This study investigates the coordination of factors in the three coupled systems of water resources, economy and ecosystem. The study data were economic, social, environmental and water resource indicators of the Henan section of the Yellow River basin from 2000 to 2019. Data were analyzed using a model of coupling coordination, grey relational analysis and a combined sparrow search algorithm–back propagation (SSA-BP) prediction model. Evaluation, analysis and prediction of the water resources–economy–ecosystem complex were undertaken. The results show that: (1) the degree of coupling coordination of the water resources–economy–ecosystem complex in the Henan section of the Yellow River basin showed an increasing trend that reached 0.8105 in 2019, which indicates good coordination; (2) the water resources subsystem had the greatest influence on the overall degree of coupling coordination, followed by the economic and natural environment subsystems; and (3) the SSA-BP model predicted that the degree of coupling coordination would remain good for the next six years and reach 0.8333 in 2025. To ensure the sustainability of expected rapid economic development, Henan Province must increase the utilization efficiency of water resources, strengthen environmental protection, and coordinate development in the region.

  • An evaluation model of the coupled water resources–economy–ecosystem development of the Henan section of the Yellow River basin was established.

  • A new method for future forecasting of regional coupling coordination is proposed.

  • The main factors affecting the degree of coordinated water resources–economy–ecosystem development in the Henan section of the Yellow River basin were identified.

Social and economic development create conflicting human demands for water resources, economic growth and environmental conservation. Balancing these demands by coordinating the development of water resources, economic growth and environmental conservation has become an area of intense research in sustainable development (Zhao 2016) to the extent that coordinated development of water resources, economic growth and environmental conservation has become a long-term goal of human development (Liu et al. 2007). China has formulated and issued a number of policies for coordinated development and green development at the macro-level in the recent past, which have great strategic significance in building a resource-conserving and environmentally friendly society (Zuo et al. 2017; Yang et al. 2019).

Haken (1978) first proposed the concept of synergetics in the 1970s. The System of Environmental-Economic Accounting (SEEA) introduced by the United Nations in 1993 (Smith 2007) is a theoretical system that integrates multiple disciplines and methods such as political science, ecology, and economics. Costanza et al. (1997) argued that ecosystem services represent a part of the total economic value of the Earth and laid a theoretical foundation for the study of resource–ecological–economic systems as entities. Mutisya & Yarime (2014) investigated the extent of coupling and the degree of coordination between Kenya's resources, the natural environment, society and economy and suggested that management of the natural environment and governance of society should be strengthened to increase the degree of coupling.

As research develops, more and more researchers are identifying the degree of coupling and the extent of coordination within composite systems. F. Q. Wang et al. (2022) modeled the degree of coupling and extent of coordination to quantify the coupled development of water resources, the economy and ecosystem in the Beijing–Tianjin–Hebei region from 2006 to 2019 and to provide a theoretical grounding for decision-making in the region. Wen & Wen (2019) also modeled the degree of coupling and extent of coordination in investigating symbiotic relationships and coordination between water resources, the economy and ecosystem in 18 key provinces (cities, districts) to support the Belt and Road Initiative. They created a system of evaluation indexes for the three subsystems suitable for use in assessing key areas temporally and spatially. The evaluation index system they developed used comprehensive subjective and objective weighting to calculate the weights of indicators at all levels and calculated the degree of coupling and the extent of coordination within a system. Based on the two dimensions of endogenous power and external dynamics, Zhang et al. (2022) introduced a fixed-effect model to measure the driving factors of water–economic and society–ecological environment coupling coordination in Gansu Province from 2010 to 2019. The analysis of regional water–economy and society–ecological environment coupling and their coordinated development is of great significance to strengthen the macro-management of natural resources and economic and social systems and promote regional sustainable development. Du et al. (2022) constructed the evaluation model for the coupling coordination of water resources, socio-economy, and ecological environment and quantitatively analyzed the development status of subsystems and their coupling systems from 2005 to 2019 of specific regions (northeast, north China, and northwest) and administrative units (provinces and municipalities directly under the central government).

With the deepening of research, scholars began to study the future coupling coordination of composite systems. Zhou et al. (2016), Ling & Yu (2016) and Cai et al. (2020) used a GM (grey model) to predict coupling and coordination in different regions and analyzed them in terms of the future development of the regions. Cao (2015) modeled the degree of coupling and extent of coordination for the development of composite systems in the western region of China using a back-propagation (BP) neural network and produced a roadmap to expedite construction in the region. Luo et al. (2022) empirically analyzed the co-evolution of the water–energy–food complex in the Yellow River basin from 2010 to 2019 and used an ARIMA model to predict the degree of coupling and extent of coordination. Wang (2019) used system dynamics to measure and simulate the coordinated development level of the energy–economy–environment (3E) system in Anhui Province and analyzed in depth the relationship between the three, which had important theoretical and practical significance for making developed scientific decisions and promoting regional sustainable development.

At present, there are very few relevant studies that evaluate or predict the coupling and coordination of water resources, the economy, and the ecosystem in the southern part of the Yellow River basin and Henan Province, which is located in the lower reaches of the Yellow River but occupies an important position in the entire Yellow River basin. This study aims to fill this gap and provide decision-making information for the problems of lagging economic development, scarcity of water resources, and serious pollution of the local natural environment in the southern part of the Yellow River basin. A comprehensive index system for the three subsystems of the water resources–economy–ecosystem complex was established. We initially established an index for each factor in each system and weighted the indexes to form a comprehensive subsystem index. Then, the water resources–economy–ecosystem model was developed to calculate the degree of coupling and extent of coordination between subsystems. We used grey relational analysis to analyze and calculate the extent of the influence of each factor index on the degree of coupling and the extent of coordination and produced a comprehensive index for the entire water resources–economy–ecosystem complex in the Henan section of the Yellow River basin. An SSA-BP model was used to verify the comprehensive index in order to ensure the prediction accuracy of the system model in order to provide theoretical support for the coordinated development of water resources, economic and social growth, and the natural environment in the Henan section of the Yellow River basin.

Study area

The Yellow River is the second longest river in China and one of the longest rivers in the world. The Yellow River basin is in a temperate zone and a plateau climate zone. Annual sunshine is 2,000–3,300 h; annual rainfall is unevenly distributed, with an average in the range 200–650 mm; the average frost-free period in the upper, middle and lower reaches of the river is in the range 20–200 d (Xiao et al. 2020). The Henan section of the Yellow River basin flows through nine cities in Henan Province (Zhengzhou, Kaifeng, Luoyang, Anyang, Xinxiang, Puyang, Sanmenxia, Jiyuan and Jiaozuo) in the lower reaches of the river. The Henan section of the Yellow River basin is extremely turbid due to the enormous amount of entrained sediment transported by the river, and the region faces problems of sediment deposition, changes in the river course, and the ‘hanging river on the ground’ (a phenomenon in which the deposited sediment has raised the level of the river bed to be above the river banks) that have not been completely resolved. The risk of flooding has increased in the region due to climate change and extreme weather events. Urban and industrial development in the Henan section of the basin has introduced problems associated with low quality and low efficiency that are characteristic of the energy and chemical industry; pollution from raw materials, agriculture and animal husbandry is obvious, and there are few industrial clusters that are strongly competitive.

Indicator system

Research into the combined water resources–economy–ecosystem complex has not yet provided a single unified index that accurately represents the complex. Following the principles and methods of index construction (Qaiser et al. 2017; Held et al. 2018; S. S. Wang et al. 2022), we constructed an index intended to reflect the state of the water resources–economy–ecosystem complex of the Henan section of the Yellow River basin. The first stage of developing the index was to identify the interactions between the various indicators shown in Table 1.

Table 1

The comprehensive index system of water resources–economy–ecosystem for the Henan section of the Yellow River basin

Criterion layerIndicator layer
Indicator numberNameDescription and calculation of indicators
Water resource indicators Per capita water resources Total water resources/total population 
Average annual rainfall Sum of annual rainfall/years 
Average annual evaporation Sum of years of evaporation/years 
Water modulus Total water resources/land area 
Total water consumption Total annual water consumption 
Proportion of agricultural water use Agricultural water consumption/total water consumption 
Proportion of industrial water use Industrial water consumption/total water consumption 
Proportion of domestic water use Domestic water consumption/total water consumption 
Modulus of water supply Water supply/land area 
Economy indicators 10 Per capita GDP GDP/total population 
11 Water consumption per million yuan of GDP Water consumption/GDP 
12 Primary production ratio Ratio of primary industry output to GDP 
13 Secondary production ratio Ratio of secondary industry output to GDP 
14 Tertiary production ratio Ratio of tertiary industry output to GDP 
15 Modulus of industrial output Gross industrial output/land area 
16 Gross industrial output as proportion of GDP Gross industrial output/GDP 
17 Arable land irrigation rate Irrigation area/arable land area 
18 Irrigation water quota Irrigation water/irrigation area 
Ecology indicators 19 Safe drinking water ratio Population/total population drinking sanitation-compliant water 
20 Sewage–runoff ratio Sewage discharge/river runoff 
21 Industrial pollutant emissions Industrial wastewater discharge 
22 Agricultural pollutant emissions Amount of agricultural pollution 
23 Domestic sewage discharge Daily domestic sewage discharge 
24 Vegetation coverage Area/total area covered by vegetation 
25 Sewage reuse rate Sewage reuse/(sewage reuse + sewage directly discharged into the environment) 
26 River sediment transport Soil erosion modulus × transport ratio × area of soil erosion 
27 Environmental water use rate Natural environment water consumption/total water consumption 
Criterion layerIndicator layer
Indicator numberNameDescription and calculation of indicators
Water resource indicators Per capita water resources Total water resources/total population 
Average annual rainfall Sum of annual rainfall/years 
Average annual evaporation Sum of years of evaporation/years 
Water modulus Total water resources/land area 
Total water consumption Total annual water consumption 
Proportion of agricultural water use Agricultural water consumption/total water consumption 
Proportion of industrial water use Industrial water consumption/total water consumption 
Proportion of domestic water use Domestic water consumption/total water consumption 
Modulus of water supply Water supply/land area 
Economy indicators 10 Per capita GDP GDP/total population 
11 Water consumption per million yuan of GDP Water consumption/GDP 
12 Primary production ratio Ratio of primary industry output to GDP 
13 Secondary production ratio Ratio of secondary industry output to GDP 
14 Tertiary production ratio Ratio of tertiary industry output to GDP 
15 Modulus of industrial output Gross industrial output/land area 
16 Gross industrial output as proportion of GDP Gross industrial output/GDP 
17 Arable land irrigation rate Irrigation area/arable land area 
18 Irrigation water quota Irrigation water/irrigation area 
Ecology indicators 19 Safe drinking water ratio Population/total population drinking sanitation-compliant water 
20 Sewage–runoff ratio Sewage discharge/river runoff 
21 Industrial pollutant emissions Industrial wastewater discharge 
22 Agricultural pollutant emissions Amount of agricultural pollution 
23 Domestic sewage discharge Daily domestic sewage discharge 
24 Vegetation coverage Area/total area covered by vegetation 
25 Sewage reuse rate Sewage reuse/(sewage reuse + sewage directly discharged into the environment) 
26 River sediment transport Soil erosion modulus × transport ratio × area of soil erosion 
27 Environmental water use rate Natural environment water consumption/total water consumption 

Data availability

The data used in this study come from the 2000–2019 publications of the China Statistical Yearbook, Henan Provincial Statistical Yearbook, Henan Province Water Resources Bulletin, Henan Province Environmental Status Bulletin, the 2000–2019 Statistical Yearbooks of cities in the Henan section of the Yellow River basin, Resources Bulletin, and the 2000–2019 Yellow River Water Resources Bulletin.

Methods

Combined weighting method

We used a weighted combination of entropy weighting and the analytic hierarchy process to weight the indicators (Cheng & Huang 2021; Zhou 2022) in order to obtain weighted indexes that accurately represent real world conditions. The equation for calculating the combined weight is:
(1)
where is the combined weighted value of index i, is the objective weight, is the subjective weight, and is the weight compromise coefficient that was set to 0.4 in this study. The calculated values of the 27 index weights of the Henan section of the Yellow River basin are shown in Figure 1.
Figure 1

Weights of 27 water resources–economy–ecosystem indicators for the Henan section of the Yellow River basin.

Figure 1

Weights of 27 water resources–economy–ecosystem indicators for the Henan section of the Yellow River basin.

Close modal

Comprehensive evaluation model

We created the following comprehensive evaluation index:
(2)
where , , and respectively represent the comprehensive value of the water resources, economy and ecosystem subsystems; , and respectively represent the weight of each index in each subsystem; and , and are respectively the dimensionless values of each individual index in the subsystem.

Coupling coordination model

The three subsystems (water resources, economy and ecosystem) were combined in the system coupling model (Liu et al. 2005):
(3)
where C is the degree of coupling and 0 ≤ C ≤ 1. When 0 ≤ C < 0.3, it is in the low-level coupling stage; when 0.3 ≤ C < 0.5, it is in the fly-down stage; when 0.5 ≤ C < 0.8, it is in the running-in stage; and when 0.8 ≤ C ≤ 1, it is in the high-level coupling stage.
The degree of coupling coordination D of the system can be calculated (Equation (4)). We assume that the three subsystems are equally important, so α = β = γ = 1/3. The comprehensive index T of the water resources–economy–ecosystem complex is also calculated (Equation (5)):
(4)
(5)
where C is the degree of coupling, D is the degree of coupling coordination, T is the comprehensive evaluation index of the water resources–economy–ecosystem complex, and α, β and γ are the respective weights of each subsystem.

We used data for the current state of water resources, economic development and the natural environment in cities in the Henan section of the basin and combined it with historic data from previous studies (Diao et al. 2020; Wang & Hu 2020; Wang et al. 2021). We also qualitatively categorized the degree of coupling and the degree of coupling coordination in coordinated development based on indicator values (Table 2).

Table 2

Categorization of degree of coupling and degree of coupling coordination

D(0.9–1](0.8–0.9](0.7–0.8](0.6–0.7](0.5–0.6](0.4–0.5](0.3–0.4](0.2–0.3](0.1–0.2](0–0.1]
Type of coordination High-quality coordination Good coordination Intermediate coordination Primary coordination Reluctant coordination On the verge of being out of balance Mildly out of balance Moderately out of balance Seriously out of balance Extremely out of balance 
D(0.9–1](0.8–0.9](0.7–0.8](0.6–0.7](0.5–0.6](0.4–0.5](0.3–0.4](0.2–0.3](0.1–0.2](0–0.1]
Type of coordination High-quality coordination Good coordination Intermediate coordination Primary coordination Reluctant coordination On the verge of being out of balance Mildly out of balance Moderately out of balance Seriously out of balance Extremely out of balance 

Grey relational analysis

Grey relational analysis is a very active branch of grey system theory. The underlying idea of the method is to determine how close is the relationship between different sequences to the geometric shape of the sequence curve. The sequence is converted into a continuous segmented polyline, and then a model that measures the degree of association is created according to the geometric characteristics of the polyline (Liu et al. 2013). In order to examine the extent of the influence of each index on the degree of coupling coordination, the degree of coupling coordination was selected as the reference sequence, and each index was used as a comparison sequence.

SSA-BP combined prediction model

SSA

The sparrow search algorithm (SSA) is a swarm intelligence optimization algorithm developed by Xue (2020). The sparrow search algorithm uses the foraging and anti-predation behaviors of sparrows as the basis of a mathematical model to simulate the behavior of sparrows in nature. The specific rules are as follows.

  • (1)

    The finder (the sparrow that finds a food source) has a high energy reserve and provides the joiner (a freeloading sparrow that observes the finder) with an area and direction for foraging. If either sparrow observes a predator, that sparrow will chirp an alarm signal. When the alarm value is greater than the safe value, the finder will take the joiner to another safe area for foraging.

  • (2)

    The identities of finders and joiners are dynamic. One sparrow becomes a finder and another sparrow becomes a joiner. Joiners can search for and follow the finder that provides the best food and then get food or forage from around the finder. In order to increase their predator avoidance rate, some joiners may constantly monitor the finder and compete among themselves for food resources.

  • (3)

    The lower the energy of the joiners, the worse the foraging position they are in, in the overall population.

  • (4)

    When aware of danger, sparrows at the fringes of the flock move quickly to a safe area to gain a better position, while those in the middle of the colony move randomly to get closer to other sparrows.

The specific algorithm based on this behavior is as follows.

(1) Establish a virtual population X composed of sparrows to search for food that is represented by:
(6)
where d is the dimension of the problem variable to be optimized, and n is the number of sparrows. The fitness value of all sparrows is given by:
(7)
where f() is the fitness value.
Finders with greater fitness values will preferentially obtain food during the search process. In each iteration, the next position of a finder at time t is described by:
(8)
where t is the current iteration, ; is a constant representing the maximum number of iterations, Xij is the location of sparrow i in dimension j, α ∊ (0,1] is a random number, R2 (R2 ∊ [0,1]) is the warning value, ST (ST ∊ [0.5,1]) is the safety value, Q is a normally distributed random number, and L is a 1 × d matrix with every element 1.
When the joiner perceives that the finder has discovered a new food source and competes with the finder for it, the joiner position is updated by:
(9)
where is the best position currently occupied by the discoverer, is the current global worst position, A is a 1 × d matrix, where each element is randomly assigned 1 or −1, and . When , the ith participant with a lower fitness value did not get food and was in a very hungry state. This sparrow needed to fly to another location to obtain more food. Sparrows that are aware of the danger from a predator are alerters, and they form about 20% of the sparrow population. The equation is:
(10)
where is the current global optimal position, is a step-size control parameter that is a normally distributed random variable with mean 0 and variance 1, is a random number that indicates the direction that the sparrow moves, is the fitness value of the current sparrow, and are the current global best and global worst fitness values, and is a small constant that is included to avoid having zeros in the denominator.
SSA-BP
Back-propagation (BP) neural network prediction models have been widely used in many fields, such as industry, agriculture, and medical care (Wang et al. 2012; Xu et al. 2021; Cheng et al. 2022). The wide use of BP neural networks has made their shortcomings, such as poor data search capability and the tendency to converge on local rather than global minimum values, increasingly prominent (He et al. 2022). The sparrow search algorithm has the advantages of strong search capability and rapid convergence. We therefore used the sparrow search to optimize the neural network and perform global searches on the sample data to find the fitness value of the sparrow that meets the desired level of accuracy, which is transferred to the neural network to use the recorded fitness value as the weight and threshold. After several network trainings, the prediction parameters were optimized (Zhao et al. 2022). The process for optimization of SSA and using the BP network is shown in Figure 2.
Figure 2

Sparrow search algorithm optimization and BP neural network flowchart.

Figure 2

Sparrow search algorithm optimization and BP neural network flowchart.

Close modal

Comprehensive evaluation time series analysis

Figure 3 shows that the comprehensive index of the water resources–economy–ecosystem complex in the Henan section of the Yellow River basin increased from 0.333 to 0.711 in 2000–2019. The overall variation in the water resources subsystem index for 2000–2019 was relatively small. The economy subsystem index showed a significant increasing trend, from 0.247 in 2000 to 1.125 in 2019. The ecology subsystems index was relatively unstable, showing a gradually increasing trend from 2000 to 2008, then a decreasing trend, and an increasing trend since 2015.
Figure 3

Water resources–economy–ecosystem indexes for the Henan section of the Yellow River basin for the years 2000–2019.

Figure 3

Water resources–economy–ecosystem indexes for the Henan section of the Yellow River basin for the years 2000–2019.

Close modal

The comprehensive index of the water resources subsystem clearly had an increasing trend in 2003, a significant decreasing trend in 2008 and 2012 and a decreasing trend in 2016. These trends occurred because precipitation in the Yellow River basin was significantly higher in 2003; average precipitation was 555.6 mm. Total precipitation volume in 2003 was 441.70 km3. In terms of regional precipitation distribution, the region between Sanmenxia and Huayuankou had the greatest precipitation. In 2008, the surface water and water resources in the Henan section of the Yellow River basin decreased by 43.7% compared with the long-term average. The Qinhe water system decreased by >50%. In 2012, the per capita water resources in Henan Province decreased significantly compared with 2011. At the end of 2016, the funnel-shaped Anyang–Puyang area in the Henan section of the Yellow River basin expanded by 80 km2, and the subsurface depth to groundwater at the center of the funnel increased by 1.84 m due to excessive groundwater exploitation that destroyed the groundwater balance; the groundwater level continues to decline.

Since 2005, the comprehensive index of economic subsystems has increased sharply. Since the introduction of the Henan Province Tenth Five-Year Plan, Henan Province has implemented the national macro-control policy and made great efforts to strategically adjust its economic structure. The region has become significantly stronger economically; per capita GDP increased from ¥200.188 million CNY in 2005 to ¥680.599 million CNY in 2019. Henan Province has completely transformed itself from a large agricultural province to a large economic/industrial province.

The Henan section of the Yellow River basin could be characterized prior to 2010 as being static and having a large population, weak infrastructure and unbalanced development. The environmental situation was dire, and basic capacity lagged behind other parts of China. The economy of the Henan section of the Yellow River basin has developed rapidly since then, as has environmental protection. It is still not perfect, and the comprehensive index of ecological subsystems shows a decreasing trend. In 2015, environmental protection in the Henan section of the Yellow River basin was still severely strained, but resource and environmental constraints were tightened. However, compared with previous years, there has been overall improvement and there has been good progress in the implementation of environmental protection specified in the 12th Five-Year Plan. During the implementation of environmental protection identified in the 13th Five-Year Plan, attempts were made to build an ecological protection system, with noticeable effect. In 2019, the comprehensive index of the ecological subsystem in the Henan section of the Yellow River basin showed a significant increasing trend.

Degree of coupling and degree of coupling coordination

We calculated and plotted the degree of coupling and degree of coupling coordination (Table 2) as shown in Figure 4.
Figure 4

Multi-indicators of water resources–economy–ecosystem for the Henan section of the Yellow River basin.

Figure 4

Multi-indicators of water resources–economy–ecosystem for the Henan section of the Yellow River basin.

Close modal

The degree of coupling was at a high level from 2000 to 2019 but it began to show a decreasing trend after 2011. This was due to the rapid development of the economy in Henan Province after 1998. Environmental protection and water resource management were neglected in the early stage of economic development. In 2013, the state increased protection of the environment and control of water resources, but protection and control did not develop at the same speed as the economy, and so the degree of coupling decreased. However, the degree of coupling showed an increasing trend in 2019, which indicates that environmental governance has been effective.

From 2000 to 2002, coupling coordination was classed as reluctant. After 2010, development in the Henan section of the Yellow River basin was more coordinated, and this classification became intermediate. Overall, the degree of coupling coordination has become good. The process guiding this change was as follows. The Yellow River had 19 no-flow events from 1972 to 1999, and the ecological balance of the river basin was seriously upset. After the completion of the Xiaolangdi project, the ecological balance of the Yellow River basin began to recover. The degree of coupling coordination was <0.6 from 2000 to 2002, and the basin was in a state of barely coordinated development. From 2003 to around 2009, the water resources–economy–ecosystem complex was transformed into a state of primary coordinated development because over-development of the economy had some effect on water resources and the natural environment. For example, secondary manufacturing in Henan Province during this period increased annually. In 2006, secondary manufacturing in Henan Province accounted for 70.70% of all manufacturing; this exceeded the national average by 1.67%.

The natural environment subsystem has gradually shifted to a stage of intermediate coordinated development. The state has done a lot of work to optimally allocate environmental and water resources, and Henan Province has implemented in-depth reforms. Examples are the implementation of the river-chief system, in which a single entity has responsibility for water quality for a particular catchment or reach, the establishment of a water and soil loss prevention system, the construction of regional water diversion projects, and better use of precipitation in the region; these activities are all related. The problem being addressed is, given the extreme shortage of water resources in the Henan section of the Yellow River basin, how can various regions all achieve effective economic development? There are no universal solutions: environmental protection is the main area for future research.

Attribution analysis

Figure 5 shows that the degree of coupling coordination of influencing factors in the Henan section of the Yellow River basin, water modulus (factor 4), total water consumption (5), modulus of water supply (9), primary production rate (12), the sewage–runoff ratio (20), river sediment transport (26), and environmental water use rate (27) were all 1; the degree of coupling coordination of per capita GDP (10), industrial pollutant emissions (21), and domestic sewage discharge (23) was between 0.5 and 0.9, and the degree of coupling coordination of other indicators was between 0.9 and 1.
Figure 5

Results of attribution analysis for the Henan section of the Yellow River basin.

Figure 5

Results of attribution analysis for the Henan section of the Yellow River basin.

Close modal

Factor identification showed that the indicators of the water resources subsystem accounted for 34.30%, of the system total, the economy subsystem indicators accounted for 32.55%, and the natural environment subsystem indicators accounted for 32.15%. The most important factor in the coordinated development of sector coupling was the influence of economic, social and environmental indicators on the degree of coupling coordination. Water resources are an indispensable basic natural resource in high-quality development of the Henan section of the Yellow River basin. The comprehensive coordination of local water resource use, external water transferred into the region and unconventional water sources with improved water conservation and water use efficiency is key to sustainably developing the Henan section of the Yellow River basin, and it is an important measure for high-quality development of water resources–economy–ecosystem.

Coupled coordination prediction

Verification of accuracy

The BP prediction model and the SSA-BP prediction model were both used to verify the accuracy of the degree of coupling coordination predictions of the water resources–economy–ecosystem model for the Henan section of the Yellow River basin from 2000 to 2019. A biennial prediction method was used to increase prediction accuracy, with 19 inputs and 19 outputs. The first 16 inputs were used as the training set, and the last three inputs were used as the test set. The test results are shown in Figure 6. The figure shows that the SSA-BP model predictions were more accurate than the BP model predictions: the average absolute error of the BP predictions was −0.0156, and the average relative error was −1.96%; the average absolute error of the SSA-BP predictions was 0.0049, and the average relative error was 0.62%.
Figure 6

Prediction results for BP and SSA-BP models.

Figure 6

Prediction results for BP and SSA-BP models.

Close modal

Evolve predictive analytics

The combined SSA-BP model was used to predict the degree of coupling coordination of the water resources–economy–ecosystem complex for the Henan section of the Yellow River basin for 2020–2025. The results are shown in Figure 7. The figure shows that good coordination will be maintained for six years after reaching the state of good coordination in 2019. The rate of increase in the degree of coupling coordination was less than for 2000–2019. This is because the cities along the Henan section of the Yellow River basin are developing rapidly. Rapid economic development is often accompanied by resource consumption and environmental pollution, while resource regeneration and environmental remediation take some time.
Figure 7

Prediction of the degree of coupling coordination of the water resources–economy–ecosystem complex for the Henan section of the Yellow River basin.

Figure 7

Prediction of the degree of coupling coordination of the water resources–economy–ecosystem complex for the Henan section of the Yellow River basin.

Close modal

Water consumption has continually increased in the Henan section of the Yellow River basin with the rapid economic and social development in the province, and the negative effects of human activity have become increasingly obvious. Limited water resources seriously restrict sustainable development of the economy in Henan Province. Much research has been directed toward finding the best balance between social and economic development, maintaining the natural environment and the use of water resources. The Henan section of the Yellow River basin was the research area for this study, in which we identified problems and deficiencies in the current research and developed a model for the coordinated development of the water resources–economy–ecosystem coupling. Our research supports the following conclusions:

  • (1)

    The comprehensive index of the water resources–economy–ecosystem complex in the Henan section of the Yellow River basin shows that the degree of coordination steadily increased over the period. The overall variation in the index was small. The comprehensive index of the economic subsystem also increased, and the comprehensive index of the natural environment subsystem varied greatly in contrast with the other two subsystem indexes. The degree of coordinated development in the water resources–economy–ecosystem coupling moved closer to being optimal annually from 2000 to 2019 as it changed gradually from the coordinated development category of barely coordinated development to good coordinated development. The degree of coupling coordination in the water resources–economy–ecosystem complex reached the stage of good coordinated development.

  • (2)

    The most important factors affecting coupling coordination were water modulus (factor 4), total water consumption (5), modulus of water supply (9), primary production ratio (12), sewage–runoff ratio (20), river sediment transport (26) and environmental water use rate (27). Future development and use of local water resources, water transported into the region and unconventional water sources should be coordinated to reduce water use and increase water use efficiency. Other necessary improvements include improving water and sediment control to reduce the conflict between supply and demand of water resources; building sewage purification lagoons, artificial wetlands and infrastructure to intercept nitrogen and phosphorus to prevent environmental degradation; constructing water purification plants; and increasing water recycling resources. These measures support the overall goal of promoting high-quality economic and social development and protecting the natural environment.

  • (3)

    The SSA-BP model showed a steady increasing trend in the degree of coupling coordination of the water resources–economy–ecosystem complex in the Henan section of the Yellow River basin. The model predicted a good coordination from 2020 to 2025 with a large amount of development. The model predictions also provide the basis for rational allocation of resources, improved efficiency in resource utilization, the avoidance of resource waste, and the prevention of resource imbalance due to rapid urban development.

Due to the restricted study period, only 20 years of data were collected in the data collection process, making the coupling coordination evaluation in this paper lack a certain degree of completeness, thus limiting the prediction of coupling coordination. Future research should combine the current situation of the Yellow River basin and fully consider other influencing factors affecting the coupling coordination of water resources, the economy, and the ecosystem to broaden the scope and increase the depth of research to achieve not only the coordination of the water resources–economy–ecological environment coupling system of the entire Yellow River basin but also the sustainable, high-quality, and stable development of the Yellow River basin.

This work was financially supported by the General Project of National Natural Science Foundation of China, No. 52079051, Key Scientific Research Project of Henan Province Colleges and Universities, No. 22A570004 & 23A570006, Fund of Innovative Education Program for Graduate Students at North China University of Water Resources and Electric Power, China, grading no. YK-2021-43.

S.W. (Professor) wrote and revised the manuscript, J.Y. (M.S. student) analyzed the data, A.W. (Associate Professor) revised the manuscript, T.L. for data visualization, Y.Y. to collect and organize data.

Data and materials are available from the corresponding author upon request.

The authors declare there is no conflict.

Cai
W. J.
,
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