Search Results (1,518)

Reliable nearshore wave information is essential for harbor engineering, coastal-hazard mitigation, and marine operations, yet field observations are often limited by sparse monitoring networks, data gaps, and few high-wave samples. Using hourly observations from three marine stations and one tide gauge, this study develops a multi-station virtual-buoy framework to reconstruct significant wave height, peak period, and wave direction. Random Forest is used as the primary model, and an independent validation set guides comparisons of feature modules, coordinate representations, lagged inputs, static spatial features, and alternative algorithms; the test set is retained for final hold-out evaluation. Validation results show that wave and wind inputs form a consistently competitive configuration: wind provides the most consistent auxiliary benefit, although the absolute improvements are generally modest, whereas current and tide provide limited and less consistent additional benefit. Hs is comparatively less sensitive to lag configuration, whereas Tp and Dir benefit more consistently from short-term lagged inputs; static features add little. Historical high-wave-event-window augmentation substantially improves Hs reconstruction during unseen high-wave events, although the most severe peak remains underestimated. Overall, validation-guided feature selection and targeted historical-event augmentation improve reconstruction under both ordinary and high-wave conditions while retaining a relatively simple model architecture. Full article

Storm-surge residuals represent one of the most destructive marine-coastal hazards, and reliable short-term surge residual prediction is critical for coastal disaster preparedness. Conventional empirical forecasting approaches suffer from limited cross-regional generalization, while high-fidelity physics-based hydrodynamic models such as ADCIRC-SWAN can reproduce complete storm-surge physical processes but demand substantial computational resources. In this study, a three-layer back-propagation neural network (BPNN) for storm-surge residual forecasting is constructed, which is driven by output datasets from the validated ADCIRC-SWAN coupled hydrodynamic model. Wind speed, significant wave height, sea-surface atmospheric pressure, and the simulated current-time storm-surge residual are selected as input predictors. Simulation-derived samples are pre-processed via data cleaning and Min-Max normalization, and two different dataset partitioning strategies (random mesh-point-based partition and time-sequential partition) are implemented for comparative experiments. After hyperparameter sensitivity tests, the optimal network configuration with 30 hidden-layer neurons is determined. Model predictive performance is quantitatively evaluated via multi-station time-series comparison and universal statistical metrics including R, NSE, and RMSE. The results show that the BPNN achieves satisfactory performance under random mesh-point-oriented partitioning, yet obvious performance degradation occurs under time-sequential temporal extrapolation, with prominent underestimation of surge peaks. On the basis of BPNN-predicted spatial surge residual fields, storm-surge intensity grading is carried out following the Chinese national standard GB/T 39418-2020. Statistical comparisons between the full computational domain and the Pearl River Estuary sub-region reveal strong spatial aggregation of high-intensity storm-surge grids within the estuary driven by funnel-shaped topographic amplification. This work demonstrates the feasibility of using a BPNN as a surrogate emulator for hydrodynamic outputs under a given typhoon condition; however, limitations in temporal extrapolation performance still need to be addressed before this approach can be practically used in operational early-warning applications. Full article

Typhoons can substantially perturb the thermal and circulation structure of marginal seas, yet in the Yellow Sea most studies have focused on surface cooling, vertical mixing, and coastal upwelling/downwelling, while the hourly-scale response of subsurface circulation below the thermocline remains poorly understood. Using the Finite Volume Community Ocean Model (FVCOM) combined with ERA5 reanalysis and satellite remote sensing, this study investigates the subsurface response of the South Yellow Sea during Typhoon Lekima (2019). Results reveal a pronounced transient sub-basin-scale anticyclonic circulation below the thermocline (30–70 m depth, maximum at 40–60 m) lasting approximately 15 h. This circulation exhibits significantly negative relative vorticity (O(10⁻⁵) s⁻¹) and coherent vertical structure, driven by barotropic pressure forcing. The barotropic sea surface height anomaly was traced to coastal wind forcing along the Shandong Peninsula and northern Jiangsu margins, which subsequently extended into the central basin. These findings reveal an overlooked pathway by which typhoons rapidly reorganize subsurface circulation in shelf seas, with implications for understanding the hourly-scale response of shelf seas to intensifying typhoon activity. Full article

High-frequency meteorological observations from automatic weather stations (AWS) are frequently affected by substantial data gaps in data-sparse regions such as West Africa, limiting their usability for climate analysis and decision-making. This study presents a machine learning-based framework for reconstructing missing hourly observations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products. The framework was applied to a network of over 50 AWS across 10 West African countries over the period 2017–2025, targeting seven meteorological variables: air temperature, relative humidity, global solar radiation, atmospheric pressure, precipitation, wind speed, and wind direction. Gradient boosting models (XGBoost, LightGBM, and CatBoost) were trained following a station-wise and variable-wise strategy, yielding over 300 variable-specific models. Detailed quantitative results are reported for four representative stations spanning distinct agro-climatic zones (Sahelian, Sudanian, coastal, and humid tropical). Air temperature and atmospheric pressure exhibit the highest reconstruction skill, with R² values typically exceeding 0.90, while relative humidity and global solar radiation achieve R² between 0.80 and 0.92. Precipitation and wind speed showed lower reconstruction skill than thermodynamic variables, reflecting their intermittency and sensitivity to local-scale processes. Wind direction, evaluated separately using circular statistics after recombination into degrees, exhibited the largest angular errors, highlighting the difficulty of reconstructing directional variability from large-scale predictors alone. Full article

Accurate prediction of near-surface meteorological variables is important for weather services and coastal risk management. However, the station-level performance of global artificial intelligence (AI) weather models remains insufficiently characterized in complex coastal environments. This study evaluated Pangu-Weather, FengWu, FuXi, and the Global Forecast System (GFS) against observations from 210 stations in eastern coastal China from July to December 2022. The assessment focused specifically on 2 m temperature, surface pressure, 10 m wind speed, and wind direction across forecast lead times, stations, and routine and typhoon conditions. FuXi had the lowest temperature RMSE (1.70 °C), whereas FengWu had the lowest pressure and wind-speed RMSE values (0.89 hPa and 1.17 m/s, respectively). The models showed distinct spatial error patterns, and wind-speed errors were concentrated at several northern coastal and transition-zone stations. During Typhoon Muifa, errors increased for all models, with the largest deterioration occurring for wind speed. FengWu retained the lowest typhoon-period wind-speed RMSE (1.87 m/s), whereas GFS had the largest value (3.03 m/s). Wind-direction distributions remained difficult for all models to reproduce. These results support variable-specific model selection, but they should not be interpreted as a general ranking of atmospheric forecast systems because the validation is limited to near-surface station data and a six-month period. Full article

Seawater reverse osmosis (SWRO) desalination is electricity-intensive, making its environmental performance highly dependent on the power supply. This study couples site-specific energy-system modelling with life cycle assessment to examine how renewable integration changes both total impacts and life-cycle hotspots. A modelled SWRO plant with a specific electricity consumption of 3.4 kWh m⁻³, derived from a process model for Mediterranean feedwater at 48% recovery with energy recovery devices, was assessed in Gela and Trapani, two grid-connected coastal sites with contrasting wind resources, and Lipari, a non-interconnected island with carbon-intensive backup generation. Grid-only, photovoltaic (PV)-grid, wind-grid, and PV-wind-grid configurations were modelled in HOMER Pro without storage and with excess electricity limited to 13%, then evaluated in SimaPro using Environmental Footprint 3.1. Hybrid configurations supplied 46.7%, 64.4%, and 53.3% renewable electricity in Gela, Trapani, and Lipari, reducing climate-change impacts by 30%, 42%, and 45%, respectively. Renewable integration also lowered fossil resource use, whereas PV-containing scenarios increased land and mineral/metal resource use. As electricity-related impacts declined, chemical consumption became the main non-energy hotspot, particularly for ecotoxicity, freshwater and eutrophication. Environmental performance therefore depends not only on renewable penetration, but also on technology choice and the residual electricity supply. Comprehensive system boundaries are essential when planning lower-carbon desalination for coastal and island water security. Full article

Coastal areas are particularly suitable study sites for kite aerial photography (KAP) surveys because of consistent wind and open space. This study uses KAP surveys conducted in January, March, June, August, and October 2025, combined with Structure from Motion photogrammetry and spatially variable error modeling for DEM of Difference (DoD) thresholding to monitor a dune revegetation project at Ocean Beach in San Francisco. Two independent ground surveys were conducted in June and October to compare measured elevation data with KAP-derived elevation. A fuzzy inference system was used to model spatially variable error for uncertainty thresholding with a 95% confidence interval based on point cloud density and surface roughness. The elevation models (raw and thresholded) were then analyzed through geomorphic change detection to assess volumetric change over time. DoD estimates from the four sampling periods between flights were used to derive mass transport rates (120.09, 415.04, 270.86, and 102.22 kg m⁻¹ d⁻¹, respectively) from cross-sections aligned with estimated wind directions, with results similar to wind model estimates from the same time periods, ranging from 50 to 80% (R² = 0.98) of the latter. This method holds promise for better connecting geomorphic to atmospheric research methods, especially important when pursuing process-based restoration. Full article

Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes $n_m$, the filter length L, the CK shift order M, and the characteristic-period scaling $T_{\mathrm{scale}}$—whose manual tuning is impractical for multi-channel floating offshore wind turbine monitoring deployments. We propose GLO-FMD, an adaptive denoising framework coupling the Gray Langurs Optimizer (GLO) with FMD. GLO autonomously optimizes the FMD parameters, thereby aligning the CK objective with structural modal periods rather than impulsive fault periods. Although the search space spans $(n_m,L,M,T_{\mathrm{scale}})$, the CK shift order M is fixed at 2 and $T_{\mathrm{scale}}$ is estimated automatically from the dominant autocorrelation peak; consequently, only $(n_m,L)$ are actively optimized. The optimized FMD decomposes multi-axis tower-base signals into band-limited modes through iterative CK-maximizing FIR filter optimization; each mode identifies a dominant periodic component, and the original signal is zero-phase band-pass filtered around the identified frequencies to preserve physical phase during reconstruction. Validation employs (i) semi-synthetic signals reproducing the measured tower-base structure (a smooth $0.15$ Hz structural mode plus an impulse-excited $3.77$ Hz resonance) with exactly known ground truth—a best-case benchmark by construction that isolates denoising capability from reference uncertainty—and (ii) real strapdown inertial sensor data acquired at 8 Hz from the tower-base interface of a floating offshore wind turbine at an operational site in Chinese coastal waters, over a six-day measurement campaign (18–23 April 2023). Six kinematic channels spanning triaxial acceleration (north, up, east) and triaxial velocity (north, up, east) are analyzed, with 200-s (1600-sample) continuous windows extracted for algorithmic evaluation. On the semi-synthetic data, GLO-FMD achieves a $9.6$–$10.2$ dB SNR improvement over default wavelet thresholding against the known ground truth, and the GLO optimization is essential for reliability—the default FMD configuration is unstable across noise realizations, whereas the optimized parameters recover the clean components consistently. GLO-FMD also achieves pseudo-reference-relative SNR gains of 5.3–7.8 dB over default wavelet thresholding across all six real-data channels. Bootstrap resampling over 12 independent segments confirms statistical significance ($p<0.001$, Cohen’s $d>8$), and a no-reference smoothness index provides complementary evaluation independent of the pseudo-reference assumption. Multi-day consistency analysis yields coefficients of variation below 5%, demonstrating short-term consistency across the environmental conditions represented in the six-day dataset. The online denoising stage requires approximately 1.5 s per channel, supporting potential deployment on edge-computing hardware at the turbine controller level. Full article

Floating offshore wind turbines (FOWTs) experience coupled aero–hydro–servo-elastic excitations that produce structurally distinct kinematic responses at different measurement heights. While field monitoring campaigns increasingly deploy multi-level inertial sensors, the quantitative spectral partitioning of response energy across measurement levels and its relationship to operational and environmental conditions remain poorly characterised for operational FOWTs. This study presents a systematic multi-level spectral decomposition of operational FOWT structural response using synchronised tower-base and nacelle strapdown inertial measurements acquired at 8 Hz over a six-day campaign (18–23 April 2023) at a semi-submersible FOWT in Chinese coastal waters. Six kinematic channels—three translational acceleration components and three translational velocity components—from each sensor are decomposed into four physically defined frequency bands: drift (0.005–0.05 Hz), wave (0.05–0.30 Hz), structural (0.30–0.50 Hz), and rotor (0.50–0.80 Hz). Three derived scalar metrics—band energy ratio (BER), Wave-to-Structural Dominance Ratio (WSDR), and Structural Amplification Factor (SAF)—are defined, with their complete computation specifications and parameter sensitivity analysis provided to ensure reproducibility. Across 36 ten-minute windows spanning diverse conditions (mean wind 4.2–12.1 m/s, $H_s$ 0.8–3.1 m), results reveal a pronounced and consistent spectral separation: the tower base is strongly wave-dominated (${\mathrm{BER}}_{\mathrm{wave}}$ = 75.9%, coefficient of variation CV = 15.0% across days), whereas the nacelle exhibits substantially elevated structural-band energy (${\mathrm{BER}}_{\mathrm{struct}}$ = 10.3%, 4.72-fold amplification relative to tower base, 95% CI [3.63, 5.81]) and rotor-band energy (11.8%, 3.77-fold amplification). The WSDR at the tower base (mean 200.9, 95% CI [101.4, 300.4]) exceeds that at the nacelle (mean 48.4, 95% CI [13.1, 83.7]) by a factor of 4.1×. One-way analysis of variance (ANOVA) reveals that nacelle structural-band BER is significantly modulated by SCADA operational regime ($F=5.20$, $p=0.024$, ${\eta }^2=0.16$) and by significant wave height ($p=0.031$), while tower-base wave-band BER is primarily driven by $H_s$ ($p=0.018$). Comparison with baseline features—root-mean-square acceleration, spectral peak frequency, and traditional broad-band energy ratio—demonstrates that the band-resolved BER provides finer discrimination between excitation mechanisms than aggregate metrics. Importantly, no structural damage events occurred during the monitoring period; therefore, the reported stability of these features is interpreted as a baseline characterisation under normal operational conditions, which could support future anomaly detection efforts but does not constitute validation of damage detection capability. A comprehensive limitations assessment is provided, covering single-turbine validation, frequency-band sensitivity, regime sample imbalance, and generalisability constraints. Full article

Coastal polynyas in the Kara–Laptev Seas regulate winter air–sea exchange and sea-ice production, but neighboring polynya systems can behave differently. This study examines January–April variability of the Ob–Yenisey (OY), western Laptev Sea (WLS), and eastern Laptev Sea (ELS) polynyas from 2003 to 2025 using passive-microwave sea-ice concentration, ERA5 winds and air temperature, AMSR-MPR sea-surface temperature, TOPAZ4b sea-ice thickness, and AO/ENSO indices. The three regions show distinct spatiotemporal variations. The OY exhibits the only significant increase in accumulated polynya area (+1.72 × 10⁴ km² day a⁻¹), the WLS has the largest interannual variability and the most frequent large-area events, and the ELS is most strongly concentrated in late March and April. NW has the clearest same-day or one-day-lead association with opening, consistent with wind-driven mechanical divergence, while coastline orientation and landfast-ice geometry are associated with differences in regional responses. Hierarchical partitioning (HP) assigns the largest daily relative statistical allocation to SST in the WLS and ELS; because this association is predominantly synchronous, it is interpreted as an ice–ocean state relationship rather than as a demonstrated antecedent control. Annual associations are more region dependent and include NW, T2M, SST, and SIT. AO is positively correlated with polynya activity, whereas ENSO has no consistent relationship. Full article

This study investigates the atmospheric and upper-ocean responses associated with Super Typhoon (TY) Lekima (2019) during 4–12 August, including its landfall over Zhejiang Province, China. Variations in sea surface temperature (SST), latent heat flux (LHF), water vapor flux (WVF), total column water vapor (TCWV), vertical integral moisture divergence (VIMD), mean sea level pressure (MSLP), wind circulation, precipitation, subsurface temperature and salinity, and Ekman pumping velocity (W_E) were analyzed throughout the typhoon life cycle. Before intensification, SSTs of 29.5–31.0 °C indicated favorable ocean-surface conditions. During and after the storm passage, SST decreased to approximately 26.0–27.5 °C along portions of the track and below 25.0 °C near the Zhejiang coast, with stronger cooling on the right-hand side of the track. Surface salinity decreased by approximately 0.3–0.8 PSU during 8–10 August, with freshening extending through the upper 30–40 m. The Ekman pumping field identified regions where wind-stress curl favored upwelling and downwelling, with stronger positive signals occurring on the right side of the track. During the active maritime stage, LHF values of approximately −300 to −200 W m⁻² indicated enhanced upward latent heat transfer. WVF reached 1800–2200 kg m⁻¹ s⁻¹, TCWV exceeded 70 kg m⁻², and VIMD decreased below approximately −100 × 10⁻⁵ kg m⁻² s⁻¹, indicating enhanced moisture transport and convergence. These conditions coincided with daily precipitation exceeding 120 mm and locally reaching approximately 160 mm over northern and northwestern coastal Zhejiang. The minimum daily mean MSLP decreased from 1000 to 972–976 hPa during peak intensity and subsequently increased as Lekima approached landfall and weakened inland. While these responses are qualitatively consistent with previous TY case studies, our study provides new quantitative benchmarks and process attribution through heat budget analysis. This integrated, stage-based analysis provides a comprehensive quantitative reference for model validation and future comparative studies of landfalling typhoons in the western North Pacific. Full article

Coastal humid-heat and photochemical-oxidant episodes are commonly studied through concentration estimation, leaving it unclear whether satellite observations improve warning decisions under explicit false-alarm constraints. This study introduces CoAST-EWS Japan, a six-station, validation-locked hindcast benchmark across Osaka Bay and Tokyo Bay. Models were developed using June–July 2023 data, calibrated and thresholded on August 2023 predictions, and retrospectively evaluated on June–August 2025 station-hour observations. The strong non-satellite route combines recent ground history, ERA5 meteorology, CAMS composition, and static station geometry. Adding previous-day MODIS thermal context to an otherwise identical XGBoost route increased average precision from 0.3153 to 0.3429, reduced the Brier score from 0.05032 to 0.04874, and improved recall/F1 under a validation-locked budget of 0.5 false alarms per station-day (FPDs) from 0.1864/0.2511 to 0.2402/0.3042. Japan-local calendar-day intervals supported the improvements in Brier score, recall, and F1. In a dimension-matched comparison using the same 18 MODIS variables, previous-day context increased average precision over the same-day route by 0.0378 (95% CI: 0.0144–0.0603), demonstrating that the timing advantage was not attributable to a larger satellite feature set. The MODIS increment was strongest during high-heat issue times and in Osaka Bay, and its ranking value was reproduced by a 36 h Temporal FLOW model. Matched spatial controls identified distance-based coastal context as the most stable 24 h graph component, while wind-aligned information operated as a complementary route. These results establish latency-aware MODIS thermal context as a measurable decision input for neighborhood-scale coastal compound warning. Strict station-level localization, cross-bay transfer, and forecast-consistent deployment define the next validation frontier. Full article

The decarbonization of maritime transportation requires shipboard energy systems to coordinate conventional generators, renewable energy sources, energy storage devices, and thermal energy units under voyage-dependent operating constraints. This paper develops a configurable hybrid multienergy ship system for coordinated electrical and thermal energy scheduling. The proposed framework functionally separates the propulsion subsystem from the service and thermal subsystem while retaining system-level coordination among photovoltaic generation, wind generation, diesel generators, micro gas turbines, energy storage batteries, and thermal energy units. A convolutional neural network is employed to provide short-term photovoltaic power forecasts for day-ahead scheduling. The resulting scheduling problem simultaneously considers voyage completion, power balance, equipment operating limits, ramp-rate constraints, battery charging and discharging restrictions, operating costs, and pollutant emission treatment costs. The nonlinear operating logic is reformulated as a mixed-integer optimization problem and solved using CPLEX. A representative coastal voyage case study is used to evaluate the proposed framework. The results demonstrate that the method can coordinate multiple shipboard energy sources, satisfy the prescribed electrical and thermal demands, and provide a set of Pareto-optimal solutions describing the trade-off between operating cost and emission-related cost. The proposed framework provides a system-level scheduling approach for supporting the economic and low-carbon operation of hybrid multienergy ships under increasingly stringent maritime emission reduction requirements. Full article

Coastal dune fields provide important ecological and geomorphic functions, while their evolution is strongly influenced by groundwater-controlled surface moisture. However, the effects of seasonal groundwater-level fluctuations and moisture-affected sand on long-term dune development remain insufficiently represented in numerical models. In this paper, a time-varying groundwater-level field and moisture-dependent entrainment thresholds are incorporated into a real-space cellular automaton model to investigate the coupled evolution of coastal dunes and interdune lakes. The results show that rising groundwater levels reduce the wind-erodible surface area, inundate interdune depressions, and delay the growth of peak dune height. Periodic water-level fluctuations also produce a sediment storage–release cycle, in which sand is temporarily stored on inundated interdune surfaces during high-water stages and is progressively remobilized during subsequent low-water stages, while newly exposed moisture-affected sand remains subject to an elevated entrainment threshold. In addition, increasing the critical threshold shear stress within the groundwater-controlled moisture-affected layer suppresses dune development and reduces final peak dune height by approximately 8–17%. Comparison with the observed wet-season water-pond area further shows that incorporating the moisture-affected layer brings the simulated relative water-pond area closer to the observed value in the Lençóis Maranhenses dune field. Overall, the results demonstrate that groundwater regulates dune evolution through both direct inundation and an enhanced entrainment resistance of moisture-affected sand above the water table. Full article

Synthetic Aperture Radar (SAR) enables high-resolution sea-surface wind speed retrieval. However, the enhanced spatial resolution of SAR imagery introduces substantial challenges, from small-scale contamination sources that significantly degrade retrieval accuracy. Particularly in coastal regions, non-wind-related backscatter signals, such as ships and oil slicks, can severely bias wind speed estimates at sub-kilometer scales. In this study, the first-order moment (average, m₁) and second-order moment (variance, m₂) are computed from the normalized radar cross-section (NRCS) within sub-images of Sentinel-1 SAR data acquired in Interferometric Wide (IW) mode. Analysis reveals that clean-sea-surface signals in both VV and VH polarizations cluster around an approximately linear empirical trend, m₂ = 2m₁ + b, in the m₁-m₂ statistical feature space, whereas the examined contamination types deviate from this trend and occupy separable regions. Based on this characteristic, a quality-control framework is proposed for the systematic separation of clean sea surface from image noise (border noise and inter-swath stripe noise) and non-ocean targets (land contamination, bright targets, and dark spots). Validation using independent SAR data from the Taiwan Strait was conducted separately for native 10 m and height-adjusted 3 m buoy observations. For the native 10 m observations, the RMSE and MBE were essentially unchanged at 1.5 m/s and −0.3 m/s, respectively. For the height-adjusted nearshore observations, the RMSE decreased from 3.2 m/s to 2.1 m/s and the MBE changed from −1.5 m/s to −1.1 m/s. Full article