Modelling Impacts of Climate Change and Anthropogenic Activities on Ecosystem State Variables of Water Quality in the Cat Ba–Ha Long Coastal Area (Vietnam)

Abstract

Different scenarios have been established and simulated based on the Delft3D model to compare and assess the impact of human activities (increased pollutants as oxygen demand, BOD, COD, nutrients, and land reclamation), climate change (rising temperatures, sea level rise), and a combined scenario of human activities and climate change on water quality in the Cat Ba–Ha Long coastal area. The findings quantify the impacts of anthropogenic activities and climate change on the water quality in the study area in 2030 and 2050. During the northeast monsoon and the two transitional seasons, the impact of humans and climate change adversely affects water quality. The impact of climate change is less significant than that of human activities and their combination, which result in a reduction in DO levels of 0.02–0.13 mg/L, 0.07–0.44 mg/L, and 0.09–0.48 mg/L, respectively. Meanwhile, during the southwest monsoon, climate change significantly reduces water quality (0.25–0.31 mg/L), more so than human activities (0.14–0.16 mg/L) and their combined effects (0.13–0.17 mg/L). This may elucidate the fact that the increase in nutrient supply from the river during the southwest monsoon in this region can result in an increase in nutrient levels and biological activity, which, in turn, causes an increase in DO. Additionally, the augmented quantity of DO may partially offset the decrease in DO resulting from climate change. Under the influence of human activities and climate change, the nutrient levels in the area increase, with average values of 0.002–0.033 g/m³ (NO₃⁻), 0.0003–0.034 g/m³ (NH₄⁺), and 0.0005–0.014 g/m³ (PO₄³⁻).

1. Introduction

Coastal waters are considered to be among the most important areas in the world because they provide about 22% of the global value of ecosystem services [1]. These regions have elevated primary productivity, biodiversity, and other advantageous conditions for human settlement. Nevertheless, they remain the most sensitive and vulnerable regions due to anthropogenic activities, including socio-economic development, urban expansion, population growth, and pollution sources [2,3,4]. Evidence indicates that human influences on the worldwide aquatic environment persistently accrue and grow more pronounced [5]. Population increase, economic development, and infrastructure expansion are expected to intensify environmental alterations in coastal waterways [6,7], resulting in the degradation and potential extinction of certain ecosystems [8]. For example, human-generated CO₂ significantly contributes to rising temperatures and ocean acidification [9,10]. Reclamation and landfilling in coastal regions influence dynamic processes, potentially resulting in coastal erosion [11]. The encroachment of human habitation into marine territories not only heightens the vulnerability to elevated sea levels [7,12] but also produces substantial wastewater from both residential and commercial activities, exacerbating environmental conditions in coastal regions [13,14]. Research indicates that anthropogenic activities are the primary contributors to the degradation of the aquatic environments in coastal regions [15].

Moreover, climate change (elevated temperatures, ocean acidification, and extreme weather events) might impact the aquatic environment. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) indicates that, by 2100, water environments in coastal regions may deteriorate due to rising sea levels, elevated water temperatures, heightened frequency and intensity of extreme weather events, and other associated factors [16]. Climate change has identified the rise in temperature as a significant element influencing water quality [17]. The rise in water temperature leads to a reduction in dissolved oxygen and saturation oxygen levels in the water [18,19,20]. Recent studies indicate that elevated water temperatures can diminish nitrogen and phosphorus nutrient levels from rivers to coastal regions, attributed to enhanced consumption and photosynthesis by algae, as phytoplankton flourish in warmer conditions [20,21]. Drought increases the concentrations of pollutants, enhances the process of nitrogen mineralisation, and impedes recovery from acidification [22,23]. Intense precipitation events may result in elevated concentrations of dissolved organic carbon and nutrients [24,25], which are recognised for their significant involvement in the transport and release of pollutants [26,27]. Bhat et al. [28] and Drewry et al. [29] showed that the concentrations of nutrients and suspended particles are directly connected with precipitation events. Intense rainfall events that elevate river flows significantly enhance the danger of contaminant transport via runoff. Moreover, the influence of precipitation on the quality of coastal waters as a non-point source of pollution has been noted [30].

In the future, the effects of economic development and climate change on coastal water quality will be a significant global concern [31]. The integrated impact of human socio-economic activities and climate change has caused significant pressure on the water quality in coastal areas. Various biochemical processes can directly or indirectly affect water quality [17,32], depending on the local characteristics of each locality [18]. The rise in temperature and alterations in precipitation, along with anthropogenic influences including population expansion, livestock activities, and land-use changes, will persist in influencing the flow and water quality of rivers and coastal areas [33]. The augmentation of nitrogen nutrient sources from human activities, combined with the impacts of climate change, can substantially elevate nitrate concentrations in certain regions [34]. Jeppesen et al. [35] indicated that predicting the effects of augmented fertiliser imports from terrestrial sources and climate change involves intricate processes characterised by interacting effects among nutrient sources, light conditions, temperature, and hydrodynamic processes. A complete approach is necessary, considering the interplay among natural conditions, the environment, and socio-economic issues [36,37].

The Cat Ba–Ha Long region was recognised as a UNESCO World Natural Heritage site in September 2023. The influx of tourists to the region is rising, accompanied by socio-economic development, urbanisation, and land reclamation, which have constricted the bay’s area and heightened the risk of water contamination. Land reclamation activities are diminishing space in Cua Luc Bay, as well as the Tuan Chau and Bai Chay coastal areas, while modifying the flow dynamics and elevating sedimentation rates in Cua Luc Bay, Ha Long Bay, and the southern region of Tuan Chau Island [38]. The simulation outcomes of the Dinh Vu industrial zone’s impact, derived from various discharge scenarios utilising the Delft3D model, indicate an elevation in substances such as NH₄⁺, COD, and BOD₅, along with a reduction in oxygen levels, in the coastal region of Hai Phong, particularly during events originating from the industrial zone [39]. The aforementioned initiatives have partially evaluated the distribution and dispersion of pollutants, together with the influence of economic activities on these pollutants in the study area.

In addition, this region is also influenced by global climate change trends. Hai et al. [40] indicated that the sea level rise in the region increased by 3.38 mm/year from 1961 to 2020 and doubled to 7.16 mm/year from 2002 to 2020. The temperature increased by 0.02 °C every year from 1995 to 2020 and escalated to 0.093 °C per year from 2008 to 2020 [41]. The evaluation and prediction of the combined effects of human socio-economic development and climate change on the water quality in the Cat Ba–Ha Long region is a multifaceted endeavour, as this area is marked by dynamic socio-economic activities and functions as a semi-closed system that continuously interacts with the mainland and external environments. The region’s water quality monitoring programs only evaluate the present condition of the aquatic environment at designated sites and struggle to forecast future impact patterns. The mathematical model can incorporate several factors and calculation objects, enabling the prediction of the effects of diverse pollution sources and their influence on water quality in each water body. Moreover, evaluations integrating the effects of anthropogenic activities and climate change on water quality in this area remain significantly constrained.

Therefore, this study aims to simulate and evaluate the individual effects of human socio-economic activities and climate change, as well as their cumulative impact on some ecosystem state variables of water quality in the Cat Ba–Ha Long region. We consider only the impact of BOD, COD, NH₄⁺, NO₃⁻, and PO₄³⁻ due to human activities, and that of sea level rise and temperature increase for climate change, assuming that precipitation, river discharges, and wind remain constant, and we simulate and analyse the 3D distributions of these parameters and of dissolved oxygen (DO).

2. Data and Methods

2.1. Cat Ba–Ha Long Coastal Area

The Cat Ba–Ha Long coastal area belongs to Hai Phong city and Quang Ninh Province, coastal area of northeastern Vietnam (Figure 1a), which are two localities of the Ha Noi–Hai Phong–Quang Ninh economic development triangle. This area is shallow coastal water with a common depth between 3 and 15 m and is bisected by 2367 small islands. Water and materials from the Red–Thai Binh River system, transported through the Hai Phong region and small rivers in Quang Ninh Province, are finally received in the Cat Ba–Ha Long coastal area.

In this area, most precipitation occurs from May to October, accounting for around 83.6% of the total rainfall. From June to September (the southwest monsoon), the major wind directions are primarily northeast, east, southeast, southwest, and south, comprising 72.2%. During the northeast monsoon (December to March), winds predominantly originate from the northeast, north, northwest, and southeast directions, accounting for 92.1% of the time [42].

Furthermore, this area is influenced by the hydrological regime of the Red River Delta. River water discharge exhibits significant seasonal variation, with 71–79% of the yearly total occurring in the rainy season and about 9.4–18% during the dry season [43]. It is affected by the diurnal tidal regime. Based on the tide gauge measurements at Hon Dau station (1960–2011), the tidal range is about 2.6–3.6 m in spring tide and about 0.5–1.0 m in neap tide [42].

2.2. Data

For the area near the coast, the digitised coastal bathymetry was obtained from topography maps of 1:50,000 and 1:25,000 by the Vietnamese People’s Navy (2017) and updated under the projects (Load assessment and transportation of pollutants from upstream to major rivers in Hai Phong coastal area, ĐT.MT.2020.85; Estimation of regional sea level change in the North of the Tonkin Gulf from satellite altimetry data, QTFR02.01/23-24; Study on the impact of increasing sources of organic pollution, nutrition from human activities and climate change on primary productivity in coastal waters of Cat Ba-Ha Long, VAST05.05/21-22 and Management of the water quality in Vietnamese coastal waters impacted by CLIMate change and human induced DISasters using a marine modelling tool, NĐT.97.BE/20) carried out at the IMER in recent years. The offshore regions utilised the GEBCO-1/8 data, also known as the Ocean of British Oceanographic Data Centre’s General Bathymetric Chart [13,44].

Tidal harmonic constants (M₂, S₂, N₂, K₂, K₁, O₁, P₁, Q₁, MF, MM, M₄, MS₄, and MN₄) at the open sea boundaries were obtained from the TPXO-8 global tidal model, with a spatial resolution of 0.25° × 0.25° [45]. The meteorological data, encompassing wind, atmospheric pressure, air temperature, solar radiation, and cloud volume at a resolution of 0.125° × 0.125°, were sourced from the ECMWF Re-analysis-5 (ERA5) meteorological dataset [46]. The Copernicus Marine Environmental Service’s Global Ocean Physics Analysis and Forecast dataset (salinity and temperature), which is structured on a standardised regular grid with a resolution of 1/12°, was utilised for the open sea boundaries [47].

Moreover, river discharge at Cam station (Cam River) and Trung Trang (Van Uc River) was measured by the Vietnam Meteorological and Hydrological Administration in 2019–2021, and the freshwater flow measurements in the Hai Phong region were obtained as part of project ĐT.MT.2020.852, serving as river boundaries. Data gathered over a 72 h period by the CLIMDIS project (NĐT.97.BE/20) were utilised to validate the model for the Cat Ba–Ha Long region, encompassing flow and nutrient measurements at some stations (Cua Luc, Ha Long, Tuan Chau, and Cam Pha) during January and November of 2021. Additionally, water level data (1-hourly) at Hon Dau and Bai Chay stations were also used for the model validation.

The total load of pollutants in 2019 and the forecast for 2030 in the Cat Ba–Ha Long area were referenced from the NĐT.97.BE/20 project (Table 1). Pollutants produced in coastal regions can either enter the Cat Ba–Ha Long coastal waters through the river system or be directly discharged into these waters. Estimating the pollution load in rivers relies on the runoff rate of each tributary and the capacity to mitigate the pollution load through the efficacy of management and treatment processes of discharge sources. We assessed the aggregate pollutant load entering the study region from several sources, including residents and tourists, livestock, aquaculture, industrial operations, maritime vessels, and soil erosion. The projected pollutant load was determined based on the socio-economic development objectives for the research area until 2030.

In addition, the analytical results from the NĐT.97.BE/20 project in 2010 (before land reclamation operations) and in 2019 (subsequent to land reclamation activities) indicate a trend of spatial contraction in the study region, attributable to land reclamation efforts. Over the past decade, the Cua Luc Bay region has undergone reclamation totalling 419.2 hectares. In the coastal region of Bai Chay–Tuan Chau, the water surface area was 4770.3 hectares in 2010; however, due to extensive land reclamation efforts in recent years, this area had diminished to 3546.1 hectares by 2019, reflecting a decrease of 1224.2 hectares (Table 2).

The scenario of increasing temperatures and sea levels in the Cat Ba–Ha Long region is based on the 2020 climate change scenario provided by the Ministry of Natural Resources and Environment. This climate change scenario is detailed for the Vietnam region, consistent with the global updates from the IPCC. The document is founded on the Fifth Assessment Report (AR5) and integrates the most recent IPCC publications from 2018 and 2019 regarding global climate change trends and sea level rise, hydrometeorological and sea-level data for Vietnam updated to 2018, digital elevation model data revised to 2020, and a dynamical downscaling technique derived from statistically corrected model outputs [48].

2.3. Methods

Delft3D is an advanced three-dimensional model that can properly simulate a variety of natural phenomena, including flow, wave dynamics, sediment transport, morphology, water quality, and ecology. Deltares, a research institute in Delft (The Netherlands), developed this open-source model, which includes modules such as Delft3D-Flow, Delft3D-Wave, and Delft3D-Water Quality.

2.3.1. Model Setup

The hydrodynamics model (Delft3D-Flow): The study area is the Cat Ba–Ha Long coastal area, which extends approximately 143 km in the cross-shore direction and 119 km in the along-shore direction (Figure 1c). The model employs an orthogonal curvilinear grid encompassing the coastal seas of Hai Phong and Ha Long, as well as the river mouths of the Red and Thai Binh rivers, including Bach Dang, Cam, Lach Tray, Van Uc, Thai Binh, and Tra Ly. The horizontal grid comprises 697 × 308 computational cells, with sizes spanning from 10 to 1000 m horizontally. The sea boundary conditions for the hydrodynamic model were derived from the findings of the parent model via the NESTING methodology. The parent model frame encompasses the entirety of the Gulf of Tonkin (Figure 1b), extending roughly 636 km in the north–south direction and 420 km in the east–west direction. The horizontal grid consists of 469 × 443 points, with a grid size ranging from 92.1 m to 5050 m. In the overall and detailed model, five sigma vertical layers in coordinate are considered, each accounting for 20% of the water depth.

The initial condition of the hydrodynamic model: In Delft3D, the initial condition might employ the prior run as a restart file. The initial condition of the model for the Cat Ba–Ha Long coastal area is based on the calculated outcomes of the previous month.

The boundary conditions of the hydrodynamic model: The water discharge of rivers (the Bach Dang, Cam, Lach Tray, Van Uc, Thai Binh, and Tra Ly) serves as the river boundary conditions. Furthermore, the daily salinity and temperature of these rivers are utilised to define the model’s river transport boundary condition. The 13 tidal harmonic constituents, along with salinity and temperature data, are employed for the model’s open sea boundary conditions.

Meteorological conditions: To evaluate the impacts of surface wind stress and air–sea temperature exchange at the sea surface boundary on hydrodynamic processes, the model utilises atmospheric forcing from the ERA-5 dataset.

The water quality model: The Delft3D-Waq is the water quality module of the Delft3D modelling system, which is used to calculate and simulate the propagation, transformation, and processes related to water quality. The Delft3D-Waq module divides substances into groups, typically consisting of one or more substances with similar or identical physical and chemical properties. For instance, the nutrient group comprises nitrate, ammonia, phosphate, and silica, grouped together due to their role in photosynthesis, which generates primary biological productivity. Meanwhile, salts, chlorine, and conservative substances are unique to Delft3D-Waq because they only participate in transport processes, not in water quality processes. Since they do not participate in water quality transformation processes, these substances will help us distinguish between the effects of transport processes and changes due to water quality processes on different substances [49].

The water quality model is established in three dimensions with five depth layers. It utilises the hydrodynamic modelling results, encompassing grid, water-level fluctuations, flow, temperature, salinity, and other pertinent elements. The primary computed parameter groups consist of dissolved organic matter (BOD, COD), dissolved nutrients of nitrogen (NH₄⁺, NO₃⁻), phosphorus (PO₄³⁻), and dissolved oxygen (DO). Even if the model was not used to simulate the full range of water quality parameters (such as chlorophyll-a, primary production, total suspended solids, transparency, faecal coliforms, etc.), the module Delf3D-Waq coupled with DELFT3D is hereafter called a “water quality” model.

Boundary conditions of the water quality model for the study area: Based on the results of analysing the concentrations of pollutant groups collected from various sources in recent times.

The initial condition in the water quality model refers to the concentration of components that need estimation and simulation at the start of the computation. In the Delft3D-Waq program, these parameters are initially configured to “0”, allowing users to enter alternative initial values based on the available data. This study uses the average concentration values from the previous month’s simulations to estimate the initial conditions.

2.3.2. Model Validation

To assess the reliability of the calculations, this study uses the Root-Mean-Squared Error (RMSE), BIAS, and the Nash–Sutcliffe efficiency (NSE). The NSE indicator [50] assesses the accuracy of model predictions. The model results are considered satisfactory when the Nash–Sutcliffe efficiency (NSE) approaches a value of 1. BIAS was utilised to denote the mean direction of the data. BIAS asymptotically approached 0, indicating favourable model performance. RMSE describes the absolute deviation of the results with different weights to errors [51]. The larger the absolute error, the larger the weight [52]. A higher value shows substantial discrepancy between the model results and observations. These variables were articulated as follows:

(1)$\mathrm{N}\mathrm{S}\mathrm{E}=1-{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{O}}_{\mathrm{i}}-{\mathrm{P}}_{\mathrm{i}}\right)}^2}{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{O}}_{\mathrm{i}}-\overline{\mathrm{O}}\right)}^2}}$

(2)$\mathrm{B}\mathrm{I}\mathrm{A}\mathrm{S}={\displaystyle \frac{1}{\mathrm{N}}}{\sum }_{\mathrm{i}=1}^{\mathrm{N}}({\mathrm{P}}_{\mathrm{i}}-{\mathrm{O}}_{\mathrm{i}})$

(3)$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{({\mathrm{P}}_{\mathrm{i}}-{\mathrm{O}}_{\mathrm{i}})}^2}{\mathrm{N}}}}$

where O_i is the observed value (time i), $\overline{\mathrm{O}}$ is the average value observed during the entire period, P_i is the simulated value (at time i), and $\overline{\mathrm{P}}$ is the average forecast value.

2.3.3. Simulation Scenarios

Model validation scenario group: This scenario group was established with the conditions of 2021, such as topography, along with meteorological, hydrological, oceanographic, and environmental conditions, in order to validate the model by comparing the model results with the measured data in 2021.

Present scenario group: This group was conducted to simulate the assessment of hydrodynamic conditions and water quality in the Cat Ba–Ha Long area in 2019.

The scenarios simulating the impacts of climate change (sea level rise and rising temperature) in 2030 (2030cc) and 2050 (2050cc) were based on the climate change scenarios of the Ministry of Natural Resources and Environment in 2020 (Table 3).

Scenario group of human impact: Increases waste discharge into the Cat Ba–Ha Long coastal area in 2030 (2030h) and before the land reclamation activities in 2010 (Table 3).

Group of scenarios integrating human and climate change impacts: This group would encompass the concurrent consequences of heightened pollutant sources, diminished bay area due to land reclamation, sea encroachment, rising water levels, and elevated sea temperatures (climate change) in 2030 (2030h-cc) and 2050 (2050h-cc) (Table 3).

Each scenario was simulated over the 4 seasons (northeast monsoon, southwest monsoon, and 2 transitional seasons), with a duration interval of 30 s.

3. Results

3.1. Model Validation’s Results

Water elevation at Hon Dau (Figure 2a,b) and Bai Chay stations (Figure 2c,d) was used to calibrate and validate the model. From the calculations, the NSE coefficients indicated excellent matching between the simulations and measurements, with values of 0.91–0.95 for Hon Dau and 0.93–0.97 for Bai Chay. The other coefficients, namely, the BIAS value of about −0.12 to 0.17 m and the RMSE fluctuating from 0.15 to 0.23 m, also matched well.

Current velocity/direction was validated by comparing the model results with data measured at Cua Luc, Ha Long, Tuan Chau, and Cam Pha stations in January and October, demonstrating a satisfactory correlation between the simulations and measurements. The NSE coefficients ranged from 0.55 to 0.65 and from 0.58 to 0.71 for magnitude and current direction, respectively (Table 2). The average difference between the simulations and measurements ranged from −0.03 to 0.06 m/s (velocity) and from −10.2 to 20.3 degrees (direction). The absolute deviation was 23.2 to 35.5 degrees for direction and 0.03–0.05 m/s for velocity (Table 2). Figure 3 compares the measured and model-simulated current velocities at different stations.

The simulations’ findings were compared to observed organic matter (BOD₅, COD) and nutrients (NO₃⁻, NH₄⁺, PO₄³⁻) at Cua Luc, Ha Long, Tuan Chau, and Cam Pha stations. They showed a good matching of the simulations and observations, with the NSE ranging from 0.54 to 0.67 and from 0.56 to 0.68, respectively (Figure 4, Table 4). The RMSE ranged from 0.15 to 0.29 mg/L for organic matter and from 3.3 to 5.2 µg/L for nutrients. The BIAS data fluctuated within the range of −0.001 to 0.004 µg/L and −1.79 to 0.91 µg/L for organic matter and nutrients, respectively, demonstrating a precise alignment between the simulations and observations (Table 4).

3.2. Impact of Human Activities and Climate Change on Hydrodynamics

The effects of human activities on hydrodynamic conditions in the Cat Ba–Ha Long region are particularly apparent due to land reclamation, filling, and topographical alterations in the coastal waters of Ha Long, Bai Chay, Tuan Chau, Cam Pha, and Cat Hai. The analytical results indicate that the current field has undergone significant changes compared to previous encroachment operations. During the northeast monsoon, the most notable effect is the rising trend of current magnitude outside the Cua Luc region. Conversely, in the southwest of Ha Long Bay, the velocity typically diminishes as a result of land reclamation (Figure 5a,b).

During the transitional wind season from northeast to southwest following land reclamation activities, the current in the Bai Chay coastal area exhibits a modest increase. The bay’s constriction, which marginally enlarges the channel’s cross-sectional area, leads to a minor increase in velocity (Figure 6a,b).

During the southwest monsoon, the impact of land reclamation activities slightly reduces the flow from the outer area of Cua Luc Bay to the southeast of Ha Long Bay. In other regions, the current field exhibits no substantial alterations (Figure 7a,b). The current field of the western region of Ha Long Bay exhibits a similar pattern during the transitional season from southwest to northeast (Figure 8a,b).

The impacts of climate change (mainly sea level rise and the increase in temperature) on the flow conditions in the study area were also simulated for the years 2030 and 2050. The results of the analysis and calculations show that the impacts of climate change on the hydrodynamic conditions of the study area are modest and do not clearly manifest as trends (Figure 6c,d, Figure 7c,d and Figure 8c,d).

3.3. Impact of Human Activities on Ecosystem Variables of Water Quality

3.3.1. Individual Impact Due to Increased Sources of Pollutants

Our findings indicate that an increase in pollution sources in the Cat Ba–Ha Long coastal area leads to significant variations in dissolved oxygen (DO) levels over time and across different locations. The distribution of DO closely resembles the present scenario (2019). The proliferation of pollution sources has markedly diminished DO levels in the water, particularly in coastal regions such as Cua Luc Bay, Ha Long Bay, the southwestern part of Cat Ba Island, and, notably, the northern region of Tuan Chau Island (Figure 9).

The analysis of average DO levels at various locations, including Cua Luc, Ha Long, Tuan Chau, Cam Pha, and Bai Chay, indicates a general trend: with increases in pollution loads by 2030, DO levels are expected to decline relative to the present scenario, displaying pronounced seasonal fluctuations. During the northeast monsoon, the DO concentration diminishes by 0.15–0.21 mg/L. The Bai Chay area exhibits the most significant decline in DO levels compared to other regions, recording a value of 0.21 mg/L, followed by the southern part of Tuan Chau Island at 0.18 mg/L, the Cua Luc and Cam Pha areas (0.17 mg/L), and the central area of Ha Long Bay at the lowest level of 0.15 mg/L (Figure 10a). During the transitional wind season from northeast to southwest, the DO content is lower than in other seasons; however, the decline is more notable, especially in the southern region of Tuan Chau Island and Ha Long Bay, where it may exceed 0.4 mg/L (Figure 10b).

During the southwest monsoon, the rise in nutritional and organic sources correlates with a decrease in DO by around 0.14–0.16 mg/L, exhibiting minimal variation between the regions (Figure 10c). Meanwhile, during the transitional season from southwest to northeast winds, the influx of pollution sources into the research region results in a modest drop in DO concentration compared to the present scenario. The mean DO value diminishes by 0.07–0.15 mg/L, representing the lowest level over the seasons (Figure 10d).

Conversely, the escalation of pollutant concentrations attributable to anthropogenic activities has elevated the levels of nutrients and organic compounds in coastal regions: BOD₅ (increased by 0.1–0.29 g/m³), COD (0.1–0.3 g/m³), NO₃⁻ (0.006–0.025 g/m³), NH₄⁺ (0.01–0.02 g/m³), and PO₄³⁻ (0.001–0.01 g/m³).

3.3.2. Specific Impacts Due to Land Reclamation

Land reclamation activities in the Cat Ba–Ha Long coastal region have modified the hydrodynamic characteristics of the coastal waters, significantly influencing the dispersion and transportation of pollutants in this area. The simulation and analysis results from scenarios prior to (2010) and subsequent to (2019) the land reclamation activities indicate that these activities exert specific effects on the region’s water environment. For example, during the transitional wind season from southwest to northeast, land reclamation might diminish the dissolved oxygen (DO) levels in Ha Long Bay, particularly in the northern region of Tuan Chau Island (Figure 11).

The data indicate that, during the northeast monsoon season, the average DO concentration in the Cua Luc area had a minor decline of 0.04 mg/L, but other locations exhibited a trend of increasing DO levels following land reclamation. The DO levels rose most significantly in the southern region of Tuan Chau Island (0.32 mg/L), followed by the coastal area of Bai Chay (0.2 mg/L) and Cam Pha (0.18 mg/L), with the lowest in Ha Long Bay (0.02 mg/L), as illustrated in Figure 12a. During the transitional wind season from northeast to southwest, land reclamation diminishes DO levels in the coastal regions of Bai Chay and southern Tuan Chau, recording values of 0.17 and 0.25 mg/L, respectively. In contrast, in the remaining regions, land reclamation marginally elevates DO by 0.03–0.12 mg/L (Figure 12b).

During the southwest monsoon season, certain regions, including the southern portion of Tuan Chau and the shores of Cam Pha, experience a slight decrease in dissolved oxygen (DO) levels (0.01–0.03 mg/L). Conversely, areas such as Cua Luc, Ha Long, and Bai Chay exhibit an increase in DO due to land reclamation, with values ranging from 0.07 to 0.11 mg/L (Figure 12c). The trend of dissolved oxygen (DO) fluctuations in the research area is quite uniform during the transitional season from southwest to northeast winds, exhibiting an increase in DO values after land reclamation activities, ranging from 0.05 to 0.16 mg/L (Figure 12d).

Following land reclamation, the average biochemical oxygen demand (BOD) typically rises over the seasons, as observed in most regions, with values between 0.002 and 0.172 g/m³. The peak increase in BOD value transpires during the northeast monsoon season, reaching 0.172 g/m³ in the Cua Luc region. In the Cua Luc region, Ha Long and Cam Pha, during the transitional wind season from northeast to southwest, and in the Cua Luc region and south of Tuan Chau Island during the southwest wind season, BOD diminished compared to the period preceding land reclamation activities, with values ranging from 0.047 to 0.068 g/m³ and from 0.009 to 0.063 g/m³, respectively.

The average COD value following land reclamation activities generally rises, particularly during the northeast monsoon and the transitional season from southwest to northeast, with values ranging from 0.002 to 0.076 g/m³, except in the Cam Pha area, which exhibits a declining trend of 0.21 g/m³. Conversely, during the transitional season from northeast to southwest winds and the southwest monsoon, COD tends to decrease, exhibiting values between 0.02 and 0.1 g/m³, with the exception of the Ha Long area, where it experiences a minor rise (0.001 g/m³).

The average NO₃⁻ concentration fluctuates across various regions and seasons. During the northeast monsoon season, a rising trend of NO₃⁻ is noted in all five regions affected by land reclamation, with a value of 0.004–0.007 g/m³. Conversely, a declining tendency is evident in the majority of regions during the subsequent seasons, with levels below 0.01g/m³.

Following reclamation activities, the average NH₄⁺ concentration tends to increase during the northeast monsoon and the transitional period from southwest to northeast, with levels reaching up to 0.0064 g/m³. The rising trend of NH₄⁺ during the transitional season from southwest to northeast is more significant than in the northeast monsoon. The declining tendency across all five areas was observed during the transition from northeast to southwest winds, manifesting in the southern regions of Tuan Chau Island and Cam Pha in the southwest monsoon.

The average PO₄³⁻ concentration exhibits an upward tendency at most locations throughout the northeast monsoon, southwest monsoon, and the transitional period from southwest to northeast monsoon (excluding the Cam Pha area), with concentrations between 0.001 and 0.007g/m³ after reclamation activities. The opposite trend occurs during the transitional season from northeast to southwest, characterised by a reduction of 0.003–0.007 g/m³, with the exception of the southern part of Tuan Chau Island, which exhibits a marginal increase of 0.001 g/m³.

3.4. Impact of Climate Change on Ecosystem Variables of Water Quality

To evaluate the specific effects of climate change on the ecosystem state variables of water quality of the study area, simulations forecasting sea level rise and sea surface temperature increases for the years 2030 and 2050 were conducted and compared to the current scenario (2019). The results indicate that the spatiotemporal variability trend of water environmental quality under the influence of climate change is not much different from the current conditions (Figure 13).

Furthermore, to quantitatively evaluate the alterations in water quality under the influence of climate change over time, the mean values of multiple ecosystem variables were analysed based on model results at various sites, including Cua Luc, Cam Pha, the southern part of Tuan Chau Island, Ha Long Bay, and Bai Chay.

The investigation indicated that climate change causes a decline in DO levels in water. During the northeast monsoon, the average DO concentration diminishes by 0.06 mg/L in the 2030 scenario and by 0.1 mg/L in the 2050 scenario, as illustrated in Figure 14a. During the transitional season from northeast to southwest winds, the DO concentration, according to climate change projections for 2030 and 2050, diminishes by 0.04 and 0.06 mg/L, respectively (Figure 14b). The declining trend is particularly evident during the southwest monsoon, with the average DO content in the monitored regions projected to fall by 0.27 mg/L and 0.29 mg/L according to the climate change scenarios for 2030 and 2050, respectively (Figure 14c). During the transitional season from southwest to northeast winds, the DO concentration diminishes by 0.03 mg/L and 0.04 mg/L for the climate change scenarios of 2030 and 2050, respectively (Figure 14d).

The increase in water level and temperature may diminish BOD₅ in the coastal area, with values of 0.005–0.011 g/m³ during the northeast monsoon and 0.019–0.046 g/m³ during the transition from northeast to southwest monsoon. In the 2030 simulation scenario, BOD₅ values exhibit a modest increase during the southwest monsoon and the transition from southwest to northeast, recording values of 0.009 g/m³ and 0.006 g/m³, respectively. The modelling scenario for 2050 revealed an opposing trend: a reduction of 0.028 g/m³ during the southwest monsoon and 0.02 g/m³ in the transitional season from southwest to northeast. The average COD values generally decline in most regions across the scenarios, particularly exhibiting a more significant reduction under the impact of climate change in 2050. During the northeast monsoon, as well as the transitional seasons from northeast to southwest and from southwest to northeast, the COD diminishes, with value ranges of 0.006–0.012 g/m³, 0.024–0.053 g/m³, and 0.009–0.019 g/m³, respectively. During the southwest monsoon, COD is projected to increase marginally in the 2030 scenario (0.01 g/m³) and decrease in the 2050 scenario (0.014 g/m³).

Due to climate change, the nutrient contents in the water exhibit little variation relative to the present scenario. The average nutrient concentration in the five locations may increase/decrease by a maximum of approximately 0.001 g/m³ for NO₃⁻ and PO₄³⁻. A decreasing trend was seen for NH₄⁺ in most regions, with a value potentially reaching 0.005 g/m³.

3.5. Impact of Human Activities and Climate Change on Ecosystem Variables of Water Quality

Alongside examining the individual effects of human activities and climate change, the scenario group also simulated the combined impacts of human activities and climate change on the water environment for the years 2030 and 2050. The spatial distribution of pollutants reveals a consistent pattern: elevated concentrations are predominantly located in the coastal regions of the river mouth, extending from Cua Luc Bay to the southern section of Cat Hai Island, particularly in areas such as Cua Luc, Chanh River, and the northern part of Tuan Chau Island. The scenario groups that combine human impact and climate change and the scenario caused solely by humans lead to a significant increase in organic and nutrient substances while simultaneously reducing DO compared to the current scenario groups and the scenario that only considers climate change. This is clearly evident at the coastal river mouth region; however, the disparities between the scenarios become nearly imperceptible as one moves inland. The temporal variation pattern of the contaminated water masses predominantly aligns with tidal oscillations, exhibiting minimal differences across the scenarios (Figure 15).

The analytical results demonstrate that a rise in pollution sources correlates with a drop in DO content relative to current conditions (2019). During the northeast monsoon season, influenced by climate change, the DO content diminishes, recording values of 0.05–0.07 mg/L in 2030 and 0.08–0.13 mg/L in 2050. The trend of diminishing DO is significantly exacerbated by anthropogenic factors, particularly the combination of climate change and human activities: 0.15–0.21 mg/L (2030h), 0.2–0.26 mg/L (2030h-cc), and 0.23–0.3 mg/L (2050h-cc) (Figure 16a, Table 5).

During the transitional wind season from northeast to southwest, DO levels dramatically decline in scenarios affected by anthropogenic activity, particularly in the context of combined human impact and climate change, with values of 0.09–0.44 mg/L (2030h), 0.15–0.48 mg/L (2030h-cc), and 0.17–0.4 mg/L (2050h-cc). In the absence of other factors, climate change alone results in a reduction in the DO concentration in water by approximately 0.03–0.06 mg/L (2030cc) and 0.04–0.09 mg/L (2050cc) (Figure 16b, Table 5).

During the southwest monsoon, the trend of declining DO persists across scenarios; however, due to climate change, the reduction in DO content is more pronounced than in the human impact scenario. Climate change results in a drop in DO by 0.25–0.28 mg/L (2030cc) and 0.27–0.31 mg/L (2050cc), whereas human impact causes a reduction of 0.14–0.16 mg/L (2030h), 0.11–0.14 mg/L (2030h-cc), and 0.13–0.17 mg/L (2050h-cc) (Figure 16c, Table 5).

During the transitional wind season from southwest to northeast, the synergistic effects of human activities and climate change diminish the DO level in water more significantly than each factor alone. The effect of climate change decreases the DO value by approximately 0.02–0.03 mg/L (2030cc) and 0.03–0.05 mg/L (2050cc), whereas human activity diminishes the DO by 0.07–0.15 mg/L (2030h), 0.09–0.18 mg/L (2030h-cc), and 0.1–0.2 mg/L (2050h-cc) (Figure 16d, Table 5).

The simulated scenarios for the combined effects of human activity and climate change in 2030 and 2050 indicate an increase in organic compounds (BOD₅ and COD) in the research area. BOD₅ and COD exhibit considerable similarity, with values increasing from 0.074 to 0.3 g/m³ and from 0.078 to 0.32 g/m³, respectively, and are often lower than those in scenarios solely influenced by human activity. A contrasting pattern emerges when only the effects of climate change are evaluated, resulting in reductions of 0.005–0.046g/m³ (BOD₅) and 0.006–0.053g/m³ (COD).

Under the combined influence of humans and climate change, the nutrient content in the areas has increased in all seasons, with an increase of 0.006–0.025g/m³ (NO₃⁻), 0.006–0.019g/m³ (NH₄⁺), and 0.002–0.011g/m³ (PO₄³⁻).

4. Discussion

4.1. Impact of Human Activities

Socio-economic development activities in the Cat Ba–Ha Long region primarily encompass population growth, tourism, aquaculture, livestock farming, and industry, which have resulted in water pollutants as of 2019: approximately 192.9 × 10⁶ kg of COD, 77.5 × 10⁶ kg of BOD₅, 765 × 10³ kg of NO₃⁻ + NO₂⁻, 18.9 × 10⁶ kg of NH₄⁺, and 846 × 10³ kg of PO₄³⁻. The projection of total waste produced from residential, tourism, industrial, livestock, and aquaculture sources in the study area indicates a substantial increase by 2030 compared to 2019: BOD₅, COD, NO₃⁻, NH₄⁺, and PO₄³⁻ levels of 95.5%, 95.3%, 38.3%, 41.4%, and 79.4%, respectively (Figure 17). These factors influencing water quality in the study area are predominantly sourced from industrial activities. Simulations indicate that the Nghi Son Industrial Zone (Thanh Hoa Province) and the Dinh Vu Industrial Zone (Hai Phong) adversely affect the water quality of coastal regions due to occurrences originating from these industrial zones [39]. Phiri et al. [53] stated that industrial zone waste pollution significantly impacts the quality of the water environment in coastal regions, and that the contamination of coastal river estuaries in developing countries is attributed to industrial waste sources. Muwanga and Barifaijo [54] revealed through their research that industrial waste is the primary culprit contributing to the significant deterioration of the water environment in Lake Victoria, Uganda.

Research indicates that anthropogenic activities are the primary contributors to the degradation of aquatic environments in coastal regions [5,15]. Pollutants pose a significant hazard to the ecosystem [55]. Urban development and population growth augment pollution sources from domestic and industrial wastewater to the aquatic environment [56]. The 2030 projections for the Cat Ba–Ha Long coastal area reveal that anthropogenic activities have elevated organic substances (BOD₅ to approximately 0.29 g/m³, COD to roughly 0.3 g/m³) and nutrient concentrations (NO₃⁻ to around 0.025 g/m³, NH₄⁺ to about 0.02 g/m³, and PO₄³⁻ to approximately 0.01 g/m³), while concurrently diminishing the dissolved oxygen levels (approximately 0.29 mg/L) in the aquatic environment. Research conducted by Vitousek et al. [57] and Malone et al. [58] similarly demonstrated that anthropogenic activities elevate dissolved nutrients and pollutants in coastal zones, resulting in algal blooms and subsequent oxygen depletion in these places.

4.2. Impact of Climate Change

The rise in temperature due to climate change has been identified as a significant factor influencing ecosystem health and water quality [17]. The temperature of seawater influences physicochemical balance processes, including nitrification and the mineralisation of organic compounds. Consequently, variations in water temperature will affect the transport and dispersion of organic groups and dissolved nutrients in coastal river estuaries [18].

Research findings in the Cat Ba–Ha Long coastal area demonstrate that climate change (elevated temperatures and increasing sea levels) leads to a reduction in dissolved oxygen levels, with an average loss of around 0.02–0.28 mg/L by 2030 and 0.03–0.31 mg/L by 2050. This research finding conforms with the investigations conducted in [18], which demonstrated that elevated temperatures cause a decrease in dissolved oxygen and saturated oxygen levels in water. The findings of Hosseini et al. [20] indicate that elevated temperatures (both air and water) in the Qu’Appelle River region (Canada) correlate with a reduction in dissolved oxygen levels for the majority of the year. Cox and Whitehead [19] predict a substantial reduction in dissolved oxygen levels in the Thames River by 2080, driven by increasing temperatures and biological oxygen demand.

Furthermore, the effects of climate change diminish NH₄⁺ levels (0.005 g/m³) and may also alter NO₃⁻ and PO₄³⁻ concentrations by approximately 0.001 g/m³ in the Cat Ba–Ha Long coastal area. A recent study on the influence of temperature on water quality [21,22] indicates that elevated water temperatures can diminish nitrogen and phosphorus nutrient levels from rivers to coastal regions, attributed to the enhanced consumption–photosynthesis processes of algae, as phytoplankton proliferate with rising water temperatures.

4.3. Combined Impact of Humans and Climate Change

The Cat Ba–Ha Long coastal area has experienced an increase in organic matter concentration (0.32 g/m³) and nutrients (0.025 g/m³) due to human activities, such as rising pollutants and land reclamation, alongside the effects of climate change, including elevated temperatures and sea level rise. Concurrently, the concentration of dissolved oxygen has diminished to 0.33 mg/L. Jeppesen et al. [35] posited that forecasting the effects of augmented nutrient input from terrestrial sources and climate change involves very intricate processes, characterised by interacting influences among nutrient sources, light conditions, temperature, and hydrodynamic factors. The augmentation of fertiliser sources along the shore, along with elevated sea temperatures, will create very favourable conditions for the proliferation of algae, particularly blue-green algae. The augmentation of nitrogen nutrients from anthropogenic activities, along with the effects of climate change, can substantially elevate nitrate concentrations in certain regions. These findings closely resemble those of Whitehead et al. [34] in the Thames Estuary, predicting that nitrate concentrations may climb from 4 to 18 mg/L due to the synergistic effects of fertiliser influx and temperature elevation resulting from climate change. An additional announcement indicated that the increase in water temperature by about 0.8–1.1 degrees Celsius in the Chesapeake Bay (USA), coupled with the augmented nutrient influx due to socio-economic development, has led to a substantial increase in phytoplankton biomass and a heightened frequency of red tide events in this region [59].

The rise in temperature and alterations in precipitation, along with anthropogenic influences like population expansion, livestock, and modifications in land use, will persist in influencing the flow and water quality of rivers and coastal areas [33]. Previous studies have reported that DO is one of the most vital indicators of coastal water quality. However, since the 1960s, DO in the global ocean has declined by 2% [60,61,62]. The rate of DO decline was predicted to continue to decline with increased global warming in the future [63,64,65]. In this study, the simulation results also show that, due to climate change, DO tends to decrease in both the northeast monsoon (dry season/winter season) and southwest monsoon (summer/rainy season), with values of 0.02–0.13 mg/L and 0.25–0.31 mg/L, respectively. This clearly shows a sharper downward trend in DO in summer due to climate change in the Cat Ba–Ha Long coastal area than in the winter season. This result is also consistent with many related studies on the decline in DO when seawater temperature rises, and the increasing trend of temperature in the coastal area of Hai Phong–Quang Ninh in summer is much greater than in winter [41].

Meanwhile, pollution sources from the coast, such as oxygen demand (e.g., BOD, COD) and nutrients, are also drivers for the decline in DO in the coastal regions [66,67]. In the Cat Ba–Ha Long coastal region, under human activities only, DO may decrease by about 0.07–0.44 mg/L in the northeast monsoon/winter and 0.14–0.16 mg/L in the southwest monsoon/summer. This suggests the complex variation in DO under the influence of both pollution sources and bio-geochemical processes taking place in this area, as shown in related studies [68].

In this study, the combined effects of human activities and climate change revealed distinct trends in DO during the rainy and dry seasons. In the dry season, this combination slightly increases DO (0.09–0.48 mg/L) compared to individual cases, surpassing both the individual effects of humans (0.07–0.44 mg/L) and the specific effects of climate change (0.02–0.13 mg/L). In contrast, in the rainy season, this combination causes DO to decrease (0.13–0.17 mg/L) less than in individual cases, especially the unique effects of climate change (0.25–0.31 mg/L). This may indicate that, in this region during the rainy season, the increase in nutrient sources from rivers can increase nutrients as well as biological activity, causing an increase in DO, and this increased amount of DO may partially compensate for the decline in DO caused by climate change, similar to the findings of some previous studies [68,69,70]. Obviously, the extent of human influence depends on the source of pollutants (oxygen demand and nutrients). However, with environmental protection policies being strengthened, pollution sources introduced into the coastal area of Cat Ba–Ha Long are increasingly controlled; the impact of human activities will likely continue to decrease compared to the impact of climate change.

5. Conclusions

The effects of anthropogenic activities and climate change on the water environment of the Cat Ba–Ha Long coastal area were evaluated through simulation scenarios: present condition (2019), climate change projections (2030, 2050), human impact projections (2030), and a combined scenario of human and climate change impacts (2030 and 2050), as well as a scenario prior to land reclamation activities (2010). Each scenario was simulated across four seasons: the northeast monsoon (January), the southwest monsoon (July), the transition from northeast to southwest (May), and the transition from southwest to northeast (October). The findings indicate that some ecosystem state variables of water quality experience specific alterations due to the synergistic effects of human activity and climate change.

Both anthropogenic activities and climate change influence water quality during the northeast monsoon and the two transitional seasons; however, the effect of climate change (DO reduction of 0.02–0.13 mg/L) is less significant than that of human activities (DO reduction of 0.07–0.44 mg/L), while their combined impact results in a DO reduction of 0.09–0.48 mg/L. During the southwest monsoon, the trend reverses: the impact of climate change significantly reduces water quality (DO declines by approximately 0.25–0.31 mg/L), surpassing that of human activities (0.14–0.16 mg/L) and the combined effect of human activities and climate change (0.13–0.17 mg/L). Conversely, due to human activity and climate change, nutrient concentrations in the area have risen, with average values of 0.002–0.033 g/m³ (NO₃⁻), 0.0003–0.034 g/m³ (NH₄⁺), and 0.0005–0.014 g/m³ (PO₄³⁻).

This study is the first to apply a water quality model to evaluate the individual effects of climate change and human activity, as well as their combined effects, on the water quality of the Cat Ba–Ha Long area. These preliminary research results will serve as a reference for local managers, guiding socio-economic development in the context of climate change (increased temperatures and rising sea levels) while simultaneously protecting water quality in the Cat Ba–Ha Long area, a UNESCO World Natural Heritage site. However, this study exclusively evaluates the effects of human activities that elevate pollutants due to socio-economic development, omitting the assessment of occurrences related to industrial zones and their impact on water quality in the Cat Ba–Ha Long region. Additionally, this study’s examination of climate change’s effects solely focuses on rising temperatures and sea level increases, neglecting extreme weather phenomena like precipitation and flooding. These features may be added in future follow-up studies.

Author Contributions

N.M.H. and V.D.V.: conceived the study; N.M.H., V.D.V. and N.T.D.: conceptualization, methodology, and writing—original draft preparation and performed preliminary analysis; N.M.H., V.D.V., S.O. and T.D.L.: supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

This study is benefited from the support of the scientific cooperation between VAST-IRD, QTFR02.02/24-25 project. This paper is also a contribution to the LOTUS International Joint Laboratory (http://lotus.usth.edu.vn).

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Cat Ba–Ha Long coastal area: (a) the East Vietnam Sea; (b) overall model grid; (c) detailed model grid (LT1: Cua Luc; LT2: Ha Long; LT3: Tuan Chau; LT4: Cam Pha; LT5: Bai Chay).

Figure 2. Comparison between simulated results and measured water elevation: (a) Hon Dau, January 2021; (b) Hon Dau, October 2021; (c) Bai Chay, January 2021; (d) Bai Chay, October 2021.

Figure 3. Comparison between simulated and measured currents: (a) direction at bottom layer, Ha Long, January 2021; (b) velocity at bottom layer, Ha Long, January 2021; (c) direction at surface layer, Tuan Chau, January 2021; (d) velocity at surface layer, Tuan Chau, January 2021; (e) direction at middle layer, Cua Luc, October 2021; (f) velocity at middle layer, Cua Luc, October 2021; (g) direction at surface layer, Cam Pha, October 2021; (h) velocity at surface layer, Cam Pha, October 2021 (see locations in Figure 1).

Figure 4. Comparison of organic matter and nutrients between observation and modelling: (a) NO3−, Cua Luc, January; (b) PO43−, Ha Long, January; (c) NH4+, Tuan Chau, January; (d) BOD5, Ha Long, October; (e) COD, Cua Luc, October; (f) NH4+, Cam Pha, October.

Figure 5. Comparison of surface current fields during the ebb tide in the northeast monsoon under several scenarios: (a) 2010, (b) 2019, (c) 2030cc, (d) 2050cc. Arrows and the colour scale represent the current direction and the current speed (m/s), respectively.

Figure 6. Comparison of surface current fields during the ebb tide in the transition season from northeast to southwest monsoon under several scenarios: (a) 2010, (b) 2019, (c) 2030cc, (d) 2050cc. Arrows and the colour scale represent the current direction and the current speed (m/s), respectively.

Figure 7. Comparison of surface current fields during the ebb tide in the southwest monsoon under several scenarios: (a) 2010, (b) 2019, (c) 2030cc, (d) 2050cc. Arrows and the colour scale represent the current direction and the current speed (m/s), respectively.

Figure 8. Comparison of surface current fields during the ebb tide in the transition season from southwest to northeast monsoon under several scenarios: (a) 2010, (b) 2019, (c) 2030cc, (d) 2050cc. Arrows and the colour scale represent the current direction and the current speed (m/s), respectively.

Figure 9. Distribution of DO during the northeast monsoon in the flood tide: (a) surface layer, 2019; (b) surface layer, 2030h; (c) bottom layer, 2019; (d) bottom layer, 2030h.

Figure 10. Average monthly DO levels in some areas according to the present scenario (2019) and increased pollution sources (2030h): (a) northeast monsoon, (b) transitional season from northeast to southwest, (c) southwest monsoon, (d) transitional season from southwest to northeast.

Figure 11. Distribution of DO content in the transitional wind season from southwest to northeast during the ebb tide: surface layer—(a) 2010, (b) 2019; bottom layer—(c) 2010, (d) 2019.

Figure 12. The average DO concentration in some areas according to the current scenarios (2019) and before land reclamation (2010): (a) northeast monsoon, (b) transition season from northeast to southwest, (c) southwest monsoon, and (d) transition season from southwest to northeast.

Figure 13. Distribution of DO during the northeast monsoon phase: (a) surface layer, current status; (b) surface layer, 2050; (c) bottom layer, current status; (d) bottom layer, 2050.

Figure 14. Average DO levels in some areas according to the current scenario (2019), climate change scenario 2030, and climate change scenario 2050: (a) northeast monsoon season, (b) transition season from northeast to southwest, (c) southwest monsoon season, and (d) transition season from southwest to northeast.

Figure 15. Distribution of DO during the ebb tide in the dry season according to the scenarios: (a) surface layer, current status; (b) surface layer, climate change; (c) surface layer, human impact; (d) surface layer, human impact and climate change; (e) bottom layer, current status; (f) bottom layer, climate change; (g) bottom layer, human impact; (h) bottom layer, human impact and climate change.

Figure 16. Fluctuations in average DO levels in the Cat Ba–Ha Long area: (a) northeast monsoon, (b) transitional season from northeast to southwest, (c) southwest monsoon, and (d) transitional season from southwest to northeast.

Figure 17. Forecast of pollutant load in the Cat Ba–Ha Long coastal area in 2030.

Table 1

Total pollution load discharged into the Cat Ba–Ha Long area in 2019 and 2030 (tons/year).

Pollutant	Domestic	Aquaculture	Livestock	Industry	Total
2019
COD	18,688.6	201.53	13,079.4	160,965	192,934.5
BOD5	11,680.4	57.21	8179.8	57,535.3	77,452.7
NO3− + NO2−	28.5	0.37	64.1	672	764.99
NH4+	854.7	8.87	1539.5	16,504.4	18,907.48
PO43−	85.5	14.92	282.9	462.7	846
2030
COD	32,092.3	206.5	16,921.3	327,670.5	376,890.6
BOD5	20,057.6	58.7	10,582.8	120,742.8	151,441.8
NO3− + NO2−	48.9	0.4	85.1	923.6	1058.04
NH4+	1467.6	9.1	2042.6	23,210.2	26,729.49
PO43−	146.7	15.3	367.5	988.6	1518.1

Table 2

The area of land reclamation in 2019 compared to 2010 in the Cat Ba–Ha Long area (ha).

Area	2010	2019	Reclaimed Land Area
Cua Luc Bay	2288.0	1868.8	419.2
Bai Chay–Tuan Chau	4770.3	3546.1	1224.2
Cam Pha	7132.5	6553.8	578.8
Cam Pha–Van Don	16,851.9	16,060.8	791.1

Table 3

Scenarios of pollution and climate change.

Year	Scenario Group 1Climate Change (RCP8.5)	Scenarios Group 2Human Activities	Scenario Group 3Combine: Climate Change Plus Human Activities
2010		Before land reclamation, sea encroachment (2010)
2019		- Present scenario (2019) - Land reclamation, sea encroachment
2030	- Increasing water temperature: 0.9 °C - Sea Level Rise: 13 cm (2030cc)	Increasing of riverine input: BOD + 95.5%COD + 95.3%NO3 + 38.3%NH4 + 41.4%PO4 + 79.4%(2030h)	Combination of scenario group 1 and group 2(2030h-cc)
2050	- Increasing water temperature: 2 °C - Sea Level Rise: 26 cm (2050cc)	Same as scenario 2030 (pollution controlled)	Combination of scenario group 1 and group 2(2050h-cc)

Table 4

Model validation.

Station	Parameters	Coefficients
		NSE	RMSE	BIAS
Hon Dau, Bai Chay	Water level	0.91–0.97	0.15–0.23	−0.12–0.17
Cua Luc, Ha Long, Tuan Chau, and Cam Pha	Current magnitude	0.55–0.65	0.03–0.05	−0.03–0.06
	Current direction	0.58–0.71	23.2–35.5	−10.2–20.3
Cua Luc, Ha Long, Tuan Chau, and Cam Pha	BOD5, COD	0.54–0.67	0.15–0.29	−0.001–0.004
	NO3−, NH4+, PO43−	0.56–0.68	3.3–5.2	−1.79–0.91

Table 5

The decrease in DO (mg/L) in the scenarios compared to the present scenario.

Season	Location	Scenarios
		2030h	2030cc	2030h-cc	2050cc	2050h-cc
Northeast monsoon	Cua Luc	0.17	0.05	0.22	0.09	0.25
	Ha Long	0.15	0.05	0.20	0.08	0.23
	Tuan Chau	0.18	0.07	0.24	0.12	0.29
	Cam Pha	0.17	0.07	0.24	0.13	0.29
	Bai Chay	0.21	0.06	0.26	0.10	0.30
	Average *	0.17	0.06	0.23	0.10	0.27
Transitional season from northeast to southwest	Cua Luc	0.15	0.03	0.18	0.04	0.39
	Ha Long	0.44	0.04	0.47	0.05	0.37
	Tuan Chau	0.42	0.06	0.48	0.09	0.40
	Cam Pha	0.36	0.03	0.39	0.04	0.28
	Bai Chay	0.09	0.06	0.15	0.09	0.17
	Average *	0.29	0.04	0.33	0.06	0.32
Southwest monsoon	Cua Luc	0.16	0.27	0.12	0.28	0.13
	Ha Long	0.14	0.25	0.11	0.27	0.13
	Tuan Chau	0.15	0.28	0.13	0.31	0.17
	Cam Pha	0.14	0.28	0.14	0.29	0.16
	Bai Chay	0.15	0.27	0.12	0.30	0.15
	Average *	0.15	0.27	0.13	0.29	0.15
Transitional season from southwest to northeast	Cua Luc	0.12	0.02	0.14	0.03	0.15
	Ha Long	0.10	0.02	0.13	0.03	0.14
	Tuan Chau	0.13	0.03	0.16	0.05	0.18
	Cam Pha	0.07	0.02	0.09	0.03	0.10
	Bai Chay	0.15	0.03	0.18	0.04	0.20
	Average *	0.12	0.03	0.14	0.04	0.15

Note: * Average is the average DO value of 5 locations: Cua Luc, Ha Long, Tuan Chau, Cam Pha, Bai Chay.

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