The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km², an ice inventory of about 1168.53 km³, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km²). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km² (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km²). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains.

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m³/s and 4.554 m³/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m³/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m³/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains.

Traditional villages along the Jinzhong section of the Wanli Tea Road possess unique geographical locations and cultural heritage, showcasing a rich variety of cultural landscapes. Among these, village temple buildings play a crucial role as the core of spiritual and belief life in traditional villages. In the context of rapid urbanization and modernization, a new perspective is needed to thoroughly explore the deeper connotations of cultural landscape characteristics in traditional villages along the Wanli Tea Road, particularly by examining the composition and inherent features of their landscape genes. This study employs landscape gene theory, using the village temple system as a lens to identify and analyze the landscape genes of traditional villages along the Jinzhong section of the Wanli Tea Road. Based on the results, a genealogy and a map of landscape genes are constructed to clearly and effectively present the typical characteristics and expressions of these genes in a logical and visual manner. The findings indicate the following: (1) The village temple system in traditional villages along the Jinzhong section of the Wanli Tea Road is closely related to the formation and expression of their landscape genes and can play a critical role in the protection and transmission of cultural landscapes. (2) As important nodes along the historical trade route, the landscape gene characteristics of these villages reflect their unique natural environments and historical development trajectories, illustrating the deep intertwining of commercial activities and local cultural landscapes. (3) This study establishes a replicable research framework that is not only applicable to local studies but also provides valuable references for broader cultural landscape research.

The traditional villages along the Wanli Tea Road carry rich historical and cultural heritage, holding significant humanistic and scientific value. However, with the rapid pace of urbanization and modernization, the inheritance and protection of the cultural landscapes in these traditional villages face numerous challenges. Based on this, this study focuses on Xiamen Village, a traditional village along the Jinzhong section of the Wanli Tea Road, utilizing the perspective of the landscape gene information chain to reveal the performance and genetic characteristics of its unique and regionally distinctive cultural landscape genes. The study provides theoretical support for the protection and inheritance of cultural landscapes in traditional villages along the Wanli Tea Road. The results show that: (1) The overall cultural landscape of Xiamen Village has been well preserved, with notable characteristics in environment, layout, architecture, and culture, demonstrating its rich historical and cultural accumulation, and offering high research and conservation value; (2) The landscape gene information chain of Xiamen Village plays a critical role in integrating the village’s cultural landscape. The landscape gene information elements and points express the village’s unique historical inheritance through regional culture and material forms. The “branch-like” structure of the landscape gene information corridors effectively connects the various landscape gene information points, while the landscape gene information network reflects the interaction between tradition and modernity; (3) The landscape gene information chain of Xiamen Village shows a relationship of coexistence between inheritance and change in its genetic characteristics. Although some landscape genes face challenges from modernization, their core traits have not been lost. The inheritance of the landscape genes is not static but adjusts and reconstructs within an evolving social and cultural context, reflecting adaptability and flexibility in response to modern demands.

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρ_XY) and random forest feature importance (FI) (ρ_XY = 0.63, FI = 0.723), followed by soil temperature (ρ_XY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost.