Search Results (71)

A remote analysis of coastal sedimentation in northern KwaZulu-Natal (KZN), South Africa, describes how summer runoff and winter wave-action operate within a highly variable climate. Despite rising sea levels, the sediment flux can sustain beaches under certain conditions. Daily satellite red-band reflectivity and ocean–atmosphere reanalysis datasets were studied over the period of 2018–2025. Statistical results indicate that streamflow discharges are spread northward by oblique wave-driven currents. Sediment concentrations peak during late winter (>1 mg/L, May–October) when deep turbulent mixing (>40 m) mobilizes sand from the seabed. A case study from September 2021 revealed that ridging high-pressure/cut-off low weather patterns can simultaneously increase streamflow, wave energy, and wind power, creating a surf-zone sediment conveyor along the coast of northern KZN. Long-term climate diagnostics from 1981 to 2025 reveal upward trends in coastal runoff, vegetation, and turbidity (0.29 σ/yr) that point to an increasingly vigorous water cycle. The warming of the southeast Atlantic intensifies the sub-tropical upper-level westerlies and late winter storms over southeast Africa. These processes occur in 5–8 year cycles and drive shoreline advance and retreat, from accretion ~1 T/m and storm surge inundations up to 5.5 m. Using Digital Earth, it was noted that ~1/4 of beaches around Africa are gaining sediment while ~1/3 are eroding. Although remote information could not close the sediment budget, realistic estimates of long-shore transport in the surf-zone (>10⁴ kg/yr/m) and on the beach (>10³ kg/yr/m) were calculated. These provide an emerging explanation for the resilience of northern KZN beaches, as sea levels rise at a rate of 0.6 cm/yr. Full article

The Sentinel-3 Ocean and Land Colour Instrument (OLCI) is designed for water monitoring. Its 21-spectral bands serve as the basis for the precise retrieval of water quality parameters. However, its coarse resolution restricts the depiction of the spatial distribution of water quality parameters in small inland water bodies. Spatial–spectral fusion is a common method to address the inherent constraints between the spatial and spectral resolutions of sensors. Central to the popular methods is the deep learning-based method. Nonetheless, deep-learning-based models still face challenges in fusing Sentinel-2 Multi-Spectral Instrument (MSI) and Sentinel-3 OLCI data. Here, we propose a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), which effectively integrates OLCI’s spectral advantage and MSI’s spatial resolution. Quantitative evaluation was conducted against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN). The results show that MSA-UGAN achieves the best overall performance: QNR (0.9709) and SSIM (0.9087) are the highest, while SAM (1.1331), spatial distortion (D_S = 0.0389), and spectral distortion (D_λ = 0.0252) are the lowest. This shows that MSA-UGAN can better preserve the spatial details of S2 MSI and the spectral features of S3 OLCI data. Moreover, ERGAS (2.2734) also performs excellently in the comparative experiments. The experiment of Chlorophyll-a inversion using the fused image in Chen Lake revealed a spatial gradient ranging from 3.25 to 19.33 µg/L, with the highest concentrations in the southwestern nearshore waters, likely associated with aquaculture. These results jointly indicate that MSA-UGAN can generate high-spatial-resolution multispectral images, and the fused images can be effectively utilized for water quality monitoring, thereby providing essential data support for the precision management and scientific decision-making regarding inland lakes. Full article

Carbon fluxes from the atmosphere to the ocean and from the ocean surface to the deep ocean are a key pathway in the long-term sequestration of anthropogenic CO₂. Particulate Organic Carbon (POC), which comprises living plankton, detritus and other microscopic organisms, is a very dynamic carbon pool in surface waters, so an ability to assess POC reliably from satellites and autonomous profilers is fundamental to the quantification of the reservoirs and fluxes of carbon within the ocean, and to assess their response to climate change. In situ records from sample filtration during dedicated hydrographic surveys are limited both in terms of spatial coverage and time, so reliable algorithms are required that make use of readily available autonomously collected data that provide much better spatial and temporal coverage. In this paper, algorithms that use ocean colour data from satellites to estimate POC are re-assessed, and then the satellite-derived products are compared with near-surface in situ observations from biogeochemical (BGC) Argo profilers. The satellites and in situ BGC-Argo records match each other to within 30%, but a regional bias persists that may be related to the BGC-Argo fluorometers overestimating the chlorophyll concentration in the Southern Ocean. A simple coarse-resolution regional correction to the observed chlorophyll-a concentration and backscatter coefficient, plus the removal of clear outliers, improves the agreement to approximately 15%. The association of POC with the surface chlorophyll value is so strong that an algorithm based on chlorophyll-a alone provides an almost equally good estimate of POC compared with more complex algorithms that incorporate additional bio-optical variables such as the backscattering coefficient. Full article

The ocean constitutes the largest actively exchangeable carbon reservoir in Earth’s surface system, with the ocean–atmosphere system functioning as an integrated entity that modulates atmospheric CO₂ concentrations over geological timescales. While carbonate and organic-rich sedimentary carbon sinks have been the subject of extensive research, their synergistic roles in long-term carbon–climate feedback loops, as well as the degree to which microbial mediation links ocean hydrographic states to basin-scale carbon sequestration efficiency, remain poorly synthesized. Here, we develop a mechanistic framework comprising five intercoupled components: (1) driving factors (tectonic–climatic forcing and anthropogenic analogs); (2) ocean state controls (basin restriction, water column stratification, and redox conditions); (3) microbial processes (microbial carbon pump-mediated transformation of dissolved organic carbon and the modulating influence of microbial carbonate formation); (4) sedimentary carbon sinks (carbonate platforms versus organic-rich shales underpinning organo-mineral stabilization); and (5) Earth system feedback expressions (e.g., carbon isotope excursions and sustained perturbations in atmospheric CO₂ levels). This framework is validated across three contrasting sedimentary basins, including the Western Tethys rift basins, the Cambrian South China platform system, and the Toarcian Lower Saxony restricted basin, and via three falsifiable propositions. Converging evidence from these case studies corroborates three key conclusions: (1) basin restriction and diminished water mass renewal foster water column stratification and hypoxic/anoxic conditions, thereby enhancing organic carbon preservation (P1); (2) the tectonic and depositional setting of a basin modulates the relative predominance of carbonate and organic carbon sinks (P2); and (3) post-extinction anachronistic facies record amplified microbial control over carbon burial pathways (P3). By emphasizing the context dependence of carbon sequestration processes and the significance of organo-mineral stabilization alongside particulate organic carbon export, this synthesis provides a transferable analytical framework for interpreting deep-time carbon cycle transitions and for contextualizing the impacts of modern ocean warming and deoxygenation on natural carbon sinks. Full article

Chlorophyll-a (Chl-a) concentration is a key indicator of coastal ecosystem health, reflecting both primary productivity and the ecosystem’s response to climate change and human activities. This study quantifies long-term Chl-a trends in the Yellow and Bohai Seas using a multi-source remote sensing reconstruction dataset generated with deep learning algorithms. Quantile regression was applied to assess changes across the 75th, 50th, and 25th percentiles, and environmental drivers—including sea surface temperature, mixed layer depth, wind speed, and sea surface height anomalies—were evaluated in representative regions such as estuaries, aquaculture zones, and offshore waters. From 2005 to 2024, Chl-a concentrations declined across the 75th, 50th, and 25th percentiles, with rates of −4.82 × 10⁻³, −4.50 × 10⁻³, and −4.09 × 10⁻³ mg·m⁻³·a⁻¹, respectively (where “a” denotes year). The decline also showed strong seasonal differences, with summer decreases (−0.0638 mg·m⁻³·a⁻¹) substantially greater than winter (−0.04 mg·m⁻³·a⁻¹). Spatially, the decline was more pronounced in high-concentration nearshore waters, with rates of −0.0283 mg·m⁻³·a⁻¹ in the Qinhuangdao region, compared to −0.0137 mg·m⁻³·a⁻¹ in deeper offshore waters. Mixed-layer depth and wind speed emerged as the primary physical controls, with nearshore declines driven by enhanced vertical mixing and offshore changes dominated by mesoscale oceanic processes. These findings provide new insights for modeling and managing coastal ecosystems under combined climate and anthropogenic pressures. Full article

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article

The Weimei Formation, the most complete Upper Jurassic sedimentary sequence in the Tethyan Himalaya, is crucial for understanding the tectono-sedimentary evolution of the northern Indian margin. However, its depositional environment remains debated, with conflicting shallow- and deep-water interpretations. This study integrates sedimentary facies, petrography, zircon geochronology, and geochemical analyses to constrain the provenance, depositional environment, and tectonic setting of the Weimei Formation. The results reveal that the sedimentary system primarily consists of shoreface, delta, and shelf facies, with locally developed slope-incised valleys. Detrital zircon ages are concentrated at ~468 Ma and ~964 Ma, indicating a provenance mainly derived from the Indian continent. Geochemical characteristics, such as high SiO₂, low Na₂O–CaO–TiO₂ contents, right-leaning REE patterns, and significant negative Eu anomalies, suggest the derivation of sediments from felsic upper crustal recycling within a passive continental margin. Stratigraphic comparison between southern and northern Tethyan Himalayan sub-zones reveals a paleogeographic “uplift–depression” pattern, characterized by the coexistence of shoreface–shelf deposits and slope-incised valleys. This study provides key evidence for reconstructing the Late Jurassic paleogeography of the northern Indian margin and the tectonic evolution of the Neo-Tethys Ocean. Full article

The current standard for sea ice engineering in the Bohai Sea implements a 1/4° grid method, which cannot satisfy the safety of oil and gas activities in the southern Bohai Sea, and therefore more detailed information on ice conditions and a more refined ice zone division are necessary. In the present study, up to 1/12° resolution sea ice characteristic data (period, thickness, concentration, and strength) were obtained based on the NEMO-LIM2 ice–ocean coupling model. On this basis, the design sea ice strength parameters were derived with different return periods from 1 to 100 years. Among the total of 53 grids, the mean ice periods in the southern Bohai Sea from 1951 to 2022 were 2–35 days, the mean ice concentration values were 8.3–64.6%, and the mean ice thicknesses were 2–15 cm. The design uniaxial compressive strengths and shear strengths at almost all grids exceeded 2.00 MPa and 1.00 MPa for return periods over 20 years, respectively. The design flexural strengths for the 100-year return period ranged from 463 to 594 kPa. For the 100-year return period scenario, all grids exhibited design tensile strengths exceeding 200 kPa. Across the southern Bohai Sea, the most severe ice conditions occur in nearshore zones, and the ice conditions display a distinct spatial gradient with Bohai Bay > offshore deep-water areas > Laizhou Bay. The mean ice thickness, concentration, design flexural and tensile strengths derived in this study were lower compared to the ice parameters suggested in the current standard, and design uniaxial compressive and shear strengths derived here were comparable to those suggested in the current standard. The refined grid used here captures more detailed spatial variations in the design strength values of sea ice engineering parameters in the southern Bohai Sea, providing more accurate data support for the anti-ice design of marine structures. Full article

Investigating biodiversity in remote and harsh environments, particularly in the Southern Ocean, remains costly and challenging through traditional sampling methods such as trawling. Environmental DNA (eDNA) sampling, which refers to sampling genetic material shed by organisms from environmental samples (e.g., water), provides a more cost-effective and sustainable alternative to traditional sampling approaches. To study the biogeographic patterns of two typical mesopelagic fishes, Antarctic lanternfish (Electrona antarctica) and Antarctic deep-sea smelt (Bathylagus antarcticus), in the Cosmonaut Sea in the Indian Ocean sector of the Southern Ocean, we conducted both eDNA and trawling sampling at a total of 86 stations in the Cosmonaut Sea during two cruises in 2021–2022. Two sets of species-specific primers and probes were developed for a quantitative eDNA analysis of two fish species. Both the eDNA and trawl results indicated that the two fish species are widely distributed in the Cosmonaut Sea, with no significant difference in eDNA concentration, biomass, or abundance between stations. Spatially, E. antarctica tended to be distributed in shallow waters, while B. antarcticus tended to be distributed in deep waters. Vertically, E. antarctica was more abundant above 500 m, while B. antarcticus had a wider range of habitat depths. The distribution patterns of both species were affected by nutrients, with E. antarctica additionally affected by chlorophyll, indicating that their distribution is primarily influenced by food resources. Our study provides broader insight into the biogeographic patterns of the two mesopelagic fishes in the remote Cosmonaut Sea, demonstrates the potential of combining eDNA with traditional methods to study biodiversity and ecosystem dynamics in the Southern Ocean and even at high latitudes, and contributes to future ecosystem research and biodiversity conservation in the region. Full article

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks. Full article

The hydrogeological study of the Rharb coastal basin, located in the semi-arid northwest region of Morocco, focuses on its two aquifers: the Plio-Quaternary aquifer characterized by high-quality water with salt concentrations ranging from 0.4 to 2 g/L, and the Upper Quaternary aquifer, with lower water quality (2 to 6 g/L). The deep aquifer is overexploited for agricultural purposes. This overexploitation has led to declining piezometric levels and the worsening of the oceanic intrusion phenomenon. The study aims to develop a numerical model for a period of 15 years, from 1992/93 to 2006/07 for monitoring groundwater quantity and quality, considering recharge, exploitation, and basin characteristics. A hydrodynamic model based on storage coefficient calibration identifies overexploitation for irrigation, increasing from 93 Mm³ in 1993 to 170 Mm³ in 2007, as the primary driver of declining water levels. A hydrodispersive model highlights higher salt concentrations in the shallow aquifer (up to 6 g/L), high nitrate concentrations due to human activity, and pinpoints areas of seawater intrusion approximately 500 m from the shoreline. Although the deeper aquifer remains relatively preserved, negative hydraulic balances from −15.4 Mm³ in 1993 to −36.6 Mm³ in 2007 indicate an impending critical period. Full article

Over the past three decades, increasing atmospheric CO₂ (AtmCO₂) has led to climate warming, sea ice reduction and ocean acidification in the Beaufort Sea (BS). Additionally, the effects of upwelling on the carbon cycle and acidification in the BS are still unknown. The Regional Arctic System Model (RASM) adequately reflects the observed long-term trends and interannual variations in summer sea ice concentration (SIC), temperature, partial pressure of CO₂ (pCO₂) and pH from 1990 to 2020. Multiple linear regression results from a control case show that surface (0–20 m) pH decline is significantly driven by AtmCO₂ and SIC, while AtmCO₂ dominates in subsurface (20–50 m) and deep layers (50–120 m). Regression results from a sensitivity case show that even if the AtmCO₂ concentration remained at 1990 levels, the pH would still exhibit a long-term decline trend, being significantly driven by SIC only in the surface layers and by SIC and net primary production (NPP) in the subsurface layers. In contrast to the nearly linearly increasing AtmCO₂ over the last three decades, the ocean pH shows more interannual variations that are significantly affected by SIC and mixed layer depth (MLD) in the surface, NPP and Ekman pumping velocity (EPV) in the subsurface and EPV only in the deep layer. The comparison of results from high and low SIC years reveals that areas with notable pH differences are overlapping regions with the largest differences in both SIC and MLD, and both cause a statistically significant increase in pCO₂ and decrease in pH. Comparison of results from high and low EPV years reveals that although stronger upwelling can lift up more nutrient-rich seawater in the subsurface and deep layers and lead to higher NPP and pH, this effect is more than offset by the higher DIC lifted up from deep water, leading to generally lower pH in most regions. Full article

Monitoring the oceans and establishing a global ocean observing system is a task of paramount importance for topics as diverse as the study of climate change, the management of marine environments, and the safety of coastal areas and marine traffic. These systems must be based on long-term observations that allow the correct modeling of the behavior of the seas and the proper environmental management of them. Despite the logical present trend toward automation, in situ measurements from oceanographic vessels are still needed at present, especially when dealing with biogeochemical variables or when seeking information from the subsurface or deep layers of the sea. Long-term measurements by oceanographic vessels can be carried out at one single fixed oceanographic station with a high sampling frequency (typically once a month) or across a grid of stations. In the latter case a larger geographical area is usually covered, but the cost is a reduction of sampling frequency. The question that arises is: what objectives can be achieved, and what questions can be answered according to the sampling frequency and the spatial coverage of the monitoring program? In this work, we analyze the influence of the sampling frequency on the capacity of observing programs to capture the temporal variability of ocean variables at different time scales and to estimate average seasonal cycles and long-term trends. This analysis is conducted through the study of sea surface chlorophyll concentrations in the Western Mediterranean. The trade-off between sampling frequency and spatial coverage is addressed. For this purpose, a monitoring program in the Spanish Mediterranean waters is used as a case study. We show that monthly and fortnightly intervals are the best sampling frequencies for describing the temporal variability of ocean variables as well as their average seasonal cycles. Quarterly sampling could also be appropriate for estimating such seasonal cycles. Surprisingly, the limitations of these low frequency samplings do not arise from the high frequency variability of ocean variables but from the shape of the seasonal cycles. Both high and low frequency sampling designs could be suitable for detecting long-linear trends, depending on the variance of the noise and that of the trend. In the case of quarterly sampling, we show that some statistics improve with the length of the time series, whereas others do not. Although some results may be related to the dynamics of this region, the results are generally applicable to any other marine monitoring system. Full article

In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term Memory (LSTM) neural network. Adopting the permutation feature importance method, time sequences of several geographical and physical variables, including longitude, latitude, time, sea surface temperature, salinity, sea level anomaly, wind field, etc., were selected and integrated to the reconstruction model as input parameters. Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. The average unbiased percentage difference (UPD) of reconstructed Chl-a concentration is 11.7%, which is 2.9%, 7.6%, 10.6%, and 10.5% smaller than that of the other four models, respectively. Over the majority of the study area, the root mean square error is less than 0.05 mg/m³ and the UPD is below 10%, indicating that the LSTM model has considerable potential in accurately reconstructing sea surface Chl-a concentrations in shallow waters. Full article

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. Full article