Oceanologia最新文献

The paper presents a prediction of bed form parameters (length and height) in the remote foreshore of the non-tidal sea. The study site is located in the south Baltic region, near the Coastal Research Station (CRS) in Lubiatowo, Poland (ca. 1–2 Nm off the shoreline at depths of around 16–20 m). The study site is an area with hydrodynamics and lithodynamics typical of the south Baltic coast, built of fine sands. Predictions are based on numerical modelling with an input of measured data. Numerical modelling uses assumptions of specific relations of sand wave height and length with shear stress, grain size, kinematic viscosity, or storm event frequency. To establish these relations for the study area, wave, sea temperature and wind data were collected near CRS Lubiatowo. To verify the predictions made in this research, bathymetric data from a field survey in the study area was used. The results show that the flow is mostly current-dominated. Sand waves formed by hydrodynamic conditions in this area have a steepness of 0.01 to 0.02, a length of 40 to 70 m and a height of 0.6 to 2.3 m.

The general characteristics of tidal currents in the entrance of the Khor Abdullah at Iraq marine water, located at the northwest tip of the Arabian Gulf, were studied based on realistic current measurements. The velocity measurements used in this study extended to about one year, which was never previously available in this vital region of the Arabian Gulf. The results illustrated that this area is characterized by strong currents exceeding 1 m/s during both ebb and flood tides, driven by local water level variations. The maximum currents recorded during the study period were 1.65 and 1.36 m/s at the ebb and flood tides, respectively. Additionally, the monthly averages of ebb currents are higher than those of flood currents. The harmonic analysis results revealed that the tidal effect explained approximately 98% of the variation in water currents, with the remaining percentage due to residual currents. Among the 35 tidal components used in harmonic analysis, the M₂ component was the main contributor to tidal currents variation in the area, followed by S₂, K₁, N₂, and O₁. The residual current seems to have a low effect on the currents variations in the area, with maximum values not exceeding 0.0677 and 0.058 m/s during the ebb and flood tides, respectively. The results obtained give a general view of the tidal current behavior and could be beneficial for several aspects of marine and coastal engineering as well as shipping and navigation activities in this region.

Arsenic is a ubiquitous chemical element, occurring naturally worldwide. Yet due to its global cycle, its concentrations in the marine environment are manifold higher than the terrestrial background and may pose harm to biota. This is especially relevant for the Baltic Sea, which is very susceptible to any kind of pollution. Arsenic transported to the sea is adsorbed on iron oxides or precipitating as flocculating particulates and finally bounded in sediments. Therefore, despite the contemporary emission cuts, the existing pollution remains or constantly circulates in marine habitats. The purpose of the research was to recognize the spatial distribution of arsenic in the surface sediments of the southern parts of the Baltic Sea. The number of 483 samples allowed us to prepare reliable interpolation of arsenic contents in surface sediments. Although arsenic concentrations in the Baltic Sea can be considered low, in particular areas the levels are significantly higher. The observed arsenic concentrations distribution pattern could be mostly explained by natural transportation and accumulation bottom-type distribution.

The Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological parameters like wind speed, sea surface temperature, and near-surface pressure. This article explores the possibility of using a non-parametric algorithm to identify different flow regimes using a one-month long time-series data of the near-surface parameters. The changes in the structure of the time series signal were statistically segmented using an unconstrained non-parametric algorithm. The non-parametric changepoint method was applied to time series of near-surface winds, sea surface temperature, sea level pressure, air temperature and salinity and the segmentations are consistent with visual observations. Identifying different data segments and their simple parameterization is a crucial component and relating them to different flow regimes is useful for the development of parametrization schemes in weather and climate models. The segmentations can considerably simplify the parametrization schemes when expressed as linear functions. Moreover, the usefulness of non-parametric automatic detection of data segments of similar statistical properties shall be more apparent when dealing with relatively long time series data.

The contrast in habitat complexity between emergent (EMV) and submerged vegetation (SUV) zones in aquatic ecosystems results from the differences in the structure of plant above- and belowground parts, subject to seasonal changes. Comparative studies on the influence of habitat complexity created by vegetation on benthic macroinvertebrates in coastal areas are scarce. In order to fill this knowledge gap, we performed a study on a seasonal basis in the brackish Vistula Lagoon (southern Baltic Sea) in two zones: EMV, dominated by a dense belt of Phragmites australis (Cav.) Trin. ex Steud, and SUV, with scattered stands of Potamogeton perfoliatus L. We assumed the following: i. Species richness, diversity, and density of invertebrates are higher in the EMV zone due to greater and less seasonally variable structural complexity than in the SUV zone, ii. High belowground complexity in the EMV zone due to the presence of the rhizome/root matrix, much more robust and denser than in the SUV zone limits the vertical distribution of macroinvertebrates. Both hypotheses were supported. Overall, our results pointing to higher animal diversity and density in more complex aquatic habitats are consistent with other studies, inferred mostly from comparative surveys of bare bottom and that covered with submerged vegetation. The results of this study highlight potentially far-reaching implications for benthic invertebrate fauna and their role in the aquatic ecosystem in the context of increasingly rapid loss of aquatic vegetation due to multiple anthropogenic stressors.

The appearance of internal solitary waves (ISWs) in the Maluku Sea is often captured by satellite imagery. However, no study has revealed details on this phenomenon to date. Here, the characteristics of such ISWs were investigated based on their appearance in synthetic aperture radar (SAR) imagery on 20 February 2015. Two different sources of ISW packets were observed: one packet propagating from the Lifamatola Passage and another from the Sangihe Passage. The vertical structure of the waves was constructed using the Korteweg-de Vries (KdV) model, which suggests an average phase speed of ∼2.8 and 2.7 m s⁻¹ for the first and the second sources, respectively. ISWs originating from the first source had a typical amplitude of O(80 m), while those from the second source were characterized by a lower amplitude of O(40 m). The waves generated horizontal and vertical currents with typical magnitudes of O(1 m s⁻¹) and O(10 cm s⁻¹) for the first source and O(0.6 m s⁻¹) and O(4 cm s⁻¹) for the second source, respectively. The mean energy densities of the first and second sources reached 461 MJ m⁻¹ and 185 MJ m⁻¹, respectively. Single leading solitary wave contained a fraction of approximately 20% and 15% of the baroclinic tidal energy generated in the Lifamatola Passage and Sangihe Passage, respectively.

The two main small pelagic fishes along the southwest coast of India in the eastern Arabian Sea, the Indian oil sardine (Sardinella longiceps) and the Indian mackerel (Rastrelliger kanagurta) were studied to see if there was any notable long-term alteration in phytoplankton in their diets. The basic oceanographic features and the long-term sea surface warming in the study region are first examined using satellite data, which reveals clear seasonality in the wind, sea surface temperature, sea surface salinity, and chlorophyll-a as well as long-term ocean surface warming. We then considered a recent gut content data set of Indian oil sardine and Indian mackerel from the 2010–2011 period from two landing places along India's southwest coast, which was compared with numerous historical data sets to find out if there was any long-term phytoplankton compositional change in the diet. The recent data revealed the same dominant phytoplankton as those collected decades ago, such as Coscinodiscus, Nitzschia, Pleurosigma, and Thalassiosira in sardines and Coscinodiscus, Thalassiosira, Ceratium, Dinophysis, Protoperidinium, and Pyrophacus in mackerel. This suggests the lack of any significant long-term qualitative shift of phytoplankton in the Indian oil sardine and Indian mackerel over the last several decades. However, several phytoplankton genera found present in the recent data were absent in the historical data, including 10 genera for mackerel and 17 for Indian oil sardine. These recent phytoplankton records are intriguing, and we feel they could be an early sign of long-term phytoplankton compositional or relative abundance shift, although additional research is needed to confirm this.

The article analyzes the impact of nutrients (nitrates and phosphates) supplied from the land on the waters of the Puck Lagoon (Gdańsk Basin, southern Baltic). The study is based on the numerical modelling. The model data was verified by comparison with the in situ measurement data. The spatial and temporal variability of the concentrations of nitrate, phosphate, and chlorophyll a were analyzed. We came to the conclusion that the load of nutrients deposited from the land side to the waters of the Puck Lagoon is relatively small (but not negligible compared to the Vistula River). However, even when a little runoff enters the reservoir with a very limited water exchange, like the Puck Lagoon, there are periods when riverine nutrients load significantly affects the functioning of the ecosystem.

As an indicator of exchanges between watersheds, rivers and coastal seas, salinity may provide valuable information about the exposure, ecological health and robustness of marine ecosystems, including especially estuaries. The temporal variations of salinity are traditionally approached with numerical models based on a physical description of hydrodynamic and hydrological processes. However, as these models require large computational resources, such an approach is, in practice, rarely considered for rapid turnaround predictions as requested by engineering and operational applications dealing with the ecological monitoring of estuaries. As an alternative efficient and rapid solution, we investigated here the potential of machine learning algorithms to mimic the non-linear complex relationships between salinity and a series of input parameters (such as tide-induced free-surface elevation, river discharges and wind velocity). Beyond regression methods, the attention was dedicated to popular machine learning approaches including MultiLayer Perceptron, Support Vector Regression and Random Forest. These algorithms were applied to six-year observations of sea surface salinity at the mouth of the Elorn estuary (bay of Brest, western Brittany, France) and compared to predictions from an advanced ecological numerical model. In spite of simple input data, machine learning algorithms reproduced the seasonal and semi-diurnal variations of sea surface salinity characterised by noticeable tide-induced modulations and low-salinity events during the winter period. Support Vector Regression provided the best estimations of surface salinity, improving especially predictions from the advanced numerical model during low-salinity events. This promotes the exploitation of machine learning algorithms as a complementary tool to process-based physical models.