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Study region: This study focuses on the upper reaches of Shule river (URSLH) and Heihe river (URHH) basins and Taolai river (URTLH) basin in Qilian Mountains. Study focus: The impact of the cryosphere changes on runoff components in basins with different cryosphere ratios. New hydrological insights for the region: Total runoff (TR) increased in URSLH and URHH and decreased in URTLH, snowmelt runoff (SR) decreased in each basin, glacier runoff (GR) increased in URSLH and URTLH but decreased in URHH during 1980–2015. In the future, GR will increase under SSP585 and slightly decrease under SSP126 in 2040–2060 in URSLH and decrease in URHH and URSLH. The peak time of SR will advance by a month in each basin. In the future, GR (The ratio of the coefficient of variation (cv) of TR to cv of non-glacial runoff) will decrease, indicating hydrological regulation of glaciers will be weakened in these basins. SR and Rs (The ratio of summer runoff to spring runoff) will show downward trends, the processes of TR increase will be smoother. Rr (The ratio of maximum to minimum monthly runoff) will show downward trends under SSPs. TR will become smoother in each basin. Furthermore, the change of each runoff components will make TR tends to be smoother in the future and reduce TR especially in summer.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC‐CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid‐low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low‐coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid‐coverage grassland (MG; 15.6%), bare land (12.5%), high‐coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water. [ABSTRACT FROM AUTHOR]