Search Results (1,643)

As sea ice recedes, the strategic importance of Arctic shipping routes has intensified, yet the complex polar environment poses severe challenges to navigational safety. Through a systematic search of the Scopus database, relevant key studies in both English and Chinese were identified and selected based on predefined inclusion criteria for in-depth review. The present study establishes a systematic categorization framework to parse existing research on Arctic navigational risk assessment. It structurally analyzes the literature across three core dimensions: sea ice characteristics, accident statistical analysis, and risk modeling methodologies. Addressing current limitations in data sparsity, factor coupling, and dynamic forecasting, this study proposes that future research should focus on the construction of structural models for risk interdependencies, multi-source data-driven environmental risk learning, and intelligent small-sample assessment based on Case-Based Reasoning (CBR), which extracts effective risk solutions from limited historical samples by interpreting past navigational successes and failures to improve decision quality. This review aims to provide a comprehensive reference for developing a systematic and intelligent risk assessment architecture for Arctic shipping. Full article

Even under a warming climate, winter sea ice in the Bohai Sea continues to threaten ships and offshore/coastal infrastructure. Reliable pre-season prediction of the overall wintertime sea ice condition in the Bohai Sea, as represented by the Bohai Sea Ice Grade (BSIG), is therefore important for disaster preparedness and mitigation. Based on the 1979–2024 BSIG record, this study compares seven statistical and AI-based seasonal prediction methods: analog year analysis, multiple linear regression, stepwise regression, Principal Component Regression, a cross-correlation-based regression model, support vector regression, and the Bayesian Ensemble Bohai Ice Grade Net (BE-BIGNet). As potential precursors, we considered sea ice extent in 14 Arctic regions together with 114 large-scale atmospheric and oceanic circulation indices. The results suggest substantial differences in predictive skill among the methods. Among the tested approaches, BE-BIGNet, which combines Bayesian regularization with bootstrap median ensembling, achieves strong full-period performance and stable skill during the independent test period, suggesting that it may provide a useful framework for operational BSIG forecasting in the Bohai Sea. Full article

The crushing failure of sea ice is a critical design issue for polar offshore structures and ship structures because ice-induced loads may generate pronounced local damage and dynamic responses. Accurately modelling this process remains challenging because ice crushing involves localized fragmentation, crack propagation, rubble accumulation, and repeated contact release. This paper presents a controlled numerical sensitivity study of level-ice crushing against a vertical structure using a coupled LS-DYNA framework that combines the Structured Arbitrary Lagrangian–Eulerian (S-ALE) formulation with the Cohesive Element Method (CEM). The study focuses on a benchmark-scale indentation configuration and examines how mesh topology, mesh size, and imposed indentation velocity affect the predicted fracture morphology and load-time histories. The results show that random triangular meshes better reproduce stochastic fragmentation and lateral flaking than regular triangular or quadrilateral meshes, while finer meshes reduce excessive load oscillations and provide more stable force histories. The velocity study indicates a transition from gradual crushing and fragment retention at lower velocities to more rapid brittle chipping and stronger dynamic fluctuations at higher velocities. A benchmark-level comparison with published ice-indentation simulations shows that the predicted peak line load is of the same order of magnitude as reference results. The proposed framework is therefore useful for investigating numerical sensitivities and failure-mode trends in ice-crushing simulations, although final design-load application requires further calibration and formal mesh-independence assessment. Full article

The Northern Sea Route (NSR) is becoming increasingly accessible as Arctic sea ice declines, motivating data-driven forecasts of seasonal navigability. We compiled a 13-year (2013–2025) monthly dataset of AMSR2 sea ice concentration (SIC) and ERA5 atmospheric reanalysis variables over the NSR corridor (68–80° N, 30–180° E) and benchmarked a hierarchy of forecasting models for 1-, 3-, and 6-month lead times. Baselines (climatology, persistence, anomaly persistence, SARIMA, ridge regression) were compared with compact deep learning architectures (LSTM, Transformer; 10,000–70,000 parameters) trained on 12-month sequences with anomaly targets and five-seed ensembles. Three findings emerge. First, the seasonal cycle explains 98.0% of the monthly SIC variance, so climatology alone yields RMSE = 4.56% and three-class navigability accuracy of 87.5%. Second, SARIMA, the compact LSTM ensemble, random forest, and MLP_small all yield small positive skill scores over climatology: SARIMA achieves the lowest 1-month RMSE (3.98%, skill score +0.239), while the compact LSTM ensemble shows positive skill at all horizons (mean skill score +0.038); however, the bootstrap confidence intervals overlap and these differences are not statistically distinguishable from climatology. Third, all skilful models converge to identical classification metrics (accuracy 0.875, macro-F1 0.78, κ = 0.76); McNemar tests and overlapping bootstrap confidence intervals show no statistically significant differences. Permutation importance confirms that AMSR2 ice-state features dominate, whereas the high raw correlations of ERA5 radiation variables collapse after detrending. These results indicate that compact statistical and deep learning models are equivalent for NSR seasonal navigability prediction and that honest baseline comparison is essential when seasonal cycles dominate. Full article

The “New Arctic” regime represents a prominent climatic feature of the Arctic Ocean under global warming, characterized by persistently low summer sea ice extent, a marked reduction in sea ice thickness, and an expansion of open water areas at high latitudes. As a key indicator of the Arctic sea ice system, the spatiotemporal evolution of sea ice thickness and its underlying driving mechanisms remain incompletely understood. Using reanalysis datasets and remote sensing observations, this study identifies major abrupt shifts in Arctic sea ice thickness under the New Arctic regime, reveals the spatiotemporal distribution characteristics of winter sea ice thickness, and examines the driving factors from both thermodynamic and dynamic perspectives. The results show that the evolution of Arctic sea ice thickness can be divided into three phases: a high-level period during the “Traditional Arctic” (1979–1992), a rapid thinning period during the New Arctic transition (1993–2012), and a low-level stabilization period in the New Arctic regime (2013–2023). The first EOF mode of winter sea ice thickness depicts a spatially consistent thinning pattern across the entire Arctic, with the most significant reduction occurring in the multi-year ice regions north of the Canadian Arctic Archipelago and Greenland. The second EOF mode exhibits an out-of-phase variation between the Atlantic and Pacific sectors of the Arctic, accompanied by a shrinking amplitude and weakened regional oscillations. The coupling between surface air temperature and sea ice thickness displays distinct phase dependence: their negative correlation is strongest during the transition period (r = −0.78, p < 0.001) but becomes statistically insignificant in the New Arctic regime. Sea ice motion speed exhibits an overall accelerating trend, which extends from the marginal seasonal ice zones toward the high-latitude multi-year ice regions, accompanied by a notably enhanced sensitivity of sea ice motion to wind forcing. Sea ice volume flux through the Fram Strait is primarily controlled by ice motion speed, whose contribution to the flux is approximately 2.6 times that of ice thickness. The recovery of ice drift speed offsets the thinning of sea ice cover, leading to a partial rebound in volume flux during the New Arctic steady state. This study identifies the evolutionary patterns and drivers of Arctic sea ice thickness under the New Arctic regime, providing a scientific basis for further understanding the changes in the Arctic climate system and associated air–sea ice interactions. Full article

With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating “dataset construction–algorithm improvement–system development”. A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety. Full article

High-latitude coastal regions are highly sensitive to climate change, yet their geomorphology is obscured by sea ice, landfast ice and seasonal snow, restricting the applicability of optical remote sensing for fine coastal classification. To address this limitation, we develop an interpretable coastal classification framework integrating ICESat-2 photon-counting LiDAR and explainable machine learning. Multi-dimensional morphometric features describing cross-shore geometry, vertical relief and local slope variability are extracted from ICESat-2 ATL03 along-track profiles to train a CatBoost classifier, with five-fold cross-validation and sample weighting to mitigate class imbalance. Introducing SHAP-based interpretability into ICESat-2-driven coastal geomorphic classification enables the identification of morphometric controls on coastal-type differentiation. Validated in the Bering Sea with 447 profiles and a 75%/25% stratified split, the framework achieved an overall accuracy of 86.6%, a macro-average recall of 89.4% and a Kappa coefficient of 0.84. SHAP analysis identifies that coastal width is the most influential feature for model-based classification of coastal geomorphic types, while slope and local steepness variability serve as important predictive indicators for distinguishing rocky and sedimentary coasts. This framework links data-driven classification to geomorphic processes and provides a potentially generalisable approach for fine-scale coastal mapping in high-latitude environments. Full article

Climate change is altering the marine environment in many ways, including increasing sea surface temperatures and decreasing sea ice. Species distributions are changing and ‘out of habitat’ marine mammals are being recorded. ‘Out of habitat’ (OOH) refers to individuals recorded outside of their natural range or within environments unsuitable for their survival. This phenomenon is currently understudied. This study aimed to identify the scale of the issue, identify consensus opinions on trends and possible causes of these OOH events, as well as assessing the preparedness of local authorities and rescue networks in responding to OOH marine mammals. This study is the first to assess and quantify this issue through a formal consultation process, conducted through an online questionnaire and a detailed examination of two case studies, from the UK and Peru. Sixty-three questionnaire responses were received from six different continents and the majority (60%) reported OOH events in their region. Through the questionnaire and case studies, 42 different marine mammal species were reported to be affected. This clearly indicates this is a global phenomenon, affecting at least 32% of all known pinniped and cetacean species. Most participants (77%) also believed these OOH events are increasing, and 55% believe these events are forerunners to distribution changes. Data from Peru showed an endangered species, the Galápagos fur seal (Arctocephalus galapagoensis), had made a range shift. Of the reported OOH species, four are classified as either endangered or critically endangered. The consensus opinion was that climate change is the leading driver of these OOH events, with sea surface temperatures and changes in prey distribution reported as the most important factors. The success of OOH responses was reported as highly inconsistent and, in many cases, requires specialist training, e.g., in human–wildlife conflict. The information derived from this study can be used to advise conservation plans, as well as provide a foundational step for future research into the possible trends in these OOH movements. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Sea buckthorn leaves are a relatively underutilised component of sea buckthorn processed products; however, various studies have indicated that they possess hypoglycaemic potential. Under alkaline solubilisation and acid-precipitation conditions, the extraction yield of sea buckthorn leaf protein (SLP) reached 19.33%. Trypsin was selected as the hydrolysing enzyme to extract SLPPs-T, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.1361 ± 0.017 mg/mL and 0.1286 ± 0.012 mg/mL, respectively. UV spectroscopy, Fourier transform infrared spectroscopy, circular dichroism spectroscopy and particle size analysis indicated that the secondary and microstructures of SLP underwent changes following its hydrolysis to SLPPs-T; following separation, purification, sequence identification and computer screening, two novel peptides, PM-8 and VG-11, were obtained; molecular docking, solid-phase synthesis and in vitro experiments confirmed that VG-11 exhibited superior inhibitory activity, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.3885 ± 0.015 mM and 0.2611 ± 0.021 mM, respectively. In summary, this study explored the potential of sea buckthorn leaf protein as a natural hypoglycaemic product through a combination of computational modelling and experimental methods, thereby significantly enhancing the value of sea buckthorn resources. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

Rivers are a key pathway for the transport of plastics into the ocean. Studies of plastic pollution in Arctic rivers remain limited due to the inaccessibility of sampling sites and work in extreme weather conditions. This work presents the results of a three-year (2019–2021) survey of floating large microplastics (0.5–5 mm) and meso/macroplastics (>5 mm) in the Northern Dvina River, an actively navigated river that drains a densely populated region into the White Sea. Sampling was conducted during the ice-free periods (May–October) along a ∼3.5 km transect using a Neuston net, providing a multi-year dataset spanning three ice-free seasons. A critical methodological advancement was the calculation of plastic river–sea flux using the discharge of the sampled surface layer (upper 20 cm), which constitutes only ∼3% of the river’s total discharge, rather than the total discharge itself. Observed microplastic concentrations (average 0.003 items ${\mathrm{m}}^{-3}$) were low compared to many European rivers, and lower than those reported in the adjacent Barents and Kara Seas. Microplastic abundance was significantly lower during the high-water season than during the low-water season, which resulted in practically no seasonal variability in microplastic fluxes from the river to the White Sea (average 0.3 items ${\mathrm{s}}^{-1}$). A notable finding was that in some cases, meso/macroplastics outnumbered microplastics by item count, underscoring the river’s role as a significant source of larger plastic debris. A geospatial assessment of Arctic rivers’ pollution potential was performed, using socio-economic indicators such as near-delta population density and port activity. This study identified the Northern Dvina River as a major contributor of microplastics among the Arctic rivers. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

The effects of environmental factors on zooplankton within the marginal ice zone (MIZ) of the Barents Sea remain poorly understood. To address this knowledge gap, we investigated mesozooplankton communities across the central, northern, and northeastern regions in April 2016. Abundance and biomass ranged from 90 to 997 individuals m⁻³ and from 1.1 to 48.6 mg dry mass m⁻³ (0.3 to 13.6 g dry mass m⁻²), respectively. Oithona similis was the most abundant taxon, while calanoid copepods, including Calanus spp., Metridia longa, and Pseudocalanus spp., dominated total biomass. The spatial distribution of mesozooplankton communities was closely linked to the physical properties of water masses. Cluster analysis identified two distinct assemblages associated with Polar Front Water and Arctic Water. Redundancy analysis and generalized linear models identified temperature, mean salinity, mean chlorophyll a concentration, and sea ice concentration as significant predictors of abundance and biomass. The dominance of older life stages within major copepod taxa indicated a winter status for the mesozooplankton community. Regional and temporal comparisons of mesozooplankton biomass with prior May–June data from central and northwestern areas highlighted higher productivity in the northern and northeastern MIZ. This increase is potentially related to the warming trends observed in the Arctic since the 2000s. Our research provides novel insights into Arctic marine zooplankton assemblages and serves as a valuable baseline for future ecological monitoring and modeling of the Barents Sea ecosystem in the context of global climate change. Full article

Metro Manila, one of the world’s most densely populated megacities, is highly vulnerable to sea-level rise because of its low-lying deltaic location, frequent tropical cyclones, and rapid anthropogenic subsidence caused mainly by groundwater extraction. This study brings together historical tide-gauge records from the Port of Manila (PSMSL) with the Sixth Assessment Report of Intergovernmental Panel on Climate Change (IPCC AR6) projections under Shared Socioeconomic Pathways, adding in vertical land motion (VLM) and sea-level fingerprints to work out local relative sea-level (RSL) changes. Assuming a constant subsidence rate, cumulative VLM reaches ~0.785 m by 2100 and ~1.289 m by 2150. When you factor in climatic contributions (amplified 10–20% by fingerprints, especially under high-emission scenarios thanks to far-field Antarctic ice-loss effects in the western Pacific), projected RSL ranges from 1.09–1.42 m (SSP1-2.6) to 1.51–2.00 m (SSP5-8.5) by 2100, and from 1.70–2.28 m to 2.41–3.54 m by 2150. Results show that 7.95–11.15 km² (1.2–1.8% of land area under SSP5-8.5) could face permanent inundation, mostly in Malabon (~18%), Navotas (~20%), and Manila (~7%). Our conservative estimates (permanent ocean-connected flooding, excluding existing aquaculture areas) come in much lower than earlier mid-century projections of up to a 30% area affected. All this will worsen chronic tidal flooding, erosion, saltwater intrusion, and risks to millions in low-lying districts. We urgently need integrated adaptation, better groundwater regulation, and a mix of nature-based and engineered solutions. Full article