Natural hazard processes, as an inherent component of mountain environments, react sensitively to global warming. The main drivers of these changes are alterations in the amount, intensity or type of precipitation, glacier melting, or thawing of permafrost ice. The hazard responses can involve a change in hazard intensity or frequency (increasing or decreasing), a shift in their location or, a shift from one type of hazard to another. As climate change impacts vary in space and time, this variability must be considered when planning measures to protect populations and infrastructure from hazardous processes. To support this, we developed a method for assessing the climate sensitivity of small individual rock releases and larger rockfall processes. The method is based on a fuzzy logic approach and uses highly resolved climate scenario data, allowing application on a regional or even larger scale. The application in a study area of 700 km2 in the central Valais (Switzerland) shows that the impacts of climate change on natural hazard processes can vary quite substantially across small spatial scales. Generally, an increase in rockfall frequency and magnitude is simulated under future warming scenarios, especially at higher altitudes. However, at lower elevations and on south-exposed slopes, a decrease in freeze-thaw cycles leads to a decrease in material availability. This knowledge is essential in discussions of how climate change should be considered in hazard and disaster management.

The Kaxgar River Basin, a key of the Tarim River Basin, is a typical ecologically fragile region that has undergone rapid changes to its spatial patterns over the preceding few decades. In particular, the expansion of salinized land has posed a severe threat to ecological restoration and economic development. This study monitored the rates and patterns of land use and land cover (LULC) changes in the plain area of Aketao County in the middle reaches of the Kaxgar River Basin. Five Landsat images (captured in 1990, 1998, 2002, 2013, and 2018) were divided into seven LULC types: built-up land, cultivated land, woodland and grassland, light-moderate salinized land, heavy salinized land, water areas, and bare land. Subsequently, their dynamic processes were analyzed. The results revealed that in 1990, the dominant LULCs were cultivated land, woodland and grassland, and bare land. Throughout the study period (from 1990 to 2018), the coverage of built-up land, cultivated land, bare land, water areas, and light-moderate salinized land increased; by contrast, that of the other LULC types decreased. The most marked LULC changes were the expansion of light-moderate salinized land (by 6.2% of the study area) and the shrinkage of woodland and grassland (by 9.4% of the study area). Almost all the analyzed LULC types underwent conversion to other types; such conversion occurred most frequently between 1998 and 2018. The conversions of woodland and grassland into cultivated land and light-moderate salinized land were the most notable phenomena. Another highly evident change was the conversion of heavy salinized land into bare land. These results revealed that the expansion of salinized land and the shrinkage of woodland and grassland in the study area were the most severe environmental changes. Therefore, ecological protection and salinization control are urgently required to enable local economic development while not exceeding the environmental carrying capacity and ensuring the safety of the green corridor in the lower reaches of the Kaxgar River Basin.

Arctic zone of the Russian Federation (AZRF) is the region of intensive economic development. In this regard, it is critical to give an adequate assessment of natural factors that may have a negative impact on the growing technological infrastructure. Rapid climate change effects show a significant influence on this activity, including the railway network development. Hence, the decision-making community requires relevant information on climatic variations that can put at hazard the construction and operation of railway facilities. This paper presents the analysis of climatic changes within the region of Central and Western Russian Arctic in 1980-2021. It was performed using the new electronic Atlas of climatic variations in main hydrometeorological parameters, created for the Russian Railways in 2023. This geoinformatic product includes about 400 digital maps reflecting the variability of seven climatic parameters over more than four decades within the studied region. These parameters are air temperature, total precipitation, wind speed, soil temperature, soil moisture content, air humidity, and snow cover thickness. The analysis of climatic maps and their comparison between selected periods showed spatial and temporal heterogeneity of climatic variations in this region. This justifies the feasibility of further research using additional analytical instruments, such as Hovm & ouml;ller diagrams, time series graphs, etc. The implementation of advanced geoinformatic products in the practice of the Russian Railways will facilitate sustainable development of its infrastructure in rapidly altering climatic conditions.

For remote communities in the discontinuous permafrost zone, access to permafrost distribution maps for hazard assessment is limited and more general products are often inadequate for use in local-scale planning. In this study we apply established analytical methods to illustrate a time- and cost-efficient method for conducting community-scale permafrost mapping in the community of Whati, Northwest Territories, Canada. We ran a binary logistic regression (BLR) using a combination of field data, digital surface model-derived variables, and remotely sensed products. Independent variables included vegetation, topographic position index, and elevation bands. The dependent variable was sourced from 139 physical checks of near-surface permafrost presence/absence sampled across the variable boreal-wetland environment. Vegetation is the strongest predictor of near-surface permafrost in the regression. The regression predicts that 50.0% (minimum confidence: 36%) of the vegetated area is underlain by near-surface permafrost with a spatial accuracy of 72.8%. Analysis of data recorded across various burnt and not-burnt environments indicated that recent burn scenarios have significantly influenced the distribution of near-surface permafrost in the community. A spatial burn analysis predicted up to an 18.3% reduction in near-surface permafrost coverage, in a maximum burn scenario without factoring in the influence of climate change. The study highlights the potential that in an ecosystem with virtually homogeneous air temperature, ecosystem structure and disturbance history drive short-term changes in permafrost distribution and evolution. Thus, at the community level these factors should be considered as seriously as changes to air temperature as climate changes.

Development of carbon polygons for monitoring the emission and deposition of carbon compounds in terrestrial ecosystems is one of the priority tasks in the case of climate and biosphere conservation. Significant is the role of soils, which are not only the main source of greenhouse gas emissions into the Earth's atmosphere but also a long-term reservoir that stores significant amounts of organic carbon in the form of soil humus. The article discusses the organization of monitoring of greenhouse gases at carbon polygons, the methods of sampling soil horizons, and methodological approaches to determine the content and stocks of organic carbon in soils. The importance of information on the qualitative and quantitative composition of soil organic matter and humic substances, which is necessary for the operation of modern simulation models and calculation of carbon units for the economic assessment of the direct and reverse carbon footprint have been revealed. Russia faces a number of challenges related to carbon offset and a low-carbon economy. The necessary volumes of monitoring data, which must be obtained at carbon polygons for the use of the ROMUL and Efimod models are considered. The necessity for an adequate spatial coverage of the territory of Russia with a network of carbon polygons is emphasized. Particular attention should be paid to the arctic territories that contain significant amounts of organic matter in permafrost and can become precursors of the formation and emission of significant amounts of carbon dioxide and methane into the atmosphere.

The most dramatic permafrost degradation is expected to occur at the southern edge of permafrost distribution, which is difficult to detect directly on a large scale. Ecological indicators can be used to provide an early signal of changes in terrestrial ecosystems for regional near-surface permafrost habitats and potentially to monitor near-surface permafrost degradation. In this study, plant composition and community structure indicate the near-surface permafrost distribution at the southern edge of the boreal forest and permafrost in northeastern China. The plant species composition and structure of aboveground vegetation were linked to the belowground near-surface permafrost distribution in order to find indicators of changes in vegetation features from permafrost melting. These indicators are essential for assessing changes in permafrost vegetation systems under climate change. Carex schmidtii and C. appendiculata in the herb layer and Benda fruticosa in the shrub layer were found to be specific near-surface permafrost plant indicator species, especially for the wetland permafrost. Shrub cover, moss mat thickness and tree canopy cover are also strongly correlated with near-surface permafrost distribution. The active layer thickness (ALT) showed negative correlations with moss thickness and shrub cover because these features may act as buffers for regional climate warming. We chose the cover of each indicator species, near-surface permafrost-specific community features and geographical information as independent variables to predict the possible distribution of near-surface permafrost in our study region using logistic regression. The results showed that the prediction model had good performance and accuracy. Our study sheds light on early caution of deepening of regional-scale permafrost active layer with vegetation indicators that can further be identified from satellite images.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.

Previously-frozen stores of organic carbon (C) are now subject to decomposition due to a warming Arctic climate and associated permafrost thaw; however, estimates of the amount of greenhouse gases (GHG) that may be released are not well constrained. Knowing more about the functions of the extant permafrost microbial community will inform this knowledge gap. The exploration of microbial functional traits may be useful to elucidate the relationship between microbial diversity and ecosystem function. We characterized the community traits and functional diversity of the bacterial and Archaeal component of the microbial community from three depths of permafrost, as well as the organic and mineral horizons of the seasonally-thawed active layer, by assessing 'substrate-use richness,' substrate preference,' 'growth rate,' and substrate specific growth rate.' We measured the microbial community response to 31 substrates with an EcoPlate (Biolog, Inc.) assay at three incubation temperatures (1, 10, and 20 degrees C) using a kinetic approach, and modeled the microbial response to each substrate with a modified logistic growth function. We hypothesized that the permafrost communities would be selected for high functional potential and activity at cold temperatures. Rather, we found that the permafrost community did not have a higher functional diversity or activity at 1 degrees C than the organic active layer soils. In addition, permafrost communities increased their growth rates with increasing temperature, indicating that the highest incubation temperature (20 degrees C) was below their temperature optimum for growth. As predicted, the permafrost communities did exhibit temperature dependent substrate preferences. Thus, permafrost microbial communities did not appear to be selected for higher metabolism and the ability to use a broad suite of substrates at low temperatures, which suggests that they may have limited function immediately following thaw when temperatures are near 0 degrees C. However, changes in community composition or additional permafrost warming will increase the functional capabilities of permafrost microbes to decompose the C stored in those soils. (C) 2015 Elsevier Ltd. All rights reserved.

High-latitude ecosystems where the mean annual ground surface temperature is around or below 0 degreesC are highly sensitive to global warming. This is largely because these regions contain vast areas of permafrost, which begins to thaw when the mean annual temperature rises above freezing. The Geophysical Institute Permafrost Lab has developed a new interactive geographical information systems (GIS) model to estimate the long-term response of permafrost to changes in climate. An analytical approach is used for calculating both active layer thickness (ALT) and mean annual ground temperatures (MAGTs). When applied to long-term (decadal or longer time scale) averages, this approach shows an accuracy of +/-0.2-0.4degreesC for MAGTs and +/-0.1-0.3 in for ALT calculations. The relative errors do not exceed 32% for ALT calculations, but typically they are between 10 and 25%. A spatial statistical analysis of the data from 32 sites in Siberia indicated a confidence level of 75% to have a deviation between measured and calculated MAGTs of 0.2-0.4degreesC. A detailed analysis has been performed for two regional transects in Alaska and eastern Siberia that has validated the use of the model. The results obtained from this analysis show that a more economical (in terms of computational time) analytical approach could be successfully used instead of a full-scale numerical model in the regional and global scale analysis of permafrost spatial and temporal dynamics. This project has been a successful contribution to the Arctic Climate Impact Assessment project. Copyright (C) 2003 John Wiley Sons, Ltd.