Assessment of Coastal Zone Vulnerability in the Context of Sea Level Rise and Climate Change

Abstract

The coastal zone is the most frequent and active area where nature and human society interact with each other on the Earth. However, the coastal zone is also an area with fragile environment and frequent disasters. Coupled with the high-intensity human activities, disaster prevention and environmental protection in the coastal zone have become eternal topics. At the same time, the trend of sea level rise and climate change is currently difficult to curb, and its impact on coastal areas cannot be ignored, and a scientific assessment of the vulnerability of coastal zones caused by them is required. Based on multi-source data, this paper constructs a coastal zone vulnerability evaluation system from two sources of ecological vulnerability, sea level rise and climate change, and reveals the impact of sea level rise and climate change on coastal zone ecosystems, providing technical support for the sustainable development of coastal cities. From the results, it can be seen that the mildly vulnerable area and slightly vulnerable area in the Jiangsu coastal zone are relatively large, accounting for 34.06 and 30.43% of the total area of the evaluation area, followed by moderately vulnerable area and highly vulnerable area accounting for 21.11 and 11.17%, respectively, and the extremely vulnerable area is the smallest, accounting for only 3.23% of the total area.

1. Introduction

The coastal zone refers to the zone of interaction between the sea and the land, that is, the intertidal zone (tidal zone) affected by tidal fluctuations and a certain range of land on both sides and the transition zone between the land and the shallow sea. As the key zone for the coordinated implementation of land and sea, the coastal zone is the area with the most frequent human economic activities in the world, and it is also the zone with the most vitality for the sustainable development of the global economy. 62% of the world’s cities with a population of more than 8 million are located in coastal areas, and about 44% of the population lives in coastal areas within 150 kilometers from the ocean [1]. From a socioeconomic perspective, the coastal zone has been the engine belt for the rapid development of the global economy in the past few decades. However, the pace of development in coastal areas is accelerating, and coastal ecological crises have also begun to emerge frequently, and have become a global problem with frequent occurrence, large scale, wide impact, and slow recovery, and there is a trend of evolution to an irreversible state.

Especially in recent years, due to the rising sea level caused by the greenhouse effect, the coastal environment has become more complicated [2]. The sixth climate change assessment report of the United Nations Intergovernmental Panel on Climate Change (IPCC) pointed out that the current sea level rise is accelerating and will continue to rise in the future with an irreversible trend [3]. Under the highest emissions scenario, average global sea level rise is likely to exceed projections - 2 meters by 2100 and 5 meters by 2150. Even under the strongest mitigation scenarios, sea levels will continue to rise for hundreds or thousands of years to come. The long-term cumulative effect of sea level rise will increase the frequency of marine disasters, such as salt tide intrusion, storm surge, and coastal erosion, and aggravate their damage, affecting the ecological and economic systems and marine economic development in coastal areas.

In order to assess the degree of regional impact of natural disasters and environmental changes, researchers in eco-economic systems and socioeconomic systems have proposed the concept of vulnerability (Vulnerability) of the system’s ability to withstand changes in external conditions. In 1981, Timmerman introduced the concept of vulnerability into the field of geosciences [4], and in 1990, Gornitez proposed the concept of coastal vulnerability [5], which has been widely used in land use/cover change, ecological environment assessment, climate change, and other research. Since the IPCC’s first assessment report, global warming and its impacts have gradually become the focus of attention around the world, and research on the vulnerability of coastal ecosystems under the influence of climate change has also become a hot spot of international concern. The vulnerability of the coastal zone is closely related to the increasing number of natural and man-made disasters, and the self-recovery and regeneration ability of the fragile coastal zone eco-economic system is poor, resulting in limited human ability to adapt to the fragile coastal zone eco-economic system [6]. Therefore, evaluating and studying the vulnerability of regional coastal ecosystems under the background of global change is of great significance to the realization of regional sustainable development goals in coastal areas.

At present, the methods for quantitative assessment of the vulnerability of coastal areas mainly include Principal Component Analysis (PCA) [7], Analytic Hierarchy Process [8], and Comprehensive Index Method [9]. These methods have their own characteristics and limitations. Among them, PCA requires large sample conditions, and the evaluation results change with the sample; the evaluation results of other methods depend on the determination of weights, and the process of determining weights is difficult to avoid subjective factors. Many scholars have carried out a large number of studies on the impact of climate change and sea level rise on coastal areas [10, 11, 12, 13, 14, 15]. For example, taking the coastal county-level administrative region as the evaluation unit, comprehensively evaluate the vulnerability and regional differences of the ecological and economic system in China’s coastal zone under the background of sea level rise from the two aspects of the coastal zone’s natural environment and social economy [10]; based on the comprehensive risk theory framework of IPCC climate change, a vulnerability evaluation index system of “exposure-sensitivity-adaptation” was constructed to evaluate the main characteristics of the vulnerability of the mangrove ecosystem in Dongzhai Port, Hainan under the background of sea level rise and typhoon events [11]; select 17 parameters representing coastal physical and socioeconomic characteristics of exposure, sensitivity, and resilience to customize the Coastal Vulnerability Index (CVI) evaluation model for sea level rise, using GIS, remote sensing technology and CVI to divide the coastal areas of Africa into regions with different vulnerability degrees [12]; using four socioeconomic variables and eight geological variables, four different iterative methods were selected based on Random Forest (RF) to integrate the pixel-based differential weighted rank values of all variables to determine the important factors affecting the coastal vulnerability index, assessing the role of development and socioeconomic activities in coastal vulnerability analysis [13]; using the comprehensive index method based on GIS, five physical variables (geomorphology, elevation, absolute sea level rise, erosion-sedimentation, tidal range) and one social variable (population density) were analyzed and identified to assess the vulnerability of the Sundarbans coastal zone in India [14]; projections of coastal zone vulnerability in India in the 2100 s were made considering different future scenarios and various issues related to adaptation and mitigation of coastal zone species were analyzed [15].

The thermal expansion of oceans and the melting of glaciers and ice caps under climate warming lead to global sea level rise. The trend of accelerated sea level rise is currently difficult to curb, and its impact on coastal areas cannot be ignored [16, 17, 18, 19]. Toimil et al. found that current exposure of coastal cities and settlements (C&S) populations to effects related to sea level rise and other climate-related consequences is significant [20]. In the area of climate and security, threats to people’s lives, livelihoods, and property are disproportionately high due to the increased frequency and severity of climate change disasters. Some of the most dangerous risks are land subsidence, tropical cyclones, storm surges, flooding from extremely high tides, and sea level rise [21]. In the past few decades, 80% of global flood deaths occurred within 100 km of the coast, coupled with the rapid growth of coastal megacities around the world. The high cost of catastrophic events such as Hurricane Katrina-storm surge in the United States (2005), Tropical Storm Nargis (2008), Hurricanes Sandy (2012), and Harvey (2017) in the United States, and the winter storms in the United Kingdom (2013–2014) and the high cost of traditional disaster prevention infrastructure highlight the importance of coastal vulnerability research. An assessment of mortality from floods, droughts, and storms in 2010–2020 shows that mortality rates are 15 times higher in highly vulnerable countries than in countries with very low vulnerability, particularly in Africa, Asia, small islands, and Central and South America [22]. The increasingly frequent extreme disaster events in coastal areas have attracted great attention from government organizations and academia around the world. The “Future Earth Plan” has established the core research project on Land-Ocean Interactions in the Coastal Zone (LOICZ), and has now been developed into the Future Earth-Coast (FEC) international program, which aims to develop and integrate multidisciplinary analytical methods in the context of global change, promote the sustainable development of coastal areas, and improve the adaptability to climate change [23]. Thus, reducing the risk of natural disasters in coastal areas has become one of the major challenges facing the international community.

Therefore, it is necessary to establish a quantitative assessment method for the vulnerability of coastal zones under the influence of climate change and sea level rise, and conduct a scientific assessment of the vulnerability of coastal zones caused by it, so as to plan some targeted measures in advance [24]. This is crucial to the rational use of coastal resources and the sustainable development of coastal areas. To this end, taking the coastal zone of Jiangsu Province of China as the research object, the vulnerability assessment index system and estimation method based on “ecological sensitivity-ecological resiliency-ecological pressure” were constructed to quantitatively analyze the vulnerability status of the coastal zone of Jiangsu Province under the background of climate change and sea level rise, and the vulnerability imbalance of different cities and counties in the coastal zone of Jiangsu Province was revealed to provide scientific basis for the protection and management of coastal zones under the background of climate change and sea level rise.

2. Study area and data

Working Group II of the IPCC Sixth Assessment Report (IPCC AR6WG II), which focuses on impacts, risks, adaptation, and vulnerability to climate change, provides a deeper understanding and description of global urban vulnerability, noting that the vulnerability of natural and human systems to climate change varies widely between and within regions. The vulnerability of human societies and ecosystems is interdependent. There is growing evidence that current patterns of economic and population growth are increasing the exposure and vulnerability of natural and human systems to climate hazards. AR6WG II also states that an estimated 3.3 to 3.6 billion people worldwide live in high-vulnerability areas, particularly in West, Central, and East Africa, South Asia, Central and South America, small island developing States, and the Arctic [22]. Another study assessed that China and India are the most vulnerable countries in Asia and the world (Figure 1). After Asia, Africa is the second most vulnerable continent, with the most vulnerable countries, including Ethiopia, Madagascar, Kenya, Nigeria, Mozambique, Tanzania, and Congo. In the Americas, the United States, Mexico, Brazil, Peru, and Colombia are the most vulnerable major countries. Human disturbances related to population size, rapid economic development, and the high-intensity devastating effects of natural disasters have made countries in South Asia and East Africa the most vulnerable regions in the world [25]. Especially in the context of increasingly frequent global climate change and accelerating sea level rise, the coastal ecological environment in these areas needs to receive more special attention [26].

Figure 1.

Map of global areas with high and very high levels of vulnerability [25].

2.1 Study area

The study area includes three coastal cities in Jiangsu, with a coastline of 876.56 km, and is currently a treasure land with the greatest potential and late-mover advantage in eastern China (Figure 2). It is located at the intersection of the coastal economic belt and the Yangtze River Economic Belt, adjacent to China’s largest economic center Shanghai in the south, connected to the Bohai Sea region in the north, across the sea from Northeast Asia in the east, and connected to the new Asia-Europe Land Bridge and the Yangtze River Golden Waterway in the west. It is an important part of China’s “T-shaped” spatial structure strategy.

Figure 2.

Study area.

The coastal counties and cities in Jiangsu are located in the subtropical and warm temperate climate transition zone, with north–south climate intersecting, with the characteristics of dual influences of continental climate and marine climate. The overall presents the characteristics of four distinct seasons, significant monsoon, rain and heat in the same season, and concentrated precipitation. Most of the coastal counties and cities in Jiangsu are located in the plain area. From north to south, they are the Yimu hilly plain area, the coastal plain area, and the Yangtze River Delta plain area. The altitude is44m ∼ 614 m, and the terrain is flat. The eastern coast has a full range of types, including sandy coasts, bedrock coasts, and silt and silt coasts. Among them, sandy tidal flats and bedrock tidal flats are mainly distributed in the northern part of Lianyungang, accounting for 7% of the total length of the coast of Jiangsu Province, and the rest are muddy tidal flats [27].

2.2 Data

The physical geographic data used in this paper include land use data, temperature data, precipitation data, relative humidity data, wind speed data, Normalized Difference Vegetation Index (NDVI) data, net primary productivity data, Land subsidence rate data, DEM data, and coastline data in 2020. The land use data come from the European Space Agency (ESA) (https://viewer.esa-worldcover.org/worldcover/), with a spatial resolution of 10 m; temperature data, precipitation data, relative humidity data, wind speed data, and NDVI data come from National Science and Technology Fundamental Conditions Platform—National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn), with a spatial resolution of 1 km; net primary productivity data come from the MOD17A3 data set (https://lpdaac.usgs.gov/products/mod17a3hv006/) regularly released by National Aeronautics and Space Administration (NASA), with a spatial resolution of 500 m; the land subsidence data come from the monitoring data of the coastal zone of Jiangsu Province [28]; DEM data from NASA’s Land Processes Distributed Active Archive Center (LP DAAC) (https://lpdaac.usgs.gov/news/release-nasadem-data-products/), the spatial resolution of 30 meters, by ArcGIS software to calculate the slope; the coastline data come from OpenStreetMap data (https://osmdata.openstreetmap.de/data/land-polygons.html), and the distance to the coastline is calculated by the Euclidean distance tool of ArcGIS.

The socioeconomic data used in this paper include population density data and Gross Domestic Product (GDP) density data in 2020, which come from the National Science and Technology Infrastructure Platform—National Earth System Science Data Center (http://www.geodata.cn).

3. Method

3.1 Inversion method of fraction vegetation coverage

The Fraction Vegetation Coverage (FVC) is based on NDVI, and the dimidiate pixel model is used to process the NDVI image to obtain the FVC data. Dimidiate pixel model can accurately distinguish pure vegetation pixel from pure soil pixel, and is now widely used to estimate large area vegetation coverage [29]. The formula is as follows:

$${\mathrm{F}}_{\mathrm{veg}}=\left(\text{NDVI}-{\text{NDVI}}_{\text{soil}}\right)/\left({\text{NDVI}}_{\mathrm{veg}}-{\text{NDVI}}_{\text{soil}}\right)$$E1

Among them, ${\mathrm{F}}_{\mathrm{veg}}$ is the vegetation coverage, ${\text{NDVI}}_{\text{soil}}$ represents the NDVI value of the area covered by naked vegetation, and ${\text{NDVI}}_{\mathrm{veg}}$ is the NDVI value of the pixel covered by the full vegetation. According to the frequency statistics table and the NDVI image characteristics of Jiangsu coastal areas, this paper takes NDVI with a confidence level of 5% as ${\text{NDVI}}_{\text{soil}}$, and NDVI with a confidence level of 95% as ${\text{NDVI}}_{\mathrm{veg}}$. In ArcGIS, the maximum value synthesis method is used to process the annual vegetation coverage to obtain the maximum vegetation coverage.

3.2 Data standardization processing

In order to ensure the objectivity and scientificity of the data, it is necessary to standardize the evaluation indicators according to certain standards. The formula for data standardization is as follows:

$${\mathrm{X}}^{\prime }=\frac{X_i-X_{\mathit{min}}}{X_{\mathit{max}-}{X}_{\mathit{min}}}$$E2

$${\mathrm{X}}^{\prime }=\frac{{\mathrm{X}}_{\mathit{max}}-X_i}{X_{\mathit{max}-}{X}_{\mathit{min}}}$$E3

where ${\mathrm{X}}^{\prime }$ is the standardized value of the i th indicator, $X_i$ represents the initial value of the i th indicator, and ${\mathrm{X}}_{\mathit{max}}、X_{\mathit{min}}$ represent the maximum and minimum values of the i th indicator, respectively. The larger the ${\mathrm{X}}^{\prime }$, the more vulnerable the ecological environment. When the indicator factor is a positive indicator, Eq. (2) is used. Eq. (3) is used when the indicator factor is a negative indicator.

3.3 Vulnerability assessment method

3.3.1 Selection of assessment index

The selection of assessment indicators of coastal zone vulnerability should take into account various influencing factors as far as possible to fully reflect the essential characteristics of coastal zone vulnerability caused by climate change and sea level rise. Combined with the actual situation of Jiangsu coastal areas, the ecological sensitivity-ecological resiliency-ecological pressure (SRP) conceptual model is used to select and construct the assessment index system (Table 1).

Project level		Index level	Index feature
Ecological sensitivity	Meteorological factors	Annual mean temperature	+
		Annual mean precipitation	—
		Annual mean relative humidity	—
		Annual mean wind speed	+
	Factors related to sea level rise	Elevation	+
		Slope	+
		Land subsidence rate	+
		Distance to coastline	—
Vegetation factors	Fraction Vegetation Coverage	—
	Ecological resiliency	Net primary productivity	—
Ecological pressure	Population	Population density	+
	Economy	GDP density	—

Table 1.

Assessment index system of vulnerability in Jiangsu coastal zone.

“+” means positive indicators; “-” means negative indicators.

The ecological sensitivity index mainly reflects the sensitivity of the coastal ecosystem to climate change and sea level rise, the ecological resiliency index mainly reflects the self-recovery ability of the coastal ecosystem, and the ecological pressure index mainly reflects the ecological benefits of coastal ecosystems [30]. Among them, the positive indicators include annual mean temperature, annual mean wind speed, elevation, slope, land subsidence rate, and population density (Figure 3). The negative indicators include annual mean precipitation, annual mean relative humidity, distance to coastline, FVC, net primary productivity, and GDP density (Figure 4).

Figure 3.

The quantitative positive indicators of the assessment index system.

Figure 4.

The quantitative negative indicators of the assessment index system.

3.3.2 Weight calculation of assessment index

The analytic hierarchy process (AHP) is used to calculate the weight of evaluation indicators [31, 32]. The analysis steps are as follows:

1. Establish a multi-level assessment index system according to the actual situation of the Jiangsu coastal zone;

2. Compare each assessment index in pairs, establish a judgment matrix P, and calculate the relative weight of each assessment index;

$$P=\left[\begin{array}{cccc}{F}_{11}& F_{12}& \cdots & F_{1j}\\ F_{21}& F_{22}& \cdots & F_{2j}\\ ⋮& ⋮& \ddots & ⋮\\ F_{i1}& F_{i2}& \cdots & F_{\mathit{ij}}\end{array}\right]$$E4

$F_{\mathit{ij}}$represents the comparison value of mutual importance between two assessment indexes$F_i$ and $F_j$,

1. Test the consistency ratio (CR) of the judgment matrix, and CR < 0.1 indicates that the judgment matrix has satisfactory consistency and the index weight is set reasonably [33]. When this condition is not true, the value of the relevant variables of the judgment matrix can be adjusted in time, and all the importance comparison values can be reasonably set to ensure that the final consistency meets the requirements. The weight distribution of specific indicators is shown in Figure 5.

Figure 5.

The index weight of vulnerability assessment index system of Jiangsu coastal zone.

3.3.3 Assessment model of coastal ecological vulnerability

The coastal vulnerability index (CVI) method is used to calculate and measure the vulnerability caused by climate change and sea level rise in the Jiangsu coastal zone [34, 35, 36]. The vulnerability index reflects the vulnerability degree of the coastal zone, and the formula for calculating the vulnerability index is as follows:

$$\mathit{CVI}=\sum _{i=1}^n{C}_i\bullet W_i$$E5

where CVI is the vulnerability index of the coastal zone caused by climate change and sea level rise; $C_i$ is the standardized data value of the i th indicator; and $W_i$ is the weight of the i th indicator.

3.4 Spatial autocorrelation analysis

Spatial autocorrelation analysis is used to measure the distribution characteristics and influence the degree of a certain variable in space. Spatial autocorrelation analysis is widely used in geostatistics, and there are many indexes that can be used now, but the two most important indexes are Moran’s I coefficient and Geary’s C coefficient [37]. In this paper, the global and local Moran’s indices of vulnerability of the Jiangsu coastal zone in 2020 are calculated. The global Moreland index can be used to measure the spatial aggregation of coastal zone vulnerability, and the local Moreland index can be used to measure the spatial aggregation of coastal zone vulnerability. The global Moran’s index (Eq. (6)) and the local Moran’s index (Eq. (7)) are calculated as follows [38]:

$$I=\frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{\mathit{ij}}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{\mathit{ij}}{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2}$$E6

$$I_i=\frac{\left(x_i-\overline{x}\right)}{s^2}{\sum }_{j=1}^n{w}_{\mathit{ij}}\left(x_j-\overline{x}\right)$$E7

In the formula, $x_i$ and $x_j$ are the vulnerability values of the i and j spatial regions; $\overline{x}$ is the average vulnerability value of all region attribute values; $w_{\mathit{ij}}$ is the spatial weight matrix between regions; s is the sum of the elements of the spatial weight matrix; and n indicates the number of regions. Local indicators of spatial association graph (LISA graph) can be obtained by spatial clustering of local Moreland index, which can be divided into five types of aggregation: High-High Cluster, High-Low Outlier, Low-High Outlier, Low-Low Cluster, and Not Significant.

4. Results and discussion

4.1 Comprehensive analysis of vulnerability in Jiangsu coastal zone

Using the CVI method to operate on the ArGIS platform, the value of the coastal zone vulnerability grid unit in Jiangsu Province is obtained, as shown in Figure 6. The coastal zone vulnerability ranges from 0.17 to 0.62, with an average value of 0.29. In general, the vulnerability of the Jiangsu coastal zone caused by climate change and sea level rise presents a spatial distribution characteristic of high in the east and low in the west, and the order of magnitude is Lianyungang city> Nantong city> Yancheng city.

Figure 6.

Vulnerability index of Jiangsu coastal zone in 2020.

Using the “natural discontinuity segmentation method” in ArGIS, the coastal zone vulnerability index is divided into five grades: slightly vulnerable area, mildly vulnerable area, moderately vulnerable area, highly vulnerable area, and extremely vulnerable area (Table 2). The vulnerability spatial distribution map of the Jiangsu coastal zone (Figure 7) and the area proportion map of the Jiangsu coastal zone, Lianyungang, Yancheng, and Nantong vulnerability levels were drawn (Figure 8). From Figures 7 and 8, it can be seen that the mildly vulnerable area and slightly vulnerable area in the Jiangsu coastal zone are relatively large, accounting for 34.06 and 30.43% of the total area of the evaluation area, followed by moderately vulnerable area and highly vulnerable area accounting for 21.11 and 11.17%, respectively, and the extremely vulnerable area is the smallest, accounting for only 3.23% of the total area.

Vulnerability class	Value	Feature description
Slightly vulnerable area	017 ∼ 0.25	The ecological environment is basically free from external interference, can provide normal functional services, has a relatively complete ecological functional structure, and shows a low vulnerability.
Mildly vulnerable area	0.25 ∼ 0.30	Ecological functions run normally, can provide good service functions, lack of a perfect ecosystem, less external interference, and showing a low vulnerability.
Moderately vulnerable area	0.30 ∼ 0.36	The ecosystem is in a barely maintained state, which can provide basic functional services and maintain basic ecological operation mechanism. It is subjected to relatively external interference and occasionally occurs a relatively low degree of disaster, which can be recovered in time with human help, but it already has a high vulnerability.
Highly vulnerable area	0.36 ∼ 0.44	The structure of the ecosystem has undergone major changes, it is difficult to provide complete service functions, and external interference is serious, resulting in disaster prone and difficult to recover, and high vulnerability.
Extremely vulnerable area	0.44 ∼ 0.62	The ecosystem structure is in a messy state, completely unable to provide basic service functions, external interference is very serious, it is difficult to effectively restore the ecology, and has an extreme vulnerability.

Table 2.

Vulnerability assessment and classification of coastal zone.

Figure 7.

Spatial distribution pattern of vulnerability in Jiangsu coastal zone in 2020.

Figure 8.

Area proportion map of vulnerability level in Jiangsu coastal zone in 2020.

From the perspective of the vulnerability of the three coastal cities in Jiangsu, Lianyungang city is mainly a mildly vulnerable area and a moderately vulnerable area, Yancheng city is mainly a slightly vulnerable area. Although there are almost no extremely vulnerable areas in Nantong, moderately vulnerable areas account for a large proportion.

Using ArcGIS’s Global Moran’s I to calculate the global Moran’s index of Jiangsu coastal zone vulnerability in 2020 for spatial autocorrelation analysis, the obtained Moran’s index is 0.70, and the P value is less than 0.01, indicating that the result is significant at the 1% level. This shows that there is a certain positive correlation and strong clustering in the vulnerability distribution of the Jiangsu coastal zone, that is, the more similar the vulnerability of the coastal zone is with the aggregation of the spatial distribution locations. With the dispersion of spatial distribution, the vulnerability of coastal zones is more different.

Based on the vulnerability index of the Jiangsu coastal zone in 2020, the local Moran’s index was calculated, and the local Moran’s index was clustered to further analyze the spatial aggregation pattern of coastal zone vulnerability (Figure 9). It can be seen that there is spatial heterogeneity in the vulnerability aggregation characteristics of the Jiangsu coastal zone. The low-low aggregation pattern is shown on the inland side, indicating that the vulnerability of the coastal zone in this area is low, and the vulnerability of the surrounding counties is also low, which is a low-value aggregation area. However, high values and clusters of high values are mainly distributed in the area to the east of the study area (especially the area near the sea in Lianyungang and Nantong), where the vegetation coverage is sparse and the vulnerability is above the medium level. Overall, the spatial distribution of most coastal zone vulnerability indices shows strong spatial correlation, and the spatial autocorrelation mode is mainly reflected in the High-High Cluster and Low-Low Cluster, while the High-Low Outlier and Low-High are scattered in the study area.

Figure 9.

Vulnerability LISA cluster map of Jiangsu coastal zone in 2020.

4.2 Discussion

Under the background of climate change and sea level rise, there are differences in the degree of vulnerability of different cities and counties in the coastal areas of Jiangsu. Overall, the vulnerability of the Jiangsu coastal zone is at a moderate level. The extremely vulnerable areas are mainly concentrated in the seaside areas of Lianyungang and Yancheng. Under similar socioeconomic conditions, higher annual average wind speed and land subsidence rate will lead to higher vulnerability risks of climate change and sea level rise. However, most areas of Nantong city are moderately vulnerable, which may be due to low vegetation coverage, high annual mean temperature and wind speed, and weak ecological resilience and resistance to external disturbances. The western part of the Jiangsu coastal zone mostly presents slight vulnerability and mild vulnerability, mainly because it is far from the coast, and has high vegetation coverage and low land subsidence rate (Figures 2 and 3, and Figure 6). However, climate change and sea level rise are universal issues that need to be paid attention to in the future, and the vulnerability of coastal zones caused by them requires the establishment and improvement of a continuous risk monitoring system.

According to the assessment results, combined with the conditions of the Jiangsu coastal zone, this study puts forward the following suggestions.

1. For extremely vulnerable areas and highly vulnerable areas, ecological environment restoration and management should be strengthened, coastal protection projects should be deployed, coastal shelter forests and coastal wetlands should be protected, ecological seawalls should be built, and should improve the ability to deal with climate change, especially natural disasters caused by sea level rise. In addition, measures should be taken to alleviate land subsidence, reduce the rate of relative sea level rise, and try to avoid the layout of large-scale industrial areas and living areas in this area.

2. For moderately vulnerable areas, attention should be paid to the natural restoration of the ecological environment in the coastal zone, reasonable allocation of forest resources, timely protection, and scientific use of resources, to ensure that the forest coverage rate can be effectively increased, and then improve the resilience of the coastal ecological environment system under climate change and sea level rise scenarios.

3. For mildly vulnerable areas and slightly vulnerable areas, it is necessary to strengthen coastal environmental protection, improve the efficiency of marine economic development, encourage the development of innovative and environmentally friendly industries, strengthen regional infrastructure construction, and improve public affairs management capabilities and social security, promote the coordinated and stable development of the economic, social, resource, and environmental systems in the coastal zone.

The vulnerability of the Jiangsu coastal zone is not only affected by natural disasters such as global climate change and sea level rise but also closely related to human activities in the coastal zone. Under the continuous and irreversible trends of global climate change and sea level rise, it is necessary to conduct in-depth research on vulnerability assessment and response. Starting from the status quo of the Jiangsu coastal zone, this study selects assessment indicators scientifically and rationally, builds a coastal zone vulnerability assessment system, and analyzes the spatial distribution characteristics and heterogeneity of Jiangsu coastal zone vulnerability in 2020. The research results provide a scientific reference for the environmental protection and sustainable development of the Jiangsu coastal zone. In addition, this is of great significance for further in-depth assessment of coastal zone vulnerability on larger spatial and temporal scales.

5. Conclusions

This paper uses the SRP model and analytic hierarchy process and the coastal zone vulnerability index model to build a vulnerability assessment system with annual mean temperature, annual mean precipitation, annual mean wind speed, annual mean relative humidity, elevation, slope, land subsidence rate, distance to the coastline, vegetation coverage, net primary productivity, population density, and GDP density as indicators. The classification of vulnerability of the Jiangsu coastal zone in 2020 was completed, and then the vulnerability difference of the Jiangsu coastal zone caused by climate change and sea level rise was evaluated, and the following conclusions were drawn.

1. The overall vulnerability of the Jiangsu coastal zone is at a moderate level, and the vulnerability of coastal cities and counties is significantly different. The overall vulnerability presents a pattern of high in the east and low in the west.

2. The mildly vulnerable area and the slightly vulnerable area in the coastal zone of Jiangsu are larger, accounting for 34.06% and 30.43% of the total area of the assessed area respectively, and the extremely vulnerable area is the smallest, accounting for only 3.23% of the total area.

3. There is a certain positive correlation and strong agglomeration in the vulnerability distribution of the Jiangsu coastal zone, which is mainly reflected in the High-High Cluster and Low-Low Cluster, while the High-Low Outlier and Low-High are scattered in the study area.

In future work, it is planned to further improve an in-depth study from the following aspects: (1) Due to the limitations of time and data acquisition in this study, the selection of indicators cannot cover all aspects, and the selection of indicators is somewhat subjective. In future, it is necessary to continue to explore the driving forces and influencing mechanisms affecting coastal zone vulnerability, and establish a more comprehensive and perfect vulnerability assessment system. (2) Coastal zone vulnerability research should be viewed from the perspective of time and space. At present, our research only focuses on the spatial assessment of coastal zone vulnerability and lacks consideration of time scale. In the future, multiple scenarios of sea level rise rate will be considered to achieve a quantitative spatial assessment of coastal zone vulnerability at different time scales.

Acknowledgments

Acknowledgment for the data support from “National Earth System Science Data Center, National Science & Technology Infrastructure of China. (http://www.geodata.cn),” ESA and NASA. This research was supported by the National Natural Science Foundation (U1901215) and the Marine Special Program of Jiangsu Province in China (JSZRHYKJ202007).

Conflict of interest

The authors declare no conflict of interest.