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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.

Debris-covered glaciers (DCGs) represent a substantial portion of global glaciers, but their automatic identification remains a challenge due to environmental complexity and glacier type variability. This study focuses on 30 glacial areas across Western China, representing continental, sub-continental, and maritime glaciers. Using four machine-learning algorithms-Random Forests (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM)-we integrated multiple environmental variables, including surface reflectance, normalized indices, surface temperature (ST), and topography, to classify DCGs through a three-process approach. Process 1 distinguished overall ice/snow cover from other land features, while Processes 2 and 3, both building on results of Process 1, further separated ice from snow cover and differentiated debris from other land features, respectively. Results showed that RF generally outperformed the other algorithms, with median Matthews correlation coefficient (MCC) values exceeding 0.999 in Process 1 and reaching 0.612 and 0.784 in Processes 2 and 3, respectively, and the average MCC value for DCGs identification reached 0.819. Regional modeling significantly improved the accuracy of DCG identification in Processes 2 and 3, particularly in continental glacier regions, with a notable increase in MCC values. Additionally, the independent binary-class models outperformed the integrated multi-class model in Processes 2 and 3, further validating the necessity of process-specific modeling. Comparison with the GGI18 glacier inventory showed a high level of consistency with the DCGs identified by the optimal algorithms, with an R 2 value of 0.971. Despite uncertainties in maritime glacier regions, the proposed method demonstrates robustness and high spatiotemporal generalization. This approach, combining machine learning and environmental variables, offers a reliable tool for automated DCG identification and can significantly aid future glacier inventory compilation and monitoring efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)