Abstract Submarine Groundwater Discharge (SGD) is often omitted from estuarine circulation modeling systems despite its potential to alter salinity, density, and stratification. So, this study employed a one‐way coupled PARallel Flow (ParFlow)–Finite Volume Community Ocean Model (FVCOM) framework to investigate the hydrodynamic influence of terrestrially derived meteoric groundwater discharge on the Chesapeake Bay. The results show that SGD effects vary spatially and seasonally, whereas the groundwater recharge/discharge is more likely to echo the precipitation events. Submarine Groundwater Discharge effects become more significant during the low river flow conditions, especially in summer and in Middle Bay due to the high SGD to river discharge ratio. Under baseline conditions, SGD slowly enhances stratification (0%–2%) by freshening the bottom layer and sharpening the adjacent halocline where river discharges being trapped above the pycnocline barely modifies the deeper and denser layers. During Hurricane Ida, prolonged period of precipitation elevated the SGD, which resulted in a 20% salinity reduction, however, wind driven mixing dominated stratification during the storm peak and SGD‐driven bottom freshening re‐establishes stratification during post‐storm recovery. Sensitivity experiments addressing uncertainties in SGD volume show that an ideal tenfold SGD intensification produces an 8% salinity reduction and a 10% (30%) stratification enhancement (reduction) throughout the bay (shallow Upper Bay). While river discharge dominates the estuarine freshwater budget, SGD exerts a physically distinct and disproportionate secondary influence on deep‐layer density, salt intrusion, exchange circulation, and stratification, with implications for oxygen dynamics and biogeochemical cycling in this vulnerable estuary.

Understanding sediment transport is fundamental to the safe and effective open-water placement of dredged material in any coastal system. The Wolf Trap Alternate Open Water Placement Site (WTAPS), located in the lower reaches of Chesapeake Bay, is a licensed placement site for sediments removed from the Virginia York Spit shipping channel and other critical dredging operations. Given the scarce sediment transport data and the potential impacts on water quality and benthic organisms (e.g. blue crabs), calculating the sediment behavior is critical for evaluating dredging activities. With limited datasets, particle model was then considered as an alternative tool to simulate the sediment dynamics, especially the surface sediment dynamics compared to the complex full sediment model. Thus, we utilized a calibrated unstructured grid-based hydrodynamic model (FVCOM) and an offline Lagrangian particle-tracking model to simulate the surface sediment (particle) dynamics in the lower reaches Chesapeake Bay. Sediments, as passive particles, were released in spring using climate and oceanographic data from 2017, to understand temporal and spatial patterns of settlement and distribution driven by the wind and riverine inflow conditions. These numerical simulations assess the impacts of particle sizes and densities, winds, and riverine influx on the subsequent sediment (particle) transport: coarser, denser particles, such as sand, settle southwest more readily, while finer silt and suspended particles remain in the upper water column. Surface-confined sediments tend to drift southeastward toward the bay's mouth, while majority of deposited, fine particles move northwestward due to bottom seawater intrusions. Wind plays an important role in altering bay circulation, influencing sediment settlement and distribution. In contrast, riverine inflow has limited impact except during flooding events when the significantly increased freshwater discharge alters sediment dispersion. These findings introduce a useful tool to understand dredging material transport and provide insights for managing dredging operations to mitigate their ecological impacts, and also offer guidance for sustainable sediment management in estuarine systems.

Accurate medium- and long-term storm surge forecasting is essential for coastal hazard mitigation, yet it remains a persistent challenge owing to the chaotic dynamics of ocean-atmosphere interactions. This study evaluates deep learning models for medium- and long-term storm surge prediction in the Bohai Sea, utilizing typhoon best-track and atmospheric forcing variables as predictors. Specifically, a hybrid CNN-BiLSTM-Attention architecture is proposed for 24-h multi-step storm surge forecasting, while a standalone BiLSTM model is applied for single-step forecasts with lead times spanning 24 to 72 h. To enhance model interpretability, three explanation approaches including Shapley Additive Explanations, Permutation Importance Method, and Sequential Forward Selection, are integrated to quantify the relative contributions of input predictors. Results demonstrate that the CNN-BiLSTM-Attention model achieves the highest predictive accuracy for 24-h multi-step forecasts, whereas the BiLSTM model maintains robust performance for single-step forecasts at longer lead times. Sensitivity analysis further reveals that typhoon location and atmospheric forcing are consistently dominant predictors, while the relative importance of typhoon central pressure, wind speed, and translational speed changes with various lead times. Overall, this study highlights the potential of explainable deep learning in improving storm surge predictability and identifying the key physical drivers governing storm surge dynamics in the Bohai Sea.

The Yellow River Estuary (YRE), located in the semi-enclosed Bohai Sea, is characterized by complex shorelines and shallow water depths and is vulnerable to high waves during extreme weather events. Therefore, a dual-nested third-generation wave model was applied to investigate wave dynamics during Typhoons In-Fa (2021) and Mui-Fa (2022) and a pair of cold wave events in 2021 and 2022. The YRE model was refined to reproduce realistic high-resolution terrain and then calibrated against observations at four long-term buoy stations. Results indicate that wave characteristics closely correlate with winds, modulated by local bathymetry. During cold waves, the temporal evolution of the significant wave height (Hs) exhibits double peaks, whereas a single peak is observed during typhoons due to alternative development responses to winds. This resulted in a 1.5-h time lag between Hs and winds. Wind waves primarily dominate sea states, while swells occur after the typhoon passage. Bathymetric refraction plays an essential role in sheltering the southern region of the YRE from remotely energetic swells. Further investigations reveal that depth-induced breaking and whitecapping jointly control wave energy dissipation. Bathymetric heterogeneity and shoaling processes substantially influence wave energy, resulting in wave attenuation and spatial variability. Intense triad wave-wave interactions and wave breaking contribute to increased Hs, causing multiple wave-breaking processes during propagation. The findings in the YRE help enhance the understanding of wave dynamics in similar shallow-water mega deltas and estuaries.

Accurate and timely storm surge prediction is critical information in coastal zone management and risk reduction strategies. The Bohai Sea, a semi‐enclosed bay in the Northwest Pacific that used to be less prone to typhoon disasters, has been witnessing a paradigm shift in typhoon activities in the recent past. Since there have been limited typhoon‐induced storm surges in the Bohai Sea, an innovative prediction system is warranted to address frequent and intense typhoon‐induced impacts. Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. In this study, the FVCOM‐ML model was driven by a hybrid wind field that superimposed the Holland wind and the reanalysis wind field. The ML models were trained via Advanced Circulation Model simulations to compensate for the limited in‐situ observations. The prediction performances were analyzed for both spatial (e.g., single and multiple sites) and temporal (e.g., single and multiple steps) scale variability. ML is trained to overcome the residual error of the FVCOM, effectively reducing the inherent uncertainty of traditional methods. FVCOM‐ML offers a significant advantage over standalone FVCOM or ML while better incorporating realistic physical constraints and improving the accuracy of storm surge forecasts.

Given the considerable hydrodynamic and ecological implications associated with riverine substances in estuaries, a comprehensive investigation on their fates, pathways, and driving mechanisms is imperative. Consequently, observed drifters were deployed in the Bohai Sea in November 2019, revealing a primary hydrodynamic pattern. To complement the observed trajectories, an unstructured-grid-based particle-tracking model was implemented for the Bohai Sea to delineate the riverine particle transport dynamics during ice-free months (April to November) in 2019. The Lagrangian particle trajectories from riverine substances showed distinct spatial and temporal characteristics. Particles from the Yellow River reached the Central Basin in July, while those from the Hai and Liao Rivers remained localized. Particles from the Luan River traveled to the Central Basin and Bohai Bay regions. Winds significantly alter the spatial scales of particle-influenced area: southerly winds transport particles offshore from the Yellow River, northerly winds move particles from the Hai and Liao Rivers, and both southerly and westerly winds aid in moving particles offshore from the Luan River. While river discharge influences the size of these area, their overall spatial distribution patterns remained mostly consistent regardless of the presence or absence of river discharge. Moreover, momentum analysis confirmed that wind-induced Ekman transport strengthens the coastal-shelf connection, dispersing riverine substances from the Yellow and Luan Rivers away from the coast. In contrast, horizontal advection and the dominance of viscous force override Ekman transport, reducing the coastal-shelf connection in the Bohai and Liaodong Bays.

Climatologically, the Bohai Sea, a semi-enclosed sea in the northwestern Pacific Ocean, has contributed minimally to typhoon hazards due to its low sea surface temperatures, high sea level pressure, weak vorticity, and landlocked basin. However, with rising sea surface temperatures and the poleward migration of typhoons in the northwest Pacific, a northward shift in typhoon intensity is evident from the increased frequency and energy of typhoons in the Bohai Sea. This study examines the typhoon climatology and triggering factors in the Bohai Sea from 1980 to 2020 and compared them with other Pacific basins. The analysis reveals the key factors including the strengthening of the Interdecadal Pacific Oscillation, rising sea surface temperatures, and weakening vertical wind shear during typhoon season potentially favoring the typhoon frequency and power in this basin. A paradigm shifts in the maximum potential intensity, with a 20–30

Shallow waters are highly susceptible to coastal storm surges, and thus a comprehensive understanding of physical dynamics is essential to coastal management and nearby communities. Given that physics-based models are more suitable in the highly dynamic and complex coastal and estuarine systems than data-driven methods, this work reviews state-of-the-art storm surge modeling and its influencing factors. Three cases were applied under strong wind conditions and during hurricane events in the nearshore of Lake Michigan and a lagoonal system called the Maryland Coastal Bays (MCBs). For the first case, a pair of wave-current coupled, two-dimensional (2D) models, which included the Advanced Circulation Model (ADCIRC) and the Finite Volume Community Ocean Model (FVCOM), were applied to the nearshore of Lake Michigan under strong wind conditions. Storm surge simulations from both models are sensitive to atmospheric datasets, wind stress calculation methods, and wave radiation stress gradients. Specifically, modeled storm surge is associated with wind speed, its direction, coastal geometry, topography, and wave-induced setup. A three-dimensional (3D), wave-current coupled FVCOM was then applied to the MCBs in the second case during Hurricane Irene (2011). With the inclusion of wave effects (e.g., wave radiation stress), the underestimated storm surge (e.g., 20 cm for the 1.01 high water surface elevation mark) was reduced by half (i.e., 10 cm or 10%). For the third case, a nested 3D FVCOM based storm surge model was used to simulate the storm surge during the passage of Hurricane Sandy (2012) over the MCBs. It confirms the findings from previous cases that winds are important in storm surge simulations. Further investigations reveal that a nesting model can provide the necessary remote forcing from a large domain and maintain the intricate shoreline and bathymetry of the inner domain in a lagoonal system. In the future, more effort and work on storm surge modeling can be focused on the comparison between 2D and 3D models, using alternative wave-current coupled mechanisms (e.g., vortex-force formalism), and considering the effects of turbulent mixing processes.

Understanding spatiotemporal variations in phytoplankton biomass is crucial to the health of marine ecosystems. Therefore, the Finite Volume Community Ocean Model (FVCOM)-based Ecological Model (Integrated Compartment Model) was implemented to assess nutrient and phytoplankton dynamics in the Bohai Sea from 2010 to 2019. From 2010 to 2013, dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) levels in the Bohai Sea were higher (similar to 0.400 and 0.030 mg/L, respectively) in nearshore areas compared to other years. During the summer and fall, the spatial distribution of the DIN/DIP ratio in the Bohai Sea exhibited a double-core structure. Higher values (>100) were primarily concentrated in the central region of the Liaodong Bay and south of the Central Basin near the Yellow River Estuary. Statistical analyses and numerical experiments revealed that increasing DIP loading from rivers had a greater effect on phytoplankton biomass in the Laizhou and Bohai Bays than increasing DIN loading. However, the phytoplankton biomass in the Liaodong Bay was strongly influenced by both increasing DIN and DIP loading from rivers. Notably, the increase in phytoplankton biomass resulting from increasing DIN loading exceeded that from increasing DIP loading by 17% in the Liaodong Bay. The reduction in river discharge weakened circulation at the river mouths, thereby partially retaining surface phytoplankton. This was more predominant in the nearshore areas of the Yellow River owing to the higher river discharge in August 2019. This study provides valuable insights for the management and conservation of marine ecosystems.

The dynamics of typhoon-induced waves in semienclosed seas become an interesting topic with the increase of typhoon intensity. Based on the calibrated Simulating Waves Nearshore (SWAN) model, wave dynamics were investigated under distinct typhoon tracks [e.g., Matmo (2014), Rumbia (2018), and Lekima (2019)] in the Bohai Sea. Distributions of significant wave heights (SWHs) are affected by the typhoon wind fields and are directly related to the typhoon tracks. The classical JONSWAP wave spectra were adopted for the analysis of sea states (e.g., wind seas or swells) to further explain variations in wave heights. Results indicate that the dominant sea state with higher energy experiences significant spatiotemporal variability under distinct tracks. For typhoons passing through the central part of the Bohai Sea (e.g., Rumbia), high-energy waves are observed under swell-dominated and mixed sea states, which are subjected to the fetch limitation in the semienclosed sea and rapid changes in typhoon winds. The high energy waves induced by other typhoons passing along the edges of the Bohai Sea correspond to the wind-sea-dominated sea state. Spatiotemporal variability of the sea state exhibits a high correlation with its position relative to the typhoon center. Therefore, a reference frame based on the radius of the maximum wind speed was established to discuss the sea states in this semienclosed sea. Further investigations reveal that swells (wind seas) dominate the regions within the radius of the maximum wind speed (elsewhere), and the double-peaked wave spectra tend to appear in the left quadrants.

Blue crab (Callinectes sapidus) supports lucrative Mid-Atlantic crustacean fisheries and plays an important role in estuarine ecology, so their larval transport and recruitment dynamics in the Maryland Coastal Bays system were investigated using simulated and observed surface drifters. Relative contributions of winds, tides, density gradients, and waves to larval recruitment success were identified during the spawning season, particularly under hurricane conditions in 2014. Based on temperature (e.g., 19-29 degrees C) and salinity conditions (e.g., 23-33 PSU), particles representing virtual blue crab larvae were released into the model domain from early June to late October 2014. During the spawning season, variations in the larval recruitment success caused by wind speed and direction, tides (e.g., affecting through inlets), density gradients (e.g., salinity variations), and surface gravity waves were 17%, 4%, -9%, and 17%, respectively. During Hurricane Arthur (2014), variability of self-recruitment success caused by density gradients are negligible while by other three factors are comparable at 3%-4%. Surface drifter experiments support the modeling results that larval recruitment success is strongly associated with the coastal circulation. The high (low) self-recruitment success in the Assawoman and Chincoteague Bays (Sinepuxent Bay) is related to the locally weak (strong) circulation; released larvae escape from inlets are likely recruited to southern Fenwick and northern Assateague Islands, and the coastal regions outside the Chincoteague Inlet. Understanding physical factors influencing larval recruitment success helps resource managers make informed decisions about habitat restoration and harvest regulations, in addition to seafood-related food security.

Mixing and transport in the estuaries and coastal waters must be informed by advanced and up-to-date research to consider the underlying natural and anthropogenic effects on physical and biogeochemical processes. To that end a session was organized during the 2021 Coastal and Estuarine Research Federation conference entitled “Mixing and Transport in Estuaries and Coastal Waters”. The focus of this session was to improve understanding through comprehensive studies associated with mixing and transport processes in estuaries and coastal waters based on observations, analytical models, laboratory experiments, and numerical models. This Special Issue (SI) ‘Mixing and Transport in Estuaries and Coastal Waters’ in ‘Estuarine Coastal and Shelf Science’ is an outcome of the talks presented at the conference session. The key research interests covered comprise of five themes: Estuarine dynamics, Wave-current-surge processes, Sediment dynamics, Plume dynamics, and Estuarine physical-biogeochemical processes. The articles contained in the SI represent the latest advances in mixing and transport in estuaries and coastal seas all over the world, as well as common methods including observations, remote sensing, and numerical modelling, a data framework is also used, which is the methodological highlight of this SI. While continued progress is still being made on understanding estuarine and coastal sea dynamics, the effects of these physical processes on biological and biogeochemical issues (i.e., larval transport and water quality dynamics) should also be considered in future studies.

The presence of wave coherence, which contributes to the inhomogeneity of wave characteristics and significantly affects wave processes over nearshore regions of the Yellow River Delta (YRD), was simulated and analyzed in this study. A phase-resolving Boussinesq-type wave model, FUNWAVE-TVD, was used to simulate waves with desirable coherency effects. Bathymetry and topography data were obtained from the Chinese nautical chart and E.U. Copernicus Marine Service Information. After the model configuration, spatial distributions of the root mean square and significant wave heights, and the maximum cross-shore current velocity and vorticity over the domain with respect to different degrees of wave coherence and energy spectrum discretization were investigated. The results indicate that the complexity of the spatial distribution and magnitude of longshore variations in wave statistics are proportional to the degree of coherence. Waves with higher coherency exhibit more complex variabilities and stronger fluctuations along the longshore direction. The influence of morphological changes on wave height in the YRD was discernible by comparing the results with and without coherency effects. The cross-shore current velocity decreased as the waves moved toward the surf zone, while the vorticity accelerated, indicating a higher shear wave magnitude. The simulated wave dissipates more than 60% (80%) of its energy when it reaches water depths of less than 5 m (2 m) and completely dissipates when it breaks at the shore.

A wave-current coupled, unstructured-grid, three-dimensional hydrodynamic model was applied to investigate the seasonal dynamics of the Maryland Coastal Bays system. The model's performance was validated successfully against hydrodynamic observations from the spring to fall of 2014, and the driving forces of water circulation and exchange flows were discussed. Results indicate that seasonal dynamics are primarily controlled by tides, modulated by winds, waves, and density variations, and regulated by the inlet orientation and geometry. Seasonal circulation in the surface layer is stronger than that near the bottom. The strong coastal circulation, net outflows via inlets, and the clockwise movements in the southern Isle of Wight Bay are primarily controlled by tides. The directional alignment between winds and the bay's principal axis and inlet orientation are key to the seasonal circulation and exchange flows in Sinepuxent Bay and via inlets. Wave-induced effects are comparable to tides in particular regions (e.g., reaching 30 cm/s in Isle of Wight Bay), and much larger than those caused by density variations overall. Additional numerical experiments indicate that spatial variations in salinity are mainly responsible for the density-induced circulation (e.g., 6 cm/s at the mouth of Newport River in Newport Bay). Further analysis indicates that the net exchange flows vary from the surface to bottom layers (e.g., different magnitudes or transporting directions) both in the lagoon and via the paired inlets. This work is beneficial to coastal communities and numerical modelers in understanding the dynamics of shallow lagoon-inlet-coastal ocean systems at a seasonal timescale.

Wave dynamics were investigated by applying the wind-wave model Simulating Waves Nearshore to the semi -enclosed Bohai Sea during cold wave events. Wind quality was examined by comparing three wind data sour-ces with buoy observations, and then numerical experiments were conducted to investigate the impacts of model physics settings on simulated waves. After model calibration, the wave dynamics were examined from the aspects of response to various wind conditions (speed, fetch, duration), wave dominant component (wind wave or swell), as well as the dissipation processes during a pair of cold wave events (e.g., northwesterly in 2014 and north-easterly in 2015) with the consideration of spatial differences. The maximum significant wave heights during two cold wave events are similar (e.g., 3.5 m in the central Bohai Basin), which attributes to the impacts of limited fetch difference and insufficient high wind duration. Wind wave is the dominant component, which is reflected in the wave spectra (e.g., wave age less than 1.2). Spatial regions of wave dissipations controlled by whitecapping, bottom friction, and depth-induced breaking during cold wave events are quantified. Whitecapping dominates the wave energy dissipation in deep-water areas, while the bottom friction dissipation controls the major dissipation process as waves propagate towards the coast (e.g., at 5-10 m water depths). Depth-induced dissi-pation only occurs over a narrow strip along the southern coastal area but is significantly beyond the others. Depth-induced dissipation decreases suddenly when reaches a flat bottom, and it decreases with the increase of the width of the bottom slope.

The circulation in a shallow lagoon–inlet–coastal ocean system is significant to material transports (e.g., debris, pollutants, and larvae). To study surface flows in this system, we deployed 35 surface drifters at various tidal phases and wind conditions during 2017 and 2018 in the Maryland Coastal Bays system (MCBs). Given that winds and tides are two important drivers of estuarine and coastal circulation, their influences on surface drifter trajectories were analyzed. Observations indicate that surface drifters exit (enter) the lagoon mostly during ebb (flood) currents, and clockwise circular movements at a length scale of 1.5 km formed at the outer edge of Ocean City Inlet (OCI). Under weak wind conditions, tides are primarily responsible for drifter movements near OCI, whereas both the long-fetch winds and tides are important near the relatively larger Chincoteague Inlet and backbays. Under strong wind conditions, surface drifter movements generally follow wind directions. In the shallow lagoonal system, relative effects of winds on surface drifters gradually become stronger in the regions further away from the adjacent inlet as tides are weaker. Further investigations indicate that the fastest and slowest surface drifters are near the small OCI and backbays with the weakened tides, respectively. The direct surface drifter observations that cover a wide spatial range and long time series can provide strong support to surface current simulations. Enhanced understanding of coastal physical oceanography in the MCBs can be beneficial to similar systems and coastal ocean communities.

The number, size and severity of aquatic low-oxygen dead zones are increasing worldwide. Microbial processes in low-oxygen environments have important ecosystem-level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low-oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity and oxygen. Sulfur-oxidizing microorganisms were found to be enriched in the low-oxygen bottom water and predictive of hypoxic conditions. Metagenome-assembled genomes demonstrate that some of these sulfur-oxidizing populations are capable of partial denitrification and transcriptionally active in a prior study. These results suggest that microorganisms capable of oxidizing reduced sulfur compounds are a previously unidentified microbial indicator of low oxygen in the Chesapeake Bay and reveal ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change.

Considering the significance of water exchange between the nearshore and offshore waters to shallow-water biogeochemical processes, the influences of wind and baroclinic processes on water exchanges across the 10m (E10) and 20-m isobaths (E20) in the Bohai Sea during ice-free cycles of 1998-2019 were investigated using a three-dimensional Finite Volume Community Ocean Model (FVCOM). The two-way analysis of variance illustrated that both E10 and E20 showed significant seasonal and spatial variations. The cross-isobath fluxes were the strongest during summer and the weakest during fall, with offshore (onshore) transport in the upper (bottom) layer. The net volume transport was at O (10-2) Mm3/s (millions of cubic meters per second) in Bohai and Liaodong Bays, which was an order of magnitude greater than that in Laizhou Bay. Further numerical experiments revealed that wind regulated the upper-layer E10 via the wind-induced advection, while it altered E20 mainly through the enhanced mixing and the subsequent weakening of the baroclinic pressure gradient force in the subsurface layer. In addition, the thermal gradient driven cross-isobath volume transports in Bohai and Liaodong Bays were most prominent during summer but negligible during fall. Those along the 10-m isobath in Laizhou Bay, however, were induced by the salinity gradient from the buoyant fluxes of the Yellow River all year round. With supplemental contributions of the baroclinic processes related to the cold pool of the Bohai Sea, and the wind-induced advection and mixing in the surface and subsurface layers, respectively, Laizhou Bay's E20 had a four-layer vertical structure during summer.

Shallow lagoon systems with limited river flow and precipitation are often regarded as being well-mixed; however, stratification can occasionally occur in shallow lagoons in response to intense river runoffs and precipitation. Understanding the stratification behaviors of a shallow lagoon system is critical for future environmental assessment and management strategies. Thus, in this study, stratification and mixing mechanisms in a shallow lagoon system (the Maryland Coastal Bays) were investigated on an episodic scale during Hurricane Sandy (2012) by using a numerical model. The strength of stratification (Brunt-Vaisala frequency, N in s(-1)) in the lagoon system varied spatially; and was determined by river flow, precipitation, winds, and remote forcing. During the pre-Sandy period (October 25-28, 2012), the bays were relatively mixed without sufficient river flow and precipitation. Driven by intense river flow from the northern creeks (peak: 82.47 m(3) s(-1)), high precipitation (peak: 3.29 x 10(-6) m s(-1)), and strong storm winds (mean: 12.15 m s(-1)) during the Sandy period (October 28-31, 2012), the water column in the northern creeks of the bays became stratified (mean N-2: similar to 0.01 s(-2)). During the post-Sandy period (October 31-November 3, 2012), the St. Martin River among the northern creeks showed strong stratification (mean N-2: similar to 0.03 s(-2)) due to weaker winds (mean: 5.70 m s(-1)) and residual high river flow and precipitation from the Sandy period. The wind was found to be the most dominant source of mixing in the bays, while remote forcing and wind regulated the stratification near the Ocean City Inlet.