Permafrost Zonation Research Articles

Improving Permafrost Mapping in Southern Tibetan Plateau Using Machine Learning and Rock Glacier Inventory

ABSTRACTAs a key component of the cryosphere, permafrost is sensitive to climate change, but mapping permafrost, especially in the Tibetan Plateau, has been challenging due to the heterogeneous mountainous landscape and limited representativeness of ground observations. Using 155 compiled ground observations and more than 20,000 rock glacier records, we developed a machine learning model to map the distribution of permafrost and produce an improved permafrost zonation index (PZI) map. The model was applied by incorporating several control variables, including terrain (elevation and relief), soil (bulk density, clay, coarse fragments, sand, and silt), and temperature (MAAT, FDD, and TDDT) to estimate the PZI at a 1‐km resolution in the southern Tibetan Plateau. Excluding glaciers and lakes, the area of permafrost estimated by the new map is approximately 103.5 × 103 km2, accounting for 47.8% of the total area of the region. The result was assessed with various datasets and compared with existing permafrost maps and achieved higher accuracy compared with previous studies. The overall classification accuracy was 96.1% in high plain areas and 84.4% in mountain areas. The results demonstrated the substantial potential for improving mapping permafrost and understanding the periglacial environment with rock glacier inventories and machine learning, especially in complex terrain and climate.

Enhancing mountainous permafrost mapping by leveraging a rock glacier inventory in northeastern Tibetan Plateau

ABSTRACT Our understanding of permafrost distribution is still limited, particularly in mountainous areas where highly heterogeneous environments and a lack of reliable field data tend to prevail. The extensive distribution of rock glaciers in the Qilian Mountains, located in the northeastern Tibetan Plateau, offers the opportunity to develop a novel approach for permafrost mapping in mountainous regions. In this study, a total of 1,530 rock glacier records were combined with in situ data to drive machine learning models for estimating permafrost presence. Three machine learning algorithms were adopted, and their accuracies were assessed in both mountains and plains by comparing the mapped permafrost to reserved field data as well as other published permafrost datasets. Among the algorithms tested, the CatBoost model presented the highest accuracy, with an overall accuracy of 83.3%. The model was thus chosen to produce a 250-m resolution permafrost zonation index (PZI) map, which identified a total area of 73.1 × 103 km2 permafrost in the Qilian Mountains, accounting for 39.1% of the area. The map also presented higher accuracy than other published permafrost maps. This study demonstrated that rock glacier records coupled with gradient-boosting machine-learning algorithms can help improve permafrost mapping, especially in the most challenging mountainous permafrost areas.

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.

Towards the incorporation of hydrogeochemistry into the modelling of permafrost environments: a review of recent recommendations, considerations, and literature

This study is a meta-analysis of recent global research articles on hydrogeochemical modelling of permafrost regions to determine trends and consensus on research gaps and future research directions. The hydrogeochemical response of permafrost to climate change remains challenging to estimate and forecast despite evidence of large-scale impacts on freshwater and ecological cycles. We investigate the feasibility, need, and potential for hydrogeochemical modelling of permafrost landscapes by reviewing recommendations from previous modelling, review, and primer papers, including discussing ways to advance this type of modelling science. Key permafrost hydrogeochemical processes are discussed, including heat transfer and associated freeze–thaw regimes, biogeochemical processes and rates, and surface and subsurface flow. Modelling considerations (i.e., model dimension, scale, heterogeneity, and permafrost zonation) and model parameters are subsequently examined. Finally, limitations and additional considerations for advancing permafrost hydrogeochemical modelling efforts are reviewed. The findings of this review are summarized in recommendations, tables, and two schematics incorporating key considerations for future hydrogeochemical modelling initiatives in permafrost environments.

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

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.

Climatic assessment of circum-Arctic permafrost zonation over the last 122 kyr

Permafrost is an important climate factor of the cold-region environments, known to evolve over a long timescale. Although direct observations are required for the exact determination of presence, this is difficult especially in remote areas. An indirect assessment of permafrost zonation can help in estimating the probable extents and providing an understanding of its spatiotemporal variability under different climate. In this paper, we adopt a previously defined indirect assessment method using freezing and thawing indices, which was revised and evaluated against new observation-based dataset. The revised method was applied to time-series of the circum-Arctic environment for the past 122 kyr constructed from a Greenland ice core, present-day reanalysis climatology, and a glacial isostatic adjustment model, and subsequently, examined in terms of the aggregation, persistence, and retreat of permafrost. Permafrost zones over exposed land varied between 9.11 million km2 (interglacial) and 26.7 million km2 (glacial); the maximum extent of subsea and subglacial permafrost was 7.7 million km2 and 0.5 million km2, respectively. The climatic sensitivity of its areal extent was examined for the meridional temperature gradient in the polar amplification (increase by 0.2 million km2 [°C/10°]−1 when warming occurs) and the global mean annual temperature (decrease by 4.3 million km2°C−1).

Climate warming over 1961–2019 and impacts on permafrost zonation in Northeast China

Climate warming over 1961–2019 and impacts on permafrost zonation in Northeast China

Biophysical permafrost map indicates ecosystem processes dominate permafrost stability in the Northern Hemisphere

The stability of permafrost is of fundamental importance to socio-economic well-being and ecological services, involving broad impacts to hydrological cycling, global budgets of greenhouse gases and infrastructure safety. This study presents a biophysical permafrost zonation map that uses a rule-based geographic information system (GIS) model integrating global climate and ecological datasets to classify and map permafrost regions (totaling 19.76 × 106 km2, excluding glaciers and lakes) in the Northern Hemisphere into five types: climate-driven (CD) (19% of area), CD/ecosystem-modified (41%), CD/ecosystem protected (3%), ecosystem-driven (29%), and ecosystem-protected (8%). Overall, 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai–Tibet Plateau. This highlights the importance of reducing ecosystem disturbances (natural and human activity) to help slow permafrost degradation and lower the related risks from a warming climate.

Quantifying Overestimated Permafrost Extent Driven by Rock Glacier Inventory

AbstractRock glaciers (RGs) are normally used as “ground‐truth” observations to indicate the presence of permafrost, and hence extensively used in training permafrost distribution models. However, the unique structure and composition of RGs enhance ground cooling effects, leading to more favorable conditions for permafrost presence than in adjacent ground. We therefore hypothesized and confirmed that permafrost extent is overestimated using RG‐driven models. The results indicate that the permafrost zonation index was overestimated by about 8.4%–13.1% in a model driven by RG observations compared to a model using in situ measurements of permafrost presence/absence. The bias is particularly pronounced in discontinuous permafrost region, where it is thought to be highly sensitive to climate change, resulting in about a 41.8%–90.8% overestimation in permafrost region and 7.0%–18.6% misclassification. In order to better use the large RG datasets available to understand permafrost conditions, we provide a method to correct this bias in a fundamental model.

Rock glacier inventory, permafrost probability distribution modeling and associated hazards in the Hunza River Basin, Western Karakoram, Pakistan

The destabilization of rock glaciers and permafrost variations is of great importance to the safety of the population and infrastructure in the Karakoram region because of their effects on land stability and river obstructions. In this study, we compiled the first complete rock glacier inventory for the Hunza Basin, western Karakoram, of 616 rock glaciers with an area of 194 km2 between 2800 and 5700 m a.s.l. We categorized the rock glaciers as intact or relict, and their distributions and destabilization were further analyzed and used along with in situ climate and elevation dataset to model the permafrost probability distribution. The modeled areas where the permafrost zonation index (PZI) is 0.5–1.00 indicate that permafrost occurs over 85% of the catchment area and lies above 3525 m a.s.l., which closely matches the zero-degree isotherm of 3800 m a.s.l. Based on the sensitivity analysis of the independent variables, elevation is the most sensitive variable, followed by net radiation, for predicting the probabilities of the presence and absence of permafrost. The model distributions are quite precise, with median posterior areas under the curve of 0.98 and 0.96 for model training and testing, respectively. We analyzed the rock glacier destabilization for 68 rock glaciers that interacted with river channels, of which 50 blocked or diverted river channels. Destabilized rock glaciers can be closely linked to the 0 °C isotherm between 3400 and 4600 m a.s.l. The significant damage caused by periodic floods from the subsequent blockage of river channels by landslides can be attributed to variations in permafrost. Which demolished infrastructure, including a hydropower plant, suspension bridge and water supply system in Hassan-abad catchment. Quantification of rock glacier dynamics and permafrost in the region can further improve policies related to the reduction in disaster risk and mitigation of associated hazards.

Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence

Permafrost is prevalent over the Qinghai–Tibet Plateau (QTP), but mapping its distribution is challenging due to the limited availability of ground‐truth data sets and strong spatial heterogeneity in the region. Based on a recently developed inventory of permafrost presence or absence from 1475in situobservations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multiple controlling variables, including near‐surface air temperature downscaled from re‐analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to available existing PZI and traditional categorical maps. Based on more than 1000in situmeasurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106km2, or 60.7 (54.5–65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106km2, or 46 (37.3–53.0)%.

Methods for mapping and monitoring global glaciovolcanism

The most deadly (Nevado del Ruiz, 1985) and the most costly (Eyjafjallajökull, 2010) eruptions of the last 100years were both glaciovolcanic. Considering its great importance to studies of volcanic hazards, global climate, and even astrobiology, the global distribution of glaciovolcanism is insufficiently understood. We present and assess three algorithms for mapping, monitoring, and predicting likely centers of glaciovolcanic activity worldwide. Each algorithm intersects buffer zones representing known Holocene-active volcanic centers with existing datasets of snow, ice, and permafrost. Two detection algorithms, RGGA and PZGA, are simple spatial join operations computed from the Randolph Glacier Inventory and the Permafrost Zonation Index, respectively. The third, MDGA, is an algorithm run on all 15 available years of the MOD10A2 weekly snow cover product from the Terra MODIS satellite radiometer. Shortcomings and advantages of the three methods are discussed, including previously unreported blunders in the MOD10A2 dataset. Comparison of the results leads to an effective approach for integrating the three methods. We show that 20.4% of known Holocene volcanic centers host glaciers or areas of permanent snow. A further 10.9% potentially interact with permafrost.MDGA and PZGA do not rely on any human input, rendering them useful for investigations of change over time. An intermediate step in MDGA involves estimating the snow-covered area at every Holocene volcanic center. These estimations can be updated weekly with no human intervention. To investigate the feasibility of an automatic ice-loss alert system, we consider three examples of glaciovolcanism in the MDGA weekly dataset. We also discuss the potential use of PZGA to model past and future glaciovolcanism based on global circulation model outputs. Combined, the three algorithms provide an automated system for understanding the geographic and temporal patterns of global glaciovolcanism which should be of use for hazard assessment, the search for extreme microbiomes, climate models, and implementation of ice-cover-based volcano monitoring systems.

Modelling the spatial distribution of permafrost in Labrador–Ungava using the temperature at the top of permafrost

Permafrost zonation in Labrador–Ungava ranges from very isolated patches through to continuous permafrost. Here we present a new estimate of the distribution of permafrost at high resolution (250 m × 250 m) using spatial numerical modelling supported by station data from 29 air and ground climate monitoring stations. Permafrost presence was estimated using a modified version of the temperature at the top of permafrost (TTOP) model. Mean ground surface temperatures were modelled using gridded air temperatures and a novel n-factor parameterization scheme that compensates for regional differences in continentality, snowfall, and land cover and is transferable to other Subarctic environments. The thermal offset was modelled using land cover and surficial material datasets. Predicted TTOP values for the average climate range regionally from −9 °C (for high elevations in northern Quebec) to +5 °C (for southeastern Labrador – Quebec). Modelling for specific temporal windows (1948–1962, 1982–1996, 2000–2014) suggests that permafrost area increased from the middle of the 20th century to a potential peak extent (36% of the total land area) in the 1990s. Subsequent warming is predicted to have caused a decrease in permafrost extent of one-quarter (95 000 km2), even if air temperatures rise no further, providing air and ground temperatures equilibrate. Zonal boundaries derived by upscaling the high-resolution model are highly scale dependent, precluding direct comparison with the Permafrost Map of Canada that was generated without the use of geographic information system based analyses.

Assessment of permafrost distribution maps in the Hindu Kush Himalayan region using rock glaciers mapped in Google Earth

Abstract. The extent and distribution of permafrost in the mountainous parts of the Hindu Kush Himalayan (HKH) region are largely unknown. A long tradition of permafrost research, predominantly on rather gentle relief, exists only on the Tibetan Plateau. Two permafrost maps are available digitally that cover the HKH and provide estimates of permafrost extent, i.e., the areal proportion of permafrost: the manually delineated Circum-Arctic Map of Permafrost and Ground Ice Conditions (Brown et al., 1998) and the Global Permafrost Zonation Index, based on a computer model (Gruber, 2012). This article provides a first-order assessment of these permafrost maps in the HKH region based on the mapping of rock glaciers. Rock glaciers were used as a proxy, because they are visual indicators of permafrost, can occur near the lowermost regional occurrence of permafrost in mountains, and can be delineated based on high-resolution remote sensing imagery freely available on Google Earth. For the mapping, 4000 square samples (~ 30 km2) were randomly distributed over the HKH region. Every sample was investigated and rock glaciers were mapped by two independent researchers following precise mapping instructions. Samples with insufficient image quality were recorded but not mapped. We use the mapping of rock glaciers in Google Earth as first-order evidence for permafrost in mountain areas with severely limited ground truth. The minimum elevation of rock glaciers varies between 3500 and 5500 m a.s.l. within the region. The Circum-Arctic Map of Permafrost and Ground Ice Conditions does not reproduce mapped conditions in the HKH region adequately, whereas the Global Permafrost Zonation Index does so with more success. Based on this study, the Permafrost Zonation Index is inferred to be a reasonable first-order prediction of permafrost in the HKH. In the central part of the region a considerable deviation exists that needs further investigations.

Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

The value of remote sensing and surface geophysical data for characterizing the spatial variability and relationships between land-surface and subsurface properties was explored in an Alaska (USA) coastal plain ecosystem. At this site, a nested suite of measurements was collected within a region where the land surface was dominated by polygons, including: LiDAR data; ground- penetrating radar, electromagnetic, and electrical-resis- tance tomography data; active-layer depth, soil tempera- ture, soil-moisture content, soil texture, soil carbon and nitrogen content; and pore-fluid cations. LiDAR data were used to extract geomorphic metrics, which potentially indicate drainage potential. Geophysical data were used to characterize active-layer depth, soil-moisture content, and permafrost variability. Cluster analysis of the LiDAR and geophysical attributes revealed the presence of three spatial zones, which had unique distributions of geomor- phic, hydrological, thermal, and geochemical properties. The correspondence between the LiDAR-based geomor- phic zonation and the geophysics-based active-layer and permafrost zonation highlights the significant linkage between these ecosystem compartments. This study suggests the potential of combining LiDAR and surface geophysical measurements for providing high-resolution information about land-surface and subsurface properties as well as their spatial variations and linkages, all of which are important for quantifying terrestrial-ecosystem evolution and feedbacks to climate.

Derivation and analysis of a high-resolution estimate of global permafrost zonation

Abstract. Permafrost underlies much of Earth's surface and interacts with climate, eco-systems and human systems. It is a complex phenomenon controlled by climate and (sub-) surface properties and reacts to change with variable delay. Heterogeneity and sparse data challenge the modeling of its spatial distribution. Currently, there is no data set to adequately inform global studies of permafrost. The available data set for the Northern Hemisphere is frequently used for model evaluation, but its quality and consistency are difficult to assess. Here, a global model of permafrost extent and dataset of permafrost zonation are presented and discussed, extending earlier studies by including the Southern Hemisphere, by consistent data and methods, by attention to uncertainty and scaling. Established relationships between air temperature and the occurrence of permafrost are re-formulated into a model that is parametrized using published estimates. It is run with a high-resolution (<1 km) global elevation data and air temperatures based on the NCAR-NCEP reanalysis and CRU TS 2.0. The resulting data provide more spatial detail and a consistent extrapolation to remote regions, while aggregated values resemble previous studies. The estimated uncertainties affect regional patterns and aggregate number, and provide interesting insight. The permafrost area, i.e. the actual surface area underlain by permafrost, north of 60° S is estimated to be 13–18 × 106 km2 or 9–14 % of the exposed land surface. The global permafrost area including Antarctic and sub-sea permafrost is estimated to be 16–21 × 106 km2. The global permafrost region, i.e. the exposed land surface below which some permafrost can be expected, is estimated to be 22 ± 3 × 106 km2. A large proportion of this exhibits considerable topography and spatially-discontinuous permafrost, underscoring the importance of attention to scaling issues and heterogeneity in large-area models.

Impact of global warming on permafrost conditions in a coupled GCM

A climate change scenario experiment conducted with the state‐of‐the‐art coupled atmosphere‐ocean general circulation model ECHAM4/OPYC3 is analysed with the objective to quantify changes in present‐day Arctic permafrost conditions. An efficient procedure is adopted which overcomes the many problems associated with an explicit treatment of soil freezing and thawing processes. The zero degree soil temperatures as well as induced permafrost index characteristics simulated by the model for present day conditions match well the observed permafrost zonation. For a future scenario of greenhouse gas emissions (SRES A2 issued by IPCC), we estimate the amounts that the permafrost zones moves poleward and how the thickness of the active layer deepens in response to the global warming by the end of the 21st century. The simulation indicates a 30–40% increase in active‐layer thickness for most of the permafrost area in the Northern Hemisphere, with largest relative increases concentrated in the northernmost locations.

PERMAFROST ZONATION AND CLIMATE CHANGE IN THE NORTHERN HEMISPHERE: RESULTS FROM TRANSIENT GENERAL CIRCULATION MODELS

Numerous studies have demonstrated that both global patterns and local details of permafrost distribution are highly responsive to climatic fluctuations, at several temporal and spatial scales. Permafrost currently underlies about one fourth of the land area of the northern hemisphere, and many qualitative predictions have been made for a severe reduction of this area in response to global warming. A map of permafrost distribution compiled using the ‘frost index’, a dimensionless number that can be related to the zonal arrangement of permafrost, shows very good correspondence with a recently published empirical map. The frost index was used in conjunction with three transient general circulation models to compile maps of permafrost zonation for conditions in the mid-21st century. Although regional patterns and local details differ substantially between the three scenarios, all result in reductions in the area occupied by each permafrost zone. Localized expansions of the area underlain by permafrost are apparent from two of the scenarios. Reductions in the areal extent of equilibrium permafrost predicted from two of the three transient models are much less than those indicated by runs using 2 × CO models.

Permafrost zonation in Russia under anthropogenic climatic change

AbstractWarming induced by the hypothesized greenhouse effect will have profound consequences for permafrost distribution throughout the high‐latitude regions of the globe. Two scenarios of climate change based on the palaeo‐reconstruction method widely employed by Russian scientists were used in conjunction with a model that produces zonal maps of permafrost distribution. Changes associated with both 1.2°C and 2.0°C warming of the mean global temperature are amplified in the high latitudes, which results in the potential for a large reduction of the area underlain by continuous permafrost. Thermokarst development is likely to be particularly acute in the West Siberian plain, where the climate model suggests that conditions will become marginal for the maintenance of permafrost.