Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

Abstract

With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.

1. Introduction

In recent decades, the increased dependence on oil as a crucial resource has led to a rise in its marine transportation. While this offers financial benefits for large-scale oil transport over distances, it has also heightened the frequency and severity of pollution events resulting from oil spills. Data released by the International Tanker Owners Pollution Federation [1] reveal a concerning figure: from 1970 to 2022, roughly 5.88 million tons of oil were spilled across global coastal areas due to tanker accidents. These spills pose devastating threats to coastal ecosystems while causing substantial economic losses. The Deepwater Horizon oil spill in the Gulf of Mexico, one of the largest marine oil spills in history, released approximately 4.9 million barrels (206 million gallons or 780,000 metric tons) of crude oil, which caused significant ecological damage and adversely impacted the tourism and fishing industries. The total economic costs, including cleanup and legal settlements, exceeded USD 65 billion for BP, profoundly affecting local economies and industries [2,3]. Another major incident, the 2018 collision between the Sanchi oil tanker and a cargo ship in the East China Sea, involved a large fire and the release of roughly 130,000 tons of condensate, resulting in 32 deaths or missing persons [4]. In 2022, marine oil transportation saw a marked increase in oil spill incidents (approximately 15,000 tons spilled), including three classified as major spills (over 700 tons each), according to ITOPF data [1]. Oil spills can have persistent and devastating impacts on marine environments, disrupting a broad range of life forms and ecosystems. While prevention measures are crucial for minimizing the risk of oil spills, accidents can still occur due to various factors, such as human error, equipment failure, or natural disasters [5,6]. When oil spills happen, they can cause significant harm to marine and coastal habitats, leading to long-term ecological, economic, and social consequences [7].

To effectively mitigate the potential impacts of oil spills, it is essential to conduct comprehensive risk assessments. These evaluations measure the potential impacts on marine and shoreline resources and estimate the likelihood of negative incidents [8]. By identifying vulnerable areas and resources at risk, risk assessments help prioritize prevention and response efforts [9]. Furthermore, they provide valuable insights for developing targeted strategies to minimize the consequences of oil spills, such as implementing stricter safety measures in high-risk zones or establishing contingency plans for sensitive habitats [10]. Comprehensive risk assessments are crucial for strengthening oil spill response measures and enhancing the overall resilience of marine environments to oil spill incidents. The oil spill risk assessment (OSRA) integrates the likelihood of oil spill incidents with an analysis of the potential consequences [11,12,13]. The probability of an oil spill incident is commonly derived from historical data, while the potential consequences are primarily determined by the hazard posed to the region from an oil spill and the vulnerability of the ecological resources that could be affected. The hazards associated with an oil spill affecting a region can be assessed using oil spill models. Widely used oil spill models include Automated Data Inquiry for Oil Spills (ADIOS), General NOAA Operational Modeling Environment (GNOME), and Oil Spill Contingency and Response (OSCAR). These models incorporate various factors, such as ocean currents, wind patterns, and the properties of the spilled oil, to predict the trajectory, fate, and potential environmental impacts of oil spills. Numerous studies have successfully applied different oil spill models, demonstrating their effectiveness and versatility in diverse geographical settings. For instance, Valis et al. utilized selected mathematical functions to analyze operation data information, enhancing the accuracy of oil spill predictions [14]. Similarly, a recent study published in Water provided insights from diverse environmental studies, highlighting the importance of considering water resources and resilience in oil spill risk assessments [15]. Moreover, Spaulding conducted a comprehensive review of oil spill modeling, discussing the state-of-the-art techniques and future directions in this field [16]. The review emphasized the significance of incorporating environmental factors and the advancements in oil spill modeling approaches. Amir-Heidari and Raie developed a decision support system for oil spill response planning in the Persian Gulf, employing a form of consequence modeling that considers various environmental factors [17]. Their study demonstrated the effectiveness of this approach in predicting oil spill impacts and supporting decision-making processes. In a comparative risk assessment study, French-McCay et al. applied oil spill modeling to evaluate the effectiveness of different spill response options for a deepwater oil well blowout. The study highlighted the importance of considering environmental factors and the role of modeling in supporting decision-making during oil spill incidents [18]. Furthermore, Guo et al. conducted a numerical simulation of oil spill trajectory and diffusion in the Bohai Sea, taking into account various environmental factors, such as ocean currents and wind patterns [19]. When reliable predicted results are obtained from oil spill models, the zones where oil spills have the potential to harm marine ecosystems can be identified. When evaluating the vulnerability of coastal regions to oil spills, the Environmental Sensitivity Index (ESI) map, developed in accordance with guidelines from the National Oceanic and Atmospheric Administration (NOAA), serves as a foundational tool. By classifying shoreline types based on a set of criteria that include their pollution sensitivity, biological productivity, and patterns of human activity, the ESI map offers a detailed analysis of the varying degrees of risk across coastal regions, from the least to the most vulnerable to damage from oil spills. The utilization of the ESI map enables the strategic prioritization of areas that are significantly at risk, thereby enhancing the efficacy of oil spill response initiatives, and has achieved global application [20,21,22]. Integrating the ESI with simulations of oil spill trajectories enhances the understanding of potential impacts on fragile environmental resources and facilitates the precise identification of risk-prone areas.

In China, despite numerous studies simulating oil spill trajectories and fates [23,24,25], the integration of such simulations with assessments of coastal vulnerabilities to quantify oil spill risks remains inadequately explored due to data limitations. Without this integration, the potential risks of oil spills in different coastal regions cannot be fully understood, which can significantly restrict the development of sophisticated risk-mitigation strategies, precise resource allocation, and the elevation of preparedness levels for oil spill events. In this study, a quantitative approach aimed at assessing the risk to coastal areas posed by oil spills is introduced. This approach integrates the ESI dataset with hypothetical oil spill trajectories, utilizing both an oil spill numerical model and Geographic Information Systems (GIS) techniques. The ESI dataset is used to estimate the sensitivity of coastal and marine ecosystems to oil spills, enabling a detailed evaluation of environmental vulnerability. The employment of numerical oil spill models is instrumental in simulating the dynamics and spread of oil following a spill, offering predictive insights into the movement and eventual fate of spilled oil across different scenarios. GIS technology is used to visualize and analyze how oil spill paths intersect with the coastal vulnerabilities identified by ESI maps. The exposure is assessed by analyzing the potential overlap between the projected spill area and the distribution of critical environmental elements within the study area. A risk assessment of oil spills is instrumental in identifying the elements most susceptible to oil spill damage in coastal zones and mapping the varying levels of risk across these areas, facilitating the early formation of management plans. Jiaozhou Bay, characterized by its critical ecological role, including a diverse array of marine mammals, alongside its economic importance due to the intensive oil industry presence, serves as the site for this case study. The bay’s unique combination of ecological sensitivity and high oil spill risk makes it an ideal location to investigate the potential impacts of oil spills on coastal environments.

Despite the advancements in oil spill modeling and risk assessment techniques, there remains a significant research gap in the integration of these approaches with coastal vulnerability assessments in China, particularly in ecologically sensitive and economically important areas like Jiaozhou Bay. Previous studies have primarily focused on simulating oil spill trajectories and fates, with limited attention being paid to the comprehensive evaluation of coastal environmental risks in such regions. Moreover, the application of advanced oil spill models, such as MEDSLIK-II, in combination with GIS-based vulnerability assessments, has not been extensively explored in the Chinese context, especially in areas with complex ecological and economic dynamics. To address this research gap and build upon the unique characteristics of Jiaozhou Bay, the primary objectives of this study are twofold: (1) to develop a comprehensive framework for a quantitative risk assessment of oil spills along China’s coastlines, integrating numerical modeling, GIS analysis, and the Environmental Sensitivity Index (ESI); (2) to demonstrate the application of this framework through a case study in the Jiaozhou Bay area, identifying high-risk zones and evaluating the potential impacts of oil spills on coastal environments. By achieving these objectives, this study aims to contribute to the advancement of oil spill risk assessment methodologies and provide valuable insights for coastal management and environmental protection strategies in China, with a particular focus on regions facing complex ecological and economic challenges.

2. Study Area and Datasets

2.1. Study Area

Located in the southern region of the Shandong Peninsula, Jiaozhou Bay is a semi-enclosed coastal area positioned between longitudes 119°30′ E and 121° E and latitudes 35°35′ N and 37°09′ N, as depicted in Figure 1a. It is connected to the Yellow Sea through a narrow eastern opening and covers roughly 1000 square kilometers, as illustrated in Figure 1b,c. The bay’s shoreline stretches over 30 km, and it boasts an average water depth of about 7 m, making it a relatively shallow marine environment.

The study area serves as an essential hub for international economic and transportation networks within the area, accommodating an array of industrial operations and harbors, notably Qingdao Port—ranked as the sixth largest globally for container throughput, with approximately 23.7 million TEUs managed in 2021 [26]. The region’s high volume of shipping activities elevates the potential for oil spill incidents during transportation or loading and unloading operations. Moreover, the bay’s ecosystem, home to diverse marine life such as fish, crabs, and shrimp, faces heightened vulnerability to the detrimental impacts of oil contamination. The bay’s relatively shallow waters promote the rapid dispersal of oil spills, posing threats to fragile ecosystems and local communities. Pollution from such incidents could severely impact Jiaozhou Bay’s delicate habitats and its tourism industry. A significant event occurred on 25 November 1983, when the Panama-registered “Oriental Ambassador” vessel spilled 3343 tons of crude oil, affecting 230 km of coastline, 900,000 square meters of tidal areas, and over 15,000 mu of aquafarms, leading to substantial economic losses.

Consequently, it is important to implement comprehensive oil spill risk assessments to protect Jiaozhou Bay’s biodiversity and economic interests, and the well-being of its community.

2.2. Datasets

The datasets employed for conducting oil spill risk assessments in the study area comprise meteorological, socioeconomic, and geographic information. These encompass the following:

(1) The historical oil spill dataset, which contains historical records of oil spills that have occurred in the study area, including the date, location, volume, type of oil, and cause of the spill. The data can be obtained from government agencies, reports, and academic research. By analyzing historical oil spill data, patterns and trends can be identified, allowing for the identification of areas with a high frequency of oil spills.

(2) Hydrological and meteorological datasets, which are used to simulate the oil spill trajectories over a large area and time period in the marine environment. Hydrological datasets include information on ocean currents, water temperature, and tidal movements. Meteorological datasets include information on wind speed and direction. These factors can affect the spread and behavior of oil spills on the surface of the water, and the potential impact to coastal areas.

(3) The ESI dataset includes information on the sensitivity and vulnerability of different environmental and socio-economic resources to oil spills, such as wetlands, wildlife refuges, coral reefs, and human-use areas. Sensitivity to the impacts of an oil spill is evaluated based on the different types of coastal sections, and levels of vulnerability can be identified for the whole coastline.

(4) The Geographic Dataset includes topographic data, bathymetric data, geographic zoning data, and satellite imagery. Understanding the bathymetric and topography of both the underwater and coastal environment can help predict how oil might move with the terrain and the direction in which it might flow, especially in regions with varying elevations. The zoning data and satellite imagery can be used to create risk assessment maps.

3. Methodology

The methodology for the Oil Spill Risk Assessment (OSRA) articulated in this study, as shown in Figure 2, involves a systematic four-step approach: (I) the establishment of oil spill scenarios informed by historical incidents; (II) the utilization of the MEDSLIK-II model for simulating oil spill dynamics, enabling the forecasting of spill trajectories and their potential impacts across predefined scenarios; (III) the integration of the Environmental Sensitivity Index (ESI) with Geographic Information System (GIS) techniques to spatially analyze and visualize the vulnerability of coastal zones to oil spill incidents; and (IV) a risk assessment of coastal areas concerning oil spills, combining the hazard levels calculated from MEDSLIK-II output data with vulnerability levels from ESI through the application of a risk matrix method.

3.1. Step1: Establishment of Hypothetical Scenarios

In order to identify areas at risk of oil spill and the subsequent potential damage, the initial step is the generation of representative oil spill scenarios. These scenarios include an array of parameters, such as the geographical origin of the spill, the type of oil involved, the start time of the spill, the total volume of oil spilled, and the duration of the spill. Moreover, environmental factors such as wind direction and sea surface temperature exhibit seasonal changes, which can influence the trajectory and eventual outcome of oil spills. Consequently, an in-depth understanding and precise quantification of these influential factors are indispensable in the construction of comprehensive oil spill scenarios and in accurately predicting potential environmental impact.

Regarding the geographical origin of the spill, an analysis of historical records of oil spill incidents from the period 1974–2022 within the specified study area was conducted [27]. The objective of this analysis was to identify the sites most frequently impacted by oil spill incidents, and subsequently incorporate them into the construction of representative oil spill scenarios. The primary variables, including the date of the incident, initial spill site, causative elements, and the quantity of oil spilled, are detailed in Appendix A,

Table A1. The oil spill pollution accident statistics in Jiaozhou bay between 1974 and 2022.

No.	Date	Location	Ship Name	Cause of Accident	Spilled Oil (t)
1	1974/9/1	Zhongsha Reef	Daqing 31	Grounding	895.0
2	1975/3/1	Huangdao Dock	Daqing 35	Oil Tank Overflow	4.0
3	1975/5/1	Channel (Horseshoe Reef)	Daqing 30	Grounding	33.0
4	1975/9/1	Huangdao Dock	Daqing 53	Collision	3.0
5	1975/9/1	Dagang Dock	Nanping	Misoperation	20.0
6	1975/10/1	Anchorage	Daqing 15	Oil Tank Overflow	7.0
7	1975/10/1	Dagang Dock	Daqing 36	Oil Tank Overflow	4.0
8	1975/12/1	Huangdao Dock	Daqing 41	Oil Tank Overflow	2.0
9	1976/7/1	Dagang Dock	Huangdao	Oil Pipe Detachment	15.0
10	1977/11/1	Huangdao Dock	Daqing 244	Oil Tank Overflow	30.0
11	1979/6/1	Huangdao Dock	Daqing 240	Misoperation	10.0
12	1979/6/1	Huangdao Dock	Sairus	Collision	350.0
13	1980/8/1	Zhongsha Reef	Daqing 256	Grounding	43.0
14	1983/11/1	Zhongsha Reef	Eastern Ambassador	Grounding	3343.0
15	1984/9/1	Zhongsha Reef	Jiacui	Grounding	757.0
16	1986/10/1	Huangdao Dock	Daqing 245	Explosion	100.0
17	1987/9/1	Huangdao Dock	Huahai 2	Oil Pipe Breakage	120.0
18	1994/7/1	Qingdao Port Anchorage	Praba Cyprus	Collision	100.0
19	2001/7/1	Dagang Dock	Huahai 78	Oil Tank Overflow	3.0
20	2001/9/1	Main Channel	Samitun Kuwait	Oil Pipe Detachment	25.0
21	2002/10/1	Huangdao Dock	Bao De 1136	Misoperation	1.0
22	2004/11/1	Huangdao Dock	Zhele Oil 7	Collision Leakage	3.0
23	2005/7/1	Qingdao Port	Titan Giant	Hull Damage and Oil Arm Break	25.0
24	2006/2/1	Qingdao Port	Fuhai	Hull Damage and Oil Leak	64.0
25	2010/6/1	Dagang Dock	Hehua	Oil Tank Crack	1.0
26	2011/4/1	Huangdao Dock	Youlan	Oil Arm Detachment	2.0
27	2011/10/1	Main Channel	Eastern Sunrise	Collision	30.0
28	2013/11/1	Huangdao Dock	None	Land Source	2000.0
29	2014/4/1	Main Channel	Huashun 88	Collision	30.0
30	2021/4/27	Main Channel	SEA JUSTICE	Collision	9400.0
31	2021/9/30	Qianwan Dock	Xin ***	Air Pipe Overflow	1.83
32	2022/3/21	Huangdao Dock	ARZOYI	Cable Break during Unloading	84.6
33	2022/4/21	Qianwan Dock	Li ***	Air Pipe Overflow	1.41
34	2022/6/17	Qianwan Dock	Zhong ****	Air Pipe Overflow	2.6

, derived from the analysis of historical data.

The data analysis from Table A1 reveals Huangdao Dock as the primary location of oil spill incidents, constituting 39.4% of all recorded accidents (n = 13) and 33.4% of the total oil spill volume. Conversely, Hidden Reefs, despite experiencing only five incidents (15.2% of the total), accounts for a significantly larger portion of the total spill volume, at 62.5%. In contrast, Dagang Dock, despite its seven incidents (accounting for 21.2% of the total), has a notably lower total spill volume, at just 1.6%. Although the oil spill volume at Dagang Dock is relatively low, the persistent recurrence of incidents requires sustained monitoring. Accounting for both spill frequency and volume, five representative locations, specifically Qianwan Port (120.23° E, 36.02° N), Huangdao Oil Port (120.24° E, 36.07° N), Dagang Port (120.31° E, 36.09° N), Horseshoe Reef (120.30° E, 36.07° N), and Channel Entrance (120.30° E, 36.03° N) were selected as potential origins for simulated oil spills, as demonstrated in Figure 3.

In the area under study, vessel collisions emerge as the leading cause of oil spill incidents, with spills predominantly consisting of fuel oil from non-oil-transporting vessels and various petroleum products from oil tankers. To assess the impact of such incidents, this study applies numerical simulations focusing on a hypothetical scenario involving two 10,000 ton oil tankers, each with a cargo of approximately 1000 tons. Based on the “Technical Guidelines on Environmental Risk Assessment of Oil Spills at Waters”, spill calculations assume a 20% loss of the cargo from a single compartment in such events, which results in a total estimated spillage of 400 tons, which are potentially released into the marine environment within a two-hour timeframe following the incident.

Employing a set of specific parameters, this research constructs scenarios to evaluate the environmental impact of hypothetical oil spills at several vital locations, including Qianwan Port, Huangdao Oil Port, Dagang Port, Horseshoe Reef, and the channel entrance. The scenarios depict a spill lasting two hours, with an oil release volume of 400 tons under conditions of approximately 25 °C, as illustrated in

Table 1. Oil spill simulation scenario parameters.

Time	Oil Type	Spill Volume	Oil Spill Duration	Forecast Time	Temperature (°C)	Locations
27 April 2021–29 April 2021	Crude oil	400 t	2 h	48 h	25 °C	Qianwan Port, Huangdao Oil Port, Dagang Port, Horseshoe Reef, and the channel entrance

. Through the integration of wind and current data simulations from 27–29 April 2021, the study simulates the dispersion of the oil across a 48 h interval, providing a comprehensive analysis of the initial effects and subsequent environmental impact.

3.2. Step 2: Simulation of Oil Spill Trajectory

The delineation of potential oil spill trajectories is crucial for estimating their environmental effects on coastal zones. The MEDSLIK-II model was employed to simulate hypothetical oil spill scenarios, delineated by specific parameters from Table 1, to forecast the movement and eventual outcome of oil spills in the study area. These simulations are informed by the FVCOM for current data, facilitating a comprehensive analysis of spill transport, weathering, and coastal absorption processes. Through the combined use of FVCOM and MEDSLIK-II, the dispersion of and alterations in oil in varying environmental scenarios, including changes in temperature, wind, and tidal forces, were modeled [28,29,30]. Consequently, it became possible to identify areas that the oil might affect and the coastal resources that could be at risk.

The MEDSLIK-II, an advanced iteration of the Mediterranean oil spill model, is a sophisticated, three-dimensional, Eulerian–Lagrangian tool adept at simulating marine oil spill behavior. By integrating regional oceanographic data, it can precisely predict the oil’s trajectory, metamorphosis, and weathering stages. Furthermore, this model divides the oil spill dynamics into two main aspects: the drift process, significantly influenced by wind and currents, and the transformation mechanism, accounting for changes the oil undergoes in the marine environment.

The oil spill equation is presented as follows:

$$\frac{\delta \mathrm{C}}{\delta \mathrm{t}}=\nabla \cdot (\mathrm{K}\nabla \mathrm{C})-U\cdot \nabla \mathrm{C}+\sum _{j=1}^M r_j(x,\mathrm{C}(x,t),t)$$

(1)

In Equation (1), the concentration of the oil spill, depicted as C (x, y, z, t), fluctuates due to random turbulence and marine influences. K stands as a representation of turbulent diffusivity, while U, portraying the oceanic dynamics (waves, winds, currents), primarily governs the spill’s movement. Factor $\mathrm{r}\mathrm{j}\left(\mathrm{x},\mathrm{C}\left(\mathrm{x},\mathrm{t}\right),\mathrm{t}\right)$ designates a transformational subprocess impacting the oil spill’s disposition. The term ${\sum }_{j=1}^M r_j(x,C(x,t),t)$ aligns with a designated computational layer.

The primary influencer of oceanic oil dispersion is the marine currents. U ∙ ∇C and ∇∙(K∇C) represent epitomize oceanic advection and turbulence effects, respectively. Cector U is derived using dx0, dy0, and dz0, which are defined in the context of Equation (2).

$$\left\{\begin{array}{l}\mathrm{dx}0=\left(\mathrm{c}\cdot \mathrm{V}\mathrm{w}\mathrm{x}+\mathrm{V}\mathrm{c}\mathrm{x}+\mathrm{V}\mathrm{s}\mathrm{x}+{\mathrm{V}}_{\mathrm{R}}\right)\cdot \mathrm{d}\mathrm{t}\\ \mathrm{dy}0=\left(\mathrm{c}\cdot \mathrm{V}\mathrm{w}\mathrm{y}+\mathrm{V}\mathrm{c}\mathrm{y}+\mathrm{V}\mathrm{s}\mathrm{y}+{\mathrm{V}}_{\mathrm{R}}\right)\cdot \mathrm{d}\mathrm{t}\\ \mathrm{dz}0=\left(\mathrm{c}\cdot \mathrm{V}\mathrm{w}\mathrm{z}+\mathrm{V}\mathrm{c}\mathrm{z}+\mathrm{V}\mathrm{s}\mathrm{z}+{\mathrm{V}}_{\mathrm{R}}\right)\cdot \mathrm{d}\mathrm{t}\end{array}\right.$$

(2)

where the variable V_R is determined through an empirical random walk approach. The velocities induced by wind, currents, and swell in the x, y, and z directions are denoted as (V_wx, V_wy, V_wz), (V_cx, V_cy, V_cz), and (V_sx, V_sy, V_sz), respectively.

The MEDSLIK-II used the output from the Finite-Volume Community Ocean Model (FVCOM) as a primary driver for current dynamics in this study. The FVCOM is a numerical simulation tool, designed specifically for coastal ocean and estuarine applications. The unstructured triangular grids are employed as the foundational units upon which the primitive governing equations operate. Integrating traditional ocean circulation paradigms with equations conserving momentum, continuity, temperature, density, and salinity, FVCOM plays an essential role in mathematically modeling water movement and elucidating the intricate dynamics between grids. The three-dimensional equations are formulated as follows:

$$\begin{array}{ll}& \frac{\partial \mathrm{u}\mathrm{D}}{\partial \mathrm{t}}+\frac{\partial {\mathrm{u}}^2\mathrm{D}}{\partial \mathrm{x}}+\frac{\partial \mathrm{u}\mathrm{v}\mathrm{D}}{\partial \mathrm{y}}+\frac{\partial \mathrm{u}\mathsf{\omega }}{\partial \mathsf{\sigma }}-\mathrm{f}\mathrm{v}\mathrm{D}=-\mathrm{g}\mathrm{D}\frac{\partial \mathsf{\eta }}{\partial \mathrm{x}}-\frac{\mathrm{D}}{{\mathsf{\rho }}_0}\frac{\partial {\mathrm{p}}_{\mathrm{a}}}{\partial \mathrm{x}}-\frac{\mathrm{g}\mathrm{D}}{{\mathsf{\rho }}_0}\left({\int }_{\mathsf{\sigma }}^0 \mathrm{D}\frac{\partial \mathsf{\rho }}{\partial \mathrm{x}}\mathrm{d}\mathsf{\sigma }-\frac{\partial \mathrm{D}}{\partial \mathrm{x}}{\int }_{\mathsf{\sigma }}^0 \mathsf{\sigma }\frac{\partial \mathsf{\rho }}{\partial \mathsf{\sigma }}\mathrm{d}\mathsf{\sigma }\right)-\\ & \frac{\mathrm{D}}{{\mathsf{\rho }}_0}\left[\frac{\partial \mathrm{q}}{\partial \mathrm{x}}+\frac{\partial \mathsf{\sigma }}{\partial \mathrm{x}}\frac{\partial \mathrm{q}}{\partial \mathsf{\sigma }}\right]+\frac{1}{\mathrm{D}}\frac{\partial }{\partial \mathsf{\sigma }}\left({\mathrm{K}}_{\mathrm{m}}\frac{\partial \mathrm{u}}{\partial \mathsf{\sigma }}\right)+\mathrm{D}{\mathrm{F}}_{\mathrm{u}}\end{array}$$

(3)

$$\begin{array}{ll}& \frac{\partial \mathrm{u}\mathrm{D}}{\partial \mathrm{t}}+\frac{\partial {\mathrm{u}}^2\mathrm{D}}{\partial \mathrm{x}}+\frac{\partial \mathrm{u}\mathrm{v}\mathrm{D}}{\partial \mathrm{y}}+\frac{\partial \mathrm{u}\mathsf{\omega }}{\partial \mathsf{\sigma }}-\mathrm{f}\mathrm{v}\mathrm{D}=-\mathrm{g}\mathrm{D}\frac{\partial \mathsf{\eta }}{\partial \mathrm{x}}-\frac{\mathrm{D}}{{\mathsf{\rho }}_0}\frac{\partial {\mathrm{p}}_{\mathrm{a}}}{\partial \mathrm{x}}-\frac{\mathrm{g}\mathrm{D}}{{\mathsf{\rho }}_0}\left({\int }_{\mathsf{\sigma }}^0 \mathrm{D}\frac{\partial \mathsf{\rho }}{\partial \mathrm{x}}\mathrm{d}\mathsf{\sigma }-\frac{\partial \mathrm{D}}{\partial \mathrm{x}}{\int }_{\mathsf{\sigma }}^0 \mathsf{\sigma }\frac{\partial \mathsf{\rho }}{\partial \mathsf{\sigma }}\mathrm{d}\mathsf{\sigma }\right)-\\ & \frac{\mathrm{D}}{{\mathsf{\rho }}_0}\left[\frac{\partial \mathrm{q}}{\partial \mathrm{x}}+\frac{\partial \mathsf{\sigma }}{\partial \mathrm{x}}\frac{\partial \mathrm{q}}{\partial \mathsf{\sigma }}\right]+\frac{1}{\mathrm{D}}\frac{\partial }{\partial \mathsf{\sigma }}\left({\mathrm{K}}_{\mathrm{m}}\frac{\partial \mathrm{u}}{\partial \mathsf{\sigma }}\right)+\mathrm{D}{\mathrm{F}}_{\mathrm{u}}\end{array}$$

(4)

$$\frac{\partial \mathrm{w}\mathrm{D}}{\partial \mathrm{t}}+\frac{\partial \mathrm{u}\mathrm{w}\mathrm{D}}{\partial \mathrm{x}}+\frac{\partial \mathrm{v}\mathrm{w}\mathrm{D}}{\partial \mathrm{y}}+\frac{\partial \mathrm{w}\mathsf{\omega }}{\partial \mathsf{\sigma }}=-\frac{1}{{\mathsf{\rho }}_0}\frac{\partial \mathrm{q}}{\partial \mathsf{\sigma }}+\frac{1}{\mathrm{D}}\frac{\partial }{\partial \mathsf{\sigma }}\left({\mathrm{K}}_{\mathrm{m}}\frac{\partial \mathrm{w}}{\partial \mathsf{\sigma }}\right)+\mathrm{D}{\mathrm{F}}_{\mathrm{w}}$$

(5)

$$\frac{\partial \mathrm{u}}{\partial \mathrm{x}}+\frac{\partial \mathrm{v}}{\partial \mathrm{y}}+\frac{\partial \mathsf{\sigma }}{\partial \mathrm{x}}\frac{\partial \mathrm{u}}{\partial \mathsf{\sigma }}+\frac{\partial \mathsf{\sigma }}{\partial \mathrm{y}}\frac{\partial \mathrm{v}}{\partial \mathsf{\sigma }}+\frac{1}{\mathrm{D}}\frac{\partial \mathrm{w}}{\partial \mathsf{\sigma }}=0$$

(6)

$$\frac{\partial \mathsf{\eta }}{\partial \mathrm{t}}+\frac{\partial \mathrm{D}\mathrm{u}}{\partial \mathrm{x}}+\frac{\partial \mathrm{D}\mathrm{v}}{\partial \mathrm{y}}+\frac{\partial \mathsf{\omega }}{\partial \mathsf{\sigma }}=0$$

(7)

where velocity components u, v, and w are identified in the x, y, and z directions, respectively. Further, ω is the altered vertical velocity corresponding to the σ coordinate, and η, f, and ρ₀ describe the water surface elevation, Coriolis factor, and reference density. Additionally, the situ density (ρ), atmospheric pressure (p_a), non-hydrostatic pressure (q), vertical eddy viscosity (K_m), gravitational acceleration (g), and horizontal diffusion terms (F_u, F_v) are noted. The total water depth (D) is computed as D = H + η, where H refers to the static water depth. Total pressure (P) is the aggregation of the surface atmospheric pressure (p_a), the hydrostatic pressure (P_H), and the non-hydrostatic pressure (q), which is defined as follows:

$${\mathrm{p}}_{\mathrm{H}}={\mathsf{\rho }}_0\mathrm{g}\mathsf{\eta }+\mathrm{g}{\int }_{\mathrm{z}}^0\mathsf{\rho }\mathrm{d}{\mathrm{z}}^{\prime }$$

(8)

A detailed description of the governing equations and primary principles in FVCOM is available in FVCOM’s user manual [31].

The numerical model’s unstructured grids extend across the entirety of the Bohai Sea and Yellow Sea. These grids comprise 104,250 nodes and 203,529 triangular sections, covering a vast expanse of approximately 446,500 square kilometers. Within these grids, Jiaozhou Bay’s geographical bounds are precisely defined, spanning from 120°10′ E to 120°46′ E longitude and 35°35′ N to 36°18′ N latitude, as illustrated in Figure 4, making it a distinct feature of the Yellow Sea region. Located along the southern shores of the Shandong Peninsula, the bay is characterized by an average depth of about 7 m.

Within the FVCOM, an advanced, non-uniform, triangular grid system is employed to accommodate the geographical complexities of the coastlines and islands. In targeted zones, the model achieves a mesh resolution as fine as 200 m. Specifically, in Jiaozhou Bay, there is a significant increase in mesh density, while the mesh sizes become larger in the adjacent areas. At the Yellow Sea’s open boundary, the grid’s resolution is roughly 1/12°. This method of varying grid sizes across different areas ensures an optimal mix of detailed computational precision and efficiency in processing. The FVCOM model utilizes bathymetric data from the latest GEBCO (2021) dataset, offering an average resolution of 15 arc-seconds, or approximately 500 m. This detailed bathymetric information is integrated into the model’s triangular grid using the inverse distance weighted interpolation technique.

The precision of the FVCOM is greatly influenced by its open boundary settings, which this research establishes within the Yellow Sea domain. Utilizing tidal data from the TPXO9-atlas v5 model enhances the model’s tidal accuracy. The model further incorporates eight principal tidal constituents (M2, S2, N2, K2, O1, K1, P1, Q1) that are essential for accurate simulation dynamics in the vicinity of the Bohai, Yellow, and East China Seas. This research investigated two separate periods for simulation: from 1 June 2010 to 31 July 2010, and from 1 October 2013 to 30 November 2013. Within the model’s setup, the internal time step was chosen to be 10 s, with an external time step of 1 s, and the outputs were generated at hourly intervals.

3.3. Step 3: Shoreline Vulnerability Assessment

To identify coastal zones at risk from oil spills, the Environmental Sensitivity Index (ESI) serves as a fundamental method for evaluating and quantifying shoreline vulnerability to such incidents [32]. The concept of ESI was first formulated by Miles O. Hayes and his team at the Research Planning Institute in the 1970s, and has been utilized by the NOAA [33,34]. Since then, the ESI framework has been instrumental in making detailed maps depicting the vulnerability of coastlines to oil spill events across the United States.

Furthermore, this methodology has been successfully adopted in coastal areas of Vietnam, India, and Brazil, reflecting its versatility and global applicability in environmental risk assessment [35,36,37]. The ESI is globally recognized as a comprehensive framework that integrates three critical aspects: types of shorelines, biologically sensitive resources, and human-use resources. This research primarily focuses on the category of shoreline types, thereby enhancing the comprehension of geomorphological attributes critical to the assessment process. The ESI framework delineates a standardized classification for evaluating shoreline vulnerabilities, distinguishing between estuarine, lacustrine, riverine, and palustrine environments. A detailed description of the shoreline types and their corresponding ESI values for the estuarine, is presented in

Table 2. Environmental Sensitivity Index value and coast types based on the modified NOAA classification.

ESI Rank	Estuarine Environment
1A	Exposed, rocky shores
1B	Exposed, solid, man-made structures
1C	Exposed, rocky cliffs with boulder talus base
2A	Exposed, wave-cut platforms in bedrock, mud, or clay
2B	Exposed scarps and steep slopes in clay
3A	Fine- to medium-grained sand beaches
3B	Scarps and steep slopes in sand
3C	Tundra cliffs
4	Coarse-grained sand beaches
4	Sand beaches
5	Mixed sand and gravel beaches
6A	Gravel beaches
6B	Riprap
6D	Boulder rubble
7	Exposed tidal flats
8A	Sheltered scarps in bedrock, mud, or clay; sheltered, impermeable, rocky shores
8B	Sheltered, solid man-made structures; sheltered, permeable, rocky shores
8C	Sheltered riprap
8D	Sheltered, rocky rubble shores
8E	Peat shorelines
9A	Sheltered tidal flats
9B	Vegetated low banks
9C	Hyper-saline tidal flats
10A	Salt and brackish water marshes
10B	Freshwater marshes
10C	Swamps
10D	Scrub and shrub wetlands
10E	Inundated low-lying

, deriving from the Environmental Sensitivity Index guidelines [38].

Table 2 illustrates how the vulnerability of estuarine shorelines is influenced by three main elements, as follows: (I) Coastal exposure to hydrodynamic forces: The interaction between wave actions and tidal currents plays a pivotal role in shaping the energy distribution along shorelines. Zones with the highest energy levels (1A–2B) consistently face strong wave and tidal forces year-round. Zones of intermediate energy (3A–7) exhibit variability, with storm events causing periodic fluctuations. In contrast, low-energy areas (8A–10E) are generally protected from wave and tidal impacts, except in rare or unusual situations. (II) Geomorphological Slope of the Shoreline: The inclination of the intertidal zone, classified as steep (>30°), moderate (between 5° and 30°), and flat (<5°), is crucial in determining vulnerability levels. (III) Type of Substrate: The nature of the shoreline substrate is also a determinant factor, classified into bedrock (which may be permeable or impermeable based on the overlaying surface deposits), sedimentary materials (categorized by grain size), and artificial structures (like riprap or seawalls).

Adopting NOAA’s guidelines, the ESI was utilized to classify the vulnerability of coastlines within the study area to oil spills. The ESI delineates a range from “Exposed, Rocky Shores” and “Solid Man-Made Structures” (1A–1B), indicating areas of minimal environmental vulnerability, to “Salt and Brackish Water Marshes” and “Inundated Low Lying” locales (10A–10E), which represent the highest vulnerability levels. In this ranking system, a level of 10 reflects the utmost vulnerability of a shoreline to oil damage, whereas a rank of 1 suggests the lowest potential impact. This study’s fieldwork and subsequent data analyses were carried out along a 26.87 km stretch of the study area’s shoreline, extending from Dingjia Mouth to Stone Old Man. The research centered on collecting primary data on sensitive coastal elements, documenting aspects such as the physical characteristics of coastlines and their slopes, alongside an evaluation of how coastal resources are utilized and classified. To achieve this, a diverse array of methods was employed to capture these coastal resource data, including the use of field record tables, handheld GPS devices for accurate positioning, and visual documentation through photographs and videos.

3.4. Step 4: Oil Spill Risk Assessment

Oil spill risk assessment is commonly delineated as a combination of three critical components: hazard evaluation, exposure analysis, and vulnerability assessments. Within this framework, hazard evaluation involves calculating the potential impact of an oil spill at each grid point by employing Equation (9):

H(_i,j) = T(_i,j) × M(_i,j)

(9)

where H(_i,j) represents the calculated hazard at each grid point (i,j), T(_i,j) denotes the thickness of the oil slick, and M(_i,j) denotes the mass of the oil spill. This formula integrates the thickness (T) and mass (M) of the oil spill, quantifying the spill’s potential severity across grid points. Subsequently, the derived hazard values H(_i,j) are categorized into one of four distinct hazard levels through the application of a quartile method, which divides the spectrum of hazard values into four equal segments, each corresponding to a specific hazard level: Low, Moderate, High, and Very High.

The exposure assessment quantitatively determines the probability of oil spills affecting ecologically sensitive regions. By conducting a thorough investigation into the pathways of oil spills from varied potential origins, the assessment offers insights into the spill’s navigation and its projected consequences for the coastal ecosystems, using GIS techniques to accurately map and assess the exposure of these areas to oil spill hazards. The assessment of vulnerability, as determined by the ESI, is integral to understanding the resilience and sensitivity of coastlines to oil spills. The results from hazard and exposure assessments are integrated with ESI-based vulnerability evaluations, utilizing the 4 × 4 Risk Matrix methodology, to derive a quantifiable risk index for each coastal segment. This approach allows for the multiplication of hazard and vulnerability to classify segments into four distinct risk levels: Low, Medium, High, or Very High.

4. Results and Discussion

This section concentrates on identifying areas at risk from oil spills, using simulations based on historical spill events and environmental sensitivity data. By examining oil spill scenarios from specific points of origin and incorporating the ESI for coastlines, we conducted a thorough risk assessment to understand the potential impacts of oil spills on different coastal zones.

4.1. Validation of Models and Simulation of Oil Spill

The FVCOM was validated through a comparison of tide data from the Qingdao Huangdao (120.25° E, 36.167° N) station with the observed data extracted from the TOPEX/Poseidon satellite database. The analysis, covering the period from 20 November 2013 to 29 November 2013, revealed minor differences between the model’s simulations and actual observations, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values calculated to be 0.065 m and 0.052 m, respectively. Further insights into FVCOM’s validation, including cotidal maps and harmonic constants, are detailed in our preceding publication [29]. This presents empirical evidence demonstrating FVCOM’s high reliability, which satisfies the accuracy prerequisites essential for oil spill modeling applications.

Building on the established hydrodynamic model, the verification of the oil spill model was conducted using the Qingdao Huangdao 11.22 oil spill event. Due to the absence of satellite observation data, this model’s validation relied uniquely on records from the provincial maritime department, which documented the spread and impact of oil within Jiaozhou Bay and its adjacent waters 48 h after the spill. The detailed results of our oil spill model simulations, including a range of comparative metrics and visualizations, are elaborately presented in our preceding study [29]. The oil spill model has proven highly accurate in forecasting the distribution patterns of oil particles, effectively reflecting observed data on oil accumulation and dispersion within the marine environment. Moreover, the model’s predictions concerning the behavior of the oil spill over time, including evaporation rates and the progression of emulsification, were validated against observed outcomes. Approximately 25% of the spilled oil was observed to reach the coastline, consistent with the simulation’s predictions of shoreline adsorption. These results demonstrate the model’s capacity to serve as an accurate instrument in marine oil spill situations.

Based on the established models, a series of simulations, each with a unique origin point for the oil spill incident, were generated to illustrate the potential trajectories and dispersion patterns of oil spills under varying conditions. For the scenarios at Dagang Port and Horseshoe Reef, the simulations reveal minimal movement over a 48 h period. At Dagang Port, the simulation shows the oil spill remaining nearly stationary, close to its point of origin, throughout the 48 h period. This suggests that the spill’s natural dispersal is impeded by environmental conditions or physical barriers, such as natural shallows or man-made structures. The observed lack of movement might also be due to the high level of cohesion among the oil particles, which prevents the spill from spreading, as would typically be expected. Similarly, the scenario at Horseshoe Reef demonstrates a tendency to move towards the shoreline over the course of 48 h. Initially, the oil maintains its proximity to the point of origin. As time advances, particularly between the 13th and 22nd hour, there is a gradual but definite displacement of the oil closer to the coast. This suggests the influence of coastal currents and possibly the topography of the seabed directing the spill’s trajectory. By the 30th hour, the simulation shows an increased concentration of oil towards the land, indicating sustained movement in that direction. As the simulation approaches the 46th hour, the oil’s reach extends further along the coastal boundary. This movement suggests a consistent drive towards the coast, shaped by environmental currents and seabed contours. In contrast, the subsequent scenarios presented in Figure 5, Figure 6 and Figure 7 show marked differences in the dispersion patterns, showing notable movements of oil spills from the initial spill moment (t = 0) up to 48 h. These visual representations, marked at eight-hour intervals, provide a depiction of how the oil spills evolve over time, offering insights into the influence of different spill origins on the dispersion trajectory.

As shown in Figure 5, the 48 h oil spill trajectory simulation, originating from the channel entrance, exhibits a distinct evolution in the spread and movement of the oil. In the early phase, specifically the first 6 h following the spill, the oil primarily remains concentrated within a limited area, marked by a high density, indicating a considerable volume of oil release. Progressing into the 7–22 h timeframe, the oil spill displays a noticeable shift towards the northeast. This direction is consistent with the dominant wind patterns in the region, which are typically northeastward, offering a reasonable explanation for the movement pattern of the observed spill. Furthermore, during the 23–38 h interval, the spill not only persists in its northeastward trajectory but also begins to fragment into smaller sections, a phenomenon that could be attributed to factors such as sea turbulence, responsive measures, and the natural breakdown process of the oil. In the final 39–48 h period, the oil spill demonstrates a more extensive dispersion, affecting a larger area but with a decrease in concentration levels.

As shown in Figure 6, the oil spill from Huangdao Oil Port exhibits distinct behavior over a 48 h period. Initially, the dispersion is limited, with the oil remaining concentrated near the port. From 8 to 16 h, there is an unexpected movement toward the north, which may be due to local currents or winds impacting the spill’s path. As the simulation advances beyond 16 h, a gradual shift towards the southeast becomes evident, suggesting that the wind or currents might have changed direction. By the 32 h point, the southeastward movement is clear, and the oil’s dispersion across the water’s surface becomes more widespread. Towards the end of the simulation, from 40 to 48 h, the oil has been drifting for a substantial amount of time and covered a more extensive area.

Figure 7 delineates the progression of an oil spill originating from Qianwan Port. Initially, the spill exhibits limited movement, remaining effectively confined for the initial 8 h, indicating effective containment. From the 9th hour, a slight expansion is observed, yet the oil remains near the port, indicating slow spreading. Transitioning from the 17th to the 24th hour, the oil’s spread visibly increases, moving eastward in response to prevailing currents. When entering the 25th–32nd hours, the eastward drift intensifies, highlighting the marine environment’s influence on the spill’s trajectory. The 33rd–40th hour phase shows the spill extending further east across a broader area. The final observation period, from the 41st to the 48th hour, shows substantial dispersion across a broad maritime region, highlighting the critical need for swift environmental response and remediation efforts.

When comparing the oil spill trajectories from the five distinct origins under the same environmental conditions, there are noticeable variations in the movement and spread patterns. The spill from the channel entrance demonstrates rapid dispersion towards the northeast, likely propelled by prevailing marine currents and wind patterns. This scenario, depicted in Figure 5, illustrates an extensive spread of oil, highlighting the impact of environmental forces in extending the spill’s reach far from its origin. Conversely, the scenarios at Dagang Port and Horseshoe Reef exhibit minimal movement, suggesting that geographical features such as natural barriers or effective immediate containment measures significantly limit dispersion. This restrained spread, particularly noted at Dagang Port, implies the presence of impediments that effectively prevent the oil from extending over broader areas. The dispersion pattern from Huangdao Oil Port presents an initial northward drift, followed by a notable shift towards the southeast, as illustrated in Figure 6. This change may reflect alterations in local currents or wind directions, altering the spill’s trajectory. This dynamic indicates the fluid nature of marine conditions and their influence on spill dispersion. Similarly, the oil spill from Qianwan Port, as outlined in Figure 7, initially shows restricted movement, followed by a gradual eastward expansion. This pattern suggests a slower adaptation to prevailing sea conditions, possibly influenced by localized currents or strategic containment that delays oil spread.

4.2. ESI Evaluation

Following the guidelines set by NOAA and illustrated in

Table 3. Risk analysis for multiple oil spill origins.

Location	Risk Level	Segments Count	Total Length	Avg. Length per Segment	ESI Range	Sensitive Areas Identified	Management Priority
Qianwan Port	Low	25	22,380.4	895.2	1–2	Widespread Biodiversity	Low
	Medium	8	8278.7	1034.8	2–3	Tidal Flats, Potential Habitat Significance	Moderate
	High	10	8561.2	856.1	3–4	Intertidal Zones, Cetacean Migration Routes	High
Horseshoe Reef	Low	2	2643.5	1321.8	2	General Marine Area	Moderate
	Medium	4	1693.8	423.5	3	Seagrass Meadows, Coral Reefs	High
Huangdao Oil Port	Low	5	5095.2	1019.0	2–3	Coastal Buffer Zone	Low
	Medium	2	1431.8	715.9	3–4	Community Use Areas	Moderate
	High	1	2165.7	2165.7	4	Mangrove Forests	High
	Very High	2	1551.8	775.9	5	Bird Nesting Areas, Fish Breeding Grounds	Urgent

, an Environmental Sensitivity Index (ESI) map was made for the study region, showing shoreline variations as depicted in Figure 8. This ESI map differentiates shoreline types using distinct color codes to represent each ESI category. Analysis reveals that the coastline of Jiaozhou Bay encompasses ten of NOAA’s shoreline classifications.

Based on a scale ranging from 1 to 10, shoreline vulnerability, as measured by the ESI numeric values, can be simplified into four distinct levels: very low, medium, high, and very high. Shorelines classified with ESIs of 1 and 2 demonstrate a diminished sensitivity to oil spills, leading to an assessment of very low vulnerability. For the intermediate levels, shorelines designated with ESI values of 3–5 exhibit a moderate degree of sensitivity, categorizing them under the medium-vulnerability bracket. Those with ESI ratings of 6–8, given their increased ecological sensitivity relative to lower ranks, although they are less severe than those in the highest category, fall into the high vulnerability category. Shorelines assigned ESIs of 9 and 10 display high ecological sensitivity and therefore are considered to have very high potential vulnerability to oil spill impacts.

The ESI shoreline types and their vulnerability levels are categorized based on a scale ranging from ESI 1 to ESI 10.

Very Low Vulnerability (ESI 1–2): Areas under the rankings of ESI 1 and ESI 2 exhibit the least vulnerability. For ESI 1, which includes a total of 170 entries, the areas range from 41.67 m to 6019.77 m. These regions are characterized by a diverse set of features, with the most common being solidified shore protection, indicating a blend of natural and man-made structures. On the other hand, ESI 2 predominantly features rock reefs, with 26 entries spanning from 31.53 m to 2261.42 m, illustrating their distinct structural resilience.

Medium Vulnerability (ESI 3–5): Medium vulnerability is captured by areas under the ESI 3–5 classifications. ESI 3 represents beaches, with all 19 entries covering lengths from 116.57 m to 1129.30 m, highlighting their ecological and socio-economic significance. ESI 4 areas are primarily estuaries; six out of the seven entries range from 26.92 m to 2337.73 m, underscoring their role as rich biological zones where rivers merge with the sea. ESI 5, with four entries, encompasses regions that blend rocks and beaches, spanning from 227.16 m to 951.43 m. These zones, due to their varied habitats, serve as crucial biodiversity zones.

High Vulnerability (ESI 6–8): ESI 6–8 classify areas that demonstrate heightened vulnerability. ESI 6 regions, characterized by breakwaters that might also function as small docks, span areas from 745.91 m to 2876.02 m across four entries, emphasizing their role in coastal safeguarding. Mudflats define ESI 7, with 12 entries ranging from 109.39 m to 2016.61 m, spotlighting their support of diverse aquatic life. ESI 8, associated with dikes or reclaimed lands and spanning from 93.10 m to 4390.13 m across 13 entries, reflects the extensive human alterations to coastal landscapes.

Very High Vulnerability (ESI 9–10): The highest vulnerability spectrum is represented by areas classified under ESI 9 and ESI 10. ESI 9 primarily includes puddles or smaller water bodies, with two entries covering 145.29–516.72 m, suggesting their importance to specific species and local biodiversity. ESI 10, the most vulnerable classification, is primarily linked to aquaculture or fish farming. These areas, totaling 82 entries, range from 87.64 m to 3474.59 m, indicating a dependency on aquaculture for livelihood and its contribution to local biodiversity.

4.3. Risk Assessment of Oil Spill

The comprehensive risk assessment conducted for oil spills originating from three distinct points—Horseshoe Reef, Huangdao Oil Port, and Qianwan Port—has produced detailed risk zoning maps, as shown in Figure 9, Figure 10 and Figure 11. These maps, characterized by color-coded risk levels, provide a visual representation that delineates areas across the study region under varying degrees of risk, from low to very high. This layered approach to risk evaluation, integrating ESI ratings with projected oil spill movements, enhances our understanding of the potential environmental consequences. The structured analysis is presented in Table 2.

As shown in Figure 9, the detailed examination of the Horseshoe Reef area reveals that the area is mostly protected from the negative effects of oil spills, with risk levels ranging between low and medium. The marking of two segments as low-risk, together measuring 2643.5 m, and four segments as medium-risk, adding up to 1693.8 m, highlights a region with notable resilience. This protection against oil spills is emphasized by the large average length of the low-risk segments, at 1321.75 m, indicating that long stretches of coast are relatively safe from major oil spill damage. Similarly, the medium-risk segments, with an average length of 423.45 m each, show areas of slightly higher risk but still demonstrate a good ability to manage oil spill effects. The resilience seen near Horseshoe Reef points to a strong mix of natural barriers and planned human actions. Natural defenses, likely including the area’s special water dynamics, variety of life, and the reef’s structure, might naturally lessen the spread and impact of oil spills. These natural features could act as a first defense, through helping to break down oil, trapping and keeping oil particles in place, or through other ways that stop the wide spread of oil pollution. Moreover, the absence of high- and very high-risk areas in this study, along with the lack of marked ecologically or biologically sensitive areas within these risk levels, clearly shows the success of current spill control strategies. This suggests that the combination of natural strength and advance planning or response efforts in the Horseshoe Reef area has been effective in reducing the risk and potential harm of oil spill incidents.

As depicted in Figure 10, the risk assessment at Huangdao Oil Port shows a complex risk depiction with different risk levels. It identifies five areas as low risk, covering a total of 5095.2 m. This large area under low risk suggests that either effective measures are already in place or these coastal areas naturally resist oil spill damage. However, the observation of two medium-risk areas, spanning 1431.8 m in total, introduces a specter of concern, indicating places where oil spill risks start to increase but can still be managed with efficient actions. The average lengths of these areas—1019.04 m for low-risk and 715.9 m for medium-risk areas—support measures to protect against the spread of risk, showing how vulnerability varies along the port’s coast. Moreover, the identification of one high-risk area of 2165.7 m and two very high-risk areas totaling 1551.8 m points to locations with more significant environmental sensitivity. These areas, close to critical habitats like mangrove forests, bird homes, and fish spawning sites, highlight the significant environmental concerns involved. The large size of the high-risk area, at 2165.7 m, underscores a major area under threat, where oil spills are more likely to occur and cause serious harm. The close presence of vital ecological spots within these high- and very high-risk areas emphasizes the need for very specific action plans. The mangrove forests, bird nesting sites, and fish breeding areas in these parts not only show the ecological value of the marine environment around Huangdao Oil Port but also the critical need to protect these places from the negative effects of oil spills.

Figure 11 illustrates the investigation into Qianwan Port, through a detailed oil spill risk assessment that shows predominantly low-risk circumstances across an extensive portion of the coastline. This conclusion is derived from the delineation of 25 segments cumulatively spanning 22,380.4 m that are designated as low-risk. This substantial low-risk domain suggests either a geographical fortification or the efficacy of spill-mitigation strategies, which together contribute to reducing the probability of negative effects from potential oil spill incidents. The calculated mean extension of these low-risk sectors is approximately 895.216 m, indicating a wide stretch of coastal areas seen as minimally at risk for serious harm from oil spills. Beyond the low-risk scenario, the port area includes eight medium-risk sections, together measuring 8278.7 m. The average size of these medium-risk areas, at around 1034.8375 m, signifies zones where the potential for detriment exceeds that in low-risk sectors, yet is mitigated through existing safety measures or inherent resilience. These spots need more readiness and specific action plans to properly reduce potential damage. More importantly, the analysis identifies ten high-risk sectors within the port’s proximity, aggregating at 8561.2 m. The average length of these high-risk sectors, estimated at 856.12 m, shows certain coastal areas that are especially at risk from oil spill events. These areas are characterized by variables that elevate their risk profile, such as their adjacency to navigational channels, their ecological fragility, or a paucity of protective interventions. Of particular concern within these high-risk domains are the delineated sensitive ecosystems, including intertidal zones and cetacean migration routes. Tidal flats are crucial areas for many marine creatures’ feeding and breeding, making them very sensitive to the bat effects of oil spills. Similarly, locations known for sea mammal migration are important ecologically, acting as routes for species movements that could be affected by pollution. Having such ecologically delicate areas in high-risk zones not only shows the complicated nature of the environment but also stresses the need for environmental care and proactive plans for spill preparedness strategies.

The findings of this study have significant implications for coastal management and emergency response strategies in the Jiaozhou Bay area. By identifying high-risk zones and vulnerable shoreline types, the risk assessment framework provides valuable information for decision-makers to effectively prioritize protection efforts and allocate resources. The integration of the risk assessment results into the Oil Spill Emergency Forecasting and Early Warning System demonstrates the practical utility of the proposed framework in enhancing oil spill preparedness and response capabilities. This integration enables the real-time monitoring of potential oil spill risks, supports the development of targeted prevention and mitigation measures, and facilitates rapid response actions in the event of a spill.

While this study provides a robust framework for oil spill risk assessment, there are several avenues for future research that could further enhance its applicability and effectiveness. One key area is the incorporation of real-time environmental data, such as ocean currents, wind patterns, and wave conditions, into the risk assessment model. This would enable more dynamic and accurate predictions of oil spill trajectories and their potential impacts. Additionally, the development of more advanced numerical models that can simulate the complex physical, chemical, and biological processes involved in the results and transport of oil spills would improve the precision and reliability of risk assessments. Another important direction for future research is the integration of socio-economic factors into the risk assessment framework. This could involve considering the potential impacts of oil spills on human health, local economies, and cultural resources, as well as evaluating the social and economic costs of different prevention and mitigation strategies. By incorporating these factors, the risk assessment framework could provide a more comprehensive and inclusive basis for decision-making in coastal management and emergency responses.

5. Conclusions

This study presents a comprehensive framework for a quantitative risk assessment of oil spills along China’s coastlines, integrating numerical modeling, GIS analysis, and the Environmental Sensitivity Index (ESI). The application of the MEDSLIK-II model and ESI data to the Jiaozhou Bay case study demonstrates the framework’s effectiveness in identifying high-risk areas and evaluating the potential impacts of oil spills on coastal environments. The results reveal 10 high-risk zones, covering a total length of 8561.2 m, with saltwater marshes, brackish water marshes, and inundated low-lying areas (ESI rankings 9–10) being the most vulnerable shoreline types.

The integration of the risk assessment framework into the Oil Spill Emergency Forecasting and Early Warning System highlights its practical value in enhancing oil spill preparedness and response capabilities. The system provides decision-makers with real-time information on potential oil spill risks and supports the development of targeted prevention and mitigation strategies. The risk maps generated using GIS techniques serve as valuable tools for coastal managers, enabling them to prioritize protection efforts and allocate resources effectively. The proposed framework has the potential to be adapted and applied to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.