Rainwater isotopic composition variability during extremely rainy years in the São Francisco River headwaters, southeastern Brazil

Rainwater isotopic composition variability during extremely rainy years in the São Francisco River headwaters, southeastern Brazil | 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 Rainwater isotopic composition variability during extremely rainy years in the São Francisco River headwaters, southeastern Brazil Marcela Aragão de Carvalho RAMOS, Rafaela Rodrigues GOMES, Tomás Lyra LUTTERBACH, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8555216/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted 8 You are reading this latest preprint version Abstract The stable isotope composition of oxygen (δ¹⁸O) and hydrogen (δ²H) in precipitation is essential for tracing hydrometeorological processes and understanding their impacts on surface and groundwater. This study investigates the seasonal isotopic variability of precipitation in the headwaters of the São Francisco River, southeastern Brazil, highlighting the influence of local and regional meteorological parameters and large-scale atmospheric phenomena such as the South Atlantic Convergence Zone (SACZ). Stable isotope data of oxygen and hydrogen were obtained from 33 monthly precipitation samples, together with meteorological records (precipitation, temperature, relative humidity). Additional information was derived from ERA‑5 satellite products (total column water vapor and vertical velocity) and backward air‑mass trajectories using the HYSPLIT model, to assess the role of moisture availability and convection in isotopic variability. Comparative analysis between dry and wet seasons revealed seasonal patterns and highlighted the influence of the South Atlantic Convergence Zone (SACZ), which intensified isotopic depletion during prolonged rainfall events. Isotopic seasonality is explained by distinct moisture transport patterns between wet and dry seasons, reflecting the dynamics of large-scale atmospheric circulation. water stable isotopes South Atlantic Convergence Zone (SACZ) ERA-5 satellite HYSPLIT model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 INTRODUCTION The isotopic composition of rain, particularly the variation in the ratios of stable oxygen (δ¹⁸O) and hydrogen (δ²H) isotopes, represents a valuable tool for tracing hydrometeorological processes within the hydrological cycle and their impacts on surface and groundwater (Araguás-Araguás et al. 2000 , Gat & Mook 2001, Rozanski et al. 1993 , Jasechko 2019 ). Its application is possible due to isotopic fractionation, in which isotopes of the same element are unevenly distributed among different phases of water, resulting in a unique isotopic composition capable of indicating its source, processes, and pathway throughout the hydrological cycle (Gat 1996 , Gat & Gonfiantini 1981 ). The isotopic variability observed in rain can be associated with multiple climatic controls, such as different sources and trajectories of vapor (Jeelani et al. 2018 , Wu & Bedaso 2022), convective activity (Moerman et al. 2013 , Lekshmy et al. 2014 ), types of rainfall (Aggarwal et al. 2016 , Zhang et al. 2024 ), and diverse atmospheric systems (Sánchez-Murillo et al. 2019 , Han et al. 2021 , Sun et al. 2022 , Santos et al. 2021 ). As the main global source of moisture, oceanic vapor interacts with local conditions of temperature, humidity, and wind during its transport into the continental interior, modulating rainfall types and reinforcing the variability observed in δ¹⁸O and δ²H (Jasechko, 2019 ). In Brazil, isotopic studies have advanced the understanding of rain variability, mainly in the Amazon (Dalollio 1976, Salati 1979, Gat & Matsui 1991 , Borma et al. 2022 , Ampuero et al. 2020 ) and in the Southeast (Gastmans et al. 2017 , Santos et al. 2019 , 2021 , 2024 a,b). These studies have revisited classical paradigms, such as the effects of amount, temperature, altitude, and latitude, emphasizing the role of rainfall amount in tropical climates, particularly at seasonal scales (Dansgaard 1964 , Rozanski et al. 1993 , Kurita et al. 2009 , Sanchez-Murillo et al. 2016). In the south-central region of the country, at daily and subsidiary scales, isotopic variability reflects more clearly the seasonality of vapor transport, the influence of the South Atlantic Convergence Zone (SACZ), cold fronts, and differences between convective and stratiform rainfall (Dos Santos et al. 2022 , Gastmans et al. 2024 , Santos et al. 2021 , Santos et al. 2024 a,b). These observations highlight the importance of isotopic monitoring networks capable of capturing variability at different temporal and spatial scales, for a better understanding of the governing processes. In line with the Global Network of Isotopes in Precipitation (GNIP) ( https://nucleus.iaea.org/wiser ), coordinated by the International Atomic Energy Agency, the Geological Survey of Brazil (SGB) ( https://www.sgb.gov.br/estudos-hidroquimicos-e-isotopicos ) is implementing a national isotopic monitoring network of rain. The vast territory and operational costs, however, hinder continuous sampling across all regions, leaving key areas under-investigated. The Serra da Canastra, in the south-central part of Minas Gerais, where the waters of the São Francisco River originate, is one of these strategic and environmentally fragile regions that will be incorporated into the national network. The inclusion of this site strengthens Brazil’s integration into the global network and expands the availability of isotopic data for water management and environmental planning. The São Francisco River Basin (BHRSF) encompasses the ecosystems of Cerrado (Brazilian savanna) and Caatinga (Brazilian semiarid), characterized by well-defined dry and wet seasons. This dynamic requires special attention to multiple water uses, such as supply, hydroelectric power, and agriculture (ANA 2021). During the rainy season, the headwaters region is influenced by the South Atlantic Convergence Zone (SACZ), which, in the context of climate change, intensifies extreme events such as floods and landslides, affecting infrastructure and fragile ecosystems. The Serra da Canastra National Park, a federal conservation unit (Brazil 1972), reinforces the environmental relevance of the area and the need for adaptation and mitigation strategies. Although stable isotopes have already been used to investigate interactions between surface and groundwater in the region (Ramos et al. 2025), the atmospheric processes associated with the isotopic composition of rain remain little explored. This study seeks to fill this gap by analyzing the seasonal variability of rain in the headwaters of the São Francisco River and exploring the influence of local and regional meteorological parameters, including the South Atlantic Convergence Zone (SACZ). The central scientific questions include: (i) how meteorological parameters modulate local isotopic composition, and (ii) what role phenomena such as the SACZ play in the observed variability? The results provide support for climate modeling based on stable water isotopes and contribute to water resource management policies, offering data for decision-making in the context of climate change, including the management of different uses during dry periods and the prediction of extremes during wet periods. 2 GENERAL SETTINGS The study area is located in the headwaters of the São Francisco River, within the Serra da Canastra National Park, Minas Gerais, southeastern Brazil. The sampling site is installed at an altitude of 1350 meters (Fig. 1 a–f). The regional climate is subtropical highland with dry winters (Cwb) (Peel et al. 2007 ), with annual rainfall ranging between 1000 and 1500 mm. The mean annual temperature is about 22°C during the warmest months and 18°C during the coldest months (Novais 2011 ). Rainfall is distributed into two well-defined seasons: a dry season from April to September (autumn/winter), which accounts for approximately 15% of the total volume, and a wet season from October to March (spring/summer), which accounts for about 85%. The mean annual relative humidity is 70% (ICMBIO 2023, INMET 2025). The influence of synoptic atmospheric phenomena during summer, combined with higher solar incidence, intensifies the ascent of warm air and cloud formation, favoring convective activity across much of South America and reaching the study area. During this period, several systems contribute to increased rainfall, such as the Low-Level Jets (LLJs), which transport moisture from the Amazon to southeastern Brazil; winds associated with the South Atlantic Subtropical Anticyclone (SASA); and convergence zones, including the Moisture Convergence Zone (MCZ), which persists for three days or more, and the South Atlantic Convergence Zone (SACZ), which lasts at least four days and is associated with large rainfall volumes that generate significant river flows (Fig. 1 g,h). In winter, lower temperatures weaken convection, while the SASA over the central continent inhibits the ascent of moist air, favoring drier conditions over the study area. Even so, cold fronts may interact with available moisture and produce rainfall (Reboita et al. 2010 , Reboita et al. 2012 , Santos et al. 2019 , Dos Santos et al. 2022 ). 3 METHODS 3.1 Sampling and isotopic techniques Monthly rain samples were collected between September 2021 and May 2024 (n = 33) at a single site near the main entrance of the Serra da Canastra National Park (SCN) (20°14’24’’S, 46°27’00’’W), using a passive PALMEX collector (Gröning et al. 2012 ) (Fig. 1 e). In the field, samples were filtered with cellulose acetate syringe filters (0.45 µm pore size) and immediately stored in 20 mL high-density polyethylene (HDPE) bottles with cap and insert. Subsequently, they were kept refrigerated until shipment to the laboratory, where they underwent a second filtration (0.22 µm pore size) and were transferred into 2 mL glass vials with cap and septum. The determination of isotopic ratios (δ²H and δ¹⁸O) was carried out at the Stable Isotopes Center of São Paulo State University (UNESP), using Isotope Ratio Mass Spectrometry with a continuous-flow system (CF-IRMS) on a Thermo Scientific instrument. For each analysis, 0.1 µL of sample was injected into the reactor at 1400°C with a helium flow of 100 mL/min to convert water into H₂ and CO₂ gases. The gases were separated in a 1.0 m chromatographic column at 90°C, where the isotopic ratios R(²H/¹H) and R(¹⁸O/¹⁶O) were determined simultaneously in the same sample (Coplen 2011 ). Results were normalized using three-point anchoring (Paul et al. 2007 ), with three internal standards calibrated against Standard Light Antarctic Precipitation 2 (SLAP2) and Vienna Standard Mean Ocean Water 2 (VSMOW2), achieving analytical precision of ± 0.50‰ (1σ) for δ²H and ± 0.11‰ (1σ) for δ¹⁸O. Deuterium excess ( d -excess), a second-order isotopic parameter, was calculated as d = δ²H − 8·δ¹⁸O (Dansgaard 1964 ) and used as an indicator of the origin and trajectory conditions of atmospheric vapor, supporting the identification of moisture sources and evaporation intensity (Pfahl & Sodemann 2014 ). 3.2 Meteorological Data Meteorological data for the study period (2021–2024), including precipitation volume (P), temperature (T), and relative humidity (RH), were obtained from a weather station installed on site and operated by agents of the Chico Mendes Institute for Biodiversity Conservation (ICMBio). For comparative purposes, long-term averages (1994–2024) for these parameters were calculated using data from the nearest meteorological station, located in Araxá (ARX) (INMET 2025a), approximately 100 km from the study area and at a similar altitude (1018 masl). Monthly Agroclimatological Bulletins from INMET were also considered for the identification of SACZ (INMET 2025b). In most of Brazil, the hydrological year begins with the onset of the wet season in October. In this context, the monthly isotopic dataset presented in this study covers two complete hydrological years (2021–2022, 2022–2023) and one incomplete year (2024). For analytical simplification, September 2021 and May 2024, which correspond to the dry season in the Brazilian hydrological calendar, are exceptionally considered as part of the wet season. The following periods are therefore defined: wet season 2021/2022 (September 2021 to April 2022; n = 8), dry season 2022 (May 2022 to September 2022; n = 5), wet season 2022/2023 (October 2022 to April 2023; n = 7), dry season 2023 (May 2023 to September 2023; n = 5), and wet season 2023/2024 (October 2023 to May 2024; n = 8). Reanalysis data were used to investigate the influence of available moisture and convection on the variability of rainfall isotopic composition over the study area. Monthly averaged reanalysis information of total column vertically-integrated water vapour (TCVWV [kg m⁻²]) and vertical velocity (Vertical velocity [Pa s⁻¹]) were obtained from ERA-5 (data available at: https://cds.climate.copernicus.eu/cdsapp#!/home ) (Copernicus Climate Change Service, 2022). Downloads were performed for each month within the study period, and seasonal averages (wet and dry seasons) were subsequently calculated. Data processing, averaging, and the preparation of regional maps were carried out using Python in Google Colaboratory. To place local climatic variations within a broader climate scenario, data from the Oceanic Niño Index (ONI) were used. The quarterly values of the index, which represent the anomaly of Sea Surface Temperature (SST) in the Niño 3.4 region (5°N–5°S, 120°–170°W), were obtained from the historical database provided by the U.S. National Oceanic and Atmospheric Administration (NOAA) ( https://ggweather.com/enso/oni.htm ). These data enabled the classification of the study period (September 2021 to May 2024) into El Niño phases (index ≥ 0.5°C), La Niña phases (index ≤ − 0.5°C), or Neutral conditions, according to official criteria. 3.3 Calculation of the Estimated Meteoric Water Line To construct the estimated meteoric water line, three unweighted regression methods were evaluated: Ordinary Least Squares Regression (OLSR), Reduced Major Axis (RMA), and Major Axis (MA), which represent classical linear regression approaches. In addition, three precipitation-weighted methods were applied: Precipitation Weighted Least Squares Regression (PWLSR), Precipitation Weighted Reduced Major Axis (PWRMA), and Precipitation Weighted Major Axis (PWMA). For the unweighted methods, slope coefficients were obtained following the formulations presented by Crawford et al. ( 2014 ), while the standard errors of these coefficients were calculated as described in Hughes & Crawford ( 2012 ). For the weighted methods, the slope coefficient of PWLSR and the intercept calculation for all three weighted approaches were determined according to Hughes & Crawford ( 2012 ). The slope coefficients for PWRMA and PWMA were derived from their unweighted counterparts, using an analogous procedure to that employed for PWLSR. All calculations were implemented in a Python script. The introduction of precipitation-weighted methods, as highlighted by Crawford et al. ( 2014 ), substantially reduces bias caused by samples representing small rainfall volumes, which may undergo evaporation during droplet fall. The selection of the most appropriate equation to represent the estimated meteoric water line was based on mean error. 3.4 Backward Trajectories of Air Masses (HYSPLIT) The HYSPLIT model (Hybrid Single-Particle Lagrangian Integrated Trajectory) developed by NOAA (Draxler & Rolph, 2013) was used to calculate the backward trajectories of air masses for each precipitation event collected in situ , based on the global reanalysis dataset (global, 1948–present, 2.5° × 2.5° resolution). Trajectories were computed for the eight days preceding (192 hours) each in situ precipitation event, reflecting the average residence time of water vapor in the atmosphere (7–10 days; Gimeno et al., 2019 ). Simulations initiated at 1500 m above ground level, consistent with the climatological height of the low-level jet, typically located between 1000 and 2000 m (Marengo et al., 2004 ). Trajectories were obtained using R software (R Core Team, 2024 ) with the “splitr” package, which enables automated configuration and execution of the HYSPLIT model (available at: https://www.rdocumentation.org/packages/SplitR/versions/0.3 ). For trajectory clustering, within each seasonal set (wet and dry), the optimal number of clusters was determined using the NbClust package, and clustering was performed with the k-means algorithm. Percentages were calculated based on the relative frequency of trajectories in each group. 3.5 Statistical Analysis In order to select the correlation test to be applied, normality tests were applied to isotopic variables (δ²H, δ¹⁸O, and d-excess) and meteorological variables (precipitation, temperature, and relative humidity) using the Shapiro–Wilk test. Based on the results (p > 0.05 indicating normal distribution; p < 0.05 indicating non-normal distribution), Pearson and Spearman correlation tests were performed, implemented in Python within the Google Colab environment. Comparison between isotopic compositions of the wet and dry seasons was conducted using the non-parametric Mann–Whitney U test (Mann & Whitney, 1947 ; McKnight & Najab, 2010 ). 4 RESULTS 4.1 Atmospheric Variations During the Monitoring Period During the analyzed period (September 2021 to May 2024), mean monthly temperature ranged from 17.1°C (June 2023) to 23.3°C (November 2023), with an overall average of 20.6 ± 1.8°C. Relative humidity varied between 61% (September 2024) and 88.6% (December 2021), with a mean of 80 ± 6.8%. Monthly precipitation ranged from 0 mm (July 2022) to 572.5 mm (January 2022), with an arithmetic monthly average of 188.3 ± 173.3 mm (Table 1 and Table 1 /SM). Seasonal rainfall distribution defines a dry season (May–September), with averages of 20.41 ± 21.58 mm/month (n = 11), and a wet season (October–April), with averages of 274.95 ± 156.85 mm/month (n = 22), accounting for approximately 85% of the annual precipitation. The Shapiro–Wilk test indicated a normal distribution only for temperature, whereas precipitation and relative humidity exhibited non-normal distributions. When comparing SCN data with the long-term average from the ARX meteorological station (1994–2004) (INMET 2025a, Tables 2 and 3/SM), it was observed that the annual mean temperature in SCN was 1°C lower than in ARX (Fig. 2 a), while relative humidity was 10.9% higher (Fig. 2 b). In the two complete years analyzed (2022 and 2023), annual precipitation in SCN (2126.5 mm) exceeded the long-term average of ARX (1553.5 mm), both during the dry season (241 mm vs. 209.7 mm) and the wet season (1810 mm vs. 1344 mm). These results demonstrate that the study period was characterized by rainfall anomalies, particularly during the wet season, associated with the occurrence of the South Atlantic Convergence Zone (SACZ) over the region (Fig. 2 c). In parallel, the analysis of Sea Surface Temperature (SST) anomaly indices (ONI) for the Niño 3.4 region (5°N–5°S, 120°–170°W) indicates the predominance of La Niña conditions during the 2021–2022 season (Moderate, ML) and the 2022–2023 season (Weak, WL). During this period, the indices remained consistently negative, reaching a peak of − 1.06 in MAM 2022. From mid-2023 onward, a rapid transition occurred toward a strong El Niño event (SE), with indices becoming positive and reaching a maximum value of 1.95 in NDJ 2023–2024. The final stage of monitoring, from May 2024, signaled a trend toward a return to neutral conditions. Table 1 – Descriptive statistics of monthly meteorological data in SCN (this study) and ARX (INMET, 2025a). A = annual; D = dry; W = wet. Min. = minimum; Max. = maximum; Avg. = average; s = standard deviation. *Mean total precipitation over two complete years of sampling. SCN P (mm) ARX P (mm) SCN T (°C) ARX T (°C) SCN U.R. (%) ARX U.R. (%) A Min. 0.0 7.0 17.1 18.7 61.0 55.5 Max. 572.5 290.3 23.3 22.9 88.6 79.9 Sum 2126.5* 1553.5 Avg. 188.3 140.2 20.6 21.6 80.0 70.9 s 173.0 103.9 1.8 1.4 6.8 8.3 D Sum 241* 209.68 Avg. 20.41 34.95 18.94 20.24 74.31 64.28 s 21.58 27.64 1.77 1.47 3.93 7.89 R Sum 1810* 1344.2 Avg. 274.95 224.05 21.54 22.53 82.83 75.91 s 156.85 61.77 1.02 0.34 6.08 5.06 4.2 Variation in the isotopic composition of precipitation The isotopic composition of rainfall in SCN ranged from 12.2‰ to − 94.8‰ for δ²H and from − 0.1‰ to − 12.9‰ for δ¹⁸O. A clear seasonal distribution was observed, with the most depleted values recorded in February 2022 (δ²H = − 94.8‰; δ¹⁸O = − 12.9‰), shortly after the occurrence of SACZ, and the most enriched values in September 2021 (δ²H = 12.2‰; δ¹⁸O = − 0.14‰) (Fig. 2 d,e). The d -excess ranged from 22.8‰ to 6.1‰, with a maximum in November 2021 and a minimum in September 2022 (Table 3/SM). Annual arithmetic means were − 24.1‰ for δ²H, − 4.7‰ for δ¹⁸O, and 13.3‰ for d -excess. During the dry season, values averaged − 0.6‰, − 1.6‰, and 12.5‰ for δ²H, δ¹⁸O, and d -excess, respectively, while in the wet season, averages were − 34.3‰, − 6.0‰, and 13.6‰ for δ²H, δ¹⁸O, and d -excess, respectively. Precipitation-weighted means were more depleted than arithmetic means for the annual average (δ²H = − 42.9‰; δ¹⁸O = − 7.1‰), the dry season (δ²H = − 1.3‰; δ¹⁸O = − 1.8‰), and the wet season (δ²H = − 44.3‰; δ¹⁸O = − 7.3‰). However, for d -excess, precipitation-weighted means were more enriched than arithmetic means (Table 2 ). The Shapiro–Wilk test indicated non-normal distributions for δ²H and δ¹⁸O, whereas d -excess, exhibited a normal distribution. The Mann–Whitney test applied to isotopic composition, precipitation, and relative humidity revealed significant differences between dry and wet periods (p < 0.001, p < 0.002, and p < 0.002). Comparison among wet seasons (2021–2022, 2022–2023, 2023–2024) and dry seasons (2022 and 2023) showed that the 2021–2022 wet season presented the most depleted precipitation-weighted means, followed by 2022–2023, coinciding with the occurrence of SACZ. The most enriched precipitation-weighted means were observed during the dry season of 2023 (Fig. 3 ). 4.3 Estimated Meteoric Water Line (EMWL) Since the period covered by this study did not reach the minimum number of collected samples, nor the minimum observation period of four years of precipitation (Hatvani et al., 2023 ), and given that climatic variables during the study differed from the historical average, an estimated meteoric water line (EMWL) was presented for the three years of observation (Fig. 3 a and Table 2 ). The precipitation-weighted methods (PWLSR, PWRMA, and PWMA) provided the best fits, with a mean error of 0.31, yielding slopes between 8.12 and 8.25, while intercepts ranged from 13.07 to 13.87. Estimates obtained using unweighted regressions showed larger errors (greater than 2.5), underestimating both slopes and intercept values. This was likely due to the possible effect of cloud-base evaporation associated with more enriched events. EMWL with smaller error follows the equation δ²H = 8.16 δ¹⁸O + 13.29. Comparison of the results shows that the calculated slope is close to that of the Global Meteoric Water Line (GMWL), whereas the intercepts are higher, because of vapor recirculation during transport. Table 2 – OLSR = Ordinary Least Squares Regression; RMA = Reduced Major Axis; MA = Major Axis; PWLSR = Precipitation Weighted Least Squares Regression; PWRMA = Precipitation Weighted Reduced Major Axis; PWMA = Precipitation Weighted Major Axis. a = slope; b = intercept; se = standard error; rmSSE = root mean-square standard error. Method a b SE_a SE_b rmSSE OLSR 7.80 12.22 0.17 0.99 2.59 RMA 7.86 12.47 0.12 0.70 2.58 MA 7.91 12.72 0.02 0.12 2.57 PWLSR 8.12 13.07 0.13 0.97 0.32 PWRMA 8.16 13.29 0.09 0.69 0.31 PWMA 8.25 13.87 0.00 0.00 0.32 5 DISCUSSION The data collected in SCN indicate statistically significant relationships between isotopic composition of rainfall and local meteorological variables. Spearman’s correlation analysis confirmed the amount effect, with larger rainfall volumes associated with more depleted δ²H (ρ = − 0.634; p < 0.001) and δ¹⁸O (ρ = − 0.638; p < 0.001) values (Rozanski et al., 1993 ). Rainfall also correlated positively with mean temperature (ρ = 0.414; p = 0.017) and relative humidity (ρ = 0.780; p < 0.001), indicating that intense rainfall events occurred under warmer and more humid atmospheric conditions, resulting in more depleted isotopic compositions. On the other hand, relative humidity correlated negatively with isotopic composition (δ²H: ρ = − 0.722; δ¹⁸O: ρ = − 0.710; p < 0.001), indicating that isotopic depletion is associated with more humid environments, likely due to reduced evaporation influence during rainfall formation. The d -excess, in turn, did not show significant correlations with any of the variables, reflecting its limited seasonal variability (Fig. 2 ). Wet seasons in SCN consistently recorded rainfall volumes above the long-term average at the ARX station. In November–December 2022 and January 2023, this was associated with SACZ occurrence (INMET, 2025b). Similarly, in the wet seasons of 2021–2022 and 2023–2024, the total rainfall exceeded the long-term average, despite the absence of SACZ. In February 2022, after three months of SACZ occurrence, rainfall exhibited the most depleted isotopic compositions (δ²H = − 94.8‰; δ¹⁸O = − 12.9‰). This underscores the role of SACZ in producing prolonged high-volume rainfall, which directly increases river discharge and may contribute to aquifer recharge (Gastmans et al., 2021 ). However, SACZ alone do not fully explain the isotopic seasonality, which results from multiple atmospheric processes operating at the daily scale. Seasonal differences in mean δ 2 H, δ¹⁸O and d -excess values (Table 1 ) reflect large-scale atmospheric phenomena such as moisture transport and convective activity. Figures 4 and 5 show consistent interannual moisture transport patterns with clear distinction between wet and dry seasons. During wet seasons, there is a wide mixture of trajectories with three predominant pathways: (i) from the Atlantic Ocean through Northeastern and Central-Eastern Brazil, as a consequence of wind circulation driven by the South Atlantic Subtropical High (SASH); (ii) across the Amazon rainforest; and (iii) from Southern Brazil via the Atlantic coastline (Figs. 4 a–c). In dry seasons, trajectories originated southwest of SCN, within the continent, possibly as a result of polar air mass incursions and cold fronts typical of this time of year (Figs. 5 a–b). The mixture of trajectories yield higher total column vertically integrated water vapor (TCVWV) during the wet season, ranging from 32 to 40 kg m⁻² over SCN across the years (Fig. 4 d–f), whereas in the dry season values were lower, < 24 kg m⁻² (Fig. 5 c,d). The same occurs for vertical velocity, with negative values (indicating convection) predominating (–0.4 to − 0.8 Pa·s⁻¹) during the wet season (Fig. 4 g–i), and positive values (0 to 0.4 Pa·s⁻¹) during the dry season (Fig. 5 e,f). These regional conditions of enhanced moisture availability and convection during the wet season allow successive condensation processes to promote a greater loss of heavy isotopes, contributing to the depleted values observed. Conversely, lower moisture availability and weaker convective processes during the dry season result in fewer precipitation events, with less removal of heavy isotopes, leading to more enriched values. These findings are consistent with previous studies in south-central Brazil (Dos Santos et al. 2022 ; Gastmans et al. 2017 ; Santos et al. 2019 , 2021 , 2024 a), reinforcing the role of seasonality in controlling the isotopic composition of precipitation and, consequently, the isotopic signal of surface and groundwater, as discussed in Ramos et al. (2025). 6 CONCLUSION In the headwaters of the São Francisco River (SCN), isotopic depletion in precipitation was strongly associated with the amount effect and with warmer, more humid atmospheric conditions. Rainy seasons consistently recorded precipitation volumes above the long-term average at the ARX station, partly due to the occurrence of the South Atlantic Convergence Zone (SACZ), which intensified isotopic depletion during prolonged rainfall events. Enhanced moisture transport from the Amazon, cold fronts and and stronger convective activity during the wet season further contributed to depleted isotopic values, whereas reduced humidity and weaker convection in the dry season resulted in enriched values. These findings confirm the role of seasonality in shaping isotopic composition and its hydrological implications, including periods of increased river discharge. Isotopic studies in headwater regions are fundamental for identifying water sources and the atmospheric processes that govern rainfall formation. Establishing a robust local meteoric water line (LMWL) based on longer time series is essential to characterize isotopic behavior under conditions closer to the long-term mean. Continued isotopic monitoring will strengthen climate modeling efforts and provide critical data for water resource management, particularly in the context of climate change, where reliable information is needed to guide decisions on water use during dry periods and to anticipate extreme events during wet seasons. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the International Atomic Energy Agency (Coordinate Research Project, F30059 code); scholarship for the first author was provided by the Coordination for Improvement of Higher Education Personnel (CAPES) of Brazil's Ministry of Education (2020 − 2022) (under process 88887.542221/2020-00); and by the National Council for Scientific and Technological Development (CNPq) of Brazil's Ministry of Science, Technology and Innovation (2022–2024) (under process 140784/2022-5). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.R., V.S., R. G., T. L., V. C. and Z. S. and D. G. supervised and validated data. The first draft of the manuscript was written by M. R. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors would like to thank Mr. Jairo Pereira, agent of the Chico Mendes Institute for Biodiversity Conservation (ICMBio) of Brazil, for collecting the rainwater samples. Data Availability I have shared the link to my data at the Attachment File step “Stable isotopes of H and O in rainfall at the headwaters of the São Francisco River, Serra da Canastra, Minas Gerais, Brazil” [https://hdl.handle.net/11449/318009] References Aggarwal PK, Romatschke U, Araguas-Araguas L, Belachew D, Longstaffe FJ, Berg P, Schumacher C, Funk A (2016) Proportions of convective and stratiform precipitation revealed in water isotope ratios. Nat Geosci 9:624–629. https://doi.org/10.1038/ngeo2739 Ampuero A et al (2020) The forest effects on the isotopic composition of rainfall in the northwestern Amazon Basin. J Geophys Research: Atmos v 125, n. 4, p. e2019JD031445, 2020. ANA – Agência Nacional de Águas (2021) Atlas Irrigação: uso da água na agricultura irrigada, 2nd ed. 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1","display":"","copyAsset":false,"role":"figure","size":199748,"visible":true,"origin":"","legend":"\u003cp\u003eA. Study area (SF1 = São Francisco 1 subbasin; SCN = Serra da Canastra; MG = Minas Gerais state). B. Casca D’anta waterfalls. C. Serra da Canastra National Park. D. Brazilian Savanna. E. Rain collector. F. Landmark of São Francisco River spring. G-H. São Francisco River in the study area during dry season (G) and rainy season (H), showing rapid changes in the river flow associated with summer precipitation, climatic condition associated with synoptic atmospheric phenomena (for details, refer to Figure 11 in Reboita et al. 2010 and/or Figure 1 in Dos Santos et al. 2022).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/c64ec13cc4e4692e4e7a74df.jpeg"},{"id":100353793,"identity":"b7826dce-85d9-45a7-b478-a18238644b44","added_by":"auto","created_at":"2026-01-16 05:04:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":274082,"visible":true,"origin":"","legend":"\u003cp\u003eMeteorological data from this study (SCN; 2021–2024) compared with the historical series from the nearest meteorological station (ARX; 1994–2024) (INMET 2025a). Arrows in panel C indicate the occurrence of SACZ (INMET 2025b). A. Temperature (°C). B. Relative humidity (%). C. Precipitation (mm). D. δ¹⁸O (‰). E. Deuterium excess (‰). Dark grey shadow indicates the wet season and light grey, the dry season.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/2d8bd6c08a3dfbed7b89c94b.jpeg"},{"id":100373378,"identity":"410d37fe-c180-432f-9e51-06b5c0d6ef9a","added_by":"auto","created_at":"2026-01-16 08:14:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88446,"visible":true,"origin":"","legend":"\u003cp\u003eA. Monthly isotopic signatures of δ²H \u003cem\u003eversus \u003c/em\u003eδ¹⁸O at SCN (Serra da Canastra station). GMWL = Global Meteoric Water Line (Craig, 1961), EMWL = Estimated Meteoric Water Line. Boxplots of isotopic composition: B. Annual; C. δ¹⁸O; D. δ²H; E. d-excess.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/67200548a19a3df64fd41840.png"},{"id":100373612,"identity":"ed9fba6c-4c70-4dde-86be-9dba9de77638","added_by":"auto","created_at":"2026-01-16 08:15:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":448397,"visible":true,"origin":"","legend":"\u003cp\u003eHumidity transportation in the rainy season. Hysplit trajectories in the rainy season (A, B, C); TCWVV = Total column vertically-integrated water (D, E, F); Vert. vel. = vertical velocity (G, H, I).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/73483fbca110f9f1c1ef54b7.jpeg"},{"id":100353809,"identity":"81cb40bb-2eda-400e-bba0-d1615a1e5a25","added_by":"auto","created_at":"2026-01-16 05:04:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":293996,"visible":true,"origin":"","legend":"\u003cp\u003eHumidity transportation in the dry season. Hysplit trajectories in the rainy season (A, B); TCWVV = Total column vertically-integrated water (C, D); Vert. vel. = vertical velocity (E, F).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/3a64ea0310ef7bd631c73891.jpeg"},{"id":108437561,"identity":"e5a70ad5-f512-4500-87a2-165c5faf70d1","added_by":"auto","created_at":"2026-05-04 15:59:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1632640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/53b2c6b4-5435-4f5f-99ba-8a2c1a110573.pdf"},{"id":100373692,"identity":"359b0498-bb43-4874-8321-fceec4761a41","added_by":"auto","created_at":"2026-01-16 08:17:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18701,"visible":true,"origin":"","legend":"","description":"","filename":"TabelasSM2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8555216/v1/eedc0ec697ad8784f5ee20e1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rainwater isotopic composition variability during extremely rainy years in the São Francisco River headwaters, southeastern Brazil","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eThe isotopic composition of rain, particularly the variation in the ratios of stable oxygen (δ\u0026sup1;⁸O) and hydrogen (δ\u0026sup2;H) isotopes, represents a valuable tool for tracing hydrometeorological processes within the hydrological cycle and their impacts on surface and groundwater (Aragu\u0026aacute;s-Aragu\u0026aacute;s et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Gat \u0026amp; Mook 2001, Rozanski et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Jasechko \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Its application is possible due to isotopic fractionation, in which isotopes of the same element are unevenly distributed among different phases of water, resulting in a unique isotopic composition capable of indicating its source, processes, and pathway throughout the hydrological cycle (Gat \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Gat \u0026amp; Gonfiantini \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe isotopic variability observed in rain can be associated with multiple climatic controls, such as different sources and trajectories of vapor (Jeelani et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Wu \u0026amp; Bedaso 2022), convective activity (Moerman et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Lekshmy et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), types of rainfall (Aggarwal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and diverse atmospheric systems (S\u0026aacute;nchez-Murillo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Han et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Sun et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Santos et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As the main global source of moisture, oceanic vapor interacts with local conditions of temperature, humidity, and wind during its transport into the continental interior, modulating rainfall types and reinforcing the variability observed in δ\u0026sup1;⁸O and δ\u0026sup2;H (Jasechko, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, isotopic studies have advanced the understanding of rain variability, mainly in the Amazon (Dalollio 1976, Salati 1979, Gat \u0026amp; Matsui \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, Borma et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ampuero et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and in the Southeast (Gastmans et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Santos et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea,b). These studies have revisited classical paradigms, such as the effects of amount, temperature, altitude, and latitude, emphasizing the role of rainfall amount in tropical climates, particularly at seasonal scales (Dansgaard \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1964\u003c/span\u003e, Rozanski et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Kurita et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Sanchez-Murillo et al. 2016). In the south-central region of the country, at daily and subsidiary scales, isotopic variability reflects more clearly the seasonality of vapor transport, the influence of the South Atlantic Convergence Zone (SACZ), cold fronts, and differences between convective and stratiform rainfall (Dos Santos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Gastmans et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Santos et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Santos et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea,b). These observations highlight the importance of isotopic monitoring networks capable of capturing variability at different temporal and spatial scales, for a better understanding of the governing processes.\u003c/p\u003e \u003cp\u003eIn line with the Global Network of Isotopes in Precipitation (GNIP) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nucleus.iaea.org/wiser\u003c/span\u003e\u003cspan address=\"https://nucleus.iaea.org/wiser\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), coordinated by the International Atomic Energy Agency, the Geological Survey of Brazil (SGB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sgb.gov.br/estudos-hidroquimicos-e-isotopicos\u003c/span\u003e\u003cspan address=\"https://www.sgb.gov.br/estudos-hidroquimicos-e-isotopicos\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is implementing a national isotopic monitoring network of rain. The vast territory and operational costs, however, hinder continuous sampling across all regions, leaving key areas under-investigated. The Serra da Canastra, in the south-central part of Minas Gerais, where the waters of the S\u0026atilde;o Francisco River originate, is one of these strategic and environmentally fragile regions that will be incorporated into the national network. The inclusion of this site strengthens Brazil\u0026rsquo;s integration into the global network and expands the availability of isotopic data for water management and environmental planning.\u003c/p\u003e \u003cp\u003eThe S\u0026atilde;o Francisco River Basin (BHRSF) encompasses the ecosystems of Cerrado (Brazilian savanna) and Caatinga (Brazilian semiarid), characterized by well-defined dry and wet seasons. This dynamic requires special attention to multiple water uses, such as supply, hydroelectric power, and agriculture (ANA 2021). During the rainy season, the headwaters region is influenced by the South Atlantic Convergence Zone (SACZ), which, in the context of climate change, intensifies extreme events such as floods and landslides, affecting infrastructure and fragile ecosystems. The Serra da Canastra National Park, a federal conservation unit (Brazil 1972), reinforces the environmental relevance of the area and the need for adaptation and mitigation strategies.\u003c/p\u003e \u003cp\u003eAlthough stable isotopes have already been used to investigate interactions between surface and groundwater in the region (Ramos et al. 2025), the atmospheric processes associated with the isotopic composition of rain remain little explored. This study seeks to fill this gap by analyzing the seasonal variability of rain in the headwaters of the S\u0026atilde;o Francisco River and exploring the influence of local and regional meteorological parameters, including the South Atlantic Convergence Zone (SACZ). The central scientific questions include: (i) how meteorological parameters modulate local isotopic composition, and (ii) what role phenomena such as the SACZ play in the observed variability? The results provide support for climate modeling based on stable water isotopes and contribute to water resource management policies, offering data for decision-making in the context of climate change, including the management of different uses during dry periods and the prediction of extremes during wet periods.\u003c/p\u003e"},{"header":"2 GENERAL SETTINGS","content":"\u003cp\u003eThe study area is located in the headwaters of the S\u0026atilde;o Francisco River, within the Serra da Canastra National Park, Minas Gerais, southeastern Brazil. The sampling site is installed at an altitude of 1350 meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;f). The regional climate is subtropical highland with dry winters (Cwb) (Peel et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), with annual rainfall ranging between 1000 and 1500 mm. The mean annual temperature is about 22\u0026deg;C during the warmest months and 18\u0026deg;C during the coldest months (Novais \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Rainfall is distributed into two well-defined seasons: a dry season from April to September (autumn/winter), which accounts for approximately 15% of the total volume, and a wet season from October to March (spring/summer), which accounts for about 85%. The mean annual relative humidity is 70% (ICMBIO 2023, INMET 2025).\u003c/p\u003e \u003cp\u003eThe influence of synoptic atmospheric phenomena during summer, combined with higher solar incidence, intensifies the ascent of warm air and cloud formation, favoring convective activity across much of South America and reaching the study area. During this period, several systems contribute to increased rainfall, such as the Low-Level Jets (LLJs), which transport moisture from the Amazon to southeastern Brazil; winds associated with the South Atlantic Subtropical Anticyclone (SASA); and convergence zones, including the Moisture Convergence Zone (MCZ), which persists for three days or more, and the South Atlantic Convergence Zone (SACZ), which lasts at least four days and is associated with large rainfall volumes that generate significant river flows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg,h). In winter, lower temperatures weaken convection, while the SASA over the central continent inhibits the ascent of moist air, favoring drier conditions over the study area. Even so, cold fronts may interact with available moisture and produce rainfall (Reboita et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Reboita et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Santos et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Dos Santos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sampling and isotopic techniques\u003c/h2\u003e \u003cp\u003eMonthly rain samples were collected between September 2021 and May 2024 (n\u0026thinsp;=\u0026thinsp;33) at a single site near the main entrance of the Serra da Canastra National Park (SCN) (20\u0026deg;14\u0026rsquo;24\u0026rsquo;\u0026rsquo;S, 46\u0026deg;27\u0026rsquo;00\u0026rsquo;\u0026rsquo;W), using a passive PALMEX collector (Gr\u0026ouml;ning et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). In the field, samples were filtered with cellulose acetate syringe filters (0.45 \u0026micro;m pore size) and immediately stored in 20 mL high-density polyethylene (HDPE) bottles with cap and insert. Subsequently, they were kept refrigerated until shipment to the laboratory, where they underwent a second filtration (0.22 \u0026micro;m pore size) and were transferred into 2 mL glass vials with cap and septum.\u003c/p\u003e \u003cp\u003eThe determination of isotopic ratios (δ\u0026sup2;H and δ\u0026sup1;⁸O) was carried out at the Stable Isotopes Center of S\u0026atilde;o Paulo State University (UNESP), using Isotope Ratio Mass Spectrometry with a continuous-flow system (CF-IRMS) on a Thermo Scientific instrument. For each analysis, 0.1 \u0026micro;L of sample was injected into the reactor at 1400\u0026deg;C with a helium flow of 100 mL/min to convert water into H₂ and CO₂ gases. The gases were separated in a 1.0 m chromatographic column at 90\u0026deg;C, where the isotopic ratios R(\u0026sup2;H/\u0026sup1;H) and R(\u0026sup1;⁸O/\u0026sup1;⁶O) were determined simultaneously in the same sample (Coplen \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Results were normalized using three-point anchoring (Paul et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), with three internal standards calibrated against Standard Light Antarctic Precipitation 2 (SLAP2) and Vienna Standard Mean Ocean Water 2 (VSMOW2), achieving analytical precision of \u0026plusmn;\u0026thinsp;0.50\u0026permil; (1σ) for δ\u0026sup2;H and \u0026plusmn;\u0026thinsp;0.11\u0026permil; (1σ) for δ\u0026sup1;⁸O.\u003c/p\u003e \u003cp\u003eDeuterium excess (\u003cem\u003ed\u003c/em\u003e-excess), a second-order isotopic parameter, was calculated as d\u0026thinsp;=\u0026thinsp;δ\u0026sup2;H\u0026thinsp;\u0026minus;\u0026thinsp;8\u0026middot;δ\u0026sup1;⁸O (Dansgaard \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) and used as an indicator of the origin and trajectory conditions of atmospheric vapor, supporting the identification of moisture sources and evaporation intensity (Pfahl \u0026amp; Sodemann \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Meteorological Data\u003c/h2\u003e \u003cp\u003eMeteorological data for the study period (2021\u0026ndash;2024), including precipitation volume (P), temperature (T), and relative humidity (RH), were obtained from a weather station installed on site and operated by agents of the Chico Mendes Institute for Biodiversity Conservation (ICMBio). For comparative purposes, long-term averages (1994\u0026ndash;2024) for these parameters were calculated using data from the nearest meteorological station, located in Arax\u0026aacute; (ARX) (INMET 2025a), approximately 100 km from the study area and at a similar altitude (1018 masl). Monthly Agroclimatological Bulletins from INMET were also considered for the identification of SACZ (INMET 2025b).\u003c/p\u003e \u003cp\u003eIn most of Brazil, the hydrological year begins with the onset of the wet season in October. In this context, the monthly isotopic dataset presented in this study covers two complete hydrological years (2021\u0026ndash;2022, 2022\u0026ndash;2023) and one incomplete year (2024). For analytical simplification, September 2021 and May 2024, which correspond to the dry season in the Brazilian hydrological calendar, are exceptionally considered as part of the wet season. The following periods are therefore defined: wet season 2021/2022 (September 2021 to April 2022; n\u0026thinsp;=\u0026thinsp;8), dry season 2022 (May 2022 to September 2022; n\u0026thinsp;=\u0026thinsp;5), wet season 2022/2023 (October 2022 to April 2023; n\u0026thinsp;=\u0026thinsp;7), dry season 2023 (May 2023 to September 2023; n\u0026thinsp;=\u0026thinsp;5), and wet season 2023/2024 (October 2023 to May 2024; n\u0026thinsp;=\u0026thinsp;8).\u003c/p\u003e \u003cp\u003eReanalysis data were used to investigate the influence of available moisture and convection on the variability of rainfall isotopic composition over the study area. Monthly averaged reanalysis information of total column vertically-integrated water vapour (TCVWV [kg m⁻\u0026sup2;]) and vertical velocity (Vertical velocity [Pa s⁻\u0026sup1;]) were obtained from ERA-5 (data available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/home\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Copernicus Climate Change Service, 2022). Downloads were performed for each month within the study period, and seasonal averages (wet and dry seasons) were subsequently calculated. Data processing, averaging, and the preparation of regional maps were carried out using Python in Google Colaboratory.\u003c/p\u003e \u003cp\u003eTo place local climatic variations within a broader climate scenario, data from the Oceanic Ni\u0026ntilde;o Index (ONI) were used. The quarterly values of the index, which represent the anomaly of Sea Surface Temperature (SST) in the Ni\u0026ntilde;o 3.4 region (5\u0026deg;N\u0026ndash;5\u0026deg;S, 120\u0026deg;\u0026ndash;170\u0026deg;W), were obtained from the historical database provided by the U.S. National Oceanic and Atmospheric Administration (NOAA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggweather.com/enso/oni.htm\u003c/span\u003e\u003cspan address=\"https://ggweather.com/enso/oni.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These data enabled the classification of the study period (September 2021 to May 2024) into El Ni\u0026ntilde;o phases (index\u0026thinsp;\u0026ge;\u0026thinsp;0.5\u0026deg;C), La Ni\u0026ntilde;a phases (index \u0026le; \u0026minus;\u0026thinsp;0.5\u0026deg;C), or Neutral conditions, according to official criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Calculation of the Estimated Meteoric Water Line\u003c/h2\u003e \u003cp\u003eTo construct the estimated meteoric water line, three unweighted regression methods were evaluated: Ordinary Least Squares Regression (OLSR), Reduced Major Axis (RMA), and Major Axis (MA), which represent classical linear regression approaches. In addition, three precipitation-weighted methods were applied: Precipitation Weighted Least Squares Regression (PWLSR), Precipitation Weighted Reduced Major Axis (PWRMA), and Precipitation Weighted Major Axis (PWMA).\u003c/p\u003e \u003cp\u003eFor the unweighted methods, slope coefficients were obtained following the formulations presented by Crawford et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while the standard errors of these coefficients were calculated as described in Hughes \u0026amp; Crawford (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For the weighted methods, the slope coefficient of PWLSR and the intercept calculation for all three weighted approaches were determined according to Hughes \u0026amp; Crawford (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The slope coefficients for PWRMA and PWMA were derived from their unweighted counterparts, using an analogous procedure to that employed for PWLSR.\u003c/p\u003e \u003cp\u003eAll calculations were implemented in a Python script. The introduction of precipitation-weighted methods, as highlighted by Crawford et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), substantially reduces bias caused by samples representing small rainfall volumes, which may undergo evaporation during droplet fall. The selection of the most appropriate equation to represent the estimated meteoric water line was based on mean error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Backward Trajectories of Air Masses (HYSPLIT)\u003c/h2\u003e \u003cp\u003eThe HYSPLIT model (Hybrid Single-Particle Lagrangian Integrated Trajectory) developed by NOAA (Draxler \u0026amp; Rolph, 2013) was used to calculate the backward trajectories of air masses for each precipitation event collected \u003cem\u003ein situ\u003c/em\u003e, based on the global reanalysis dataset (global, 1948\u0026ndash;present, 2.5\u0026deg; \u0026times; 2.5\u0026deg; resolution). Trajectories were computed for the eight days preceding (192 hours) each \u003cem\u003ein situ\u003c/em\u003e precipitation event, reflecting the average residence time of water vapor in the atmosphere (7\u0026ndash;10 days; Gimeno et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimulations initiated at 1500 m above ground level, consistent with the climatological height of the low-level jet, typically located between 1000 and 2000 m (Marengo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Trajectories were obtained using R software (R Core Team, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with the \u0026ldquo;splitr\u0026rdquo; package, which enables automated configuration and execution of the HYSPLIT model (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdocumentation.org/packages/SplitR/versions/0.3\u003c/span\u003e\u003cspan address=\"https://www.rdocumentation.org/packages/SplitR/versions/0.3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For trajectory clustering, within each seasonal set (wet and dry), the optimal number of clusters was determined using the NbClust package, and clustering was performed with the k-means algorithm. Percentages were calculated based on the relative frequency of trajectories in each group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eIn order to select the correlation test to be applied, normality tests were applied to isotopic variables (δ\u0026sup2;H, δ\u0026sup1;⁸O, and d-excess) and meteorological variables (precipitation, temperature, and relative humidity) using the Shapiro\u0026ndash;Wilk test. Based on the results (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating normal distribution; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating non-normal distribution), Pearson and Spearman correlation tests were performed, implemented in Python within the Google Colab environment. Comparison between isotopic compositions of the wet and dry seasons was conducted using the non-parametric Mann\u0026ndash;Whitney U test (Mann \u0026amp; Whitney, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1947\u003c/span\u003e; McKnight \u0026amp; Najab, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Atmospheric Variations During the Monitoring Period\u003c/h2\u003e \u003cp\u003eDuring the analyzed period (September 2021 to May 2024), mean monthly temperature ranged from 17.1\u0026deg;C (June 2023) to 23.3\u0026deg;C (November 2023), with an overall average of 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u0026deg;C. Relative humidity varied between 61% (September 2024) and 88.6% (December 2021), with a mean of 80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8%. Monthly precipitation ranged from 0 mm (July 2022) to 572.5 mm (January 2022), with an arithmetic monthly average of 188.3\u0026thinsp;\u0026plusmn;\u0026thinsp;173.3 mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e/SM). Seasonal rainfall distribution defines a dry season (May\u0026ndash;September), with averages of 20.41\u0026thinsp;\u0026plusmn;\u0026thinsp;21.58 mm/month (n\u0026thinsp;=\u0026thinsp;11), and a wet season (October\u0026ndash;April), with averages of 274.95\u0026thinsp;\u0026plusmn;\u0026thinsp;156.85 mm/month (n\u0026thinsp;=\u0026thinsp;22), accounting for approximately 85% of the annual precipitation.\u003c/p\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test indicated a normal distribution only for temperature, whereas precipitation and relative humidity exhibited non-normal distributions. When comparing SCN data with the long-term average from the ARX meteorological station (1994\u0026ndash;2004) (INMET 2025a, Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and 3/SM), it was observed that the annual mean temperature in SCN was 1\u0026deg;C lower than in ARX (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while relative humidity was 10.9% higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In the two complete years analyzed (2022 and 2023), annual precipitation in SCN (2126.5 mm) exceeded the long-term average of ARX (1553.5 mm), both during the dry season (241 mm vs. 209.7 mm) and the wet season (1810 mm vs. 1344 mm). These results demonstrate that the study period was characterized by rainfall anomalies, particularly during the wet season, associated with the occurrence of the South Atlantic Convergence Zone (SACZ) over the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eIn parallel, the analysis of Sea Surface Temperature (SST) anomaly indices (ONI) for the Ni\u0026ntilde;o 3.4 region (5\u0026deg;N\u0026ndash;5\u0026deg;S, 120\u0026deg;\u0026ndash;170\u0026deg;W) indicates the predominance of La Ni\u0026ntilde;a conditions during the 2021\u0026ndash;2022 season (Moderate, ML) and the 2022\u0026ndash;2023 season (Weak, WL). During this period, the indices remained consistently negative, reaching a peak of \u0026minus;\u0026thinsp;1.06 in MAM 2022. From mid-2023 onward, a rapid transition occurred toward a strong El Ni\u0026ntilde;o event (SE), with indices becoming positive and reaching a maximum value of 1.95 in NDJ 2023\u0026ndash;2024. The final stage of monitoring, from May 2024, signaled a trend toward a return to neutral conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Descriptive statistics of monthly meteorological data in SCN (this study) and ARX (INMET, 2025a). A\u0026thinsp;=\u0026thinsp;annual; D\u0026thinsp;=\u0026thinsp;dry; W\u0026thinsp;=\u0026thinsp;wet. Min. = minimum; Max. = maximum; Avg. = average; s\u0026thinsp;=\u0026thinsp;standard deviation. *Mean total precipitation over two complete years of sampling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eARX\u003c/p\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003cp\u003eT\u003c/p\u003e \u003cp\u003e(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eARX\u003c/p\u003e \u003cp\u003eT\u003c/p\u003e \u003cp\u003e(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSCN\u003c/p\u003e \u003cp\u003eU.R. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eARX\u003c/p\u003e \u003cp\u003eU.R. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMin.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMax.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2126.5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1553.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAvg.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e209.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAvg.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1810*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1344.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAvg.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variation in the isotopic composition of precipitation\u003c/h2\u003e \u003cp\u003eThe isotopic composition of rainfall in SCN ranged from 12.2\u0026permil; to \u0026minus;\u0026thinsp;94.8\u0026permil; for δ\u0026sup2;H and from \u0026minus;\u0026thinsp;0.1\u0026permil; to \u0026minus;\u0026thinsp;12.9\u0026permil; for δ\u0026sup1;⁸O. A clear seasonal distribution was observed, with the most depleted values recorded in February 2022 (δ\u0026sup2;H = \u0026minus;\u0026thinsp;94.8\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;12.9\u0026permil;), shortly after the occurrence of SACZ, and the most enriched values in September 2021 (δ\u0026sup2;H\u0026thinsp;=\u0026thinsp;12.2\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;0.14\u0026permil;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed,e). The \u003cem\u003ed\u003c/em\u003e-excess ranged from 22.8\u0026permil; to 6.1\u0026permil;, with a maximum in November 2021 and a minimum in September 2022 (Table\u0026nbsp;3/SM).\u003c/p\u003e \u003cp\u003eAnnual arithmetic means were \u0026minus;\u0026thinsp;24.1\u0026permil; for δ\u0026sup2;H, \u0026minus;\u0026thinsp;4.7\u0026permil; for δ\u0026sup1;⁸O, and 13.3\u0026permil; for \u003cem\u003ed\u003c/em\u003e-excess. During the dry season, values averaged \u0026minus;\u0026thinsp;0.6\u0026permil;, \u0026minus;\u0026thinsp;1.6\u0026permil;, and 12.5\u0026permil; for δ\u0026sup2;H, δ\u0026sup1;⁸O, and \u003cem\u003ed\u003c/em\u003e-excess, respectively, while in the wet season, averages were \u0026minus;\u0026thinsp;34.3\u0026permil;, \u0026minus;\u0026thinsp;6.0\u0026permil;, and 13.6\u0026permil; for δ\u0026sup2;H, δ\u0026sup1;⁸O, and \u003cem\u003ed\u003c/em\u003e-excess, respectively. Precipitation-weighted means were more depleted than arithmetic means for the annual average (δ\u0026sup2;H = \u0026minus;\u0026thinsp;42.9\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;7.1\u0026permil;), the dry season (δ\u0026sup2;H = \u0026minus;\u0026thinsp;1.3\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;1.8\u0026permil;), and the wet season (δ\u0026sup2;H = \u0026minus;\u0026thinsp;44.3\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;7.3\u0026permil;). However, for \u003cem\u003ed\u003c/em\u003e-excess, precipitation-weighted means were more enriched than arithmetic means (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test indicated non-normal distributions for δ\u0026sup2;H and δ\u0026sup1;⁸O, whereas \u003cem\u003ed\u003c/em\u003e-excess, exhibited a normal distribution. The Mann\u0026ndash;Whitney test applied to isotopic composition, precipitation, and relative humidity revealed significant differences between dry and wet periods (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.002, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.002). Comparison among wet seasons (2021\u0026ndash;2022, 2022\u0026ndash;2023, 2023\u0026ndash;2024) and dry seasons (2022 and 2023) showed that the 2021\u0026ndash;2022 wet season presented the most depleted precipitation-weighted means, followed by 2022\u0026ndash;2023, coinciding with the occurrence of SACZ. The most enriched precipitation-weighted means were observed during the dry season of 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Estimated Meteoric Water Line (EMWL)\u003c/h2\u003e \u003cp\u003eSince the period covered by this study did not reach the minimum number of collected samples, nor the minimum observation period of four years of precipitation (Hatvani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and given that climatic variables during the study differed from the historical average, an estimated meteoric water line (EMWL) was presented for the three years of observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe precipitation-weighted methods (PWLSR, PWRMA, and PWMA) provided the best fits, with a mean error of 0.31, yielding slopes between 8.12 and 8.25, while intercepts ranged from 13.07 to 13.87. Estimates obtained using unweighted regressions showed larger errors (greater than 2.5), underestimating both slopes and intercept values. This was likely due to the possible effect of cloud-base evaporation associated with more enriched events. EMWL with smaller error follows the equation δ\u0026sup2;H\u0026thinsp;=\u0026thinsp;8.16 δ\u0026sup1;⁸O\u0026thinsp;+\u0026thinsp;13.29. Comparison of the results shows that the calculated slope is close to that of the Global Meteoric Water Line (GMWL), whereas the intercepts are higher, because of vapor recirculation during transport.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; OLSR\u0026thinsp;=\u0026thinsp;Ordinary Least Squares Regression; RMA\u0026thinsp;=\u0026thinsp;Reduced Major Axis; MA\u0026thinsp;=\u0026thinsp;Major Axis; PWLSR\u0026thinsp;=\u0026thinsp;Precipitation Weighted Least Squares Regression; PWRMA\u0026thinsp;=\u0026thinsp;Precipitation Weighted Reduced Major Axis; PWMA\u0026thinsp;=\u0026thinsp;Precipitation Weighted Major Axis. a\u0026thinsp;=\u0026thinsp;slope; b\u0026thinsp;=\u0026thinsp;intercept; se\u0026thinsp;=\u0026thinsp;standard error; rmSSE\u0026thinsp;=\u0026thinsp;root mean-square standard error.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE_a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE_b\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ermSSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOLSR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePWLSR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePWRMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePWMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\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 \u003c/div\u003e"},{"header":"5 DISCUSSION","content":"\u003cp\u003eThe data collected in SCN indicate statistically significant relationships between isotopic composition of rainfall and local meteorological variables. Spearman\u0026rsquo;s correlation analysis confirmed the amount effect, with larger rainfall volumes associated with more depleted δ\u0026sup2;H (ρ = \u0026minus;\u0026thinsp;0.634; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and δ\u0026sup1;⁸O (ρ = \u0026minus;\u0026thinsp;0.638; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) values (Rozanski et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRainfall also correlated positively with mean temperature (ρ\u0026thinsp;=\u0026thinsp;0.414; p\u0026thinsp;=\u0026thinsp;0.017) and relative humidity (ρ\u0026thinsp;=\u0026thinsp;0.780; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that intense rainfall events occurred under warmer and more humid atmospheric conditions, resulting in more depleted isotopic compositions. On the other hand, relative humidity correlated negatively with isotopic composition (δ\u0026sup2;H: ρ = \u0026minus;\u0026thinsp;0.722; δ\u0026sup1;⁸O: ρ = \u0026minus;\u0026thinsp;0.710; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that isotopic depletion is associated with more humid environments, likely due to reduced evaporation influence during rainfall formation. The \u003cem\u003ed\u003c/em\u003e-excess, in turn, did not show significant correlations with any of the variables, reflecting its limited seasonal variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWet seasons in SCN consistently recorded rainfall volumes above the long-term average at the ARX station. In November\u0026ndash;December 2022 and January 2023, this was associated with SACZ occurrence (INMET, 2025b). Similarly, in the wet seasons of 2021\u0026ndash;2022 and 2023\u0026ndash;2024, the total rainfall exceeded the long-term average, despite the absence of SACZ. In February 2022, after three months of SACZ occurrence, rainfall exhibited the most depleted isotopic compositions (δ\u0026sup2;H = \u0026minus;\u0026thinsp;94.8\u0026permil;; δ\u0026sup1;⁸O = \u0026minus;\u0026thinsp;12.9\u0026permil;). This underscores the role of SACZ in producing prolonged high-volume rainfall, which directly increases river discharge and may contribute to aquifer recharge (Gastmans et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, SACZ alone do not fully explain the isotopic seasonality, which results from multiple atmospheric processes operating at the daily scale.\u003c/p\u003e \u003cp\u003eSeasonal differences in mean δ\u003csup\u003e2\u003c/sup\u003eH, δ\u0026sup1;⁸O and \u003cem\u003ed\u003c/em\u003e-excess values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) reflect large-scale atmospheric phenomena such as moisture transport and convective activity. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show consistent interannual moisture transport patterns with clear distinction between wet and dry seasons. During wet seasons, there is a wide mixture of trajectories with three predominant pathways: (i) from the Atlantic Ocean through Northeastern and Central-Eastern Brazil, as a consequence of wind circulation driven by the South Atlantic Subtropical High (SASH); (ii) across the Amazon rainforest; and (iii) from Southern Brazil via the Atlantic coastline (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c). In dry seasons, trajectories originated southwest of SCN, within the continent, possibly as a result of polar air mass incursions and cold fronts typical of this time of year (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;b).\u003c/p\u003e \u003cp\u003eThe mixture of trajectories yield higher total column vertically integrated water vapor (TCVWV) during the wet season, ranging from 32 to 40 kg m⁻\u0026sup2; over SCN across the years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u0026ndash;f), whereas in the dry season values were lower, \u0026lt; 24 kg m⁻\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d). The same occurs for vertical velocity, with negative values (indicating convection) predominating (\u0026ndash;0.4 to \u0026minus;\u0026thinsp;0.8 Pa\u0026middot;s⁻\u0026sup1;) during the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg\u0026ndash;i), and positive values (0 to 0.4 Pa\u0026middot;s⁻\u0026sup1;) during the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee,f).\u003c/p\u003e \u003cp\u003eThese regional conditions of enhanced moisture availability and convection during the wet season allow successive condensation processes to promote a greater loss of heavy isotopes, contributing to the depleted values observed. Conversely, lower moisture availability and weaker convective processes during the dry season result in fewer precipitation events, with less removal of heavy isotopes, leading to more enriched values. These findings are consistent with previous studies in south-central Brazil (Dos Santos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gastmans et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea), reinforcing the role of seasonality in controlling the isotopic composition of precipitation and, consequently, the isotopic signal of surface and groundwater, as discussed in Ramos et al. (2025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6 CONCLUSION","content":"\u003cp\u003eIn the headwaters of the S\u0026atilde;o Francisco River (SCN), isotopic depletion in precipitation was strongly associated with the amount effect and with warmer, more humid atmospheric conditions. Rainy seasons consistently recorded precipitation volumes above the long-term average at the ARX station, partly due to the occurrence of the South Atlantic Convergence Zone (SACZ), which intensified isotopic depletion during prolonged rainfall events. Enhanced moisture transport from the Amazon, cold fronts and and stronger convective activity during the wet season further contributed to depleted isotopic values, whereas reduced humidity and weaker convection in the dry season resulted in enriched values. These findings confirm the role of seasonality in shaping isotopic composition and its hydrological implications, including periods of increased river discharge.\u003c/p\u003e \u003cp\u003eIsotopic studies in headwater regions are fundamental for identifying water sources and the atmospheric processes that govern rainfall formation. Establishing a robust local meteoric water line (LMWL) based on longer time series is essential to characterize isotopic behavior under conditions closer to the long-term mean. Continued isotopic monitoring will strengthen climate modeling efforts and provide critical data for water resource management, particularly in the context of climate change, where reliable information is needed to guide decisions on water use during dry periods and to anticipate extreme events during wet seasons.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the International Atomic Energy Agency (Coordinate Research Project, F30059 code); scholarship for the first author was provided by the Coordination for Improvement of Higher Education Personnel (CAPES) of Brazil's Ministry of Education (2020\u0026thinsp;\u0026minus;\u0026thinsp;2022) (under process 88887.542221/2020-00); and by the National Council for Scientific and Technological Development (CNPq) of Brazil's Ministry of Science, Technology and Innovation (2022\u0026ndash;2024) (under process 140784/2022-5).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.R., V.S., R. G., T. L., V. C. and Z. S. and D. G. supervised and validated data. The first draft of the manuscript was written by M. R. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Mr. Jairo Pereira, agent of the Chico Mendes Institute for Biodiversity Conservation (ICMBio) of Brazil, for collecting the rainwater samples.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eI have shared the link to my data at the Attachment File step \u0026ldquo;Stable isotopes of H and O in rainfall at the headwaters of the S\u0026atilde;o Francisco River, Serra da Canastra, Minas Gerais, Brazil\u0026rdquo; [https://hdl.handle.net/11449/318009]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal PK, Romatschke U, Araguas-Araguas L, Belachew D, Longstaffe FJ, Berg P, Schumacher C, Funk A (2016) Proportions of convective and stratiform precipitation revealed in water isotope ratios. 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Theor Appl Climatol 155:1093\u0026ndash;1102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-023-04681-0\u003c/span\u003e\u003cspan address=\"10.1007/s00704-023-04681-0\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"water stable isotopes, South Atlantic Convergence Zone (SACZ), ERA-5 satellite, HYSPLIT model","lastPublishedDoi":"10.21203/rs.3.rs-8555216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8555216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe stable isotope composition of oxygen (δ\u0026sup1;⁸O) and hydrogen (δ\u0026sup2;H) in precipitation is essential for tracing hydrometeorological processes and understanding their impacts on surface and groundwater. This study investigates the seasonal isotopic variability of precipitation in the headwaters of the S\u0026atilde;o Francisco River, southeastern Brazil, highlighting the influence of local and regional meteorological parameters and large-scale atmospheric phenomena such as the South Atlantic Convergence Zone (SACZ). Stable isotope data of oxygen and hydrogen were obtained from 33 monthly precipitation samples, together with meteorological records (precipitation, temperature, relative humidity). Additional information was derived from ERA‑5 satellite products (total column water vapor and vertical velocity) and backward air‑mass trajectories using the HYSPLIT model, to assess the role of moisture availability and convection in isotopic variability. Comparative analysis between dry and wet seasons revealed seasonal patterns and highlighted the influence of the South Atlantic Convergence Zone (SACZ), which intensified isotopic depletion during prolonged rainfall events. Isotopic seasonality is explained by distinct moisture transport patterns between wet and dry seasons, reflecting the dynamics of large-scale atmospheric circulation.\u003c/p\u003e","manuscriptTitle":"Rainwater isotopic composition variability during extremely rainy years in the São Francisco River headwaters, southeastern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 05:04:47","doi":"10.21203/rs.3.rs-8555216/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T06:58:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T04:04:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230940643207998734601228373749483497920","date":"2026-02-11T19:44:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220635564992124624639691663468814986787","date":"2026-02-04T23:57:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T16:47:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T02:14:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T02:14:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-01-08T22:03:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ad447922-f0c3-41eb-bb58-31d92ef39b1c","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:58:59+00:00","versionOfRecord":{"articleIdentity":"rs-8555216","link":"https://doi.org/10.1007/s00704-026-06271-2","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2026-04-30 15:57:09","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2026-01-16 05:04:47","video":"","vorDoi":"10.1007/s00704-026-06271-2","vorDoiUrl":"https://doi.org/10.1007/s00704-026-06271-2","workflowStages":[]},"version":"v1","identity":"rs-8555216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8555216","identity":"rs-8555216","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}