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Article

Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta

1
School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
2
Guangdong Key Laboratory of Oceanic Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
3
Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-sen University, Zhuhai 519000, China
4
School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Water 2022, 14(7), 1103; https://doi.org/10.3390/w14071103
Submission received: 7 March 2022 / Revised: 26 March 2022 / Accepted: 27 March 2022 / Published: 30 March 2022
(This article belongs to the Special Issue Urban Water Security and Sustainable Development)

Abstract

:
Water plays an essential role in social and economic sustainable development, and the relationship between socio-economic development and water resources sustainable utilization has been an important issue in water resources management. We aim to propose a water resources vulnerability assessment index with the dimensions of exposure, sensitivity and adaptability, and establish a water resources system model over the Pearl River Delta network river region based on the Vulnerability Scoping Diagram (VSD) framework and System Dynamics (SD) model. The city of Zhongshan, which is located in the Pearl River Delta is used as a case study. The vulnerability of the water resources in Zhongshan from 2021 to 2050 is simulated in four different scenarios (normal, technical innovation, social economic improvement, and comprehensive development models). The results showed that the vulnerability in all four scenarios span three grades in 30 years, including moderately vulnerable, slightly vulnerable, and not vulnerable. Among them, similar trends were found between scenarios 1 and 3, and between scenarios 2 and 4. Furthermore, the vulnerability level in scenarios 2 and 4 was lower than that in scenarios 1 and 3. The vulnerability of both scenarios 2 and 4 decreased first and then increased, with the average values of 24.64 and 27.63, respectively. Scenario 2 experienced 7 years of not vulnerable (2034 to 2040) and 23 years of slightly vulnerable (2021–2033, 204–2050), scenario 4 experienced 5 years of not vulnerable (2032–2036), 21 years of slightly vulnerable (2021–2031, 2037–2046), and 4 years of moderately vulnerable (2047–2050). Although the vulnerability of scenario 4 was slightly worse than scenario 2, its adaptability to economic and social development, water resources, and water environment was much higher than scenario 2. Considering the extent of socio-economic development and the level of adaptability of the local water resources and water environment, the study concluded that the comprehensive development model is more suitable for cities in the network river area. In this scenario, sustainable water use and management can be made possible through policy regulation that encourages higher water efficiency, sewage reuse rate, and centralized sewage treatment rate.

1. Introduction

Water resources play a critical role in maintaining human life and our ecosystem, and sustainable utilization of water resources is the key to sustainable development of society and economy [1,2]. With the continuous economic development and accelerated urbanization, the increase in industrial waste and urban sewage production may lead to more severe water pollution, which in turn, may further increase the vulnerability of the water resources system [3]. The degree of vulnerability of the water resources system, refers to the sensitivity and adaptability of a system [4]. Studies have shown that vulnerability assessment can explain the ability of water resources to meet the regional water demand under pressure [5,6,7,8].
Research on the vulnerability of water resources began in 1968, when Marget presented the term “underground water vulnerability” [9]. Since then, numerous scholars have presented different opinions on this concept. The water resources are vulnerable when external disturbance (e.g., climate change, human activities) makes it difficult for the system to recover to its original status [10]. Vladimir Živanović pointed out that the groundwater vulnerability assessment concept was used to describe how groundwater is vulnerable to contaminants released at the surface as a function of geological, hydrological, and hydrogeological environmental condition [11]. Albinet et al. believed that underground water vulnerability refers to the possibility of pollution sources infiltrating or spreading from the surface into the underground water [12]. Foster et al. highlighted that the vulnerability of the aquifer and the pollution load produced by human activities are the main causes of underground water pollution and proposed the concept of “aquifer vulnerability” [13]. Early research mostly focused on underground water and water quality. In recent years, under the demands of continued socio-economic development, the increasing industrial water consumption and sewage production are responsible for further deterioration of the surface water quality. Meanwhile, such conditions have also spurred an increase in research on water resources vulnerability, especially surface water vulnerability. Kulshreshtha considered that water resource vulnerability is the nature of the water resource being vulnerable to damage [14]. Perveen et al. believed that this concept involved regional vulnerability because of the limitation of water availability and concentrated water utilization [15]. Afterwards, the concept and connotation of water resources vulnerability have been improved and developed continuously and experienced a process from single groundwater or surface water vulnerability to comprehensive water resources vulnerability, from single water quality to water quality and water quantity combination [16]. Existing studies have shown that water resources vulnerability represents a characteristic and status in which the quantity and quality of groundwater and surface water are affected by internal and external factors and lead to system imbalance, and includes the adaptability and sensitivity of water resources system to external driving factors. The DRASTIC, Legrand, EPIK, and SEPPAGE vulnerability index models have been widely used for the vulnerability analysis [17,18]. Combining the above models with new technologies, such as global information systems is a research hotspot. Rahman applied the GIS-based DRASTIC model, to evaluate the vulnerability of the table water aquifer in Aligarh in India [19]. Pathak et al. analyzed the vulnerability in Kathmandu using the same method and highlighted that vulnerability is important to underground water management [20]. In addition, the study of water resource vulnerability under climate change is also a current research hotspot with the global warming. Strzepek et al. analyzed the water resource vulnerability in the Nile River in Egypt under the conditions of the changing climate [21]. Vörösmarty et al. studied water resource vulnerability through population growth and climate change from 1985 to 2025 [22]. Fowler et al. studied the vulnerability of the water resource system in Yorkshire in England in relation to climate change [23]. Gui et al. analyzed the vulnerability of the Lancang River Basin water resources system due to climate change and mapped the spatiotemporal characteristics of the water resource vulnerability in the LRB [24]. Existing research mainly focusses on vulnerability comparisons among different areas and rarely refers to the evolution law of the vulnerability of the water resource system under different development models.
In this study, Zhongshan City, an archetypal city in the Peral River Delta network river region in Guangdong Province, China is selected as case study. With its advantages in geography and rapid economic development, the Pearl River Delta river network region brings about a sharp rising in water utilization, as well as industrial waste and urban wastewater emissions, which easily leads to more severe water pollution. Considering the characteristics of the network river region, the water resource system model is constructed using the system dynamics method based on the VSD evaluation index system. The vulnerability of the water resource system from 2021 to 2050 is then evaluated using the synthetic index method. The changes in the water resources systems vulnerability are modeled under four different modes of development. The results can enable policy makers to choose the most appropriate policy interventions for the socio-economic development and sustainable water use in the network-river regions.

2. Study Area

Zhongshan City is located in the middle of the Pearl River Delta in China at 22°11′ to 22°47′ N and 113°09′ to 113°46′ E, in the subtropical monsoon climate. The city covers a total urban area of 1783 square kilometers with a population of 4.4181 million and the GDP of RMB 315.159 billion in 2020, including RMB 140.417 billion of the industrial added value [25]. Zhongshan City is located downstream of the river network area of the Pearl River Delta, going through Modaomen, Hengmen, and Hongqili waterways and flowing to the ocean. The Hongqili waterway is in the north of the Beijiang River. The lower branches of the Donghai waterway in the north, Jiya and Xiaolan waterways, join to form the Hengmen waterway. There are Xihai and Modaomen waterways in the west and Guizhou, Huangpu, and Huangshali waterways run across Zhongshan City to create the crisscross river network zone (Figure 1). Zhongshan is rich in water resources, but the majority is originated or channeled from elsewhere. The multi-year average total water storage volume is 1.738 billion m3, in which the average surface water storage is 1.686 billion m3, and the average groundwater storage is 258 million m3 [26].
In recent years, along with the rapid economic and industrial development, the demand for water resources increased drastically as well. From 2001 to 2020, the total consumption of water in the city increased about 167% from 0.88 billion to 1.469 billion m3. Agricultural water consumption increased from 0.474 billion to 0.493 billion m3, essentially unchanged. Industrial water consumption rose 0.89 times from 0.249 billion to 0.471 billion m3. Domestic water consumption increased 2.24 times from 0.156 billion to 0.505 billion m3, indicating that the shortage of water resources and the contradiction between water supply and demand have intensified. The main water resource problems in Zhongshan include: (1) the growing population and urbanization increases domestic water demand; (2) the industrial production (including the thermal power industry) consumes a large amount of water with a low reclamation rate; (3) the quantity of COD discharged into the river increases, leading to serious pollution caused by water shortage which has an adverse impact on economic growth.

3. Methodology

3.1. System Dynamics (SD) Method

Presented by Forrester in 1956, SD combines the relevant theories and methods of the application control, information, and decision theories and integrates structure and function, material and information, science, and experience [27]. SD integrates natural science and social science horizontally and is suitable for dealing with the high-order, nonlinear, multi-feedback, and complex time-varying system problems. Hence, SD is an ideal method to explore the motion law of the complex system [28]. SD is used to study the system problems through combination of quantitative and qualitative, overall thinking and analysis, reasoning, and synthesis. As an effective tool of modern scientific decision-making and prediction and known as the lab of strategies, it is widely used in regional macro development strategies’ decision making [29]. SD utilizes the first-order differential equation to describe how the rate of change of the system state variables relates to other state variables or specific inputs. The state of change is then decomposed into several flow rates according to the target system and research needs. This way, the system can be more clearly defined, which is conducive to modeling and finding the control points of the system.
The essence of SD is a set of first-order differential equations with time-delay, which describes the dependence of the rate of change of each state variable or specific input according to the actual condition of the system and the requirement of the research to make the concept of the system clear and conducive for modeling and identifying the control points of the system. In the SD model, the flow rate equation describes the change regularity of the state variables (flow level) and is expressed using Euler’s numerical integration. The general form of the equation is:
L . K = L . J + ( I R . J K O R . J K ) × D T
where L . K and L . J are the flow bit vectors at times K and J , respectively; I R . J K and O R . J K are the inflow rate vector and the outflow rate vector, respectively; D T is the time period from the past J time to the present K time.
By modifying the Formula (1), we obtain Equation (2), which is the derivative of the flow position that is equal to the algebraic sum of the inflow rate and the outflow rate. Obviously, the system dynamics model is a first-order differential equation set determined by the above-mentioned vector equations.
L . K L . J D T = D L D T = I R . J K O R . J K
where D L is the difference of the stream bit vector at the time of K and J .
SD can be divided into seven steps [30,31]: a. determination of the simulation target; b. determination of the system boundary; c. design of the system feedback mechanism; d. construction of the system flow diagram; e. establishment of the equation; f. model calibration and verification; and g. model implementation.
The water resource system is mainly composed of subsystems such as water supply, water demand, economy, population, and water environment. Each subsystem is the result of interaction of many factors, and the multiple feedback relations of the water resource system are formed through the mutual influence and restriction relations of variables among subsystems. The SD model has four main variables: level (L), rate (R), auxiliary (A), and constant variables (C). According to the present situation and development trend of the research area, we use the observed yearly data from 2015 to 2020 to validate the model and then simulate from 2021 to 2050. The water resource system model of Zhongshan City is established based on the Vensim-PLE platform, which is industrial-strength simulation software for improving the performance of real systems. The basic framework of the model is shown in Figure 2, and the flow graph of the water resource system is shown in Figure 3. The full names of the variables are presented in Table A1.

3.2. Construction of Water Resources Vulnerability Index System

3.2.1. Evaluation Index System Construction

The Vulnerability Scoping Diagram (VSD) conceptual framework was first proposed by Polsky in 2007 [32] which has been widely used in the analysis of the vulnerability of coupled systems. The construction of the vulnerability evaluation index system of the water resource system is designed to seek a series of representative characteristic variables that can directly reflect the degree of vulnerability of the system. According to the principles of opposition, relative stability, comparability, and operability for index selection and referring to the research results related to the water resource evaluation index system [33,34], this study focuses on three subsystems of water resource, water environment, and social economy to establish the vulnerability evaluation index system of the water resource system. The three dimensions of analysis are exposure, sensitivity, and adaptability. We adopt the “criteria layer–element layer–indicator layer” to establish a water resource system vulnerability assessment index system with three sub-systems of water resources, water environment, and social economy. As each level involves a lot of content, this article selects more important indicators that can reflect the characteristics of the research area for research.
The water resource subsystem selects per capita water resources (PCWRQ), water consumption per RMB 10,000 of industrial added value (WUAPIAV), and annual water supply guarantee rate (YWSGR) as the evaluation indices of the vulnerability of the system [35]. PCWRQ is a recognized standard index for measuring the renewable freshwater resource condition of a country or a region and reflecting the water condition of the regional development. WUAPIAV reflects the industrial production level and water consumption level of a region, where a larger value indicates greater industrial water demand and industrial water pressure. YWSGR is an important index used to evaluate the annual regional water supply and demand capability.
Zhongshan City is a textbook example of the river network area. Existing research has emphasized that sewage discharge is the main source of damage to the water environment [36]. In this study, RWCT and CODEI, which reflects the intensity of sewage treatment and the emission situation of COD, respectively, are used to evaluate the water environment sub-system. PCGDP, UR, and GRGDP are critical indexes for the vulnerability of the system [37]. PCGDP is an important index used to measure the economic power of a country or a region. UR measures the degree of urbanization. GRGDP reflects the sustainability of the regional economic development. The exposure indicators include the rate of centralized sewage treatment, sensitivity indicators include per capita water resources, GDP annual growth rate and water consumption of RMB 10,000 of industrial added value, and adaptability indicators include per capita GDP, urbanization rate, and water supply guarantee rate.

3.2.2. Weight Determination of the Index System and the Scoring Criterion

The weight of each evaluation index determines the contribution of each factor to the vulnerability of the water resource system. Here, we use the analytic hierarchy process (AHP) to confirm the weight of each evaluation index. Firstly, a judgment matrix is constructed by pairwise comparison of the level indicators through expert scoring. Then, the level single sorting and total level sorting and their consistency test are carried out. More detailed calculations can be retrieved from [38].
The index system adopts a scoring range from 0 to 100 with five levels. The scores are determined by comparing the actual and predicted indices to the scoring criteria. The limit theory is adopted for the indexes in the open interval. The scoring criterion of the index systems is based on existing research results [39,40], as shown in Table 1.

3.2.3. Vulnerability Calculation

The synthetic index shows the degree of comprehensive influence of the factors on the system. This index establishes the mathematical induction and statistics with the contribution indexes of these factors and calculates the value representing the condition of the system through the mathematical relationship based on the single index. As a common evaluation method, the synthetic index has the advantages of strong comprehensiveness and comparability [41]. This method is applied in this paper to evaluate the vulnerability of the water resource system.
Firstly, the raw data is made dimensionless and homogenized. The larger the value, the greater the exposure, sensitivity, and adaptability, as shown in Equation (3). The smaller the value, the larger the exposure, sensitivity and adaptability larger, as shown in Equation (4).
Z i * = Z i Z n Z m Z n
Z i * = 1 Z i Z n Z m Z n
where i is the number of indexes, i = 1 , 2 , ...8 ; Z i * is the standardized index value; Z i is the original index value; Z m is the maximum value; Z n is the minimum value.
Exposure ( f E j ), sensitivity ( f S k ), and adaptability ( f A l ) are, respectively, composed of the indicator variables in Table 1, and the three indexes are calculated by weighting and summing, respectively, see Formulas (5)–(7). For calculation of the vulnerability of a water resources system, see Formula (8).
f E j = j = 1 2 w j E j
f S k = k = 1 3 w k S k
f A l = l = 1 3 w l A l
W R S V D = f E , S , A
where j , k , l are the number of indicators; w is the weight; E j is the exposure index value; S k is the sensitivity index value; A l is the adaptability index value; W R S V D represents the vulnerability of the water resources system; E is the exposure; S is the sensitivity; A is the adaptability. A larger W R S V D indicates higher vulnerability and instability of the system. Conversely, a lower vulnerability indicates higher stability of the system. The index values are between 0 and 100.

4. Results and Discussion

4.1. Model Validation

After constructing the SD water resources model, four methods (i.e., visual test, running test, historical test, and sensitivity) are generally used to calibrate the SD model in order to ensure the consistency between the simulation results and the actual system. Given that the first two methods have been implemented in the modeling process, here we mainly verified the last two.

4.1.1. Historical Test

The parameters of the model are based on population, economy, resources, environment, and other data in the Statistical Yearbook and Water Resources Bulletin of Zhongshan City from 2005 and 2010. The state equation, constant equation, and the table function are used to establish the quantitative relationship among all variables. By intuitively and operationally testing the model, it was found that the causality and units of the model were in line with reality. The simulated total population (TP), GDP, and total water demand (TWD) were compared with the real values from 2015 and 2020 for the historical test. The result shows that the errors between the simulation values and the historical values are less than 10% (Table 2), which is the standard basis to judge whether a model is credible [42]; thus indicating a high reliability of the model.

4.1.2. Sensitivity Analysis

Sensitivity analysis is an important method to validate model effectiveness. An effective model with good robustness should have low sensitivity [40]. Sensitivity analysis examines the influence of parameter changes on the variable output results of the model by adjusting the parameters in the model [43]. This paper uses the sensitivity model to undertake the sensitivity analysis of the system. The formula is as follows [44]:
S Q = Δ Q t Q t X t Δ X t
where t refers to the time; S Q refers to the sensitivity of the level variable Q to the parameter X ; Q t and X t refer to the values of Q and X at Time t , respectively; and Δ Q t and Δ X t refer to the increments of Q and X at Time t , respectively.
For n level variables ( Q 1 , Q 2 , , Q n ), the average sensitivity of any parameter at Time t is
S = 1 n i = 1 n S Q i
where n refers to the number of level variables, S Q i refers to the sensitivity of Q i , and S refers to the average sensitivity of parameter X to n level variables.
The simulation and modeling of the water resource system involves numerous parameters and variables. Thus, five key parameters and six variables in the system were selected to analyze the data between 2005 and 2010. By changing one parameter at a time (increasing by 10%), we were able to analyze its influence on the other six variables. The sensitivity of the parameter to each variable can be calculated according to Formula (9). The average sensitivity of each variable to a parameter, that is, the sensitivity of this parameter to the system model, can be calculated according to Formula (10). The sensitivity analysis results (see Table 3) show that the sensitivity of parameters is less than 10%, indicating the low sensitivity of the system to the parameters and strong system stability [40,44]. Combining the historical test results, this model is suitable for simulating the actual system of Zhongshan City.

4.2. Water Resource System Vulnerability in Different Scenarios

The vulnerability of the water resource system was simulated under four different scenarios. The Normal development model describes the business-as-usual scenario, where all the indicators are steadily increasing. Based on this scenario, three other scenarios are developed. The technical innovation development model is a scenario where parameters affecting water usage efficiency are modified; the social-economic improvement mode is a scenario where parameters related to social development are modified; and the comprehensive development mode is a combination of the previous two scenarios. By comparing the variations in the vulnerability of the water system under different developmental scenarios, it is then possible to demonstrate how policy regulations can be a useful approach for improving the reliability of the water resource system.
The schematics of the four scenarios are presented in Table 4. By using the simulation results of the four scenarios, in combination with the scoring and weights of the evaluation indexes, the vulnerability of the water resource system of Zhongshan City between 2021 and 2050 was calculated using the synthetic index weighted sum method introduced in Section 3.2.3. The results are shown in Table 5 and the specific parameters are shown in Table A2.

4.3. Discussion

The vulnerability of a city’s surface water resource system is generally divided into five grades: not vulnerable (<40 points), slightly vulnerable (40–50 points), moderately vulnerable (50–60 points), very vulnerable (60–70 points), and extremely vulnerable (> 70 points) [45]. The annual vulnerability in each scenario is calculated by considering the vulnerability score results of the four scenarios and the water resources system vulnerability scoring criteria, and results are exhibited in Figure 4a–d.
(a) Scenario 1—the normal development model: In this scenario, the indicators (e.g., social economy) are steadily increasing. The vulnerability scores of the water resource system of Zhongshan City under the normal development model and the grading results are shown in Table 5 and Figure 4a, respectively. The results indicate that the vulnerability score of the water resources system decreased year by year from 2021 to 2029, but it began to increase annually thereafter. The vulnerability score was the lowest in 2029, with a score of 22.36 (slightly vulnerable) and the highest in 2050, which is 42.37 (moderately vulnerable). From 2021 to 2050, the vulnerability score of the water resource system of Zhongshan City spans two grades, including slightly vulnerable for 19 years (2021–2039) with an average vulnerability score of 27.26, and moderately vulnerable for 11 years (2040–2050) with an average vulnerability score of 41.82. Thus, with the development of the economy and society, the water resources system has gradually stabilized, but gradually deteriorated with the passage of time.
(b) Scenario 2—the technical innovation development model: This scenario is a water-saving development model that aims at increasing the water usage efficiency through the technical innovation. In this scenario, the quota of agricultural, production, and domestic water consumption is reduced to 80% of the normal development mode, and the sewage reuse rate and sewage centralized treatment rate are increased to 120% of the conventional development mode after the renovation of the sewage treatment plant and the improvement of technology. Table 5 and Figure 4b show the vulnerability scores of the water resource system in this scenario and the grading results, respectively. The results show that the vulnerability score of the water resources system first showed a rapid downward trend, and then reached to relative stability status in 2034, and then shows a rapid upward trend in 2041. The vulnerability score is the highest in 2050 at 39.53 (slightly vulnerable) and the lowest in 2039 at 19.46 (not vulnerable). In the span to 30 years between 2021 and 2050, the vulnerability score of the water resources system of Zhongshan city has changed two grades; there is a period of 7 years that is not vulnerable (2034–2040) with an average vulnerability score of 19.72, and a period of 23 years of slightly vulnerable (2021–2033, 2041–2050), with an average vulnerability score of 26.13. Therefore, we can see that although technological innovation plays a significant role in the stability of a water resources system, as the economic and social development reach a certain level, the stability of the water resources system starts to gradually deteriorate.
(c) Scenario 3—improving the social and economic development mode: This scenario is a development mode that promotes social development by adjusting the proportion of industrial structure, GDP growth rate, population growth rate, and urbanization rate through the current development status. In this scenario, PPI and PSI are adjusted to 95% and 97% of the normal development model, respectively. GRGDP, GRTP, and UR are increased to 120% of the normal development mode, as shown in Table 5 and Figure 4c, respectively. The results showed that the vulnerability of water resources system decreased first and then increased until it gradually stabilized. The vulnerability score was the lowest in 2028, with a score of 22.64 (slightly vulnerable) while the highest is in 2050 with a score of 43.74 (moderately vulnerable). In the 30 years between 2021 and 2050, the vulnerability of the water resources system in Zhongshan city spans two levels, and the average vulnerability of slightly vulnerable is 28.20 in between 2021 and 2036, and moderately vulnerable for between 2037 and 2050 with an average vulnerability of 42.56. Compared with the normal development model, the social economic improvement development model can obviously contribute to an increased vulnerability of the water resource system.
(d) Scenario 4—the comprehensive development model: This scenario is a water-saving development model that is based on the first model and satisfies the second and third models. The parameters of this scenario are adjusted according to scenarios 2 and 3. The vulnerability scores of the water resource system in this scenario and the grading results are shown in Table 5 and Figure 4d, respectively. The results show that the vulnerability of the water resource system first showed a downward trend, then stabilized, followed by a rapid upward trend, before finally stabilizing. The vulnerability score value is the highest in 2050 at 40.63 (moderately vulnerable) and the lowest in 2036 at 19.44 (not vulnerable). In the 30 years between 2021 and 2050, the vulnerability score of the water resource system of Zhongshan City spans three grades, including not vulnerable for 5 years (2032–2036) with an average vulnerability score of 19.67, slightly vulnerable for 21 years (2021–2031, 2037–2046) with an average vulnerability score of 27.10, and moderately vulnerable for 4 years (2047–2050) with an average vulnerability score of 40.38. Thus, we can see that the comprehensive development model is greatly influenced by the technological innovation model, and that they follow similar trends.
Figure 4a–d shows the vulnerability scores and grading predictions of the water resource system of Zhongshan City in the next 30 years in four development models. The vulnerabilities of scenarios 1 and 3 span two grades, namely, slightly vulnerable and moderately vulnerable. Scenario 2 spans two levels of not vulnerable and slightly vulnerable, and scenario 4 spans three levels of not vulnerable, slightly vulnerable, and moderately vulnerable. As shown in Table 6, among the not vulnerable levels, scenario 2 has the highest number of years, 7 years, and the average vulnerability is 19.72, accounting for 0.23; scenario 4 is the second, with 5 years, and the average vulnerability is 19.67, accounting for 0.17; scenarios 1 and 3 have no non-vulnerable years. Among the slightly vulnerable levels, four scenarios are involved, scenario 2 has the highest number of years, 23 years, and average vulnerability of 26.13, accounting for 0.77; scenario 3 has the lowest number of years, 16 years, and average vulnerability is 28.2, the proportion is 0.53. In the middle level of vulnerability, except for scenario 2, all other scenarios are involved. Among them, scenario 3 has the highest number of years, 14 years, the average vulnerability is 42.56, and the proportion is 0.47; scenario 4 has the lowest number of years, 4 years, and average vulnerability is 40.38, the proportion is 0.13.
The average vulnerability score of the four scenarios, in descending order: scenario 3 (34.90), scenario 1 (32.91), scenario 4 (27.63), and scenario 2 (24.64). Scenarios 1 and 3 are relatively similar, and scenario 2 is more similar to scenario 4. All four scenarios entered the slightly vulnerable level at the same time, in 2021, but ended at different times. In scenarios 1 and 3, it switches to moderately vulnerable after the end of 2039 and 2036, respectively. In scenarios 2 and 4 it switches to invulnerability mode after the end of 2033 and 2031, respectively, but then switches to slightly vulnerable again. Compared to scenario 3, although scenario 4 is slightly weaker, its economic and social development is far greater than in scenario 3. This shows that both technological innovation and a certain level of economic and social development have a more obvious effect on the stability of the water resources system.
The vulnerability of the water resources system is influenced by a combination of several factors. Even if the vulnerability level of the water resource systems is similar or even the same, the contributing factors and, therefore, the reflected water resource problems can be very different [46]. With unique geographical advantages and rich natural resources, the river network area enjoys rapid economic development. However, the inharmonious relationship between the water environment, the development, and the utilization of the water resources has become a major factor that restricts social and economic development. The common water resource problems include water-quality-induced water shortage, low-water resource utilization rate, imperfect water conservancy facilities, and unsound water resources management system [47]. To solve these problems, we analyzed four scenarios to identify the optimal solution to ensure the safety of the system. Xiong et al. used the SD method to establish a model in research on the water resource supply and demand system of the Changsha–Zhuzhou–Xiangtan City Group, which has a developed water system. After setting four scenarios, namely, the traditional development, economic development, water-saving, and coordinated models, the results show that the coordinated model is the optimal one for the water resource development and utilization in the Changsha–Zhuzhou–Xiangtan City Group [48]. Li et al. adopted similar methods and drew the conclusion that the coordinated model is the optimal one for the water resource development and utilization around the Dongting Lake area with the same method [49]. The research results of this paper highlight that by increasing the water utilization efficiency, WRR, and RWCT can ensure the sound development of the social economy in the river network area and maintain the stable water resource system. This is confirmed by a recent study on the control measures of water security of Zhongshan, where it showed that the percentage of wastewater reuse and wastewater centralized treatment are among the main contributing factors towards the alleviation of water scarcity, and that policy regulation is an effective approach for improving the city’s water security [50]. Therefore, we believe this is currently the most rigorous and accurate model for the sustainable development of the network-river regions.

5. Conclusions

With the acceleration of urbanization and the rapid increase in population as well as economic development, the water environment problems become more prominent, further threatening the water resources security of the Pearl River Delta’s river network region. In the key period of social development, it is critical to maintain the stability of the water resource system. Using the VSD-based evaluation index system, this study used the SD method to establish a water resource system model and evaluated it in four scenarios (normal, technical innovation, social economic improvement, and comprehensive development models). According to the simulation results of the model, the comprehensive index method was adopted to evaluate the vulnerability of the water resources system of Zhongshan City from 2021 to 2050. The main findings are illustrated as follows:
(1)
In all four scenarios, the annual vulnerability score first shows degrees of decline and then degrees of growth. Scenarios 1 and 3 show similar trends, while scenarios 2 and 4 are similar. The results indicate that a certain degree of government regulations and control measures, technical innovation, and industrial structure adjustment serve a positive function in the stability of the water resource systems.
(2)
In the 30 years between 2021 and 2050, the vulnerabilities of all four scenarios are classified into three grades, namely, not vulnerable, slightly vulnerable, and moderately vulnerable. Scenario 2 has the lowest average score of vulnerability with an average value score of 24.64, while scenario 3 has the highest vulnerability with an average value of 34.90. Scenario 3 focuses on economic and social development and fails to effectively balance water resources. Scenario 4, although slightly weaker than scenario 2, in terms of vulnerability, has achieved greater economic and social development.
(3)
Compared to scenario 1, scenario 3 shows higher vulnerability, while both scenarios 2 and 4 have lower vulnerability scores. Scenario 4 is a combination of scenarios 2 and 3, that is, pursuing rapid economic development while increasing water use efficiency. While pursuing economic and social development to ensure water resources security, scenario 4 is more suitable for the development needs of cities in the network-river area, but we also need to consider whether the limited resources can support the healthy development of society when the economic and social development reaches a certain level. Particularly, in the Pearl River Delta, an economically developed are, what we should focus on is how to make sure that the limited resources can be used to maximize the benefits of future water resources management through timely and effective policy adjustments.

Author Contributions

Conceptualization, S.W. and K.L.; methodology, S.W. and Z.C.; software, S.W.; validation, S.W.; formal analysis, S.W. and L.H.; investigation, X.B.; resources, S.W.; data curation, X.B.; writing—original draft preparation, S.W.; writing—review and editing, K.L. and Z.Y.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Excellent Young Scientist Foundation of NSFC (51822908), the Fundamental Research Funds for the Central Universities (20lgjc04), and the Baiqianwan project’s young talents plan of special support program in Guangdong Province (42150001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Appendix A.

Acknowledgments

The authors greatly appreciate the water authority of Zhongshan for providing the valuable regional basic data.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Abbreviations for variables in the water resources system model.
Table A1. Abbreviations for variables in the water resources system model.
No.AbbreviationVariablesUnit
1TPTotal Population104 persons
2GDPGross Domestic Product104 RMB
3LALivestock Amount104 heads
4FFWAFishpond Filling Water Area104 mu
5FOIAForestland and Orchard Irrigation Area104 mu
6FIAFarmland Irrigation Area104 mu
7UELWDUrban Ecological Landscaping Water Demand104 m3
8WRQWater Resources Quantity104 m3
9GATPGrowth Amount of Total Population104 persons/year
10GAGDPGrowth Amount of GDP104 RMB/year
11GALNGrowth Amount of Livestock Number104 heads/year
12GAFFWAGrowth Amount of Fishpond Filling Water Area104 mu/year
13GAFOIAGrowth Amount of Forestland and Orchard Irrigation Area104 mu/year
14GAFIAGrowth Amount of Farmland Irrigation Area104 mu/year
15GAUELWDGrowth Amount of Urban Ecological Landscaping Water Demand104 m3/year
16GAWRQGrowth Amount of Water Resources Quantity104 m3/year
17CODEICOD Emissions Intensitykg/(104 RMB)
18CODEACOD Emission Amountkg
19GRGDPGrowth Rate of GDP1/year
20AVPIAdded Value of Primary Industry104 RMB
21PPIProportion of Primary IndustryDmnl
22WDPIWater Demand of Primary Industry104 m3
23WUAPTIAVWater Used Amount Per 104 RMB Tertiary Industry Added Valuem3/(104 RMB)
24CODGAPIAVCOD Generated Amount of Per 104 RMB Industry Added Valuekg/(104 RMB)
25WUAPIAVWater Used Amount Per 104 RMB Industry Added Valuem3/(104 RMB)
26WUAPAAVWater Used Amount Per 104 RMB Architectural Added Valuem3/(104 RMB)
27TIAVTertiary Industry Added Value104 RMB
28WDTIWater Demand of Tertiary Industry104 m3
29SIAVSecondary Industry Added Value104 RMB
30PSIProportion of Secondary IndustryDmnl
31WDSIWater Demand of Secondary Industry104 m3
32GRTPGrowth Rate of Total Population1/year
33PCGDPPer Capita GDPRMB/person
34PCWRQPer Capita Water Resources Quantitym3/person
35TWSTotal Water Supply104 m3
36WSCWater Supply Capacity104 m3
37RPRural Population104 persons
38RPCWQRural Per Capita Water QuotaL/(person·day)
39RDWDRural Domestic Water Demand104 m3
40FIWQFarmland Irrigation Water Quotam3/mu
41GRFIAGrowth Rate of Farmland Irrigation Area1/year
42FLWDFarmland Water Demand104 m3
43UPUrban Population104 persons
44UPCWQUrban Per Capita Water QuotaL/(person·day)
45URUrbanization RateDmnl
46GRUELWDGrowth Rate of Urban Ecological Landscape Water Demand1/year
47UDWDUrban Domestic Water Demand104 m3
48CODGAICOD Generated Amount of Industrialkg
49IAVIndustrial Added Value104 RMB
50IWECIndustrial Wastewater Emission CoefficientDmnl
51IWEAIndustrial Wastewater Emission Amount104 m3
52WDIWater Demand of Industry104 m3
53CVAconstruction value added104 RMB
54PCIProportion of Construction industryDmnl
55WDCIWater Demand of construction industry104 m3
56ITCODDTEInfluence of Total COD Dischargment To Economic104 RMB/year
57TWATotal Wastewater Amount104 m3
58QDWQuantity of Delivery Water104 m3
59CODEAWFWTPCOD Emission Amount Without From Wastewater Treatment Plantkg
60GRFOIAGrowth Rate of Forestland and Orchard Irrigation Area1/year
61FOWDForestland and Orchard Water Demand104 m3
62WSDGWater Supply and Demand Gap104 m3
63GRWRQGrowth Rate of Water Resources Quantity1/year
64WRRWastewater Reuse RateDmnl
65WRAWastewater Reuse Amount104 m3
66CODEAFWTPCOD Emission Amount From Wastewater Treatment Plantkg
67RWCTThe rate of Wastewater Concentrated TreatmentDmnl
68FIWDFishery Water Demand104 m3
69GRLNGrowth Rate of Livestock Number1/year
70WDLWater Demand of Livestock104 m3
71DCODGADomestic COD Generated Amountkg
72DWECDomestic Wastewater Emission CoefficientDmnl
73DWEADomestic Wastewater Emission Amount104 m3
74DWDDomestic Water Demand104 m3
75EWDEconomic Water Demand104 m3
76WSDWater Shortage DegreeDmnl
77WSWater Storage104 m3
78TWDTotal Water Demand104 m3
79PCCODGAPer Capita COD Generated Amountkg/(104 persons)
80CODSECOD Standard of Effluentkg/(104 m3)
81IUCODEEInfluence of Unit COD Emission to Economic104 RMB /(kg·year)
82UCCUnit Conversion Coefficientm3/L
83APAvailability ProportionDmnl
84DWQDiversion Water quantity104 m3
85FOIWQForestland and Orchard Irrigation Water Quotam3/mu
86LWQLivestock Water QuotaL/(d·head)
87LSTWUALimitation Standard of Total Water Used Amount104 m3
88DWADiversion Water Amount104 m3
89FWQFishpond Water Quotam3/mu
90GRFFWAGrowth Rate of Fishpond Filling Water Area1/year
91YWSGRYearly Water Supply Guarantee RateDmnl
Table A2. Parameter setting of the four different scenarios.
Table A2. Parameter setting of the four different scenarios.
ParametersScenario 1Scenario 2Scenario 3Scenario 4
VariablesYearUnitNormal Development ModeTechnical Innovation
Development Mode
Social Economic Improvement Development ModeComprehensive
Development Mode
WUAPIAV2020m3/(104 RMB)28.00022.400-22.400
203025.00020.000-20.000
WUAPAAV2025m3/(104 RMB)31.00024.800-24.800
203528.00022.400-22.400
205022.00017.600-17.600
WUAPTIAV2025m3/(104 RMB)7.8006.240-6.240
20356.5005.200-5.200
20504.5003.600-3.600
UPCWQ2025L/(person·day)205.000164.000-164.000
2030210.000168.000-168.000
2035215.000172.000-172.000
2050220.000176.000-176.000
RPCWQ2025L/(person·day)145.000116.000-116.000
2030150.000120.000-120.000
2035155.000124.000-124.000
2050160.000128.000-128.000
FIWQ2025m3/mu640.000512.000-512.000
2030605.000484.000-484.000
2035569.000455.200-455.200
2050498.000398.400-398.400
FOIWQ-m3/mu189.000151.200-151.200
FWQ-m3/mu1000.000800.000-800.000
LWQ-L/(person·day)50.05040.040-40.040
WRR2025Dmnl0.1500.180-0.180
20350.2000.240-0.240
20500.3000.360-0.360
RWCT2025Dmnl0.9501.000-1.000
20350.9701.000-1.000
20500.9801.000-1.000
PPI2025Dmnl0.011-0.01050.0105
20350.009-0.00860.0086
20500.007-0.00620.0062
PSI2025Dmnl0.413-0.40100.4010
20350.351-0.34090.3409
20500.300-0.29100.2910
GRGDP20241/year0.065-0.07800.0780
20250.065-0.07800.0780
20340.065-0.07800.0780
20500.070-0.08400.0840
GRTP20241/year0.035-0.04200.0420
20250.002-0.00260.0026
20340.002-0.00260.0026
20350.015-0.01810.0181
20500.015-0.01810.0181
UR2020Dmnl0.930-1.00001.0000
20350.950-1.00001.0000
20500.970-1.00001.0000

References

  1. Brown, K. Water Scarcity: Forecasting the Future with Spotty Data. Science 2002, 297, 926–927. [Google Scholar] [CrossRef] [PubMed]
  2. Tong, F.; Dong, Z.C. Water security scenario analysis: Methodology and a case study. In Proceedings of the 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS, Hohai University, Nanjing, China, 20–23 October 2008; pp. 307–313. [Google Scholar]
  3. Chen, W.; Chen, Y.; Feng, Y. Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China. Entropy 2021, 23, 882. [Google Scholar] [CrossRef]
  4. Intergovernmental Panel on Climate Change. Forth Assessment Report: Climate Change; IPCC: Geneva, Switzerland, 2007. [Google Scholar]
  5. Xiang, X.; Li, Q. Water Resources Vulnerability Assessment and Adaptive Management Based on Projection Pursuit Model. J. Coast. Res. 2020, 103 (Suppl. S1), 431–435. [Google Scholar] [CrossRef]
  6. Li, R.; Guo, P.; Li, J. Regional Water Use Structure Optimization Under Multiple Uncertainties Based on Water Resources Vulnerability Analysis. Water Resour. Manag. 2018, 32, 1827–1847. [Google Scholar] [CrossRef]
  7. Mirauda, D.; Ostoich, M. Surface water vulnerability assessment applying the integrity model as a decision support system for quality improvement. Environ. Impact Assess. Rev. 2011, 31, 161–171. [Google Scholar] [CrossRef]
  8. Padowski, J.C.; Jawitz, J.W. Water availability and vulnerability of 225 large cities in the United States. Water Resour. Res. 2012, 48, 1–6. [Google Scholar] [CrossRef] [Green Version]
  9. Vrba, J.; Zaporozec, A. Guidebook on Mapping Groundwater Vulnerability; International Association of Hydrogeologists, Ed.; Verlag Heinz Heise: Hannover, Germany, 1994. [Google Scholar]
  10. Huang, Y.; Cai, M.T. Methodological Guidelines-Vulnerability Assessment of Freshwater Resources to Environmental Change; United Nations Environmental Programme: Nairobi, Kenya, 2009. [Google Scholar]
  11. Živanović, V.; Atanacković, N.; Stojadinović, S. Vulnerability Assessment as a Basis for Sanitary Zone Delineation of Karst Groundwater Sources—Blederija Spring Case Study. Water 2021, 13, 2775. [Google Scholar] [CrossRef]
  12. Albinet, M.; Margat, J. Cartographie de la vulnérabilité à la pollution des nappes d’eau souterraine Orleans, France. Bull BRGM 1970, 4, 13–22. [Google Scholar]
  13. Foster, S.S.D. Fundamental concepts in aquifer vulnerability, pollution risk and protection strategy. Vulnerability Soil. Groundw. Pollut. 1987, 38, 69–86. [Google Scholar]
  14. Kulshreshtha, S.N. A Global Outlook for Water Resources to the Year 2025. Water Resour. Manag. 1998, 12, 167–184. [Google Scholar] [CrossRef]
  15. Perveen, S.; James, L.A. Scale invariance of water stress and scarcity indicators: Facilitating cross-scale comparisons of water resources vulnerability. Appl. Geogr. 2011, 31, 321–328. [Google Scholar] [CrossRef]
  16. Ma, F.B.; Wang, H.; Li, C.H. Research progress in water resources vulnerability assessment. J. Water Resour. Eng. 2012, 23, 30–37. [Google Scholar]
  17. Doerfliger, N.; Jeannin, P.Y.; Zwahlen, F. Water vulnerability assessment in karst environments: A new method of defining protection areas using a multi-attribute approach and GIS tools (EPIK method). Environ. Earth Sci. 1999, 39, 165–176. [Google Scholar] [CrossRef] [Green Version]
  18. Fu, S.R.; Wang, Y.X.; Cai, H.S.; Li, Y.L. Vulnerability to contamination of groundwater in urban regions. Earth Sci. 2000, 25, 482–486. [Google Scholar] [CrossRef]
  19. Rahman, A. A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Appl. Geogr. 2008, 28, 32–53. [Google Scholar] [CrossRef]
  20. Pathak, D.R.; Hiratsuka, A.; Awata, I.; Chen, L. Groundwater vulnerability assessment in shallow aquifer of Kathmandu Valley using GIS-based DRASTIC model. Environ. Earth Sci. 2008, 57, 1569–1578. [Google Scholar] [CrossRef]
  21. Strzepek, K.; Yates, D.; El Quosy, D. Vulnerability assessment of water resources in Egypt to climatic change in the Nile Basin. Clim. Res. 1996, 6, 89–95. [Google Scholar] [CrossRef] [Green Version]
  22. Vörösmarty, C.J.; Green, P.; Salisbury, J.; Lammers, R.B. Global Water Resources: Vulnerability from Climate Change and Population Growth. Science 2000, 289, 284–288. [Google Scholar] [CrossRef] [Green Version]
  23. Fowler, H.J.; Kilsby, C.; O’Connell, P.E. Modeling the impacts of climatic change and variability on the reliability, resilience, and vulnerability of a water resource system. Water Resour. Res. 2003, 39, 1222–1232. [Google Scholar] [CrossRef] [Green Version]
  24. Gui, Z.; Chen, X.; He, Y. Spatiotemporal analysis of water resources system vulnerability in the Lancang River Basin, China. J. Hydrol. 2021, 601, 126614. [Google Scholar] [CrossRef]
  25. Zhongshan Statistics Authority. The Information Network of Zhongshan Statistics, Zhongshan, China. Available online: http://www.zs.gov.cn/main/about/infoview/index.action?id=99593 (accessed on 10 May 2021).
  26. Zhongshan Water Authority. Zhongshan Water Resources Bulletin, Zhongshan, China. Available online: http://www.zswater.gov.cn/main/open/resource/show/index.action?id=36925 (accessed on 10 May 2021).
  27. Chen, Z.; Wei, S. Application of System Dynamics to Water Security Research. Water Resour. Manag. 2013, 28, 287–300. [Google Scholar] [CrossRef]
  28. Wang, Q.F. Advanced System Dynamics; Tsinghua University Press: Beijing, China, 1995. [Google Scholar]
  29. Dong, Z.J. Systems Engineering and Operational Research; National Defense Industry Press: Beijing, China, 2003. [Google Scholar]
  30. Rodriguez-Ulloa, R.; Paucar-Caceres, A. Soft System Dynamics Methodology (SSDM): Combining Soft Systems Methodology (SSM) and System Dynamics (SD). Syst. Pract. Action Res. 2005, 18, 303–334. [Google Scholar] [CrossRef]
  31. Vincenot, C.E.; Giannino, F.; Rietkerk, M.; Moriya, K.; Mazzoleni, S. Theoretical considerations on the combined use of System Dynamics and individual-based modeling in ecology. Ecol. Model. 2011, 222, 210–218. [Google Scholar] [CrossRef]
  32. Polsky, C.; Neff, R.; Yarnal, B. Building comparable global change vulnerability assessments: The vulnerability scoping diagram. Glob. Environ. Chang. 2007, 17, 472–485. [Google Scholar] [CrossRef]
  33. Zuo, D.Q.; Dai, S.S.; Yuan, R.H.; Li, H.Y.; Rui, X.F. Study on the Water Resources Assessment Indexes System. Adv. Water Sci. 1996, 7, 367–374. [Google Scholar] [CrossRef]
  34. Daly, D.; Dassargues, A.; Drew, D.; Dunne, S.; Goldscheider, N.; Neale, S.; Popescu, I.; Zwahlen, F. Main concepts of the “European approach” to karst-groundwater-vulnerability assessment and mapping. Appl. Hydrogeol. 2002, 10, 340–345. [Google Scholar] [CrossRef] [Green Version]
  35. Wang, W.S.; Jin, J.L.; Ding, J.; Li, Y.Q. A new approach to water resources system assessment-set pair analysis method. Sci. China Ser. E-Tech. Sci. 2009, 52, 3017–3023. [Google Scholar] [CrossRef]
  36. Huo, Y.; Wang, L.C.; Jiao, S.X.; Teng, J. Water pollution driving forces and pollution prevention measures priority of medium/small-sized cities in southern China. China Environ. Sci. 2009, 29, 1052–1058. [Google Scholar] [CrossRef]
  37. Xiang, H.M. Research on Construction and Application of Index System for Regional Water Security Assessment. Master’s Thesis, Jinan University, Guangzhou, China, 2011. [Google Scholar]
  38. Chen, Z.H. The Response, Diagnosis and Regulation of Water Security in Water Network Region under Changing Environment: A Case Study of Zhongshan City, Guangdong; Postdoctoral Research Report; Sun Yat-sen University: Guagnzhou, China, 2010. [Google Scholar]
  39. Wang, S.; Xu, L.; Yang, F.; Wang, H. Assessment of water ecological carrying capacity under the two policies in Tieling City on the basis of the integrated system dynamics model. Sci. Total Environ. 2014, 472, 1070–1081. [Google Scholar] [CrossRef]
  40. Chen, X.; Liu, G.Y. System Dynamics in the Ash River Basin Water Pollution Control Planning. Environ. Sci. Manage. 2010, 35, 73–76. [Google Scholar] [CrossRef]
  41. Ting, H.; Liangen, Z.; Yan, Z.; Chuanying, Z.; Jing, L. Water Quality Comprehensive Index Method of Eltrix River in Xin Jiang Province using SPSS. Procedia Earth Planet. Sci. 2012, 5, 314–321. [Google Scholar] [CrossRef] [Green Version]
  42. Chen, Y.; Chen, W. Simulation Study on the Different Policies of Jiangsu Province for a Dynamic Balance of Water Resources under the Water–Energy–Food Nexus. Water 2020, 12, 1666. [Google Scholar] [CrossRef]
  43. Guo, H.C. A Study on Water Pollution Control System in Luoyang City. Acta Geogr. Sin. 1988, 43, 318–328. [Google Scholar] [CrossRef]
  44. Zhang, X.; Zhang, H.; Chen, B.; Chen, G.; Zhao, X. Water resources planning based on complex system dynamics: A case study of Tianjin city. Commun. Nonlinear Sci. Numer. Simul. 2008, 13, 2328–2336. [Google Scholar] [CrossRef]
  45. Zhou, J.; Liu, L.F.; Tian, Y.P.; Xie, X.L. Concept and Quantitative Assessment of Vulnerability of Surface Water Resource. Resour. Sci. 2007, 29, 92–98. [Google Scholar] [CrossRef]
  46. Xia, J.; Weng, J.W.; Chen, J.X.; Qiu, B. Multi-scale Water Vulnerability Assessment Research. J. Basic Sci. Eng. 2012, 20, 1–14. [Google Scholar] [CrossRef]
  47. Gao, W.P.; Lin, W.H. Characteristics of Temporal and Spatial Distribution of Precipitation in Pearl River Area. J. China Hydrol. 2009, 29 (Supp. S1), 19–21. [Google Scholar]
  48. Xiong, Y.; Li, J.; Jiang, D. Optimization research on supply and demand system for water resources in the Chang-Zhu-Tan urban agglomeration. J. Geogr. Sci. 2015, 25, 1357–1376. [Google Scholar] [CrossRef]
  49. Li, J.Z.; Zhu, X.; Li, J.B. Research on the Dynamics Analysis and Optimization Decision-Making of Water Resources Supply and Demand System in Dongting Lake District. J. Nat. Resour. 2013, 28, 199–210. [Google Scholar] [CrossRef]
  50. Wei, S.; Chen, Z.H.; Yi, J.J.; Zeng, L.; Kang, H. Research on the Control Measures of Water Security of Zhongshan Based on System Dynamics. Pearl River 2016, 37, 5–9. [Google Scholar] [CrossRef]
Figure 1. Location of Zhongshan city.
Figure 1. Location of Zhongshan city.
Water 14 01103 g001
Figure 2. The framework of the water resource system.
Figure 2. The framework of the water resource system.
Water 14 01103 g002
Figure 3. The flow graph of the water resource system.
Figure 3. The flow graph of the water resource system.
Water 14 01103 g003
Figure 4. Vulnerability score and grading in different scenarios. (a) Scenario 1; (b) scenario 2; (c) scenario 3; (d) scenario 4.
Figure 4. Vulnerability score and grading in different scenarios. (a) Scenario 1; (b) scenario 2; (c) scenario 3; (d) scenario 4.
Water 14 01103 g004
Table 1. Evaluation index scoring criterion and weight.
Table 1. Evaluation index scoring criterion and weight.
Criteria LayerElement LayerIndex LayerUnitWeightThe Vulnerability Grade of Water Resource System
Extremely VulnerableVery
Vulnerable
Moderately VulnerableSlightly VulnerableNot
Vulnerable
ExposureWater environmentRWCT (−)%0.1038[0, 20](20, 40](40, 60](60, 80](80, 100)
CODEI (+)kg0.3130(6, +∞)(3, 6](2.25, 3](1.5, 2.25][0, 1.5]
SensitivityLoad on population and economyPCWRQ (−)m3/people0.1528[0, 1000](1000, 1500](1500, 2000](2000, 3000][3000, +∞)
GRGDP (−)%0.0221[0, 8](8, 11](11, 14](14, 20](20, +∞)
WUAPIAV (+)m3/104 RMB0.0447(200, +∞)(90, 200](30, 90](10, 30][0, 10]
AdaptabilityEconomic, social and water supplyPCGDP (−)104 RMB0.0802[0, 1](1, 2.5](2.5, 5](5, 10](10, +∞)
UR (−)%0.0221(80, 100)(50, 80](35, 50](20, 35][0, 20]
YWSGR (−)%0.2613[0, 75](75, 82](82, 90](90, 95](95, 100)
Scoring Criterion100~8080~6060~4040~2020~0
Note: (+) indicates a positive indicator, the larger the indicator, the more fragile the system is; (−) indicates a negative indicator, the larger the indicator is, the better it is to reduce the fragility of the system.
Table 2. The error statistics of model simulation results.
Table 2. The error statistics of model simulation results.
YearTP (104 People)GDP (104 RMB)TWD (104 m3)
Simulation ValueHistorical ValueError
(%)
Simulation ValueHistorical
Value
Error
(%)
Simulation ValueHistorical ValueError
(%)
20153.21 × 1023.21 × 1020.002.71 × 1072.71 × 1070.001.57 × 1051.58 × 105−1.01
20163.23 × 1023.23 × 102−0.042.71 × 1072.83 × 107−4.221.54 × 1051.50 × 1052.11
20173.26 × 1023.26 × 102−0.062.83 × 1072.94 × 107−3.731.52 × 1051.44 × 1055.56
20183.31 × 1023.31 × 102−0.102.94 × 1073.05 × 107−3.771.51 × 1051.42 × 1056.59
20193.38 × 1023.38 × 102−0.113.05 × 1073.10 × 107−1.571.52 × 1051.48 × 1052.28
20204.41 × 1024.42 × 102−0.123.10 × 1073.15 × 107−1.561.57 × 1051.47 × 105−1.48
Table 3. Results of sensitivity analysis.
Table 3. Results of sensitivity analysis.
X GRTPGRGDPGRFOIAGRLNWRR
Q
TP0.033470.000000.000000.000000.00000
GDP0.000000.046450.000000.000000.00000
WDPI0.000000.000000.004400.000170.00000
DWD0.032870.000000.000000.000000.00000
CODEA0.014830.028950.000000.000000.04386
WRA0.009600.035760.000000.000000.49590
S 0.015130.018530.000730.000030.08996
Table 4. Parameter setting schematics of the four different scenarios.
Table 4. Parameter setting schematics of the four different scenarios.
No.Scenario NameParameter Setting
1Normal Development modeAutomatically feedback the analogue simulation in the normal development according to the development situation of the research area.
2Technical Innovation
Development mode
Strengthen water saving according to the development situation of the research area; adjusting WUAPIAV, WUAPAAV, WUAPTIAV, UPCWQ, RPCWQ, FIWQ, FOIWQ, FWQ, LWQ, and other parameters to be 80% of those of the normal development model and increase WRR and RWCT to be 1.2 times of those of the normal development.
3Social Economic Improvement Development modeAdjust the industrial structure proportions according to the development situation of the research area. Adjust PPI to be 95%, PSI to be 97%, and GRGDP, GRTP, and UR to be 1.2 times of those of the normal development model.
4Comprehensive
Development mode
Integrate scenarios 2 and 3 based on the normal development model.
Table 5. The WRSVD of Zhongshan City in the four scenarios.
Table 5. The WRSVD of Zhongshan City in the four scenarios.
Scenario
Year
202120222023202420252026202720282029203020312032203320342035
125.4525.0524.6724.3223.9823.5423.1022.6622.3623.0024.8626.7828.7430.7632.34
225.0224.2423.4722.7322.0321.6621.2820.9020.6320.4220.3020.1720.0519.9219.80
325.4224.9824.5724.1723.8123.3022.8022.6424.7526.9429.5132.0533.7135.4237.31
424.9924.1723.3622.5921.8721.4421.0120.6420.4120.1820.0519.9219.7919.6619.53
Scenario
Year
203620372038203920402041204220432044204520462047204820492050
133.8935.4837.2339.1841.1741.4441.5341.6241.7241.8241.9242.0342.1442.2542.37
219.7119.6319.5519.4619.9522.3624.8227.3429.7631.3733.0134.7136.6638.6439.53
339.8441.5041.6441.7841.9442.0942.2642.4342.6142.7942.9843.1743.3643.5543.74
419.4421.2924.4627.7330.5232.6534.8937.4939.6439.8039.9640.1340.3040.4640.63
Table 6. Vulnerability grade comparison of different scenarios.
Table 6. Vulnerability grade comparison of different scenarios.
ScenarioModerately VulnerableSlightly VulnerableNot VulnerableTotal Average Value
YearsAverage ValueProportionYearsAverage ValueProportionYearsAverage ValueProportion
11141.820.371927.760.63000.0032.91
2000.002326.130.77719.720.2324.64
31442.560.471628.20.53000.0034.90
4440.380.132127.10.70519.670.1727.63
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Wei, S.; Lin, K.; Huang, L.; Yao, Z.; Bai, X.; Chen, Z. Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta. Water 2022, 14, 1103. https://doi.org/10.3390/w14071103

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Wei S, Lin K, Huang L, Yao Z, Bai X, Chen Z. Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta. Water. 2022; 14(7):1103. https://doi.org/10.3390/w14071103

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Wei, Shuai, Kairong Lin, Liyan Huang, Zeyu Yao, Xiaoyan Bai, and Zhihe Chen. 2022. "Assessing the Vulnerability of Water Resources System Using VSD-SD Coupling Model: A Case of Pearl River Delta" Water 14, no. 7: 1103. https://doi.org/10.3390/w14071103

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