Siberia occupies vast areas underlain by permafrost, and understanding its land cover changes is important for ecological environmental protection in a warming climate. Based on the land cover and climate datasets, we analyzed the land cover changes and their drivers in Siberia from 1992 to 2020. The results show that (1) From 1992 to 2020, the areas of evergreen needleleaf trees and deciduous needleleaf trees in Siberia decreased by 9% and 2.5%, and the areas of grassland, shrub, cropland, and construction land increased by 1.5%, 14.2%, 2.8%, and 39.2%, respectively. Cropland expansion had the fastest rate of 1.85% in the continuous permafrost zone, and construction land expansion had the fastest rate of 3.07% in the non-permafrost zone. (2) The center of gravity of agricultural land continues to migrate to the northeast, and the center of gravity of construction land continues to migrate to the southwest. (3) The primary drivers for the land cover changes were temperature and precipitation, and active layer thickness also affected grassland, cropland, and deciduous needleleaf trees. The correlation coefficient between active layer thickness and cropland area is 0.74 in the continuous permafrost zone. The interaction between factors is mostly manifested as a two-factor enhancement, with the highest q-value of the interaction of temperature and precipitation for explanatory power. Our results suggest that climate change and permafrost degradation significantly changed land cover in Siberia. This finding deepens our understanding of the mechanisms of land cover change under the influence of permafrost degradation and provides a new perspective on the land cover changes in permafrost regions.

The Qinghai-Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai-Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai-Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km(2), and there was a significant increasing trend (=322 km(2)/year, p < 0.01) during 2000-2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.

Surface albedo exerts substantial control over the energy available for glacier melting. For Urumqi Glacier No.1 in the Tien Shan Mountains, China, represented as a summer accumulation glacier, the variations in albedo driven by surface processes are complex and still poorly understood. In this study, we examined the interannual trends in ablation-period albedo from 2000 to 2021 using MOD10A1 products, evaluated the variation in bare-ice albedo retrieved from 13 end-of-summer Landsat images obtained between 2002 and 2019, and investigated the seasonal variation and diurnal cycle of surface albedo collected near the equilibrium line of the glacier by an AWS from September 2018 to August 2021. During the period of 2000-2021, the average ablation-period albedo presented a slight but not statistically significant downward trend, with a total decrease of 1.87%. Specifically, the decrease in glacier albedo was quicker in July than that in August, and there was a slight increase in May and June. The blackening phenomenon was shown on the east branch glacier, but not on the west branch glacier. For seasonal variability, a bimodal pattern was demonstrated, different from the unimodal seasonal variation in other midlatitude glaciers. The albedo peaks occurred in December and April or May. Under clear sky conditions, the diurnal cycle presented three patterns: a symmetric pattern, an asymmetric pattern, and a progressive decreasing pattern. Air temperature and solid precipitation are the main drivers of variations in glacier albedo, but in different periods of the ablation season, two climate variables affect albedo to varying degrees. The effect of surface albedo reduction enhanced glacier melting by about 20% over the past 20 years. The short-term increase in albedo caused by summer snowfall can considerably reduce glacier melting by as much as 80% in June.

The timing and duration of snow cover critically affect surface albedo, surface energy budgets, and hydrological processes. Previous studies using in-situ or satellite remote sensing data have mostly been site-specific (Siberia and the Tibetan Plateau), and remote sensing and/or modeling data include large uncertainties. Here, we used 1103 stations with long-term (1966-2012) ground-based snow measurements to investigate spatial and temporal variability in snow cover timing and duration and factors impacting those changes across the Eurasian continent. We found the earliest annual onset and latest disappearance of snow cover occurred along the Arctic coast, where the long-term (1971-2000) mean annual snow cover duration (SCD) was more than nine months which was the longest in this study. The shortest SCD, <= 10 days, was found in southern China. The first and last dates of snow cover (FD and LD, respectively), SCD, and the ratio of SCD to snow season length (RDL) were generally latitude dependent over the Eurasian Continent, while were elevation dependent on the Tibetan Plateau. During the period from 1966 through 2012, FD delayed and LD advanced by similar to 1 day/decade, and RDL increased by about 0.01/decade. The LD, SCD, and RDL anomalies (relative to the period 1971-2000) were also significantly correlated with latitude. Advances in LD and positive RDL were more significant in low-latitude regions, decreases in SCD were more significant in high-latitude regions. Changes in SCD were related to air temperature and snowfall in autumn and warming in spring. SCD specifically increased in the northern Xinjiang and northeastern China to increased snowfall. The significant reduction in SCD in southwestern Russia, the Tibetan Plateau and along the Yangtze River was mainly affected by climate warming. (C) 2020 Elsevier B.V. All rights reserved.

Rain on snow (ROS) is a complex phenomenon leading to repeated flooding in many regions with a seasonal snow cover. The potential to generate floods during ROS depends not only on the magnitude of rainfall but also on the areal extent of the antecedent snow cover and the spatio-temporal interaction between meteorologic and snowpack properties. The complex interaction of these factors makes it difficult to accurately predict the effect of snow cover on runoff formation for an upcoming ROS event. In this study, the detailed physics-based snow cover model SNOWPACK was used to assess the influence of snow cover properties on converting rain input to available snowpack runoff during 191 ROS events for 58 catchments in the Swiss Alps. Conditions identified by the simulations that led to excessive snowpack runoff were a large snow-covered fraction, spatially homogeneous snowpack properties, prolonged rainfall events, and a strong rise in air temperature over the course of the event. These factors entail a higher probability of snowpack runoff occurring synchronously within the catchment, which in turn favours higher overall runoff rates. The findings suggest that during autumn and late spring, flooding due to ROS is more likely to happen, whereas during winter a coincidence of the above conditions in the study area is quite rare. For example, an autumn event which occurred in October 2011 resulted from a combination of spatially homogeneous snowpack conditions following a recent snowfall and high, but not exceptional rainfall, and led to major flooding. The results of this study provide key factors to assess in advance of an incoming ROS event and emphasize the importance of detailed snow monitoring for flood forecasting in snow-affected watersheds.