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Article

Relationship Between the Water Vapor Transport from the Amazon Basin and the Rainfall Regime over a Watershed on Brazil’s Southern Border

by
Maicon Moraes Santiago
1,*,
André Becker Nunes
2,
Flavio Tiago Couto
3,
Danielle de Almeida Bressiani
1,
Rose Ane Pereira de Freitas
2 and
Iulli Pitone Cardoso
1
1
Center for Technological Development, Federal University of Pelotas, Pelotas 96010-610, RS, Brazil
2
School of Meteorology, Federal University of Pelotas, Capão do Leão 96160-000, RS, Brazil
3
Center for Sci-Tech Research in Earth SysTem and Energy (CREATE), Institute for Advanced Research and Training—IIFA, Department of Physics, School of Science and Technology—ECT, University of Évora, Romão Ramalho Street, 59, 7000-671 Évora, Portugal
*
Author to whom correspondence should be addressed.
Earth 2025, 6(1), 13; https://doi.org/10.3390/earth6010013
Submission received: 29 December 2024 / Revised: 15 February 2025 / Accepted: 18 February 2025 / Published: 24 February 2025
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Abstract

:
The climate of the south of Brazil is characterized by northern winds in a hegemonic way for the transfer of moisture. Thus, the goal here is to verify the impact of the meridional water vapor transport on the rainfall of the Mirim–São Gonçalo Watershed (MSGW), located in the extreme south of Brazil and essential for regional development. The study is based on the precipitation data from MSGW weather stations and ERA5 reanalysis data for the period 1981–2020, which allowed the analysis of the interactions between different climatological variables. The water vapor transport was analyzed using the vertically integrated water vapor flux (VIVF). Coefficients were obtained according to the VIVF values in two locations placed between the Amazon basin and southern Brazil, namely in Bolivia and Paraguay. The results show that the MSGW is directly impacted by moisture transport from the north in all seasons, and this transport is most significant at the 850 hPa level. In addition, the moisture and rainfall in the MSGW are also influenced by changes in the magnitude and direction of this flow, with an increase in transport in periods of El Niño, especially during spring. Therefore, the study brings insights into how changes in tropical South American climate, through a cascading effect, may affect the Mirim–São Gonçalo Watershed development in the middle latitudes from changes in the meridional water vapor transport, highlighting the importance of studying the tropical and extratropical interactions in South America for the MSGW management and sustainable development.

1. Introduction

The meridional water vapor transport in the lower levels of the troposphere plays an important role in the hydrological cycle in higher latitudes, being associated with heavy rainfall events, often due to orographic effects. Also known as atmospheric rivers (ARs), this transport is well recognized for producing torrential rains and floods worldwide. For instance, these long and narrow bands of enhanced water vapor content can produce heavy orographic precipitation events when striking the coastal mountains of the Western United States [1,2,3,4]. The same impact was documented on Madeira Island, in the North Atlantic Ocean [5,6]. The authors showed that orographic precipitation over the island can be intensified by large-scale patterns, namely by atmospheric rivers that provided the necessary moisture conditions for the intensification of orographic precipitation. However, this horizontal transport of tropical moisture can also reach the European continent, influencing the precipitating systems, the occurrence of high streamflow, and flooding events [7,8,9]. In Asia, particularly in China, the ARs have the same role in producing torrential rains, flooding and landfalls [10,11,12].
Under a climate change context, several regions worldwide are becoming vulnerable to extreme hydrological phenomena directly linked to extreme weather events. Recently, Veloso et al. [13] showed how different moisture sources and their interaction with local topography could be affecting arid regions like the Atacama and Namib deserts. In the Atacama, for instance, Veloso [14] showed a case study of extreme rainfall caused by an “atmospheric river-like” structure along the southeast Pacific, also highlighting the role of the water vapor transport in generating extreme precipitation through an orographic lifting effect. The impact of atmospheric rivers is also recognized for producing significant rainfall in south-central Chile [15]. From a surface-atmosphere interaction perspective, McGowan et al. [16] draw attention to atmospheric rivers producing rainfall-on-snow events in Australia and accelerating the loss of snow cover. All these studies are examples of how important the understanding of tropical and extratropical interactions is, since this tropical water vapor can be essential for rainfall events and regional development in higher latitudes.
In South America (SA), tropical moisture is transported toward midlatitudes, also influencing the precipitation regime. For instance, the south of Brazil experiences low-level flow predominantly from the north. This northern component transports moisture from the tropical region of South America to the southern region [17,18], with the contribution of the South American Low-Level Jet (SALLJ) in this process [19,20]. The SALLJ may promote extreme rainfall events over southern Brazil [21,22], affecting the state’s water regime. The southern coast of Brazil is a cyclogenetic region [23,24,25], and the transport of heat and moisture from the Amazon, as well as from the South Atlantic Subtropical High (SASH), is fundamental for the warm advection of extratropical cyclogenesis to the leeward of the Andes [26,27,28,29]. The impacts of these northern flow contributions can result in Mesoscale Convective Complexes (MCCs) in southeastern South America, contributing to the occurrence of rainfall and cyclogenesis activity in the region [25,30,31,32].
The rainfall in the southern region of Brazil is approximately uniform throughout the year, with relatively higher volumes in winter. In this region, the Mirim–São Gonçalo Watershed (MSGW) is located in the south/southeast of Rio Grande do Sul (RS) State. The maximum rainfall in the southeast of RS occurs during July, August, and September, with these months being the rainiest, and varying between 1250 mm to 2000 mm of annual rainfall on average in the MSGW [17,33].
The MSGW is an interborder basin, with part of its territory in Brazil and another part in Uruguay, composed of 20 Brazilian municipalities. Its biome is the Pampa, and its water resources are applied to irrigation and human consumption [34]. The MSGW was chosen for this study because of its role in regional development, namely by representing 15% of rice production in RS [35], which can be directly influenced by hydrological extremes. In this basin, on average (2018–2020), it produced between 850,000 and 2.6 million tons/year of paddy rice [36], contributing to USD 7 billion exported by agribusiness in RS from January to June 2022 [37].
The basin region is influenced by climatic anomalies of sea surface temperature, such as ENSO (El Niño–Southern Oscillation). Rao and Hada [38], and other subsequent studies, demonstrated that during El Niño (La Niña), rainfall above (below) normal is expected, especially in spring. Furthermore, some studies, such as [21], indicate that the SALLJ becomes more intense and frequent during the El Niño phase.
The goal of this study is to analyze the influence of moisture transport from the Amazon basin on rainfall in the Brazilian part of the Mirim–São Gonçalo Watershed. In addition, its relationship with ENSO (El Niño–Southern Oscillation) is also explored, since a direct relationship between the northern flow and the warm phase of ENSO is expected for Southern Brazil [22,39,40,41,42].
The study is structured as follows: Section 2 presents the materials and methods, followed by the results and discussion in Section 3. Section 4 summarizes the findings and offers conclusions.

2. Materials and Methods

2.1. Study Area

The Mirim–São Gonçalo Watershed (MSGW) is a transboundary hydrographic basin, where 29,250 km2 (47%) of its area is in Brazil, specifically in the State of Rio Grande do Sul (Figure 1), and 33,000 km2 (53%) is situated in Uruguay, totaling an area of 62,250 km2 [34]. Mirim Lagoon connects to Patos Lagoon to the north via the São Gonçalo channel. To represent the transport of moisture from the Amazon, the cities Mariscal José Félix Estigarribia, Paraguay (named here as Mariscal), and Santa Cruz de La Sierra, Bolivia (named here as Santa Cruz), were chosen as representative points for the Eulerian analysis due to their location along the climatological trajectory of the transport of moisture to southern South America through the Low-Level Jet (LLJ) [22,43,44]. Furthermore, the coast of the state is recognized as a cyclogenetic region [23,24,25], where heat transport from the north makes up the warm sector of extratropical cyclones.

2.2. Obtaining the Data

Precipitation data from the meteorological stations of the National Water and Basic Sanitation Agency (ANA) and the National Institute of Meteorology (INMET) [45] (Table 1) were used, as well as the grid point ERA5 reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for the analysis of meteorological variables and their interactions. The monthly meteorological variables were analyzed through the ERA5 reanalysis in the period 1981–2020, with a temporal resolution of 1 h, a horizontal resolution of 31 km, and 137 vertical levels [46]. These data are available via the Copernicus platform (https://cds.climate.copernicus.eu/datasets, accessed on 17 February 2025). The reference points were set at 17.5° S, 63° W for Santa Cruz and 22° S, 60° W for Mariscal.

2.3. Rainfall Data Handling

Firstly, it was necessary to check the quality of the observed precipitation data and then whether it could be represented by ERA5 reanalysis. Lavers et al. [47], based on precipitation data from 5637 weather stations, recommended the monthly ERA5 data to analyze the rainfall regime in extratropical regions. The methodology used here for handling the precipitation data, both from ERA5 and observed in rain gauges, is presented in Cardoso et al. [48]. Basically, in the treatment of data use, we applied the nearest-neighbor method to select the coordinates representative of the meteorological and rainfall stations to set up the ERA5 data. This selection was made using the Python 3.7 language via the NumPy library [49]. The locations of the stations are shown in Table 1, together with their representative coordinates in the ERA5 data (columns “Lon ERA5” and “Lat ERA5”).
To fill gaps in the observed data from the stations, multiple linear regression was used [50]. Among the stations surveyed found in the MSGW, series with more than 10% missing data in the period 1981–2020 were discarded. The presence of gaps is mainly due to equipment errors [51].
After this, the rainfall climatological normals for each pluviometric or meteorological station (PMS) in the MSGW were calculated for 1981–2020. To verify the representativeness of the spatially averaged climatological normal of the entire basin, the Pearson coefficient between the average of each PMS and the average of the MSGW was evaluated.

2.4. Data Analysis

2.4.1. VIVF and Rainfall: Averages

To analyze the influence of moisture transport from the Amazon basin to the MSGW, the vertically integrated water vapor flow (VIVF) was selected, as it accounts for the total amount of water vapor present in the troposphere. The values of the VIVF and rainfall in the MSGW were compared. For this, the VIVF coefficients were generated. Such coefficients were obtained from the average of monthly VIVF between the cities of Mariscal and Santa Cruz, as mentioned in Section 2.1, for 1981–2020.
As for the monthly rainfall data, the rainfall monthly averages (1981–2020 climatological normal) obtained from the PMSs (observed rainfall) were calculated using a simple average between the monthly rainfall values (Table 1). After this procedure, it was verified whether the average rainfall of the MSGW could represent the average rainfall behavior of the PMSs. Pearson coefficients between the PMSs and the mean MSGW were obtained to evaluate the mean MSGW representativeness [48].
Subsequently, it was verified whether the ERA5 rainfall data (PrecERA5) could satisfactorily describe the average behavior of the MSGW. For this, the 1981–2020 rainfall monthly normal of the ERA5 data was generated and compared with the normal of the monthly observed rainfall data. In Table 1, the coordinates in the columns ‘Lon ERA5’ and ‘Lat ERA5’ were considered as spatially representative points of the basin, and an average was made between these points. With the acquisition of PrecERA5 data, a comparison with the spatial average of the observed rainfall (PrecObs) was conducted using the Pearson coefficient. The representativeness between the data (PrecERA5 and PrecObs) is reached when the Pearson coefficient presents a high-magnitude value, following Mukaka’s [52] classification, found, for example, in Debnath et al. [53].
To determine the relationship between the El Niño–Southern Oscillation (ENSO) and the VIVF, the Pearson’s correlation coefficient was also calculated. ENSO information was obtained from the National Oceanic and Atmospheric Administration (NOAA), according to the Oceanic Niño Index (ONI) to state whether there was a warm phase (El Niño) or cold phase (La Niña).

2.4.2. Statistical Analysis

After obtaining the correlations between ENSO, VIVF, and rainfall, statistical significance at 0.05 was verified. Trend analysis of the variables was carried out according to the following steps:

Sequence Test (Run Test)

Since the Mann–Kendall test requires the data to be independent and random [54], the sequence test (Run Test), applied through the Python language via the Statsmodels package [55], was performed.
Within the scope of data independence, because Pymannkendall (a Python package for performing the Mann–Kendall test [56]) has a 5% significance level as a default [56], this procedure was not necessary, as this percentage of significance promotes trend removal in the time series.

Mann–Kendall Test

Significance was verified using the Mann–Kendall test [57,58], which compared the p-value provided by the test with 0.05 significance level. The calculation of the Mann–Kendall test was performed by the Pymannkendall package in Python, using the so-called “Original Mann–Kendall”, because it does not consider serial correlation or seasonal influences, and the “Seasonal Mann–Kendall” for autocorrelated series and treatment of possible seasonalities in the data [54,56]. The test was performed on the VIVF and rainfall from ERA5 (PrecERA5) data, and observed rainfall data (PrecObs). The Mann–Kendall test was also chosen because it is appropriate to analyze climate change in climatological series and can identify the direction of the trend and its starting point [54,59,60].

2.4.3. VIVF and Rainfall Anomalies

The investigation of the relationship between the vertically integrated water vapor flow (VIVF) and rainfall anomalies was performed using percentiles. Values within the 95th percentile and 10th percentile were considered positive and negative anomalies, respectively, since the percentile technique is usually applied to define positive extreme rainfall events [61]. According to the Bureau of Meteorology (Australia), the 10th percentile can be used to classify an area as “affected by drought” (negative anomaly) [62]. It should be noted that the Bureau of Meteorology methodology was adapted here, using only the value indicated by the 10th percentile method as a criterion for identifying an anomalous month, regardless of the number of months.
The analysis was performed by season (summer (DJF), autumn (MAM), winter (JJA), and spring (SON)). To investigate the causes of the VIVF and rainfall anomalies, the u (zonal) and v (meridional) components of the wind at 850 hPa were plotted for these months, as well as the u and v anomalies. Here, u and v anomalies were calculated as the difference between the monthly data and their respective 1981–2020 climatological normal.

3. Results and Discussion

3.1. VIVF and Low Levels Flow

Figure 2 shows the VIVF coming from the Amazon basin, directly reaching the MSGW in all seasons. This dynamic found in Figure 2 is well known, as shown, for example, in Ruiz-Vásquez et al. [63] and Martinez et al. [64], where the flow of moisture was observed, acquiring a southern component when undergoing the topographic blockage of the Andes Mountains.
The VIVF values found in the Amazon in Figure 2A,C align with those from Rocha [65], which presented 209 kg/m·s in summer (DJF) and 213 kg/m·s in winter (JJA) for 1985–2005. The mean VIVF values shown in Figure 2B–D over the MSGW also correspond to Rocha et al. [66], who, employing the 1980–2005 ERA-Interim dataset, found values between 50 and 100 kg/m·s in autumn and spring, and a range of 100 to 150 kg/m·s in winter.
The 850 hPa flow is from the north/northwest in all seasons along the MSGW (Figure 3B,E,H,K). At the level of 925 hPa, it is northeast in all seasons except winter, in which the winds have a north/northwest component (Figure 3I). At 700 hPa, the flow is westward in all seasons (Figure 3A,D,G,J). Through this wind dynamics, in general, relative to the MSGW, we can observe the contribution of moisture from the Amazon: indirectly at 700 hPa (Figure 3A,D,G,J), directly at 850 hPa (Figure 3B,E,H,K), and without direct contribution at 925 hPa (Figure 3C,I,J,L). In winter, the 925 hPa flow comes from the Atlantic (Figure 3C,F,L) and/or from the continent (the southeast region of Brazil) (Figure 3I).
By means of the analysis of the streamline field at the different pressure levels, a low-pressure center was observed over Bolivia (Figure 3A–C,J–L) in spring and summer. Due to its position in summer, we can call this system the Chaco Low (CL) [67,68]. This configuration agrees with that found in Wong et al. [69], who discuss that the CL manifests itself in summer, and Ferreira and Reboita [70], who indicate the CL’s influence on the weather conditions of southern Brazil and north-central Argentina. At 700 hPa, the influence of this low-pressure center decreased in winter, which allowed a more northerly wind component and an increase in the VIVF over the MSGW (Figure 3G–I). In the other seasons, there was no change in the magnitude of the VIVF (Figure 3A–F,J–L).
At 850 hPa, the MSGW is most influenced by the South Atlantic Subtropical Anticyclone (SASA) [26,71] in winter (Figure 3H) and by the Northwestern Argentinean Low (NAL) [72] during autumn, promoting diffluence over the basin (Figure 3E). At 925 hPa, the influence of the pressure systems observed at 850 hPa is raised (Figure 3F,I). This scenario is acceptable because the SASA is more intense in winter and contributes to the transport of moisture toward southern Brazil [71,73].
Considering the above and Figure 3B,E,H,K, the 850 hPa level is the most significant in representing the northern flow from the Amazon as it reaches the MSGW directly across the seasons.

3.2. Relationship Between Variables: Statistical Results

Table 2 shows a weak Pearson’s correlation between the VIVF and the ONI over time when considering the entire period (1981–2020). When analyzed by seasons (Table 3), the correlation is weak in summer and winter, insignificant in autumn, but the relationship is moderate in spring. This may indicate that ENSO moderately influences the VIVF in the MSGW in spring, agreeing with the literature, for example, Kousky et al. [74], Rao and Hada [38], and Grimm et al. [75], who indicate higher rainfall in Rio Grande do Sul (RS) State under El Niño conditions. Studies such as Lemes et al. [76] show that during ENSO events, there is a change in the magnitude of the VIVF over the Amazon in summer. Also, in the south of Brazil during East La Niña events [77], there is a weakening of the LLJ and a southern component for the VIVF during spring [78], which is consistent with the findings in Table 3.
As observed in Table 2, the VIVF has a moderate relationship with rainfall, as a whole. This indicates that external sources of moisture from the north can influence rainfall in the basin, as discussed below. A strong relationship, with a correlation coefficient of 0.93, was found between the normal of ERA5 rainfall (PrecERA5) and the observed rainfall (PrecObs). This means that we can use the ERA5 data to represent the behavior of the climatological monthly rainfall in the MSGW. Figure 4 corroborates this statement. According to the t-test, all correlations presented in Table 2 and Table 3 were statistically significant at the 0.05 level.
The linear trends (line in red) are shown in Figure A1, Figure A2 and Figure A3. The angular coefficient shown in Figure A1 indicates a small positive trend of the VIVF. Figure A2 shows a negative trend for PrecERA5, and Figure A3 shows a slight decrease over time in PrecObs. Table 4 shows the significance and randomness test of the trends, where we can see the p-value of PrecERA5 obtained a value above 0.05 (significance level). The VIVF and PrecObs values gave a p-value below 0.05, indicating non-random values. In this way, Seasonal Mann–Kendall was applied to the VIVF and PrecObs data. However, as shown in Table 4, the p-value results of the Mann–Kendall tests were above 0.05, thus rendering them non-significant. Therefore, the linear regression seen in Figure A1, Figure A2 and Figure A3 indicates the variability of each variable.

3.3. Rainfall in the MSGW and Moisture Transport (VIVF)

Figure 5 shows the monthly climatological normal (1981–2020) of each PMS within the MSGW. All PMS agree among themselves regarding seasonality, with all quarterly cumulative averages up to 325 mm for summer, 275 mm for autumn, 400 mm for winter, and 325 mm during spring. Thus, the average rainfall in the MSGW is reasonably homogeneous throughout the year (as can be seen in Table 5), with variation over the months ranging from 96.78 mm to 141.78 mm. Table 6 shows that the average of the normal of the PMSs can represent the basin due to the high (following Mukaka [52]) Pearson coefficients, which are statistically significant at 0.05 level.
Regarding the climatological behavior of moisture transport, the VIVF coefficients agree with Figure 2, showing an increase in the autumn months, culminating in a peak in June, reaching values around 293.16 kg/m·s (Figure 6). It is also seen in Figure 6 that July, August, September, and October presented similar VIVF means: 262.61 kg/m·s, 254.73 kg/m·s, 262.55 kg/m·s, and 262.08 kg/m·s, respectively. Not all months have the same climatological behavior between the VIVF and PrecERA5, given that different atmospheric systems can be observed in these regions (MSGW, Mariscal, and Santa Cruz). The weather in Rio Grande do Sul State is greatly influenced by frontal systems, especially in the coldest semester [17]. The greatest differences between the PrecERA5 and VIVF behavior were observed from January to April. As observed in Figure 2, the most intense normal flows were observed during winter and spring, leading to a greater agreement between the variables. During other seasons, it is assumed that with less intense flows, precipitation is more influenced by other external sources, such as the Atlantic Ocean, or even by the local humidity of the basin.
The VIVF and precipitation anomalies were identified from the seasonal values in Table 7. It was found that in 87.5% of the months with positive rainfall anomaly (21 out of 24 months), the VIVF was above its climatological normal. On the other hand, in 80% of the positive VIVF anomaly months (20 out of 25 months), above-normal rainfall was observed in the MSGW. Regarding the negative anomalies of the VIVF and rainfall, in 90.2% of the negative rainfall anomalies months (46 out of 51 months) there was the VIVF below the climatological normal. As for 72.55% of the VIVF negative anomalous months (37 out of 51 months) below average rainfall were observed. The number of anomalous months per decade is shown in Table 8, where a greater agreement between the negative anomalies is observed.
The occurrence of months with double anomaly, that is when there was an anomaly of rainfall and the VIVF in the same month, was observed. Thus, 16.67% of the positive anomalous months occurred for both rainfall and VIVF. Regarding the negative anomalies, 14.61% were observed in both the VIVF and rainfall. The highest values of rainfall and the VIVF occurred in months with double positive anomalies, and the lowest values of the rainfall and the VIVF were observed in months with double negative anomalies.
In addition, we present a brief analysis of the 850 hPa ‘u’ and ‘v’ wind components during the double negative anomaly case of January 2012. Previous analysis indicates January 2012 as a representative month for double negative anomalies. The summer climatological normal (Figure 7) shows low-level wind from the west over Bolivia and from the north over much of the continent, especially in the direction of the two reference cities, that is, on the way to transport of moisture from the Amazon basin to the MSGW. During January 2012, there was a negative anomaly of both VIVF and rainfall, an eastern anomaly was observed over Bolivia and over MSGW, in addition to a southern anomaly over Paraguay and Bolivia (Figure 8), that is, a behavior opposite to that expected for summer. In the analysis of other anomalous cases, we can observe in most seasons (summer, autumn, and spring) there is a change of direction in most of the continent, with the wind gaining a zonal component, justifying the presence of anomalous easterly winds over Bolivia and the reduction of magnitude of northern flow during these double negative anomalous months at these seasons. Thus, the rainfall decrease in the MSGW is a consequence of the reduction in the contribution of northern flow from the Amazon, and consequent decrease of LLJ role, due to the presence of the southern anomaly in the northern region of Paraguay and south-central Bolivia, as pointed out in the study by Tedeschi et al. [78].
The drought of the summer of 2019/2020 in Pelotas, RS, is an example of this configuration, in which there was a change in low-level flow in January 2020, with the northern component not being observed in this month’s average [79]. Without this transport of moisture from the Amazon, precipitation systems in southern Brazil, such as Mesoscale Convective Complexes, can be inhibited, since the development of convective systems is facilitated by this northern flux [30]. In most of these months (8 out of 13 months), there was a manifestation of ENSO, with La Niña present in six cases (Table 9). In contrast, this is a differential concerning the double positive anomalous months because in these La Niña was not observed. Thus, this work agrees with Tedeschi et al. [78] about the occurrence of anomalous southerly winds and decreased rainfall in southeastern South America during La Niña events.
In cases of double positive anomalous months, a strengthening of the meridional flow (mainly the northern one) along the continent was observed, which contributed to increased rainfall in the MSGW by fostering convective systems driven by the moisture brought by the northerlies. The El Niño phenomenon was observed in 4 out of 7 months with double positive anomalies. As found in Montini et al. [22], during El Niño events an increase of northern flow is observed (mainly via LLJ). Also, the East El Niño contributes to the increase in rainfall in the southern region of South America [80].
In summary, this section shows that the VIVF coming from the Amazon basin reaches the MSGW in all seasons, with the flow of moisture acquiring a southern component when undergoing the topographic blockage of the Andes Mountains. In addition, the 850 hPa flow, originating from the north/northwest in all seasons along the MSGW, is considered the most significant level to represent this vapor transport when compared with 925 hPa and 700 hPa levels.
When analyzing the relationship between the variables VIVF, ENSO, and rainfall (PrecERA5 and PrecObs), the results showed a strong relationship between PrecERA5 and PrecObs, indicating that the ERA5 data can be used to represent the behavior of the climatological monthly rainfall of the MSGW, as shown in Cardoso et al. [48]. Moreover, the VIVF had a moderate relationship with rainfall, whereas ENSO had a moderate influence on the VIVF in the MSGW during the spring season.
Concerning rainfall in the MSGW and VIVF, the average rainfall in the MSGW is reasonably homogeneous throughout the year. However, our findings show that in the months with positive rainfall anomaly, the VIVF was above its climatological normal. On the other hand, in the case of months with negative rainfall anomalies, the VIVF was below the climatological normal. Finally, the results confirmed that the rainfall regime in the MSGW is sensitive to changes in the magnitude and direction of moisture transport from the South America tropical region, which are directly influenced by climate oscillations.

4. Conclusions

The study presents the analysis of the impact of the meridional water vapor transport on the rainfall of the Mirim–São Gonçalo Watershed (MSGW), located in the extreme south of the southern region of Brazil. The MSGW belongs to an essentially agricultural region and, therefore, it is dependent on the water regime and sensitive to its variability.
The analysis used ERA5 reanalysis monthly data and showed that the Amazon transport of moisture, here identified by the vertically integrated water vapor flow (VIVF) over two reference cities, contributes directly to the rainfall in the MSGW. Among the pressure levels studied in this work, 700 hPa, 850 hPa, and 925 hPa, the level of 850 hPa was the most relevant due to the consistent occurrence of northern winds over the basin in all seasons at this level. It was also possible to conclude that the VIVF anomalies were related to the MSGW rainfall and vice versa. The study indicated that in 87.5% of the positive anomalous months of rainfall, there was the VIVF above the climatological normal, and in 80% of positive anomalous months of the VIVF, there was rainfall above the climatological normal in the MSGW. It was also found that in 90.2% of the negative extremes of rainfall, there was the VIVF below the climatological normal. In the negative extremes of the VIVF, 72.55% of these months presented rainfall below the normal.
This study shows that changes in the magnitude and direction of moisture transport from the South America tropical region promote changes in the rainfall regime in the MSGW. The months with negative anomaly for both the VIVF and precipitation (double anomaly) occurred when anomalous southerly winds in the region around the Bolivia–Paraguay border, and anomalous easterly winds over Bolivia were observed. The presence of these anomalous winds promotes the inhibition of the Low-Level Jet, which contributes to the reduction of moisture and rainfall in the MSGW. During the double positive anomalies, there is an intensification of northern flow, which promotes more favorable weather conditions for rainfall to occur over the MSGW. In general, during negative cases, the La Niña was observed, corroborating that this phenomenon contributes to decreasing rainfall on the MSGW. On the other hand, El Niño was associated with the double positive anomalous months.
Since the study confirms the dependence of rainfall regime on the MSGW with the large-scale pattern, our findings can be used for decision-making processes and the development of sustainable strategies for the region, aiming to mitigate the impacts that changes in this large-scale pattern can have on the MSGW. Given that changes in climate oscillations, its direction, or the amount of moisture associated with it affect rainfall, these factors should be taken into consideration by decision-makers.
The MSGW is a complex system, which is not only vulnerable to climate changes in a global context but also to human activities that are impacting the Pampa biome. On the other hand, this study shows how the MSGW can be sensitive to changes also in other biomes. For instance, deforestation or agricultural expansion in the Amazon basin influence moisture transport and rainfall in the MSGW. In addition, changes in the precipitation regime directly affect water resources availability, agriculture, biodiversity, and infrastructure in the MSGW. On the other hand, studies about regional circulations, such as sea breeze intrusions (e.g., [81,82]), and its role in some meteorological conditions in the MSGW, should also contribute to a better understanding of the rainfall regime in the region, not considering only the meridional water vapor transport. Therefore, these topics remain open to future studies to better understand how global changes may impact smaller-scales processes.

Author Contributions

Conceptualization, M.M.S. and A.B.N.; methodology, M.M.S. and A.B.N.; validation, M.M.S.; formal analysis, M.M.S., A.B.N. and F.T.C.; investigation, M.M.S., A.B.N. and F.T.C.; data curation, M.M.S. and I.P.C.; writing—original draft preparation, M.M.S.; writing—review and editing, M.M.S., A.B.N., F.T.C., D.d.A.B. and R.A.P.d.F.; visualization, M.M.S.; supervision, A.B.N., F.T.C., D.d.A.B. and R.A.P.d.F.; project administration, A.B.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The first author thanks CAPES for the scholarship provided.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Figure A1, Figure A2, and Figure A3 show the trend of the VIVF, PrecERA5, and PrecObs variables over time, respectively.
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Figure A1. VIVF trend over time. VIVF in kg/m·s.
Figure A1. VIVF trend over time. VIVF in kg/m·s.
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Figure A2. Precipitation trend (PrecERA5) over time.
Figure A2. Precipitation trend (PrecERA5) over time.
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Figure A3. Precipitation trend (PrecObs) over time.
Figure A3. Precipitation trend (PrecObs) over time.
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Figure 1. Location of the Brazilian territory within the Mirim–São Gonçalo Watershed (in red).
Figure 1. Location of the Brazilian territory within the Mirim–São Gonçalo Watershed (in red).
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Figure 2. 1981–2020 climatological normal of the VIVF: (A) summer, (B) autumn, (C) winter, and (D) spring. The streamlines represent the directions and the shading the magnitude (kg/m·s) of the VIVF. The rectangle highlights the region where the MSGW is located. The city of Santa Cruz demarcated by a circle (in black) and Mariscal by a square (in purple).
Figure 2. 1981–2020 climatological normal of the VIVF: (A) summer, (B) autumn, (C) winter, and (D) spring. The streamlines represent the directions and the shading the magnitude (kg/m·s) of the VIVF. The rectangle highlights the region where the MSGW is located. The city of Santa Cruz demarcated by a circle (in black) and Mariscal by a square (in purple).
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Figure 3. Climatological normal of the VIVF and streamlines for DJF: (A) 700 hPa, (B) 850 hPa, and (C) 925 hPa. For MAM: (D) 700 hPa, (E) 850 hPa, and (F) 925 hPa. For JJA: (G) 700 hPa, (H) 850 hPa, and (I) 925 hPa. For SON: (J) 700 hPa, (K) 850 hPa, and (L) 925 hPa. The VIVF (shaded) in kg/m·s and streamlines in vectors. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is marked by a circle (in black) and Mariscal by a square (in purple).
Figure 3. Climatological normal of the VIVF and streamlines for DJF: (A) 700 hPa, (B) 850 hPa, and (C) 925 hPa. For MAM: (D) 700 hPa, (E) 850 hPa, and (F) 925 hPa. For JJA: (G) 700 hPa, (H) 850 hPa, and (I) 925 hPa. For SON: (J) 700 hPa, (K) 850 hPa, and (L) 925 hPa. The VIVF (shaded) in kg/m·s and streamlines in vectors. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is marked by a circle (in black) and Mariscal by a square (in purple).
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Figure 4. 1981–2020 Climatological Normal of Precipitation (mm).
Figure 4. 1981–2020 Climatological Normal of Precipitation (mm).
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Figure 5. Monthly climatological normal (1981–2020) of precipitation for all MSGW stations and the watershed average (gray line).
Figure 5. Monthly climatological normal (1981–2020) of precipitation for all MSGW stations and the watershed average (gray line).
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Figure 6. Climatological normals (1981–2020) of the VIVF (kg/m·s) and ERA5 precipitation (mm).
Figure 6. Climatological normals (1981–2020) of the VIVF (kg/m·s) and ERA5 precipitation (mm).
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Figure 7. DJF Climatological normal (1981–2020) of the wind (850 hPa): (A) u component, (B) v component. Magnitude (shaded) in m/s. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is marked by a circle (in black) and Mariscal by a square (in purple).
Figure 7. DJF Climatological normal (1981–2020) of the wind (850 hPa): (A) u component, (B) v component. Magnitude (shaded) in m/s. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is marked by a circle (in black) and Mariscal by a square (in purple).
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Figure 8. Double negative anomalous month—January 2012: (A) u component (850 hPa) and (B) v component (850 hPa). Magnitude (shaded) in m/s. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is demarcated by a circle (in black) and Mariscal by a square (in purple).
Figure 8. Double negative anomalous month—January 2012: (A) u component (850 hPa) and (B) v component (850 hPa). Magnitude (shaded) in m/s. Rectangle represents the region where the MSGW is located. The city of Santa Cruz is demarcated by a circle (in black) and Mariscal by a square (in purple).
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Table 1. Location of pluviometric/meteorological stations and representative coordinates in ERA5.
Table 1. Location of pluviometric/meteorological stations and representative coordinates in ERA5.
No.StationCityLongitudeLatitudeLon ERA5Lat ERA5Data Source
1Arroio GrandeArroio Grande−53.0897−32.2372−53.00−32.25ANA
2CanguçuCanguçu−52.6733−31.4044−52.75−31.50ANA
3Capão do LeãoCapão do Leão−52.4166−31.7833−52.50−31.75INMET
4Estação Experimental de PiratiniPiratini−53.1078−31.4308−53.00−31.50ANA
5FerrariaPiratini−53.0539−31.7375−53.00−31.75ANA
6Granja CerritoRio Grande−52.54−32.3506−52.50−32.25ANA
7Granja Coronel Pedro OsórioArroio Grande−52.6528−32.0061−52.75−32.00ANA
8Granja OsórioSanta Vitória do Palmar−53.1189−32.9539−53.00−33.00ANA
9Granja Santa MariaRio Grande−52.5386−32.5381−52.50−32.50ANA
10HervalHerval−53.3978−32.0286−53.50−32.00ANA
11Pedras AltasPedras Altas−53.5881−31.7333−53.50−31.75ANA
12Pedro OsórioPedro Osório−52.8103−31.8797−52.75−32.00ANA
13Pinheiro MachadoPinheiro Machado−53.3769−31.5775−53.50−31.50ANA
14Ponte Cordeiro de FariasPelotas−52.4631−31.5739−52.50−31.50ANA
15Vila FreireCerrito−52.7728−31.6694−52.75−31.75ANA
Table 2. Correlation coefficient between the variables ENSO, VIVF, PrecERA5, and PrecObs.
Table 2. Correlation coefficient between the variables ENSO, VIVF, PrecERA5, and PrecObs.
VariablesPearson
ENSO vs. VIVF0.32
VIVF vs. ERA5 Precipitation0.45
Climatological Normal: ERA5 rainfall data vs. Precipitation Observed0.93
Table 3. Correlation coefficients of Pearson between VIVF and ENSO by season.
Table 3. Correlation coefficients of Pearson between VIVF and ENSO by season.
SeasonPearson
Summer (DJF)0.39
Autumn (MAM)0.25
Winter (JJA)0.30
Spring (SON)0.50
Table 4. p-Value of each variable for the Run Test and Mann–Kendall tests.
Table 4. p-Value of each variable for the Run Test and Mann–Kendall tests.
VariableRun TestOriginal Mann–Kendall
PrecERA50.070.09
VariableRun TestSeasonal Mann–Kendall
PrecObs0.010.64
VIVF3.92 × 10−50.95
Table 5. Average seasonal precipitation (mm) in the MSGW.
Table 5. Average seasonal precipitation (mm) in the MSGW.
SeasonMean (mm)Standard Deviation (mm)
DJF121.0769.25
MAM119.1172.37
JJA123.2166.32
SON128.1760.79
Table 6. Pearson coefficient between the climatological normal of each station vs. climatological normal of the MSGW.
Table 6. Pearson coefficient between the climatological normal of each station vs. climatological normal of the MSGW.
StationPearsonStationPearsonStationPearson
Canguçu0.87Pedras Altas0.68Arroio Grande0.97
Vila Freire0.95Pinheiro Machado0.83Granja Osório0.82
Pedro Osório0.96Coronel Pedro Osório0.95Herval0.83
Ponte Cordeiro de Farias0.88Granja Cerrito0.86Pelotas0.80
Ferraria0.88Granja Santa Maria0.88Ext. Exp. Piratini0.93
Table 7. Reference values for classification as an anomalous month. Values obtained from the 95th percentile and 10th percentile.
Table 7. Reference values for classification as an anomalous month. Values obtained from the 95th percentile and 10th percentile.
SeasonVIVF (kg/m·s)
Positive Anomalous		Precipitation (mm) Positive AnomalousVIVF (kg/m·s)
Negative Anomalous		Precipitation (mm) Negative Anomalous
Summer (DJF)375.95204.7965.9841.97
Autumn (MAM)359.95223.8086.2047.29
Winter (JJA)373.41188.22196.2448.28
Spring (SON)365.27185.76131.6455.06
Table 8. Number of anomalous months per decade.
Table 8. Number of anomalous months per decade.
DecadeVIVF PositivePrecipitation PositiveVIVF NegativePrecipitation Negative
1980–1989451114
1990–19999111114
2000–200953119
2010–2020751414
Table 9. Double negative anomalies of the VIVF with manifestation the ENSO.
Table 9. Double negative anomalies of the VIVF with manifestation the ENSO.
10th percentile—DJF
DateVIVF (kg/m·s)ENSO phase
  February 201821.08La Niña
  January 201251.33La Niña
  December 200846.87La Niña
  December 199556.24El Niño
  February 198964.28La Niña
10th percentile—MAM
DateVIVF (kg/m·s)ENSO phase
  March 202077.99Neutral
  April 198260.32El Niño
10th percentile—JJA
DateVIVF (kg/m·s)ENSO phase
  July 1996115.36Neutral
  July 1990152.47Neutral
  July 1986148.41Neutral
10th percentile—SON
DateVIVF (kg/m·s)ENSO phase
  November 2012113.03Neutral
  November 2011108.86La Niña
  November 2008111.10La Niña
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Santiago, M.M.; Nunes, A.B.; Couto, F.T.; Almeida Bressiani, D.d.; Freitas, R.A.P.d.; Cardoso, I.P. Relationship Between the Water Vapor Transport from the Amazon Basin and the Rainfall Regime over a Watershed on Brazil’s Southern Border. Earth 2025, 6, 13. https://doi.org/10.3390/earth6010013

AMA Style

Santiago MM, Nunes AB, Couto FT, Almeida Bressiani Dd, Freitas RAPd, Cardoso IP. Relationship Between the Water Vapor Transport from the Amazon Basin and the Rainfall Regime over a Watershed on Brazil’s Southern Border. Earth. 2025; 6(1):13. https://doi.org/10.3390/earth6010013

Chicago/Turabian Style

Santiago, Maicon Moraes, André Becker Nunes, Flavio Tiago Couto, Danielle de Almeida Bressiani, Rose Ane Pereira de Freitas, and Iulli Pitone Cardoso. 2025. "Relationship Between the Water Vapor Transport from the Amazon Basin and the Rainfall Regime over a Watershed on Brazil’s Southern Border" Earth 6, no. 1: 13. https://doi.org/10.3390/earth6010013

APA Style

Santiago, M. M., Nunes, A. B., Couto, F. T., Almeida Bressiani, D. d., Freitas, R. A. P. d., & Cardoso, I. P. (2025). Relationship Between the Water Vapor Transport from the Amazon Basin and the Rainfall Regime over a Watershed on Brazil’s Southern Border. Earth, 6(1), 13. https://doi.org/10.3390/earth6010013

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