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.

There is 78 % permafrost and seasonal frozen soil in the Yangtze River's Source Region (SRYR), which is situated in the middle of the Qinghai-Xizang Plateau. Three distinct scenarios were developed in the Soil and Water Assessment Tool (SWAT) to model the effects of land cover change (LCC) on various water balance components. Discharge and percolation of groundwater have decreased by mid-December. This demonstrates the seasonal contributions of subsurface water, which diminish when soil freezes. During winter, when surface water inputs are low, groundwater storage becomes even more critical to ensure water supply due to this periodic trend. An impermeable layer underneath the active layer thickness decreases GWQ and PERC in LCC + permafrost scenario. The water transport and storage phase reached a critical point in August when precipitation, permafrost thawing, and snowmelt caused LATQ to surge. To prevent waterlogging and save water for dry periods, it is necessary to control this peak flow phase. Hydrological processes, permafrost dynamics, and land cover changes in the SRYR are difficult, according to the data. These interactions enhance water circulation throughout the year, recharge of groundwater supplies, surface runoff, and lateral flow. For the region's water resource management to be effective in sustaining ecohydrology, ensuring appropriate water storage, and alleviating freshwater scarcity, these dynamics must be considered.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high -precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of highprecision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking -DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen 's slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R 2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking -DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high -precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.

The Qilian Mountains (QMs), located in the northeast part of the Qinghai-Tibetan Plateau in China, have a fragile ecological environment, complex and sensitive climate, and diverse land-cover types. It plays an important role in the Qinghai-Tibetan Plateau Ecological Barrier and Northern Sand Control Belt in China's two screens and three belts ecological security strategy. Based on land use data of 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we utilized GIS technology, land use dynamic degree, and land use transition matrixes to analyze the spatial and temporal evolution of land use in the QMs from 1980 to 2020. The results showed the following: (1) From 1980 to 2020, grassland, forest land, and unused land were the main land-use types in the QMs, and the proportion of construction land accounted for only 0.31% of all land-use types. (2) The single land use dynamic degree showed that the dynamic degree of construction land was the highest and the fastest change rate from 2010 to 2015. The comprehensive land use dynamic degree showed that the intensity of land-use change was relatively drastic in the three time periods of 1990-1995, 1995-2000, and 2015-2020. (3) The land-use types in the study area switched infrequently during 2000-2005, 2005-2010, and 2010-2015. (4) The main transition directions of land-use types were grassland and unused land to other land-use types. These changes altered the spatial distributions of different land-use types. The study is critical for understanding the spatial and temporal change patterns of land-use change in the QMs and providing guidance for the optimization of land use in the study area and the improvement of regional eco-environmental protection.

Wind erosion can cause desertification and sandstorms in arid and semiarid areas. However, quantitative studies of the dynamic changes in wind erosion over long time periods are relatively rare, and this knowledge gap hinders our un-derstanding of desertification under the conditions of a changing climate. Here, we selected the Mongolian Plateau as the study area. Using the revised wind erosion equation (RWEQ) model, we assessed the spatial and temporal dy-namics of wind erosion on the Mongolian Plateau from 1982 to 2018. Our results showed that the wind erosion inten-sity on the Mongolian Plateau increased from northeast to southwest. The annual mean wind erosion modulus was 46.5 t center dot ha-1 in 1982-2008, with a significant decline at a rate of -5.1 t center dot ha-1 center dot 10 yr-1. The intensity of wind erosion was the strongest in spring, followed by autumn and summer, and was weakest in winter. During 1982-2018, wind erosion showed a significant decreasing trend in all seasons except winter. The wind erosion contribution of spring to the total annual wind erosion significantly increased, while that of summer significantly decreased. These results can help decision-makers identify high-risk areas of soil erosion on the Mongolian Plateau and take effective measures to adapt to climate change.

The Amazon Basin is at the center of an intensifying discourse about deforestation, land-use, and global change. To date, climate research in the Basin has overwhelmingly focused on the cycling and storage of carbon (C) and its implications for global climate. Missing, however, is a more comprehensive consideration of other significant biophysical climate feedbacks [i.e., CH4, N2O, black carbon, biogenic volatile organic compounds (BV0Cs), aerosols, evapotranspiration, and albedo] and their dynamic responses to both localized (fire, land-use change, infrastructure development, and storms) and global (warming, drying, and some related to El Nino or to warming in the tropical Atlantic) changes. Here, we synthesize the current understanding of (1) sources and fluxes of all major forcing agents, (2) the demonstrated or expected impact of global and local changes on each agent, and (3) the nature, extent, and drivers of anthropogenic change in the Basin. We highlight the large uncertainty in flux magnitude and responses, and their corresponding direct and indirect effects on the regional and global climate system. Despite uncertainty in their responses to change, we conclude that current warming from non-CO2 agents (especially CH4 and N2O) in the Amazon Basin largely offsets- and most likely exceeds-the climate service provided by atmospheric CO2 uptake. We also find that the majority of anthropogenic impacts act to increase the radiative forcing potential of the Basin. Given the large contribution of less-recognized agents (e.g., Amazonian trees alone emit similar to 3.5% of all global CH4), a continuing focus on a single metric (i.e., C uptake and storage) is incompatible with genuine efforts to understand and manage the biogeochemistry of climate in a rapidly changing Amazon Basin.

Our study highlights the usefulness of very high resolution (VHR) images to detect various types of disturbances over permafrost areas using three example regions in different permafrost zones. The study focuses on detecting subtle changes in land cover classes, thermokarst water bodies, river dynamics, retrogressive thaw slumps (RTS) and infrastructure in the Yamal Peninsula, Urengoy and Pechora regions. Very high-resolution optical imagery (sub-meter) derived from WorldView, QuickBird and GeoEye in conjunction with declassified Corona images were involved in the analyses. The comparison of very high-resolution images acquired in 2003/2004 and 2016/2017 indicates a pronounced increase in the extent of tundra and a slight increase of land covered by water. The number of water bodies increased in all three regions, especially in discontinuous permafrost, where 14.86% of new lakes and ponds were initiated between 2003 and 2017. The analysis of the evolution of two river channels in Yamal and Urengoy indicates the dominance of erosion during the last two decades. An increase of both rivers' lengths and a significant widening of the river channels were also observed. The number and total surface of RTS in the Yamal Peninsula strongly increased between 2004 and 2016. A mean annual headwall retreat rate of 1.86 m/year was calculated. Extensive networks of infrastructure occurred in the Yamal Peninsula in the last two decades, stimulating the initiation of new thermokarst features. The significant warming and seasonal variations of the hydrologic cycle, in particular, increased snow water equivalent acted in favor of deepening of the active layer; thus, an increasing number of thermokarst lake formations.

In this study we assess the total storage, landscape distribution, and vertical partitioning of soil organic carbon (SOC) stocks on the Brogger Peninsula, Svalbard. This type of high Arctic area is underrepresented in SOC databases for the northern permafrost region. Physico-chemical, elemental, and radiocarbon (C-14) dating analyses were carried out on thirty-two soil profiles. Results were upscaled using both a land cover classification (LCC) and a landform classification (LFC). Both LCC and LFC approaches provide weighted mean SOC 0-100 cm estimates for the study area of 1.0 +/- 0.3 kg C m(-2) (95% confidence interval) and indicate that about 68 percent of the total SOC storage occurs in the upper 30 cm of the soil, and about 10 percent occurs in the surface organic layer. Furthermore, LCC and LFC upscaling approaches provide similar spatial SOC allocation estimates and emphasize the dominant role of vegetated area (4.2 +/- 1.6 kg C m(-2)) and solifluction slopes (6.7 +/- 3.6 kg C m(-2)) in SOC 0-100 cm storage. LCC and LFC approaches report different and complementary information on the dominant processes controlling the spatial and vertical distribution of SOC in the landscape. There is no evidence for any significant SOC storage in the permafrost layer. We hypothesize, therefore, that the Brogger Peninsula and similar areas of the high Arctic will become net carbon sinks, providing negative feedback on global warming in the future. The surface area that will have vegetation cover and incipient soil development will expand, whereas only small amounts of organic matter will experience increased decomposition due to active-layer deepening.