Supra-glacial debris cover is key to glacier ablation through increasing (thin debris layer) or decreasing (thick debris layer) melt rates, thereby regulating the mass balance of a glacier and its meltwater runoff. The thickening or lateral expansion of supra-glacial debris cover correlates with a reduction of glacier ablation and, consequently, runoff generation, which is also considered to be an influential factor on the rheology and dynamics of a glacierized system. Studies on supra-glacial debris cover have recently attracted wide attention especially for glaciers in the Himalayas and Karakoram, where the glaciers have heterogeneously responded to climate change. In this study, we used 32 images from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager archive, going back to 1990, which are available on the Google Earth Engine cloud-computing platform, to map the supra-glacial debris cover in the Hunza Valley, Karakoram, Pakistan, based on a band ratio segmentation method (normalized difference snow index [NDSI] < 0.4), Otsu thresholding, and machine learning algorithms. Compared with manual digitization, the random forest (RF) model was found to have the greatest accuracy in identifying supra-glacial debris, with a Kappa coefficient of 0.94 ± 0.01 and an overall accuracy of 95.5 ± 0.9%. Overall, the supraglacial debris cover in the study area showed an increasing trend, and the total area expanded by 8.1-21.3% for various glaciers from 1990 to 2019. The other two methods (Otsu thresholding and NDSI < 0.4) generally overestimated the supra-glacial debris covered area, by 36.3 and 18.8%, respectively, compared to that of the RF model. The supra-glacial debris cover has migrated upward on the glaciers, with intensive variation near the equilibrium-line altitude zone (4,500-5,500 m a.s.l.). The increase in ice or snow avalanche activity at high altitudes may be responsible for this upward expansion of supra-glacial debris cover in the Hunza Valley, which is attributed to the combined effect of temperature decrease and precipitation increase in the study area.

Lake ice, one of the most direct lake physical characteristics affected by climate change, can reflect small-scale environmental changes caused by the atmosphere and hydrology, as well as large-scale climate changes such as global warming. This study uses National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer (NOAA AVHRR), MOD09GQ surface reflectance products, and Landsat surface reflectance Tier 1 products, which comprehensively used RS and GIS technology to study lake ice phenology (LIP) and changes in Qinghai Lake. Over the past 38 years, freeze-up start and freeze-up end dates were gradually delayed by a rate of 0.16 d/a and 0.19 d/a, respectively, with a total delay by 6.08 d and 7.22 d. The dates of break-up start and break-up end showed advancing trends by −0.36 d/a and −0.42 d/a, respectively, which shifted them earlier by 13.68 d and 15.96 d. Overall, ice coverage duration, freeze duration, and complete freeze duration showed decreasing trends of −0.58 d/a, −0.60 d/a, and −0.52 d/a, respectively, and overall decreased by 22.04 d, 22.81 d, and 9.76 d between 1980 and 2018. The spatial pattern in the freeze–thaw of Qinghai Lake can be divided into two areas; the west of the lake area has similar spatial patterns of freezing and ablation, while, in the east of the lake area, freezing and ablation patterns are opposite. Climate factors were closely related to LIP, especially the accumulated freezing degree-day (AFDD) from October to April of the following year. Furthermore, freeze-up start time was more sensitive to changes in wind speed and precipitation.

The southeastern Tibetan Plateau, where monsoonal temperate glaciers are most developed, has a huge number of glacial lakes. Based on Landsat Operational Land Imager (OLI) images, 192 glacial lakes with a total area of 45.73 ± 6.18 km2 in 2016 were delineated in the Yi’ong Zangbo River Basin. Glacial lakes with areas of less than 0.1 km2 accounted for 81.77% of the total number, and glacial lakes located above 4500 m elevation comprised 83.33%. Dramatic glacier melting caused by climate warming has occurred, resulting in the formation and expansion of glacial lakes and the increase of potential glacial lake outburst floods (GLOFs) risk. From 1970 to 2016, the total area of glaciers in the basin has decreased by 35.39%, whereas the number and total area of glacial lakes have, respectively, increased by 86 and 1.59 km2. In that time, 110 new glacial lakes emerged, whereas 24 of the original lakes disappeared. The newly formed lakes have a smaller mean area but higher mean elevation than the lakes that disappeared. Based on five indicators, a first-order method was used to identify glacial lakes that pose potential threats. We identified 10 lakes with very high, 7 with high, 31 with medium, and 19 with low GLOF susceptibility, out of 67 moraine-dammed glacial lakes with areas larger than 0.02 km2. Understanding the behavior of glaciers and glacial lakes is a vital aspect of GLOFs disaster management, and the monitoring of glacial lakes should be strengthened.

Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing (RS) datasets including the MOD09GQ surface reflectance product, Landsat TM/ETM+ images, and meteorological records to analyze spatiotemporal variations of ice phenology of Qinghai Lake between 2000 and 2016 applying both RS and GIS technology. We also identified the climatic factors that have influenced lake ice phenology over time and draw a number of conclusions. First, data show that freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) on Qinghai Lake usually occurred in mid-December, early January, mid-to-late March, and early April, respectively. The average freezing duration (FD, between FUE and BUE), complete freezing duration (CFD, between FUE and BUS), ice coverage duration (ICD, between FUS and BUE), and ablation duration (AD, between BUS and BUE) were 88 days, 77 days, 108 days and 10 days, respectively. Second, while the results of this analysis reveal considerable differences in ice phenology on Qinghai Lake between 2000 and 2016, there has been relatively little variation in FUS times. Data show that FUE dates had also tended to fluctuate over time, initially advancing and then being delayed, while the opposite was the case for BUS dates as these advanced between 2012 and 2016. Overall, there was a shortening trend of Qinghai Lake's FD in two periods, 2000-2005 and 2010-2016, which was shorter than those seen on other lakes within the hinterland of the Tibetan Plateau. Third, Qinghai Lake can be characterized by similar spatial patterns in both freeze-up (FU) and break-up (BU) processes, as parts of the surface which freeze earlier also start to melt first, distinctly different from some other lakes on the Tibetan Plateau. A further feature of Qinghai Lake ice phenology is that FU duration (between 18 days and 31 days) is about 10 days longer than BU duration (between 7 days and 20 days). Fourth, data show that negative temperature accumulated during the winter half year (between October and the following April) also plays a dominant role in ice phenology variations of Qinghai Lake. Precipitation and wind speed both also exert direct influences on the formation and 116 Journal of Geographical Sciences melting of lake ice cover and also cannot be neglected.

The China–Pakistan international Karakoram Highway passes through the core area of the “Karakoram Anomaly,” whose glaciers have maintained or increased their mass during a period when most glaciers worldwide have receded. We synthesized the literature and used remote-sensing techniques to review the types, distribution, characteristics, causes and frequency of major glacial hazards along the Karakoram Highway. We found that the glacier-related hazards could be divided into direct and indirect hazards, including glacier surges, glacial lake outburst floods, and glacial floods, which are concentrated in East Pamir and the Hunza River Basin. In the past 100 years, hazards from glaciers surges and glacial floods only occurred once and twice, respectively, which appear suddenly, with the hazard-causing process being short-lived and occurring mainly in the summer. Glacial lake outburst floods mainly occur in the spring and summer in the Hunza River Basin. Among these, ice-dammed lakes have the highest frequency of flooding, their formation and outbursts being closely related to the sudden advancement of surge-type glaciers. Under the background of global climate warming, we speculate that the glacier surge cycle may shorten and the frequency of the formation and outbursts in the glacial lakes may increase. In the future, we should combine models and new field observations to simulate, and deepen our understanding of the physical mechanisms of different glacier-related hazards. In particular, on-site monitoring should be carried out, to include the evolution of glaciers subglacial hydrological systems, the thermal state at the base of the glaciers, and the opening and closing of drainage channels at the base of the ice dams.