In the context of continued global population growth and various environmental challenges, the coordinated protection of water resources, energy, and food security has become increasingly important. This study focuses on the uneven distribution of water resources, food, and energy in China, examining the coupling coordination relationship within the Water–Energy–Food nexus (WEF) system from 2006 to 2020 and conducting a quantitative assessment of its spatial equilibrium state. The results indicate the following: (1) The WEF system coupling degree across China’s provinces (municipalities and autonomous regions) is above 0.75, suggesting a close interdependence and synergy between water resources, energy, and the food system. Most regions maintain a dynamically balanced coupling coordination state over time, providing theoretical feasibility for optimizing the allocation of cross-regional and cross-system resources. (2) Natural endowments and human activities have a decisive impact on spatial equilibrium differences in the water resources system. The layout of energy production is closely related to equilibrium, and overly concentrated production areas can disrupt spatial equilibrium. Future energy planning must consider the synergistic relationship between production and consumption areas. The proportion of grain output is not positively or negatively correlated with the spatial equilibrium coefficient; if regions with concentrated or sparse grain production cannot coordinate their development, the overall spatial equilibrium level will decline. (3) The spatial equilibrium of the WEF system is superior to that of any single subsystem. Inherent deficiencies in natural endowments can be gradually adapted and maintained through policy adjustments, resource coordination, and regional collaboration, allowing the system to maintain a dynamic balance.

Unbalanced and inadequate development has led to challenges such as resource shortage, ecological fragility, and economic backwardness in the water-receiving area of Henan Province, which has hindered the high-quality development of the region. This research aims to construct a high-quality development regulation model. The single-indicator quantification–multi-indicator synthesis–multicriteria integration (SMI-P) method was used to evaluate the level of regional development; using the method of embedded system dynamics, the regulation model was constructed and applied. The results show that (1) from 2011 to 2020, the degree of regional high-quality development showed an increasing trend, rising from a low level (0.34) to a medium level (0.57), an increase of one level, but the level of high-quality development still has a lot of room for improvement and faces great challenges; (2) the development of a resource–ecology–economy–social system was in a state of non-harmonious balance, and the proportion of contributions of each system was very different, but the system was developing towards a state of harmony and balance; (3) based on the constructed high-quality development regulation model, the key constraints affecting the high-quality development of the Yellow River water supply area in Henan Province are per capita water consumption, per capita energy consumption, carbon emissions per capita, GDP per capita, etc. The model constructed in this paper can improve the quality development level through the application of examples. The high-quality development degree of the study area was increased from 0.57 to 0.8.

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.

Surface water is a crucial part of terrestrial ecosystems and is crucial to maintaining ecosystem health, ensuring social stability, and promoting high-quality regional economic development. The surface water in the Yellow River Basin (YRB) has a high sediment content and spatially heterogeneous sediment distribution, presenting a significant challenge for surface water extraction. In this study, we first analyze the applicability of nine water indexes in the YRB by using the Landsat series images (Landsat 5, 7, 8) and then examine the correlation between the accuracy of the water indexes and suspended particulate matter (SPM) concentrations. On this basis, we propose a surface water extraction method considering the SPM concentrations (SWE-CSPM). Finally, we examine the dynamic variations in the surface water in the YRB at four scales: the global scale, the secondary water resource zoning scale, the provincial scale, and the typical water scale. The results indicate that (1) among the nine water indexes, the MBWI has the highest water extraction accuracy, followed by the AWEInsh and WI2021, while the NDWI has the lowest. (2) Compared with the nine water indexes and the multi-index water extraction rule method (MIWER), the SWE-CSPM can effectively reduce the commission errors of surface water extraction, and the water extraction accuracy is the highest (overall accuracy 95.44%, kappa coefficient 90.62%). (3) At the global scale, the maximum water area of the YRB shows a decreasing trend, but the change amount is small. The permanent water area shows an uptrend, whereas the seasonal water area shows a downtrend year by year. The reason may be that the increase in surface runoff and the construction of reservoir projects have led to the transformation of some seasonal water into permanent water. (4) At the secondary water resource zoning scale, the permanent water area of other secondary water resource zonings shows an increasing trend in different degrees, except for the Interior Drainage Area. (5) At the provincial scale, the permanent water area of all provinces shows an uptrend, while the seasonal water areas show a fluctuating downtrend. The maximum water area of Shandong, Inner Mongolia Autonomous Region, and Qinghai increases slowly, while the other provinces show a decreasing trend. (6) At the typical water scale, there are significant differences in the water area variation process in Zhaling Lake, Eling Lake, Wuliangsuhai, Hongjiannao, and Dongping Lake, but the permanent water area and maximum water area of these waters have increased over the past decade. This study offers significant technical support for the dynamic monitoring of surface water and helps to deeply understand the spatiotemporal variations in surface water in the YRB.

This study evaluates the performance of Global Climate Models (GCMs) in simulating extreme climate in three northeastern provinces of China (TNPC). A total of 23 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected and compared with observations from 1961 to 2010, using the 12 extreme climate indices defined by the Expert Team on Climate Change Detection and Indicators. The Interannual Variability Skill Score (IVS), Taylor diagrams and Taylor Skill Scores (S) were used as evaluation tools to compare the outputs of these 23 GCMs with the observations. The results show that the monthly minimum of daily minimum temperature (TNn) is overestimated in 55.7% of the regional grids, while the percentage of time when the daily minimum temperature is below the 10th percentile (TN10p) and the monthly mean difference between the daily maximum and minimum temperatures (DTR) are underestimated in more than 95% of the regional grids. The monthly maximum value of the daily maximum temperature (TXx) and the annual count when there are at least six consecutive days of the minimum temperature below the 10th percentile (CSDI) have relatively low regional spatial biases of 1.17 °C and 1.91 d, respectively. However, the regional spatial bias of annual count when the daily minimum temperature is below 0 °C (FD) is relatively high at 9 d. The GCMs can efficiently capture temporal variations in CSDI and TN10p (IVS < 0.5), as well as the spatial patterns of TNn and FD (S > 0.8). For the extreme precipitation indices, GCMs overestimate the annual total precipitation from days greater than the 95th percentile (R95p) and the annual count when precipitation is greater than or equal to 10 mm (R10 mm) in more than 90% of the regional grids. The maximum number of consecutive days when precipitation is below 1 mm (CDD) and the ratio of annual total precipitation to the number of wet days (greater than or equal to 1 mm) (SDII) are underestimated in more than 80% and 54% of the regional grids, respectively. The regional spatial bias of the monthly maximum consecutive 5-day precipitation (RX5day) is relatively small at 10.66%. GCMs are able to better capture temporal variations in the monthly maximum 1-day precipitation (RX1day) and SDII (IVS < 0.6), as well as spatial patterns in R95p and R10mm (S > 0.7). The findings of this study can provide a reference that can inform climate hazard risk management and mitigation strategies for the TNPC.

Affected by global climate change and rapid socio-economic development, China faces serious water security issues, especially in terms of water shortages, flood disasters, and water-related ecological and environmental problems