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Uptake of anthropogenic carbon dioxide (CO2) from the atmosphere has acidified the ocean and threatened the health of marine organisms and their ecosystems. In coastal waters, acidification is often enhanced by CO2 and acids produced under high rates of biological respiration. However, less is known about buffering processes that counter coastal acidification in eutrophic and seasonally hypoxic water bodies, such as the Chesapeake Bay. Here, we use carbonate chemistry, mineralogical analyses and geochemical modelling to demonstrate the occurrence of a bay-wide pH-buffering mechanism resulting from spatially decoupled calcium carbonate mineral cycling. In summer, high rates of photosynthesis by dense submerged aquatic vegetation at the head of the bay and in shallow, nearshore areas generate high pH, an elevated carbonate mineral saturation state and net alkalinity uptake. Calcium carbonate particles produced under these conditions are subsequently transported downstream into corrosive subsurface waters, where their dissolution buffers pH decreases caused by aerobic respiration and anthropogenic CO2. Because this pH-buffering mechanism would be strengthened by further nutrient load reductions and associated submerged aquatic vegetation recovery, our findings suggest that the reduction of nutrient inputs into coastal waters will not only reduce eutrophication and hypoxia, but also alleviate the severity of coastal ocean acidification.Calcium carbonate formed in seagrass beds that is transported and dissolved in deeper waters offshore helps buffer coastal acidification in the Chesapeake Bay, according to geochemical modelling of a transect of carbonate chemistry measurements.

Upper layer thickness (mixing depth) is an essential parameter for estimating the dissolved inorganic carbon and carbon flux at the water surface based on their association with the vertical flux of dissolved inorganic carbon. Previous studies quantified the mixing depth without SAV meadow or penetration depth in the SAV meadow without stratification and wind stress. However, mixing depth related to interaction with submerged aquatic vegetations (SAVs), stratification, and wind stress has yet to be quantified in the previous studies. Our study is the first to quantify the theoretical mixing depth with SAVs according to wind stress, SAV height, and drag coefficient. Theoretical mixing depth was quantified from modeled vertical temperature profile, vertical profile of horizontal velocity, and gradient Richardson number (Rig,veg). We found that mixing depth at a Rig,veg of 100 demonstrated good agreement with numerical results on average, with the mixing depth estimated in this study (hU,this study) showing high applicability to observations at Komuke Lagoon. Moreover, hU,this study increased with the increasing wind stress and decreasing drag coefficient and SAV height. Further, we found that SAV meadows with stratification and wind stress could be divided into four hydrodynamic regimes: non‐vegetated layers, upper vegetated layers, thermoclines, and benthic boundary layers. Our findings help us estimate mixing depth or vertical flux without complicated numerical simulation and understand flow interaction with SAV, wind stress, and stratification. Plain Language Summary Upper layer thickness (mixing depth) and flow fields are important to estimate the carbon flux (e.g., “blue carbon”) and the transportation of dissolved materials (e.g., dissolved oxygen, dissolved inorganic carbon, dissolved inorganic nitrogen, etc) in submerged aquatic vegetation (SAV) meadows. However, it may not be easy to estimate mixing depth without complex numerical simulations. Additionally, we have not understood the interaction of SAV meadows with stratification, currents, or waves. Our study is the first to quantify the mixing depth analytically and to show the hydrodynamic regimes in SAV meadows with stratification. Our finding helps us to estimate carbon flux and the transportation rate of dissolved materials easily without complicated numerical simulation. Key Points Mixing depth with submerged aquatic vegetation (SAV) meadows was estimated using an average gradient Richardson number 100 SAV meadows with stratification and wind stress were divided into four hydrodynamic regimes Wind stress, SAV, and stratification effects were used to accurately estimate the mixing depth

The Everglades Stormwater Treatment Areas (STAs) are a complex of large constructed wetlands that are an integral component of the State and Federal efforts to restore the Everglades ecosystem. The overall objective of this study was to determine the accumulation rates of macro‐elements including carbon (C), nitrogen (N), phosphorus (P), sulfur (S), and associated secondary elements including calcium (Ca), magnesium (Mg), aluminum (Al), and iron (Fe) in two Everglades STAs over their periods of operation. The study was conducted in STA‐2 with parallel flow‐ways consisting of emergent aquatic vegetation (EAV) and submerged aquatic vegetation (SAV) and the Western flow‐way of STA‐3/4 with EAV and SAV cells operated in series. Elemental accumulation rates were determined in the unconsolidated surficial sediments (floc) and recently accreted soil (RAS) that have accumulated on top of the antecedent soil over the 14‐ and 10‐yr periods of operation for STA‐2 and STA‐3/4, respectively. Flow‐ways with SAV were more efficient than EAV in accreting mineral matter, resulting in increased bulk density and higher accumulation rates of elements. Average C accumulation in the floc and RAS of SAV flow‐ways was 320 g·m−2·yr−1 with approximately equal proportions of inorganic and organic C, while in the EAV flow‐ways accumulation rates of C ranged from 116 to 147 g·m−2·yr−1 with mostly organic C. Phosphorus accumulation rates were approximately 2–3 times higher in SAV than in EAV flow‐ways. Differences in accumulation of elements between SAV and EAV were largest for Ca with 17–42 times more Ca in SAV than EAV systems. This suggests that in the SAV systems, possible occlusion of macro‐elements and metals during CaCO3 precipitation facilitated accretion of material with high mineral content. In EAV, biomass turnover and associated biotic processes regulated organic matter accumulation rates. The spatial accumulation patterns of P, C, and N in the EAV areas of STA‐2 and STA‐3/4 were similar to those observed in the EAV areas of the natural wetlands in Water Conservation Areas, suggesting that constructed wetland systems function similarly to natural wetlands dominated by EAV areas in retaining and storing macro‐ and secondary elements.

Aquatic vegetation, including floating-leaved and emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV), and algal blooms (AB), are primary producers in eutrophic lake ecosystems and hold significant ecological importance. Aquatic vegetation and AB dominate in clear and turbid water states, respectively. Monitoring their dynamics is essential for understanding lake states and transitions. Sentinel imagery provides high-resolution data for capturing changes in aquatic vegetation and AB. However, the existing mapping algorithms for aquatic vegetation and AB based on Sentinel data only focused on one or two types. There are still limited algorithms that comprehensively reflect the dynamic changes of aquatic vegetation and AB. Additionally, the unique red-edge bands of Sentinel-2 MSI have not yet been fully exploited for mapping aquatic vegetation and AB. Therefore, we developed an automated mapping algorithm that utilizes Sentinel data, especially red-edge bands, to comprehensively reflect the dynamic changes of FEAV, SAV, and AB. The key indicator of the algorithm, the second principal component (PC2) derived from four red-edge bands and four other bands of Sentinel-2 MSI, can effectively distinguish between FEAV and AB. SAV was mapped by the Sentinel-based submerged aquatic vegetation index (SSAVI), which was constructed by fusing Sentinel-1 SAR and Sentinel-2 MSI data. The algorithm was tested in three representative lakes, including Lake Taihu, Lake Hongze, and Lake Chaohu, and yielded an average accuracy of 87.65%. The algorithm was also applied to track changes in aquatic vegetation and AB from 2019 to 2023. The results show that, over the past five years, AB coverage in all three lakes has decreased. The coverage of aquatic vegetation in Lake Taihu and Lake Hongze is also declining, while coverage remains relatively stable in Lake Chaohu. This algorithm leverages the high spatiotemporal resolution of Sentinel data, as well as its band advantages, and is expected to be applicable for large-scale monitoring of aquatic vegetation and AB dynamics. It will provide valuable technical support for future assessments of lake ecological health and state transitions.

Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013 to 2016), in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R2 = 0.77) to determine the water clarity (2013–2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg Dry Weight (DW) m-2. The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m-2) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1–8 m water depth with a spatial resolution in the decimeter scale. The survey area was known to be populated with the seagrass Zostera marina and the bathymetric soundings were highly influenced by this habitat. The depth values often coincide with the canopy of the seagrass. Instead of classifying the data with a digital terrain model and the given derivatives, we derive predictive features from the native point cloud of the MBES soundings in a similar way to terrestrial LiDAR data analysis. We calculated the eigenvalues to derive nine characteristic features, which include linearity, planarity, and sphericity. The features were calculated for each sounding within a cylindrical neighborhood of 0.5 m radius and holding 88 neighboring soundings, on average, during our survey. The occurrence of seagrass was ground-truthed by divers and aerial photography. A data model was constructed and we applied a random forest machine learning supervised classification to predict between the two cases of “seafloor” and “vegetation”. Prediction by linearity, planarity, and sphericity resulted in 88.5% prediction accuracy. After constructing the higher-order eigenvalue derivatives and having the nine features available, the model resulted in 96% prediction accuracy. This study outlines for the first time that valuable feature classes can be derived from MBES point clouds—an approach that could substantially improve bathymetric measurements and habitat mapping.

Information of interactions between waves and aquatic vegetation is becoming increasingly important, in particular, due to the trend of plant-induced wave attenuation towards sustainable coastal management systems. This study aims to understand monotypic vegetation-wave interactions through three-level, four factors, response surface methodology (RSM) using laboratory wave flume under controlled conditions. Cymodocea Serrulata is one of the prevalent monotypic seagrass species found in the Gulf of Mannar, Tamilnadu, South India. It was physically simulated using synthetic plant imitations to create a relationship between wave attenuation and four direct control factors, i.e., water depth (h), wave period (T), plant density (A/) and bed roughness factor (/) using an empiric model. The model developed was tested using the analysis of variance technique (ANOVA) and evaluated for the main and interaction effects of the studied parameters. The findings showed that both individually and in combination, all of the parameters considered were significantly effective on. All model-based findings were compared with a new collection of experimental data and validation tests were performed. The comparison of experimental results with model predictions was at a good agreement with a high coefficient of determination (R2) of 0.98 (with p-value < 0.05).

Coastal hypoxia is increasing worldwide in response to human‐caused changes in global climate and biogeochemical cycles. In this paper, we view anthropogenic trends in coastal hypoxia through the lens of disturbance ecology and complexity theory. Complexity theory provides a framework for describing how estuaries and other coastal aquatic ecosystems respond to hypoxia by understanding feedback loops. Can it also be valuable in understanding how these ecosystems behave under shifting (i.e., unnatural) disturbance regimes? When viewed as a disturbance regime, shifts in the spatial (areal extent and fragmentation) and temporal (frequency and duration of events) characteristics of coastal hypoxia can be used to track changes into a non‐stationary future. Here, we consider options for increasing the resilience of coastal aquatic ecosystems to future, unnatural hypoxic regimes. To start, we define desirable states as ecosystems with long trophic chains and slow nutrient and carbon dynamics that produce many ecosystem services. We then work backward to describe circumstances dominated by positive feedbacks that can lead ecosystems toward an undesirable state (i.e., depauperate communities and chemically reduced sediments). Processes of degradation and recovery can be understood in the context of island biogeography whereby species diversity in habitats fragmented by hypoxia is determined by the balance between rapid local extinction, slow recolonization from the edges of hypoxic patches, and opportunities for ecological succession during between disturbance events. We review potential future changes associated with changing global climate and highlight ways to enhance coastal resilience. In addition to efforts to slow climate change, potential interventions include reduced nutrient and carbon loadings from rivers, restoration of aquatic vegetation, and managing for key species, including those that promote sediment oxygenation, that enhance water clarity, or that promote grazing on epiphytic algae through top‐down control.

The aim of this study was to carry out a floristic survey of aquatic macrophytes in the municipality of Chapadinha, eastern Maranhão, and classify their biological forms. The study was done between September 2021 and September 2022. A total of 31 families, 49 genera and 72 species of aquatic macrophytes were catalogued, of which 65 are angiosperms. Among them, Bacopa stricta (Plantaginaceae), Staurogyne diantheroides (Acanthaceae), and Xanthosoma aristeguietae (Araceae) are new records for the flora of Maranhão, with the last two new records for Northeast Brazil. The richest family was Cyperaceae, with 11 species, followed by Plantaginaceae (seven taxa), Fabaceae (five taxa) and Lentibulariaceae (five taxa). Six biological forms were recorded, amphibious (27 taxa) and emergent (26 taxa) being the most common. The aquatic environments of Chapadinha are home to a considerable number of species, families, and life forms of macrophytes. The results show that due to the lack of surveys, evidenced by the new records presented, the state aquatic flora is still underestimated. Further studies in poorly explored areas are suggested, especially in the eastern part of the state, to improve understanding of species richness.