Introduction

Water resources are fundamental natural resources that are essential for human survival and socioeconomic development1,2. With the rapid growth of the population and economic progress, the demand for water resources has significantly increased. Consequently, many regions are now confronted with the challenge of water scarcity, which has posed a threat to the sustainable development of human society3,4. Therefore, it is crucial to address the urgent issue of coordinating water resources with socioeconomic and ecological development. Measuring the water resources carrying capacity (WRCC) and exploring its internal driving mechanism are necessary steps in resolving the imbalance between the resource supply and demand, ultimately promoting societal sustainability5.

As a hot topic in water security and sustainable development research, the WRCC is often utilized to determine whether regional water resources can sustain the coordinated development of the population, economy and environment. Currently, the definition of WRCC can be classified into the following three categories. In the first category, the WRCC is defined as the maximum amount of regional water resources that can be exploited and utilized under specific social production conditions and economic and technological levels6,7. In the second category, the WRCC is characterized as the maximum population that can be sustained by the available water resources within a given region while ensuring a normal material living standard8,9. In the third category, the WRCC is regard as the maximum scale or capacity to support social and economic development while maintaining sustainable social and economic development conditions and encouraging a virtuous cycle of the ecological environment10,11. Despite various interpretations, scholars generally emphasize the interconnectedness of water resources, the ecological environment and socioeconomic development.

The systematic evaluation of the WRCC is an important part of relevant research from theory to practice. Various approaches have been developed for WRCC evaluation, with the index-based evaluation method as the most widely utilized approach. This method is characterized by the use of multiple methods to assess the WRCC on the basis of a constructed index system. The widely adopted evaluation systems include support-pressure12, water resources-social-economic-ecological environment10 and pressure-state-response (PSR)13,14. The fuzzy comprehensive evaluation method15, principal component analysis method16, ecological footprint method17 and the technique for order preference by similarity to ideal solution (TOPSIS)18 are frequently employed for the comprehensive assessment of the WRCC. However, the aforementioned methods also exhibit certain limitations. For example, the fuzzy comprehensive evaluation method can effectively address fuzzy and difficult-to-quantify issues, but its calculation process is relatively complex19. The TOPSIS method is straightforward and intuitive in regard to decision-making, but it overlooks the correlation between the candidate solutions20. To increase the accuracy of the evaluation results, scholars have endeavored to integrate multiple methods to compensate for existing research shortcomings. Zare et al.21 combined the driver-pressure-state-impact-response (DPSIR) framework and system dynamics to improve the water resources model and illustrated the benefits of this approach using the Gorganroud-Gharesu Basin in Iran as an example. Wu et al.22 quantified the WRCC in Anhui Province of China and explained its carrying mechanism via a multidimensional precondition cloud and risk matrix coupling model.

Research on the driving mechanism of the WRCC helps in understanding the influence of various elements within the WRCC system on its evolution, which is important for the integrated management and regulation of regional water resources. At present, there are few direct studies on the driving mechanism of WRCC evolution, and existing studies have focused mainly on the analysis of the driving factors of WRCC evolution. Related studies in the resource and environmental carrying capacity field have focused largely on exploring the impacts of different indicators, such as the population density, land use and environmental pollution12,23. Conventional statistical analysis methods, including principal component analysis, factor analysis, correlation analysis and regression analysis methods, are widely applied in this area to identify the main driving factors. Yang et al.24 analyzed the factors influencing the WRCC in the Shiyang River Basin via geographic detectors model, and the results revealed that social and ecological elements notably influenced the WRCC in this basin. Chen et al.25 identified the enhancements in the wastewater treatment efficiency and water conservation technology as the main factors influencing the increase in the WRCC in the Yellow River Basin (YRB) on the basis of the logarithmic mean Divisia index.

In general, WRCC research mainly focuses on the conceptual discussion, the selection of evaluation methods and the analysis of influencing factors. The existing research results have greatly enriched the study of the WRCC, but further improvements are still needed: (1) the limitations of using a single evaluation method should be addressed, and instead, integrated evaluation methods that couple the advantages of multiple methods should be adopted; (2) the WRCC exhibits temporal and spatial attributes, but its spatial attributes are often neglected in evaluation and driving analysis studies; and (3) the determination of the WRCC level is influenced by multiple systems and factors, yet its influencing mechanism remains unclear. Therefore, the main objective and contribution of this study is to emphasize the spatiotemporal attributes of the WRCC and to evaluate the spatiotemporal evolution trend of the WRCC by using a comprehensive evaluation model coupled with multiple methods. Moreover, combined with the evaluation results, the driving mechanisms of the spatiotemporal differences in the WRCC are explored in depth. This study provides a reference for fully understanding the evolutionary mechanism of the WRCC and developing targeted measures.

As a crucial population and economic hub, the Henan section occupies an important position in the ecological protection and high-quality development strategy of the YRB. However, owing to the impacts of climate change and human activities, the YRB (Henan section) has faced major challenges in recent years, such as an insufficient WRCC, a fragile ecological environment and unbalanced regional development. These issues have severely hindered the sustainable development of the basin26,27. Therefore, it is essential to evaluate the WRCC in the YRB (Henan section) and clarify the intrinsic driving effects of WRCC evolution. The remainder of this paper is structured as follows: “Methods” section provides a description of the study area and methodology, and “Results” section presents the research results. Moreover, a discussion and recommendations are outlined in “Discussion” section, and the main conclusions are summarized in “Conclusion” section.

Methods

Study area and data resources

The YRB (Henan section), located in the middle-lower reaches of the Yellow River, is a traditional agricultural area and a core grain-producing area in China (Fig. 1). The study area encompasses 10 municipal administrative districts in Henan Province, namely, Sanmenxia, Luoyang, Jiyuan, Jiaozuo, Zhengzhou, Xinxiang, Kaifeng, Hebi, Anyang and Puyang, which are important ecological protection zones within the YRB. However, owing to rapid and sustained industrial and agricultural development, a series of resource problems have emerged caused by the overexploitation and utilization of water resources, leading to an increasing imbalance between the supply and demand for water resources28,29. More stringent criteria for regional water resources should be employed for high-quality economic and social development of the YRB (Henan section) in the future. Therefore, it is necessary to evaluate the WRCC and explore its driving factors, which is important for ensuring water resource security and increasing the WRCC in the basin.

Fig. 1
figure 1

Geographical location of the YRB (Henan section). The map was created using ArcGIS 10.8 (https://www.arcgis.com). The base map was from the standard map with review number GS (2023) 2767 downloaded from the website of Standard Map Service of the Ministry of Natural Resources (http://bzdt.ch.mnr.gov.cn/), with no modification of the base map.

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Statistical data on the social, economic, water resources and ecological environment of the above 10 cities in the YRB (Henan section) from 2010 to 2022 were employed in this study. Socioeconomic data, such as population density, urbanization rate and gross domestic product (GDP) per capita data, were collected mainly from the Henan Statistical Yearbook (2010–2022) (https://tjj.henan.gov.cn/tjfw/tjcbw/tjnj/) and the statistical yearbooks of each city (2010–2022). Water resources data, such as precipitation, water resources per capita and water production coefficient data, were obtained primarily from the Henan Water Resources Bulletin (2010–2022) (https://slt.henan.gov.cn/bmzl/szygl/szygb/) and the water resources bulletins of each city (2010–2022). Ecological environment data, such as the green coverage rate, ecological water use rate and wastewater discharge data, were collected largely from the statistical yearbooks, water resources bulletins, national economic and social development statistical bulletins of each city (2010–2022), and the Henan Province Ecological and Environmental Condition Bulletin (2010–2022) (https://sthjt.henan.gov.cn/hjzl/hjzkgbggnb/).

Construction of the evaluation index system

WRCC research aims to achieve coordinated development between humans and nature by addressing the conflicts between water resources and socioeconomic advancement30. This necessitates a WRCC evaluation index system that can not only capture the regional water resources level and ecological environment status but also determine whether human activities conform with the carrying capacity of regional resources and the environment. Therefore, the supporting capacity of water resources, the level of social and economic development and the status of the ecological environment should be included in the evaluation index system for the WRCC. In this study, a comprehensive WRCC evaluation index system was established, comprising water resources, social, economic and ecological environment subsystems. Furthermore, representative evaluation indicators were selected to characterize the WRCC subsystems on the basis of the availability of regional data and existing research findings31,32,33. Finally, on the basis of correlation and information contribution rate analyses, indicators with correlation coefficient values greater than 0.8 and cumulative information contribution rates in the bottom 20% of each subsystem were excluded, and an index system for the WRCC was established (Table 1).

Table 1 WRCC evaluation index system.
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Evaluation model of WRCC

In this study, an improved comprehensive evaluation model coupled with multiple methods was established via a systematic analysis of existing evaluation methods. The technique for order preference by similarity to ideal solution (TOPSIS) approach is a widely adopted multiattribute decision analysis method34. The calculation process of this method is straightforward and easy to understand. However, the evaluation results only indicate the relative position between data curves on a distance scale, thereby failing to capture dynamic changes in the data series. Additionally, there is a limitation in assigning equal weights to all evaluation objects in the calculation process18. The gray correlation analysis method can be used to measure the dynamic development of the evaluation object via the evaluation of the correlation between the curves of the evaluation and reference series via geometrical shape similarity. Therefore, in this study, the TOPSIS method was improved by coupling gray correlation analysis and assigning weights to the evaluation indicators. The calculation process of the proposed improved TOPSIS and gray correlation analysis model is expressed in Eqs. (1)–(8).

  1. 1.

    Construction of the weighted evaluation matrix

    $$X = \left( {x_{ij} } \right)_{m \times n} = \left( {w_{j} \times z_{ij} } \right)_{m \times n} = \left[ {\begin{array}{*{20}c} {x_{11} } & {x_{12} } & \cdots & {x_{1n} } \\ {x_{21} } & {x_{22} } & \cdots & {x_{2n} } \\ \vdots & \vdots & {} & \vdots \\ {x_{m1} } & {x_{m2} } & \cdots & {x_{mn} } \\ \end{array} } \right]$$
    (1)

    where xij is the weighted evaluation indicator value; wj is the weight of the jth indicator; zij is the standardized value of the indicator; m is the number of evaluation objects; and n is the number of evaluation indicators.

  2. 2.

    Calculation of positive and negative ideal solutions

    $$X^{ + } = \left\{ {\left( {\max x_{ij} \left| {j \in U} \right.} \right),\left( {\min x_{ij} \left| {j \in V} \right.} \right)} \right\} = \left( {x_{1}^{ + } ,x_{2}^{ + } , \cdots ,x_{n}^{ + } } \right)$$
    (2)
    $$X^{ - } = \left\{ {\left( {\min x_{ij} \left| {j \in U} \right.} \right),\left( {\max x_{ij} \left| {j \in V} \right.} \right)} \right\} = \left( {x_{1}^{ - } ,x_{2}^{ - } , \cdots ,x_{n}^{ - } } \right)$$
    (3)

    where \(X^{ + }\) and \(X^{ - }\) are the positive and negative ideal solutions, respectively; U is a positive indicator; and V is a negative indicator.

  3. 3.

    Determination of the gray correlation coefficient

    $$l_{ij}^{ + } = \frac{{\mathop {\min }\limits_{i} \mathop {\min }\limits_{j} \left| {x_{n}^{ + } - z_{ij} } \right| + \rho \mathop {\max }\limits_{i} \mathop {\max }\limits_{j} \left| {x_{n}^{ + } - z_{ij} } \right|}}{{\left| {x_{n}^{ + } - z_{ij} } \right| + \rho \mathop {\max }\limits_{i} \mathop {\max }\limits_{j} \left| {x_{n}^{ + } - z_{ij} } \right|}}$$
    (4)
    $$l_{ij}^{ - } = \frac{{\mathop {\min }\limits_{i} \mathop {\min }\limits_{j} \left| {x_{n}^{ - } - z_{ij} } \right| + \rho \mathop {\max }\limits_{i} \mathop {\max }\limits_{j} \left| {x_{n}^{ - } - z_{ij} } \right|}}{{\left| {x_{n}^{ - } - z_{ij} } \right| + \rho \mathop {\max }\limits_{i} \mathop {\max }\limits_{j} \left| {x_{n}^{ - } - z_{ij} } \right|}}$$
    (5)

    where \(l_{ij}^{ + }\) and \(l_{ij}^{ - }\) denote the gray correlation coefficients; and \(\rho\) is the discrimination coefficient.

  4. 4.

    Calculation of the relative closeness of the gray correlations

    $$l_{i}^{ + } = \frac{1}{n}\sum\limits_{j = 1}^{n} {l_{ij}^{ + } }$$
    (6)
    $$l_{i}^{ - } = \frac{1}{n}\sum\limits_{j = 1}^{n} {l_{ij}^{ - } }$$
    (7)
    $$Q_{i} = \frac{{l_{i}^{ + } }}{{l_{i}^{ + } + l_{i}^{ - } }}$$
    (8)

    where \(l_{i}^{ + }\) and \(l_{i}^{ - }\) are the gray correlations of the ith evaluation object; and Q is the relative gray correlation closeness, which is employed to represent the WRCC in this study.

Geographically and temporally weighted regression (GWTR)

WRCC driving research adopts the idea of regression analysis, i.e., the driving role of each subsystem is treated as a variable, and the WRCC evaluation result is considered a dependent variable in regression model construction. Traditional regression analysis models are established on the premise that the study area is internally homogeneous without spatial variations. However, due to the spatial and temporal properties of the WRCC, there are differences between the contributions of subsystem variables to WRCC evolution across various study areas. Therefore, in this study, a GWTR model was developed for the research area that accounts for temporal and spatial nonstationarity in regression analysis. This model can be employed to effectively explore the driving effects of various forces on WRCC evolution35,36. The calculation method of the GWTR model is as follows:

$$y_{i} = \beta_{0} \left( {u_{i} ,v_{i} ,t_{i} } \right) + \sum\limits_{k} {\beta_{k} } \left( {u_{i} ,v_{i} ,t_{i} } \right)x_{ik} + \varepsilon_{i} ,\;\;\;i = 1,2, \cdots n$$
(9)

where \(y_{i}\) is the WRCC value; \(\left( {u_{i} ,v_{i} ,t_{i} } \right)\) denotes the spatiotemporal coordinates; \(\beta_{0} \left( {u_{i} ,v_{i} ,t_{i} } \right)\) is the spatiotemporal intercept term; k denotes the water resources, social, economic and ecological environment subsystems; \(x_{ik}\) is the carrying capacity of the kth subsystem; \(\varepsilon_{i}\) is the random error term; and \(\beta_{k} \left( {u_{i} ,v_{i} ,t_{i} } \right)\) is the regression coefficient of the carrying capacity of the kth subsystem.

Geodetector model

The explanatory power of each driving factor for WRCC evolution was calculated via the geodetector model to comprehensively investigate the driving forces of the spatial variability pattern of the WRCC. Developed by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, the geodetector model comprises a suite of statistical techniques for investigating the spatial variability in a given phenomenon and identifying its underlying determinants37. The geodetector model has been widely utilized across various research fields, including natural, social and environmental sciences38,39,40. The geodetector model comprises four components: divergence and factor detection, ecological detection, risk zone detection and interaction detection modules. In this study, the factor detection module was utilized to elucidate the effects of various factors on WRCC evolution. The explanatory power, as determined by the q value, is commonly used in factor detection and is expressed in Eq. (10).

$$q = 1 - \frac{1}{{N\sigma^{2} }}\sum\limits_{h = 1}^{m} {N_{h} } \sigma_{h}^{2}$$
(10)

where h is the stratification of the driving factors; N and Nh are the number of global and stratified units, respectively; \(\sigma^{2}\) and \(\sigma_{h}^{2}\) are the global and stratified variance values, respectively; and q is the explanatory power of driving factor X for dependent variable Y. The greater the magnitude of q is, the greater the explanatory power of a given factor for the variations in the dependent variable.

Results

Temporal evolution pattern of the WRCC

The WRCC in the YRB (Henan section) and its 10 cities from 2010 to 2022 was calculated via the comprehensive evaluation model (Fig. 2). From the perspective of the entire YRB (Henan section), the WRCC value increased from 0.47 in 2010 to 0.59 in 2022, with an increase rate of 25.53%. This indicates that initial results have been achieved in water resources protection and rational utilization under the concepts of ecological protection and high-quality development of the YRB. From the perspective of individual cities, the WRCC also exhibited a growth trend with fluctuations during the 2010–2022 period, largely influenced by changes in natural precipitation over this period. Specifically, facilitated by rapid economic growth and continuous improvement in the ecological environment, Hebi exhibited the most rapid increase in the WRCC from 0.42 in 2010 to 0.62 in 2022, with an increase rate of 47.62%, followed by Puyang, with an average growth rate of 39.16%. Although the WRCC in Puyang was still the lowest among the remaining cities, reaching 0.45 in 2022, it exhibited a favorable development trend. Zhengzhou attained the lowest WRCC growth rate (1.87%) with the WRCC increasing from 0.53 in 2010 to 0.54 in 2022, which could be attributed to its relatively high initial WRCC and the increased water demand caused by the increasing population and rapid economic development.

Fig. 2
figure 2

Trends of the WRCC in the 10 cities in the YRB (Henan section) from 2010 to 2022.

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The carrying capacity of the four subsystems in the 10 studied cities within the YRB (Henan section) from 2010 to 2022 was calculated (Fig. 3). As shown in Fig. 3a, the water resources subsystem carrying capacity (WRSCC) in the 10 cities exhibited irregular temporal variations, which were highly influenced by local precipitation. For example, Henan Province experienced a once-in-a-century extreme rainstorm event in July 2021, which caused enormous floods accross Henan Province, especially in Zhengzhou (the “7–20” event). Influenced by this extreme rainfall event, the WRSCC values in all the cities were much higher in 2021 than they were in the other years. Figure 3b shows that the social subsystem carrying capacity (SSCC) varied across the 10 cities from 2010–2022. Specifically, the SSCC values in Puyang and Hebi increased with growth rates of 12.00% and 7.46%, respectively. The SSCC values in Zhengzhou, Kaifeng, Luoyang, Anyang and Jiaozuo exhibited significant decreasing trends. Zhengzhou attained the worst performance, with a decrease in the SSCC of 36.67% (from 0.60 in 2010 to 0.38 in 2022), which resulted from the growing population and increasing water consumption. The SSCC values in Jiyuan, Sanmenxia and Xinxiang remained relatively stable. Both the economic subsystem carrying capacity (ESCC) and the ecological environment subsystem carrying capacity (EESCC) increased in all the studied cities (Fig. 3c,d). Specifically, Kaifeng and Puyang exhibited the greatest increase in the ESCC, with growth rates of 261.11% and 258.82%, respectively. However, Zhengzhou and Sanmenxia attained the lowest growth rates of the ESCC, at 38.80% and 90.56%, respectively. The remaining cities all exhibited growth rates of the ESCC above 100% (Fig. 3c). According to Fig. 3d, Jiaozuo and Kaifeng attained the greatest EESCC growth, by 75.72% and 75.62%, respectively. However, Zhengzhou exhibited the smallest increase (4.01%) in the EESCC from 2010–2022.

Fig. 3
figure 3

Trends in the subsystem carrying capacity in the 10 cities within the YRB (Henan section).

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Spatial evolution pattern of the WRCC

The spatial distributions of the average WRCC and the average subsystem carrying capacity in the 10 cities within the YRB (Henan section) from 2010 to 2022 were analyzed (Fig. 4). In this study, the natural breaks (Jenks) method was utilized to categorize the WRCC and the subsystem carrying capacity into four levels, which allows for a more comprehensive understanding of their spatial distribution characteristics. As shown in Fig. 4a, there was significant spatial heterogeneity in the WRCC in the YRB (Henan section), with upstream cities exhibiting notably higher WRCC values than those in downstream cities. Specifically, upstream cities, such as Sanmenxia, Jiyuan, and Luoyang, attained the highest average WRCC values of 0.67, 0.64, and 0.59, respectively. These three cities are likely to possess superior water resource endowments and have gained greater ecological and environmental protection in recent years. However, downstream cities, such as Puyang and Xinxiang, exhibited the lowest WRCC performance, with average WRCC values of 0.45 and 0.46, respectively. This could be attributed to the relatively underdeveloped socioeconomic status of these cities, as well as the significant disruptions to the ecological environment caused by human activities.

Fig. 4
figure 4

Spatial distributions of the WRCC and the subsystem carrying capacity in the 10 cities within the YRB (Henan section).

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In terms of subsystems, Sanmenxia, Jiyuan and Luoyang exhibited the highest average WRSCC values of 0.58, 0.49 and 0.48, respectively, since these three cities are well endowed with water resources, with multiyear per capita water resources of 744, 493 and 362 m3, respectively (Fig. 4b). In contrast, Puyang and Zhengzhou demonstrated the lowest WRSCC performance, with average WRSCC values below 0.3, due to a the much lower multiyear per capita water resources (155 and 92 m3, respectively). As shown in Fig. 4c, Sanmenxia and Jiyuan attained the best SSCC performance, with an average SSCC value of 0.74, due to their relatively sparse populations and high domestic water use levels. However, Zhengzhou, Anyang and Puyang exhibited the lowest SSCC performance, with SSCC values less than 0.60, resulting from the high per capita domestic water consumption in these areas. In addition, the SSCC in Zhengzhou has been challenged by a large population and an increasing water demand. In the economic subsystem (Fig. 4d), as the capital of Henan Province, Zhengzhou exhibited the highest ESCC of 0.76, facilitated by its advanced economic development level and high water use capacity. Puyang and Xinxiang exhibited the lowest average ESCC values of less than 0.45 due to their low water use efficiency and poor economic development. As shown in Fig. 4e, Sanmenxia and Jiyuan exhibited the highest average EESCC values of 0.75 and 0.72, respectively, due to the extensive forest coverage and minimal anthropogenic disturbances. However, Xinxiang displayed the lowest average EESCC value of 0.40, resulting from its low forest coverage and high pollutant emissions.

Evolution of the key driving force of WRCC development

Variations in the driving intensity of the four subsystems for WRCC evolution in the YRB and its 10 cities from 2010 to 2022 were analyzed (Fig. 5). At the scale of the YRB (Henan section), the driving effect of the water resources and ecological environment subsystems exhibited a pattern of gradual increase followed by a slight decrease, resulting in an overall increase in the driving intensity. The driving effect of the social subsystem indicated a rapid declining trend before stabilization occurred. The driving effect of the economic subsystem initially exhibited a sharp decrease and then a rapid increase before stabilizing, resulting in a slight decrease in the overall driving intensity. Overall, the primary driving force of WRCC evolution in the YRB (Henan section) was dominated by social and economic development in the early years before it shifted to the ecological environment and economic development.

Fig. 5
figure 5

Driving effects of the WRCC subsystems in the 10 cities within the YRB (Henan section).

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In the different cities, the driving intensity of the various subsystems for WRCC evolution varied over time. The driving effect of the water resources subsystem in most cities generally showed an increasing trend with fluctuations, whereas that in Jiaozuo and Jiyuan indicated slight decreasing trend. The driving effect of the social subsystem exhibited an increasing trend in Anyang, Hebi and Puyang, and a significant decreasing trend in the other cities. The driving effect of the economic subsystem indicated a significant initial decreasing trend followed by an increase in all the cities, and a higher driving intensity was observed in 2022 than in 2010 in Zhengzhou, Kaifeng, Xinxiang and Jiaozuo. The driving effect of the ecological environmental subsystem generally followed a fluctuating trend encompassing an initial increase and a subsequent decrease across the 10 cities. A greater driving effect in 2022 than in 2010 was also observed in all the other cities except for Zhengzhou, Kaifeng and Jiaozuo.

In terms of the spatial distribution, the evolution of the core driving forces in the 10 cities within the YRB (Henan section) from 2010 to 2022 can be divided into the following three categories (Fig. 6): (1) the primary driving force in Luoyang, Sanmenxia and Jiyuan (located upstream) shifted from social development to ecological environmental protection; (2) the main driving force in Zhengzhou, Kaifeng, Xinxiang and Jiaozuo (located midstream) gradually changed from social development to economic development; and (3) the dominant driving force in Anyang, Hebi and Puyang (located downstream) transformed from economic development to ecological environmental protection. The main driving force of WRCC evolution exhibited notable regional characteristics. In 2022, WRCC development in the midstream cities in the YRB (Henan section) was driven primarily by the economic subsystem, whereas WRCC development in the upstream and downstream cities was driven mainly by the ecological environment subsystem. This could be largely attributed to the variations in the natural resource endowment conditions and recent developmental priorities across each city.

Fig. 6
figure 6

Spatial evolution of the driving effects of the WRCC subsystems.

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Identification of the key driving factors of WRCC development

The driving intensity of each influencing factor for WRCC development in the 10 studied cities from 2010 to 2022 was quantified via the geodetector model. The results revealed that the core driving factors for WRCC advancement in the YRB (Henan section) were the per capita disposable income of urban residents (X18), urbanization rate (X12), GDP per capita (X17), proportion of the tertiary industry (X16) and water use per 104 yuan industrial added value (X13) (Table 2). These factors were primarily concentrated in the economic subsystem, suggesting that economic advancement exerted a substantial driving effect on increasing the WRCC. The dominant driving factors of WRCC development in the different cities also varied with their natural resource conditions and development strategies. For example, the driving factors of WRCC development in Zhengzhou were dominated by the ecological water use rate (X19), water production coefficient (X3) and precipitation (X1); in Puyang, they were dominated by the per capita water use (X10), the water supply modulus (X5) and the water use per 104 yuan industrial added value (X13); and in Jiyuan, they were dominated by the GDP per capita (X17), the per capita disposable income of urban residents (X18) and the water consumption rate (X15). Although the main driving factors varied with city, the differences in the q values of these factors were relatively small, suggesting that WRCC development was influenced by a combination of multiple factors.

Table 2 Core driving factors of WRCC changes in the YRB (Henan section) and its 10 cities.
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Figure 7 shows the driving effects of the 24 factors on WRCC development in the 10 cities from 2010 to 2022. The driving effects of these factors on WRCC development clearly exhibited both consistency and variability across the different cities. Specifically, the driving intensity of the population density (X7), the per capita disposable income of urban residents (X18) and the permanent population (X9) exhibited relatively similar patterns across the different cities, with standard deviation values of 0.10, 0.13, and 0.14, respectively, suggesting that these factors could affect WRCC development in a similar manner across the 10 cities within the YRB (Henan section). Significant differences were also observed in the driving intensities of the per capita water use (X10), the ecological water use rate (X19) and the utilization ratio of water resources (X6) among the 10 cities, with standard deviation values of 0.31, 0.28 and 0.27, respectively. This finding indicates that the driving effects of these factors varied across the different cities because of their diverse levels of socioeconomic development and resource endowments.

Fig. 7
figure 7

Driving effects of the WRCC factors in the 10 cities within the YRB (Henan section).

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Discussion

The evolution process of WRCC driving forces in the YRB (Henan section) from 2010 to 2022 can be categorized into the following four stages (Fig. 8).

Fig. 8
figure 8

Change trends of the WRCC driving force from 2010 to 2022.

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At Stage I (from 2010 to 2013), WRCC development was driven primarily by social and economic progress. During this phase, social development exhibited significant vitality, with a slight increase in the driving intensity, thus serving as the primary driving force. Although there was a steady increase in the total economic volume during this period, the driving intensity of economic development demonstrated a declining trend. Overall, the driving intensity of social and economic development surpassed that of water resources and the ecological environment, mainly because of their notable reliance on natural resource consumption and occupation of the ecological environment to sustain social and economic progress during this period.

At Stage II (from 2013 to 2015), WRCC development in the YRB (Henan section) was largely driven by social and ecological progress, although the driving intensity of social development decreased. During this period, the driving intensity of economic development also exhibited a continuous decreasing trend. Despite the declining driving intensity of social and economic development, positive effects of their initial development began to emerge, effectively promoting the rational use of water resources and increasing the level of ecological environmental protection. However, the driving intensity of both water resources and the ecological environment showed significant upward trends at this stage, surpassing that of economic development.

At Stage III (from 2015 to 2019), there was a steady increase in the driving intensity of water resources and ecological development, which became the primary driving force of WRCC development in the YRB (Henan section). At this stage, the positive effects resulting from high-quality social and economic development remained prominent, contributing to further increases in water resource utilization and ecological protection. The driving intensity of water resources and ecological development remained high, whereas that of social development remained relatively stable. Additionally, there was a notable growth trend in the driving intensity of economic development, but it remained lower than those of the other subsystems.

At Stage IV (from 2019 to 2022), the driving intensity of economic development significantly increased, ranking second only to that of ecological development. In recent years, with the continuous promotion of ecological protection and high-quality development, there has been a notable improvement in the overall quality of the water environment in the YRB (Henan section), with a continuous increase in ecosystem function and sustained high momentum for ecological environmental development. Furthermore, this basin has adhered to green, low-carbon and innovative standards in its developmental practices, effectively facilitating the expansion of leading industrial clusters and high-quality agricultural development. Wang et al.41 also reported that the development of the ecological environment plays a crucial role in the advancement of water resources, society and the economy, thus emphasizing its importance for WRCC development. Therefore, future development should prioritize the leading role of ecological environmental protection.

The temporal and spatial patterns of WRCC evolution and its driving effects in the YRB (Henan section) from 2010 to 2022 were investigated in this study. Via the thorough analysis and comprehensive examination presented in this study, future development should emphasize the following aspects. First, given the significant temporal and spatial heterogeneity in the WRCC and the subsystem carrying capacity across the cities in the YRB (Henan section), each city should formulate targeted development strategies according to the regional resource endowments and development characteristics. For example, cities in the middle and lower reaches of the YRB (Henan section), particularly Zhengzhou and Puyang, face challenges such as water shortages, high exploitation and utilization of water resources and relatively dense populations. In the future, the efficient utilization of regional water resources should be promoted by enhancing water resources management, popularizing water-saving technology and increasing the water utilization efficiency. Second, the driving effect of economic development on increasing the WRCC can be enhanced by improving the mode of economic growth. Despite the high driving intensity of economic development, the recent economic growth mode in the YRB (Henan section) is too singular, and its industrial structure is relatively backward. To promote high-quality development of the YRB (Henan section), continuous efforts to upgrade industries and foster innovative development should be made to increase regional competitiveness levels. Third, it is important to further enhance ecological preservation and construction to promote the pivotal role of ecological development. The existing ecological environment remains a major obstacle to the high-quality development of the YRB (Henan section). Therefore, future development should prioritize ecology and green development, with comprehensive efforts focused on increasing ecological restoration and protection. Additionally, more attention should be given to the development of green industries and a circular economy to promote sustainable utilization of the ecosystem.

Conclusion

Research on WRCC evaluation is important for promoting ecological protection and high-quality development of the YRB (Henan section). In this study, the spatiotemporal pattern of WRCC evolution in the YRB (Henan section) was systematically analyzed, and the primary driving forces and factors of WRCC development were explored in detail. The research results could provide effective reference data for clarifying the driving mechanism of WRCC development and promoting the sustainable utilization of water resources in the basin.

The main conclusions are as follows: (1) from 2010 to 2022, the WRCCs of the YRB (Henan section) and its 10 cities showed increasing trends, with Hebi exhibiting the highest growth rate of 47.62%; (2) the WRCC demonstrated significant spatial heterogeneity within the YRB (Henan section), in which cities in the upstream region performed better than those in the downstream region did; (3) the core driving force of WRCC evolution in the YRB (Henan section) gradually changed from social and economic development to ecological and economic development; and (4) the primary driving factors of WRCC development in the YRB (Henan section) were the per capita disposable income of urban residents, the urbanization rate and the GDP per capita.