Permafrost Distribution Map Research Articles

Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal

Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.

Response of alpine ground temperatures to a rising atmospheric 0 °C isotherm in the period 1955–2021

Ground temperatures in alpine terrain vary considerably over short distances, particularly due to differences in elevation and incoming short-wave radiation but also snow and surface conditions. To allow for direct cross-site, interregional, and cross-disciplinary comparisons, we introduce a parameter called the potential 0 °C isotherm (PZDI) in the ground. This parameter represents an aspect- and elevation independent geo-projection of ground temperatures. It was calculated for several ground temperature datasets from the (sub-)permafrost zone in the European Alps. We analyse the reaction of the PZDI at different depths to long-term changes in atmospheric temperature, represented by the atmospheric 0 °C isotherm (AZDI). The close correspondence of PZDI and AZDI mainly provided two new insights: Based on 15 boreholes, the PZDI/AZDI interaction allowed a depth-dependent analysis of the magnitude and temporal evolution of the disequilibrium between atmosphere and ground and the potential heat transfer processes involved. Moreover, it allowed a reconstruction of the development of ground temperatures in the European Alps during the second half of the 20th century, adding 50 years to the longest existing time series measured in ice-poor permafrost. This extension into the past reveals a major warming at the end of the 1980s, which occurred before the start of the available permafrost temperature observations. In the 1955–2021 period, the 5-year running mean of the AZDI rose by 400 m, while the PZDI at 15 m depth rose by about 300 m. Projecting this warming into a permafrost distribution map shows a decline in shallow, ice-poor permafrost area by roughly 60 % since the 1980s. We expect the PZDI to follow the AZDI and to increase by another 100 m in the coming decade and up to at least 14 m depth. This would increase the area with loss of shallow permafrost to approximately 80 % since the 1980s.

A Framework to Identify Rock Glaciers and Model Mountain Permafrost in the Jhelum Basin, Kashmir Himalaya, India

AbstractDeglaciation has led to the transformation of glaciers into rock glaciers in various mountainous regions worldwide. However, the science of permafrost and rock glaciers remains under‐researched in the Himalayan region. This study presents a detailed inventory, dynamics, and permafrost distribution map for the Jhelum basin in the Kashmir Himalaya. The Permafrost Zonation Map (PZM) is created using a Logistic Regression Model based on topographic and climatic variables. High‐resolution satellite images are used to identify rock glaciers as visual indicators. Active and relic rock glaciers are classified based on surface topography and geomorphology. Results reveal 207 rock glaciers in the Jhelum basin, covering ∼50 km2, with over 100 falling into the active category. The PZM aligns well with the global permafrost zonation index map. Slope, aspect, and elevation of rock glaciers are computed using the ASTER Digital Elevation Model. The average elevations range from 3,700 m to 4,550 m, and the average surface slope ranges from 12° to 26°, with maximum slopes from 25° to 65°. Most rock glaciers are oriented toward the south or southeast. Field investigations confirm that these rock glaciers occur in highly elevated regions with steep slopes. This study provides valuable information on the high areal abundance of permafrost in the Himalayan region and suggests increased risks of thawing permafrost due to climate warming in the future. The findings contribute to the understanding of permafrost and rock glaciers, filling knowledge gaps in the Himalayan context.

Impacts of Land Use/Land Cover Distributions on Permafrost Simulations on Tibetan Plateau

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.

A New Method for Bare Permafrost Extraction on the Tibetan Plateau by Integrating Machine Learning and Multi-Source Information

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

Simulation of Spatiotemporal Distribution and Variation of 30 m Resolution Permafrost in Northeast China from 2003 to 2021

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index η with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index η. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003–2007, 2008–2012, 2013–2017, and 2018–2021, and the permafrost area is, respectively, 28.35 × 104 km2, 35.14 × 104 km2, 28.96 × 104 km2, and 25.21 × 104 km2. The proportion of extremely stable permafrost (<−5.0 °C), stable permafrost (−3.0~−5.0 °C), sub-stable permafrost (−1.5~−3.0 °C), transitional permafrost (−0.5~−1.5 °C), and unstable permafrost (0~−0.5 °C) is 0.50–1.27%, 6.77–12.45%, 29.08–33.94%, 34.52–39.50%, and 19.87–26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of ±1 °C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

Biennial analysis of probable permafrost distribution for Kullu district, North‐west Himalaya using Landsat 8 satellite data

AbstractClimate change and increased anthropogenic activity affect permafrost distribution, threatening ecosystem stability, water resources, and greenhouse gas emissions. In this study, we present updated probable permafrost maps at 30 m resolution for Kullu district of Himachal Pradesh derived from the predicted mean annual air temperature (MAAT) (2013 to 2020) based on Landsat 8 land surface temperature (LST). We also analysed the mean annual snow cover extent of the study area for this time period. Our probable permafrost distribution map shows the areas falling under permafrost regions in snow‐covered areas; permafrost regions in non‐snow‐covered areas; non permafrost regions along with glacierised regions. The temporal variation of permafrost over 7 years from 2013 to 2020 shows that permafrost distribution follows a cyclic variation ranging between 4.42% and 7.51% of the total area of the district. The permafrost maps were validated with the presence of rock glaciers as seen on high resolution satellite images and the ground photographs. The maps display the expected spatial pattern of near‐surface air temperatures that match with the known permafrost boundary. The findings of this study provide more precise information on permafrost distribution and essential data for future permafrost research in Kullu district.

The Zonation of Mountain Frozen Ground under Aspect Adjustment Revealed by Ground-Penetrating Radar Survey—A Case Study of a Small Catchment in the Upper Reaches of the Yellow River, Northeastern Qinghai–Tibet Plateau

Permafrost distribution is of great significance for the study of climate, ecology, hydrology, and infrastructure construction in high-cold mountain regions with complex topography. Therefore, updated high-resolution permafrost distribution mapping is necessary and highly demanded in related fields. This case study conducted in a small catchment in the northeast of the Qinghai Tibet Plateau proposes a new method of using ground-penetrating radar (GPR) to detect the stratigraphic structure, interpret the characteristics of frozen ground, and extract the boundaries of permafrost patches in mountain areas. Thus, an empirical–statistical model of mountain frozen ground zonation, along with aspect (ASP) adjustment, is established based on the results of the GPR data interpretation. The spatial mapping of the frozen ground based on this model is compared with a field survey dataset and two existing permafrost distribution maps, and their consistencies are all higher than 80. In addition, the new map provides more details on the distribution of frozen ground. In this case, the influence of ASP on the distribution of permafrost in mountain areas is revealed: the adjustment of ASP on the lower limit of continuous and discontinuous permafrost is 180–200 m, the difference in the annual mean ground temperature between sunny and shady slopes is up to 1.4–1.6 °C, and the altitude-related temperature variation and uneven distribution of solar radiation in different ASPs comprehensively affect the zonation of mountain frozen ground. This work supplements the traditional theory of mountain permafrost zonation, the results of which are of value to relevant scientific studies and instructive to engineering construction in this region.

Development of a three-dimensional geological model, based on Quaternary chronology, geological mapping, and geophysical investigation, of a watershed in the discontinuous permafrost zone near Umiujaq (Nunavik, Canada)

Among the few positive impacts of climate warming in cold regions, permafrost degradation can increase the availability of groundwater as a potential source of drinking water for northern communities. Near the Inuit community of Umiujaq in Nunavik, Canada, a watershed in a valley in the discontinuous permafrost zone was instrumented to monitor the impacts of climate change on permafrost and groundwater, and assess the groundwater availability and quality. Based on Quaternary chronology, knowledge of periglacial processes, and an investigation carried out in the valley (including mapping of Quaternary deposits and ice-rich permafrost distribution, drilling and sampling of deposits, and geophysical surveys), a three-dimensional (3D) geological model of the watershed was built into GoCAD to assess the hydrogeological context in this degrading permafrost environment. In total, six units were identified within the watershed including an upper aquifer in marine sediments, a lower aquifer at depth in glaciofluvial and glacial sediments, and the bedrock acting as a low-permeability boundary. An aquitard, made of frost-susceptible silty sand and discontinuously invaded by ice-rich permafrost, confines the lower aquifer. This 3D geological model clarifies the local stratigraphic architecture and geometries of Quaternary deposits, especially the stratigraphic relationship between the two aquifers, aquitard, and bedrock, and the extent of ice-rich permafrost within the watershed. It is the cornerstone to understand the groundwater dynamics within the watershed and to carry out numerical modelling of coupled groundwater flow and heat transfer processes to predict the impacts of climate change on groundwater resources in this degrading permafrost environment.

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research.

A new map of permafrost distribution on the Tibetan Plateau

Abstract. The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs) and validated this map using various ground-based data sets. The soil thermal properties of five soil types across the TP were estimated according to an empirical equation and soil properties (moisture content and bulk density). The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution. Permafrost, seasonally frozen ground, and unfrozen ground covered areas of 1.06 × 106 km2 (0.97–1.15 × 106 km2, 90 % confidence interval) (40 %), 1.46 × 106 (56 %), and 0.03 × 106 km2 (1 %), respectively, excluding glaciers and lakes. Ground-based observations of the permafrost distribution across the five investigated regions (IRs, located in the transition zones of the permafrost and seasonally frozen ground) and three highway transects (across the entire permafrost regions from north to south) were used to validate the model. Validation results showed that the kappa coefficient varied from 0.38 to 0.78 with a mean of 0.57 for the five IRs and 0.62 to 0.74 with a mean of 0.68 within the three transects. Compared with earlier studies, the TTOP modelling results show greater accuracy. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

Study on the Permafrost Distribution Based on RS/GIS

Permafrost in Qinghai-Tibet Plateau is the world’s highest elevation and largest permafrost regions in the lower latitude. For its vast area and less measured data, traditional mapping methods rely on the observations of station have been largely restricted. So it is necessary to build permafrost distribution model. Based on the GIS, using the RS data as the main data source and considering the distribution characteristics of permafrost in Qinghai-Tibet Plateau, we build Multivariate analysis model. Considered the elevation, land-surface temperature, vegetation, soil moisture and surface albedo and other factors related to the permafrost distribution in Tibet Plateau, and selected the Qinghai Tibet highway as the study area to simulate the permafrost distribution. Compared with the Retrieval Model of Ground Temperature based on RS/GIS, elevation model and frost number model, it is found that simulation result of Multivariate analysis model fit best with the permafrost distribution map and it reflected the permafrost distribution well along the Qinghai-Tibet highway.

Data-driven mapping of the potential mountain permafrost distribution

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping.

Effects of local factors and climate on permafrost conditions and distribution in Beiluhe basin, Qinghai-Tibet Plateau, China.

Beiluhe basin is underlain by warm and ice-rich permafrost, and covered by vegetation and soils characteristic of the Qinghai-Tibet Plateau. A field monitoring network was established to investigate permafrost conditions and to assess potential impacts of local factors and climate change. This paper describes the spatial variations in permafrost conditions from instrumented boreholes, controlling environmental factors, and recent thermal evolution of permafrost in the basin. The study area was divided into 10 ecotypes using satellite imagery based classification. The field investigations and cluster analysis of ground temperatures indicated that permafrost underlies most of the ground in swamp meadow, undisturbed alpine meadow, degrading alpine meadow, and desert alpine grassland, but is absent in other cover types. Permafrost-ecotope relations examined over a 2-year (2014-2016) period indicated that: (i) ground surface temperatures varied largely among ecotopes; (ii) annual mean ground temperatures ranged from -1.5 to 0°C in permafrost, indicating sensitive permafrost conditions; (iii) active-layer thicknesses ranged from 1.4m to 3.4m; (iv) ground ice content at the top of permafrost is high, but the active-layer soil is relatively dry. Long-term climate warming has driven thermal changes to permafrost, but ground surface characteristics and soil moisture content strongly influence the ground thermal state. These factors control local-scale spatial variations in permafrost conditions. The warm permafrost in the basin is commonly in thermal disequilibrium, and is sensitive to future climate change. Active-layer thicknesses have increased by at least 42cm and the mean annual ground temperatures have increased by up to 0.2°C in the past 10years over the basin. A permafrost distribution map was produced based on ecotypes, suggesting that permafrost underlies 64% of the study region.

Permafrost distribution map of San Juan Dry Andes (Argentina) based on rock glacier sites

Rock glaciers are frozen water reservoirs in mountainous areas. Water resources are important for the local populations and economies. The presence of rock glaciers is commonly used as a direct indicator of mountain permafrost conditions. Over 500 active rock glaciers have been identified, showing that elevations between 3500 and 4500 m asl., a south-facing or east-facing aspect, areas with relatively low solar radiation and low mean annual air temperature (−4 to 0 °C) favour the existence of rock glaciers in this region. The permafrost probability model, for Dry Andes of San Juan Province between latitudes 28º30′S and 32°30′S, have been analyzed by logistic regression models based on the active rock glaciers occurrence in relation to some topoclimatic variables such as altitude, aspect, mean annual temperature, mean annual precipitation and solar radiation, using optical remote sensing techniques in a GIS environment. The predictive performances of the model have been estimated by known rock glaciers locations and by the area under the receiver operating characteristic curve (AUROC). This regional permafrost map can be applied by the Argentinean Government for their recent initiatives which include creating inventories, monitoring and studying ice masses along the Argentinean Andes. Further, this generated map provides valuable input data for permafrost scenarios and contributes to a better understanding of our geosystem.

Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map near-surface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of ~ 85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies. • Novel-baseline assessment of permafrost properties in Alaska • Mapping permafrost in Alaska using Landsat and empirical modeling • Approximately 85% overall accuracy for near-surface permafrost distribution • Projected 16 to 24% loss of frozen ground in Alaska during 21st Century • Spatially explicit estimates of uncertainty and probability

Noah Modelling of the Permafrost Distribution and Characteristics in the West Kunlun Area, Qinghai‐Tibet Plateau, China

AbstractThe Noah land surface model (LSM) can simulate well the hydrological and thermal processes of permafrost in the Qinghai‐Tibet Plateau (QTP) and provides more permafrost metrics than those of statistical empirical models. The aim of this study was to develop a prototype for permafrost modelling by Noah and validate the model with field data. This was accomplished by modifying Noah, introducing a new thermal roughness scheme, a parameter calibration method and extending the simulation depth to allow for soil heterogeneity. The modified Noah LSM was validated using observations from the Tanggula meteorological station. Key permafrost metrics were simulated, including mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA), active layer thickness (ALT) and ground ice content of the West Kunlun area in the QTP. The permafrost distribution of the West Kunlun was mapped using the simulated MAGT and compared to a permafrost distribution map based on field observations. Data from ten boreholes were used for verification. The simulation error of the MAGT is less than 1.0 °C for eight boreholes, and the ALT simulations have relative errors of less than 25 per cent for seven boreholes. The Kappa coefficient for the two maps is 0.70. Permafrost characteristics including the distribution of different permafrost types, DZAA, ALT, MAGT and ground ice content in the West Kunlun are strongly influenced by altitude and the local environment. Such permafrost modelling can be extended to the rest of the QTP. Copyright © 2015 John Wiley & Sons, Ltd.

Remote sensing and field‐based mapping of permafrost distribution along the Alaska Highway corridor, interior Alaska

AbstractWe employed an integrated approach that combined remote sensing techniques with field measurements to predict the presence/absence of near‐surface permafrost in a section of the Alaska Highway corridor. We investigated the correlative relationships among vegetation type, topography, moss thickness, tussock condition and near‐surface permafrost in the study area. Analysis of moss thickness and active‐layer depth in low‐lying plains (slope <8°) showed an inverse relationship in different vegetation classes. The maximum likelihood classification of remotely sensed data mapped 80% of the study area as covered with vegetation. We developed an empirical‐statistical (Binary Logistic Regression) model to establish the statistical relationship between surface variables and permafrost. The statistical relationships between the surface variables (vegetation, aspect and elevation classes) and permafrost were used within a spatial model to estimate the probability of permafrost presence at any given location (or pixel) in the study area. The statistical model yielded an overall classification accuracy of 80%. The spatial model mapped 62% of the total vegetated area as underlain by permafrost, including 99.5% of the scattered short spruce, 92.5% of the dense tall spruce, and 35.8% of the mixed spruce and deciduous vegetation classes. Comparison of our probabilistic permafrost map with an independently produced photo‐interpreted permafrost map showed 75% agreement for the continuously frozen class and very close agreement for the discontinuously frozen class. Copyright © 2010 John Wiley & Sons, Ltd.

Using ground-penetrating radar to detect permafrost degradation in the northern limit of permafrost on the Tibetan Plateau

We used 50-MHz ground-penetrating radar to detect the boundary of permafrost in Xidatan region, the northern limit of the marginal and sporadic permafrost area on the Tibetan Plateau, China. We interpreted the lower limit of permafrost from nine radar profiles to determine the boundary between the permafrost and non-permafrost region. We also sketched the locations of permafrost table along the nine profiles and acquired the lower alpine limit of permafrost in this area. Based on the lower alpine limit of permafrost determined in this field survey, a new permafrost distribution map was drawn with the help of GIS analyses. Comparing with the research work in 1975, permafrost in Xidatan is reduced 12% in area and the lower alpine limit has increased by 25 m. The situation of global warming, apparent climatic warming recorded at two stations near Xidatan, could be responsible for permafrost degradation in this area.

Changes in soil temperature and active layer thickness during the twentieth century in a region in western Canada

Increases in active layer thickness and permafrost degradation induced by climate warming may have profound socio‐economic and eco‐environmental consequences. Using a process‐based model of Northern Ecosystem Soil Temperature (NEST) and data from climate records, remote sensing vegetation parameters, and soil features, we simulated soil temperature and active layer thickness (ALT) for a region in western Canada during the twentieth century. The results showed that the region‐averaged annual mean soil temperatures at different depths responded to air temperature forcing consistently during the twentieth century, but with reduced magnitudes in both long‐term trend and interannual variation. From the 1900s to 1986–1995, ALT increased 124 cm (or 79%) in the isolated and sporadic discontinuous permafrost zones, 59 cm (or 37%) in the extensive discontinuous permafrost zone, and 20 cm (or 21%) in the continuous permafrost zone on the basis of the current permafrost distribution map. The simulated results also indicated the disappearance of 17% of the permafrost in the discontinuous permafrost zone from the 1900s to the 1940s, and another 22% from the 1940s to 1986–1995. General agreement was found when comparing the simulated results with soil temperature records at climate stations, ALT at survey sites, and rates of permafrost degradation interpreted from aerial photographs. Owing to differences in spatial scales, spatial coverage, and time periods, many of the comparisons were not strict 1‐to‐1 comparisons and should instead be viewed as indirect supporting evidence.