Rapid economic and social development has created significant ecological and resource problems in the Beijing-Tianjin-Hebei (BTH) region, making it necessary to identify ways of implementing sustainable regional development. The interactions between water, energy, food, and ecology are characterized by a high degree of relevance and complexity. In studying the relationships between the four systems in depth and choosing representative indicators for each system, a comprehensive development model of the water-energy-food-ecology (WEFE) system in the BTH region has been established. The coupling coordination degree model was used to analyze the coupling synergy relationship between the WEFE systems in the BTH region from 2001 to 2020. The primary contributing elements determining the development of linked synergy in the WEFE system were investigated using a gray correlation model. According to the findings, Beijing’s total coupling coordination development level shows a gradual upward trend and is in excellent coordination; Hebei has progressed the most, experiencing a significant change from little coordination to good coordination; and Tianjin has had the least improvement, only improving from basic to good coordination. The exploitation of water resources and ecological protection of the environment are the aspects that have the greatest impact on the WEFE system. Additionally, the linked and synergistic growth of the WEFE system in the BTH region is significantly influenced by economic, social, and technological advancements in the industrial and agricultural sectors. The coupling coordination development of regional WEFE systems, which takes into consideration the synergistic optimization of many subsystems, is provided by this study as a scientific foundation.

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed.

Fractional vegetation cover (FVC) is an important indicator of regional ecological environment change, and quantitative research on the spatial and temporal distribution of FVC and the trend of change is of great significance to the monitoring, evaluation, protection, and restoration of regional ecology. This study estimates the FVC of the eastern Tibetan Plateau margin from 2000 to 2020 using the image element dichotomous model based on the Google Earth Engine platform using MODIS-NDVI images. It also investigates the temporal and spatial changes of the FVC in this region and its drivers using the Theil–Sen and Mann–Kendall trend tests, spatial autocorrelation analysis, geodetector, and machine learning approaches impact. The results of this study indicated a generally erratic rising tendency, with the Min River Basin (MRB) near the eastern tip of the Tibetan Plateau having an annual average FVC of 0.67 and an annual growth rate of 0.16%. The percentage of places with better vegetation reached 60.37%. The regional FVC showed significant positive spatial autocorrelation and was clustered. Driver analyses showed that soil type, DEM, temperature, potential evapotranspiration, and land use type were the main drivers influencing FVC on the eastern margin of the Tibetan Plateau. In addition, the random forest (RF) model outperformed the support vector machine (SVM), backpropagation neural network (BP), and long short-term memory network (LSTM) in FVC regression fitting. In summary, this study shows that the overall FVC in the eastern margin of the Tibetan Plateau is on an upward trend, and the regional ecological environment has improved significantly over the past two decades.

Against the backdrop of insufficient accuracy and adaptability of satellite precipitation products in complex terrain areas, this study focused on the Min River Basin (MRB) on the eastern edge of the Qinghai–Tibet Plateau. A two-step machine learning fusion framework was established, which integrates precipitation event identification and quantitative intensity estimation in a systematic manner. This framework incorporated 5 precipitation products (PERSIANN-CDR, CMORPH, GSMaP, IMERG, MSWEP), measured data, and environmental variables. The study compared the precipitation estimation performance of Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGBoost), Bagging, and Double Machine Learning (DML) models, and analyzed the models’ performance under different precipitation intensities and altitudes, as well as their variable sensitivity. The results showed that: (1) DML models outperformed Single Machine Learning (SML) models and original precipitation products, with RF-Bagging being the optimal model. The daily-scale Correlation Coefficient (CC) of RF-Bagging was over 50% higher than that of original products, while the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced by more than 40% and 35%, respectively. (2) For moderate-to-heavy precipitation, the RF-Bagging and RF-RF models maintain a stable Critical Success Index (CSI) of 0.7. In high-altitude regions, their Probability of Detection (POD) approaches 1, and the Heidke Skill Score (HSS) is 30–40% higher than that in mid-altitude areas, significantly outperforming other models and demonstrating strong adaptability to complex terrain. For light precipitation, while the POD values of these two models are comparable to those of other models, their False Alarm Rate (FAR) is reduced by 15–20%, effectively mitigating precipitation false alarms. (3) GSMaP, IMERG, and MSWEP were the core input variables for all models. RF and ELM models were more dependent on environmental variables, while XGBoost and Bagging models relied more on satellite data. This framework can provide technical references for precipitation estimation in complex terrain areas and contribute to watershed water resource management as well as flood prevention and mitigation.

Hydrological processes and the sustainable use of water resources in a river basin are altered by climate change and changes in human variables. This study examined the significant effects of vegetation and hydrological, climatic, and human activity changes on the basin’s biological environment and usage of water resources. The Min River Basin (MRB) in the upper Yangtze River served as the study location. Mann–Kendall and Pettitt mutation test techniques were used to examine the features of runoff changes in the basin. The effects of meteorological and anthropogenic factors on runoff and vegetation changes in the MRB from 1982 to 2020 were quantitatively evaluated using the expanded Budyko equation. Following this, spatial and temporal variations in land use and the NDVI in the basin were studied. The results of the research demonstrated the following: (1) The MRB yearly runoff trended downward and that an abrupt change in runoff happened in 1994. (2) Precipitation (Pr) showed a decreasing tendency from the base period (S1) to the change period (S2), but potential evapotranspiration (ET₀) showed an increasing trend. (3) From 1985 to 2020, the land use area of the MRB changed rapidly, and the construction land and water area increased by 322% and 58.85%, respectively, while the cultivated land area decreased by 11.72%. (4) From S1 to S2, there was a rising trend in both the NDVI and the Budyko parameter n. The contributions of Pr, ET₀, NDVI, and n to the runoff change were 32.41%, 9.43%, 27.51%, and 30.65%, respectively.