Search Results (185)

Before Cyclone Senyar made landfall on Sumatra in November 2025, it drew moisture from across the South China Sea, the Gulf of Thailand, and the Indian Ocean. The substantial damage its heavy rainfall caused on the NE and W coasts of Sumatra urges us to rethink the relationships between climate, forests, hydrology, land use, human presence, and vulnerability. Cyclones will recur, but flood damage does not have to be repeated. The simple narrative that “deforestation causes floods” is not adequate for guiding a “building back better” strategy. To account for the space-time pattern of Senyar effects with its multiple landfalls, we reviewed key concepts and framing of the links between ocean temperature, atmospheric moisture transport, rainfall extremes, saturation of existing buffers (“sponges”), river flow, and flooding. We hypothesize how atmospheric roughness slowing down rivers in the sky can induce congestion and precipitation. Surface infiltration and water retention in the soil profile matter. Mid- and downstream flow delays due to sponge effects depend on land cover beyond what a simple forest–non-forest terminology can represent. Rather than indiscriminate tree planting, adaptation efforts to avoid future disasters should balance reductions in human exposure (effective land use planning) and efforts to reduce hazards by restoring and managing vegetation and drainage systems. Full article

The Gulf of Aden (GA) is a strategically dynamic water basin linking the Red Sea basin with the Arabian Sea via the Bab el Mandeb Strait. In spite of its importance for oil transportation globally and regional hydrodynamics, tidal propagation remains relatively understudied. Thus, in this study, a depth-averaged 2D barotropic hydrodynamic model based on the Delft3D modelling system with a uniform resolution of 5 km was set up, calibrated, and validated to model the tidal propagation within the GA. The open boundaries were forced with eight primary astronomical tidal constituents, four semidiurnal (M₂, S₂, N₂, K₂) and four diurnal (K₁, O₁, P₁, Q₁) components. The model performance was assessed using hourly water level observations as well as current measurements. Sensitivity analyses were carried out on key parameters, with the Chézy bottom roughness coefficient of 35 m^1/2 s⁻¹ providing the optimal performance. Statistical parameters revealed a strong match between the modelled and the observations. Mean Absolute Error (MAE) was observed to vary from 0.07 to 0.08 m (about 2.7–5.1% of the local mean tidal range), with large inconsistencies found at ME and MW stations, while the Root Mean Square Error (RMSE) varied from 0.08 to 0.10 m (about 3.1–6.4% of the local mean tidal range). The Index of Agreement (IOA) was found significant across all stations (0.91 to 0.95). Among all constituents, the semidiurnal M₂ and diurnal K₁ tidal waves exhibit the largest amplitudes. The amplitudes of S₂ and N₂ attain approximately 43% and 30% of M₂, while O₁ represents about 38% of K₁. The co-tidal charts show that semidiurnal and diurnal tides propagate as coastally influenced long waves with a double-maximum structure along coastal boundaries, reaching maximum surface elevation amplitudes near Bab el Mandeb Strait and the eastern boundary (~0.48 m to 0.60 m for M₂, and up to 0.48 m for K₁). Rossby radius of deformation and quarter-wave resonance indicate that the semidiurnal band lies close to the fundamental resonant period of the GA, generating a co-oscillation-like response with small phase gradients, while the diurnal band more closely resembles a freely propagating Kelvin wave. Form Factor values within the GA range between 0.7 and 0.9, indicating a predominantly mixed, mainly semidiurnal tidal regime throughout the Gulf. Full article

To address the limitations of poor adaptability and insufficient versatility of current surface capture technologies for autonomous underwater vehicles (AUVs) under high sea state conditions, this study proposes an ROV-based capture system equipped with guidance and clamping mechanisms for efficient and stable recovery of AUVs and similar floating targets in rough seas. This work is intended to provide technical support for solving AUV capture challenges in high sea states. Through a systematic investigation of capture methods and associated operational systems, the overall design of the dynamic surface AUV capture device is established. A three-dimensional model is developed to verify the feasibility of the overall operational sequence. Computational fluid dynamics (CFD) simulations are performed to analyze the hydrodynamic performance of the ROV carrier, with particular focus on the drag force and pressure distribution under flow velocities corresponding to Sea State 4 and below. The simulation results indicate that when the ROV inflow velocity reaches 3 m/s, the maximum drag force is approximately 5862 N and the maximum pressure on the frontal area is about 4570 Pa. Based on these data, the overall structural stability is verified, and the results confirm that the structure maintains adequate stability under the target operating conditions. Kinematic simulations are further conducted to investigate the collision force between the target AUV and the guidance/capture device under various initial attitudes and velocities in Sea State 4, thereby determining the overall capture tolerance envelope. Through design manual buffer parameter tuning and comparative improvement analysis, the collision force between the guidance device and the target is reduced by approximately 60%. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

The present work concerns the further development of a simplified approach for predicting the average power output of offshore wind farms developed by Sørensen and Larsen. In this paper a correction factor, accounting for the finite size of a wind farm, was averaged over all operating wind speeds before deriving the convolution of the production of the wind farm wind turbines over the site wind speed distribution. In the present work, the wind-speed-dependent correction factor is redefined and consistently included in the wind speed Weibull distribution convolution. In addition, a simple model of the dependence of sea surface roughness on wind speed is introduced to refine the derived correction factor. Finally, a revised version of the simplified Geostrophic Drag Law has been derived to improve the dependence of the magnitude of the geostrophic wind speed on the friction Rossby number. The derived improved model is validated against a comprehensive set of quality-ensured full-scale production data from six different operating wind farms covering a diverse segment of Danish waters and further benchmarked against a series of commonly used state-of-the-art engineering simulation models. The comparison of computed results to actual monthly wind farm production data over a series of years shows mean prediction deviations and maximum standard deviations of the errors of about 4% and 8%, respectively. This is very much in accordance with predictions from the other engineering models against which the present model was benchmarked. The advantage of the present model, however, is its simplicity, the demand of only very few input data, and its low computational time, which is an order of magnitude lower than other engineering models. Full article

Synthetic Aperture Radar (SAR) is widely used for marine oil spill surveillance due to its all-weather capabilities and sensitivity to sea surface roughness. However, oil slicks often appear as dark formations that can be confounded with visually similar “look-alikes”, making automated detection and boundary delineation challenging. This study proposes a two-stage deep learning framework for oil spill mapping in Sentinel-1 SAR imagery. First, a ConvNeXt-T classifier screens image patches for likely slick presence, reducing the search space for dense prediction. Second, spill boundaries are extracted with a domain-adapted Segment Anything Model (SAM) configured for prompt-free, single-shot segmentation. The input representation is enhanced by combining preprocessed Sentinel-1 VV backscatter with Gray-Level Co-occurrence Matrix (GLCM) texture measures (homogeneity and variance) to better separate oil from heterogeneous background sea at the segmentation level. Quantitative evaluation against established segmentation baselines demonstrates that our adapted SAM achieves the highest overall accuracy, reaching an F1-score of 0.86. This outperforms traditional models such as UNet and CBDNet (0.83), as well as DeepLabV3, SegNeXt, and OFCNet (all at 0.82). Furthermore, an analysis of the wind speed on the test set shows that wind speed affects detectability but does not by itself determine segmentation quality. The results indicate that combining transformer-based screening with efficient foundation-model adaptation can provide accurate and scalable oil spill mapping for operational SAR monitoring. Full article

Based on ERA5 reanalysis data, best-track data of tropical cyclones, and satellite nighttime light data from 1995 to 2024, this study employs a statistical composite method to analyse spatiotemporal evolution characteristics and impact mechanisms of gale events in the South China Sea. The results indicate: ① The gale days exhibit a pattern of ‘high in the northeast and southwest, low in the middle’ with three high-value regions located in the Taiwan Strait, the Bashi Strait, and the offshore region southeast of Vietnam, where the average wind speed at the centres reaches 8 m/s. Maximum wind speeds show a ‘high in the north, low in the south’ pattern, with the dividing line near 10° N. The number of gale days peaks in winter, while maximum wind speeds are higher in summer and autumn than in winter and spring. ② The spatial distribution of gales is primarily influenced by the combined effects of land–sea topography and weather systems. Cold air masses in winter and spring are the dominant cause of gales in the South China Sea. Although typhoons in summer and autumn occur less frequently, they are more likely to trigger extreme gales. ③ Most regions of the South China Sea show an increasing trend in the gale days, while a few areas in the south and near Guangdong exhibit a decrease. The overall increase is primarily attributed to the intensification of the subtropical high, whereas the reduction near Guangdong is mainly due to increased surface roughness caused by urbanisation, which enhances friction and suppresses wind speeds. 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

The complex surface architecture of natural oyster reefs is widely considered to promote biological attachment, yet the underlying mechanisms and the relevance to the design of artificial reefs are not fully understood. Here, we combined field experiments, 3D surface characterization, and numerical modelling to quantify how reef-like roughness regulates biofouling development and near-wall flow around artificial substrates. Surface morphological characteristics of natural oyster reefs were first obtained by 3D scanning and used to fabricate concrete panels with simulated rough textures, while traditional smooth concrete panels served as controls. The two types of panels were simultaneously deployed in the target sea area for a hanging-panel experiment. Samples were collected after 3, 6, 9, and 12 months to track changes in biofouling communities. At each sampling time, the panel surfaces were quantified by canopy roughness ($R_C$), surface heterogeneity ($\sigma$), and fractal dimension ($D$), and these metrics were integrated into numerical simulations combined to resolve the flow field, turbulence kinetic, and near-wall shear stress around the colonized panels. The research results show that, after 12-month immersion, the mean thickness of the biofouling layer on rough and control panels reached 6.39 mm and 5.91 mm, respectively. Rough panels exhibited consistently higher $R_C$ and $\mathsf{\sigma }$ than controls, and these two parameters are strongly linearly correlated ($R^2=0.891)$. Numerical simulations reveal that increased $R_C$ enlarges the oyster settlement shear-stress window (OSSW), indicating more favorable hydrodynamic conditions for oyster settlement and growth on rough panels. Nevertheless, the hydrodynamic differences between the initial rough panels and control panels gradually diminish over time, suggesting that biological growth can progressively naturalize initially smooth substrates. These findings advance the mechanistic understanding of how small-scale roughness and biofouling co-evolve to shape oyster habitat quality and provide a quantitative basis for the eco-engineering design of artificial oyster reefs. Full article

This study proposes a method for retrieving ocean sea surface salinity (SSS) using C/X-band ocean emissivities in coastal regions, aiming to verify the performance of these unconventional frequencies for SSS retrieval in warm, high-salinity-variation coastal oceans. Since C/X-band brightness temperatures are less sensitive to sea surface salinity than L-band brightness temperatures, it becomes particularly important to develop a sophisticated and effective method for extracting salinity-related signals from C/X-band brightness temperatures. To this end, a wind effect correction process is developed to remove rough sea surface emissivity contributions from total emissivity and derive calm sea emissivity from WindSat’s brightness temperatures. The wind-induced effects are modeled with a third-order polynomial. Then, based on emissivity analysis, a weighted combination of C/X-band calm sea emissivities (with parameter λ) is introduced to reduce SST sensitivity. This λ-based combination is used to retrieve SSS in the Bay of Bengal. Based on the triple-match method and buoy data, the salinity retrieval results are verified and compared with the Soil Moisture Active Passive (SMAP) SSS and Argo in situ SSS. The results show that the use of parameter λ reduces the RMS error of SSS by 0.1–0.2 psu. The RMSE of SSS retrieval is about 0.64 psu, which is comparable to the error of SMAP data. Simultaneously, the SSS retrieval accuracy is significantly influenced by offshore distance. At an offshore distance of 100 km, the salinity retrieval error exceeds 1 psu, while when the offshore distance exceeds 500 km, the salinity retrieval error is better than 0.6 psu. Full article

During tropical cyclones (TCs), intense exchanges of momentum, heat, and moisture occur across the air–sea interface. The present study was conducted to investigate the role of surface roughness parameterizations under such conditions. To this end, a series of sensitivity experiments was conducted with a focus on Tropical Cyclone Biparjoy, which originated from the Indian Ocean in 2023. The experiments evaluate the impact of different schemes for momentum, thermal, and moisture roughness length on TC track, intensity, significant wave height, and air–sea heat fluxes. The results indicate that the momentum roughness length scheme is critical for accurately forecasting TC track and intensity and for simulating significant wave height; furthermore, Drennan’s parameterization yielded slightly better results in this case, with the smallest track error (72.0 km MAE) among the momentum schemes. Under the premise that Drennan’s parameterization scheme has high accuracy in momentum roughness, sensitivity experiments on thermal and moisture roughness parameterization were conducted. The Drennan–Fairall2014 combination achieved the lowest errors in TC central pressure (4.25 hPa RMSE) and the maximum sustained wind speed (5.31 m/s RMSE). Thermal and moisture roughness mainly affects the efficiency of turbulent heat transfer between the ocean and the atmosphere and thus has a limited impact on the cooling of sea surface temperature, with SST RMSE differences among schemes within 0.3 °C. This effect is mainly confined to the uppermost ocean layer and does not significantly change the thermal structure of the upper layers. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article

The valorization of shrimp shell waste is crucial for promoting sustainability and a circular economy. This study aimed to extract chitin from the exoskeletal residues of deep-water rose shrimp (Parapenaeus longirostris) sourced from the Marmara Sea and synthesize low-molecular-weight chitosan (LMWC) via conventional, microwave-, and autoclave-assisted deacetylation pathways. The shell biomass was subjected to sequential demineralization (1 M HCl) and deproteinization (1 M NaOH), yielding 14.42% chitin. The extracted chitin was then converted to LMWC using the three methods, and the products were characterized using FT-IR spectroscopy, titration, viscometry, SEM, and TGA. The results demonstrated that the autoclave-assisted method achieved the highest degree of deacetylation (DD) at 95%, significantly outperforming the conventional method (81%) and the microwave-assisted method (67%). The autoclave-synthesized chitosan also exhibited the lowest viscosity (33 cP), confirming its low molecular weight. Morphological analysis showed that chitin exhibited a well-defined fibrous structure. After deacetylation, this structure transformed into a rough and porous surface morphology. Thermal analysis further demonstrated that the laboratory-synthesized chitosan exhibited higher thermal stability than the commercial chitosan sample. In conclusion, the autoclave-assisted method proved to be highly efficient for producing low-molecular-weight chitosan with a high degree of deacetylation. However, the conventional method remains the most practical option for scalable industrial production due to its simplicity and well-established infrastructure. Moreover, the laboratory-synthesized chitosan exhibited higher thermal stability, increased porosity, and a higher degree of deacetylation compared to commercially available chitosan, which may offer functional advantages in applications requiring enhanced reactivity, solubility, or thermal resistance. Overall, the findings provide valuable insights into selecting appropriate deacetylation strategies for producing low-molecular-weight chitosan with tailored properties, thereby bridging the gap between laboratory-scale synthesis and potential industrial applications. Full article

Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 (${\sigma }^0$), is strongly related to the small-scale roughness of the ocean surface and is used to estimate near-surface wind speed. However, a number of other factors affect ${\sigma }^0$, and these need to be estimated and compensated for when developing long-term consistent ${\sigma }^0$ records spanning multiple missions. Aside from unresolved issues of absolute calibration, there are various geophysical factors (sea surface temperature, wave height and rain) that have an effect. The choice of the waveform retracking algorithm also affects the ${\sigma }^0$ values, with the four-parameter Maximum Likelihood Estimator introducing a strong dependence on waveform-derived mispointing and the use of delay-Doppler processing leading to apparent variation with spacecraft radial velocity. As all of these terms have strong geographical correlations, care is required to disentangle these various effects in order to establish a long-term consistent record. This goal will enable a better investigation of the long-term changes in wind speed at sea. Full article

When using optical remote sensing methods for Kelvin wake imaging, the imaging is affected by sea-surface stochastic fluctuation, imaging noise, and weak reflectivity contrast, resulting in weak wake image signals. In order to better obtain wake optical remote sensing images, this article proposes a multi-objective parameter stochastic optimization method for a time-delayed integration optical remote sensing imaging system. By constructing the wake imaging mechanism framework integrating a hydrodynamic model, rough sea surface probability and statistics model, and Time-Delay Integration Charge-Coupled Device (TDI-CCD) imaging link model, a stochastic multi-objective optimization model with constraints is established. The multi-objective function of this model is specifically defined as follows: maximizing the digital number difference between the crest and trough of Kelvin wakes in imaging results, maximizing the F number, minimizing the integration stages, and minimizing the quantization bits. Meanwhile, a two-stage solution method based on sample average approximation (SAA), branch and bound method (B&B), and the complex method is designed. The model can be used to obtain optimized design results for remote sensing imaging parameters, providing theoretical and methodological support for the design of remote sensing imaging systems. Numerical simulation results show that the optimized parameter combination can achieve clear imaging of the Kelvin wake, and the core indicators meet the design requirements. Full article