Synoptic variability of the Pechora plume in the Barents Sea

Abstract

Previous studies have described the general spreading patterns of the Pechora plume in the southeastern Barents Sea, but its synoptic variability during the ice-free season remains largely unknown. In this study, we analyze Pechora plume dynamics on synoptic time scales during the ice-free season from 2015 to 2024 using satellite-derived sea surface salinity, sea surface temperature, and in situ observations. We reveal that the maximal plume area, reaching up to 55,000 km², is not confined to the spring freshet period and may occur throughout the entire ice-free season under favorable wind forcing conditions. Wind primarily controls plume spreading through Ekman transport, while the timing and magnitude of peak river discharge determine freshwater volume and stratification. A time lag between the peak river discharge and freshwater inflow from Pechora Bay, i.e., the estuary of the Pechora River, to the open sea modifies the plume response to external forcing. Also, based on satellite data and in situ measurements, we reveal and describe two spreading patterns of the Pechora plume that received limited attention before. First, we identify large-scale northwestward advection of the outer part of the Pechora plume and its subsequent separation from the main part of the plume. The separated part of the plume (hereafter referred to as low-salinity lens) could drift more than 300 km towards Novaya Zemlya under northeasterly wind forcing. Second, we identify more than 30 wind-driven inflow events of the Pechora plume through the Kara Strait into the Kara Sea, which intensify inter-basin freshwater exchange between the Barents and Kara seas. These results significantly expand the current understanding on the Pechora plume dynamics and variability. It provides new insights for assessment the influence of the Pechora plume on the hydrological structure of the entire southeastern part of the Barents Sea.

1 Introduction

River plumes play an important role in freshwater cycle in the Arctic Ocean. Despite its small area (4% of the World Ocean area), the Arctic Ocean receives unproportionally large river runoff (Nummelin et al., 2016). In particular, 75 rivers with mean annual runoff > 2000 m³/s inflow to the World Ocean and 15 of them inflow to the Arctic Ocean. River inflow forms salinity stratification of sea surface layer and preconditions freeze-up of almost the entire area of the Arctic Ocean during cold season (Carmack et al., 2016). Strong spatial inhomogeneity of sea surface salinity in the Arctic Ocean is caused by entrainment of river runoff within river plumes at shelf seas (mainly in Eurasian Arctic), as well as freshwater entrainment by the Beaufort Gyre in the Amerasian Arctic (Timmermans and Toole, 2023; Savin and Osadchiev, 2025).

The largest river plumes in the Arctic Ocean are formed by the Yenisei, Lena, and Ob rivers with average annual runoff equal to 17000–20000 m³/s each (the Ob River is considered together with smaller rivers that inflow to their joint estuary). The Ob-Yenisei and Lena plumes are among the largest in the World Ocean in terms of area (Kang et al., 2013; Konik and Osadchiev, 2026). Medium-size river plumes are formed by the Mackenzie (10000 m³/s), Yukon (7000 m³/s), Pechora (4000 m³/s), Nelson (4000 m³/s), Northern Dvina (4000 m³/s), La Grande (4000 m³/s) rivers. Several medium-size rivers (Kolyma, Pyasina, Indigirka) do not form individual plumes, because their discharges become embedded into large Ob-Yenisei and Lena plumes. Several medium-size river plumes, namely, Khatanga, Northern Dvina, and La Grande, experience intense tidal forcing (Arbic et al., 2007; Webb, 2014; Magritsky et al., 2017; Osadchiev et al., 2020a).

One of the main features of river plumes in the Arctic Ocean is strong seasonality of river runoff. The majority of annual discharge (up to 80-90%) inflow to sea during 2–3 months in late spring and early summer, which is followed by long draught in autumn and winter. Little is known about river plumes during cold season, when they are covered by sea ice (Peck et al., 2022; Osadchiev et al., 2023b), however, their area decreases during draught winter period, which is followed by abrupt increase in response to freshet discharge (Spivak et al., 2021; Osadchiev et al., 2021b; Bertin et al., 2025). After peak discharge, areas of the largest Ob-Yenisei and Lena plumes do not show any dependence on discharge volume (Savin and Osadchiev, 2025), while medium-size Yukon and Mackenzie plumes show relation of their area with discharge rate (Chikita et al., 2021; Bertin et al., 2025). Wind forcing is the main driver of variability of Arctic river plumes of any size on daily and synoptic time scales (Mulligan et al., 2010; Mulligan and Perrie, 2019; Clark and Mannino, 2022; Konik and Osadchiev, 2025; Rogozhin et al., 2025), which is generally typical for river plumes in the World Ocean (Horner-Devine et al., 2015).

Many papers addressed structure and variability of the Ob-Yenisei and Lena plumes. Medium-size plumes received less attention; however, their structure and dynamics could be significantly different than those of the Ob-Yenisei and Lena plumes. In order to fill this gap, in this study, we focused on the Pechora plume that is formed in the southeastern part of the Barents Sea, which is commonly referred to as the Pechora Sea. The Pechora plume strongly affects local ecosystem, including zooplankton (Dvoretsky and Dvoretsky, 2013; Usov et al., 2019) and macrozoobenthos communities (Denisenko et al., 2000, 2003; Gebruk et al., 2023). The spatial and temporal variability of the Pechora plume still remain poorly studied. With the exception of Rogozhin et al. (2023), few studies focused on this issue. In Rogozhin et al. (2023), significant synoptic variability of the Pechora plume during the freshet period was described. In particular, the plume area could increase from approximately 6000 km² to 36000 km² within several days of strong easterly winds. Variations in river discharge and sea-ice meltwater input also modified the plume area. However, the analysis in Rogozhin et al. (2023) is focused on observations collected during the freshet period (June–July). Variability of the Pechora plume during the entire ice-free season (June–October) therefore remains unknown.

Frontal zones separating river plumes from ambient seawater are typically characterized by strong horizontal gradients of temperature and salinity (Horner-Devine et al., 2015; Osadchiev et al., 2021a). Due to large spatial extents of river plumes and presence of well-defined frontal zones, satellite observations are an effective tool for investigating structure and dynamics of different river plumes in the World Ocean. Harsh climate conditions and presence of sea ice substantially limit ship-based in situ observations in the Arctic Ocean. These conditions increase the importance of satellite observations in studies of the Arctic river plumes associated with sharp surface thermohaline gradients (Osadchiev et al., 2020b; 2021b; 2021c; Frey and Osadchiev, 2021; Zhuk and Kubryakov, 2021; Rogozhin et al., 2025; Savin and Osadchiev, 2025).

Synoptic variability of river plume extent is typically analyzed using satellite-derived sea surface temperature (SST) and sea surface salinity (SSS). SST is commonly used to identify spatial distribution of river plumes, including those in the Arctic Ocean, due to increased temperatures of the inflowing river water as compared to seawater (Vasilenko et al., 2022). However, usage of SST is limited by the short persistence of strong surface temperature contrasts during the freshet season (temperature contrasts become very low in autumn due to atmospheric cooling). In addition, optical-based SST products generally provide low temporal coverage due to common overcast conditions at the study area.

In contrast, microwave-based SSS enable observations under any cloud conditions. It provides continuous coverage of river plumes distribution during ice-free season. Most of SSS retrieval algorithms were originally developed for open-ocean conditions, where salinity gradients are typically weak and rarely exceed 0.1 psu. River plumes, in contrast, generate sharp surface freshening and strong horizontal gradients. Comparisons between satellite-derived SSS and in situ measurements in the Arctic Ocean generally show good agreement (Olmedo et al., 2018; Tang et al., 2018; Fournier et al., 2019; Supply et al., 2020). However, the available datasets remain too limited to constrain high SSS accuracy in regions with strong surface freshening.

To investigate synoptic variability of the Pechora plume we use a novel SSS dataset based on SMAP observations and processed with neural network–based algorithms. These algorithms were trained on extensive in situ temperature and salinity measurements in the Arctic shelf seas affected by river discharge (Savin et al., 2023; 2024). However, SSS data provides limited coverage in the coastal zone which is important in case of the Pechora plume. To improve the nearshore resolution, we combine SSS and SST data during cloud-free periods.

In this study, we investigate variability of the Pechora plume using satellite-derived SSS and SST data for the ice-free period (1 June–15 October) from 2015 to 2024. We pay special attention to periods of large plume expansion, which provide the greatest influence on the local ecosystem. Also, we examine wind-driven dynamics of the Pechora plume structure, including separation of the outer part of the plume and formation of low-salinity lenses. This process is rarely observed for river plumes and still remains poorly addressed.

2 Data and methods

2.1 Satellite data

In this study, synoptic variability of the Pechora plume in the Pechora Sea was described based on satellite observations of sea surface brightness temperature (SST) and salinity (SSS) from 2015 to 2024. The study period is limited by the availability of satellite-derived sea surface salinity data from the SMAP mission (Meissner et al., 2022). SMAP data are used because this mission provides higher temporal resolution (3 days) and spatial resolution (25 km) of SSS as compared to the Aquarius and SMOS satellites. However, standard satellite salinity retrieval algorithms are not optimized for the Arctic shelf seas and generally have low accuracy due to low salinity and low temperature of sea surface layer typical for this region (Supply et al., 2020; Osadchiev et al., 2023a). To improve the quality of the SMAP SSS data, additional processing was performed using a gradient boosting composite neural network algorithm, which is described in detail in (Savin et al., 2024; Savin and Osadchiev, 2025).

SSS data is not available near sea coasts. This empty zone for SSS data extends from 40 to 80 km from the Pechora Sea coastline (grey color in Figure 1). Due to this reason, synoptic variability of the Pechora plume area was investigated during the period of its large extent hereafter referred as maximal state of the Pechora plume. This state was defined if the Pechora plume expanded northward from the line connecting Cape Russkiy Zavorot (Gulyaevskie Koshki Archipelago) and Vaygach Island (Rogozhin et al., 2023) (red line in Figure 1). Note that this area is outside the empty zone for SSS data, so the outer boundary of the Pechora plume in its maximal state could be detected and its area could be calculated using SSS imagery. In this study, we also analyzed SST images of the Pechora Sea taken by satellite missions MODIS Terra/Aqua, NOAA-20/VIIRS, and Suomi NPP/VIIRS in 2015–2024 on a daily basis with spatial resolution of 250 m. We use SST data to see certain processes in more detail which partly occur in the SSS empty zone close to sea coast.

Figure 1

2.2 Thermohaline, wind and river discharge data

The in situ thermohaline data were used in this study to validate SST and SSS data in the Pechora Sea. These in situ data were obtained during field surveys in the Pechora Sea at R/V “Professor Molchanov” in July 2022 and July 2024 (colored circles in Figure 1). Thermohaline measurements were performed using SBE 911plus CTD profiler with a vertical resolution of 0.2 meters.

Wind forcing conditions were examined using ERA5 atmospheric reanalysis with a 0.25° spatial and hourly temporal resolution (Hersbach et al., 2020). The Ekman transport was calculated using the standard equations. First, we calculated zonal and meridional wind stress components τ_x and τ_y:

where u and v are the zonal and horizontal wind components, ρ_a is the atmospheric density equal to 1.225 kg/m³, C_d is the drag coefficient equal to 1.2×10⁻³. Further, we calculated the zonal and meridional Ekman transport (Q_x and Q_y) using the equations.

where ρ is the seawater density equal to 1025 kg/m³, f is the Coriolis parameter equal to 1.35·10^-4.

The Pechora River discharge measurements analyzed in this study were acquired from the most downstream gauge station located at Ust-Tsilma. Relationships between river discharge and plume characteristics were assessed using Pearson and Spearman correlation coefficients, with statistical significance evaluated at 95% (p < 0.05). The calculation of these statistical parameters was performed on the time series 2015-2024 (n = 10). The analysis accounted for the time lag between discharge measured at Ust-Tsilma and the arrival of riverine freshwater to the open part of the Pechora Sea, as described in Section 3.1.

2.3 Validation of SSS and SST data against in situ data

The boundary of the Pechora plume was calculated as the maximal gradient line at SSS and SST imagery. Gradient computation was performed using the Sobel filter. The error of SSS measurements ranges from 0.5 to 1.5 psu, which does not have a significant negative effect on identification of the frontal zone, where the salinity gradient is up to 5–7 psu. In the nearshore area, offshore SSS gradient was connected with the coastal SST gradient to cover the plume border at the empty zone for SSS data. In case of inappropriate cloud conditions for the synchronous SSS and SST data, SST imagery was selected ±4 days of the corresponding SSS observations. This duration was prescribed according the to the typical response time of the Pechora plume to external wind forcing variability (Rogozhin et al., 2023).

The SSS errors (0.5-1.5 psu) are much smaller than the observed SSS gradients (>3 psu/25 km) minimally impacting detection of the Pechora plume border and calculation of its area (Rogozhin et al., 2023). Accuracy of calculation of the Pechora plume area was limited by spatial resolution of SSS data (25 km). Once the length of the outer border of the Pechora plume is ~100 km, the uncertainty in the Pechora plume area estimates is equal to ~1000 km². Therefore, in this study we rounded the calculated Pechora plume areas to the nearest 1000 km².

To verify plume detection in coastal zones, the Pechora plume extent estimates were validated using in situ salinity and temperature measurements collected in the Pechora Sea in July 2022 (Figure 2a). Additional verification was conducted by comparing the plume frontal boundary identified from SST imagery in July 2016 with synchronous SSS-based estimates (Figure 2b). Figure 2 shows that salinity values received from in situ and satellite measurements are highly consistent.

Figure 2

3 Results

3.1 Interannual variability of the Pechora River discharge

Discharge data of the Pechora River was analyzed for 2015–2024 to investigate its influence on variability of the Pechora plume. The analysis is focused on the beginning and end dates of the spring freshet, the date of the volume-weighted midpoint of the freshet period, the peak discharge value, and the total discharge volume during the ice-free season. These hydrological characteristics were compared with characteristics of the Pechora plume area, including the number of days per year with observed plume spreading beyond the Zavorot–Vaygach line and daily plume area derived from SSS data.

To estimate the time lag between discharge observations at Ust-Tsilma and the inflow of freshwater to the open sea, we calculated the time of water flow from Ust-Tsilma to the Pechora River mouth (based on river flow velocity) and transit time of water from the Pechora River mouth through the Pechora Bay. Using previously published Pechora River flow velocity estimates of 0.6 m/s (Zhakovskaya et al., 2010), we obtained a 7-day transit between Ust-Tsilma to the Pechora Bay. Freshened waters require an additional 7 days to pass through the bay (Savelyeva et al., 1987). As a result, inflow of freshwater to the open part of the Pechora Sea occurs approximately 14 days after measurements at Ust-Tsilma downstream gauge station (Figure 3). Accordingly, river discharge characteristics were compared with plume characteristics using a 14-day lag.

Figure 3

Comparison of freshet timing with plume characteristics shows that the Pechora plume area responds more strongly to discharge timing than to discharge magnitude. Maximal plume area increases when the freshet starts later and when the volume-weighted midpoint shifts toward later dates. Correlation coefficients between maximal plume area and freshet beginning are Pearson ≈ 0.73 and Spearman ≈ 0.75. Similar values were obtained for the volume-weighted midpoint (Pearson ≈ 0.72; Spearman ≈ 0.64). The duration of maximal plume extent also increases with later freshet timing (Spearman ≈ 0.61–0.62). In contrast, correlations with peak river discharge are weaker (Spearman ≈ 0.59), and correlations with the total discharge volume are even lower (Spearman < 0.40). These results indicate that the timing of freshwater inflow to the open part of the Pechora Sea governs the Pechora plume expansion more strongly than the volume of total river runoff.

3.2 Variability of the Pechora plume area

SSS data from 2015 to 2024 were used to identify the presence and spatial distribution of the Pechora plume during the ice-free season (June–October) and its daily area variability, which was not previously analyzed at synoptic time scale. The duration of maximal plume state (i.e., beyond the Zavorot–Vaygach line) varied widely between years, ranging from 9 to 50 days (Table 1). On average, the Pechora plume remained at this state during 23 days per year, which corresponds to 17% of the entire ice-free period. The longest durations occurred in 2017 (40 days) and 2024 (50 days). In contrast, the maximal plume state was not reached in 2021 and was limited to 10 days in 2019 and 2023. In 2015, 2016, 2018, and 2020, the Pechora plume was in its maximal state for 18–29 days. Maximal state of the Pechora plume was most frequently observed in June (9 days on average) and July (11 days on average). In August, only isolated episodes of maximal plume state were detected, except for the period of 1–19 August 2015. Maximal plume state was not observed in September and October.

The average area during the maximal plume state in 2015–2024 is 31000 km². The largest area of the Pechora plume (55000 km²) was observed on 24–27 July 2024. In most years, the minimal area during the maximal plume state ranged between 21000 and 25000 km². A lower value was registered in 2018 (~18000 km²), whereas in 2021 the Pechora plume remained in the coastal zone and did not extend to maximal state during the entire ice-free season.

SSS data also shows strong interannual variability in the Pechora plume salinity values. In 2017 (5 July–5 August), 2018 (16 July–3 August), and 2024, salinity within the plume ranged from 10 to 25 psu. In contrast, in 2022 surface salinity varied within a narrower range of 20–26 psu despite one of the longest periods of maximal plume state registered in this year. This pattern is consistent with in situ measurements of vertical plume structure collected in July 2022 in the western part of the Pechora Sea during westward advection of the Pechora plume (Figure 4). The water column was strongly stratified, with low-salinity surface waters overlying more saline bottom waters. Salinity increased from 24 psu in the surface layer to 34 psu near the bottom. The lowest salinity was observed in the southern part of the section (near Cape Russkiy Zavorot), which is consistent with SSS data. The Pechora plume thickness ranged from 3 to 7 m and increased northward. Surface temperature showed weaker spatial gradients compared to salinity and ranged from −1 to 10 °C. The vertical structure observed in the western part of the Pechora Sea is similar to that previously reported near Vaygach Island.

Figure 4

Satellite SSS observations revealed two main directions of the Pechora plume advection. In most cases (≈80%), the plume advected north-westward towards Novaya Zemlya or northward, resulting in a widespread distribution of low-saline waters across the Pechora Sea. Westward advection of the Pechora plume along Cape Russkiy Zavorot towards Kolguev Island was less frequent (≈20%).

Both northward and westward advection of the Pechora plume are clearly illustrated by conditions observed in 2022 (Figure 5). The initial plume configuration with surface salinity of 15–20 psu was formed by 10 June 2022 (Figure 5a). During the following three weeks (until 28–30 June) (Figures 5b–d), the Pechora plume advected in northwestward direction. The outer plume boundary, defined from SSS, propagated at speed of 20–40 km/day. Surface salinity in the outer boundary of the Pechora plume remained stable during this advection (22–24 psu), which is consistent with in situ measurements. Dissipation of the Pechora plume near Novaya Zemlya began only on 9–12 July after approximately one month of sustained advection (Figures 5g, h). During this period, the area of the outer plume ranged from 23000–24000 km² to 35000–37000 km².

Figure 5

3.3 Wind forcing conditions during periods of maximal and minimal plume extent

Wind forcing conditions during ice-free season in 2015–2024 were highly variable. We analyzed wind direction and speed (Figure 6) together with the Ekman transport (Figure 7) for most typical cases: 2015 (maximal plume area in August), 2017 (maximal plume area in July), 2021 (the lowest plume area during the ice-free season), and 2022 (maximal plume area in June).

Figure 6

Figure 7

In 2015, periods of maximal plume state were primarily associated with easterly and northeasterly winds (60–120°) with speeds of 4–8 m/s (occasionally up to ~10 m/s). These winds promoted offshore spreading of the Pechora plume. In June–July, westerly winds (240–300°) prevailed and caused decreasing of the plume area (Figure 6a). Similar pattern was observed in 2017. During June–July, easterly winds up to 10–11 m/s promoted plume expansion to its maximal state. In August, wind regime shifted towards westerly directions, which was accompanied by decreasing of the plume area (Figure 6b). In 2021, persistent northwesterly winds (250–270°) dominated throughout the ice-free season. Under these wind conditions, the Pechora plume was located in the coastal zone (i.e., in the minimal plume state) and did not reach maximal plume state (Figure 6c). In 2022, northeasterly winds prevailed in June and early July, whereas southwesterly winds dominated in late July–August. The shift in wind direction modified the Ekman transport and redirected the Pechora plume from northward spreading to westward advection. Southwesterly wind events were already present in June 2022 and became more frequent later in the season (Figure 6d). Overall, easterly and northeasterly winds are favorable for the Pechora plume northward expansion. Under westerly and northwesterly winds, onshore Ekman transport increases and the Pechora plume is shifted towards the coast.

The distribution of Ekman transport and wind stress magnitude is consistent with the wind regimes described above (Figure 7). In 2015, Ekman transport during periods of maximal plume state was primarily directed towards north and northeast (Figure 7a). This direction of Ekman transport is consistent with the Pechora plume spreading towards the Kara Strait. Southward transport prevailed during periods of minimal plume state. This process presses the Pechora plume to the coast and reduces its area. A similar pattern was observed in 2017 (Figure 7b). In June–July, northward Ekman transport caused northward spreading of the Pechora plume to the open sea, which resulted in maximal plume state. In August, Ekman transport turned southward, confined the plume towards the coast and reduced its area. In 2021, Ekman transport was directed towards south and southwest (150–270°) throughout the ice-free season (Figure 7c). Under this persistent southward transport, the Pechora plume remained in the coastal zone and did not reach maximal plume state. In 2022, the Ekman transport direction shifted during the season (Figure 7d). Northward transport dominated at the beginning of the ice-free period, whereas southward transport became more frequent in late summer, when the Pechora plume shifted from offshore spreading to minimal state.

3.4 Inflows of the Pechora plume to the Kara Strait and the Kara Sea

SSS data shows that the Pechora plume could spread across the entire eastern part of the Pechora Sea and reach the Kara Strait. Rogozhin et al. (2023) reported an inflow of the Pechora plume to the Kara Sea through the Kara Strait in 2020 based on SST data under favorable wind forcing conditions. However, direct in situ measurements to validate the presence of this inflow were not yet available. Our current analysis of SST data identified more than 30 cases of similar inflows of the Pechora plume to the Kara Strait and the Kara Sea. The most typical events of this inflow occurred in July in 2011, 2016, 2017, and 2024. Note that the Pechora plume inflows were not detected eastward from the Kara Strait in the western part of the Kare Sea presumably due to strong tidal mixing in the strait (Morozov et al., 2003; 2008; 2017; Kozlov et al., 2015; 2023).

In addition, we describe in situ measurements in the Kara Strait that confirm inflow of the Pechora plume in July 2024 (Figure 8). Waters with salinities of 25–27 psu, which typical for outer boundary of the Pechora plume, occupied the upper 2–4 m at the northeastern part of the transect (stations 5 and 6). In the southwestern part of the transect (stations 1 and 2), salinity remained 31–32 psu throughout the water column. Temperature decreased with depth from values more than 11 °C at the surface to less than 2 °C near the bottom, indicating strong vertical stratification.

Figure 8

Synchronous SST and SSS maps acquired on 24 July of 2024 show that the Pechora plume visible in satellite imagery occupied the same area as the inflow of warm and low-saline water observed by in situ data (Figure 9a). Stations located within the inflow area show reduced surface salinity. In contrast, stations in the southern part of the section, outside the influence of the Pechora plume, are characterized by colder and more saline waters.

Figure 9

Note that the inflow of freshened waters is not connected to the remaining sea ice mass which was located northward of the Kara Strait in the Kara Sea. Surface temperatures within the inflow are higher than values typical of sea-ice meltwater (Osadchiev et al., 2022, 2024), indicating their river-plume origin. Satellite imagery from 29 July 2024 (Figure 9b) provides a clear contrast between cold meltwater and warm Pechora plume water. SST data shows that under favorable wind forcing, a cold meltwater inflow to the Kara Strait from the Kara Sea was formed resulting in cold and low-saline surface layer.

4 Discussion

4.1 Influence of wind and river discharge conditions on the Pechora plume spreading

The Pechora plume is characterized by significant spatial variability on synoptic and seasonal time scales, controlled by the combined influence of riverine input and wind forcing. Previous studies connected the maximal state of the Pechora plume mainly to the freshet river discharge based on SST and in situ observations (Rogozhin et al., 2023). Using daily SSS data for 2015–2024, we demonstrate that maximal plume state is not limited to the freshet period and could occur throughout the entire ice-free season. The longest occurrence of maximal plume state was observed in June–July 2017, June 2022, and July 2024.

Our results show that the Pechora plume expansion is primarily controlled by wind forcing, while volume of freshwater in the river plume and its salinity values are mainly influenced by timing of peak river discharge. Strong easterly winds cause offshore spreading and northward advection of the Pechora plume. At the same time, the timing of freshet onset relative to sea ice melting onset influenced the efficiency of freshwater transport from river mouth to the open sea. In 2017 and 2024, late freshet peak occurred after sea ice melting. As a result, large volume of freshwater entered directly the ice-free Pechora Sea, which resulted in strong local stratification.

In 2022, despite an early freshet period, wind conditions were favorable for wide expansion of the Pechora plume. However, the maximal plume state in 2022 was observed only during 29 days, which is much smaller than 40 and 50 days in 2017 and 2024. This suggests that wind forcing determines spatial distribution, while freshwater volume influences the presence of maximal plume state. In 2021, early freshet combined with relatively low discharge volume did not result in maximal plume state during the entire ice-free season, even under favorable winds. It shows that the Pechora plume response to wind forcing is limited by the volume of freshwater discharge.

Statistical analysis reinforces this mechanism. Pearson and Spearman coefficients for 2015–2024 reveal significant positive relationships (R ≈ 0.6–0.75) between freshet timing, river discharge volume, plume area, and duration of maximal state of the Pechora plume. The importance of freshwater volume is further supported by river plume variability in July 2018, when the initial spreading of the Pechora plume occurred during period of active sea ice melting. In that year, additional freshwater input of sea ice meltwater slightly increased stratification, resulting in large plume area. Duration of maximal plume state in 2018 was comparable to high discharge years with prevailing western winds, which are unfavorable for maximal plume state.

In general, wind forcing determines the configuration of the Pechora plume and its spreading direction. However, river discharge conditions strongly regulate area of the Pechora plume. In particular, late freshet season causes greater area of the Pechora plume. We presume that early freshet discharge results in intense mixing of riverine water in the Pechora Bay and the open sea that results in smaller area of the Pechora plume. Late freshet discharge, on the opposite, is associated with steady expansion of the Pechora plume in the open sea, which hinders plume-sea mixing. It results in greater area of the Pechora plume.

Short freshet period (1–2 months long) and significant interannual variability of peak discharge timing are typical features of almost all large Arctic rivers (Gordeev et al., 1996). However, to the extent of our knowledge, previous studies of river plumes in the Arctic Ocean did not address dependence of river plume area to peak discharge timing. Moreover (Dmitrenko et al., 2008) reported absence of relation between interannual variability of the Lena River discharge and the Lena plume area. Similar result was later confirmed for the Ob-Yenisei and Lena plumes (Osadchiev et al., 2021c; 2021d). This difference is caused by large spatial scales of the Ob-Yenisei and Lena plumes and presence of surface freshening at least at near river estuaries during the entire year including draught season. Peak discharge timing could be an important factor for medium-size river plumes in the Arctic Ocean, such as Yukon and Mackenzie (Mulligan and Perrie, 2019; Clark and Mannino, 2022). However, this analysis is hindered by typical absence of long-term discharge measurement for Arctic rivers, except the largest ones.

4.2 Separation of low-salinity lenses from the Pechora plume

Spreading of the Pechora plume towards Novaya Zemlya was observed in 2022 and 2024. During these periods the low-saline surface area extended more than 300 km from the Pechora Bay. Northwestward advection of the Pechora plume occurred in June 2022 under typical timing of freshet onset and peak discharge (early–mid June). Northward plume advection resulted from long-term northeasterly winds that generated stable Ekman transport of plume waters towards Novaya Zemlya (Figure 10a). Separation of a low-salinity lens from the main plume body occurred between 22 and 26 July after approximately one week of strong winds (6–8 m/s). Short-term variability in wind direction and speed in early July (Figure 10b) caused advection of the low-salinity lens between the Novaya Zemlya coast and the southern part of the Pechora Sea near Cape Russkiy Zavorot. Presence of this process was confirmed by in situ measurements at the transect across the western part of the Pechora Sea (Figure 4).

Figure 10

Similar spreading of the Pechora plume towards Novaya Zemlya was observed in 2024. However, wind conditions during this period were highly variable (Figures 10c, d). The freshet period in 2024 with peak discharge in late June was among the latest within 2015–2024. Peak discharge in late June resulted in large flux of warm riverine waters to the Pechora plume in early–mid July. It resulted in large area of the Pechora plume despite variable wind forcing (Figure 11).

Figure 11

The registered separation of the outer part of the Pechora plume and its further advection towards Novaya Zemlya as an isolated low-salinity lens is a rarely studied mechanism of freshwater transport. Low-salinity lenses are formed in the Arctic Ocean as a result of sea ice melting (Supply et al., 2022), however, the river plume origin of low-salinity lenses was reported before at the Ob-Yenisei plume only by (Zatsepin et al., 2010). The Mackenzie plume, which has similar spatial scales to the Pechora plume and similar response time to atmospheric forcing (1–3 days) (Mulligan et al., 2010; Mulligan and Perrie, 2019), potentially could form isolated low-salinity lenses in case of favorable and long-term wind forcing. Nevertheless, this process was not yet observed and reported.

5 Conclusions

In this study, we provide the daily-scale analysis of spatial variability of the Pechora plume during ice-free season in 2015–2024 based on daily satellite-derived sea surface salinity (SSS) and sea surface temperature (SST) data supported by in situ measurements. The Pechora plume is spreading over wide area in the central part of the Pechora Sea during 23 days (on average) of the warm season, which is 17% of the ice-free season duration. During these periods the mean area of the Pechora plume is 31000 km² and the maximal registered area of the Pechora plume is 55000 km², which is significantly larger than was previously reported (Rogozhin et al., 2023). This area is only 5–6 times smaller than area of the Ob–Yenisei plume despite twice greater difference in the related annual river discharges (Osadchiev et al., 2021d, 2023).

The large extent of the Pechora plume is not always observed during the freshet period. It could occur any time in June-August under favorable wind forcing conditions. Timing of freshet discharge at the Pechora River is another important factor that influence area of the Pechora plume. Later freshet results in steady expansion of the Pechora plume in the sea, which results in reduced plume-sea mixing and greater plume areas, as compared to intense expansion and mixing of the Pechora plume in the sea under early river freshet conditions.

We describe and analyze two spreading patterns of the Pechora plume, which are associated with large-scale freshwater transport in the Pechora Sea. First, we show that under strong and long-term northeasterly wind forcing, northward advection of the Pechora plume could result in separation of its outer boundary to an isolated low-salinity lens. This process was registered by satellite and in situ data in 2022 and 2024, when the low-salinity lens was advected to the southwestern coast of Novaya Zemlya. Second, satellite and in situ observations confirmed presence of episodic inflows of the Pechora plume to the Kara Sea through the Kara Strait. More than 30 inflow events were identified in 2002–2025 mainly caused by southeasterly winds.

Overall, the obtained results improve our understanding of mechanisms that control synoptic variability of the Pechora plume. They demonstrate the important role of not only wind forcing, but also the timing of the peak freshwater input to the Pechora Sea. Formation of isolated low-salinity lenses and inflows of the Pechora plume to the Kara Strait are independent and yet understudied elements of sea surface circulation. These mechanisms provide freshwater transport on a distance >300 km that could affect the entire area of the Pechora Sea. These results highlight the role of the Pechora plume in forming hydrological structure across the entire southeastern part of the Barents Sea.

References

• 1

  ArbicB. K.St‐LaurentP.SutherlandG.GarrettC. (2007). On the resonance and influence of the tides in Ungava Bay and Hudson Strait. Geophys. Res. Lett.34, L17606. doi: 10.1029/2007GL030845. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  BertinC.Le FouestV.CarrollD.DutkiewiczS.MenemenlisD.MatsuokaA.et al. (2025). Colored dissolved organic matter (CDOM) alters the seasonal physics and biogeochemistry of the Arctic Mackenzie River plume. Biogeosci.22, 6607–6629. doi: 10.5194/bg-22-6607-2025

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  CarmackE. C.Yamamoto-KawaiM.HaineT. W.BaconS.BluhmB. A.LiqueC.et al. (2016). Freshwater and its role in the Arctic Marine System: Sources, disposition, storage, export, and physical and biogeochemical consequences in the Arctic and global oceans. J. Geophys. Res. Biogeosci.121, 675–717. doi: 10.1002/2015JG003140. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  ChikitaK. A.WadaT.KudoI.SaitohS. I.TorataniM. (2021). Effects of river discharge and sediment load on sediment plume behaviors in a coastal region: The Yukon River, Alaska and the Bering Sea. Hydrology8, 45. doi: 10.3390/hydrology8010045. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  ClarkJ. B.ManninoA. (2022). The impacts of freshwater input and surface wind velocity on the strength and extent of a large high latitude river plume. Front. Mar. Sci.8, 793217. doi: 10.3389/fmars.2021.793217

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  DenisenkoS. G.DenisenkoN. V.LehtonenK. K.AndersinA. B.LaineA. O. (2003). Macrozoobenthos of the Pechora Sea (SE Barents Sea): community structure and spatial distribution in relation to environmental conditions. Mar. Ecol. Prog. Ser.258, 109–123. doi: 10.3354/meps258109. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  DenisenkoS. G.SandlerH.DenisenkoN.RachorE. (2000). Current state of zoobenthos in two estuarine bays of the Barents and Kara Seas. ICES J. Mar. Sci.56, 187–193. doi: 10.1006/jmsc.1999.0633. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  DmitrenkoI. A.KirillovS. A.TremblayL. B. (2008). The long-term and interannual variability of summer freshwater storage over the eastern Siberian shelf: Implication for climatic change. J. Geophys. Res.113, C03007. doi: 10.1029/2007JC004304. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  DvoretskyV. G.DvoretskyA. G. (2013). Structure of mesozooplankton community in the Barents Sea and adjacent waters in August 2009. J. Nat. Hist.47, 2095–2114. doi: 10.1080/00222933.2013.772670. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  FournierS.LeeT.TangW.SteeleM.OlmedoE. (2019). Evaluation and intercomparison of SMOS, Aquarius, and SMAP sea surface salinity products in the Arctic Ocean. Remote Sens.11, 3043. doi: 10.3390/rs11243043. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  FreyD. I.OsadchievA. A. (2021). Large river plumes detection by satellite altimetry: case study of the Ob–Yenisei plume. Remote Sens.13, 5014. doi: 10.3390/rs13245014. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  GebrukA.DgebuadzeP.RogozhinV.ErmilovaY.ShabalinN.MokievskyV. (2023). Macrozoobenthos of the Pechora Bay in 2020–2021 indicates a likely change of common bivalve mollusks in the Arctic estuary. Polar Biol.46, 473–487. doi: 10.1007/s00300-023-03138-4. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  GordeevV. V.MartinJ. M.SidorovI. S.SidorovaM. V. (1996). A reassessment of the Eurasian river input of water, sediment, major elements, and nutrients to the Arctic Ocean. Am. J. Sci.296 (6), 664–691. doi: 10.2475/ajs.296.6.664

  • CrossRef
  • Google Scholar

• 14

  HersbachH.BellB.BerrisfordP.HiraharaS.HorányiA.Muñoz-SabaterJ.et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc146, 1999–2049. doi: 10.1002/qj.3803. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  Horner-DevineA. R.HetlandR. D.MacDonaldD. G. (2015). Mixing and transport in coastal river plumes. Annu. Rev. Fluid Mech.47, 569–594. doi: 10.1146/annurev-fluid-010313-141408. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  KangY.PanD.BaiY.HeX.ChenX.ChenC.-T. A.et al. (2013). Areas of the global major river plumes. Acta Oceanol. Sin.32, 79–88. doi: 10.1007/s13131-013-0269-5. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  KonikA.OsadchievA. (2025). Dynamics of the Iokanga River plume according to SAR and optical satellite data. J. Mar. Sys.250, 104093. doi: 10.1016/j.jmarsys.2025.104093. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  KonikA.OsadchievA. (2026). Spreading and mixing of large river plumes in the World Ocean. Front. Mar. Sci.13, 1714561. doi: 10.3389/fmars.2026.1714561

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  KozlovI. E.KopyshovI. O.FreyD. I.MorozovE. G.MedvedevI. P.ShiryborovaA. I.et al. (2023). Multi-sensor observations reveal large-amplitude nonlinear internal waves in the Kara Gates, Arctic Ocean. Remote Sens.15, 5769. doi: 10.3390/rs15245769. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  KozlovI. E.KudryavtsevV. N.ZubkovaE. V.ZiminA. V.ChapronB. (2015). Characteristics of short-period internal waves in the Kara Sea inferred from satellite SAR data. Izv. Atmos. Ocean. Phys.51, 1073–1087. doi: 10.1134/S0001433815090121

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  MagritskyD. S.LebedevaS.SkripnikE. (2017). Hydrological hazards at mouths of the Northern Dvina and the Pechora rivers, Russian Federation. Nat. Haz.88, 149–170. doi: 10.1007/s11069-016-2673-6. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  MeissnerT.WentzF. J.ManasterA.LindsleyR.BrewerM.DensbergerM. (2022). Remote Sensing Systems SMAP ocean surface salinities (Level 2C, Level 3 running 8-day, Level 3 monthly), version 5.0. Remote Sens. Syst. doi: 10.5067/SMP50-2SOCS

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  MorozovE. G.KozlovI. E.ShchukaS. A.FreyD. I. (2017). Internal tide in the kara gates strait. Oceanology57, 8–18. doi: 10.1134/S0001437017010106

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  MorozovE. G.PakaV. T.BakhanovV. V. (2008). Strong internal tides in the Kara Gates Strait. Geophys. Res. Lett.35, L16603. doi: 10.1029/2008GL033804. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  MorozovE. G.Parrilla-BarreraG.VelardeM. G.ScherbininA. D. (2003). The straits of Gibraltar and Kara Gates: a comparison of internal tides. Oceanol. Acta26, 231–241. doi: 10.1016/S0399-1784(03)00023-9

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  MulliganR. P.PerrieW. (2019). Circulation and structure of the Mackenzie River plume in the coastal Arctic Ocean. Cont. Shelf Res.177, 59–68. doi: 10.1016/j.csr.2019.03.006. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  MulliganR. P.PerrieW.SolomonS. (2010). Dynamics of the Mackenzie River plume on the inner Beaufort shelf during an open water period in summer. Estuar. Coast. Shelf Sci.89, 214–220. doi: 10.1016/j.ecss.2010.06.010. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  NummelinA.IlicakM.LiC.SmedsrudL. H. (2016). Consequences of future increased Arctic runoff on Arctic Ocean stratification, circulation, and sea ice cover. J. Geophys. Res. Oceans121, 617–637. doi: 10.1002/2015JC011156. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  OlmedoE.GabarróC.González-GambauV.MartínezJ.Ballabrera-PoyJ.TurielA.et al. (2018). Seven years of SMOS sea surface salinity at high latitudes: variability in Arctic and sub-Arctic regions. Remote Sens.10, 1772. doi: 10.3390/rs10111772. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  OsadchievA. A.MedvedevI. P.ShchukaS. A.KulikovM. E.SpivakE. A.PisarevaM. A.et al. (2020a). Influence of estuarine tidal mixing on structure and spatial scales of large river plumes. Ocean Sci.16, 1–18. doi: 10.5194/os-16-1-2020

  • CrossRef
  • Google Scholar

• 31

  OsadchievA. A.SilvestrovaK. P.MyslenkovS. A. (2020b). Wind-driven coastal upwelling near large river deltas in the Laptev and East-Siberian seas. Remote Sens.12, 844. doi: 10.3390/rs12050844. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  OsadchievA. A.SedakovR. O.BarymovaA. A. (2021a). Response of a small river plume on wind forcing. Front. Mar. Sci.8, 809566. doi: 10.3389/fmars.2021.809566

  • CrossRef
  • Google Scholar

• 33

  OsadchievA. A.FreyD. I.ShchukaS. A.TilininaN. D.MorozovE. G.ZavialovP. O. (2021b). Structure of the freshened surface layer in the Kara Sea during ice-free periods. J. Geophys. Res. Oceans126, e2020JC016486. doi: 10.1029/2020JC016486. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  OsadchievA. A.FreyD. I.SpivakE. A.ShchukaS. A.TilininaN. D.SemiletovI. P. (2021c). Structure and inter-annual variability of the freshened surface layer in the Laptev and East-Siberian seas during ice-free periods. Front. Mar. Sci.8, 735011. doi: 10.3389/fmars.2021.735011

  • CrossRef
  • Google Scholar

• 35

  OsadchievA. A.KonovalovaO. P.GordeyA. S. (2021d). Water exchange between the Gulf of Ob and the Kara Sea during ice-free seasons: the roles of river discharge and wind forcing. Front. Mar. Sci.8, 741143. doi: 10.3389/fmars.2021.741143

  • CrossRef
  • Google Scholar

• 36

  OsadchievA.VitingK.FreyD.DemeshkoD.DzhamalovaA.NurlibaevaA.et al. (2022). Structure and circulation of Atlantic water masses in the St. Anna Trough in the Kara Sea. Front. Mar. Sci.9, 915674. doi: 10.3389/fmars.2022.915674

  • CrossRef
  • Google Scholar

• 37

  OsadchievA.ZabudkinaZ.RogozhinV.FreyD.GordeyA.SpivakE.et al. (2023a). Structure of the Ob–Yenisei plume in the Kara Sea shortly before autumn ice formation. Front. Mar. Sci.10, 1129331. doi: 10.3389/fmars.2023.1129331

  • CrossRef
  • Google Scholar

• 38

  OsadchievA.SedakovR.FreyD.GordeyA.RogozhinV.ZabudkinaZ.et al. (2023b). Intense zonal freshwater transport in the Eurasian Arctic during ice-covered season revealed by in situ measurements. Sci. Rep.13, 16508. doi: 10.1038/s41598-023-43524-w. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  OsadchievA. A.KuskovaE. G.IvanovV. V. (2024). The roles of river discharge and sea ice melting in formation of freshened surface layers in the Kara, Laptev, and East Siberian seas. Front. Mar. Sci.11, 1348450. doi: 10.3389/fmars.2024.1348450

  • CrossRef
  • Google Scholar

• 40

  PeckC. J.KuzykZ. Z. A.HeathJ. P.LameboyJ.EhnJ. K. (2022). Under-ice hydrography of the La Grande river plume in relation to a ten-gold increase in wintertime discharge. J. Geophys. Res. Oceans127, e2021JC018341. doi: 10.1029/2021JC018341. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  RogozhinV. S.OsadchievA. A.KonovalovaO. P. (2023). Structure and variability of the Pechora plume in the southeastern part of the Barents Sea. Front. Mar. Sci.10, 1052044. doi: 10.3389/fmars.2023.1052044

  • CrossRef
  • Google Scholar

• 42

  RogozhinV.OsadchievA.SavinA.RogozhinaE.GeorgievA.KonovalovaO. (2025). Sea ice decline in the Eastern Arctic results in expansion of the freshened surface layer. Front. Mar. Sci.12, 1558240. doi: 10.3389/fmars.2025.1558240

  • CrossRef
  • Google Scholar

• 43

  SavelyevaS. P.PotaninV. A.PavlovV. K.SavchenkoE. M. (1987). On the dynamics of the waters of the Pechora Bay of the Barents Sea. Proc. Arct. Antarct. Res. Inst.410, 97–105. in Russian.

  • Google Scholar

• 44

  SavinA.KrinitskiyM. A.OsadchievA. A. (2023). SMAP sea surface salinity improvement in the Arctic region using machine learning approaches. Mosc. Univ. Phys. Bull.78, 210–216. doi: 10.3103/S0027134923070299

  • CrossRef
  • Google Scholar

• 45

  SavinA. S.KrinitskiyM. A.OsadchievA. A. (2024). Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches. Front. Mar. Sci.11, 1358882. doi: 10.3389/fmars.2024.1358882

  • CrossRef
  • Google Scholar

• 46

  SavinA.OsadchievA. (2025). Ob–Yenisei and Lena plumes: wind-driven dynamics and variability. Front. Mar. Sci.12, 1681563. doi: 10.3389/fmars.2025.1681563

  • CrossRef
  • Google Scholar

• 47

  SpivakE. A.OsadchievA. A.SemiletovI. P. (2021). Structure and variability of the Lena River plume in the southeastern part of the Laptev Sea. Oceanology61, 839–849. doi: 10.1029/2021JC018341

  • CrossRef
  • Google Scholar

• 48

  SupplyA.BoutinJ.KolodziejczykN.ReverdinG.LiqueC.VergelyJ.-L.et al. (2022). Meltwater lenses over the Chukchi and the Beaufort seas during summer 2019: From in situ to synoptic view. J. Geophys. Res. Oceans127, e2021JC018388. doi: 10.1029/2021JC018388. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  SupplyA.BoutinJ.VergelyJ.-L.KolodziejczykN.ReverdinG.ReulN.et al. (2020). New insights into SMOS sea surface salinity retrievals in the Arctic Ocean. Remote Sens. Environ.249, 112027. doi: 10.1016/j.rse.2020.112027. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  TangW.YuehS.YangD.ForeA.HayashiA.LeeT.et al. (2018). The potential and challenges of using SMAP sea surface salinity to monitor Arctic Ocean freshwater changes. Remote Sens.10, 869. doi: 10.3390/rs10060869. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  TimmermansM. L.TooleJ. M. (2023). The arctic ocean’s beaufort gyre. Ann. Rev. Mar. Sci.15, 223–248. doi: 10.1146/annurev-marine-032122-012034. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  UsovN.KhaitovV.SmirnovV.SukhotinA. (2019). Spatial and temporal variation of hydrological characteristics and zooplankton community composition influenced by freshwater runoff in the shallow Pechora Sea. Polar Biol.42, 1647–1665. doi: 10.1007/s00300-018-2407-1. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  VasilenkoA. N.MagritskyD. V.FrolovaN. L.ShevchenkoA. I. (2022). Features of a long-term heat flux formation of the large Russian Arctic rivers and its transformations in estuaries under the influence of climate-induced and dam-induced effects. Geogr. Environ. Sustain.15, 158–170. doi: 10.24057/2071-9388-2022-105

  • CrossRef
  • Google Scholar

• 54

  WebbD. J. (2014). On the tides and resonances of Hudson Bay and Hudson Strait. Ocean Sci.10, 411–426. doi: 10.5194/os-10-411-2014

  • CrossRef
  • Google Scholar

• 55

  ZatsepinA. G.ZavialovP. O.KremenetskiyV. V.PoyarkovS. G.SolovievD. M. (2010). The upper desalinated layer in the Kara Sea. Oceanology50, 657–667. doi: 10.1134/S0001437010050036

  • CrossRef
  • Google Scholar

• 56

  ZhakovskayaZ. A.PetrovaV. N.KhoroshkoL. O.KukharevaG. I.LukinA. A. (2010). Polychlorinated biphenyls and hydrocarbons in bottom sediments of Pechora Basin rivers. Water Resour.37, 75–83. doi: 10.1029/2021JC018341

  • CrossRef
  • Google Scholar

• 57

  ZhukV. R.KubryakovA. A. (2021). Interannual variability of the Lena River plume propagation in 1993–2020 during the ice-free period based on satellite salinity, temperature, and altimetry measurements. Remote Sens.13, 4252. doi: 10.3390/rs13214252. PMID:

  • Pubmed Abstract
  • CrossRef
  • Google Scholar