Aufeis is a common phenomenon in cold regions of the Northern Hemisphere that develops during winter by successive water overflow and freezing on ice-covered surfaces. Most studies on aufeis occurrence focus on regions in North America and Siberia, while research in High Mountain Asia (HMA) is still in an exploratory phase. This study investigates the extent and dynamics of icing processes and aufeis in the Tso Moriri basin, eastern Ladakh, India. Based on a combination of 235 Landsat 5 TM/8 OLI and Sentinel-2 imagery from 2008 to 2021 the occurrence of icing and aufeis was classified using a random forest classifier. A total of 27 frequently occurring aufeis fields with an average maximum extent of 9 km(2) were identified, located at a mean elevation of 4,700 m a.s.l. Temporal patterns show a distinct accumulation phase (icing) between November and April, and a melting phase lasting from May until July. Icing is characterized by high seasonal and inter-annual variability. Successive water overflow mainly occurs between January and March and seems to be related to diurnal freeze-thaw-cycles, whereas higher daytime temperatures result in larger icing areas. Aufeis feeding sources are often located within or in close vicinity to wetland areas, while vegetation is largely absent on surfaces with frequent aufeis formation. These interactions require more attention in future research. In addition, this study shows the high potential of a machine learning approach to monitor icing processes and aufeis, which can be transferred to other regions.

As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai-Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales.

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60-120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.