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Article

Enhanced Transport Induced by Tropical Cyclone and River Discharge in Hangzhou Bay

by
Hongquan Zhou
1,* and
Xiaohui Liu
1,2
1
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2
State Key Laboratory of Satellite Ocean Environment Dynamics, Ministry of Natural Resources, Hangzhou 310012, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(2), 164; https://doi.org/10.3390/w17020164
Submission received: 2 December 2024 / Revised: 1 January 2025 / Accepted: 8 January 2025 / Published: 9 January 2025
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Ocean Engineering)
Browse Figures
Figure 1
<p>Bathymetry (m) of the Yellow Sea and East China Sea (<b>a</b>) and Hangzhou Bay and its adjacent seas (<b>b</b>). The bathymetry data are from the ETOPO Global 1-min dataset. The red circles in (<b>b</b>) represent the tide gauges used to validate the model results in <a href="#water-17-00164-f002" class="html-fig">Figure 2</a>.</p> "> Figure 2
<p>Sea surface elevation (m) of the model simulation (blue curves) and the observational data from tidal gauges (black dots) in Hangzhou Bay. The stations S1 to S4 are marked in <a href="#water-17-00164-f001" class="html-fig">Figure 1</a>b.</p> "> Figure 3
<p>Wind speed at 10 m above sea surface (<b>a</b>–<b>e</b>), precipitation rate (<b>f</b>–<b>j</b>), and air temperature at 2 m above sea surface (<b>k</b>–<b>o</b>) during the Severe Tropical Storm Ampil passing Hangzhou Bay. All the data are from the CFSR hourly dataset.</p> "> Figure 4
<p>Temperature (colors, °C) and velocity (vectors) in Hangzhou Bay and the Changjiang Estuary before the Tropical Storm Ampil (<b>a</b>–<b>d</b>) and during the storm (<b>e</b>–<b>h</b>). The red triangle is the center of the storm on day 0, and the black line represents the track of the storm.</p> "> Figure 5
<p>Locations of the particles simulated by the model released in the surface layer before the Tropical Storm Ampil (noTC, (<b>a</b>–<b>d</b>)) and during the storm (TC, (<b>e</b>–<b>h</b>)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.</p> "> Figure 6
<p>Locations of the particles simulated by the model released in the bottom layer before the Tropical Storm Ampil (noTC, (<b>a</b>–<b>d</b>)) and during the storm (TC, (<b>e</b>–<b>h</b>)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.</p> "> Figure 7
<p>Accumulated number of particles in the surface layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the noTC experiment of the CONTROL case.</p> "> Figure 8
<p>Accumulated number of particles in the surface layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the TC experiment of the CONTROL case.</p> "> Figure 9
<p>Accumulated number of particles in the bottom layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the noTC experiment of the CONTROL case.</p> "> Figure 10
<p>Accumulated number of particles in the bottom layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the TC experiment of the CONTROL case.</p> "> Figure 11
<p>Number of particles inside Hangzhou Bay after release.</p> "> Figure 12
<p>Accumulated number of particles in the surface layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the TC experiment of the NO-RIVER case.</p> "> Figure 13
<p>Accumulated number of particles in the bottom layer from the releasing time to after 5 days (<b>a</b>), 10 days (<b>b</b>), 20 days (<b>c</b>), and 30 days (<b>d</b>) in Hangzhou Bay in the TC experiment of the NO-RIVER case.</p> ">
Versions Notes

Abstract

:
Sediment transport in Hangzhou Bay and the adjacent Changjiang Estuary is extremely complex due to the bathymetry and hydrodynamic conditions in this region. Using the particle tracing method based on the ROMS model, three-dimensional (3D) passive particle transport in Hangzhou Bay and the Changjiang Estuary was simulated. Ocean temperature, salinity, and circulation patterns before and during Severe Tropical Storm Ampil (2018) were reproduced by the model. The circulation in Hangzhou Bay is significantly influenced by the passing of the storm with an enhanced southeastward surface current. The along-front current offshore of the Changjiang Estuary, accompanied by the Changjiang River plume, is weakened by strong mixing under the storm. The transport of passive particles before and during the storm was also simulated based on the current fields of the model. The results show that the passing of the tropical storm enhances mass exchange in Hangzhou Bay by the storm-induced southeast circulation, while particle transport near the Changjiang Estuary decreases as the estuarine plume is weakened by the intense mixing of strong winds of the storm.

1. Introduction

Mass transport in a semi-closed bay and its exchange with the open sea are crucial processes for the coastal environment. Hangzhou Bay is located in the south of the Changjiang Estuary, with an average water depth of about 10 m. The whole bay is manifested as a funnel shape, with its east end connected to the East China Sea by the channels among the Zhoushan Islands (Figure 1). As the river discharge of Changjiang flows southward due to the geostrophy, Hangzhou Bay is one of the regions with the highest suspended sediment concentration (SSC) in the world [1]. It was reported that the SSC near the water surface tends to exceed 1 kg/m3 and reach 5 kg/m3 near the bottom of Hangzhou Bay according to in situ measurements [2]. The sediment deposited in Hangzhou Bay mainly comes from the Changjiang River [3,4,5,6]. It was reported that the mean water discharge of the Changjiang River (at Datong station) is 28,400 m3/s, with its highest monthly transport reaching about 50,000 m3/s in July and its lowest around 11,000 m3/s in January [7,8]. The annual mean sediment load is 358 Mt/year [9]. Both the river discharge and the sediment load of the Changjiang River decrease due to water consumption, reservoir construction, and climate variability [10]. With a slow exchange rate due to the funnel-shaped structure [11], mass transport is limited, resulting in a significant lag in the mixing of water and pollutants within the bay. The slow water exchange in Hangzhou Bay can also lead to the accumulation of pollutants, raising concerns about water quality [12]. High sediment loads carried by the Qiantang River contribute to significant sedimentation within the bay. It was reported that the sedimentation rate reached about 11 mm per year [13]. Therefore, understanding the dynamics of mass transport and water exchange is crucial for effective coastal management strategies, including pollution mitigation and shoreline protection.
Strong tidal currents (which mean a high carrying capacity for suspended sediment) and a large fine-grained sediment supply from the Changjiang River result in the fine-grained distribution in Hangzhou Bay [2]. The grain size distribution in Hangzhou Bay shows significant spatial variability, with mean grain sizes ranging from 5.60 to 137.39 µm [14]. Fine-grained sediments with a grain size of less than 20 µm mainly cover the north and northeast of the bay. Coarse-grained sediments are distributed off the Qiantang River mouth and in the southeast of Hangzhou Bay. The bottom sediments in Hangzhou Bay mainly consist of sandy silt and silt.
Mass transport and water exchange in Hangzhou Bay are primarily driven by tidal forces [15]. The dominant mechanism for mass transport is the movement of suspended sediments through tidal pumping, with the Qiantang River significantly contributing freshwater inflow to the western part of the bay, while the East China Sea provides saltwater influx at the bay mouth (Figure 1). There are several other factors affecting mass transport and water exchange in Hangzhou Bay. Since Hangzhou Bay is a strongly tidal-controlled region, the magnitude of the tidal range significantly impacts the intensity of sediment transport and water mixing within the bay. Meanwhile, wind forces can influence the surface currents and further affect the transport of pollutants and sediments [16]. Furthermore, variations in the freshwater discharge from the Changjiang Estuary and Qiantang River can influence the salinity distribution and sediment transport patterns [17].
Typhoons and tropical storms are extreme atmospheric dynamic processes in the China Seas, significantly influencing mass transport in coastal regions. Studies have shown that the suspended sediment concentration (SSC) in Hangzhou Bay is highly affected by typhoons. Li et al. (2022) [18] demonstrated through FVCOM modeling that waves during Typhoon Chan-hom were the dominant factor influencing the SSC. Furthermore, Li et al. (2022) [19] highlighted that the combined wave–current bottom stress was the principal interaction driving sediment resuspension and enhancing the SSC. However, despite the critical role of typhoons in altering sediment dynamics, the scarcity of direct observational data during such extreme conditions limits the comprehensive understanding of the SSC characteristics in Hangzhou Bay during typhoons. Besides the stronger waves induced by tropical cyclones, the enhanced mixing and Ekman transport of the storms are also key factors in the sediment and pollutant transport in Hangzhou Bay. On average, the East China Sea experiences approximately four typhoons annually [20], with these events generating intense waves that propagate into nearshore zones, inducing wave reflection, refraction, and fragmentation. The increasing intensity and frequency of extreme weather events, including typhoons, have drawn heightened attention to their impacts on sediment resuspension and redistribution in coastal waters [21,22,23,24]. Understanding the role of typhoon-generated strong mixing and residual circulations in sediment transport and the associated processes of deposition and transport is vital for advancing coastal geomorphological studies and ecological research [25,26].
Typhoons are frequent air–sea processes [27,28]. The strong kinetic energy of the wind forcing brings extreme mixing, while the transport induced by larger-scale surface circulation also causes the advection of the water mass. Severe Tropical Cyclone Ampil (2018), also known as Tropical Storm Inday in the Philippines, occurred in July 2018 and passed the coastal region of Hangzhou Bay and the Changjiang Estuary during the western North Pacific typhoon season. Ampil developed as a tropical depression on 17 July and intensified into a severe tropical storm by 20 July, with sustained winds of approximately 90 km/h at its peak. It made landfall in Shanghai, China, on 22 July, marking a rare event, as it was only the third tropical cyclone to directly hit the city since 1949. According to official statistics, the economic losses amounted to USD 246 million.
In this study, we simulate the circulations in Hangzhou Bay and the Changjiang Estuary using a high-resolution numerical model. Although incorporating granulometric composition and settling velocity into the model to simulate the sediment transport in Hangzhou Bay could yield more accurate results, the lack of detailed in situ observations of the sediment parameters in the bay makes the setup of our numerical model more challenging and complex. To focus the influence of tropical storms on the bay-scale circulations and the resulting sediment and pollutant transport driven by the circulations, we apply an indirect, idealized approach using passive floats driven by the water velocity in the model simulations. The transport of passive particles is calculated and analyzed using the current fields derived from these model simulations. Through sensitivity experiments, which include scenarios with and without the influence of typhoons and Changjiang River discharge, the respective impacts of these factors on circulation and particle transport are systematically evaluated. This paper is organized as follows. The model setup and configuration as well as data description are described in Section 2. The main results are reported in Section 3. Finally, we provide the Discussion in Section 4 and the Conclusion in Section 5.

2. Model and Data

2.1. Model Setup

The ocean model used in this study is the Regional Ocean Modeling System (ROMS, http://www.myroms.org/, accessed on 24 October 2021). The ROMS is a free-surface, terrain-following, primitive-equation ocean model [29,30]. The ROMS includes accurate and efficient physical and numerical algorithms and several coupled models for biogeochemical, bio-optical, sediment, and sea ice applications. The model also includes several vertical mixing schemes [31], multiple levels of nesting, and composed grids. The ROMS has been widely used by the scientific community for a diverse range of applications including larger-scale circulations (e.g., [32,33]) and coastal mass transport (e.g., [34,35]). The present model setup is similar to the ocean model setup in Wu et al. (2021) [34]. The model domain for the ROMS (Figure 2 of Wu et al., 2021 [34]) covers the entire eastern China coastal regions, parts of the South China Sea, and the open waters of the northwest Pacific Ocean. The model has a spatially variable grid resolution ranging from ~1 km in the nearshore region of the East China Sea to ~18 km in the open water regions. Horizontal coordinates are arranged as a 403 × 880 array, and the domain is vertically discretized into 40 sigma levels with the vertical stretching parameters set to 8, 0.5, and 150 m. This grid arrangement covers the coastal waters of the Hangzhou Bay region at a relatively fine resolution. The bottom topography for the model domain was obtained from the General Bathymetric Chart of the Ocean (GEBCO; https://www.gebco.net) with a 30 s spatial resolution, which was smoothed to ensure a maximum slope (r = ∇h/h) of less than 0.35 [36].
The temperature, salinity, and barotropic and baroclinic currents at the open boundary were nudged to the reanalysis outputs of the Hybrid Coordinate Ocean Model (HYCOM; https://hycom.org, accessed on 24 October 2021). The open boundary was forced by tide elevation and velocity data represented by thirteen different constituents (K1, O1, P1, Q1, K2, M2, N2, S2, MF, MM, M4, MS4, and MN4) derived from the TPXO global tide model ([37,38] https://www.tpxo.net/global, accessed on 11 November 2021). To prevent severe drift, the modeled SSTs were relaxed to satellite-derived daily SSTs. The configuration for the open boundary conditions was as follows: Chapman conditions for the free surface, Flather conditions for the two-dimensional (2D) momentum, radiation plus nudging conditions for the 3D momentum and temperature, and salinity with a 3-day nudging coefficient. The internal time step of the model is 90 s.
To simulate the processes of the ocean, the surface momentum and heat and freshwater fluxes should be given to the model as model input information. The surface forcing fields, including wind stress and heat and freshwater fluxes, used in the model were estimated using National Centers for Environmental Prediction (NCEP) Climate Forecasting System Reanalysis (CFSR) (6-hourly products; http://rda.ucar.edu/datasets/ds094.0/ (accessed on 25 October 2021) [39]) 10 m surface winds, atmospheric pressure, relative humidity, air surface temperature, precipitation, and net shortwave and longwave radiation, following the bulk formula provided by the Coupled Ocean–Atmosphere Response Experiment (COARE [40]). The motions of surface water, the air–sea heat flux, and the freshwater flux (evaporation and precipitation) were then calculated in the model. A generic length–scale scheme (k–kl) was chosen for vertical mixing [31]. For the bottom boundary layer, the interaction between waves and bed shear stress was estimated by selecting the “SG_BBL” function in the CPP [41]. The complete model setup and validation can be found in Wu et al. (2018) [28] and Wu et al. (2021) [34].
The model simulations were conducted for the period from 1 April to 31 August 2018. Two parallel simulation cases were performed to evaluate the influence of the river discharge on the mass transport in Hangzhou Bay: the CONTROL case included the discharge from the Changjiang and Qiantang Rivers, and the NO-RIVER case excluded these two river inputs. After generating the current field of the two simulations, we released a series of particles across two periods based on the results of these two cases [42]. The particle tracking is based on Lagrangian theory. The movement of the released particles in a certain time t was calculated based on the velocity of the particle locations ( ∆ x , ∆ y , ∆ z ) = u , v , w ∆ t .
The first particle tracking period, named noTC, representing pre-storm conditions, spanned from 20 June to 20 July 2018. The second particle tracking period, named TC, corresponding to the influence of Tropical Storm Ampil, was from 20 July to 19 August 2018. The settings of these four different experiments are listed in Table 1. The particles were modeled as neutrally buoyant floats, flowing passively with the velocity of the model, without any settling or other active motion. We released the particles at horizontal intervals of 200 per degree (approximately 0.5 km apart) within a defined region bounded by 121° E to 121.9° E and 29.9° N to 30.9° N. The main reason for selecting a particle density of 200 per degree is that the horizontal resolution of the model in Hangzhou Bay is approximately 1 km. Thus, the particle density reflects a balance between the model’s resolution and the need for statistical significance. In the vertical direction, the particles were released in the surface and bottom layers of the model coordinates separately. The released particles covered a critical area for studying particle transport and interactions within the bay under varying hydrodynamic conditions.

2.2. Data

Location, sea level pressure at center, and intensity (1 min sustained maximum wind speed) information for the typhoon was obtained from the best-track dataset of the China Meteorological Administration (CMA; http://tcdata.typhoon.org.cn/, accessed on 22 October 2024). The satellite-derived daily sea surface temperatures (SSTs; spatial resolution = 0.25°) used to adjust modeled SSTs were acquired from Remote Sensing Systems (http://www.remss.com/, accessed on 1 December 2021). The gridded daily absolute dynamic topography data (spatial resolution = 0.25° × 0.25°) used to validate simulated larger-scale dynamic features were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS; http://marine.copernicus.eu, accessed on 25 October 2021).
To validate the model’s performance, we also collected the in situ sea surface elevation data from four tidal gauges during the passage of Tropical Storm Ampil, from 13 July to 12 August 2018. The tidal gauges were deployed as part of hydrological experiments for the Cross Hangzhou Bay Railway construction project [43]. The modeled surface elevation aligns well with the in situ measurements in the 30-day observational period. At S3 and S4, the simulated amplitude of the surface elevation was slightly smaller than the observation. This discrepancy is attributed to the fact that the numerical model could not simulate the tidal bore of the Qiantang Estuary due to the high nonlinearity of the dynamics.

3. Results

3.1. The Severe Tropical Storm Ampil

The Severe Tropical Storm Ampil (2018) formed around 12:00 on 19 July and made landfall in Shanghai at 06:00 22 July 2018. The storm reached a max intensity of 28.3 m/s wind speed, with the lowest air pressure recorded at 980 mb. The storm began affecting Hangzhou Bay and the adjacent region on 21 July (Figure 3a–e). The center of the storm passed the eastern edge of Hangzhou Bay and made landfall in Shanghai around 4:00 on 22 July. In Hangzhou Bay, northly winds dominated during the first half of the storm on 21 July, and then the wind direction turned anticyclonically to westward in the morning of 22 July. After the storm made landfall, the wind in the bay became southward and weakened significantly. The main rainband was located to the right side of the storm, and the precipitation rate in Hangzhou Bay increased on 22 July (Figure 3f–j). Additionally, the air temperature dropped near the storm center, with a cooler region of approximately 26 °C on 22 July when the storm was making landfall (Figure 3k–o).

3.2. Basic Circulation and Typhoon-Influenced Flow in Hangzhou Bay

In the later summer season, the circulation patterns in Hangzhou Bay and the Changjiang Estuary are mainly controlled by the discharge from the Changjiang River input and the river plume formed by the interaction between the low-salinity freshwater onshore and the higher-salinity seawater offshore. Near the Changjiang Estuary, substantial outflows from the South Branch of the Changjiang River flow in a southeastward direction. In the region further offshore, a southward current pattern forms along the front due to the balance between the density gradient created by river discharge and the Coriolis force between the river discharge and the water in the East China Sea (Figure 4a–d).
In contrast, the river discharge in Hangzhou Bay from the Qiantang River is significantly weaker than that in the Changjiang Estuary. The main offshore current in Hangzhou Bay consists of a southeastward flow near the Zhoushan Islands, situated in the southeast of the bay. The temperature distribution in Hangzhou Bay reveals a gradient, with higher temperatures in the west end of the Bay and lower temperatures in the East China Sea, separated by a front in the middle region. It was reported that the front system formed in Hangzhou Bay is a branch of the Changjiang Estuary plume [3]. The existence of the front creates the main surface circulation along the front, so the front blocks the water of Hangzhou Bay from being transported outward to the East China Sea (Figure 4a–d).
The Severe Tropical Storm Ampil in 2018 passed the Hangzhou Bay and Changjiang Estuary regions from 22 July to 24 July. As the center of the tropical storm was located east of Hangzhou Bay with a northwestward direction, the wind field during the tropical cyclone was mainly a northern wind in Hangzhou Bay (Figure 4e). A strong southward surface current was accompanied by the wind field from the Changjiang Estuary to Hangzhou Bay. The flow in Hangzhou Bay is a southeastward flow, so the outflow near the Zhoushan Islands is strengthened. As the center of the storm moved northwestward, the surface current also turned eastward in the south part of the Changjiang Estuary and the north part of Hangzhou Bay (Figure 4f,g). The eastward current in Hangzhou Bay decreased with the movement of the storm. Compared with the circulation without a tropical storm, the surface current was mainly modulated by a rotating process as the storm passed across the east part of the region (Figure 4h). The outflow inside Hangzhou Bay is stimulated by the wind-driven southeastward current.
The surface temperature decreased during the tropical storm at the eastern edge of Hangzhou Bay, with the maximum decreasing center near the Zhoushan Islands. This decrease might have been caused by the upwelling of the Ekman pumping under the tropical storm and a strong mixing by the stirring of the strong winds. The front near the Changjiang Estuary was weakened by the storm, and the strong current along the front was also decreased (Figure 4d,h).
In general, the circulation and temperature patterns were influenced by the passing of the Tropical Storm Ampil in Hangzhou Bay and the Changjiang Estuary. The most dominant feature of the influence is a strengthened cyclonic circulation and a cooling near the center of the cyclone. The front of the Changjiang River plume was then weakened by the cooling.

3.3. Tracer Transport Influenced by the Storm

The mass transport in Hangzhou Bay is governed by the circulation in the bay. By simulating the passive neutral particle in the velocity field of the model, the general evolution of mass transport in the summer in the bay was revealed before and during the tropical storm (Figure 5). There are two distinguished pathways for the tracer flowing out of the bay. The first pathway involves northward transport through the northern edge of the bay’s mouth. A significant portion of the particles, about 28.2% of the total released particles, had escaped near the corner between Hangzhou Bay and Changjiang River 20 days after the initial release (Figure 5c). Some of the particles aligned into a thin concentrated band where the water converges near the estuarine front (Figure 4), driven primarily by circulation along this front. The second pathway is the direct eastward transport. The particles, about 0.8% of the total released particles at 20 days after release, flowed along the water to cross the Zhoushan Islands. This path of particle transport was mainly controlled by the river discharge from the Yangtze and Qiantang Rivers, which guide particles along the bay’s eastward-moving waters. These distinct pathways illustrate the combined influence of estuarine dynamics and riverine input on particle transport in the region.
The transport and dispersion of the particles are significantly influenced by the tropical storm, as suggested by the TC experiment in the CONTROL case. Along the first pathway, the number of particles decreases, and their distributions become more dispersed compared to the noTC experiment of the CONTROL case. The percentage of the released particles following the first pathway decreases to about 21.9%. Conversely, more particles are transported eastward along the second pathway during the tropical storm, particularly within the first 10 days (Figure 5e,f). The percentage of particles following this pathway reaches 14.4% at 20 days after release, significantly higher than the 1.3% in the noTC case. Furthermore, as the tropical cyclone passes over the northern part of Hangzhou Bay, the cyclonic circulation generated by the storm redistributes particles, bringing more particles from the northern to the southern regions of the bay compared with the noTC experiment. Thirty days after the releasing time, the number of residual particles in the bay is much lower in the TC experiment than in the noTC experiment (Figure 5d,h), suggesting a more sufficient water exchange process during the storm. This highlights the role of storm-induced circulation in altering particle transport dynamics within the bay.
In the bottom layer, the particle transport is similar to that in the surface layer, except that the particles move more slowly (Figure 6). After 30 days of simulation, a significant portion of particles remains within the bay. For the first path northward of Hangzhou Bay, the particle distributions are similar in both the TC and noTC experiments (Figure 6d,h). However, for the second pathway, the TC case exhibits stronger transport compared to the noTC experiment, with more particles carried eastward toward the islands east of the bay. Additionally, the eastward extent of particle transport in the TC case is greater than that in the noTC experiment, highlighting the enhanced dispersal effect induced by the tropical cyclone (Figure 5d,h).
Comparing the surface and bottom transport of the particles, we note that the surface transport in the west end of Hangzhou Bay is mainly outward, while the bottom transport is inward, with a significant number of particles flowing into the Qiantang River Estuary. This contrast arises from the estuarine circulation in Hangzhou Bay, which drives opposing flow directions between the surface and bottom layers.
To evaluate the cumulative effects of the tropical storm on tracer transport, the accumulated particle distribution over 5, 10, 20, and 30 days (a tide period) was calculated (Figure 7 and Figure 8). The results show significant transport patterns influenced by tidal effects.
Comparisons between the noTC and TC experiments show distinct differences in particle accumulation and distribution. The impact of a tropical cyclone on tracer transport is profound. The strong wind forcing and resultant currents during the storm caused intense mixing and residual transport under the cyclonic circulation in Hangzhou Bay.
In the TC experiment, on the other hand, the accumulated number of particles offshore of Jinshan is much lower than that in the noTC experiment, even after 20 and 30 days (Figure 7c,d). The reason for this is that the front and its accompanied along-front current were weakened by the strong mixing of the storm. The respective higher number on the north coast of the bay is even higher due to a weaker northeastward transport. However, it is apparent that there was a higher number of particles along the south coast of the bay. The southward particle transport is mainly related to the cyclonic circulation of the tropical storm out of the bay, which then brings a southward circulation.
In contrast, the TC experiment shows a much lower particle accumulation offshore of the Changjiang Estuary after both 20 and 30 days (Figure 8c,d). This reduction is attributed to the weakening of the front and its associated along-front current caused by the intense mixing induced by the tropical cyclone. Furthermore, while particle density along the northern coast of the bay increases slightly due to reduced northeastward transport, there is a notably higher concentration along the southern coastline. This distribution is similar to the modeling study on sediment transport during Typhoon Chan-hom (2015) [18]. A higher SSC was found on the southern shore of Hangzhou Bay in their modeling. This enhanced southward transport is primarily driven by the cyclonic circulation generated by the tropical storm, which induces a southward flow that redistributes particles within the bay and beyond.
Model simulations indicate that cyclones cause a marked increase in sediment or pollutant transport, promoting extensive transport and dispersion throughout the bay. These effects persist for several days to more than 20 days following the storm, dramatically altering the distribution patterns of the tracers in the bay. This process can have profound implications for the ecological health of the bay, potentially influencing biological productivity and water quality. Such findings highlight the critical role of tropical cyclones in driving sediment and nutrient dynamics in coastal environments.
The impact of the tropical storm on bottom-layer particle transport is notably more pronounced. In the noTC experiment, particle concentrations in the bottom layer remain confined to the central region of Hangzhou Bay throughout the 30-day simulation, with the highest concentration centered around 121.5° E, 30.4° N (Figure 9). This confinement is primarily due to the slower velocities near the bottom, limiting particle dispersion to tidal mixing processes. The accumulation in the center of Hangzhou Bay is largely driven by convergence near the frontal zone. In the TC experiment, particle concentrations in the bottom layer are significantly reduced compared to in the noTC experiment (Figure 10). While the core concentration of particles persists in the center, its maximum value is lower, and the location of the core shifts eastward relative to the noTC experiment, suggesting a strong bottom transport in the lower layer. Additionally, a secondary concentration core emerges near the western end of the bay. This secondary peak is likely linked to the stronger southward transport induced by the tropical storm, which enhances bottom-layer circulation and redistributes particles within the bay.
The accumulated number of particles in Hangzhou Bay after release in both the surface and bottom layers was counted (Figure 11). The number of particles was decreased 5 days after release, with a decreasing rate of about 280 per day. The number in the TC case is significantly lower than that in the noTC case after 5 days, with their maximum difference at about 8 days after release. The lower number in the TC case suggests a stronger outflow of particles induced by the tropical storm.

3.4. Influence of Discharge of Changjiang and Qiantang Rivers

The discharge from the Changjiang and Qiantang Rivers plays a crucial role in driving mass transport out of Hangzhou Bay. To further explore the combined effects of river input and the tropical storm, we conducted two additional experiments (TC and noTC experiments in the NO-RIVER case in Table 1) in which the river discharges were set to zero. By comparing the particle distributions between cases with and without river discharge, we quantitatively evaluated the influence of river input on transport dynamics.
The averaged particle distributions over 5, 10, and 20 days in the absence of river discharge reveal significant differences compared to scenarios with normal river input (Figure 12). Without discharge from the Changjiang and Qiantang Rivers, particles are more constrained within the bay, with a substantial portion of the distribution shifted westward along the northern coastline. Similarly, particles in the southern region exhibit a westward shift, with their maximum concentration centered around 121.5° E. This altered distribution reflects the reduction in buoyancy-driven circulation caused by the absence of river input. In the bottom layer, the absence of river discharge results in even slower particle movement (Figure 13). The core of the maximum particle concentration shifts further westward within the bay, indicating reduced mass exchange with the open East China Sea. These findings underscore the critical role of river discharge in facilitating sediment and pollutant transport out of Hangzhou Bay, highlighting its contribution to maintaining the bay’s hydrodynamic connectivity with offshore waters.

4. Discussion

The mass transport in Hangzhou Bay during Severe Tropical Storm Ampil (2018) was simulated to analyze the distribution and behavior of particles before and during the storm. The results provide insights into sediment and pollutant transport dynamics within the bay. The intensified wind speeds (Figure 3a–e) and wave heights associated with the storm significantly increased the bed shear stress, leading to greater sediment resuspension. Although the particles in this study were modeled as neutrally buoyant, the findings offer valuable indications of sediment transport rates during typhoons. This effect is particularly pronounced in shallow regions of the bay, where the wave-induced bed shear stress is most intense.
The spatial and temporal variations in particles observed in the simulations highlight the dynamic nature of sediment transport in Hangzhou Bay. Under calm weather conditions, sediment concentrations remain stable, with localized peaks near river mouths. However, during storm events, sediment levels rise significantly across the entire bay. Temporal analysis reveals a peak in sediment concentration immediately following the storm, with gradual redistribution and settling driven by tidal currents.
This study also underscores the critical role of frontal systems both offshore of the Changjiang Estuary and within Hangzhou Bay in modulating circulation and mass transport. Along-front circulation facilitates the transport of particles out of the bay, while the fronts act as barriers, limiting direct cross-frontal flow. Additionally, these fronts promote localized particle accumulation through convergence processes, further influencing sediment distribution patterns. These findings enhance the understanding of sediment dynamics under extreme weather conditions and their implications for coastal management.
These findings emphasize the role of tropical cyclones and river discharge in shaping sediment and pollutant transport in coastal environments. The results are relevant for understanding sediment dynamics, pollutant dispersion, and ecological impacts in semi-enclosed bays. This study highlights the importance of incorporating both riverine and storm-induced processes in coastal management and modeling efforts, particularly in the context of increasing storm intensity and frequency due to climate change. Future research could benefit from integrating additional observational data and exploring the interactions between sediment dynamics, biogeochemical processes, and ecological health.
There are limitations in this study that should be addressed in future work. One factor affecting the accuracy of our results is the use of reanalysis wind fields as the forcing in our model. Reanalysis data cannot precisely describe the key characteristics of tropical cyclones, including wind fields [44]. Li et al. (2022) [18] incorporated an idealized formula to reconstruct typhoon wind fields. While our modeling results reproduced the ocean response to the tropical storm to a certain extent, future studies should aim to improve the accuracy of the reanalysis wind fields of storms. Another critical limitation is that our model treated the particles as neutral floats carried by water currents. The movement of the particles could not reflect the settling and resuspension processes of the sediment. We will also improve the sediment simulations in our future work.

5. Conclusions

This study explores the impact of Severe Tropical Storm Ampil (2018) on circulation and particle transport in Hangzhou Bay and the Changjiang Estuary. Based on the model simulations, circulation in Hangzhou Bay and the Changjiang Estuary is dominated by river discharge and the formation of estuarine fronts in late summer. The Changjiang River’s substantial outflow creates southeastward currents and a front that governs particle transport along its edge. In contrast, the weaker discharge from the Qiantang River primarily drives southeastward currents near the Zhoushan Islands. These currents and fronts act as significant pathways and barriers for sediment and tracer movement. The passing storm introduced pronounced changes to circulation and particle transport. Cyclonic circulation generated strong southward and southeastward currents, enhancing outflows near the Zhoushan Islands. Simultaneously, intense wind-driven mixing weakened the Changjiang plume front, reducing its associated along-front current. The storm’s influence was evident in decreased surface temperatures, particularly near the Zhoushan Islands, due to upwelling and strong wind-driven mixing.
The general mass transport in Hangzhou Bay follows two primary routes: northward along the estuarine front and eastward across the Zhoushan Islands. During the storm, particle dispersal increased, with greater eastward transport and redistribution from the northern to southern regions of the bay. The storm’s cyclonic circulation promoted enhanced water exchange, reducing residual particle concentrations in the bay. The discharge from the Changjiang and Qiantang Rivers plays a critical role in facilitating particle flow out of Hangzhou Bay. Without river input, particles were more constrained within the bay, with reduced buoyancy-driven circulation. This results in slower transport, particularly in the bottom layer.
Surface and bottom transports showed contrasting patterns due to estuarine circulation. Surface transport was primarily outward, while bottom transport directed particles inward, particularly into the Qiantang River Estuary. The storm intensified southward bottom transport, creating a secondary core of particle concentration in the western bay.

Author Contributions

Conceptualization, H.Z. & X.L.; Methodology, H.Z.; Software, X.L.; Writing—original draft, H.Z.; Writing—review & editing, X.L.; Visualization, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of China (42176009).

Data Availability Statement

Restrictions apply to the availability of these data. The tropical cyclone data were obtained from the China Meteorological Administration and are available http://tcdata.typhoon.org.cn/. The SST data were acquired from Remote Sensing Systems and are available http://www.remss.com/. The forcing data of the model were obtained from the NCEP/CFSR reanalysis and are available http://rda.ucar.edu/datasets/ds094.0/.

Acknowledgments

The authors thank Renhao Wu at the Sun Yat-sen University for his help on the model setup. The authors thank the editors and three anonymous reviewers for their constructive comments.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Bathymetry (m) of the Yellow Sea and East China Sea (a) and Hangzhou Bay and its adjacent seas (b). The bathymetry data are from the ETOPO Global 1-min dataset. The red circles in (b) represent the tide gauges used to validate the model results in Figure 2.
Figure 1. Bathymetry (m) of the Yellow Sea and East China Sea (a) and Hangzhou Bay and its adjacent seas (b). The bathymetry data are from the ETOPO Global 1-min dataset. The red circles in (b) represent the tide gauges used to validate the model results in Figure 2.
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Figure 2. Sea surface elevation (m) of the model simulation (blue curves) and the observational data from tidal gauges (black dots) in Hangzhou Bay. The stations S1 to S4 are marked in Figure 1b.
Figure 2. Sea surface elevation (m) of the model simulation (blue curves) and the observational data from tidal gauges (black dots) in Hangzhou Bay. The stations S1 to S4 are marked in Figure 1b.
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Figure 3. Wind speed at 10 m above sea surface (a–e), precipitation rate (f–j), and air temperature at 2 m above sea surface (k–o) during the Severe Tropical Storm Ampil passing Hangzhou Bay. All the data are from the CFSR hourly dataset.
Figure 3. Wind speed at 10 m above sea surface (a–e), precipitation rate (f–j), and air temperature at 2 m above sea surface (k–o) during the Severe Tropical Storm Ampil passing Hangzhou Bay. All the data are from the CFSR hourly dataset.
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Figure 4. Temperature (colors, °C) and velocity (vectors) in Hangzhou Bay and the Changjiang Estuary before the Tropical Storm Ampil (a–d) and during the storm (e–h). The red triangle is the center of the storm on day 0, and the black line represents the track of the storm.
Figure 4. Temperature (colors, °C) and velocity (vectors) in Hangzhou Bay and the Changjiang Estuary before the Tropical Storm Ampil (a–d) and during the storm (e–h). The red triangle is the center of the storm on day 0, and the black line represents the track of the storm.
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Figure 5. Locations of the particles simulated by the model released in the surface layer before the Tropical Storm Ampil (noTC, (a–d)) and during the storm (TC, (e–h)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.
Figure 5. Locations of the particles simulated by the model released in the surface layer before the Tropical Storm Ampil (noTC, (a–d)) and during the storm (TC, (e–h)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.
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Figure 6. Locations of the particles simulated by the model released in the bottom layer before the Tropical Storm Ampil (noTC, (a–d)) and during the storm (TC, (e–h)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.
Figure 6. Locations of the particles simulated by the model released in the bottom layer before the Tropical Storm Ampil (noTC, (a–d)) and during the storm (TC, (e–h)) in the CONTROL case. The panels from left to right show the particles released after 5 days, 10 days, 20 days, and 30 days.
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Figure 7. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the noTC experiment of the CONTROL case.
Figure 7. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the noTC experiment of the CONTROL case.
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Figure 8. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the CONTROL case.
Figure 8. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the CONTROL case.
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Figure 9. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the noTC experiment of the CONTROL case.
Figure 9. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the noTC experiment of the CONTROL case.
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Figure 10. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the CONTROL case.
Figure 10. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the CONTROL case.
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Figure 11. Number of particles inside Hangzhou Bay after release.
Figure 11. Number of particles inside Hangzhou Bay after release.
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Figure 12. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the NO-RIVER case.
Figure 12. Accumulated number of particles in the surface layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the NO-RIVER case.
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Figure 13. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the NO-RIVER case.
Figure 13. Accumulated number of particles in the bottom layer from the releasing time to after 5 days (a), 10 days (b), 20 days (c), and 30 days (d) in Hangzhou Bay in the TC experiment of the NO-RIVER case.
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Table 1. Setup of the tracer experiments.
Table 1. Setup of the tracer experiments.
NameRiver DischargeModeling Period
CONTROL/noTCChangjiang/Qiantang Rivers20 June to 20 July 2018
CONTROL/TCChangjiang/Qiantang Rivers20 July to 19 August 2018 (Ampil passing)
NO-RIVER/noTCNo river input20 June to 20 July 2018
NO-RIVER/TCNo river input20 July to 19 August 2018 (Ampil passing)
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Zhou, H.; Liu, X. Enhanced Transport Induced by Tropical Cyclone and River Discharge in Hangzhou Bay. Water 2025, 17, 164. https://doi.org/10.3390/w17020164

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Zhou H, Liu X. Enhanced Transport Induced by Tropical Cyclone and River Discharge in Hangzhou Bay. Water. 2025; 17(2):164. https://doi.org/10.3390/w17020164

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Zhou, Hongquan, and Xiaohui Liu. 2025. "Enhanced Transport Induced by Tropical Cyclone and River Discharge in Hangzhou Bay" Water 17, no. 2: 164. https://doi.org/10.3390/w17020164

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Zhou, H., & Liu, X. (2025). Enhanced Transport Induced by Tropical Cyclone and River Discharge in Hangzhou Bay. Water, 17(2), 164. https://doi.org/10.3390/w17020164

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