Analysis of Carrying Capacity and Obstacle Factors of Water Resources in Longnan City, China, Based on Driving–Pressure–State–Response and Technique for Order Preference by Similarity to an Ideal Solution Models

Abstract

Measuring the carrying capacity of water resources and identifying obstacle factors are critical prerequisites for the rational allocation of regional water resources and the high-quality development of economic society. This study took Longnan City, a typical city in northwest China with abundant water resources but an underdeveloped economy, as the research object. Based on the DPSR (Driving–Pressure–State–Response), an evaluation indicator system was constructed. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and an obstacle model were used to calculate the water resource carrying capacity and main obstacle factors of Longnan City from 2009 to 2019. The results showed that the carrying capacity of water resources in Longnan City had steadily improved, and the comprehensive closeness had increased from 0.44 (2009) to 0.60 (2019). From the perspective of the ruler layer, the carrying capacity of the driving force system increased from 0.05 in 2009 to 1.00 in 2019. The capacity of the state system increased during the change, with the highest value (0.85) appearing in 2013 and the lowest value appearing in 2016. All subsystems, except the pressure system, had a load-carrying capacity of 0.60 or more in 2019. The utilization of unconventional water resources, the proportion of eco-environmental water use, the volume of wastewater discharge, and the per capita urban daily water consumption are the primary factors affecting the water resource carrying capacity of Longnan City. Developing unconventional water sources, improving sewage treatment technology, promoting efficient water-saving technology, and strengthening environmental protection is the key to improving the water resources carrying capacity of Longnan City. This research provides the basis for enhancing the carrying capacity of water resources and sustainable urban development in Longnan City and other cities in China and water scarcity cities in other countries.

1. Introduction

Water is a fundamental natural resource, a strategic economic resource, and a restrictive driving factor for the ecological environment [1,2,3]. The carrying capacity of water resources represents the maximum socio-economic development scale that water resource systems can support [4]. With the acceleration of industrialization and urbanization, sustained population growth, and rapid economic development, globally available water resources are continuously decreasing. The demand for water resources in some regions has exceeded regional water resource carrying capacity [3]. Overloading water resources can trigger a series of social, ecological, and environmental security issues and severely restrict regional economic and social development [5]. Under the most stringent water resource management system and rigid constraints of water resource carrying capacity, evaluating water resource carrying capacity is essential for the healthy development of the regional economy and society, optimal allocation of water resources, and ecological environment protection [6].

Water resource carrying capacity is a typical complex system, which is affected by multiple factors such as the economy, society, and ecological environment. Combining models to construct an evaluation indicator system is the first step in calculating regional water resource carrying capacity. However, due to scholars’ different research perspectives on water resource carrying capacity, indicator systems are diverse. Yang et al. [4] and Xu et al. [7] took into account socio-economic, water resources, and water ecology to build indicator systems and comprehensively evaluated the water resource carrying capacities of Nanjing and Weifang cities in China, respectively. Al Kalbani et al. [8] synthetically assessed the water resource carrying capacity of Oman using the framework of DPSIR (Driving–Pressure–State–Influence–Response). Liu et al. [9] constructed an indicator system from the perspective of “using water to determine land, people, and production” and assessed the water resource carrying capacities of cities in Henan Province. In terms of the construction of the above indicator system, DPSR, as a fixed model for the construction of the indicator system, can better reflect the main role of the target system, the social and economic system, and the ecological environment system and is widely used by scholars.

There are many factors affecting the carrying capacity of water resources and various evaluation methods. Selecting the evaluation method is the second step in the calculation of regional water resources carrying capacity. Evaluation methods mainly include TOPSIS [10], fuzzy comprehensive evaluation [11], set pair analysis [12], and cloud models [13]. Shi et al. [14] studied the water resource carrying capacity and obstacle factors in Hebei Province based on the DPSIR-TOPSIS model. Gulishengmu et al. [15] evaluated the water resource carrying capacity of Northwest Oasis using GRA-TOPSIS. Zhi et al. [16] adopted the methods of entropy weight and analytic hierarchy process to determine indicator weights and evaluated the water resource carrying capacity of the northern slope of the Tianshan Mountains using the fuzzy comprehensive evaluation method. Wang et al. [17] analyzed the changing trend of water resource carrying capacity in Changchun City based on system dynamics and the improved fuzzy comprehensive evaluation method. Xiao et al. [18] calculated the water resource carrying capacity of Wuhan according to a modified ecological footprint model. Jia et al. [19] analyzed the change in the water resource carrying capacity of Zhengzhou City based on the DPSIR-TOPSIS model. Yang et al. [20] used a fuzzy variable method to assess the water resource carrying capacity of the Yangtze River Basin. Hu et al. [21] proposed an advanced method for calculating water resource carrying capacity based on a two-dimensional model and the analysis of water resource supply–demand, which was applied to the Inner Mongolia Autonomous Region. All of the methods above have certain advantages and disadvantages. For example, a fuzzy comprehensive evaluation is suitable for systems with diverse factors and complex structures, but it has no systematic method to determine membership function and is more complicated. The TOPSIS model can objectively reflect the dynamic changes of the target with a low number of samples, simple operation, and calculation results.

In summary, most studies on water resource carrying capacity focus on provincial [22,23,24] and prefecture [25,26,27] level cities. However, there are few studies on regions with abundant water resources but average economic development levels, especially in northwest China. Longnan City is located in the southeast of Gansu Province, China. Water resources here are relatively abundant but unevenly distributed in time and space, resulting in a severe engineering water shortage. The distribution of water resources in this area is out of kilter with economic and social development. Moreover, severe water pollution has led to a sharp decrease in available water resources. In view of this, this study took Longnan City as an example, constructed a water resource carrying capacity indicator system based on the DPSR model, calculated the water resource carrying capacity from 2009 to 2019 using the TOPSIS model, and introduced an obstacle model to diagnose the main obstacle factors of water resource carrying capacity. The objectives of this research were (1) to clarify the carrying capacity of water resources in Longnan City and its key influencing factors and (2) to provide a theoretical basis for the rational utilization of water resources and the sustainable development of the economy and society in Longnan City and other cities in China and water scarcity cities in other countries.

2. Data Sources and Research Methods

2.1. Overview of the Research Area

Longnan City (104°01′19″ E–106°35′20″ E, 32°35′45″ N–34°30′00″ N) is located in the Qinba Mountains, the southeastern part of Gansu Province, western China, with a land area of 27,800 km², an average annual temperature of higher than 12 °C, average annual precipitation of 450~800 mm, and a frost-free period of 280 days. It is rich in water resources, belonging to the Jialing River system. The per capita water resources and per mu water resources have been higher than the average levels of Gansu Province for many years. However, it is challenging to manage water resources due to a low utilization rate and prominent engineering water shortage. It is mainly reflected in inadequate agricultural irrigation facilities, small scales of rural water supply projects, low guaranteed degrees of water supply, and the arduous task of allocating water resources across counties and districts. Therefore, it is urgent to promote water reform in the city.

2.2. Data Sources

The data are from the 2009–2019 Gansu Development Yearbook, Gansu Provincial Water Resources Bulletin, and Longnan City National Economic and Social Development Statistical Bulletin.

2.3. Research Methods

2.3.1. Indicator System Construction

As a conceptual framework for constructing an indicator system, DPSR can well reflect the relationship among regional water resources, economic society, and ecological environment. The indicator of driving force (D) means the driving of population and the development of economic society results in water resource demand and wastewater discharge pressure (P). The indicator of state (S) is the current state of water resources under the driver of the above-mentioned factors. The indicator of response (R) refers to a series of practical measures to approach water resource problems, solve supply–demand conflicts, and improve wastewater treatment efficiency through human responses and actions [27].

Therefore, on the basis of summarizing existing research results [14,28,29], combined with the actual situation of Longnan City, complying with the principles of accessibility, scientificity, and authenticity, and according to the DPSR model, this study selected 16 indicators in terms of driving force, pressure, state, and response to evaluate the water resource carrying capacity of Longnan City (

Table 1. Evaluation indicator system and weights of water resource carrying capacity in Longnan City.

Target Layer	Ruler Layer	Indicator Layer	Code	Unit	Indicator Definition	Indicator Property	Weight
Water resource carrying capacity	Driving	per capita GDP	x1	CNY	reflecting the level of economic development	+	0.0147
		urbanization rate	x2	%	reflecting the level of urbanization	−	0.0425
		natural population growth rate	x3	‰	reflecting the growth of population	−	0.1507
		urban population density	x4	per/km2	reflecting the magnitude of population pressure	−	0.0585
	Pressure	per capita urban daily water consumption	x5	L	reflecting the situation of water utilization by urban residents	−	0.0114
		proportion of eco−environmental water use	x6	%	reflecting the demand and importance of ecological water utilization	+	0.0785
		wastewater discharge volume	x7	10,000 t	reflecting the pollution of the environment by wastewater	−	0.0391
		water consumption per 10,000 CNY of GDP	x8	m3/10,000 CNY	reflecting the relationship between economic development and water consumption	−	0.0750
	State	per capita water resources	x9	m3/per	reflecting the number of regional water resources	+	0.0957
		rate of the development and utilization of groundwater	x10	%	accessing the development and utilization of groundwater resources	−	0.0761
		water production modulus	x11	10,000 m3/km2	representing regional water production capacity per unit area	+	0.0850
		annual precipitation	x12	mm	reflecting regional precipitation	+	0.0165
	Response	popularity rate of urban water	x13	%	reflecting the popularity level of urban water utilization	+	0.1151
		daily capacity of wastewater treatment	x14	10,000 m3	reflecting the capacity of regional wastewater treatment	+	0.0063
		unconventional water resource utilization	x15	a hundred million m3	reflecting the treatment and utilization of regional unconventional water resources	+	0.1060
		water-saving irrigation area	x16	hm2	reflecting the degree of regional agricultural water-saving	+	0.0288

). The indicator system consists of a target layer, a ruler layer, and an indicator layer. The target layer is the water resource carrying capacity; the ruler layer contains subsystems of the driving force, pressure, state, and response; the indicator layer is a refinement of the ruler layer, externalized as each indicator. The weight of each indicator was determined using the entropy weight method. The indicator system and weights are shown in Table 1.

2.3.2. Entropy Weight Method

Using the entropy weight method to determine weights can effectively avoid the influence of subjective and objective factors. The specific process of calculation is as follows [30]:

• Construct a matrix of i rows and j columns:

  $$\mathrm{A}=\left[\begin{array}{ccc}{x}_{11}& \dots & x_{1j}\\ \dots & \dots & \dots \\ x_{i1}& \dots & x_{\mathit{ij}}\end{array}\right]$$

  (1)

  where x_ij is the j-th indicator of the i-th year.
• Calculate the proportion of the j-th indicator P_j:

  $$P_j=\frac{X_{\mathit{ij}}}{{\sum }_{i=1}^m{X}_{\mathit{ij}}}(i=1,2, \dots , m)$$

  (2)
• Calculate the entropy of the j-th indicator E_j:

  $$E_j=-{\ln \left(n\right)}^{-1}{\sum }_1^n{P}_{\mathit{ij}}·\ln P_{\mathit{ij}}$$

  (3)
• Determine the weights of indicators w_j:

  $$w_j=\frac{1-E_j}{n-\sum E_j}(j=1,2, \dots , n)$$

  (4)

2.3.3. TOPSIS Model

The TOPSIS model uses the positive and negative ideal solutions in the evaluation system to calculate and rank the distance and closeness between these solutions and each evaluation object, thus determining the quality of each evaluation object. Based on weighted distance, the TOPSIS model overcomes the possibility of reverse order in traditional TOPSIS models, possessing the advantages of a flexible and convenient calculation process and accurate and reasonable evaluation results [31,32]. The specific steps are as follows.

• Normalize the raw data to obtain a normalized matrix X = (X_ij)_m×n:
  The larger, the better the type of indicator:

  $$X_{\mathit{ij}}=\left(x_{\mathit{ij}}-x_{\mathit{jmin}})/(x_{\mathit{jmax}}-x_{\mathit{jmin}}\right)$$

  (5)
  The smaller, the better the type of indicator:

  $$X_{\mathit{ij}}=\left(x_{\mathit{jmax}}-x_{\mathit{ij}})/(x_{\mathit{jmax}}-x_{\mathit{jmin}}\right)$$

  (6)
• Multiply the normalized matrix with the weights of indicators w_j to obtain the weighted standard matrix R = (r_ij)_m×n:

  $$R={\left(r_{\mathit{ij}}\right)}_{m\times n} =\left[\begin{array}{ccc}\begin{array}{cc}{x}_{11}^{\prime }\times & w_1^{\prime }\end{array}& \cdots & \begin{array}{cc}{x}_{1n}^{\prime }& w_n^{\prime }\end{array}\\ ⋰& \ddots & ⋰\\ \begin{array}{cc}{x}_{m1}^{\prime }\times & w_1^{\prime }\end{array}& \cdots & \begin{array}{cc}{x}_{\mathrm{mn}}^{\prime }& w_n^{\prime }\end{array}\end{array}\right]$$

  (7)
• Determine the positive ideal solution R_j⁺ and the negative ideal solution R_j⁻:

  $$\left\{\begin{array}{c}{R}_j^{+}=max{\displaystyle \left\{r_{1j}{, r}_{2j}{, \cdots , r}_{\mathrm{mj}}\right\}}\\ R_j^{-}=min{\displaystyle \left\{r_{1j}{, r}_{2j}{, \cdots , r}_{\mathrm{mj}}\right\}}\end{array}\right.$$

  (8)
• Determine the distances between the evaluation object and the positive and negative ideal solutions, D_i⁺ and D_i⁻:

  $$\left\{\begin{array}{c}{D}_i^{+}=\sqrt{\sum _{j=1}^n{\left[\left(R_j^{+}-r_{\mathit{ij}}\right)\right]}^2}\\ D_i^{-}=\sqrt{\sum _{j=1}^n{\left[\left(R_j^{-}-r_{\mathit{ij}}\right)\right]}^2}\end{array}\right.$$

  (9)

The larger D_i⁺, the farther the evaluation object is from the positive ideal solution, and the less ideal the result is; the larger D_i⁻, the farther the evaluation object is from the negative ideal solution, and the better the result.

5.

Calculate the closeness K:

$$K=D_i^{-}/(D_j^{+}+D_j^{-})$$

(10)

The closer K is to 1, the better the evaluation result.

2.3.4. Obstacle Model

The obstacle model was introduced to identify the main factors constraining the carrying capacity of water resources in Longnan City. It adopts factor contribution degree, indicator deviation, and obstacle degree for diagnosis. The factor contribution degree represents the impact of the i-th indicator on water resource carrying capacity, which can be obtained by the product of indicator weight and ruler layer weight. The indicator deviation is the difference between the i-th indicator and the target of water resource carrying capacity. The obstacle degree represents the limiting degree of an indicator or a subsystem to water resource carrying capacity [33].

• The factor contribution degree F_ij:

  $$F_{\mathit{ij}}=w_j\times W_i$$

  (11)
• The indicator deviation V_ij:

  $$V_{\mathit{ij}}=1-X_{\mathit{ij}}$$

  (12)
• The obstacle degree of an indicator p_ij:

  $$p_{\mathit{ij}}=F_{\mathit{ij}}{V}_{\mathit{ij}}/{\sum }_1^n{F}_{\mathit{ij}}{V}_{\mathit{ij}}$$

  (13)
• The obstacle degree of a subsystem P_ij:

  $$P_{\mathit{ij}}=\sum p_{\mathit{ij}}$$

  (14)

W_i denotes the weight of the ruler layer, X_ij refers to the standardized value of an indicator, and n is the number of indicators.

3. Results and Discussion

3.1. Evaluation of Water Resource Carrying Capacity of Longnan City

3.1.1. Evaluation of Water Resource Carrying Capacity of DPSR Comprehensive System

According to the comprehensive evaluation results of water resource carrying capacity in Longnan City from 2009 to 2019 (Figure 1), overall water resource carrying capacity exhibited an upward trend from 2009 to 2019. It increased year by year from 2009 to 2011 and decreased from 0.47 to 0.37 from 2011 to 2013, falling 21.28% each year. This is mainly due to the greater wastewater discharge volume in 2011, while the proportion of environmental water use in 2011 was less, compared with 2009 and 2010, and then raised in fluctuations from 2013 to 2019, with a trough (0.354) in 2016. It is mainly because 2016 was a drought year, with the minimum annual precipitation during the study period (2009–2019) and relatively low unconventional water resource utilization. On the whole, there were small fluctuations in the water resource carrying capacity of Longnan City. After 2016, the water resource carrying capacity and the per capita water resource all significantly increased, and indicators of per capita GDP and annual precipitation gradually improved.

According to the changing trends of water resource carrying capacity (D_i⁺ and D_i⁻) in Longnan City from 2009 to 2019 (Figure 2), D_i⁺ decreased from 0.22 in 2009 to 0.16 in 2019, and D_i⁻ increased from 0.17 in 2009 to 0.24 in 2019. D_i⁺ and D_i⁻ achieved the maximum and the minimum in 2016, respectively, indicating that the carrying capacity in 2016 was the lowest. It is consistent with the results in Figure 2. Overall, the two showed great fluctuations. During 2012–2014 and 2015–2016, D_i⁺ increased, while D_i⁻ decreased. The distance from the positive ideal solution tended to grow, and the distance from the negative ideal solution presented a reducing tendency, implying poor water resource carrying capacity in this period. The main reason may be that from 2012 to 2014, the per capita water resources decreased, the degree of groundwater development and utilization gradually increased, and the proportion of eco-environmental water use was low from 2015 to 2016.

3.1.2. Evaluation of Water Resource Carrying Capacity of DPSR Subsystems

The TOPSIS model was used to determine the water resource carrying capacity of each subsystem in Longnan City (Figure 3).

(1)

The driving force subsystem

It steadily rose from 2009 to 2019. The closeness increased from 0.05 in 2009 to 1.00 in 2019. It is mainly attributed to the continuous improvement of the economic development of Longnan City. Per capita GDP increased from 5248 CNY in 2009 to 16,868 CNY in 2019, with a rising amplitude of 221.42%. The rate of urbanization also significantly improved, with a rising amplitude of 66.48% over the past 11 years.

(2)

The pressure subsystem

From 2009 to 2019, the closeness increased from 0.45 in 2009 to 0.77 in 2012, then decreased from 0.77 in 2012 to 0.26 in 2018. After 2018, it gradually increased and reached 0.36 in 2019. Overall, the pressure subsystem had a good carrying capacity. From the analysis of the original indicator data of the pressure subsystem, the water consumption per 10,000 CNY of GDP reduced from 171.13 m³/10,000 CNY in 2009 to 4286 m³/10,000 CNY, with a decrease of 74.95%, which may be the main reason for the gradual improvement of carrying capacity. In addition, a significant decrease in the proportion of eco-environmental water use from 2012 to 2018 may also cause a decline in carrying capacity in that year. The low carrying capacity in 2019 may be due to the remarkable enhancement in the per capita urban daily living water consumption, the decrease in ecological environment water utilization, and a large volume of wastewater discharge.

(3)

The state subsystem

From 2009 to 2019, the closeness fluctuated. The carrying capacity increased with fluctuations. The maximum value (0.85) appeared in 2013, and the minimum value emerged in 2016. This is mainly because the year 2016 was a drought year, with low annual precipitation, insufficient per capita water resources, small water production modulus, and high groundwater development and utilization rate. However, after 2016, the carrying capacity of the state subsystem continued to increase and achieved 0.67 by 2019.

(4)

The response subsystem

From 2009 to 2019, the response subsystem presented a fluctuating downward trend. However, after 2018, it tended to increase. Due to the steady increase in the popularity rate of urban water, the carrying capacity reached 0.70 in 2019, reaching 98.25%. The daily capacity of wastewater treatment also achieved the maximum. From 2009 to 2018, the overall capacity of wastewater treatment was low, and the utilization of unconventional water resources decreased. Furthermore, the area of water-saving irrigation presented a drop in fluctuations. These may be the primary reasons for the low carrying capacity of the response subsystem during this period. It also manifests that there is a large promotion space for wastewater treatment and agricultural water-saving in Longnan City.

3.2. Obstacle Factor Determination of Water Resource Carrying Capacity in Longnan City

3.2.1. Obstacle Analysis of Indicators

The obstacle model was adopted to measure the indicators of water resource carrying capacity in Longnan City. This study selected 2009, 2012, 2016, and 2019 as representative years to determine the obstacle degree of each indicator (Figure 4).

Figure 4a shows that the main obstacle factors in 2009 are distributed in each ruler layer. Specifically, x₃ (natural population growth rate) has the highest obstacle degree (26.26%), followed by x₁₃ (popularity rate of urban water, 20.18%). The obstacle degree of x₈ (water consumption per 10,000 CNY of GDP) is 14.50%, and those of other indicators are all less than 10%, implying that they had little influence on the water resource carrying capacity in Longnan City.

The factors that affected the water resource carrying capacity of Longnan City in 2012 are concentrated at the driving force layer (Figure 4b). Among them, the obstacle degree of x₃ (natural population growth rate) increased continuously from 26.26% in 2009 to 28.59% in 2012; the obstacle degrees of x₁₅ (unconventional water resource utilization) and x₁₆ (water-saving irrigation area) gradually decreased to 0; and the obstacle degree of x₁₃ (popularity rate of urban water) slowly declined to 14.67%. However, the impacts of x₅ (per capita urban daily water consumption), x₆ (proportion of eco-environmental water use), and x₇ (wastewater discharge volume) on water resource carrying capacity were gradually enhanced. Among them, x₆ (proportion of eco-environmental water use) increased from 0.16% in 2009 to 6.79% in 2012, with the largest amplification. The obstacle degree of x₇ (wastewater discharge volume) increased to 7.80%, with an amplification of 6.58% compared to 2009.

The obstacle factors to the water resource carrying capacity in Longnan City in 2016 are mainly at the driving force and state layers (Figure 4c). The main obstacle factors are x₃ (natural population growth rate), x₉ (per capita water resources), and x₁₅ (unconventional water resource utilization). Among them, the impact of x₁₅ (unconventional water resource utilization) on water resource carrying capacity gradually increased, with an obstacle degree of 14.36%. Between 2012 and 2016, the utilization of unconventional water resources in Longnan City presented a downward trend. Unconventional water resources can replace some conventional water resources, thus alleviating the contradiction between water supply and demand to some extent. The obstacle degrees of x₂ (urbanization rate), x₇ (wastewater discharge volume), and x₈ (water consumption per 10,000 CNY of GDP) declined to a certain degree, while x₁₃ (population rate of urban water) decreased significantly from 14.67% to 1.00%.

The obstacle factors to the carrying capacity of water resources in Longnan City in 2019 are mainly at the layers of pressure and response (Figure 4d). Among them, the impact of x₆ (proportion of eco-environmental water use) on water resource carrying capacity gradually increased, with an obstacle degree of 21.27%. In recent years, the proportion of eco-environmental water use in Longnan City has gradually decreased. Ensuring eco-environmental water use is the first step in Longnan City’s ecological environment construction. The obstacle degree of x₁₆ (unconventional water resource utilization) increased significantly (24.90%). The utilization of unconventional water resources was still relatively low. However, the obstacle degrees of some indicators were still reduced gradually. Among them, indicators of x₁ (per capita GDP), x₂ (urbanization rate), and x₃ (per capita natural growth rate) gradually declined to 0.

3.2.2. Obstacle Analysis of Subsystems

The obstacle model was used to calculate the obstacle degree of each subsystem of water resource carrying capacity in Longnan City (Figure 5). The results indicate that the changing trends of subsystems are different from 2009 to 2019. The obstacle degrees of the subsystems of driving force and state decreased in fluctuations, while those of the subsystems of pressure and response increased. Between 2009 and 2013, the obstacle degree of the driving force subsystem was the highest. Its steady increase is due to the gradual rising of the natural population growth rate. From 2014 to 2016, the obstacle degree of the state subsystem increased and became the main obstacle system affecting the water resource carrying capacity of Longnan City. This is attributed to the drought years from 2014 to 2016, with less precipitation and lower water production modulus. From 2017 to 2019, the main obstacle system shifted from the driving force subsystem to the pressure subsystem. The major reasons for the sharp increase in the pressure subsystem in 2019 are the increases in per capita urban daily water consumption (about twice the amount in 2018) and wastewater discharge volume (an amplification of 49.83%), and the proportion of ecological environment water consumption decreased. Overall, the pressure subsystem showed a significant upward trend in 2019, indicating that it is necessary to emphasize the pressure subsystem while taking into account the subsystems of response and driving force to improve the water resource carrying capacity of Longnan City in the future.

4. Conclusions and Suggestions

This study built an evaluation indicator system based on DPSR, quantified the water resource carrying capacity of Longnan City using the TOPSIS model, and introduced an obstacle model to measure the main influencing factors of water resource carrying capacity. The following conclusions can be drawn.

(1)

From 2009 to 2019, the overall water resource carrying capacity of Longnan City showed an upward trend;

(2)

From 2009 to 2019, the closeness of the driving force subsystem increased continuously, possessing the optimal carrying capacity; the closeness of the pressure subsystem presented a tendency of increase-decrease-increase; the carrying capacity of the state subsystem reached the peak of 0.85; and the carrying capacity of the response subsystem tended to decrease;

(3)

From the perspective of obstacle factors, the stress subsystem had the highest obstacle degree, followed by the subsystems of response, driving force, and state. The primary obstacle factors to the carrying capacity of water resources in Longnan City are the utilization of unconventional water resources, the proportion of eco-environmental water use, the volume of wastewater discharge, and the per capita urban daily water consumption.

A series of measures should be taken to improve the carrying capacity of water resources in Longnan City. First, strengthen ecological environment construction, increase investment in ecological environment protection, reduce pollution emissions, and build an Eco-City. Second, intensify the rigid constraints of water resources, strengthen the control of water-using quantity, and improve the development and utilization of unconventional water resources. Third, promote the coordinated development between water resources and economic society, accelerate the transformation and upgrading of industrial structure, raise residents’ awareness of water-saving, promote water resource utilization efficiency, and further exert the leverage role of water prices. Fourth, improve the capacity of wastewater treatment and the introduction of new and high technologies. Fifth, reasonably plan population distribution and avoid environmental pressure caused by excessive population concentration in the promotion of urbanization.

This paper analyzed the water resource carrying capacity of Longnan City from 2009 to 2019 and the obstacle factors and could provide a theoretical basis for the rational utilization of water resources and the sustainable development of the economy and society in Longnan City and other cities in China, and water scarcity cities in other countries. In the follow-up study process, the indicator system should be further optimized, and a dual-scale evaluation in space and time should be conducted to improve the evaluation accuracy.