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

By inergency On Mar 29, 2024

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, 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. 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:

$\frac{δ C}{δ t} = \nabla \cdot (K \nabla C) - U \cdot \nabla C + \sum_{j = 1}^{M} r_{j} (x, C (x, t), t)$

(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 $r j (x, C (x, t), t)$ 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).

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

(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:

$\begin{array}{l} \frac{\partial u D}{\partial t} + \frac{\partial u^{2} D}{\partial x} + \frac{\partial u v D}{\partial y} + \frac{\partial u ω}{\partial σ} - f v D = - g D \frac{\partial η}{\partial x} - \frac{D}{ρ_{0}} \frac{\partial p_{a}}{\partial x} - \frac{g D}{ρ_{0}} (\int_{σ}^{0} D \frac{\partial ρ}{\partial x} d σ - \frac{\partial D}{\partial x} \int_{σ}^{0} σ \frac{\partial ρ}{\partial σ} d σ) - \\ \frac{D}{ρ_{0}} [\frac{\partial q}{\partial x} + \frac{\partial σ}{\partial x} \frac{\partial q}{\partial σ}] + \frac{1}{D} \frac{\partial}{\partial σ} (K_{m} \frac{\partial u}{\partial σ}) + D F_{u} \end{array}$

(3)

(4)

$\frac{\partial w D}{\partial t} + \frac{\partial u w D}{\partial x} + \frac{\partial v w D}{\partial y} + \frac{\partial w ω}{\partial σ} = - \frac{1}{ρ_{0}} \frac{\partial q}{\partial σ} + \frac{1}{D} \frac{\partial}{\partial σ} (K_{m} \frac{\partial w}{\partial σ}) + D F_{w}$

(5)

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

(6)

$\frac{\partial η}{\partial t} + \frac{\partial D u}{\partial x} + \frac{\partial D v}{\partial y} + \frac{\partial ω}{\partial σ} = 0$

(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:

$p_{H} = ρ_{0} g η + g \int_{z}^{0} ρ d z^{'}$

(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, 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 (

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.