Evaluation and spatial-temporal evolution of water resources carrying capacity in Dongting Lake Basin Open Access

Water is one of the important resources for the survival and development of human society. With the growth of population and development of the economy, water shortage and the deterioration of the water environment are becoming increasingly serious and are gradually turning into important factors restricting the sustainable development of society and the economy (Zuo & Zhang 2015). Water resources carrying capacity (WRCC) is an important indicator to characterize the state of regional water resources. Scientific measurement of regional WRCC is not only a necessary prerequisite for carrying out water resource regulation, but also an important basis for sustainable economic and social development in the region (Song & Zhan 2011; Yang et al. 2015, 2019). Comprehensive evaluation of regional WRCC helps grasp the interactive relationship between water resources and economic development, and provides theoretical support and a realistic basis for coordinating economic, social, and environmental development (Simanjuntak et al. 2020).

Research on WRCC is relatively abundant, mainly focusing on the definition, the evaluation index system, and evaluation methods of WRCC. At present, there is still no consensus on the definition of WRCC. Hui et al. (2001) believed that WRCC is the water system's largest supporting capacity for socioeconomic development in the region during certain development stages, and emphasize the supporting role of water resources for social and economic development. Xia (2002) believed that WRCC is the ability to support population size and sustainable socioeconomic development, and reflects the mutual relationship between water resources and social economy, which is more scientific and reasonable. As for the evaluation index system, a three-dimensional evaluation index system is often constructed based on water resources–social economy–ecological environment (Li et al. 2020). A five-dimensional rating index system about driving force–pressure–status–influence–response (DPSIR) is used to evaluate WRCC (Chen et al. 2004; Zhu & Wang 2005; Li et al. 2012; Weng et al. 2020). An evaluation index system from the perspectives of quantity–quality–watershed-flow comprehensively evaluated the WRCC of the Beijing–Tianjin–Hebei area (Yu et al. 2020). In terms of quantitative research of WRCC, the commonly used evaluation methods are mainly the conventional trend method, principal component analysis method, fuzzy comprehensive evaluation method, and system dynamics method. Cui et al. (2018) adopted set pair analysis and the improved entropy weight method to determine the objective weight of indicators, making the evaluation results more real and accurate. Qu & Fan (2000) used the conventional trend method to study the WRCC of the Hei River Basin as well as analyzing the supply and demand relationship of water resources in the basin under different schemes. Some authors used the system dynamics method to establish a comprehensive assessment model of water resources in Yiwu and Xi'an (Feng et al. 2008; Haddeland et al. 2014). Li et al. (2014) used the principal component analysis method to comprehensively evaluate the resource carrying capacity of Zhengzhou and found the main factors affecting the resource carrying capacity through the principal component score. Yuan et al. (2017) applied the improved fuzzy comprehensive evaluation method to evaluate the WRCC in Jiangyin City. He et al. (2019) used the TOPSIS comprehensive evaluation method to study and analyze the dynamic changes and spatial differences of the WRCC of the Yangtze River Economic Belt from 2007 to 2016. Lin et al. (2020) applied the TOPSIS model based on the entropy weight method, analysis, and evaluation of the WRCC in Kubuqi desert area from 2013 to 2018, and put forward suggestions for improvement.

The above research shows that WRCC has gradually become a research hotspot in regional economics and economic geography. In general, existing studies have produced good research on the evaluation of WRCC, which has created strong progress concerning WRCC. However, existing studies still have the following limitations. First, most of the research areas for WRCC are local areas, such as the western, central, and northeastern provinces, as well as specific urban groups like the Yangtze River Delta in China. There are few studies on the comprehensive evaluation of WRCC based on river basins. Second, the index system needs to be further improved. Most existing studies adopt socioeconomic and water resource indicators (Rijiberman & van de Venb 2000; Wu et al. 2018), and there is no consideration of water environment indicators that affect WRCC. Third, most of the existing literature uses subjective weighting such as the principal component analysis method or objective weighting such as the entropy weighting method, which may lead to insufficient accuracy of the evaluation results of WRCC.

Based on the current research work, this paper takes the Dongting Lake Basin as the research area. Considering water environment indictors' impact on WRCC, the paper applies the water environment monitoring data of each basin monitoring point of the Ecological Environment Monitoring Center of Dongting Lake, and selects indicators from the three subsystems of water resources, water environment, and social economy, and then constructs a comprehensive evaluation index system of the WRCC. Finally, the program takes the TOPSIS model combined with composite weighting based on the improved analytic hierarchy process (AHP) and entropy value method to determine the comprehensive weight of each indicator, and the temporal and spatial dynamic changes of WRCC in the Dongting Lake Basin from 2009 to 2018 are evaluated.

The remainder of this paper is organized as follows. The next section introduces the study area and data source, followed by a section describing the research methods. Then, a section presents the empirical research results and the final section discusses the research findings and concludes the study.

The Dongting Lake Basin is located in the northeast of Hunan Province, on the south bank of the Jingjiang River in the middle reaches of the Yangtze River, forming a complex interaction relationship with the Yangtze River. The Ouchikou, Songzikou, and Taipingkou (referred to as ‘Three Outlets’) to the north divert the Yangtze River water from the Oujichi River, Songzi River, and Hudu River into Dongting. Xiangjiang, Zijiang, Yuanjiang, and Lishui (referred to as the ‘Four Rivers’) come from the south and are injected into the Dongting Lake and, after being stored in the lake, the water system flows through Yueyang City into the Yangtze River. Dongting Lake is an important throughput lake in the Yangtze River Basin. With the advancement of industrialization, farming modernization, and urbanization in the lake area, the relationship between the Yangtze River and Dongting Lake evolved after the Three Gorges Project became operational. The water resource shortage and worsened water resource environment has evolved into the restricted condition of Dongting Lake regional economic development. As such, studying the spatiotemporal evolution of the WRCC of the Dongting Lake Basin is of great significance for solving the contradiction between the supply and demand of water resources. It also promotes the sustainable use of water resources and creates the Dongting Lake eco-economic zone and the green development demonstration zone.

Based on Dongting Lake Basin from 2008 to 2019, Hunan Statistical Yearbook, Hunan Water Resources Bulletin, and Hunan Dongting Lake Ecological Environment Monitoring Center obtained water resources data and socioeconomic data. Ten representative Dongting Lake Basin monitoring stations (Figure 1) were screened to obtain the water environment data, and included: S1 Songzi River's Xinjiangkou; S2 Hudu River's Mituo Temple; S3 Oujichi River's Butler Shop; S4 Xiangjiang's Zhuting Town; S5 Zijiang's Pingkou; S6 Yuanshui's Guanyin Temple; S7 Shimenxinguan of Lishui; S8 Yueyang Tower in the east of Dongting Lake; S9 Xiaohezui in the west of Dongting Lake; and S10 Hengling Lake on the south bank of Dongting Lake.

According to the definition of WRCC and the principles of comprehensiveness, representativeness, comparability, and data availability of the evaluation index system, this paper constructs the three-dimensional indicator system of water resources system, water environment system, and social economic system. The water resource system includes four indicators: water resources per capita, water production modulus, river patency, and average annual runoff. The water environment system includes total water phosphorus content (TP), total water nitrogen content (TN), chemical oxygen demand (COD), and water dissolved oxygen content (DO). The socioeconomic system includes urban population density, urbanization rate, regional GDP, 10,000 industrial value-added water consumption and 10,000 agricultural value-added water consumption. Table 1 shows the selection of specific indicators and their impact on WRCC.

In order to accurately reflect the weights of the indicators of the water resources system, water environment system, and socioeconomic system of Dongting Lake Basin's WRCC, this paper combines with subjective weighting by AHP and objective weighting by entropy weighting method to determine the weight.

AHP was proposed by the American scholar A. L. Saaty (1990). It introduces the experience and professional knowledge of decision-makers into the evaluation process to determine the subjective weight of each evaluation index. The specific calculation steps are presented:

• (i)

  the judgment matrix ⁠, where is the relative importance of index u compared with indicator v, and its value range is 1–9.

• (ii)

  The second step is to calculate the maximum eigenvector of the judgment matrix. According to the formula ⁠, the maximum eigenvalue and corresponding eigenvector of the judgment matrix can be obtained.

• (iii)

  The third step is the consistency test. When the consistency ratio CR <0.1, it shows that the judgment matrix has passed the consistency test, and after normalization, the weight of each index is obtained.

Compared with the AHP, the entropy weight method can objectively determine its weight based on the information provided by each evaluation index, to avoid the deviation of the results caused by the randomness of subjective weighting, where the entropy weight can measure the degree of disorder of the system, that is, the smaller the information entropy of the indicator, the larger the entropy weight, that the indicator is more important, and vice versa. The main calculation steps are as follows:

• (i)

  Data standardization. Construct the original data matrix: ⁠. Due to the different dimensions and index attributes of the data, the data will be standardized using a standardized method:

The normalization matrix is obtained, where is the normalized value of the i-th index at the j-th monitoring point; and and represent the maximum and minimum values of the i-th target at the j-th observation point.

• (ii) Calculation indicator weights. According to the definition of information entropy in information theory, the information entropy of a set of data is as follows:

  

  

  (2)
• (iii) is the characteristic proportion of the indicator. Therefore the weight of each indicator is

  

  (3)
• (iv) Upon determining the weights of the indicators, we established an indicator weighted criterion matrix using the following formula:

  

  (4)

Technique for order preference by similarity to an ideal solution (TOPSIS) is one of the methods for comprehensive evaluation of multi-objective decision-making. The basic idea is to detect the relative distance between the evaluation index and the optimal and the worst solutions. To sort, the concept is clear and operable, and it can be used for horizontal and vertical comparative analysis. Specific evaluation steps are as follows:

• (i)
  Calculate relative distance. We calculated the ideal solution and the negative solution of the indicator weighted criterion assessment value using formula (5):

  

  (5)

The value range of is [0–1]. When T is larger, it is closer to the ideal solution, and the WRCC is higher. When T is smaller, the distance from the ideal solution is farther, and the WRCC is lower. As such, the status of WRCC must be evaluated. Comprehensively referring to the research results of many scholars, the final calculation result T is divided into five levels representing severe overload, overload, reasonable, good, and high quality of WRCC, as shown in Table 2.

According to the calculation steps of combination weighting, the weights of each index are calculated by formulas (1)–(4). The specific results are shown in Table 3.

According to the calculation results of the combined weights, using formulas (5) and (6), the evaluation results of the WRCC of each basin can be calculated as shown in Table 4.

During the inspection period, the average WRCC of the entire basin of Dongting Lake decreased from 0.472 in 2009 to 0.415 in 2018 (Figure 2). Among them, the WRCC of the Dongting Lake district decreased from 0.509 in 2009 to 0.456, from a good level to a reasonable level, showing a steady decline in general. The average value of Three Outlets Basin declined from 0.413 in 2009 to 0.305 in 2018, the evaluation level of the WRCC going from a reasonable stage to an overloaded state (Figure 3). The value of the WRCC of the Xiangjiang Basin increased from 0.291 in 2009 to 0.397 in 2018, and its evaluation level increased from severe overload to an overload state (Figure 4). The WRCC basically showed a steady downward trend (Figure 4). According to Figures 2–4, the time evolution of the WRCC of the Dongting Lake Basin can be divided into two stages.

The first stage is from 2009 to 2014. During this period, the WRCC of the Dongting Lake Basin generally declined. The carrying capacity of water resources decreased from 0.472 to 0.398, and the evaluation level dropped from reasonable to overloaded. The main reason was that the total GDP of Hunan Province increased from 130.6 billion to 270.7 billion yuan in 2009–2014, a growth rate of 7.9%. The water consumption per yuan of GDP and the value-added industrial water consumption per 10,000 yuan were 135 m³ and 83 m³. The total discharge of TN, TP, and COD from industrial wastewater into the lake from 856,000 tons, 67,000 tons, and 1.1 million tons, respectively, increased to 984,000, 83,000, and 1.316 million tons. Due to the impact of the operation of the Three Gorges Project, the total runoff of Dongting Lake water resources from 213 billion m³ decreased to 198 billion m³. The WRCC of Ouchi River, Songzi River, and Hudu River decreased from 0.421, 0.404, and 0.413 in 2009 to 0.345, 0.332, and 0.337 in 2014, respectively. WRCC level dropped from reasonable to overload level. The main reason is the total runoff of water resources in the Three Outlets Basin decreased from 47.8 billion m³ to 36.3 billion m³, and the river channel drying extensions from 69 to 129 days. The carrying capacity of the water resources in Xiangjiang increased from 0.291 in 2009 to 0.359 in 2014, and the evaluation level has rebounded from a severe overload level to an overload level. The main reason is that the Xiangjiang is the concentration area for traditional industries such as metals, steel, coal, food processing, etc. in the Dongting Lake Basin. The total industrial wastewater discharge over the past five years was 83.09 million tons, accounting for about 82% of the total wastewater discharge in the Dongting Lake Basin. Until very recently, people have only gradually realized the importance of environmental protection, and the government has adopted environmental control policies to strictly limit the discharge of industrial sewage, which has made the carrying capacity of water resources in the Xiangjiang River Basin improve.

The second stage is from 2015 to 2018. The WRCC of the Dongting Lake watershed fluctuated in a reasonable scope between 0.398 and 0.415, and the difference between the WRCC of the watersheds of Dongting Lake gradually narrowed. The main reason is the planning and implementation of the Dongting Lake ecological economic zone and the construction of the Chang-Zhu-Tan type experimental area. The government plays a key role in coordinating economic development and environmental protection. It mainly implements the policy of adjusting and optimizing the layout of high water-consumption industry such as non-ferrous metal mining and smelting, petrochemicals, papermaking, and power and energy. The special governance policy of the Dongting Lake ecological environment has optimized the socioeconomic system and the water environment system and gradually increased the WRCC of Dongting Lake basin. However, the state of water resources has been affected by the river–lake relationship. The continuous decline of water resources in total supply negatively affects the WRCC.

Table 4 shows the WRCC of the Dongting Lake Basin in 2018. The WRCC of the Ouchi River, Songzi River, Hudu River, Xiangjiang, and East Dongting Lake areas are in overload level, and their bearing capacity values are 0.303, 0.311, 0.302, 0.397, and 0.396, respectively. Zijiang, Yuanjiang, and South Dongting Lake areas are at a reasonable level, and the carrying capacity values are 0.469, 0.418, and 0.448, respectively. Lishui and West Dongting Lake are at a good level, with carrying capacity values of 0.582 and 0.522. The overall ranking of WRCC is Lishui > West Dongting Lake > Zijiang > South Dongting Lake > Yuanjiang > Xiangjiang > East Dongting Lake > Songzi River > Ouchi River > Hudu River. In order to more intuitively reflect the changes in WRCC of the Dongting Lake Basin in 2009 and 2018, Arc GIS was used to draw a comparison chart of WRCC levels (Figure 5).

The water resources per capita, river patency, water production modulus and average annual runoff in the water resources system of the Three Outlets Basin are 1,873 m³, 286 days/year, 3,348 m³/km², respectively. The annual average runoff of 5.7 billion m³ greatly impacts the carrying capacity of the Three Outlets Basin. The TN content in the water environment system exceeds the standard, and the value-added agricultural water consumption per 10,000 yuan is 359 m³/10,000 yuan. The contribution rate of agricultural non-point source pollution to the total nitrogen in the basin exceeds 70% which adversely affects the status of WRCC. The population density of cities in the Xiangjiang, represented by Changsha, Zhuzhou, Xiangtan, and Hengyang, has reached 655 per/km, and total GDP has reached 1,842.238 billion yuan, accounting for 50% of Hunan Province. The urbanization development and the total value of industrial production are significantly and positively related to TN, TP, and COD which negatively affect the carrying capacity of water resources in the Xiangjiang. With the decrease of the amount of water entering the East Dongting Lake, the self-purification capacity of the water body decreases. Moreover, the accumulation of pollutants is obvious, and the average annual range of total phosphorus is in the range of 0.063–0.127 mg/L, exceeding the Class III water quality limit value of lakes and reservoirs, thus placing great pressure on WRCC.

Based on the construction of a comprehensive evaluation index system of the WRCC of the Dongting Lake Basin, the entropy-weighted TOPSIS method was used to analyze the temporal evolution and spatial distribution characteristics of the WRCC of the Dongting Lake watershed, to investigate the impact of the WRCC of the Dongting Lake watershed factors, and the main research conclusions are as follows: (1) From 2009 to 2018, the WRCC of the Dongting Lake basin went from a good level to a reasonable level, showing a steady decline. The carrying capacity of the Ouchi River, Hudu River, Songzi River basin, and East Dongting Lake has decreased significantly. (2) Obvious differences exist in the spatial distribution of WRCC in the Dongting Lake Basin. The WRCC of Lishui, Zijiang, and West Dongting Lake is relatively high, whereas that of Xiangjiang, Yuanshui, Ouchi River, Hudu River, Songzi River, and East Dongting Lake is poor. The spatial distribution of WRCC in the Dongting Lake basin is in the order of Lishui > West Dongting Lake > Zijiang > South Dongting Lake > Yuanjiang > Xiangjiang > East Dongting Lake > Songzi River > Ouchi River > Hudu River.

The study of the WRCC of the Dongting Lake Basin provides the policy basis for the construction of the Dongting Lake ecological economic zone and the green development demonstration zone in the middle reaches of the Yangtze River. Possible policy measures to improve and enhance the water carrying capacity of the Dongting Lake Basin include:(1) Strengthen the construction of water conservancy engineering facilities in the Three Outlets Basin. Dredge the Ouchi River, Songzi River, and Hudu River. Increase the amount of water flowing into the Three Outlets Basin and reduce the main river channel cut-off time; implement dredging and dredging projects to restore the connectivity and water delivery capacity of the channel, and solve the problems of serious siltation. (2) Use ‘source reduction–process control–end treatment’ to reduce industrial, agricultural, and domestic pollution emissions in the Dongting Lake Basin. Guide and optimize the industrial layout of the Xiangjiang River; promote the industrial transfer of Xiangjiang River to Industry shifted to Yueyang and Yiyang cities; strengthen the supervision and management of large water industry emissions; promote the transformation and upgrading of petrochemical, steel, and non-ferrous metal industries; strengthen the supervision and governance of sewage discharges of papermaking and petrochemical enterprises in the Dongting Lake area, and encourage key enterprises to implement cleanliness. Recycling of industrial water; carry out special rectification of the water environment of Dongting Lake and repair the water ecological environment of Dongting Lake Basin. Adjust the agricultural production structure in the Three Outlets Basin, prominent agricultural non-point source pollution control, reducing the use of chemical fertilizers and pesticides, vigorously promoting high-efficiency, low-toxic, low-residue chemical and biological pesticides, piloting and promoting agricultural water-saving technology demonstrations. Increasing the concentration of urban and rural water pollution treatment and accelerating urban and rural life with the construction and reconstruction of centralized sewage treatment facilities will achieve full coverage of the construction of sewage treatment facilities in cities, key towns, and villages along the lake.

The authors are grateful to two anonymous reviewers from the editorial department of the Journal of Water and Climate Change, and Jianxin Xiong, professor of Hunan Institute of Science and Technology, for their suggestions about the manuscript. This research is funded by the Natural Science Foundation of Hunan Province, China, grant number 2019JJ40107; the Natural Science Foundation of Hunan Province, grant number 2018JJ2157.

All relevant data are included in the paper or its Supplementary Information.