Glaciers have proven to be a particularly sensitive indicator of climate change, and the impacts of glacier melting on downstream water supplies are becoming increasingly important as the world’s population expands and global warming continues. Data scarcity in mountainous catchments, on the other hand, has been a substantial impediment to hydrological simulation. Therefore, an enhanced glacier hydrological model combined with multi-source remote sensing data was introduced in this study and was performed in the Upper Yarkant River (UYR) Basin. A simple yet efficient degree-day glacier melt algorithm considering solar radiation effects has been introduced for the Soil and Water Assessment Tool Plus model (SWAT+), sensitivity analysis and auto calibration/validation processes were integrated into this enhanced model as well. The results indicate that (i) including glacio-hydrological processes and multi-source remote sensing data considerably improved the simulation precision, with a Nash–Sutcliffe efficiency coefficient (NSE) promotion of 1.9 times and correlated coefficient (R²) of 1.6 times greater than the original model; (ii) it is an efficient and feasible way to simulate glacio-hydrological processes with SWAT+Glacier and calibrate it using observed discharge data in data-scarce and glacier-melt-dominated catchments; and (iii) glacier runoff is intensively distributed throughout the summer season, accounting for about 78.5% of the annual glacier runoff, and glacier meltwater provides approximately 52.5% (4.4 × 10⁹ m³) of total runoff in the study area. This research can serve the runoff simulation in glacierized regions and help in understanding the interactions between streamflow components and climate change on basin scale.

Continuously monitoring and mapping glacial lake variation is of great importance for determining changes in water resources and potential hazards in alpine cryospheric regions. The semi-automated glacial lake mapping methods used currently are hampered by inherent subjectivity and inefficiency. This study used improved YOLOv5 strategies to extract glacial lake boundaries from Sentinel-2 imagery. These strategies include using the space-to-depth technique to identify small glacial lakes, and adopting the coordinate attention and the convolution block attention modules to improve mapping performance and adaptability. In terms of glacial lake extraction, the improved YOLOv5-seg network achieved values of 0.95, 0.93, 0.96, and 0.94 for precision (P), recall (R), mAP_0.5, and the F1 score, respectively, indicating an overall improvement in performance of 12% compared to that of the newest YOLOv8 networks. In High Mountain Asia (HMA), 23,108 glacial lakes with a total area of 1847.5 km² were identified in imagery from 2022 using the proposed method. Compared with the use of manual interpretation for lake boundary extraction in test sites of HMA, the proposed method achieved values of 0.89, 0.87, and 0.86 for P, R, and the F1 score, respectively. Our proposed deep learning method has improved accuracy in glacial lake extraction because it can address the challenge represented by frozen or high-turbidity glacial lakes in HMA.

Arctic river discharge is one of the important factors affecting sea-ice melting of Arctic shelf seas. However, such effects have not been given much attention. In this study, the changes in discharge of the Ob, Yenisei, and Lena Rivers and the sea ice of the Kara and Laptev Seas during 1979–2019 were analyzed. Substantial increases in discharge and heat from the discharge and decreases in sea ice concentration (SIC) were detected. The effects of changes in discharge and riverine heat on sea ice changes were investigated. The results showed that the influence of the discharge, accumulated discharge, heat, and accumulated heat on SIC mainly occurred at the beginning and final stages of sea-ice melting. Discharge accelerated the melting of sea ice by increasing the absorption of solar radiation as the impurities contained in the discharge washed to the sea ice surface during the initial and late stages of sea-ice melting. Changes in cumulative riverine heat from May to September greatly contributed to the SIC changes in the Kara and Laptev Seas at the seasonal scale. The SIC reduced by 1% when the cumulative riverine heat increased by 213.2 × 10⁶ MJ, 181.5 × 10⁶ MJ, and 154.6 × 10⁶ MJ in the Lena, Yenisei, and Ob Rivers, respectively, from May to September. However, even in the plume coverage areas in the Kara and Laptev Seas, discharge changes from the three rivers had a limited contribution to the reduction in SIC at annual scales. This work is helpful for understanding the changes in Arctic sea ice.

Glaciers have proven to be a particularly sensitive indicator of climate change, and the impact of glacier melting on downstream water supplies is becoming increasingly important as the world’s population expands and global warming continues. Data scarcity in mountainous catchments, on the other hand, has been a substantial impediment to hydrological process simulation. Therefore, an integrated glacier hydrological process module was introduced for the Soil and Water Assessment Tool Plus model (SWAT+), in which an enhanced temperature-index glacier melt algorithm considering solar radiation was employed to maintain model clarity and favorable performance in this study. Furthermore, SWATplusR was introduced for sensitivity analysis using the Sobol approach, and Integrated Parameter Estimation and Uncertainty Analysis Tool Plus (IPEAT+) was coupled with this enhanced model (SWAT+Glacier) to perform calibration and validation in the Upper Yarkant River (UYR) basin. The result indicated that (i) including glacial-hydrological processes considerably improved simulation precision, with an NSE promotion of 2.6 times and R ² of 1.7 times greater than the original model; (ii) it is an efficient and feasible way to simulate glacial-hydrological processes with SWAT+Glacier and calibrate it using observed discharge data in data-scarce and glacier melt dominated catchments; and (iii) we discovered that glacier runoff is intensively distributed throughout the summer season, accounts for about 78.5% of the annual glacier runoff, and glacier meltwater provides approximately 52.5% (4.4×10 ⁹ m ³ ) of total runoff in the study area.

Light-absorbing particles, which are vital components of aerosols, can cause significant snow albedo darkening and accelerate melting. However, restricted by the poor quality of remote sensing-based aerosol products in High Mountain Asia (HMA), previous studies have seldom reported the long-term pattern of aerosols. In this study, we analyzed the spatial and temporal distribution characteristics of AOD in HMA and surrounding areas using Moderate Resolution Imaging Spectroradiometer and Ozone Monitoring Instrument data from 2004 to 2023. The Mann-Kendall test was applied to analyze the temporal trend and abrupt changes in AOD, while Rotated Empirical Orthogonal Function was used to identify subregions and investigate spatiotemporal variations. Moreover, random forest and XGBoost-Shap models were employed to quantify the contributions of the aerosols to changes in snow albedo and melting. The results indicate that the annual (monthly) average highest and lowest AOD occurred in 2021 (April) and 2022 (September) between 2004 and 2023, respectively. The AOD first increased and then decreased during our study period and an abrupt decline was detected in 2013. The REOF model revealed three regions in HMA (northern, southwestern, and southeastern parts) with strong variations in AOD load, which are strongly correlated with atmospheric circulation and monsoon driving. Specifically, REOF1, REOF2, and REOF3 are primarily associated with frequent dust events during springtime atmospheric circulation and anthropogenic emission transport during the monsoon season. Aerosol types were divided into four types, BC aerosol, DUST aerosol, MIX aerosol, and clean conditions, whose proportions were 16.7%, 16.1%, 6.6%, and 60.6%, respectively. The clean conditions constituted the main aerosol type in the region. The AOD notably decreased snow albedo (17.8%) and increased snowmelt (9.0%); we highlight the contribution of AOD to the intensification of snowmelt. These results could provide guidance for further studies on the relationship between snowmelt and AOD.