Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models.

Land degradation under the influence of global warming and ecological environmental destruction due to poor land management is the main challenge facing the Mongolian Plateau, and its future ecological risk change trends and drivers are also unclear. Therefore, to address the context relevant to this challenge, planning based on measured information from land use patterns is required. Based on land use and land cover (LULC), this study evaluates the landscape ecological risk (LER) of the Selenga River Basin by calculating the landscape pattern index. The spatiotemporal pattern and influencing factors of landscape ecological risk in the Selenga River Basin from 1990 to 2040 were analyzed. According to the results of LULC analysis, forest and grassland were the primary land use types in the Selenga River Basin. The built area, forest, and cropland showed an increasing trend, while the grassland area showed a fluctuating decreasing trend. From 1990 to 2010, the comprehensive land use dynamic degree showed a trend of rising first and then falling, specifically from 0.13% in 1990 to 0.29% in 2010, and will drop to 0.06% by 2040, indicating that the range of land use change is becoming more and more stable. The landscape ecological risk assessment shows a distribution pattern of “low at the edge and high in the middle”. The landscape ecological risk index (LER) first increases and then decreases, with the peak value in 2010 (0.085). By calculating the spatial aggregation of LER and the partial correlation with climate, we found that the Moran’s I index showed an “anti-V”-shaped change trend from 1990 to 2040, and the average landscape ecological risk presents positive spatial correlation, primarily with high-value aggregation, and peaked in 2010. Precipitation had a negative correlation with landscape ecological risk controlling for temperature, while there was a positive relationship between temperature and landscape ecological risk under the influence of controlling precipitation. This study provides a scientific basis for LULC planning in the Selenga River Basin, and is of great significance for maintaining the ecological security of the Mongolian Plateau.

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region’s socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R² = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10⁻³ y⁻¹. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with “good” and “excellent” ecological grades, whereas unused land types represent more than 44% of the total area classified with “poor” ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions.

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10⁻³ y⁻¹, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR.

The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

One of the main glacier-related natural hazards that are common to alpine locations is the occurrence of glacial lake outburst floods (GLOFs), which can seriously harm downstream towns and infrastructure. GLOFs have increased in frequency in the central Himalayas in recent years as a result of global warming, and careful management of glacial lakes is a crucial step in catastrophe prevention. In this study, field surveys were conducted on 28 August 2020 and 1 August 2021 with the help of an unmanned aerial vehicle (UAV) and a boat bathymetric system on an unmanned surface vessel (USV), combined with 22 years of Landsat series imagery and Sentinel-2 MSI imagery data. Spatial analysis was then used to investigate changes in lake surface conditions, dam stability, and surrounding topography before and after an integrated project of the Jialong Co lake. The results show that: (1) from 2000 to 2020 (before engineering management), the area of the Jialong Co glacial lake increased from 0.2148 ± 0.0176 km² to 0.5921 ± 0.0003 km². The glacial lake expansion rate from 2000 to 2010 (0.0145 km²/a) was greater than the rate from 2011 to 2020 (6.92 $\times$ 10⁻⁶ km²/a). In 2021 (after engineering treatment), the glacial lake perimeter, area, and volume decreased by 0.6014 km, 0.1136 km², and 1.90 × 10⁷ m³, respectively. The amount of excavation during the project treatment was 8.13 million square meters, and the amount of filling was 1.24 million square meters. According to the results of the unmanned surface vessel (USV), the elevation of the lake surface dropped from 4331 m to 4281 m, and the water level dropped by 50 m (the designed safe water level line dropped by 30 m). (2) The results of the UAV topographic survey and geomorphological analysis showed that the engineered reinforcement of the outlet channel and surrounding dam effectively mitigated severe scouring of the foot of the final moraine at the outlet of the spillway, as well as the likelihood of glacial lake outbursts caused by ice avalanches and landslides. (3) The comprehensive engineering treatment of this typical glacial lake effectively lowered the water level and improved the stability of the moraine ridge and lake dam, providing a scientific foundation for other glacial lake outburst risk assessments and disaster mitigation and management measures. Thus, it is critical to evaluate the impact of comprehensive engineering management of key glacial lakes to support glacial lake management.