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Article

A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province

by
Zhiqiang Zhang
1,2,
Weiwei Wang
1,
Xiuyu Zhang
3,*,
Hui Zhang
1,
Li Yang
1,
Xizhi Lv
4 and
Xu Xi
5,*
1
College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
2
Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475000, China
3
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
4
Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China
5
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
*
Authors to whom correspondence should be addressed.
Water 2024, 16(17), 2497; https://doi.org/10.3390/w16172497
Submission received: 22 July 2024 / Revised: 25 August 2024 / Accepted: 29 August 2024 / Published: 3 September 2024
(This article belongs to the Special Issue Yellow River Basin Management under Pressure: Present State, Restoration and Protection III)
Browse Figures
Versions Notes

Abstract

:
Water security, as a crucial component of national security, plays a significant role in maintaining regional stability and ensuring the healthy and rapid development of the economy and society. The Yellow River water-receiving area of Henan Province (YRWAR-HN) is selected as the research area in this study. Firstly, a comprehensive evaluation index system is constructed based on the actual water security problems of the research area, and the single index quantification–multiple indices syntheses–poly-criteria integration method (SMI-P) is introduced to quantify the water security degree of 14 cities in the YRWAR-HN from 2010 to 2021. Then, the obstacle degree model is used to identify the key obstacle indexes that restrict the improvement of water security. Finally, the harmonious behavior set optimization method is adopted to carry out the regulation of water security, and the improvement path of water security in the YRWAR-HN is formulated. The results indicate the following: (1) the water security degree of the YRWAR-HN shows a fluctuating upward trend, increasing from 0.4348 (2010) to 0.6766 (2021), a significant rise of 55.61%. The water security level improves from the relatively unsafe level to the relatively safe level. Hebi City exhibits the fastest rate of water security improvement, while Xinxiang City shows the slowest rate. (2) The density of the river network (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indexes with the highest obstacle degree, with the average obstacle degrees being 15.09% and 10.79%, respectively. (3) The combination of the composite regulation scenario and improvement Path 2 is the optimal regulation strategy for water security in the YRWAR-HN. From the implementation process, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu may prioritize improving their flood and drought disaster defense capabilities and emergency response capabilities, continuously enhancing the flood prevention and disaster reduction system. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou may prioritize resolving the regional water supply and demand conflicts, balancing development and conservation, actively seeking transboundary and external water transfers, and strengthening the capacity for water conservation and intensive utilization. Puyang City may prioritize enhancing its comprehensive water environment management capabilities, increasing investment in water conservancy and the environment, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution.

1. Introduction

Water resources, as the source of life, essential for production, and the foundation of ecology, are critical to national food security, economic security, and ecological security. They are important strategic resources and security guarantees [1]. In the context of rapid urbanization and economic development, the demand for water resources development and utilization has increased greatly [2]. Since the 1980s, global freshwater use has been growing at a rate of about 1% per year, but the living water needs of more than 1 billion people remain unmet [3]. The global future water security situation is not optimistic. In 1999, the United Nations warned the world that if measures were not taken promptly, nearly 30% of the global population would be unable to access safe drinking water by 2025. Since the 21st century, water security has gradually become a research hotspot in the field of water resources [4,5,6], attracting the attention of many government and non-government organizations, including the United Nations, the World Bank, and the Asian Development Bank [7,8]. The World Ministerial Conference held in The Hague, Netherlands, in March 2000 [9], and the 10th discussion of the World Water Forum held in Stockholm, Sweden, in August 2000, both set “21st Century Water Security” as their conference theme. At the 2005 International Water Science Conference, water security was elevated to the level of national security. On World Water Day in 2013, the UN Water Organization released the “Water Security Analytical Brief,” discussing the inclusion of a water-related sustainable development goal in the United Nations’ Sustainable Development Goals [10]. The United Nations 2023 Water Conference held in New York, USA, in March 2023 called for countries to unite in addressing the water resource crisis and adopted a milestone “Water Action Agenda”.
At present, the definition of water security is not fully unified. WaterAid defines water security from the perspective of water demand assurance and water disaster risk management, considering it as the reliability of obtaining sufficient quantity and quality of water to meet basic human needs, small-scale livelihoods, and local ecosystem services, while ensuring good risk management of water-related disasters [11]. Xia et al. [12] believes that water security is the ability to provide an adequate and quality-assured water supply needed for human production and living, to ensure society is protected from the erosion of water and drought disasters and water environment pollution, and to maintain the health of the natural environment and people’s living conditions within the basin. Deng et al. [13] defines water security as the ability to sustainably supply sufficient and quality-compliant water at an affordable cost, ensuring the needs of human life, socioeconomic and ecological health, and guaranteeing the safety of water-related disaster prevention and control. Wang et al. [14] points out from a national perspective that water security is the state and capacity to ensure the stability of the nation, enhance people’s well-being, eliminate water and drought threats, and ensure sustainable social development and ecological environment health. Although the definition of water security is not unified, it is generally agreed that water security involves water systems, socioeconomic systems, and ecological environment systems [15,16,17]. It primarily includes the ability to meet the water demands of economic, social, and ecological environments [18], the sustainability of the water system itself, and the risk management capabilities for water-related disasters such as droughts and floods [19].
Water security evaluation is fundamental to addressing water security issues and is an important branch of water security research. The key to water security evaluation lies in constructing a scientifically comprehensive water security evaluation index system without deviating from the core connotations of water security. Early water security evaluations focus on using single indicators to evaluate the sustainability of water systems. Widely used indicators include the Water Resources Stress Index (WRSI), the Water Resources Vulnerability Index (WRVI), and the development and utilization degree of water resources. For instance, Falkenmark et al. [20] developed the Water Scarcity Index (IWS) based on per capita water resources to measure the degree of regional water scarcity, which has been widely used in global water security evaluation [21,22,23]. Raskin et al. [24] introduced the coefficient of variation of the precipitation to construct the IWRV, assessing the vulnerability and sustainability of water resources at both national and regional scales. As the concept of water security has been continuously refined, existing studies have constructed comprehensive water security evaluation systems from different evaluation dimensions. According to different concepts of constructing indicator systems, they can be roughly divided into two categories. One category approaches water security from a systems theory perspective, dividing the complex system into different subsystems, each of which selects representative indicators to construct a water security evaluation system. The advantage of this method is the clear connotation of water security, emphasizing the coordination between subsystems, facilitating the analysis of key indicators affecting water security, and the formulation of regulation plans. For example, Deng et al. [13] constructed a water security evaluation index system based on water quantity, water quality, sustainability, water price and supply affordability, and flood safety. Wang et al. [25] developed an indicator system from the dimensions of water resources balance capacity, water resources pressure and driving force, and water resources development and utilization capacity, evaluating the comprehensive water resource carrying capacity of 31 provinces in China. Another category is centered around water systems, considering the pressures from the economic–social–ecological system on the water system, the state and response of the water system itself, and human management of the water system; this approach uses fixed models as the framework for constructing indicator systems. These models include the Driver-State-Response (DSR) model [26], the Pressure-State-Response (PSR) model [27], the Driver-Pressure-State-Impact-Response (DPSIR) model [28], and the Driver-Pressure-State-Impact-Response-Management (DPSIRM) model [29].
On the basis of the index system construction, the appropriate comprehensive evaluation method is usually selected to quantify the degree of regional water security. Widely used comprehensive evaluation methods include fuzzy mathematics, artificial neural networks, set pair analysis (SPA), the cloud entropy model, and the SMI-P. Cai et al. [30] used the fuzzy comprehensive evaluation and entropy weight methods to establish a water security evaluation model based on the water footprint. Deng et al. [31] utilized the SPA to establish a comprehensive evaluation index system covering water resources, socioeconomics, and the ecological environment, and systematically quantified the water resource carrying capacity for the current and future periods of the Hanjiang River Basin in China. Qiu et al. [32] constructed a water ecological security evaluation index system for the Yellow River Basin based on the PSR model and calculated the water ecological security index for 62 cities using the SMI-P method. It is worth noting that due to influencing factors, regional water security is usually at a low level, necessitating optimization and regulation research to develop strategies for improving regional water security. The behavior set optimization method is a commonly used optimization and regulation method. Its basic idea is to establish the constraints and objective functions for optimization and regulation, build the optimization and regulation behavior set, and finally determine the optimal regulation behavior based on the principle of maximizing the objective function [33]. For example, Zuo et al. [34,35] treated the human and water systems as a composite system and constructed a harmonious behavior set of human–water relationships based on the quantitative evaluation of human–water relationships, with the objective function of maximizing harmony.
The Yellow River water-receiving area of Henan Province (YRWAR-HN) is a densely populated and economically significant area in the basin, playing a crucial role in implementing the national strategy for the Yellow River [36]. Due to long-term sediment deposition, the riverbed elevation has continually risen, resulting in the famous elevated river in the Henan section, which frequently suffers from flood disasters, making water security issues particularly prominent [37]. To determine the water resource carrying capacity in the YRWAR-HN, Zhang et al. [38] developed an index system encompassing three criteria: water resources, ecological environment, and socioeconomic factors. They applied a TOPSIS model with combined weights based on the analytic hierarchy process (AHP) and the entropy weight method (EWM) to evaluate the water resource carrying capacity in the region from 2010 to 2021. Additionally, Zhang et al. [39] constructed a high-quality development evaluation index system from four perspectives: resources, ecology, economy, and society. They employed the SMI-P method to assess the development level of the YRWAR-HN, and used an embedded system dynamics approach to develop and apply a regional high-quality development regulation model. However, there are few studies on water security evaluation and regulation for the YRWAR-HN, and the spatiotemporal characteristics and enhancement strategies of water security in this section remain unclear, necessitating in-depth research. Given this, this study selects the YRWAR-HN as a study area, and introduces the harmony-based method (including the SMI-P and the HBSO) to conduct water security evaluation and regulation research. It analyzes the spatiotemporal characteristics and key obstacle factors of water security in the YRWAR-HN from 2010 to 2021 and proposes pathways for improving water security in this section, aiming to provide guidance for enhancing water security in the YRWAR-HN.

2. Study Area

The YRWAR-HN includes 14 prefecture-level cities: Zhengzhou, Kaifeng, Luoyang, Pingdingshan, Anyang, Hebi, Xinxiang, Jiaozuo, Puyang, Xuchang, Sanmenxia, Shangqiu, Zhoukou, and Jiyuan (Figure 1). This area covers a total of approximately 104,000 km2, accounting for 62.3% of the total area of Henan Province. The terrain is higher in the southwest and lower in the northeast. The southwestern part belongs to the Funiu Mountain, characterized mainly by mountains and hills. The central and northeastern parts are relatively flat, forming part of the North China Plain, which is a major grain-producing area in China. Most of the YRWAR-HN are located in the warm temperate, semi-humid, and semi-arid regions, characterized by a temperate continental monsoon climate with distinct transitional features. Due to the influence of the monsoon climate, rainfall distribution is uneven, with humid and rainy summers and dry winters. Precipitation is concentrated mainly between June and September, accounting for 55% to 75% of the annual total, with a long-term average precipitation of 658.46 mm and an average annual temperature ranging from 12.1 °C to 15.7 °C [40]. The summer season is sometimes affected by typhoons, bringing extreme heavy rainfall events, such as the extreme downpour in July 2021, which led to severe urban flooding [41].
The YRWAR-HN is a densely populated area and a major economic belt in the Yellow River Basin, playing a significant role in implementing the national strategy for the Yellow River. However, due to long-term sediment deposition, the riverbed elevation has continuously risen, forming an above-ground “hanging river,” which frequently subjects the region to flood disasters. According to incomplete statistics, over the recorded history of more than 2000 years, the lower reaches of the Yellow River have breached and flooded over 1500 times, with more than twenty major course changes and six significant course alterations. Simultaneously, with the rapid economic and social development, the region’s water consumption and pollutant discharge have continuously increased, leading to prominent contradictions between water supply and demand, and severe water environment pollution issues.

3. Methodology

3.1. Evaluation Index System

This study, based on the actual issues of the YRWAR-HN, constructs a comprehensive water security evaluation index system with a four-tier hierarchical structure (Table 1). First, the evaluation objective is determined, which is to calculate the water security degree of the YRWAR-HN. Then, considering the prominent water security issues in this area—flood disasters, water supply–demand imbalances, and water environment pollution—the evaluation dimensions are determined as flood and drought disaster defense, water supply–demand conflict, and water environment pollution. In the flood and drought disaster defense dimension, the focus is on evaluating the flood disaster defense capability, drought defense capability, and emergency disaster response capability. In the water supply–demand conflict dimension, emphasis is placed on the regional characteristics of water resources, the development and utilization degree of water resources, and water use efficiency. In the water environment pollution dimension, the focus is on the pressure and response regarding water environment pollution prevention. Based on the determination of the evaluation content, the corresponding evaluation indicators are comprehensively established by integrating existing research [42,43,44,45,46] and adhering to the principles of systematicity, representativeness, and operability. For example, when selecting the evaluation indicators of regional characteristics of water resources, per capita water resources, the water yield modulus and the precipitation are usually selected in the reference [43,44]. In this study, the per capita water resources are finally selected as the evaluation index according to the data availability of the YRWAR-HN.

3.2. Calculation of Evaluation Index Data

The indicator data for river network density is derived from satellite remote sensing images. First, cloud-free landsat TM/ETM+/OLI images (2010–2021) of the YRWAR-HN from March to May are downloaded from the USGS website and preprocessed. The Modified Normalized Difference Water Index (MNDWI) is used to extract annual surface water bodies, and the results are corrected through human–machine interaction. Then, the river network centerline is extracted by mathematical morphology, and the river length of each city in the YRWAR-HN from 2010–2021 is calculated. Finally, the river network density for each city in the YRWAR-HN is calculated. The calculation formula is shown in Appendix A (Equation (A1)).
The other indicator data are obtained from the “Statistical Yearbook”, “Water Resources Bulletin”, and “Economic and Social Development Bulletin” of Henan Province and its cities from 2010 to 2021. A small amount of missing data is obtained through interpolation and extended calculations.

3.3. Single Index Quantification–Multiple Indices Syntheses–Poly-Criteria Integration Method (SMI-P)

The SMI-P is a widely used comprehensive evaluation method [47], and it has been extensively applied in the fields of water resources management [48], environmental health evaluation, economic management, and more. This study extends its application to water security evaluation, and introduces the concept of “water security degree (WSD)” to measure the water security level of each city in the YRWAR-HN. Correspondingly, each evaluation dimension has a sub-water security degree (SWSD), and each indicator has an index water security degree (IWSD), with values ranging from [0, 1].

3.3.1. Single Index Quantification

Based on the fuzzy mathematics theory, this study establishes a piecewise linear membership function to calculate the SWSD for each indicator. It is assumed that each indicator has five characteristic values: (a) the worst value, (b) the worse value, (c) the medium value, (d) the better value, and (e) the optimal value [49,50,51]. Table 2 shows the characteristic values of each indicator. For positive indicators, the SWSD corresponding to the five characteristic values are 0, 0.3, 0.6, 0.8, and 1.0, respectively. For negative indicators, the SWSD corresponding to the five characteristic values are 1.0, 0.8, 0.6, 0.3, and 0, respectively. The calculation formula is shown in Appendix A, Equations (A2) and (A3).

3.3.2. Weighted Calculation of Multiple Indicators

Based on single index quantification, a multi-indicator weighted average model is established to calculate the SWSD of each evaluation dimension. The calculation formula is shown in Appendix A (Equation (A4)). In this study, the entropy weight method is used to determine the weights of each indicator, and the results are shown in Table 2.

3.3.3. Multi-Criteria Integrated Calculation

Based on the evaluation results of all dimensions, a multi-dimensional integration model is established to calculate the water security degree of each city for each year. The calculation formula is shown in Appendix A (Equation (A5)). Based on existing research [52], the water security levels of the YRWAR-HN are divided into six levels using the equal interval principle, as shown in Table 3.

3.4. Obstacle Degree Model

The obstacle degree model can identify factors that have significant restrictive effects on the evaluation results and has been widely used in ecological security research [53]. In this study, it is applied to identify key obstacle indicators for water security improvement in the YRWAR-HN. The calculation formula is shown in Appendix A (Equations (A6)–(A8)).

3.5. Water Security Regulation

This study draws on the Harmony Behavior Optimization Method and proposes a water security regulation method based on the optimization of solution sets. The core idea of this method is to first establish a set of water security regulation strategies, then calculate the water security degree of each strategy, and finally determine the optimal or relatively optimal regulation strategy with the objective function of maximizing the water security degree.
This study initially sets up five water security regulation schemes, with each scheme having five improvement paths (increasing key obstacle indicators by 10%, 20%, 30%, 40%, and 50% based on the increments in Scheme 1). Taking Path 1 as an example, the five regulation schemes are introduced as follows, which are detailed in Appendix B. Scheme 1 (baseline scenario): maintain the current development model, with each evaluation indicator keeping the original growth rate over the past 12 years (2010–2021). Scheme 2 (enhanced flood and drought disaster defense scenario): increase the top 50% of the indicators in the flood and drought disaster defense dimension by 1.1 times the growth rate of Scheme 1, with the remaining indicators maintaining their original growth rates. Scheme 3 (optimized supply and demand scenario): increase the top 50% of the indicators in the water supply and demand dimension by 1.1 times the growth rate of Scheme 1, with the remaining indicators maintaining their original growth rates. Scheme 4 (ecological protection scenario): increase the top 60% of the indicators in the water environment pollution dimension by 1.1 times the growth rate of Scheme 1, with the remaining indicators maintaining their original growth rates. Scheme 5 (composite regulation scenario): increase the eight indicators enhanced in Schemes 2–4 by 1.1 times the growth rate of Scheme 1, with the remaining indicators maintaining their original growth rates. Based on data from 2010–2021, the water security degree of each city in the YRWAR-HN in 2030 is simulated and calculated for the five schemes under the five improvement paths to determine the optimal regulation scheme.

4. Result

4.1. Spatiotemporal Variation in Water Security in the YRWAR-HN

4.1.1. Spatial Pattern

This study selects three representative years, 2010, 2015, and 2021, to reveal the spatial pattern of water security in the YRWAR-HN, as shown in Figure 2. It is evident that in all three years, the spatial pattern of water security in the YRWAR-HN exhibits significant regional characteristics, generally showing a spatial aggregation that decreases radially from the provincial capital, Zhengzhou. In 2010, the areas with basically safe water security levels are concentrated in the western part of Zhengzhou, gradually forming a basically safe spatial cluster around Jiyuan and Luoyang. The surrounding cities, however, formed a relatively unsafe spatial cluster, with the northern cluster around Jiaozuo-Xinxiang-Puyang-Anyang centered around Zhengzhou, and the southern cluster around Pingdingshan-Xuchang-Zhoukou-Shangqiu, with Anyang and Kaifeng at the outer edge of the study area falling into the unsafe spatial cluster. In 2015, water security in the YRWAR-HN presents a patchy distribution. With Luoyang as the center, the basically safe spatial cluster expanded further, gradually forming a basically safe spatial cluster centered around Sanmenxia-Luoyang-Jiyuan-Zhengzhou-Xuchang. The relatively unsafe areas are primarily located to the northeast and east of Zhengzhou. By 2021, the water security in the YRWAR-HN exhibits a core–periphery distribution, forming a relatively safe spatial cluster with dual centers of Zhengzhou and Hebi, involving Sanmenxia-Luoyang-Jiyuan and Pingdingshan-Xuchang-Zhoukou-Shangqiu. The surrounding areas gradually formed basically safe spatial clusters around Jiaozuo-Xinxiang-Kaifeng-Anyang-Puyang.

4.1.2. Temporal Variation

Figure 3 shows the temporal variation in water security in all cities in the YRWAR-HN from 2010 to 2021. It is evident that the water security levels of all cities in the YRWAR-HN exhibit an overall upward trend. Among them, Hebi City has the fastest growth rate (0.0388 yr−1), while Xinxiang City has the slowest growth rate (0.0130 yr−1). Hebi City shows the largest increase, rising from an unsafe level to a relatively safe level. Xinxiang City shows the smallest increase, rising from a relatively unsafe level to a basically safe level. During the study period, the water security degree of Hebi City increased by 35.11% from 2020 to 2021, mainly due to a 106.53% improvement in the supply–demand balance of water resources in 2021 compared to 2020. Notably, the per capita water resources saw the most significant increase, rising by 626.92%. According to relevant data [54], the total water resources in all prefecture-level cities within the YRWAR-HN increased in 2021 compared to the long-term average. Hebi City experienced the largest increase, reaching 321.1%. Sanmenxia City experienced a trend of first increasing and then decreasing, with the highest degree recorded in 2020 (0.7267). This is likely due to a significant increase in the proportion of investments in water conservancy and environmental protection in 2020 compared to 2019, which rose by 17.48%. Zhengzhou, Luoyang, Sanmenxia, and Jiyuan consistently maintained a high level, with values exceeding 0.5 since 2015. In contrast, Kaifeng City had generally low water security levels, with values below 0.5 in all years before 2019.

4.2. Diagnosis of Water Security Obstacle Factors in the YRWAR-HN

Figure 4 shows the obstacle degrees of water security evaluation indicators for all cities in the YRWAR-HN. It is evident that the key obstacle indicators vary slightly among different cities, and the proportions of the same indicator differ between cities. Overall, the key indicators affecting water security in the YRWAR-HN include river network density (X1), hospital beds per capita (X4), per capita water resources (X5), groundwater exploitation rate (X7), water consumption per 104 CNY GDP (X9), fertilizer application per unit sown area (X13), urban sewage treatment rate (X14), and the proportion of investment in water conservancy and environmental protection in the total investment (X15). Comparing the proportions of the key obstacle indicators across all cities, X1 is the primary obstacle indicator for all cities except Zhengzhou, Jiaozuo, Jiyuan, and Puyang, indicating that X1 is a significant constraint on water security in the YRWAR-HN. The obstacle degree of X15 is greater than 10% in all cities except Luoyang, Jiyuan, and Hebi, reflecting that efforts to control water environment pollution need to be improved, making this proportion another constraint on water security. As the capital of Henan Province, Zhengzhou has a high population density and a per capita water resource obstacle degree of 16.21%, making it the primary factor restricting water security in the city. This is consistent with the results of Zhang et al. [38], who also find that Zhengzhou has a low water resource carrying capacity rating, with limited per capita water resources and severe water shortages. This indicates that the supply–demand imbalance of water resources in Zhengzhou is particularly acute, highlighting the severity of the water scarcity issue.

4.3. Water Security Regulation in the YRWAR-HN

Figure 5 compares the water security degree in 2030 for all cities along the YRWAR-HN under five improvement paths and five schemes, while Table 4 displays the average water security degree for these cities under the different schemes and improvement paths by 2030. It is evident that there has been a significant improvement in water security across different paths and schemes for all cities compared to 2021. The composite scenario (Scheme 5) achieves the highest water security degree, indicating that improving indicators with greater obstacles can effectively enhance the water security degree. Comparing Schemes 2, 3, and 4, Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou cities show optimal results under Scheme 3, suggesting a priority to resolve water supply and demand issues. Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu cities show optimal results under Scheme 2, indicating a need to prioritize the enhancement of regional flood and drought disaster defense capabilities. Puyang City performs best under Scheme 4, suggesting a priority to strengthen ecological environment construction, increase investment in ecological efforts, improve production processes, and reduce pollutant emissions. Among the different improvement paths in the composite scenario, the order of performance from highest to lowest is Path 5 > Path 4 > Path 3 > Path 2 > Path 1, with average increases of 0.32%, 0.37%, 0.48%, and 0.63%, respectively. This shows that a 20% increase in key obstacle indicators results in the largest water security improvement and cost-effectiveness for the YRWAR-HN, with diminishing returns as investments continue to rise.

5. Discussion

This study, based on an analysis of the prominent water security issues in the YRWAR-HN, constructs a water security evaluation index system. This system is significant for quantifying the water security status in these areas, identifying key factors that constrain improvements in water security, and developing scientifically sound optimization and regulation strategies. Although the applicability of this index system to other study areas requires further validation, the concepts underlying its construction, the selection of indicators, and the methods of calculation can serve as a reference for the development of comprehensive water security evaluation systems in other regions.
Overall, the water security degree in the YRWAR-HN shows a fluctuating upward trend, and the conclusion is basically consistent with that of Zuo et al. [42]. Compared to 2010 (0.4348), the water security degree of the YRWAR-HN in 2021 has significantly improved, with the average water security degree reaching 0.6766, upgrading from a relatively unsafe level to a relatively safe level. Since 2010, China’s water affairs have rapidly developed, with the national level formulating water security plans for the “12th Five-Year Plan” and “13th Five-Year Plan”. Henan Province has thoroughly implemented the national water security plans and issued a series of planning documents, such as the “Henan Province Comprehensive Disaster Prevention and Reduction Plan” and the “13th Five-Year Plan for Building a Water-Saving Society in Henan Province”, leading to a significant enhancement in disaster defense capabilities and water use efficiency. This may be the fundamental reason for the significant improvement in overall water security in the YRWAR-HN. Among the all cities in the YRWAR-HN, Hebi City has seen the largest increase in water security during the study period. This could be due to the city’s water security being at a relatively unsafe level in 2010 with substantial potential for improvement, and the significant increase in water conservancy construction investment in recent years, where the proportion of X15 increased from 6.75% in 2010 to 19.7% in 2021, a rise of 191.77%.
There is still considerable room for improvement in the water security of all cities along the YRWAR-HN. According to the water security evaluation results of 2021, no city has reached a safe level, while Jiaozuo, Kaifeng, Xinxiang, Anyang, and Puyang cities remained at a basically safe level, indicating significant water security challenges in the YRWAR-HN, particularly in Kaifeng and Xinxiang. Through the analysis of obstacle degree, it is found that X1, X5, and X15 are the top three indexes of obstacle degree in Kaifeng City. Therefore, it is imperative for Kaifeng to increase investment in water conservancy projects, implement river–lake connectivity projects, dredge congested rivers, and excavate new channels. Meanwhile, Kaifeng City should actively seek external water transfers and through-flow water via water rights trading to increase the regional available water resources. For Xinxiang City, X1, X13, and X15 are the top three indexes of obstacle degree. Xinxiang City should therefore focus on improving its capabilities in flood and drought disaster defense and water environment pollution control, including carrying out river–lake connectivity projects, increasing the regional river network density, and advancing the comprehensive management of agricultural non-point source pollution by implementing precise fertilization and replacing chemical fertilizers with organic ones. For Zhengzhou City, X5, X15, and X1 are the top three obstacle indexes. Therefore, Zhengzhou City should prioritize obtaining more external water resources (such as South-to-North Water Transfer Project water and Yellow River water) through increased water conservancy investment and water rights trading. Meanwhile, it should also increase the number of agricultural irrigation wells to enhance the drought resistance of agricultural production.
According to the regulation results, employing Scheme 5 will allow the achievement of the maximum increase in water security across all cities in the YRWAR-HN, albeit with substantial investment costs. Given limited investment capacity, priority should be given to enhancing flood and drought disaster defense capabilities and emergency response systems in Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu. This includes continuously improving flood prevention and disaster mitigation systems, advancing the construction of stormwater drainage channels in urban areas, dredging and clearing drainage ditches, and enhancing monitoring, forecasting, early warning, rehearsal, contingency planning, and flood control scheduling. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou should prioritize resolving regional water supply and demand conflicts by improving water conservancy projects, water network operation scheduling plans, and emergency water dispatch plans, as well as implementing inter-basin and inter-regional water resource scheduling projects. A long-term mechanism for the integrated regulation of atmospheric water, surface water, and groundwater should be established. Puyang should focus on enhancing its water environment management capabilities, strengthening collaboration with neighboring cities and counties, promoting integrated ecological management and restoration of trans-regional river basins, increasing investments in water and environmental projects, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution.

6. Conclusions

This study focuses on the actual water security issues in the YRWAR-HN. By establishing a water security evaluation index system and employing the SMI-P evaluation methodology, a quantitative evaluation of the water security degrees of 14 prefecture-level cities in the YRWAR-HN from 2010 to 2021 is conducted. Furthermore, using the obstacle model, key obstacle indicators are identified. Through comparative analysis of regulation strategies, an optimized strategy for water resources in the YRWAR-HN is formulated. The main conclusions are as follows:
(1) From 2010 to 2021, the water security degree in the YRWAR-HN shows a fluctuating upward trend. The water security degree increases from 0.4348 in 2010 to 0.6766 in 2021, an increase of 55.61%, with the water security level improving from relatively unsafe to relatively safe. Hebi City experiences the fastest increase in water security degree, rising from 0.2997 in 2010 to 0.7788 in 2021, an increase of 159.86%. Xinxiang City has the slowest rate of improvement, with water security degree of 0.4372 in 2010 and 0.5294 in 2021, an increase of 21.1%.
(2) As of 2021, Jiaozuo, Xinxiang, Kaifeng, Anyang, and Puyang remain at a basically safe level. Spatially, a relatively safe cluster is centered around Zhengzhou and includes Sanmenxia, Luoyang, Jiyuan, Pingdingshan, Xuchang, Zhoukou, and Shangqiu. Notably, the water security levels of Xinxiang and Kaifeng remain below 0.55.
(3) Based on the average obstacle degree of each indicator, river network density (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indicators with the highest obstacle degree, with average obstacle degrees of 15.09% and 10.79%, respectively. Therefore, it can be inferred that increasing water conservancy and environmental investments, and carrying out scientifically sound river–lake connectivity projects, can significantly enhance the water security level of the YRWAR-HN.
(4) The optimal water security regulation strategy for the YRWAR-HN is a combination of Regulation Scheme 5 and Enhancement Path 2. In terms of implementation, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu should prioritize improving their flood and drought disaster prevention capabilities and emergency response capacity, continuously refining their flood control and disaster mitigation systems. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou should focus on resolving regional water supply–demand conflicts by balancing water sourcing and conservation, actively securing transboundary and external water transfers, and enhancing the efficient use of water resources. Puyang should prioritize improving its comprehensive water environment management capacity by increasing investment in water conservancy and environmental protection, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution.
This study addresses the actual water security issues in the YRWAR-HN and, based on data availability in the study area, establishes a water security evaluation index system. This provides a reference and guidance for water security evaluation in other regions. It is important to note that water security challenges and data availability vary across regions, so the applicability of this index system in other areas requires further validation. In future research, we aim to develop a more universally applicable water security evaluation index system and to further refine the water security regulation strategies for the YRWAR-HN, offering more specific regulation recommendations tailored to different prefecture-level cities.

Author Contributions

Overall design, Z.Z.; methodology, W.W. and X.Z.; software, H.Z. and L.Y.; formal analysis, X.L. and X.X.; writing—original draft preparation, W.W.; writing—review and editing, Z.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education (GTYR202106); the Henan Province science and technology research and development plan joint fund project (232103810102); the Science and Technology Research Project of Henan Province (212102311156); the Open Research Fund Project of Key Laboratory of Yellow River Sediment of Ministry of Water Resources (HHNS202005), the Research Fund Project of Key Laboratory of Water Management and Water Security in Yellow River Basin, Ministry of Water Resources (2023-SYSJJ-04); and the National Natural Science Foundation of China (42007423; 42201097).

Data Availability Statement

Data available on request due to privacy: The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to express our respect and gratitude to the anonymous reviewers and editors for their professional comments and suggestions. In addition, thanks to Yongfeng Zhao, Zhiwei Xu, Wenzhe Wang, and Yuyang Qi for collecting and preprocessing the experimental data.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

R D i , t = R L i , t A r e a i , t
where R D i , t , R L i , t , and A r e a i , t denote the river network density, river length, and area of city i in year t, respectively.
I W S D i , j t = 0 x i , j t ≤ a j 0.3 × x i , j t − a j b j − a j a j < x i , j t ≤ b j 0.3 + 0.3 × x i , j t − b j c j − b j b j < x i , j t ≤ c j 0.6 + 0.2 × x i , j t − c j d j − c j c j < x i , j t ≤ d j 0.8 + 0.2 × x i , j t − d j e j − d j d j < x i , j t ≤ e j 1 x i , j t > e j
I W S D i , j t = 1 x i , j t ≤ e j 0.8 + 0.2 × x i , j t − d j e j − d j e j < x i , j t ≤ d j 0.6 + 0.2 × x i , j t − c j d j − c j d j < x i , j t ≤ c j 0.3 + 0.3 × x i , j t − b j c j − b j c j < x i , j t ≤ b j 0.3 × x i , j t − a j b j − a j b j < x i , j t ≤ a j 0 x i , j t > a j
where I W S D i , j t and x i , j t are the SWSD and the indicator value of indicator j for the city i in year t, respectively. a j , b j , c j , d j , and e j denote the worst value, the worse value, the medium value, the better value, and the optimal value of indicator j, respectively.
S W S D i , k t = ∑ j = 1 n ω j × I W S D i , j t
In the formula, S W S D i , k t represents the SWSD of evaluation dimension k for the city i in year t. n denotes the number of evaluation indicators in evaluation dimension k. ω j represents the weight of indicator j, with the sum of the weights of all indicators in each evaluation dimension equaling 1.
W S D i t = ∑ k = 1 3 ω k × S W S D i , k t
In the formula, W S D i t represents the water security degree of city i in year t. ω k denotes the weight of evaluation dimension k.
r j = α j × β j ∑ j = 1 15 ( α j × β j ) × 100 %
α j = 1 − X j
β j = ω j × ω k
In the formula, r j , α j , and β j represent the obstacle degree, deviation degree, and factor contribution degree of indicator j, respectively. A larger obstacle degree indicates a greater restrictive effect of the indicator on the final evaluation result. X j represents the standardized result of indicator j. ω j and ω k denote the weights of indicator j and its corresponding evaluation dimension k, respectively.

Appendix B

Table A1. Water Security Regulation Schemes for the YRWAR-HN under Path 1.
Table A1. Water Security Regulation Schemes for the YRWAR-HN under Path 1.
CityScheme 1Scheme 2Scheme 3Scheme 4Scheme 5
ZhengzhouAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X2 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X6, X7 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX11, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X2, X5, X6, X7, X11, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
KaifengAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X2 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X2, X5, X7, X9, X13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
LuoyangAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X3 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X10 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X3, X5, X7, X10, X12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
XinxiangAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X2 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X2, X5, X7, X9, X12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
JiaozuoAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X2 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X8 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X2, X5, X7, X8, X12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
SanmenxiaAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X2 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X9, X10 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X2, X5, X9, X10, X12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
PuyangAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X9, X10 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X5, X9, X10, X13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
JiyuanAll indicators adjusted by 1 times the growth rate from 2010–2021X3, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X8, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX11, X12, X13 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX3, X4, X5, X8, X9, X11, X12, X13 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
AnyangAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX7, X8, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX11, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X7, X8, X9, X11, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
HebiAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX7, X8, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X7, X8, X9, X13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
PingdingshanAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X3 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX7, X9, X10 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X3, X7, X9, X10, X12, X13, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
XuchangAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X5, X7, X9, X12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
ShangqiuAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X5, X7, X9, X13, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates
ZhoukouAll indicators adjusted by 1 times the growth rate from 2010–2021X1, X4 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX5, X7, X9 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth ratesX1, X4, X5, X7, X9, X12, X14, X15 adjusted by 1.1 times the growth rate, with the remaining indicators maintaining their original growth rates

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Figure 1. Overview Map of the Study Area.
Figure 1. Overview Map of the Study Area.
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Figure 2. Spatial Distribution of Water Security in the YRWAR-HN.
Figure 2. Spatial Distribution of Water Security in the YRWAR-HN.
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Figure 3. Temporal Variation in Water Security in the YRWAR-HN from 2010 to 2021.
Figure 3. Temporal Variation in Water Security in the YRWAR-HN from 2010 to 2021.
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Figure 4. Obstacle Degrees of Water Security Evaluation Indicators for All Cities in the YRWAR-HN.
Figure 4. Obstacle Degrees of Water Security Evaluation Indicators for All Cities in the YRWAR-HN.
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Figure 5. Water Security Degree in 2030 for All Cities along the YRWAR-HN under Different Regulation Strategies.
Figure 5. Water Security Degree in 2030 for All Cities along the YRWAR-HN under Different Regulation Strategies.
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Table 1. Comprehensive Evaluation Index System for Water Security.
Table 1. Comprehensive Evaluation Index System for Water Security.
Objective LayerDimension LayerContent LayerIndicator LayerIndex
Number		Unit
water security evaluation in the YRWAR-HNflood and drought disaster defenseflood disaster defense capabilityriver network densityX1km−1
drainage pipeline density in built-up areasX2km−1
drought defense capabilitynumber of electromechanical wells per unit area of cultivated landX3number/103 hm2
emergency disaster response capabilityhospital beds per capitaX4number/104 capita
water supply and demandregional characteristics of water resourcesper capita water resourcesX5m3/capita
development and utilization of water resourcesutilization rate of surface water resources X6%
exploitation rate of groundwater resourcesX7%
water use efficiencyper capita comprehensive water consumptionX8m3/capita
water consumption per 104 CNY GDPX9t/104 CNY
water consumption per 104 CNY of industrial value addedX10t/104 CNY
water environment pollutionpressureper capita COD emissionsX11t/capita
per capita SO2 emissionsX12t/capita
fertilizer application per unit of sown areaX13t/103 hm2
responseurban sewage treatment rateX14%
the proportion of investment in water conservancy and environmental protection in the total investmentX15%
Table 2. Quantitative Indicator Node Characteristic Values and Indicator Weights.
Table 2. Quantitative Indicator Node Characteristic Values and Indicator Weights.
Index
Number		Worst ValueWorse ValueMedium Value Better ValueOptimal ValueIndicator WeightsIndicator
Type
X10.01 0.02 0.03 0.05 0.22 0.31 ↑
X23.47 6.78 8.00 9.00 18.70 0.22 ↑
X319.35 145.96 200.46 246.36 933.03 0.23 ↑
X423.10 42.94 51.78 63.08 147.57 0.24 ↑
X557.78 168.60 212.37 295.66 1304.09 0.22 ↑
X60.65 0.55 0.40 0.25 0.10 0.12 ↓
X70.90 0.70 0.55 0.40 0.20 0.19 ↓
X8628.46 466.88 305.30 231.99 158.69 0.16 ↓
X980.00 65.00 50.00 30.00 15.00 0.17 ↓
X1070.00 55.00 40.00 25.00 10.00 0.15 ↓
X110.0402 0.0142 0.0096 0.0031 0.0001 0.15 ↓
X120.0899 0.0175 0.0062 0.0026 0.0001 0.18 ↓
X13619.90 607.40 584.10 564.40 481.20 0.23 ↓
X1460.00 70.00 85.00 90.00 100.00 0.18 ↑
X154.00 7.00 12.00 17.00 22.00 0.26 ↑
Notes: “↑” indicates a positive indicator, meaning the higher the indicator value, the higher the water security degree. Conversely, “↓” indicates a negative indicator, meaning the higher the indicator value, the lower the water security degree.
Table 3. Classification Standards for Water Security Levels.
Table 3. Classification Standards for Water Security Levels.
Serial NumberWater Security LevelRange of Wsd Values
1Safe0.83 < WSD ≤ 1.00
2Relatively Safe0.67 < WSD ≤ 0.83
3Basically Safe0.50 < WSD ≤ 0.67
4Relatively Unsafe0.33 < WSD ≤ 0.50
5Unsafe0.17 < WSD ≤ 0.33
6Severely Unsafe0.00 < WSD ≤ 0.17
Table 4. Average Water Security Degree by 2030 for the YRWAR-HN under Different Regulation Strategies.
Table 4. Average Water Security Degree by 2030 for the YRWAR-HN under Different Regulation Strategies.
Scheme 1Scheme 2Scheme 3Scheme 4Scheme 5
Path 10.8901 0.8923 0.8939 0.8909 0.8970
Path 20.8901 0.8945 0.8967 0.8915 0.9026
Path 30.8901 0.8965 0.8984 0.8922 0.9069
Path 40.8901 0.8980 0.8998 0.8927 0.9103
Path 50.8901 0.8993 0.9009 0.8931 0.9132
Notes: Scheme 1 represents the baseline scenario. Scheme 2 corresponds to the enhanced flood and drought disaster defense scenario. Scheme 3 represents the scenario with optimized supply and demand. Scheme 4 corresponds to the ecological protection scenario. Scheme 5 represents the composite regulation scenario. Detailed regulation schemes can be found in Section 3.5 on Water Security Regulation.
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Zhang, Z.; Wang, W.; Zhang, X.; Zhang, H.; Yang, L.; Lv, X.; Xi, X. A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province. Water 2024, 16, 2497. https://doi.org/10.3390/w16172497

AMA Style

Zhang Z, Wang W, Zhang X, Zhang H, Yang L, Lv X, Xi X. A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province. Water. 2024; 16(17):2497. https://doi.org/10.3390/w16172497

Chicago/Turabian Style

Zhang, Zhiqiang, Weiwei Wang, Xiuyu Zhang, Hui Zhang, Li Yang, Xizhi Lv, and Xu Xi. 2024. "A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province" Water 16, no. 17: 2497. https://doi.org/10.3390/w16172497

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