Forested wetlands such as mangroves are considered highly valuable for nature-based mitigation of coastal flooding. However, their wave attenuation capacity during extreme storms, when risks are highest, is rarely measured and remains challenging to predict. Here, we compile a unique dataset on the largest incident wave heights (0.39 to 1.44 m) ever recorded in forested wetlands, including our own measurements and literature data. Our analysis reveals that forested wetlands can significantly attenuate storm waves (35% over 3 wavelengths) except in rare near-submerged cases. Notably, 19 of the 20 existing formulations for vegetation drag coefficient, a key parameter for wave attenuation modeling, are inapplicable in storm conditions. Hence, we introduce an new approach to reliably predict wave attenuation during storms, without the need for drag coefficient determination nor modeling expertise. This approach offers coastal practitioners a new user-friendly tool to assess the effectiveness of nature-based solutions for storm hazard mitigation.

Microplastics have been widely detected in the natural water environment, which brings inevitable risks to the water ecosystem and human health. However, the understanding of the potential impact of microplastics on aquatic animals, plants, and human health is still limited, and technical methods to control microplastic pollution in natural water are still rare. Hence, this paper summarizes the progress of research on microplastic pollution in water systems in terms of microplastic source, attributes, distribution characteristics, environmental effects, and prevention and control methods according to the relevant research reports on water microplastic pollution. It also expounds the basic ways for the prevention, control, and treatment of water microplastics, and looks forward to the research direction of water microplastic pollution in the future. The results show that the abundance of fresh water microplastics in China is higher than that in other regions, but the pollution level of marine microplastics is at the middle level. Compared with other countries, the pollution degree of microplastics in aquatic organisms in China is at the middle and lower level, but the spatial heterogeneity is more obvious. Through hydraulic control and the substitution of degradable plastic products, water microplastic pollution can be greatly reduced. This paper can provide a reference basis for the formulation of microplastic pollution prevention and control in China.

We apply a 2‐D horizontal process‐based model (Delft3D) to study the feedback mechanisms that control the long‐term evolution of a fringing intertidal flat in the Western Scheldt Estuary. The hydrodynamic model is validated using a comparison with measurements on the intertidal flat and the sediment transport module is calibrated against long‐term morphology data. First, the processes that lead to net sediment exchange between channel and flat are studied. Then, long‐term simulations are performed and the dependency of sediment fluxes on the tidal flat bathymetry, and the corresponding morphodynamic feedback mechanisms are explained. In the long run, relatively stable states can be approached, which are shown to be typical for wave‐dominated fringing mudflats. The system behavior can be explained by the typical feedback mechanisms between the intertidal bathymetry and the hydrodynamic forces on the flat. In the subtidal domain, the impact of small (5–10 cm) wind waves increases with a rising elevation due to decreasing water depths. In the intertidal domain, the wave impact increases with increasing cross‐sectional slope due to wave shoaling. These relationships result in negative (stabilizing) morphodynamic feedback loops. The tidal current velocities and tide‐induced bed shear stresses, on the other hand, are largely determined by the typical horizontal geometry. A stabilizing feedback loop fails, so that there is no trend toward an equilibrium state in the absence of wind waves.

Contemporary research on terrestrial carbon exchange processes is paramount for a nuanced comprehension of global and local climatic fluctuations and their interaction with anthropogenic activities. This study delves into the spatiotemporal dynamics of vegetation carbon exchanges within the Three Northern Protection Forest Project Area, leveraging two decades of MODIS NPP data and an innovative NEP estimation model. Our analysis highlights a generally increasing trend in Net Ecosystem Productivity (NEP) from 2000 to 2020, with significant growth in approximately 32.97% of the study area and slight increases in 24.18%. Notably, lower NEP values were found in desert and arid zones, whereas higher values were observed in more vegetated regions like Ningxia, Hebei, Inner Mongolia, and the northeast. The study also assesses the impact of climate variables and land-use changes on NEP, identifying both negative and positive correlations in specific regions. Despite the overall positive trend towards ecological restoration and enhancement, significant uncertainties remain, emphasizing the urgent need for further research to support ecosystem resilience and sustainable management practices.

Maintaining the balance between power station operation and environmental carrying capacity in the process of cooling water discharge into coastal waters is an essential issue to be considered. Earth observations with airborne and sea surface sensors can efficiently estimate distribution characteristics of extensive sea surface temperature compared with traditional numerical and physical simulations. Data acquisition timing windows for those sensors are designed according to tidal data. The airborne thermal infrared data (Thermal Airborne Spectrographic Imager, TASI) is preprocessed by algorithms of atmospheric correction, geometric correction, strip brightness gradient removal, and noise reduction, and then the seawater temperature is inversed in association with sea surface synchronous temperature measurement data (Sea-Bird Electronics, SBE). Verification analyses suggested a satisfied accuracy of less than about 0.2 °C error between the predicted and the measured values in general. Multiple factors influence seawater temperature, i.e., meteorology, ocean current, runoff, water depth, seawater convection, and eddy current; tidal activity is not the only one. Environmental background temperature in different seasons is the governing factor affecting the diffusion effect of seawater temperature drainage according to analyses of the covariances and correlation coefficients of eight tidal states. The present study presents an efficient and quick seawater temperature monitoring technique owing to industrial warm drainage to sea by means of a complete set of seawater temperature inversion algorithms with multi-source thermal infrared hyperspectral data.

In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, COD_Mn, and NH₃-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction.