In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing'an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

As a vital freshwater resource for one-sixth of the world's population, snowmelt provides great convenience for residents in terms of livelihood and production, agricultural irrigation, and hydroelectric power generation. However, snowmelt can also have an important impact on the formation of surface runoff and the process of soil erosion. In contrast to glacier melt, snowmelt erosion has received relatively little attention in the past. This paper reviewed the generation of snowmelt runoff, the characteristics of erosion and sediment yield during snowmelt, the snowmelt erosion mechanism, and the applications of snowmelt modeling. The published results of sediment yield driven from snowmelt runoff ranged from 1 to 300 t km-2 a-1, with the largest value of 1114 t km-2 a-1. Snowmelt erosion is extremely sensitive to warming climate. With global warming, there is a trend towards earlier snowmelt periods and a significant increase in runoff volume, as well as a significant increase in sediment yield from snowmelt in most of the study cases. Moreover, snowmelt erosion compared to rainfall erosion has more complex mechanistic processes which can be influenced by various factors such as snowfall, freeze-thaw, topography, etc. In particular, the occurrence of rain-on-snow events will lead to more severe soil erosion. In addition, current studies of sediment yield from snowmelt erosion account for a small percentage of snowmelt, and snowmelt erosion modeling is rarely applied in practical studies. In future research, the field monitoring of snowmelt erosion in the context of climate change needs to be further strengthened and the effects of multiple factors on snowmelt erosion need to be investigated. The inclusion of rain-on-snow and specific erosion types in the model will improve the applicability of models under climate change scenarios and in multiscale environments. This paper is intended to show the achievements as well as the limitations of snowmelt erosion research, while suggesting future research directions that need to be further explored and developed for better understanding and forecasting of snowmelt erosion.

Surface freezing and thawing processes pose significant influences on surface water and energy balances, which, in turn, affect vegetation growth, soil moisture, carbon cycling, and terrestrial ecosystems. At present, the changes in surface freezing and thawing states are hotspots of ecological research, but the variations of surface frozen days (SFDs) are less studied, especially in the permafrost areas covered with boreal forest, and the influence of the environmental factors on the SFDs is not clear. Utilizing the Advanced Microwave Scanning Radiometer for EOS (AMSRE) and Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data, this study applies the Freeze-Thaw Discriminant Function Algorithm (DFA) to explore the spatiotemporal variability features of SFDs in the Northeast China Permafrost Zone (NCPZ) and the relationship between the permafrost distribution and the spatial variability characteristics of SFDs; additionally, the Optimal Parameters-based Geographical Detector is employed to determine the factors that affect SFDs. The results showed that the SFDs in the NCPZ decreased with a rate of -0.43 d/a from 2002 to 2021 and significantly decreased on the eastern and western slopes of the Greater Khingan Mountains. Meanwhile, the degree of spatial fluctuation of SFDs increased gradually with a decreasing continuity of permafrost. Snow cover and air temperature were the two most important factors influencing SFD variability in the NCPZ, accounting for 83.9% and 74.8% of the spatial variation, respectively, and SFDs increased gradually with increasing snow cover and decreasing air temperature. The strongest explanatory power of SFD spatial variability was found to be the combination of air temperature and precipitation, which had a coefficient of 94.2%. Moreover, the combination of any two environmental factors increased this power. The findings of this study can be used to design ecological environmental conservation and engineer construction policies in high-latitude permafrost zones with forest cover.

The lakes on the Qinghai-Tibetan Plateau have undergone substantial changes. As intensive cryospheric components change, the response of the lake dynamics to climatic factors, glacier-snow melting, and permafrost thawing has been complex. Based on Landsat images, meteorological data, and glacier and permafrost data, the spatial-temporal changes in the lake area on the northeastern Tibetan Plateau between 1988 and 2019 were analyzed and the driving factors behind the lake changes were further explored. The results suggest that the regional lake area increased from 1988 to 2019 at rates of 0.01-16.03 km(2)/yr. It decreased during 1988-2000, quickly increased during 2000-2012, and rapidly increased during 2012-2019. The most significant lake expansion occurred in sub-region I, which is the source region of the Yangtze River Basin. There was a sharper increase during 2012-2019 than during 2000-2012 in sub-region II (the source region of the Yellow River Basin and the Qinghai Lake Basin) and sub-region III (the Qaidam Basin). The significant lake expansion occurred about 12 years earlier in sub-region I than in sub-regions II and III. This dramatic change in the lake area was closely associated with the annual precipitation, and precipitation was the primary driving factor. Although serious glacier retreat occurred, most of the lakes in the sub-regions were non-glacier-fed lakes. The correlation between glacier ablation and the change in the lake area was poor, which suggests that glacial meltwater was not the replenishment source of most of the lakes in this region. A more accelerated increase in the active layer thickness occurred (1.90 cm/yr), which was consistent with the more rapid lake expansion, and the permafrost degradation further intensified the lake expansion.

Study region: The source region of the Yellow River, China (SAYR) Study focus: This study focuses on demonstrating the impact of seasonal freeze-thaw process on the seasonally arsenic (As) and lead (Pb) concentration in the water bodies, such as river, lake, and spring. 113 surface water samples in April (freeze permafrost), 164 in July (active layer in permafrost thawed), and 86 soil samples at various depths in July were collected. Statistical correlation and principle analysis were applied to find the connection between tracer metals in water bodies and the various environmental factors. The percentage of soil particle size (5-50 mu m), which can reflect the intensity of the freeze-thaw process in the permafrost soil, influenced the soil and water As and Pb trace metal concentrations differently in the permafrost area. New hydrological insights for the region: In April, the average As concentrations were 23.4 +/- 16.7, 39.4 +/- 32.6, 26.5 +/- 24.4 mu g/L, respectively in river, lake and spring water samples, and Pb concentrations were 34.9 +/- 27.1, 47.4 +/- 38 and 48.9 +/- 33.4 mu g/L. While the As concentrations in waters in July all decreased by 2 or 3 times compared with those in April, Pb concentrations slightly increased. Permafrost thawing enhanced the weathering of As and Pb, but the high As adsorption on fine soil particles, resulting from the seasonal freeze-thaw cycles, leaded to the significant decrease in the water As concentration in July, in addition to the rainfall dilution. The slight increase in Pb water concentration in July suggesting the effects of enhanced weathering and dilution were equally important. The higher As and Pb concentrations around the large Gyaring and Ngoring lakes than other SAYR area, shaping the spatial distribution of As and Pb, might be due to evaporative enrichment and the high phosphate content in the lakes. Results are helpful in assessing the ecological impact of trace metals in the permafrost area with climate change.

The implementation of China's Beautiful Village Initiative was an extraordinary achievement and aroused extensive public attention. However, existing research mostly focuses on the construction and seldom on public attention towards the Beautiful Village Initiative. For this reason, this paper investigated the spatiotemporal characteristics of public attention based on the Baidu index using time-constrained clustering and the spatial autocorrelation test. Our results showed that the evolutionary process can be divided into three stages: very little national attention (2011-2012), injection of a strong impetus (2013-2015), and rooted in the people's minds (2016-2020). Spatially, provincial public attention demonstrated obvious spatial differentiation and stable spatial autocorrelation, with Low-Low clusters in Northwest China and High-High Clusters in East, Central, and North China. Spatial econometric models were further utilized to quantify the effects of socioeconomic factors on public attention. The results of the SEM model proved the existence of spatial spillover effects and indicated that the urbanization rate, population density, education level, and network popularity rate all positively affected public attention. The relationship between Beautiful Village construction and public attention was uncoordinated and, in most provinces, advances in public attention were ahead of the construction level. Our findings contribute to the understanding of public attention towards the Beautiful Village Initiative, and policy suggestions we proposed would be applied to increasing public awareness and participation.

Chinese skiing tourism is rapidly developing, but it will be potentially affected by snow resources and climate conditions. This study systematically analysed the spatial characteristics of snow resources, climatic conditions and ski resorts in China; revealed the potential impacts of climatic environment on current ski resorts by using Regional Climate Model (RegCM4.4); and proposed suggestions for climate resource assessment and spatial planning of ski resorts. The mean snow depth and snow cover days are more than 4.50 cm and 150 d between the years 2000 and 2014, respectively, in high-latitude areas, where fewer ski resorts are located. In the middle-low latitude areas, the mean snow depth and snow cover days are less than 3.00 cm and 100 d, respectively, with ski resorts being intensively distributed. In most of these areas, the pronounced climate warming exceeded 0.50 & DEG;C during the ski period of 2009-2015 relative to 1961-2015, and mean annual precipitation was less than 800 mm, with some even less than 400 mm. In the 2030s and 2050s, the regions with drastic temperature increases are mainly in the Qinghai-Tibetan Plateau, Northeast China, and the Yangtze River Basin. The upper and middle reaches of the Yangtze River are expected to experience reduced snowfalland temperature increases with an almost 1-1.6 & DEG;C, respectively. In this context, some ski resorts will face major threats because of shortened skiing durations and rising management costs. Skiing tourism should not pursue a one-sided growth rate in the number of ski resorts in areas with short snowpack periods. Instead, it should rely highly on snow resources, climatic conditions, geographical environment, population and economic basis, and rationalise its spatial layout to promote the industry towards sustainable development.