Cross-County Characteristics of Water–Ecology–Economy Coupling Coordination in the Wuding River Watershed, China

Abstract

Investigating the coupling coordination relationship between water resources, ecology and the economy is the basis process for watershed governing to achieve sustainable development. Taking the Wuding River watershed (one of the largest tributaries of the Yellow River) as an example, we used the coupling coordination model to analyze the coupling coordination relationship of the water–ecology–economy system between 2001 and 2020, and then used grey correlation and partial correlation analyses to explore the main influencing factors and cross-county characteristic of the coupling coordination. The results show that the water, ecology and economy subsystems changed slightly before 2007. After 2007, the economy developed rapidly, while the water and ecology increased for a short time and then decreased after 2013. The water–ecology–economy coupling coordination was on the verge of dysfunctional decline. Water and ecology were the main influencing factors on the coupling coordination. The coupling coordination showed a cross-county characteristic. Water and ecology in upstream counties had significant positive correlations with the coupling coordination in downstream counties. The economy subsystem in upstream counties, however, had significant negative correlations with water and ecology in downstream counties. Our findings provide an empirical mode to measure transregional characteristics of coupling coordination and could support the construction of a coordination governance mode in the Wuding River watershed.

1. Introduction

Optimizing coordinated relationships between water resources and economic development is a key process for sustainable development in a watershed. Water is an indispensable natural resource for human survival and development [1,2]. A watershed provides clean and sufficient freshwater to support human production and living activities, and helps settlements to grow into cities [3]. The world’s big cities (population > 750,000) cover less than 1% of the land surface; however, their water source watersheds cover around 41% of the land surface [4,5]. From 1900 to 2010, the global water demand has increased by almost eightfold [6]. Driven by future economic development and population growth, more than 2.5 billion people will be exposed to water scarcity in 2050 [7,8]. Economic development, on the other hand, increasingly impacts watersheds because of increased water consumption, point and non-point source pollutions [9,10], which pose a significant constraint on water usability [11]. The conflict between increasing water demand and constrained water usability is one of the most severe challenges for sustainable development [12,13].

Ecological conservation in a water source watershed has been recognized as being more cost effective than investing in additional treatment facilities. The watershed ecosystem links the natural environment and human society [14], and its ecological factors such as vegetation and water quality are affected by socio-economic development [15]. Since 1900, 90% of urban water source watersheds have experienced a degradation in water quality, mainly caused by increasing pollution with sediment, phosphorus and nitrogen [5,16]. Treatment and monitoring, as well as infrastructure investment, have increased to ensure the delivery of clean water, increasing their cost by around 53% for one-third of cities globally [17]. Natural ecological conservation is a more economic path to protect the freshwater supply. The natural ecosystem provides multiple ecosystem services, including water and soil conservation, as well as cleaning up pollutants [18]. In United States, the forest ecosystem provides 50% of the total water yield, although it covers only 36% of the total land surface [19]. In Europe, nutrient pollution is the leading cause of watershed ecosystem degradation [20]. Nutrient pollution weakens ecosystem services and aggregates high treatment costs [21]. In China, the natural ecosystem provides 7.7% of the global freshwater to feed 18.5% of the world population [22]. However, due to the rapid development of industrialization and urbanization, the overuse of water resources, pollutant emissions and aggregated sediment discharge in upper and middle watersheds have caused ecological degradation and limited water resource utilization in downstream areas [23,24]. The contribution of human activity to river flow reduction and aggregated sediment discharge in the Wei River reaches 82.8% and 95.6%, respectively [25]. Establishing the coordinating relationships between water resources, ecology and the economy and eliminating the conflict between upstream and downstream areas are crucial measures for watershed sustainable development in China [26].

The Yellow River watershed is a core birthplace of ancient Chinese civilizations. It supports more than 150 million people and 15% of China’s cropland [27]. In recent decades, siltation sediment, degradation in water quality and river flow reduction have increased substantially in the Yellow River watershed [28,29]. Moreover, the teleconnection between water supply and human activities aggravates conflict between increasing water demand and constrained water usability [17,29,30]. Improved knowledge of the relationship between water resources, ecology and economic development is critical for sustainable development in the Yellow River watershed. As one of the largest tributaries of the Yellow River, Wuding River is known as “the Pocket Yellow River”. It is the main source of the sediment load of the Yellow River [31]. Its average annual sediment load is over 2 × 10⁸ t, which accounts for one eighth of the total sediment load of the Yellow River [32]. Water resources are the basic driving factor for the ecological situation and economic development in the watershed [33]. Since the 1960s, in order to reduce soil erosion and improve the ecological environment, a series of engineering measures including vegetation restoration and dam construction [34,35,36] have been carried out and have changed the hydrological processes in the Yellow River watershed [37,38,39]. These measures have improved vegetation cover [40,41] and resulted in significant reductions in runoff and annual sediment discharge [33,40,42,43,44,45]. However, few studies have explored the relationship between water resources, ecology and economic development, as well as the teleconnection between upstream and downstream areas. Thus, we integrated remote sensing data and meteorological data with economic and social statistical records to investigate the coupling coordination relationship between water resources, ecology and economic development in the Wuding River watershed during the period of 2001–2020. Then, we explored the cross-county characteristics of the coupling coordination relationship between the upstream and downstream areas. Based on the coupling coordination relationship and its cross-county characteristics, our study can provide decision support to construct a collaborative governance mode in the watershed. Specifically, we addressed the following questions: (1) what is the coupling coordination relationship between water resources, ecology and economic development in the Wuding River watershed? (2) Does the coupling coordination relationship have cross-county characteristics?

2. Materials and Methods

2.1. Study Area

The Wuding River rises at the northern foot of Baiyu Mountain in Shaanxi Province and has a total length of 491.2 km. The river flows through 12 counties and banners in total. Figure 1 shows spatial patterns of elevation (a), vegetation leaf area index (b), average annual temperature (c) and total annual precipitation (d) of the Wuding River watershed. The elevation of the watershed rises from the downstream area of the eastern watershed to the upstream area of the western watershed. The vegetation leaf area index increases from west and northwest to southeast. The Wuding River watershed has a temperate monsoon climate. The mean annual temperature decreases from low elevations in river valleys to high elevations in northern and southern parts of the watershed. The mean annual precipitation increases from northwest to southeast.

2.2. Evaluation Index System and Data

Following evaluation indices in previous studies and taking data availability into account, we established the evaluation index system (

Table 1. Evaluation index system of the water–ecology–economy coupling coordination relationship in the Wuding River watershed.

Target Layer	Guideline Layer	Index Layer	Unit	Data Sources	Property 1	Weight
Water	Water resource situation	Annual total precipitation	mm	China Meteorological Data Network	+	0.033
		Surface water storage	108 m3	Statistical yearbook NASA CSR GRACE data	+	0.043
		Groundwater storage	cm		+	0.065
	Water resource utilization	Water consumption per capita	m3	Statistical yearbook	-	0.051
		Water consumption per million yuan	m3		-	0.030
		Domestic water	108 m3		-	0.046
Ecology	Ecosystem function	Net primary productivity	kgC m−2 year−1	NASA MODIS data	+	0.045
		Water conservation capacity	m3 m−2 year−1		+	0.033
		Vegetation leaf area	m2 m−2		+	0.045
	Pollutant emission	Wastewater emission	104 t	Statistical yearbook	-	0.039
		Solid waste emission	104 t		-	0.051
		Waste gas emission	108 t		-	0.056
Economy	Economic and social situation	Gross domestic product per capita	Yuan	Statistical yearbook	+	0.098
		Fixed investment per capita	Yuan		+	0.116
		Disposable income	Yuan		+	0.079
	Industrial structure	Proportion of primary industry	%	Statistical yearbook	+	0.117
		Proportion of secondary industry	%		+	0.055
		Proportion of tertiary industry	%		+	0.069

¹ “+” is a positive indicator; “-” is a negative indicator.

) to investigate the water–ecology–economy coupling coordination relationship. The water, ecology and economy subsystems were the target layers. Water resources provide freshwater for economic development, and should be used efficiently to achieve sustainable development. The water subsystem thereby contained the water resource situation (including annual total precipitation, surface water and groundwater storages) and water resource utilization (including water consumption per capita and per million yuan, and domestic water). The watershed ecosystem produces freshwater and cleans up pollutants, so the ecology subsystem contained ecosystem functions (including net primary productivity, water conservation capacity, vegetation leaf area index) and pollutant emissions (including wastewater, solid waste and waste gas emissions). Different economic development qualities and structures have different water utilization efficiencies. The economy subsystem consequently contained the economic and social situation (including gross domestic product per capita, fixed investment per capita, disposable income) and the industrial structure (including proportions of different industries). The index weights were determined by the entropy weight method.

The precipitation records for between 2001 and 2020 were derived from the China Meteorological Data Network (http://data.cma.cn/, accessed 15 July 2022), and were interpolated to the spatial raster data by means of ANUSplin software. The ANUSplin software incorporates the elevation factor as a covariate with the independent spline variables using the partial thin plate spline method, and has a higher accuracy than other interpolation methods in obtaining spatially continuous meteorological data in mountainous area [46]. The surface water storage, pollutant emissions and economic indices subsystems were derived from the official statistical yearbook (http://www.stats.gov.cn/tjsj/, accessed 15 July 2022). Groundwater storage was derived from the Gravity Recovery and Climate Experiment (GRACE) dataset of the Center for Space Research (CSR), National Aeronautics and Space Administration (NASA) [47]. Net primary productivity (NPP) and vegetation leaf area (LAI) data were derived from the Moderate Resolution Imaging Spectro radiometer (MODIS) of the National Aeronautics and Space Administration (NASA) [48,49]. The water conservation capacity was calculated by annual total precipitation minus annual total evapotranspiration (ET). The ET data were derived from the MODIS ET dataset [50].

2.3. Statistical Methods

2.3.1. Coupling Coordination Model

First, we calculated the subsystem evaluation index and comprehensive index of all subsystems [51]. The original data were standardized to eliminate different magnitudes and unit between different indices. The calculation equation is as follows:

Positive indices:

$${ \mathrm{X}}^{\prime }{}_{\mathrm{tij} }=\frac{{\mathrm{X}}_{\mathrm{tij} }-{ \mathrm{X}}_{\min }}{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\min }}\hspace{1em}\hspace{1em}{ \mathrm{X}}_{\min }\le { \mathrm{X}}_{\mathrm{tij} }\le { \mathrm{X}}_{\max }$$

(1)

Negative indices:

$${ \mathrm{X}}^{\prime }{}_{\mathrm{tij} }=\frac{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\mathrm{tij}}}{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\min }}\hspace{1em}\hspace{1em}{ \mathrm{X}}_{\min } \le { \mathrm{X}}_{\mathrm{tij}} \le { \mathrm{X}}_{\max }$$

(2)

where X_tij is the original jth value in the index array of county i in t year; X′_tij is the standardized value; and X_max and X_min are the maximum and minimum values of the original index array, respectively.

The subsystem evaluation index of water, ecology and economy is calculated as follows, respectively:

$${ \mathrm{U}}_{\mathrm{i}=1,2,3}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\omega }}_{\mathrm{i}}{\mathrm{X}}^{\prime }{}_{\mathrm{tij}}, {{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\omega }}_{\mathrm{i}}=1$$

(3)

where U_i is the evaluation index of the ith subsystem, and $\mathsf{\omega }$_i is the index weight of the ith index.

The comprehensive index of water–ecology–economy is calculated as follows:

$${\mathrm{T}=\mathsf{\alpha }\mathrm{U}}_1{+\mathsf{\beta }\mathrm{U}}_2{+\mathsf{\gamma }\mathrm{U}}_3$$

(4)

where T is the comprehensive index of water–ecology–economy; α, β, and λ are the subsystem weights of water, ecology and economy, respectively. Following previous studies [51,52,53], the subsystem weights of water resources (α), ecology (β) and economy (λ) are defined as 0.3, 0.4 and 0.3, respectively.

Second, the coupling degree is calculated as follows:

$$\mathrm{C}=\mathrm{n}{\left\{\left({\mathrm{U}}_1{,\mathrm{U}}_2{,\mathrm{U}}_3\right) / \left[{\displaystyle \prod }\left({\mathrm{U}}_1{+\mathrm{U}}_2{+\mathrm{U}}_3\right)\right]\right\}}^{\frac{1}{\mathrm{n}}}$$

(5)

where C is the coupling degree and it ranges from 0 to 1; n is the number of all subsystems; When C = 1, there is the strongest coupling relationship between the three subsystems.

Third, to measure the coupling coordination relationship of water resources, ecology and the economy, the coupling coordination degree is calculated as follows:

$$\mathrm{D}=\sqrt{\mathrm{C} \times \mathrm{T}}$$

(6)

where D is the coupling coordination degree.

Table 2. Classification of coupling coordination degree.

Type	Coupling Coordination Degree	Category
Coordinated development	0.90–1.00	High-quality coordinated development
	0.80–0.89	Good coordinated development
	0.70–0.79	Intermediate coordinated development
	0.60–0.69	Primary coordinated development
Excessive status	0.50–0.59	Barely coordinated development
	0.40–0.49	On the verge of dysfunctional decline
Dysfunctional decline	0.30–0.39	Mild dysfunctional decline
	0.20–0.29	Moderate dysfunctional decline
	0.10–0.19	Severe dysfunctional decline
	0–0.09	Extremely dysfunctional decline

shows the classification of the coupling coordination degree.

2.3.2. Grey Correlation Analysis Model

We used grey correlation analysis to compare contributions of different subsystems on their coupling coordination relationship in each county. The grey correlation analysis uses the similarity degree between geometric shapes of parent sequence and subsequences to judge their correlation degree [54]. The coupling coordination degree (D) of each county in Wuding River Watershed in 2001–2020 was set as a parent sequence, while the concurrent water resource evaluation index (U₁), ecology evaluation index (U₂) and economy evaluation index (U₃) were set as subsequences, respectively.

First, the original datasets were standardized using Equations (1) and (2). Then, the standardized data were used to calculate the correlation coefficient. The calculation equation is as follows:

$${\mathrm{R}}_{\mathrm{ij}}\left(\mathrm{t}\right)=\frac{{\min }_{\mathrm{i}}{\min }_{\mathrm{j}}|{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{t}\right)-{\mathrm{y}}_{\mathrm{i}}\left(\mathrm{t}\right)| \times \mathsf{\rho }|{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{t}\right) -{ \mathrm{y}}_{\mathrm{j}}\left(\mathrm{t}\right)|}{|{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{t}\right)-{\mathrm{y}}_{\mathrm{j}}\left(\mathrm{t}\right)| {+\mathsf{\rho }\max }_{\mathrm{i}}{\max }_{\mathrm{j}}|{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{t}\right) -{ \mathrm{y}}_{\mathrm{j}}\left(\mathrm{t}\right)|}$$

(7)

where y_i and x_i are the parent sequence and subsequence at the sampling point t, respectively, ρ is the resolution coefficient, and we took ρ = 0.5 in the study.

The correlation degree of each subsequence x_ij is calculated as follows:

$${\mathrm{x}}_{\mathrm{ij}}=\frac{1}{\mathrm{n}}{{\displaystyle \sum }}_{\mathrm{i},\mathrm{j}=1}^{\mathrm{n}}{\mathrm{R}}_{\mathrm{ij}}\left(\mathrm{t}\right) 0 \le { \mathrm{x}}_{\mathrm{ij} }\le 1$$

(8)

where x_ij is the correlation degree; n is the number of samples.

3. Results

3.1. Spatio-Temporal Patterns of Evaluation Indices of Water Resource, Ecology and Economy Subsystems and Their Comprehensive Index

Figure 2 shows interannual change trends in evaluation indices of water, ecology and economy subsystems and their comprehensive index from 2001 to 2020. The water evaluation index was stable during the period of 2001–2020. From 2001 to 2007, evaluation indices of the ecology and economy subsystems were stable between 0.1 and 0.2. After 2007, the economy evaluation index rose rapidly with a percentage increase rate of 7.88% (R² = 0.97, p < 0.01), showing an increasing gap with other subsystems. The ecology evaluation index experienced a short increase from 2007 to 2013, and then decreased non-significantly. The comprehensive index was stable before 2007, then increased between 2007 and 2013 with a percentage increase rate of 5.58% (R² = 0.92, p < 0.01), and changed non-significantly after 2013.

Figure 3 shows the interannual change trends in the evaluation indices of the water resource, ecology and economy subsystems and their comprehensive index in each county from 2001 to 2020. During the period of 2001–2007, the evaluation indices of the water, ecology and economy subsystems in all counties experienced non-significant changes. From 2007, the economy evaluation indices increased significantly in all counties. The ecology evaluation index increased significantly in Yanchuan, but decreased significantly in Dingbian and Yuyang.

Figure 4 shows the spatial patterns of the mean value of the water, ecology and economy subsystem evaluation indices and their comprehensive index, respectively, during the period of 2001–2020. The water subsystem evaluation indices were higher in Otog Front Banner and Yuyang, but were lower in Zizhou and Qingjian counties in downstream areas than in other counties. The ecology subsystem evaluation indices were higher in middle and downstream counties, but were lower in Dingbian and Jingbian than in other counties. The economy subsystem evaluation indices were higher in Jingbian, Zichang and Yanchuan, but lower in Mizhi, Zizhou and Otog Front Banner than in other counties. The water–ecological–economic comprehensive indices were higher in Yuyang, Zichang and Yuyang, but were lower in Mizhi, Dingbian, Jingbian and Wushen than in other counties.

3.2. Spatio-Temporal Patterns of the Coupling Degree and Coupling Coordination Degree of the Water–Ecology–Economic System

Figure 5 shows the interannual change trends in the water–ecology–economy coupling degree and coupling coordination degree from 2001 to 2020. The coupling degree was stable (mean: 0.84; STD: 0.13) during the period of 2001–2013, and then decreased significantly (R² = 0.40, p < 0.01). Generally, the coupling coordination degree was on the verge of dysfunctional decline during the period of 2001–2020 (mean: 0.42; STD: 0.02). The interannual change trend in the coupling coordination degree, however, showed different trends between the period before 2013 and the period after 2013. The coupling coordination degree increased significantly by a percentage increase rate of 2.12% (R² = 0.62, p < 0.01) from 2001 to 2013, then decreased significantly (R² = 0.10, p < 0.01).

Figure 6 shows interannual change trends in the water–ecology–economy coupling degree and coupling coordination degree in each county from 2001 to 2020. The water–ecology–economy coupling coordination degrees of all counties were on the verge of unbalanced development. From 2001 to 2020, the coupling coordination degree of Zichang and Jingbian increased significantly, but the coupling coordination degrees of Dingbian, Hengshan, Mizhi, Qingjian and Suide reached the highest value in 2013 and then decreased significantly.

3.3. Contributions of Water, Ecology, Economy Subsystems on Their Coupling Coordination Degree

Figure 7 shows the spatial patterns of grey correlation degree between the water resource (U₁), ecological (U₂), and economic (U₃) subsystem evaluation indices and their coupling coordination degree, respectively. The grey correlation degrees of the water resource, ecology and economy subsystems were ranked as U₁ > U₂ > U₃ in Dingbian, Otog Front Banner, Wushen, Yuyang, Hengshan, Jingbian, Mizhi, Suide and Zichang, indicating that the water resource subsystem was the main influencing subsystem on their coupling coordination degree. The grey correlation degrees of the water resource, ecology and economy subsystems were ranked as U₂ > U₁ > U₃ in Zizhou and Qingjian, indicating that ecology was the main influencing subsystem on their coupling coordination degree.

3.4. Cross-County Characteristic between Upstream and Downstream

Figure 8 shows cross-county patterns of partial correlation between the water, ecology, economy subsystem evaluation indices and their coupling coordination degree. Water resources in upstream counties had a significant positive correlation with water resources in downstream counties, but the water resources in Hengshan in the upstream area were negatively correlated with the ecology of Zizhou and Qingjian in downstream areas (Figure 8a). The water resources of Otog Front Banner in the upstream area were negatively correlated with the economy of Mizhi and Zichang in downstream areas (Figure 8b). The ecology of Wushen in the upstream area was positively correlated with the water resources of Yanchuan in the downstream area (Figure 8c). The ecology of Yuyang in the upstream area was negatively correlated with the economy of Mizhi, Zizhou, Zichang and Yanchuan in downstream areas. The ecology of Hengshan in the upstream area was negatively correlated with the economy of Zichang and Yanchuan in downstream areas. The ecology of Yuyang, Hengshan and Jingbian in upstream areas was positive correlated with the coupling coordination degree of Mizhi, Zizhou and Suide in downstream area (Figure 8d). The economy of Yuyang in the upstream area was negatively correlated with the water resources of Zizhou and Qingjian in downstream areas. The economy of Jingbian in the upstream area was negatively correlated with the water resources of Zichang and the ecology of Mizhi in downstream areas (Figure 8e). The economy in upstream counties had a significant positive correlation with the economy in downstream counties, but the economy of Jingbian in the upstream area was negatively correlated with the coupling coordination degree of Mizhi (Figure 8f).

4. Discussion

Different change trends in the evaluation indices of water resources, ecology, and the economy and their comprehensive index indicated a conflict between water resources, ecology and the economy after 2007 in the Wuding River watershed. From 2007 to 2013, urbanization and economic development needed a greater supply of water resources than before 2007 [55], which resulted in non-significant changes in the water evaluation index under an increasing ecology evaluation index. From 2013 to 2020, rapid urban expansion, a population surge and economic development consumed large quantities of water resources and impacted ecosystem health [55], which caused a large variability in the water evaluation index and a decreasing ecology evaluation index. Ecological degradation has become the major challenge for the sustainable development of the Wuding River watershed [56]. The increasing gap between the economy evaluation index and the water resource and ecology evaluation indices indicated increasing conflicts between economic development and water resource utilization and ecological protection [57,58]. This phenomenon could result in a water resource crisis in the watershed in future [59]. Spatial differences in water resources and ecology were related to land cover and ecosystem condition in the watershed. Counties with a higher ecology evaluation index were located in the middle and downstream of the watershed, where were covered by more forest and grassland than other counties [60]. Counties located in the western part of the watershed had lower ecology evaluation indices, which was due to soil erosion and desertification in these counties [61].

The coupling coordination relationship of the water–ecology–economy system is on the verge of dysfunctional decline, mainly influenced by water resources and the ecology condition [62]. The water demands of industry, agriculture and residents squeeze ecological water, resulting in a lack of ecological restoration capacity in the watershed [25,63]. Industrial wastewater, solid waste and waste gas emissions in the Wuding River watershed increased from 42.90 × 10⁶ t, 4.83 × 10⁶ t and 111.07 × 10⁸ m³ in 2001 to 68.45 × 10⁶ t, 36.97 × 10⁶ t and 455.88 × 10⁸ m³ in 2020, respectively. The industrial wastewater, solid waste and waste gas emissions in upstream counties were 12.78, 24.29 and 13.4 times those in downstream counties, respectively. However, the water conservation capacity of the upstream counties was 2.05 times that in the downstream counties. Industrial and agricultural pollutants not only damaged ecological and environmental health, but also caused spillover effects on the surrounding areas, which strengthens the negative externality of ecology degradation and environmental pollution and constrains economic and social development [64]. The overuse of water resources in the upstream counties leads to the degradation of the ecosystem function in the downstream counties, which seriously affects the sustainable development of the whole watershed [24]. Improvements in water resource and ecological protection exerted a crucial role in driving the coordinated development of the water–ecology–economy system in the watershed [62]. However, most counties paid more attention to economic development rather than water resource and ecological protection in the watershed, leading to an uncoordinated relationship between water, ecology and the economy [65].

The coupling coordination relationship of the water–ecology–economy system showed cross-county characteristics in the Wuding River watershed. The water–ecology–economy system has complex multiple relationships between upstream and downstream counties. Upstream counties are located in the water source area and pay more attention and investments to water source conservation and ecological protection, but downstream counties pay little to secure water resources to develop the economy [66]. To eliminate the unilateral economic imbalance, upstream counties have begun to focus on urban expansion and economic development, bringing about large quantities of water resource consumption, pollutant emissions and ecological degradation, which limit water yield and water usability in downstream counties and consequently hinder their economic developments [67]. This can be demonstrated by the significant negative correlations of water resources in Hengshan with ecology in Zizhou and Qingjian. Hengshan is located upstream of Zizhou and Qingjian. In 2020, the population of Hengshan was 2.04 and 2.44 times that of Zizhou and Qingjian, respectively, while the gross domestic product (GDP) of Hengshan was 2.93 and 3.16 times that of Zizhou and Qingjian, respectively. The industrial wastewater emissions of Hengshan were 8.79 × 10⁶ t, which is 7.95 times and 16.4 times those in Zizhou and Qingjian, respectively. Water resource consumption and industrial effluent emissions induced by economic development lead to a higher concentration of pollutants in upstream areas [68], which flow to Zizhou and Qingjian and finally result in ecological degradation in these two counties [57].

Benefit maldistribution triggered by the unilateral imbalance between water resource conservation and ecological protection in upstream areas and economic development in downstream areas is the intrinsic source of the failure of cross-border governance of water resources in the watershed [69]. To solve this public problem, balancing the relationships of economy–water resources and economy–ecology between upstream and downstream areas has become a common choice for watersheds around the world [70,71]. Watershed collaborative governance is an effective and efficient way to achieve this balance. This method incorporates various direct or indirect stakeholders including governmental and non-governmental bodies to participate in watershed governance to deal with the imbalance between water resources, ecology and economic development [72]. The cooperation between governments, the private sector and society can transfer the responsibility of environmental governance to multiple subjects and relieve the pressure on a single government subject in environmental governance [73]. At present, there are various attempts at coordinated governance in the Wuding River watershed and the Yellow River watershed. For example, the macro-ecological governance emphasizes unified management of the central government, and the central government coordinates the benefit relationship between upstream and downstream areas [74]. Ecological compensation, water rights trading and cross-region coordination policies [75,76] have been explored to improve benefit distribution between upstream and downstream areas in the Yellow River watershed. Moreover, integrating enterprise operation in government-led governance in water resource conservation and ecology protection in a watershed is an advocated measure in practice [69].

Through the above analysis, it can be found that the water–ecology–economy systems have different characteristics between upstream and downstream counties in the Wuding River watershed, affecting the coordinated development of the whole watershed. Water resources in the watershed flow freely, and the natural ecological factors are closely interrelated across the whole watershed [21]. The water resources in the watershed not only provide a basic supply of freshwater directly, but also act as a carrier of pollutants and sediment to trigger conflict between upstream and downstream areas or between two sides of the river [28]. However, the river watershed is divided into different administrative regions. Establishing how to resolve the barrier between different administrative regions is the key process in constructing a transregional coordination mode. Our findings could provide decision-making support for policy makers in the Wuding River watershed.

5. Conclusions

In this study, we take the Wuding River watershed, one of the largest tributaries of the Yellow River, as an example to investigate the coupling coordination relationship of the water–ecology–economy system and its cross-county characteristics between the upstream and downstream counties. The conclusions are as follows:

The interannual change trends in the water resource, ecology and economy evaluation indices and their comprehensive indexes showed different trends. During the period of 2001–2007, all indices were stable. From 2007, however, the economy subsystem developed rapidly and showed an increasing gap with the water resource and ecology subsystems.

The coupling coordination relationship of the water–ecology–economy system was on the verge of dysfunctional decline. This relationship was mainly influenced by the water resource consumption of industry, agriculture and residents, as well as ecological degradation.

The coupling coordination of the water–ecology–economy system showed cross-county characteristics between upstream and downstream counties. The water–ecology–economy coupling coordination in downstream areas was positively correlated with water resources and ecology in upstream areas. Water resources and ecology in downstream counties, however, were negatively impacted by the economy in upstream areas.

The results not only remind us to coordinate water resources, ecology and economic development, but also clarified the cross-county characteristics of the water–ecology–economy coupling coordination between upstream and downstream areas to provide decision-making support for constructing trans-domain coordination governance in the Wuding River watershed.