculation of trends (Tau) and p-value, we used the \u0026lsquo;Kendall\u0026rsquo; package (McLeod \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The graphs were prepared using the functions of the \u0026lsquo;ggplot2\u0026rsquo; package (Wickham \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSpatial hydroclimatic variability\u003c/h2\u003e \u003cp\u003eThe results showed statistically significant variations in hydroclimatic variables between reservoirs. The annual spatiotemporal average of maximum air temperature over all reservoirs, during the study period, 2006\u0026ndash;2018, was 30.8\u0026deg;C. In two of the seven reservoirs studied (SS and SM), they were below this average, with average annual temperatures of 29.4 and 25.9\u0026deg;C respectively, which statistically differentiated them from the others (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 - Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The difference between the lowest average temperature (25.9\u0026deg;C in SS) and the highest (33\u0026deg;C in PA, LA and ES) was 8\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe reservoirs follow an increasing trend of precipitation and discharge, i.e., from upstream to downstream (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The average annual precipitation during the studied period for all reservoirs was 1584 mm. The lowest precipitation recorded was in PA (1,048 mm) and the highest in TU (2,511 mm), resulting in a difference of 1461 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The ES and TU reservoirs were significantly different from the others (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In three of the seven reservoirs (SM, CB and PA) the average annual precipitation was less than 1584 mm. These spatial differences of 8\u0026deg;C in temperature and 1461 mm/year in precipitation highlight the spatial diversity of hydroclimatic conditions in the reservoirs of the Tocantins River.\u003c/p\u003e \u003cp\u003eThe average flow rate of the reservoirs was 1,536 m\u003csup\u003e3\u003c/sup\u003e/s, but it was less than 700 m3/s in three reservoirs (SM, CB and SS), which made them statistically similar (Kruskal-Wallis test: p\u0026thinsp;=\u0026thinsp;0.432; p\u0026thinsp;=\u0026thinsp;0.418 and p\u0026thinsp;=\u0026thinsp;0.453). The ES and TU reservoirs were similar to each other (Kruskal-Wallis test: p\u0026thinsp;\u0026gt;\u0026thinsp;0.001), but different from the others (Kruskal-Wallis test: p\u0026thinsp;\u0026gt;\u0026thinsp;0.052) and recorded an average flow rate higher than 2,500 m\u003csup\u003e3\u003c/sup\u003e/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The average monthly evaporation, calculated from the area of ​​each reservoir, was 8,320 m\u003csup\u003e3\u003c/sup\u003e/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The highest evaporations occurred in the two largest reservoirs, SM (13.73 m3/s) and TU (24.85 m\u003csup\u003e3\u003c/sup\u003e/s), but SM had similar net vaporization to CB, SS, PA and LA TU (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TU differed significantly from the others (Kruskal-Wallis test: p\u0026thinsp;=\u0026thinsp;0.001 - Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, g). The spatial patterns for solar radiation and temperatures showed differences. Although temperatures in PA, LA and ES were similar, radiation varied. Statistically, solar radiation was similar in CB and ES (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in SM, CB and PA (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.012), and in LA and TU (Kruskal-Wallis test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 - Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The monthly average solar radiation was 22 MJ/m\u003csup\u003e2\u003c/sup\u003e, with only the TU reservoir above this average, at 40 MJ/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results indicated that the reservoirs presented significant differences in their hydroclimatic variability. The dissimilarity analysis and the comparison in the nMDS plots applied to the hydrological variables revealed statistically significant differences between the reservoirs (ANOSIM R\u0026thinsp;=\u0026thinsp;0.023; p\u0026thinsp;=\u0026thinsp;0.001). The TU reservoir stood out as the most distinct, exhibiting high hydroclimatic variability, mainly due to the higher flows and precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, blue dots). The spatial pattern also varied according to seasonality, showing a clear dissimilarity between the dry and rainy periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Greater similarity between the reservoirs was observed in the dry period. The greatest dissimilarities occurred in the rainy period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), suggesting that the climatic variations of the dry and rainy periods significantly influence the hydrodynamic variations (ANOSIM R\u0026thinsp;=\u0026thinsp;0.256; p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTemporal\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSeasonality\u003c/h2\u003e \u003cp\u003eThe average monthly precipitation is highest in the months of January-February-March, which together accounted for 47% of the annual total, while the lowest values ​​were found in the months of June and August, representing only 1% and 0.6% of the annual total (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). At the peak of precipitation in January, the reservoirs reached extreme flow values ​​(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Likewise, the lower precipitation resulted in lower flow levels in August. We observed a two-month delay in the response of flow to increased precipitation and a one-month delay in the response to reduced rainfall. In October and November, the increase in precipitation did not increase flow, which began to increase only in December. In contrast, the response in the reduction of rainfall was faster; at the beginning of the dry season, in May, reductions in flow were already observed. Evaporation and solar radiation follow a pattern that is inverse to precipitation, with an increase from June onwards and a decrease in October (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed,e). The maximum temperature values ​​(38\u0026ordm; C) occurred in the driest months, August and September, recording the largest temperature ranges of 12\u0026ordm; C. In January and March, we recorded the smallest temperature ranges of 8\u0026ordm; C (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTrends\u003c/h2\u003e \u003cp\u003eThe Mann-Kendall analysis revealed a clear trend of dry conditions and hot periods. All reservoirs showed a trend of decreasing precipitation and streamflows, together with increasing temperatures (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, c, d, f, g). The Kruskal-Wallis analysis followed by Dunn's post-hoc analysis indicated significant statistical differences in precipitation between the years 1970 and 2023. Specifically, the precipitation pattern in 2016 was different from the years 1973, 1977 and 1989 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh).\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\u003eMann-Kendall trend test applied to time series of precipitation (1970 to 2023), flow (1995 to 2023), temperature and radiation (2006 to 2018). P-value indicates the significance of the trend of the time series with a 95% confidence level (α\u0026thinsp;=\u0026thinsp;0.05).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoir\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroclimatic Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKendal tau\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerra Mesa -SM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePrecipitation (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCana Brava \u0026ndash; CB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeixe Angical \u0026ndash; PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026atilde;o Salvador \u0026ndash; SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLajeado \u0026ndash; LA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstreito \u0026ndash; ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTucuru\u0026iacute; \u0026ndash; TU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerra Mesa -SM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eFlow rate (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCana Brava \u0026ndash; CB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeixe Angical \u0026ndash; PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026atilde;o Salvador \u0026ndash; SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLajeado \u0026ndash; LA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstreito \u0026ndash; ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTucuru\u0026iacute; \u0026ndash; TU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerra Mesa -SM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eTemperature (\u0026ordm; C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCana Brava \u0026ndash; CB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeixe Angical \u0026ndash; PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026atilde;o Salvador \u0026ndash; SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLajeado \u0026ndash; LA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstreito \u0026ndash; ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTucuru\u0026iacute; \u0026ndash; TU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\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 \u003c/p\u003e \u003cp\u003eAccording to the analysis of long-term flow trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the reservoirs show a reduction rate of 575 m3/s. The natural flow data showed that the largest reductions were in the filling years. The largest reductions, of up to 1,382 m3/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), occurred during the filling of the SM (1998) and PA (2006) and CB (2004) reservoirs, and they were more pronounced in the filling of SM and PA, due to the fact that these reservoirs are of the accumulation type that store water.\u003c/p\u003e \u003cp\u003eOther reductions occurred in the LA filling in 2002 and ES filling in 2011 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). However, the reductions observed in the years 2017, 2018, 2020 and 2022, in which there was no implementation of new dams, reflected the hydroclimatic effects. These reductions were statistically significant, as indicated by the Kruskal-Wallis test followed by the Dunn test, applied to the period from 1995 to 2023. The years 2015 and 2016 were statistically different within the time series, with 2016 the year with the lowest flow in the entire series studied (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Mann-Kendall trend test revealed an increasing trend in temperature in all reservoirs during the years 2006 to 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). However, for four reservoirs (PA, SS, LA and TU) this trend between years was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2016, we recorded the highest temperatures in the time series, but without statistically significant differences compared to the years 2006, 2010, 2012 and 2013 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing hydrological changes\u003c/h2\u003e \u003cp\u003eThe results of the correlation analyses for the individual reservoirs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) indicated that precipitation, evaporation and solar radiation are explanatory variables for the variability of flows in the reservoirs. The positive and significant correlations between flow and precipitation (R\u0026thinsp;=\u0026thinsp;0.618, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) confirm this relationship, while negative and significant correlations between flow and evaporation (R = -0.369, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between flow and solar radiation (R = -0.473, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) reinforce the influence of these factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea, b, c).\u003c/p\u003e \u003cp\u003eFurthermore, other climatic variables of the reservoir area also showed significant correlations. We detected positive correlations between evaporation and solar radiation (R\u0026thinsp;=\u0026thinsp;0.310, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between evaporation and air temperature (R\u0026thinsp;=\u0026thinsp;0.365, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ed, f). On the other hand, negative correlations were found between precipitation and solar radiation (R = -0.472, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between precipitation and air temperature (R = -0.539, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ee, g). These results indicate that increasing air temperatures and radiation are strongly correlated with evaporation, while high levels of precipitation are associated with lower solar radiation and evaporation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHydroclimatic changes and regional development\u003c/h2\u003e \u003cp\u003eThe socioeconomic data involving the states where the reservoirs are located showed that the state with the highest HDI and GDP is also the one that withdraws the most water (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed, c, d). In 2021, the total water withdrawn in the states from the reservoirs totaled 249 m3/s, with Goi\u0026aacute;s, where the Serra da Mesa -SM reservoir is located, accounting for 43% of this total (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed). In addition, we found a high positive correlation between GDP and water withdrawal (R\u0026thinsp;=\u0026thinsp;0.851; p\u0026thinsp;=\u0026thinsp;0.005) and a high negative correlation between rainfall and water withdrawal (R = -0.624; p\u0026thinsp;=\u0026thinsp;0.0321). On the other hand, the reservoirs of Estreito (ES), in the state of Maranh\u0026atilde;o, and Tucuru\u0026iacute; (TU), in the state of Par\u0026aacute;, which have the largest populations and lowest HDI, withdraw 19% and 24% of the total water, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe carried out a hydroclimatic assessment of the hydroelectric reservoirs installed in cascades on the Tocantins River, based on historical series of precipitation (between the years 1970 to 2023), flow (years 1995 to 2023), temperature and radiation (years 2006 to 2018). Our results indicated that the reservoirs are different in their hydroclimatic conditions, both spatially and temporally, forming a hydroclimatic gradient. We observed trends of reduction in precipitation and flow, as well as an increase in temperatures. Flow rates increased from upstream to downstream, following the rhythm of precipitation defined by seasonality (dry and rainy periods). It was evident that, due to seasonality, the reservoirs suffered from the increase in thermal amplitudes, evaporation and radiation, with evaporation being an important indicator of drought and increasing as temperatures increase (Han and Singh \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also revealed drier scenarios, with low flow and high temperatures. Climatic conditions conditioned by the seasonality of precipitation, evaporation and radiation showed a strong correlation with reservoir flow. In addition, socioeconomic factors exerted strong anthropogenic pressure on the river basin. These implications suggest that the functioning of these ecosystems is being governed by hydroclimatic and anthropogenic changes, capable of influencing energy production, reducing ecosystem resilience and hindering the survival of aquatic organisms (Costa et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Domingues and da Rocha \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; K\u0026aring;resdotter et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Von Randow et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spatial differences in hydroclimatic variables demonstrate the geographic diversity of hydroclimatic conditions. This was corroborated by the climate mapping carried out by the National Water Agency (ANA, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which classified the climate of the reservoirs into three categories, according to K\u0026ouml;eppen, in Am, Aw and Cwa. From upstream to downstream, we observed an increasing gradient of precipitation, discharge and net evaporation. A group of four reservoirs, located at the beginning of the cascade (SM, CB, SS and PA), presented high hydroclimatic similarity, with little variability in discharge, precipitation and evaporation. In contrast, the last three reservoirs, which are further away (LA, ES and TU), showed significant differences between themselves and in relation to the others. The hydroclimatic similarity found in the first four reservoirs generates hydrological interdependence controlled by the first reservoir (SM), which was designed to regulate the discharges of the others. This indicates that climate impacts affect these reservoirs equally.\u003c/p\u003e \u003cp\u003eHydroclimatic and geomorphological differences, such as discharge, precipitation, and altitude, are the main drivers of river forces (Larkin 2020). As latitudes and altitudes decrease, the greater the climatic differences found. Nutrient and sediment transport is also reduced in these similar reservoirs, especially because the first, SM, is of the accumulation type with a high retention time (750 days). Excess sediment implies a reduction in the useful life of the reservoir, and sedimentation increased by the effect of the cascade installation of dams leads to a situation of oligotrophication downstream (Maavara et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wei 2020).\u003c/p\u003e \u003cp\u003eOur results suggest that hydroclimatic variations in reservoirs are reflections of seasonal cycle characteristics, with greater hydroclimatic dissimilarity during the rainy season. During this period, flow rates and temperatures have greater standard deviations and the reservoirs present greater hydroclimatological dynamics. Unlike the dry season, especially in July and early August, flow rates are stabilized by hydroelectric operators to meet the regional beach season. Reservoirs are influenced by the regional climate, which reveals consistent patterns of dry and rainy weather, with flow rates drastically decreasing during dry periods and slowly increasing during rainy periods. This generates a water deficit that is slowly replenished, generating a low water period of more than 6 months. Seasonality is a critical factor for these reservoirs, whose purpose is to generate electricity. A hydroelectric reservoir in the Tapaj\u0026oacute;s River sub-basin in the Amazon River basin revealed a 27% loss in installed capacity during the dry season (Arias et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hofmann et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Tocantins River reservoirs experienced alarming reductions in their flows during the dry season. Serra da Mesa-SM, the largest reservoir in Brazil and one of the largest in the world in terms of water volume, reached the end of the 2020 drought with only 9% of its useful volume (ONS, 2021). Other Amazonian reservoirs, such as Belo Monte (Xingu River), Girau and Santo Ant\u0026ocirc;nio (Madeira River), produced below-projected targets due to strong regional seasonality and climate change (Hofmann et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our findings are consistent with recent long-term analyses for Brazil, which have observed streamflows being influenced by precipitation seasonality (Junqueira et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Swanson et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ecologically, longer periods of drought have widespread implications for freshwater ecosystems. Droughts reduce habitat areas, increase water residence time, alter biogeochemical cycles, and increase solute concentrations in the water. This impacts aquatic food chains (G\u0026oacute;mez-Gener et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and species population densities, excluding sensitive species and increasing species more adapted to drought (Aspin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur trend analysis applied to hydroclimatic variables showed negative trends for precipitation and flow, and positive trends for temperatures in the studied reservoirs. Recent studies in tropical regions agree with our results, indicating negative trends in precipitation in Brazil in the Cerrado and Amazon biomes (Dai \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu and Wang \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies pointed to the occurrence of droughts caused by reduced rainfall, increased temperature, high evaporation, and changes in vegetation cover. From the high correlation between precipitation and flow and their negative trends presented in our study, we expect that years with lower precipitation also present the lowest flows in the Tocantins River reservoirs. Other studies corroborate our findings, revealing climate trends for the Cerrado biome, where six of the seven reservoirs analyzed here are located, indicated a reduction of up to 50% in the total rainfall recorded in the dry period (Hofmann et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) with a consequent reduction in flow (Jong et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For the Amazon, the biome of the last reservoir of the cascade analyzed, there are trends for dry periods to become even more severe (Liang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The National Water Agency confirmed that 2015, 2016, and 2017 were the driest years with the lowest flows in the last 87 years. The National Electric System Operator (ONS) reported that, in TU, the last reservoir of the cascade and with the highest precipitation index, 2016 was the year with the lowest annual flow in 80 years (ONS \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These events are attributed to the El Ni\u0026ntilde;o meteorological phenomenon which, in 2015 and 2016, led to an increase in drought risks (Dai \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) influenced by global warming (Shin et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the magnitude of the hydroclimatic changes that already occurred in 2015, 2016 and 2017, which resulted in water deficits in the reservoirs analyzed here, it is clear that any hydrological and management study must take hydroclimatic trends into account in its planning.\u003c/p\u003e \u003cp\u003eThe results presented here support the idea that climate is a key driver of the contrasting patterns in reservoir flows. It is evident that the observed climate patterns are consistent with streamflows, and the high linear correlation between streamflows and precipitation, as well as between streamflow and evaporation, clearly indicate that streamflows were dependent on regional climate. In addition to dams themselves, precipitation is the main driver of hydrological processes (Tang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and has the potential to influence streamflows in hydropower reservoirs (X. Wu et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies have warned about the significant influence of climate on hydropower systems (Mekonnen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moran et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with predictions of a reduction in safe water levels for electricity generation. Under these hydroclimatic conditions, with reduced precipitation, increased temperatures and reduced flows, improving water consumption management and reinforcing the monitoring of licenses are suggested, to maintain water flow downstream (Sigalla et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to climate change, it is important to highlight the clear human influences on the reservoirs. The greatest demand for water withdrawal in the Tocantins-Araguaia basin is for irrigation (44%), with areas exceeding 30,000 hectares (ANA \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). We observed a high demand for water, especially in the State of Goi\u0026aacute;s, where the Serra da Mesa reservoir (SM) is located. Paradoxically, we found a high negative correlation between precipitation and water withdrawal. This contrast suggests that lower precipitation led to increased water withdrawal in the portion of the river with the lowest average annual precipitation and flow. In a scenario of a trend of reduced precipitation and increasing demand for water, the reductions in flow rates may be aggravated. In addition to the withdrawal of water directly from the reservoirs, there is a large extraction concentrated in the main tributaries that supply water to these reservoirs, such as large irrigation projects that use water from the Araguaia River, the main tributary to the TU Reservoir (ANA \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high withdrawal of water from the tributaries of the micro-basins is a strong indication that the loss of flow and the increase in water deficits may be exacerbated. This represents a major challenge to maintaining the balance between the growing demand for water and the conservation of ecological functions in the basin. The significant increase in population in all states where the reservoirs are located, together with the increased withdrawal of water, raises major concerns not only in terms of electricity production, but also ecological ones (Arias et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jong et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Torn\u0026eacute;s et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The hydrographic region of the Tocantins River includes the six largest states of the federation, in addition to the federal capital (Bras\u0026iacute;lia), in terms of economic development, with GDPs above the national average. The sectors of the economy revolve around animal production, irrigated agriculture, industry, mining and thermoelectricity (ANA, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This could lead to water shortages if there is no efficient management of water resources throughout the river basin.\u003c/p\u003e \u003cp\u003eWe note that the basin\u0026rsquo;s strategic water resources plan, prepared in 2006 to 2009 and not yet implemented, foresees the growth of agricultural, hydroelectric and mining ventures, but does not consider the possible impacts of global climate change on a regional scale. This scenario is common in developing countries, where there is little or no water resources legislation that addresses climate change (Moran et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, concerns about hydroclimatic changes have grown in recent years (Hong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moshir Panahi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sigalla et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies have shown concerns about the supply of water to humanity (Drenkhan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jongman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), demonstrating that changes in precipitation and evaporation have changed population density and increased human conflicts (K\u0026aring;resdotter et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, some authors claim that the reduction in available water can cause food and water insecurity (Shin et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tiwari et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Trisurat et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, it is important to include in the basin's strategic plan the mitigation of conflicts resulting from increased demand for water and hydroclimatic changes. It should also include the identification of where there is the greatest withdrawal of water, what the implications are for river flow and ecosystems, and how future increases in water withdrawal may affect the sustainable use of water in the Tocantins-Araguaia river basin.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe conclude that the hydroelectric reservoirs of the Tocantins River presented heterogeneous hydrological and climatological characteristics, both spatially and temporally. The flows of these reservoirs were significantly influenced by precipitation, high rates of liquid evaporation and high solar radiation. These combined factors reduce not only the quantity of water, but also the water quality of these reservoirs. In relation to historical trends, analyses showed a reduction in precipitation and flows, accompanied by an increase in air temperature. Given this context, and considering scenarios of constant climate change, with projections of increasingly severe droughts, it is essential to think about the hydrological and ecosystem resilience of the reservoirs of the Tocantins River. This resilience is essential for maintaining biodiversity, food production and electricity generation. In addition, future ecological research should address the resilience of species to these hydroclimatic variations. Another relevant question is how extreme droughts can influence the water quality of these reservoirs. In drought scenarios, water temperatures increase and oxygen concentrations decrease, which can lead to critical levels of anoxia and biota mortality. Therefore, it is essential to investigate and understand these dynamics to ensure the sustainability and functionality of the Tocantins River reservoirs in the face of hydroclimatic changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eDeclarations of interest: none.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author received no funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.G.S., J.L.C.S., and B.D. devised the study. Silva, I.G. designed the project, collected the data and led the writing of the manuscript. J.L.C.S. and B.D. reviewed the data analysis, revised and edited the manuscript. All authors contributed to the writing and proofreading of the paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Postgraduate Program in Ecology of the Federal University of Par\u0026aacute; and the Laboratory of Ecology of Primary Producers (ECOPRO) and the Laboratory of Aquatic Ecology and Tropical Aquaculture of the Federal Rural University of the Amazon for the infrastructure and support. The Foundation for the Improvement of Higher Education Personnel (CNPq) granted a scholarship to Idelina Gomes da Silva. We would also like to thank ANA, INMET and IBGE, as well as the State Center for Meteorology and Water Resources of the State University of Tocantins-Unitins.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003eThe data used in this study are not available in any database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eANA. 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Stochastic Environmental Research and Risk Assessment, \u003cem\u003e26\u003c/em\u003e(7), 993\u0026ndash;1011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00477-012-0583-z\u003c/span\u003e\u003cspan address=\"10.1007/s00477-012-0583-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Evaporation, Precipitation, Solar radiation, Flow rate","lastPublishedDoi":"10.21203/rs.3.rs-4849979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4849979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch on hydroclimatic variations explains the relationships between water masses and global climate factors. Climate change causes changes in river flow regimes and impacts ecosystems, the economy, and society. In this study, we characterized the hydroclimatology of the seven reservoirs of the Tocantins River, along 1,500 km of river and during more than 12 years of sampling, where we analyzed climatic variables such as precipitation, global solar radiation, net evaporation, and air temperature, in addition to hydrological variables such as discharge and net evaporation of the reservoirs. We identified that the discharge of the reservoirs recovered more slowly after the dry period and that these discharges decreased at a rate of 575 m3/s between 1995 and 2023, followed by a negative and significant downward trend. As with discharge, precipitation showed a downward trend. The water deficit caused by prolonged droughts between 2015 and 2017 resulted in lower flows and higher air temperatures. In addition to climatic factors, the socioeconomics of the reservoir areas demand high water withdrawals, associated with population growth and agricultural production. We conclude that the reservoirs have a hydroclimatic gradient with latitudinal variations. These gradients are mainly due to differences in precipitation and flows, but are highly dependent on temperature conditions, solar radiation, evaporation, and water withdrawal. These factors are important and should be discussed in order to mitigate the ecological and socioeconomic impacts on the Tocantins River basin.\u003c/p\u003e","manuscriptTitle":"Hydroclimatic variability and trends suggest improvements in water resource management in the cascade reservoirs of the Tocantins River","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-23 05:26:34","doi":"10.21203/rs.3.rs-4849979/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"625c68f9-09e0-4d29-b131-b245a4c0fffe","owner":[],"postedDate":"August 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T08:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-23 05:26:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4849979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4849979","identity":"rs-4849979","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}