Evaluation of water resource carrying capacity in Qianping Irrigation District obstacles

1 Introduction

The study of water resource carrying capacity is a core issue for resolving water security dilemmas and achieving regional sustainable development (Chen M. et al., 2023; Chen Q. et al., 2023; Wang et al., 2024). Since the concept of “carrying capacity” was introduced into the field of water resources in the 1980s, the research has undergone an evolution process from the definition of the concept to the systematization of methods, and from static evaluation to dynamic coordination (Zuo and Zhang, 2015; Li et al., 2022). Early studies were limited by data and methods, and mostly employed simple supply–demand balance analysis. Harris and Kennedy (1999) and others defined it at the socio-economic level as the constraint threshold for population and economic activities. Since the 21st century, the indicator system has formed a tripartite structure of “water resources—socio-economy—ecological environment” (Malakar and Lu, 2021), a four-dimensional water dimension of “water volume—water quality—water body—water flow” (ZHANG et al., Zhang et al., 2019), and representative paradigms such as the PSR model (Plieninger et al., 2021). In terms of evaluation methods, comprehensive assessment methods such as AHP and TOPSIS (Wei et al., 2023) are developing in parallel with the system dynamics (Wang et al., 2021) method. The spatial scale gradually narrows from the national level (He and Wang, 2022; Khorsandi et al., 2023), to the provincial level (Defeng et al., 2021), and further down to the watershed (Jia et al., 2018; Dai et al., 2022) and cities (Chen M. et al., 2023; Chen Q. et al., 2023; Wei et al., 2023). In recent years, research has further shown a trend of multi-model coupling and intelligent integration. The entropy-weight TOPSIS-coupled coordination degree-barrier degree model chain has been widely applied. Machine learning methods have begun to be used for dynamic prediction and uncertainty quantification (Sun et al., 2022). The deep application of the DPSIR framework has also provided new tools for system analysis.

Although the aforementioned progress has been significant, the current research still has obvious weaknesses. One issue is the insufficient systematic research at the district scale. Currently, most of the research results are concentrated at the municipal or river basin scale (Xia et al., 2002). However, as a “engineering-nature-society” integrated system, the district is highly dependent on engineering regulation, cross-administrative-region water resource transfer, and seasonal water usage conflicts, and these characteristics have not been fully incorporated into the evaluation system. Second, there is a lack of long-term dynamic evaluation for newly constructed irrigation areas. Particularly, there is a lack of tracking of the transitional characteristics and the lagging effect of the engineering performance during the transition period from construction to operation for the projects that were put into operation during the “14th Five-Year Plan” period. Thirdly, the coupling mechanism under the “storage-intake integration” system is unclear. The existing models are unable to accurately depict the asymmetric contributions of local water supply and external water diversion, as well as the competitive relationship between ecological flow release and operation scheduling. Fourth, the spatial heterogeneity of the barrier analysis has been overlooked, and the evolution patterns over time have not been effectively integrated with the spatial differentiation. The fifth issue is that the data-driven dynamic early warning system has not yet been established. In terms of multi-source data integration, extreme scenario simulation, and dynamic threshold setting, these aspects are still lacking.

The Qianping Irrigation District, as a key water security project in Henan Province’s “14th Five-Year Plan”, is built on the basis of the Qianping Reservoir to establish a “storage and diversion combined” water supply system. It covers three counties (cities) in Luoyang and Pingdingshan cities, and is responsible for the water supply for millions of people and the irrigation of tens of thousands of acres of farmland. It holds a strategic position in the food security and urban–rural development of central and western Henan Province. This area is located in the transitional zone between the northern subtropical zone and the warm temperate zone. The annual precipitation is approximately 680 millimeters, but the distribution is uneven in time and space. The precipitation during the flood season from June to September accounts for over 63% of the annual total, and the problem of temporal and spatial mismatch of water resources is particularly prominent. In view of this, this study takes the period from 2010 to 2023 as the research period. A 14-item evaluation index system covering four subsystems of water resources, ecological environment, economy, and society is constructed. The entropy weight TOPSIS method, coupling coordination degree and obstacle degree model are employed to analyze the variation characteristics of water resource carrying capacity in the irrigation area, the coordination level of subsystems, and the key restrictive factors from the temporal and spatial dimensions. The aim is to provide theoretical support for the refined management of water resources in the irrigation area, and at the same time, to offer a reference model for the research on new irrigation areas in similar monsoon climate regions.

2 Research area overview and data sources

2.1 Research area overview

The Qianping Irrigation District is a regional water resource allocation system developed around Qianping Reservoir. Located in the upper reaches of the Beiyu River within the Huai River Basin, its geographical coordinates span between 112°26′ and 113°07′ east longitude and 33°49′ and 34°21′ north latitude. The irrigation district covers four counties (cities) within Henan Province: Ruyang County in Luoyang City, and Ruzhou City, Jia County, and Baofeng County in Pingdingshan City. It belongs to the continental monsoon climate zone transitioning from the northern subtropical to the warm temperate zone, with an average annual precipitation of approximately 680 millimeters. Precipitation distribution is uneven both temporally and spatially, with the rainy season from June to September accounting for over 63% of the annual total. The irrigation district’s water supply coverage explicitly includes Ruyang County in Luoyang City, as well as three counties (cities) under Pingdingshan City: Ruzhou City, Jia County, and Baofeng County. The water supply recipients are divided into two major categories: On one hand, it provides irrigation water for agricultural production in the three counties (cities), supporting the cultivation of local grain crops and cash crops; On the other hand, the focus is on ensuring the domestic water supply needs of the urban populations in Ruyang County, Ruzhou City, and Jia County, providing water resources support for urbanization development and the improvement of residents’ quality of life (see Figure 1).

2.2 Data source

Data for the water resources subsystem is sourced from the Henan Provincial Water Resources Bulletin (2010–2023) and the China Water Resources Statistical Yearbook, with reference to water resources bulletins from two cities and three counties. The agricultural water usage data are directly obtained from the actual irrigated area and the water intake volume at the sluice gates provided by the management units of the irrigation districts, in order to enhance the accuracy. Data for the ecological environment subsystem are sourced from the China Ecological Environment Status Bulletin and the China Environmental Statistics Yearbook for the period 2010–2023. Data for the economic and social subsystems are sourced from the Henan Statistical Yearbook (2010–2023), supplemented by statistical yearbooks of Luoyang and Pingdingshan cities, as well as statistical bulletins on national economic and social development. The data is based on county-level statistical information, sourced from the statistical yearbooks of each county (city) and the statistical bulletins on national economic and social development. The data is down-scaled using the “area-weighted interpolation method”. Specifically, the proportion of the controlled area of irrigation districts in the total area of each county is used as a coefficient to convert the social data such as GDP and population. Through sensitivity analysis, it is shown that if the uncorrected county-level data is used, the carrying capacity of Ruzhou City is underestimated by approximately 12%. Therefore, the results using the corrected data are adopted in this paper.

3 Research methods

3.1 Indicator system construction

Based on the established criteria for constructing the indicator system, the indicator selection follows the principles of scientificity, representativeness, and operability. It also comprehensively draws on the “Water Resources—Economy—Ecological Environment—Society” structure (Li et al., 2025). This paper has constructed an indicator system for evaluating water resource carrying capacity, which includes 4 subsystems (water resources, ecological environment, economy, and society) and 14 indicators, as shown in Table 1. Among them, given the “storage and diversion combined” water supply feature of the Qianping Irrigation District, the utilization rate of water resources (X₄) to some extent reflects the degree of water resource supply guarantee under the engineering regulation; at the same time, through spatial comparison, the differentiated impact of external water diversion on the carrying capacity of different regions can be identified.

Table 1. Water resource carrying capacity evaluation index system for Qianping Irrigation District.

To test the robustness of the index system, we conducted a sensitivity analysis using the “stepwise elimination method”. After randomly eliminating a certain index and recalculating the comprehensive score, the results showed that the Spearman correlation coefficient of the ranking order in each year was greater than 0.85 (p < 0.01), indicating that the index system has good stability and there is no situation where a single index dominates the evaluation results.

3.2 Entropy-weighted TOPSIS method

The Entropy Weight TOPSIS method is a comprehensive evaluation approach that integrates the “entropy weight method” with the “TOPSIS method.” Among these, the Entropy Weighting Method objectively determines weights through indicator information entropy, thereby avoiding subjective bias; the TOPSIS method ranks the superiority or inferiority of evaluation objects by calculating the distance between them and the “positive ideal solution” and “negative ideal solution.” The combination of these two approaches enables a scientific and objective multi-indicator comprehensive evaluation, widely applied in data analysis across resource management, economics, environmental studies, and other fields (Lv et al., 2023). The specific calculation steps are as follows:

• (1) Construct the raw data matrix:

The constructed water resource carrying capacity evaluation comprises m years and n indicators, resulting in the raw data matrix X = (x_ij)_{m × n}, Where x_ij denotes the jth evaluation indicator in the ith year.(i = 1,2,.,m; j = 1,2,.,n).

• (2) Standardization of indicators:

Positive indicators. The formula is shown in Equation 1:

x ' ij = x ij − min ( x j ) max ( x j ) − min ( x j ) ( i = 1 , 2 , … , m ; j = 1 , 2 , … , n ) (1)

Reverse indicator. The formula is shown in Equation 2:

x ' ij = max ( x j ) − x ij max ( x j ) − min ( x j ) ( i = 1 , 2 , … , m ; j = 1 , 2 , … , n ) (2)

Among these,max ( x j ) and min ( x j ) represent the maximum and minimum values of the jth indicator, respectively.

• (3) Calculate the information entropy of the indicators:
• I. Non-negative transformation of indicator data. The formula is shown in Equation 3:

χ ' ' ij = χ ' ij + A (3)

• II. Weighting of calculation indicators. The formula is shown in Equation 4:

p ij = x ' ij ∑ i = 1 m ∑ j = 1 n x ' ij (4)

• III. Calculate the entropy weight of the indicator. The formula is shown in Equation 5:

e j = − 1 ln n ∑ i = 1 n p ij ln ( p ij ) (5)

• (4) Calculate indicator weights:
• I. Calculate the redundancy of indicators. The formula is shown in Equation 6:

g j = 1 − e j (6)

• II. Calculate the weight of the jth indicator. The formula is shown in Equation 7:

w j = g j ∑ j = 1 n g j (7)

• (5) Calculate the weight normalization matrix S. The formula is shown in Equation 8:

S = ∑ j = 1 n w j p ij ( i = 1 , 2 , … , m ; j = 1 , 2 , … , n ) (8)

• (6) Determine the positive ideal solution and negative ideal solution for each indicator. The formula is shown in Equations 9, 10:

s j + = max { s 1 j , s 2 j , s 3 j , … , s ij } (9)

s j − = min { s 1 j , s 2 j , s 3 j , … , s ij } (10)

• (7) Calculate the Euclidean distance between each evaluation unit and both the positive ideal solution and the negative ideal solution:

Ideal solution distance. The formula is shown in Equation 11:

D i + = ∑ j = 1 n ( s ij − s j + ) 2. (11)

Negative ideal solution distance. The formula is shown in Equation 12:

D i − = ∑ j = 1 n ( s ij − s j − ) 2 (12)

• (8) Calculate the closeness index C_i. The formula is shown in Equation 13:

C i = D i − D i + + D i − ( i = 1 , 2 , … , m ) (13)

In the formula: C_i represents the water resource carrying capacity level in year i, where 0 ≤ C_i ≤ 1. A higher C_i value indicates a higher carrying capacity level.

The comprehensive evaluation index for water resource carrying capacity is divided into five levels (Zhao et al., 2022), as shown below (see Table 2).

Table 2. Water resource evaluation index grading criteria.

3.3 Coupling coordination model

The Coupling Coordination Degree Model quantifies the level of coordinated development among multiple systems (or subsystems) through their interactions and interdependencies. It assesses whether systems are in a state of “positive interaction” or “imbalanced conflict,” and is widely applied in evaluating synergistic relationships across diverse fields such as water resources, socio-economics, and ecological environments. The specific steps are as follows, the formula is shown in Equations 14−16:

U = 4 [ C 1 · C 2 · C 3 · C 4 ( C 1 + C 2 + C 3 + C 4 ) 4 ] 1 4 (14)

T = a C 1 + b C 2 + c C 3 + d C 4 (15)

D = U · T (16)

In the formula: C₁, C₂, C₃ and C₄ represent the carrying capacity indices of the water resources, ecological environment, economy, and society subsystems, respectively; U denotes the coupling degree; T denotes the comprehensive coordination index; a, b, c, and d denote the weight coefficients of the four criterion layers—water resources, ecological environment, economy, and society, respectively; According to the literature (Tang et al., 2025), this paper believes that in the water resource carrying system, the four systems are considered equally important. Therefore, a = b = c = d = 1/4 is taken. D denotes the coupling coordination degree, with values ranging from 0 to 1.

The coupling coordination types of water resource carrying capacity in this paper are shown in Table 3.

Table 3. Types of coupling coordination in water resource carrying capacity.

3.4 Obstacle degree model

The Barrier Degree Model is an analytical tool designed to quantitatively identify core constraints affecting system development. Its core logic involves calculating the deviation between evaluation indicators and optimal solutions to assess the degree to which each indicator impedes system objectives—such as water resource carrying capacity and coordinated development levels—ultimately pinpointing critical areas requiring priority improvement. The introduction of the barrier degree model aims to identify the primary limiting factors hindering the enhancement of water resource carrying capacity in the Qianping Irrigation District. The formula is shown in Equation 17.

Q ij = w j ( 1 − x ij ) ∑ j = 1 n w j ( 1 − x ij ) (17)

Here, w_j denotes the factor contribution level, representing the weight assigned to each index; Q_ij denotes the constraint level, measuring the degree of constraint or influence each indicator exerts on the entire system.

4 Results and analysis

4.1 Analysis of comprehensive evaluation results for water resource carrying capacity

From 2010 to 2023, the water resource carrying capacity of the three counties (cities) showed differentiated changes (Figure 2): Ruyang maintained a relatively optimal level (0.582–0.564), Ruzhou had the largest increase (0.156–0.471, +201.9%), and Jiaxian steadily improved (0.260–0.487, +87%). The significant improvement in Ruzhou was attributed to the optimization of the industrial structure and the promotion of water-saving technologies, resulting in a substantial decrease in water consumption per unit of GDP; Ruyang slightly declined but still maintained advantages in indicators such as per capita water resources and ecological water use rate; Jiaxian benefited from the expansion of agricultural water-saving irrigation areas.

Based on the water resource carrying capacity evaluation index system, the comprehensive score for the water resource carrying capacity of the Qianping Irrigation District from 2010 to 2023 has been calculated. As shown in the carrying capacity grades in Table 4, the overall water resource carrying capacity of the Qianping Irrigation District from 2010 to 2023 remained at a relatively low level, consistently falling within the range of “shortage to adequate” over the years. Overall, the trend shows fluctuating increases over time. In 2010, the evaluation scores for Ruyang County, Ruzhou City, and Jia County were 0.562, 0.160, and 0.256 respectively, with multiple areas falling into the categories of water scarcity and shortage. By 2021, Ruyang County achieved a score of 0.660, reaching the abundant level, while Ruzhou City and Jia County also advanced to the coordinated and adequate levels of water resources. Spatially, Ruyang County has consistently demonstrated superior water resource carrying capacity compared to Ruzhou City and Jia County. Ruzhou City has long endured water scarcity or shortages, with scores predominantly below 0.2 between 2010 and 2015. Although improvements have been made in Jia County, water scarcity and shortage remained the primary issues from 2010 to Zhang et al. (2019). This disparity is closely linked to Ruyang County’s proximity to Qianping Reservoir, which grants it greater advantages in water resource allocation, while Ruzhou City and Jia County face substantial industrial water demands and room for improvement in water conservation levels. It reflects that the spatial distribution of water resources within the irrigation district and its alignment with regional development needs require further optimization.

Table 4. Water resource carrying capacity evaluation scores for Qianping Irrigation District, 2010–2023.

4.2 Evaluation of subsystem analysis

From 2010 to 2023, the carrying capacity indices of the four subsystems—water resources, ecological environment, economy, and society—of the Qianping Irrigation District showed an overall upward trend and gradually moved toward coordinated development (Figure 3). The water resources subsystem showed the most significant fluctuation, with the index dropping from 0.58 in 2010 to the lowest value of 0.17 in 2016, then rebounding to the peak value of 0.73 in 2021, and falling back to 0.51 in 2023, reflecting its high sensitivity to climate and human regulation. The ecological environment subsystem steadily improved, increasing from 0.15 in 2010 to 0.50 in 2020, with the effectiveness of ecological protection gradually emerging. The economic subsystem rose from 0.07 to 0.39 in 2023, an increase of 457%, indicating an enhanced coordination between economic development and water resource carrying capacity. The social subsystem steadily increased, with the index remaining between 0.19 and 0.27, maintaining a dynamic balance between social water demand and water supply guarantee. Overall, the subsystems showed differentiated development, with significant fluctuations in water resources, continuous improvement in the ecological environment, steady economic growth, and a steady improvement in the region’s carrying capacity. Strengthening the optimization of water resource allocation and ecological governance remains a key path to enhance the regional carrying capacity.

4.3 Evaluation of coupling coordination analysis in subsystems

The coupling coordination degree of the irrigation area showed a three-stage evolution from 2010 to 2023 (Figure 4): From 2010 to 2016, the system experienced a period of low-level fluctuation, with coordination fluctuating within the range of 0.34–0.40, indicating a state of mild imbalance. From 2017 to 2020, the transition and adjustment period saw coordination steadily improve to 0.46, achieving a qualitative shift from imbalance to harmony.2From 2021 to 2023, the coordination level stabilized at a high range. Except for an abnormal decline in 2022, the coordination index remained around 0.50, indicating a level ranging from barely coordinated to minimally coordinate.

From a spatial correlation perspective, the coordinated evolution of the Qianping Irrigation District maintains strong linkage with its core county areas. Among these, Ruyang County, as the development hub, consistently leads in coordination levels, providing stable support for the district’s overall performance. The leapfrog development of Ruzhou City proved particularly pivotal, with its coordination index soaring from a low of 0.22 in 2012 to 0.49 in 2023, significantly enhancing the balanced development of the system. Although Jiaxian experienced a significant decline in 2022, it rebounded rapidly to 0.51 in 2023, demonstrating the system’s self-repairing capacity. Although Jiaxian experienced a significant decline in 2022, it rebounded rapidly to 0.51 in 2023, demonstrating the system’s self-repairing capacity.

4.4 Barrier analysis

Between 2010 and 2023, the structure of key obstacles to enhancing water resource carrying capacity in the Qianping Irrigation District underwent significant evolution (see Figure 5). The water resources subsystem constituted the primary limiting factor in the vast majority of years, with its limiting degree fluctuating upward from 28.03% in 2010 and peaking at 60.80% in 2020. This indicates that the baseline conditions of water resources and their development and utilization patterns have long been, and increasingly become, a key bottleneck constraining the enhancement of regional comprehensive carrying capacity. The ecological environment subsystem, as another significant barrier factor, exhibits a V-shaped change in its impact intensity, first decreasing and then increasing. The degree of ecological constraints declined from 41.45% in 2010 to 26.72% in 2020, but rebounded to 40.41% by the end of the study period (2023). This reflects the dynamic and fluctuating nature of ecological constraints, indicating that the effectiveness of governance measures remains unstable. In contrast, the constraints imposed by the economic and social subsystems on water resource carrying capacity steadily diminished throughout the study period, declining from 21.63 and 8.89% in 2010 to 4.97 and 6.53% in 2023, respectively. This trend indicates that as the region’s economy and society developed, the limiting effects of these two subsystems on water resource carrying capacity have significantly eased.

Overall, the pattern of constraints on water resource carrying capacity in the Qianping Irrigation District has gradually shifted from being jointly dominated by water resources and ecological environment in the early stages to being primarily constrained by the water resource subsystem. Evolutionary patterns suggest that future strategies for enhancing carrying capacity should focus on optimizing water resource allocation and promoting efficient utilization, while consolidating achievements in ecological and environmental governance. This approach aims to overcome critical bottlenecks and achieve coordinated systemic development.

The degree of constraint for the 14 evaluation indicators of water resource carrying capacity in the Qianping Irrigation District from 2010 to 2023 was calculated using the formula. To better visualize the distribution of indicator obstacle levels, the indicators were sorted in descending order of obstacle severity. The top 6 indicators by obstacle level were selected for statistical analysis, as shown in Table 5. At the same time, specific obstacles in each county were analyzed, as shown in Figure 6.

Table 5. Degree of constraint for primary limiting factors of water resource carrying capacity in Qianping Irrigation District.

As shown in Table 5, X₅, X₂, and X₃ are the main obstructive factors. The obstruction degree of X₅ remains at a high level, and the insufficient guarantee of ecological water use is a long-term bottleneck; X₂ and X₃ have significantly increased, and the pressure from population has highlighted the constraint on the background water resources.

The county-level heat map (Figure 6) reveals the differentiated constraint mechanism. Jia County (Figure 6a) has a deep red high-value area for X₅, which is particularly prominent from 2019 to 2020. Agricultural water use has encroached upon ecological water use, and X₂ and X₃ have gradually deepened year by year. The dual pressure of a lack of background resources and competition with agriculture is significant. Ruyang (Figure 6b) has a significant X₅ but relatively low X₁ and X₆. The advantage of reservoir water sources is obvious, and X₈ (water consumption rate) has a white high-value point in 2017, indicating the risk of efficiency fluctuations. Ruzhou (Figure 6c) has a continuous red-pink area for X₂ and X₃. X₅ weakened for a period from 2012 to 2015, but intensified again after 2020, and industrial water conservation has not effectively transformed into ecological water space.

Further analysis shows that although the efficiency of irrigation water utilization has increased, the proportion of high-water-consuming crops such as wheat and tobacco remains above 52%, and the total agricultural water use still reduces the ecological share. Simple water conservation is not enough; a combination of planting structure adjustment is necessary. X₄ has fluctuated and risen to above 10%, and the development intensity has nearly reached the limit; X₁₀ has decreased from 9.29% to below 5%, and the improvement in water resource utilization efficiency is significant.

The regulatory mechanism has evolved in stages: from 2010 to 2016, the main contradiction was the guarantee of ecological water use; from 2017 to 2023, it shifted to the joint constraints of the background conditions of water resources (X₂, X₃) and the guarantee of ecological water use (X₅). At the end of the study, X₁₄ entered the list of major obstacles. The pressure on water resources brought about by social development will further intensify. In the future, a systematic governance strategy needs to be adopted to jointly protect the background water resources and ecological water use, strictly control the development intensity, in order to cope with the increasingly complex challenges of water resource pressure.

5 Discussion

The temporal and spatial variation of the water resource carrying capacity of the Qianping Irrigation District is essentially the result of the coupling effect between engineering control capabilities and the inherent conditions of the resources. In 2010, the proximity index in Ruyang County reached 0.56. This was not due to abundant precipitation, but rather because it was adjacent to the Qianping Reservoir Hub and received engineering compensation. After the reservoir was put into operation in 2021, the growth rate in Ruzhou reached 115.8%, which verified the leapfrog improvement effect of the external water diversion on resource-poor areas. However, in 2023, the proximity degree in Ruyang slightly decreased, revealing the marginal decline of the engineering benefits—the expansion of the water supply area diluted the advantage of per capita water resources, and the increase in ecological scheduling was limited, indicating that it is still constrained by the regional water resources total volume ceiling in the long term.

The evolution of coupling coordination degree shows a “engineering-driven leap.” From the time the reservoir was closed and filled to the trial water release period in 2017–2020, the index of the water resources subsystem rose from 0.17 to 0.51, driving the overall coordination degree to cross from mild imbalance to primary coordination. This overall leap triggered by a single subsystem mutation confirms the positive intervention of the “storage-introduction combination” project. However, in 2022, the coordination degree dropped sharply to 0.43, revealing a structural imbalance: The extreme drought in the Huai River Basin led to a significant reduction in water inflow to the reservoirs, and the reduction in downstream water allocation disrupted the coordination relationship between the economic and social subsystems of Ruzhou and Jiaxian. This also revealed the vulnerability of water diversion-dependent irrigation areas—during the flood season, the improvement in engineering connectivity enhanced the coordination level, but during the dry season, it instead exacerbated spatial inequality.

The pattern of barriers has shifted from dual dominance of ecology and resources to single core of resource background, reflecting the institutional substitution of the regulatory mechanism. The persistent high obstacle degree of the ecological water use rate (X₅) confirms the bottleneck of insufficient ecological water resource guarantee. In the future, it is necessary to combine the ecological dispatch procedures of the Qianping Reservoir, optimize the ecological flow release system of the downstream river, and adjust the proportion of high-water-consuming crops to alleviate the competitive contradiction between ecological water use and agricultural water use. After 2017, the per capita water resources volume and water production index exceeded each other. Due to the establishment of the ecological flow release system, ecological water use was guaranteed. However, the population growth and industrial development led to a continuous decline in the per capita water resources volume. The system solved the problem of fair distribution but failed to alleviate the overall scarcity of water resources. The decline in the water consumption per 10,000 yuan of GDP is actually a passive outcome of the evolution of the industrial structure—the proportion of high-water-consuming industries in RuZhou has dropped from 38 to 21%, but the water consumption per 10,000 yuan of industrial added value has only slightly decreased, indicating that the internal water-saving technological transformation in the industry is lagging behind. “Structural water saving” rather than “technological water saving” implies that efficiency improvement will encounter bottlenecks. The issue of population density will enter the main obstacle category in 2023 and deserves greater attention. From 2020 to 2023, the urbanization rate rose from 41 to 47%, yet the leakage rate of water supply pipelines remained as high as 18%. The increase in the volume of social subsystems did not accompany an improvement in quality. The population pressure has become more obvious as a result.

The Qianping Irrigation District, as a “nested engineering” management unit, has a carrying capacity that is a function of the engineering capacity, the scheduling system, and the resource background. The management strategy needs to shift from a resource-adaptive model to an engineering-regulated model: establish a dynamic water rights allocation mechanism, and link the reservoir scheduling with the real-time carrying capacity of each county. In Jiaxian County, the proportion of high-water-consuming crops is reduced, and the water saved is allocated specifically for ecological use; a red line for industrial water efficiency is set to prevent rebound in efficiency in the industrial transformation areas. The spatial heterogeneity of the barriers requires a break from the unified management model. Ruyang needs to shift from being a water source provider to an ecological service provider, and obtain downstream support through ecological compensation. Ruzhou and Jiaxian need to shift from being dependent on engineering projects to becoming local resource exploiters. By utilizing reclaimed water, they can reduce their reliance on external water supply. This regional functional positioning and benefit-sharing mechanism serves as the institutional foundation for maintaining long-term coordination.

6 Conclusion

This paper employs the entropy-weighted TOPSIS method to measure the comprehensive water resource carrying capacity of the Qianping Irrigation District based on the established evaluation indicator system. Using the coupling coordination degree model, the coupling coordination levels of the four subsystems within the Qianping Irrigation District’s water resource carrying capacity were calculated for the period from 2010 to 2023. Additionally, an obstacle model was employed to identify the factors hindering the coupling coordination levels of these four subsystems. The main research findings are as follows:

• (1) The overall water resource carrying capacity of the Qianping Irrigation District has increased while exhibiting regional differentiation. From 2010 to 2023, the average proximity value rose from 0.34 to 0.43. Ruyang County consistently led in this metric, while Ruzhou City recorded the largest increase. With an endowment of only 358 cubic meters of water resources per capita in the water supply area, the improvement of carrying capacity primarily relies on engineering regulation and efficiency optimization. The cumulative supply of 480 million cubic meters from Qianping Reservoir serves as the core support for enhancing carrying capacity.
• (2) The level of subsystem coupling and coordination has steadily improved but remains insufficient. The overall coordination index for 2023 was 0.51. While Ruzhou (0.49) and Jiaxian (0.52) approached the threshold for barely adequate coordination, the irrigation district still faces issues such as uneven spatiotemporal distribution of water resources and inadequate ecological water supply. Synergistic effects between subsystems have yet to be fully realized.
• (3) The pattern of constraints is characterized by water resources and the ecological environment subsystem as its core. Insufficient per capita water resources and low ecological water supply security rates constitute the primary bottlenecks. While economic constraints have generally eased, internal disparities remain pronounced. Simultaneously, pressures from the social dimension are gradually intensifying, emerging as a new limiting factor.

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Footnotes

Edited by: Upaka Rathnayake, Atlantic Technological University, Ireland

Reviewed by: Liangliang Du, Gansu Agricultural University, China

Zheng Pan, Central South University, China