Zizhen Jin

Based on the lack of understanding of the attribution of land use change and the resulting ecological effects in the Datong River Basin (DTRB), we analyzed the spatial-temporal characteristics of the land use change and explored the drivers of the changes, and calculated the effect of the land use change on the net primary productivity (NPP) from 1990 to 2020. The main conclusions are as follows: The predominant land use types comprised grassland, forest, cropland, bare land, ice/snow and other categories. The cropland, grassland and shrubs showed a decreased trend. However, forest, bare land and impervious surface were increased. The comprehensive index of the land use degree showed a significant downward trend, indicating that land use intensity will likely remain at a low level. Additionally, these conversions occurred among different land use types. Cropland and Grassland were dominated by transfer-out; Forest, bare land and impervious surface were dominated by transfer-in. Regarding the analysis of the driving factors of land use changes. The low elevation area was affected by human activities, and under the impact of “green gain” protection policy, the area of grassland increased; coupled with the impact of “returning farmland to forests,” the area of cropland decreased. Bare land was significantly and negatively correlated with GDP and population, while impervious surface was positively correlated. However, high-altitude areas were affected by natural factors, with grassland partial negatively correlated with precipitation. For bare land, it partial positively correlated with temperature. Snow/ice partial positively correlated with precipitation. Regarding the ecological effect of land use change, the multi-year average NPP was about 167.0 gC·m⁻²·a⁻¹ and showed a decreased trend during the period of 1990–2020, and the NPP decreased in the northwest and increased in the southeast. This study provides scientific suggestions for the management of land resources and ecological environmental protection.

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

The Qilian Mountains are a climate-sensitive area in northwest China, and extreme precipitation events have an important impact on its ecological environment. Therefore, considering the global warming scenario, it is highly important to project the extreme precipitation indices over the Qilian Mountains in the future. This study is based on three CMIP6 models (CESM2, EC-Earth3, and KACE-1-0-G). A bias correction algorithm (QDM) was used to correct the precipitation outputs of the models. The eight extreme precipitation indices over the Qilian Mountains during the historical period and in the future were calculated using meteorological software (ClimPACT2), and the performance of the CMIP6 models to simulate the extreme precipitation indices of the Qilian Mountains in the historical period was evaluated. Results revealed that: (1) The corrected CMIP6 models could simulate the changes in extreme precipitation indices over the Qilian Mountains in the historical period relatively well, and the corrected CESM2 displayed better simulation as compared to the other two CMIP6 models. The CMIP6 models performed well while simulating R10mm (CC is higher than 0.71) and PRCPTOT (CC is higher than 0.84). (2) The changes in the eight extreme precipitation indices were greater with the enhancement of the SSP scenario. The growth rate of precipitation in the Qilian Mountains during the 21st century under SSP585 is significantly higher than the other two SSP scenarios. The increment of precipitation in the Qilian Mountains mainly comes from the increase in heavy precipitation. (3) The Qilian Mountains will become wetter in the 21st century, especially in the central and eastern regions. The largest increase in precipitation intensity will be observed in the western Qilian Mountains. Additionally, total precipitation will also increase in the middle and end of the 21st century under SSP585. Furthermore, the precipitation increment of the Qilian Mountains will increase with the altitude in the middle and end of the 21st century. This study aims to provide a reference for the changes in extreme precipitation events, glacier mass balance, and water resources in the Qilian Mountains during the 21st century.

Laohugou glacier No. 12 (LHG12), located in the northeast of the Qinghai–Tibet Plateau, is the largest valley glacier in the Qilian mountains. Since 1957, LHG12 has shrunk significantly. Due to the limitations of in situ observations, simulations and investigations of LHG12 have higher levels of uncertainty. In this study, consumer-level, low-altitude microdrones were used to conduct repeated photogrammetry at the lower part of LHG12, and a digital orthophoto map (DOM) and a digital surface model (DSM) with a resolution at the centimeter scale were generated, from 2017 to 2021. The dynamic parameters of the glacier were detected by artificial and automatic extraction methods. Using a combination of GNSS and drone-based data, the dynamic process of LHG12 was analyzed. The results show that the terminus of LHG12 has retreated by 194.35 m in total and by 19.44 m a⁻¹ on average during 2008–2021. The differential ablation leading to terminus retreat distance markedly increased during the study period. In 2019–2021, the maximum annual surface velocity was 6.50 cm day⁻¹, and during ablation season, the maximum surface velocity was 13.59 cm day⁻¹, 52.17% higher than it is annually. The surface parameters, motion, and mass balance characteristics of the glacier had significant differences between the west and east branches. The movement in the west branch is faster than it is in the east branch. Because of the extrusion of the two ice flows, there is a region with a faster surface velocity at the ablation area. The ice thickness of LHG12 is decreasing due to intensified ablation, leading to a deceleration in the surface velocity. In large glaciers, this phenomenon is more obvious than it is in small glaciers in the Qilian mountains.