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Marine heatwaves (MHWs) have recently been recognized as extreme climate events considering their devastating impacts on marine ecosystems. Our study explored the spatial and temporal variability of short (duration

Coastal flooding induced by storm surges and heavy rainfall is one of the most frequent climate-related natural hazards along the southeast Australian coast, home to more than 55% of the Australian population. Flooding in this densely populated region is a threat to public safety, coastal infrastructure, ecological systems and the economy. Although climate change is expected to cause an increase in major floods, few studies have quantified the potential changes in flood severity. This study quantifies the changes in flood peak discharge flowing to the Georges River estuary in Australia due to climate-change. An event-based hydrological model, Watershed Bounded Network Model (WBNM), was used to predict flood discharge. This hydrological model was forced by rainfall data obtained from the New South Wales and Australian Capital Territory Regional Climate Modelling Project version 1.5 (NARCliM1.5) for both historical and the Representative Concentration Pathway 8.5 (RCP8.5) conditions. Model calibration for the floods of March 1978 and March 2022 achieved a general agreement between the predicted and observed hydrographs, with an overall average 14% error in the peak values, further demonstrating that the modelling approach is generally reliable in projections of flood severity. Using high resolution climate model projections, the present study observed an increase of 22% in the model ensemble average from historical conditions to the RCP8.5 scenario for the 20-year average recurrence interval (ARI) 24 h extreme rainfall. This heightened extreme rainfall consequently resulted in the changes in flood discharge with an average rise of 55%. This study provides specific assessment of climate-generated risks for densely-populated regions, especially those on Australian east coast. Global studies have suggested that extreme precipitation events will increase under climate change. This study supports and enhances these assertions by using high resolution downscaling to quantify the specific changes within a large catchment.

This study developed a geo-spatial framework for assessing multi-hazard threat on the Bangladesh coast, integrating environmental hazards (EH), geo-environmental attributes (GA), and anthropogenic modifications (AM) based on their potential contribution to overall threat. For this purpose, a fuzzy logic based analytical technique was integrated with geospatial mapping. Thematic layers were prepared for twenty-three theoretically important factors representing the three components of threat. The spatial variations of threat and its components were delineated through spatial overlaying of the respective layers in a GIS environment. The final threat map revealed 32% (5338 km2) and 4% (646 km2) of the area of the western deltaic coast, which encompassed >50–60% of the areas of Khulna, Bagerhat, and Satkhira districts, was under high and very high threat, respectively, owing primarily to frequent cyclones, salinity ingression, and subsidence, and secondarily to the low elevation of the coast, high astronomical tide, shallow bathymetry, excessive groundwater extraction, and polder construction. High and very high threat zones within the central estuarine coast corresponded to 27% (4518 km2) and 16% (2618 km2) of the area, including most of Bhola, Barguna, and Patuakhali districts, which was attributable to the effects of coastal erosion, sea-level rise, flooding and the ancillary effects of strong wave action, high river discharge, deforestation, and land transformation. Around 14% (948 km2) and 3% (164 km2) of the eastern cliff coast, comprising the southwestern part of the Coxsbazar district, was found to be under high and very high threat, respectively, due to the direct effects of sea-level rise, storm surge, erosion, and indirect effects of closeness to the shoreline, alluvial composition of the beach, tourism, and pollution from industries. The outcomes of this study could guide the coastal managers of Bangladesh in prioritizing actions aimed at disaster risk reduction and sustainable development of this region.

The hydrodynamics and sediment characteristics of muddy estuaries and coasts during typhoons are closely related to the geomorphic evolution, ecological environment, and economic development of coastal zones. Taking the macro-tidal turbid Hangzhou Bay (HZB) as an example, the sediment characteristics and effects of wave-current interactions on sediment dynamics during Typhoon Mitag were studied using a fully-calibrated numerical model. The model considered tide-wave sediment interactions and the reconstructed typhoon wind field. Net sediment fluxes were controlled by residual currents and suspended sediment concentration (SSC). The combined interactions of currents and waves led to a high SSC during the typhoon. Under calm weather conditions, the impact of wave-current interactions (wave dissipation, form drag, wave radiation stress, mean current advection and refraction) was small, except for the combined bottom stresses. The combined bottom stress was the primary wave-current interaction that changed sediment resuspension and increased SSC, particularly in shallow waters or during storms. The advection term, which played an essential role in reducing SSC in HZB, mainly affected SSC by increasing the velocity. The wave dissipation term enhanced vertical mixing, which involved the vertical exchange of suspended sediment and currents. In the shallow waters of the southern bay, the wave dissipation term mostly led to decreased bottom stresses, increased currents, decreased SSC, and increased SSC in deep waters. The effects of form drag, wave radiation stress, and refraction terms on the suspended sediment dynamics were relatively small. These findings provide a theoretical foundation for the study of dynamic geomorphology in macro-tidal estuaries.

Multispectral remote sensing data have proven to be useful in deriving tidal flat topography. However, limited satellite observations over a certain period have large uncertainties. In this study, we used MODIS time-series data with a high observational frequency to generate accurate tidal flat topography in Nantong, China based on the relationship between the T_Tide-derived tidal levels and the MODIS-derived inundation frequency map. First, 8-day MOD09Q1 data from 2007 to 2008 were used to perform the land-water classification. Second, 92 land-water maps were stacked to generate the inundation frequency map of the tidal flat. Then, the T_Tide package was applied to calculate the tidal levels at Lvsi Tide Gauge Station. Finally, the inundation frequency map and the tidal levels were integrated to derive the tidal flat topography, which agreed well with the in-situ elevation data (RMSE = 0.40 m, r = 0.89) and the Landsat-based elevation data (RMSE = 0.18 m, r = 0.98). In addition, the derived slopes agreed well with the slopes from the in-situ elevation data (RMSE = 1.00‰, r = 0.85). We highlighted the necessity of using all MODIS data for deriving an accurate tidal flat topography. Our proposed method has a potential to derive tidal flat topography and temporal changes over the past 20 years from MODIS data.

Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multidate images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this article, using only satellite data, including multitemporal Sentinel-2 images and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, a multitemporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multitemporal Sentinel-2 images can achieve a mean root mean square error (RMSE) of 1.08 m (R2 = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R2 = 0.92, RMSE = 1.46 m) derived from single date image based methods.Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

The Ganges-Brahmaputra-Meghna delta, located in the southern part of Bangladesh, is periodically exposed to severe tropical cyclones. It is estimated that two-fifths of the world’s total impact from tropical-cyclone-induced storm surges occur in this region, and these cause fatalities and economic losses every year. A barotropic numerical 3D model is used to investigate wave dynamics during a cyclone-induced storm-surge event. The model is calibrated and validated for Cyclone Sidr (2007) and applied to ten idealized cyclonic scenarios. Numerical experiments with different coupling configurations are performed to understand wave–current interactions on significant wave heights. Results show that the water level is the dominant factor in significant wave height modulation when the wave propagates into shallower regions from the deeper ocean, whereas the current modulates the deep ocean wave height. The WCI causes higher significant wave heights in shallower waters close to the coast compared with the deep ocean. Wave energy dissipation related to whitecapping processes plays a greater role in reducing the wave height nearshore than the dissipation due to depth-induced breaking and bottom friction in the GBMD during a cyclone-induced storm-surge event.

The Ganges–Brahmaputra–Meghna Delta (GBMD) located in the head of the Bay of Bengal is regularly affected by severe tropical cyclones frequently. The GBMD covers the Bangladesh coast, which is one of the most vulnerable areas in the world due to cyclone-induced storm surges. More than 30% of the total country’s population lives on the Bangladesh coast. Hence, it is crucial to understand the underlying processes that modulate the storm surge height in the GBMD. A barotropic numerical 3D model setup is established by using Delft3D and SWAN to investigate a cyclone-induced storm surge event. The model is calibrated and validated for Cyclone Sidr in 2007 and applied to six idealized cyclonic scenarios. Numerical experiments with different coupling configurations are performed to distinguish the contribution of wind, tides, waves, and wave–current interactions (WCI) on the storm surge height. Results show that the wind-driven setup is the dominant contributor to the storm surge height during cyclonic events. Based on the tidal phase and wind direction, the interaction between tide and wind can increase or decrease the magnitude of the storm surge height. Finally, considering the wind-driven wave may increase the surge height up to 0.3 m along the coastline through a strong wave setup.

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.

Coastal slope is a fundamental land characteristic that can influence the hydrodynamic and morphological processes, which is the essential parameter to calculate the wave setup and wave run up for further estimating extreme coastal water levels. Slope information of coastal zones also plays a key role in estimating the coastline erosion and evaluating the coastal vulnerability under sea level rise. However, accurate estimates of coastal slopes are currently limited, especially over sparsely populated and remote areas. The recent ICESat-2 photon-counting lidar provides unprecedented along-track dense and accurate height measurements in coastal zones. This study aims to demonstrate the potential of ICESat-2 measurements to estimate coastal slope of sandy beach at a large scale, and the proposed method is tested in Texas, USA. The validation with local airborne lidar data (with an average slope of 0.023 in Texas) indicates that, the ICESat-2 derived coastal slopes (0.026) have much better accuracy than current large-scale coastal slopes (0.0032) derived from SRTM and MERIT DEMs. With globally covered ICESat-2 datasets, this method can be expanded to estimate coastal slopes even at a global scale.

In San Francisco Bay (SFB), light availability is largely determined by the concentration of suspended particulate matter (SPM) in the water column. SPM exhibits substantial variation with time, depth, and location. To study how SPM influences light and phytoplankton growth, we coupled a sediment transport model with a hydrodynamic model and a biogeochemical model. The coupled models were used to simulate conditions for the year of 2011 with a focus on northern SFB. For comparison, two simulations were conducted with ecosystem processes driven by SPM concentrations supplied by the sediment transport model and by applying a constant SPM concentration of 20 mg l–1. The sediment transport model successfully reproduced the general pattern of SPM variation in northern SFB, which improved the chlorophyll-a simulation resulting from the biogeochemical model, with vertically integrated primary productivity varying greatly, from 40 g[C] m–2 year–1 over shoals to 160 g[C] m–2 year–1 in the deep channel. Primary productivity in northern SFB is influenced by euphotic zone depth (Ze). Our results show that Ze in shallow water regions (

The East Australian Current (EAC) is a highly dynamic western boundary current of the South Pacific Gyre. The EAC frequently encroaches shoreward, drives upwelling, changes coastal bio-physical dynamics, and thus exerts significant impacts on coastal marine ecosystems. In this study, with high frequency (HF) radar and mooring data, for the first time accurate daily mapping and tracking of a rapid EAC intrusion event was conducted and the impacts of the EAC intrusion on the shelf water off Coffs Harbor were monitored. The results show that, during the event, the EAC was highly dynamic with a mean daily onshore/offshore movement of ~5 km/day. In addition, we found that the bottom ocean temperature and the surface current speed on the shelf varied linearly with the EAC-to-coast distance. This study thus demonstrates the value of HF remotely sensed data for the ongoing quantitative monitoring of the highly dynamic EAC fluctuations.

This study quantifies some of the potential impacts of climate change and nutrient pollution to identify the most important factors on water quality changes and algal blooms in the study region. Three variables, air temperature and streamflow, representing climate change, and nutrient runoff, were varied in eight hypothetical scenarios to determine their impact on water quality and algal blooms by the calibrated and validated water quality model QUAL2K. Water quality was assessed by the concentrations of dissolved oxygen, total nitrogen, and phosphorus. Algal blooms were identified by phytoplankton concentration. An increase in air temperature of up to 2 °C resulted in an average increase of 3% in water temperature and 4.79% in phytoplankton concentration, and an average decrease of 0.48% in dissolved-oxygen concentration. Projected decreases in streamflow not only made the above phenomenon more significant but also significantly increased the concentration of total nitrogen, total phosphorus, and phytoplankton with the same pollution inputs. Under climate change, the biggest cause of concern for estuarine water quality is reduced streamflow due to decreases in rainfall. Water quality improvement is possible by regulating the concentration of non-point-source pollution discharge. By reducing nutrient runoff, the total nitrogen and phosphorus concentrations were also reduced, resulting in a significant increase in the dissolved oxygen concentration. This study highlights the most significant factors for managing water quality in estuaries subject to climate change.

Monitoring global lake/reservoir water level changes is needed to understand the global water cycle and investigate its potential drivers. The existing global water level products only cover lakes/reservoirs with large sizes (>100 km ^2 ). Here, Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 altimetry data with small footprints are employed to examine global water level changes for 22 008 lakes/reservoirs greater than 1 km ^2 . We report that 77.56% of them exhibited rising water levels over 2003–2021. Across the globe, 78.84% of lakes exhibit a rising water level, while the proportion for reservoirs is only 56.01%. Global lake/reservoir is estimated to experience a median water level change rate of +0.02 ± 0.02 m yr ^−1 over 2003–2021, and lakes have a larger water level rise (+0.02 ± 0.02 m yr ^−1 ) than reservoirs (+0.008 ± 0.14 m yr ^−1 ). We detect large-scale rising water levels in the Tibetan Plateau, the Mississippi River basin, and high-latitude regions of the Northern Hemisphere. Our calculation also suggests a negative relationship between the percentage of water level rise in lakes/reservoirs and population density for global river basins ( r = −0.41, p -value < 0.05) and 11 hotspots ( r = −0.48, p -value < 0.05). Our result suggests that inland water level has tended to rise in recent years under natural processes while human activities (i.e. with higher population density) can balance the water level rise via reservoir regulation. We find the existing datasets underestimated global water level rise, which may be caused by the exclusion of numerous small lakes/reservoirs. Our estimated global water level change rates (that include numerous small lakes with areas of 1–10 km ^2 ) can improve the understanding of global hydrological cycle and water resource management under the double pressure of climate warming and human activities.

Extreme wind-driven storm events have the potential to erode beach systems. Along the East Coast of Australia, storm events have been responsible for beach erosion in many coast-facing, open beaches. This paper investigates the potential impacts of wind-driven storms on partly sheltered estuarine beaches—a niche found within Batemans Bay, New South Wales (NSW), along the East Coast. It combines beach geomorphological data with meteorological and oceanographic data to evaluate the impacts of large storm events on three partly sheltered estuarine embayed beaches (Cullendulla Beach, Corrigans Beach, and Maloney’s Beach). The results show that while embayed beaches are protected from some storm events, storm impacts may vary with the season due to wind speed and direction changes, the presence of nearby rivers or creeks, and anthropogenic modifications such as dredging and coastline alterations. This study may contribute to the understanding of the erosive impacts of storms and help improve management strategies used to prevent recession, particularly on embayed beaches.

For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement (“distance” and “area”) of the EAC’s shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast.

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

Serum interleukin-17 is closely related to inflammatory liver injury after hepatitis B virus (HBV) infection. The objective is to study the expression of interleukin-17 (IL-17) in different immune phases in patients with chronic hepatitis B. Whole venous blood was obtained from normal controls (n = 20) and four patient groups (n = 213): immune tolerance phase (IT, n = 57), immune clearance phase (IC, n = 59), non (low)-replicating phase (LR, n = 44), and reactivation phase (RA, n = 53). We measured IL-17, IL-6, and tumor necrosis factor alpha (TNF-α) levels in serum samples and measured IL-17, IL-6, and TNF-α messenger RNA (mRNA) levels in peripheral blood mononuclear cell (PBMC). Compared to normal controls, IL-17, IL-6, and TNF-α protein levels in serum and mRNA levels in PBMC were significantly higher in the four groups. IL-17 protein levels in serum and mRNA levels in PBMC were positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), IL-6, and TNF-α. Compared to patients in the IT phase and LR phase, patients in the IC phase exhibited the highest serum IL-17 and PBMC mRNA levels. The IL-17 mRNA levels of patients in RA phase were higher than those in patients in IT phase. IL-17 plays an important part in the various immune phases of chronic hepatitis B (CHB).

Since 2008, the world’s largest blooms of the green macroalgae, Ulva prolifera, have occurred every summer in the Yellow Sea, posing the question of whether these macroalgal blooms (MABs) have changed the phytoplankton biomass due to their perturbations of nutrient dynamics. We have attempted to address this question using long-term Moderate Resolution Imaging Spectroradiometer (MODIS) observations. A new MODIS monthly time-series of chlorophyll-a concentrations (Chl-a, an index of phytoplankton biomass) was generated after removing the macroalgae-contaminated pixels that were characterized by unexpectedly high values in the daily Chl-a products. Compared with Chl-a during July of 2002–2006 (the pre-MAB period), Chl-a during July of 2008–2012 (the MAB period) exhibited significant increases in the offshore Yellow Sea waters (rich in macroalgae), with mean Chl-a increased by 98% from 0.64 µg/L to 1.26 µg/L in the study region. In contrast, no significant Chl-a changes were observed during June between the two periods. After analyzing sea surface temperature, photosynthetically available radiation, and nutrient availability, we speculate that the observed Chl-a changes are due to nutrient competition between macroalgae and phytoplankton: during the MAB period, the fast-growing macroalgae would uptake the increased nutrients from the origin of Jiangsu Shoal; thus, the nutrients available to phytoplankton were reduced, leading to no apparent increases in biomass in the offshore Yellow Sea in June.

Spatial and temporal ocean colour data are increasingly accessible through remote sensing, which is a key tool for evaluating coastal biogeochemical and physical processes, and for monitoring water quality. Coastal infrastructure such as cross-sea bridges may impact ocean colour remote sensing due to the different spectral characteristics of asphalt and the seawater surface. However, this potential impact is typically ignored during data post-processing. In this study, we use Jiaozhou Bay (East China) and its cross-bay bridge to examine the impact of coastal infrastructure on water-quality remote-sensing products, in particular on chlorophyll-a concentration and total suspended sediment. The values of these products in the bridge area were significantly different to those in the adjacent water. Analysis of the remote-sensing reflectance and application of the Normalised Difference Water Index demonstrate that this phenomenon is caused by contamination of the signal by bridge pixels. The Moderate Resolution Imaging Spectroradiometer (MODIS) products helped estimate the pixel scale that could be influenced by contamination. Furthermore, we found similar pixel contamination at Hangzhou Bay Bridge, suggesting that the impact of large coastal infrastructure on ocean colour data is common, and must therefore be considered in data post-processing.

Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future.

Cold atmospheric-pressure He + N(2) and He + O(2) plasmas are chosen as the representatives for electropositive and electronegative plasmas, of which the discharge characteristics are studied and then compared to each other by fluid models. As the increase of the impurity (N(2) or O(2)) fraction from 0 to 10%, for He + N(2) plasmas the electron density and ion density increase, the spatiotemporal distributions of electron density, ion density, electron temperature and electron generation rate change a little. On contrast, for He + O(2) plasmas the electron density decreases, the ion density first increases and then decreases, the electron temperature increases in the bulk region, but decreases in the sheath region, and the plasmas transform from γ mode to α mode as the significant change of electron generation rate distributions. Larger electric field is needed in the bulk region to sustain the electronegative plasma, so the electrical characteristics of He + O(2) plasmas transform form capacitive to resistive with increasing O(2) fraction. Meanwhile, the ion-coupling power increases dramatically, which can be estimated by a formula based on the electronegativity. A new criterion for determining the sheath boundary, |∇E| = 5 kV/cm^2, is put forward, which is found suitable for both the electropositive and electronegative plasmas.

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE.

The increasing prevalence of obesity-related glomerulopathy (ORG) poses a significant threat to public health. Sodium-glucose cotransporter-2 (SGLT2) inhibitors effectively reduce body weight and total fat mass in individuals with obesity and halt the progression of ORG. However, the underlying mechanisms of their reno-protective effects in ORG remain unclear. We established a high-fat diet-induced ORG model using C57BL/6J mice, which were divided into three groups: normal chow diet (NCD group), high-fat diet (HFD) mice treated with placebo (ORG group), and HFD mice treated with empagliflozin (EMPA group). We conducted 16S ribosomal RNA gene sequencing of feces and analyzed metabolites from kidney, feces, liver, and serum samples. ORG mice showed increased urinary albumin creatinine ratio, cholesterol, triglyceride levels, and glomerular diameter compared with NCD mice (all P < 0.05). EMPA treatment significantly alleviated these parameters (all P < 0.05). Multitissue metabolomics analysis revealed lipid metabolic reprogramming in ORG mice, which was significantly altered by EMPA treatment. MetOrigin analysis showed a close association between EMPA-related lipid metabolic pathways and gut microbiota alterations, characterized by reduced abundances of Firmicutes and Desulfovibrio and increased abundance of Akkermansia (all P < 0.05). The metabolic homeostasis of ORG mice, especially in lipid metabolism, was disrupted and closely associated with gut microbiota alterations, contributing to the progression of ORG. EMPA treatment improved kidney function and morphology by regulating lipid metabolism through the gut-kidney axis, highlighting a novel therapeutic approach for ORG. [ABSTRACT FROM AUTHOR]