In the globalized world, water utilization and carbon emissions are two important indicators for water and energy resources evaluation. This paper investigates the inter-sectoral linkage and external trade of virtual water (water embodied in products) and embodied carbon emissions in China based on input–output tables during 1997–2015. Results indicate that: inside China, agriculture, the electric and water industry are major virtual water suppliers, while heavy industrial sectors including the metal products industry, the petrochemical industry, other nonmetallic mineral products industry, and the mining industry are major embodied carbon emissions suppliers. China is the net exporter of virtual water (137.15 × 10⁹ m³) and embodied carbon emissions (16.05 × 10⁸ t). From the perspective of industrial chain, about 81% of virtual water export come from agriculture, the electric and water industry ultimately, and about 85% of embodied carbon emissions export come from the mining industry, the petrochemical industry, other nonmetallic mineral products industry, the metal products industry, and the electric and water industry ultimately.

The urban groundwater system is complex and affected by the interaction of natural and human factors. Groundwater scarcity can no longer reflect this complex situation, and the concept of groundwater drought can better interpret this situation. The groundwater drought cycle is the time interval in which groundwater droughts occur repeatedly and twice in a row. The study of the groundwater drought cycle can more comprehensively grasp the development characteristics of the groundwater drought, which is of great importance for the development, utilization, and protection of groundwater. This study used monthly observation data from seven groundwater wells in Xuchang, China, in the period 1980–2018. We applied the Kolmogorov–Smirnov test to select the best fitting distribution function and constructed a Standardized Groundwater Index (SGI). We analyzed groundwater drought at different time scales and used Morlet’s continuous complex wavelet transform to analyze the groundwater drought cycles. The following results were obtained: 1) the maximum intensity of groundwater drought in the seven observation wells ranged from 104.40 to 187.10. Well-3# has the most severe groundwater drought; 2) the drought years of well-5# were concentrated in 1984–1987 and 2003–2012 and those in the other wells in 1994–1999 and 2014–2018; and 3) the groundwater drought cycles in the seven observation wells were 97–120 months, and the average period is about 110 months. The cycle length had the following order: well-7# > well-4# > well-5# > well-2# > well-1# > well-3# > well-6. Therefore, Morlet wavelet transform analysis can be used to study the groundwater drought cycles and can be more intuitive in understanding the development of regional groundwater droughts. In addition, through the study of the Xuchang groundwater drought and its cycle, the groundwater drought in Xuchang city has been revealed, which can help local relevant departments to provide technical support and a scientific basis for the development, utilization, and protection of groundwater in the region.

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.

Conducting ecological monitoring assessments and revealing the effects of driving factors are crucial for enhancing ecological safety and promoting sustainable development. Taking the Chushandian Reservoir basin as the research object, this paper employed the Remote Sensing Ecological Index (RSEI), constructed based on remote sensing data, to monitor and assess the ecological environment of the study area from 1990 to 2021, and predicted its future development trend through the Hurst index. On this basis, we integrated land use data to elucidate the response of the ecological environment to human activities. The results show that: (1) The mutation test indicates that selecting 1990, 2004, 2008, 2013, and 2021 as the study time nodes can comprehensively reflect the spatio-temporal information regarding changes in ecological quality in the study area. Specifically, both 1990 and 2021 exhibit higher ecological quality ratings, while 2008 has the lowest ecological quality rating. The spatial distribution of ecological quality is strongly clustered, with high–high clustering and low–low clustering dominating. (2) The overall trend of ecological quality in the study area appears in a pattern of initial decline followed by subsequent improvement. From 1990 to 2004, the degraded area constituted the largest proportion, accounting for 87.82%. After 2008, the quality of the ecological environment began to rebound. Between 2008 and 2013, the proportion of regions with improved ecological conditions was 57.91%, and from 2013 to 2021, 46.74% of the regions showed improvement. (3) In the research area, 36.70% of the regions exhibit a trend of sustainable stability into the future, representing the highest proportion. Approximately 34.3% of the areas demonstrate a trend of sustainable improvement, while the regions exhibiting sustainable degradation account for only 5.72%. While the ecological environment is demonstrating a positive overall developmental trend, it is crucial to stay vigilant regarding areas of ongoing degradation and implement appropriate protective measures. (4) Land use change significantly impacts the ecological environment, with the expansion of land for urban build up causing some ecological deterioration, while the later expansion of forest improves ecological quality. The results provide theoretical approaches and a foundation for decision-making in the ecological management of the Chushandian Reservoir basin.

Vegetation carbon sinks are an important component of the terrestrial carbon cycle, and net ecosystem productivity (NEP) is widely used to indicate ecosystem carbon-sink strength. However, the long-term dynamics and environmental associations of vegetation carbon sinks in the middle and lower Yellow River Basin remain insufficiently understood. Based on remote-sensing net primary productivity (NPP) and an empirical heterotrophic-respiration model, annual NEP was estimated for 2001–2024. Theil–Sen trend analysis, the Mann–Kendall test, coefficient of variation, optimal-parameter geographical detector (OPGD), and regression residual analysis were applied. Basin-mean annual NEP increased from approximately 214 to 425 g C m⁻² yr⁻¹, with significantly increasing areas accounting for 89.93% of the study area. Areas with NEP above 300 g C m⁻² yr⁻¹ expanded from 18.51% to 78.86%. Precipitation and solar radiation showed the highest explanatory power for NEP spatial differentiation, with mean q values of 0.538 and 0.490, and their interaction reached 0.722. Comparison with a published NEP product, parameter-sensitivity analysis, and an NPP-based robustness test supported the main temporal patterns and factor rankings. The residual-derived non-climatic component exceeded 40% of the combined component-trend magnitude across approximately 94% of the study area, but should not be interpreted as a direct measure of human activities. These findings support regional carbon-sink monitoring, water-constrained ecological restoration, and land-use management.

The Danjiangkou Reservoir Area is a critical water source for the Middle Route of the South-to-North Water Diversion Project, making its ecological security of regional importance. Using land-use data from 1995, 2005, 2015, and 2023, this study classified the area into production, living, and ecological spaces and assessed the spatiotemporal evolution of landscape ecological risk. Spatial autocorrelation analysis and the optimal parameters-based geographical detector were further applied to identify spatial clustering and driving factors. Ecological space remained dominant throughout the study period, accounting for more than 77% of the total area, whereas production space declined and living space expanded. Landscape ecological risk was predominantly moderate to high, with high-risk areas concentrated around the reservoir, along the Hanjiang and Danjiang rivers, and in zones of intensive human activity, while low-risk areas were mainly located in peripheral mountainous and forest-dominated regions. Risk exhibited significant positive spatial autocorrelation, with Moran’s I decreasing slightly from 0.585 to 0.573. Population density had the strongest individual explanatory power, whereas the interaction between elevation and population density showed the highest explanatory power (q = 0.880–0.888). These findings support spatially differentiated ecological protection and territorial optimization in the reservoir area.