Permafrost Mapping Research Articles

An indicator of relative distribution probability of field-scale permafrost in Northeast China: Using a particle swarm optimization (PSO)-based indicator composition algorithm

Under the influence of climate changing, permafrost in Northeast China (NEC) has been consistently degrading in recent years. Numerous scholars have investigated the spatial and temporal distribution patterns of permafrost in the NEC region. However, due to constraints in data availability and methodological approaches, only a limited number of studies have extended their analyses to the field scale. In this study, we established a particle swarm optimization (PSO)-based indicator composition algorithm (PSO-ICA) to obtain an indicator factor, η, that indicates the relative distribution probability of permafrost at the field scale. PSO-ICA screened and combined 12 high-resolution environmental variables to compose η. The spatial distribution data of permafrost with a length of 765.378 km provided by the engineering geological investigation report (EGIR) of six highways were used to train and validate the effectiveness of η in indicating permafrost. At the field scale, η was found to be similar to the surface freezing number (SFN) in its ability to indicate permafrost, with AUC values of 0.7046 and 0.7063 for the two by the ROC test. In addition, η has a good performance in predicting highway distresses in the permafrost region in the absence of survey data. This study also confirmed that the resolution and accuracy of permafrost mapping results can be improved by utilizing η. After downscaling the 1 km resolution SFN to 30 m resolution using η, the R2 of the linear relationship between SFN and permafrost temperatures from 43 monitoring boreholes was improved from 0.7010 to 0.8043. If η can help understand the distribution of permafrost at field scale, many engineering and environmental practices could potentially benefit.

Transient Modeling of Permafrost Distribution From 1986 to 2016 in Mongolia

ABSTRACTThe distribution of permafrost in the southernmost zones is complicated in both time and space. We assessed this complexity by applying transient heat transfer schemes to model permafrost in Mongolia between 1986 and 2016. The modeling took into account the spatial distribution of soil thermal properties and time series of ground temperature profiles observed at 69 borehole sites. Spatial modeling was forced by downscaled ERA5 air temperatures that were further debiased with local meteorological records. The results indicated that permafrost warmed by 0°C to 0.4°C/decade, most significantly in eastern and northern Hangai and at high elevations in the Altai Mountains. The extent of permafrost remained stable, but the active layer was thickened, especially after the late 1990s. Forested wet soils experienced less warming, while temperatures in soils with low organic matter and low moisture content increased by 0.5°C over the modeling period. The lower limits of mountain permafrost were estimated to be between 1320 and 2030 m a.s.l., depending on the latitude. Our permafrost map allows the delineation of a complex distribution of permafrost in Mongolia, which is strongly controlled by the spatially complex topography, landcovers, and soil wetness.

Modelling permafrost distribution using the temperature at top of permafrost model in the boreal forest environment of Whatì, NT

Current permafrost models in Canadian boreal forests are generally of low spatial resolution as they cover regional or continental scales. This study aims to understand the viability of creating a temperature at the top of permafrost (TTOP) model on a local scale in the boreal wetland environment of Whatì, Northwest Territories from short-term field-collected temperature data. The model utilizes independent variables of vegetation, topographic position index, and elevation, with the dependent variables being ground surface temperature collected from 60 ground temperature nodes and 1.5 m air temperature collected from 10 temperature stations. In doing this, the study investigates the relationship vegetation and disturbance have on ground temperature and permafrost distribution. The model predicts that 31% of the ground is underlain by permafrost, based on a mean annual temperature at TTOP of <0 °C. This model shows an accuracy of 62.5% when compared to cryotic assessment sites (CAS). Most inaccuracies, showing the limitations of the TTOP model, came from peat plateaus that had been burned in the most recent forest fire in 2014. These resulted in out-of-equilibrium permafrost and climatic conditions that TTOP cannot handle well. Commonly, permafrost mapping places Whatì in the extensive discontinuous zone, estimating that between 50% and 90% of the ground is underlain by permafrost. The study shows that a climatically driven TTOP model calibrated with CAS can be used to illustrate ground temperature heterogeneity from short-term data in boreal forest wetland environments. However, this approach likely underestimates permafrost extent and is perhaps not the best-suited modelling choice for near-surface permafrost, which is currently out of equilibrium with the current 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.

Permafrost Probability Mapping at a 30 m Resolution in Arxan Based on Multiple Characteristic Variables and Maximum Entropy Classifier

High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas.

Earth to Mars: A Protocol for Characterizing Permafrost in the Context of Climate Change as an Analog for Extraplanetary Exploration.

Abstract Permafrost is important from an exobiology and climate change perspective. It serves as an analog for extraplanetary exploration, and it threatens to emit globally significant amounts of greenhouse gases as it thaws due to climate change. Viable microbes survive in Earth's permafrost, slowly metabolizing and transforming organic matter through geologic time. Ancient permafrost microbial communities represent a crucial resource for gaining novel insights into survival strategies adopted by extremotolerant organisms in extraplanetary analogs. We present a proof-of-concept study on ∼22 Kya permafrost to determine the potential for coupling Raman and fluorescence biosignature detection technology from the NASA Mars Perseverance rover with microbial community characterization in frozen soils, which could be expanded to other Earth and off-Earth locations. Besides the well-known utility for biosignature detection and identification, our results indicate that spectral mapping of permafrost could be used to rapidly characterize organic carbon characteristics. Coupled with microbial community analyses, this method has the potential to enhance our understanding of carbon degradation and emissions in thawing permafrost. Further, spectroscopy can be accomplished in situ to mitigate sample transport challenges and in assessing and prioritizing frozen soils for further investigation. This method has broad-range applicability to understanding microbial communities and their associations with biosignatures and soil carbon and mineralogic characteristics relevant to climate science and astrobiology.

Time-Domain Electromagnetics for Subsea Permafrost Mapping in the Arctic: The Synthetic Response Analyses and Uncertainty Estimates from Numerical Modelling Data

Subsea permafrost stability is the key to whether pre-performed methane sequestered in hydrate deposits escapes to the overlying strata. By making use of the 1D numerical modeling and field data, we analyze the capabilities of the time-domain (transient) electromagnetic method (TDEM) when being applied for subsea permafrost mapping, and study the effect of the background resistivity structure on the inversion models’ accuracy for a series of settings typical for the East Siberian Arctic Shelf—the broadest and shallowest shelf in the world ocean, which represents more than 70% of the subsea permafrost. The synthetic response analysis included the construction of a series of resistivity models corresponding to different settings (presence/absence of ice-bonded permafrost layer, different position of its top and bottom boundaries, different width and thickness of thawed bodies or taliks, variable seawater depth and its resistivity), and calculation of synthetic apparent resistivity responses used to assess their sensitivity to changes in the target parameters of the resistivity structure. This was followed by regularized inversion of synthetic responses and comparing resulting models with original (true) ones, which allowed us to understand the possible uncertainties in the geometry and resistivity of the reconstructed permafrost layer, depending on seawater depth and unfrozen layer thickness, as well as confirm the overall efficacy of TDEM technology for the subsea permafrost imaging. That is crucially important for understanding the current state of the subsea permafrost-hydrate system and possible future dynamics.

Polarimetric L-Band ALOS2-PALSAR2 for Discontinuous Permafrost Mapping in Peatland Regions

Recently, it has been shown that the long penetrating polarimetric L-band ALOS is very promising for boreal and subarctic peatland mapping and monitoring. The unique information provided by the Touzi decomposition, and the dominant-scattering-type phase in particular, on peatland subsurface water flow permits an enhanced discrimination of bogs from fens, two peatland classes that can hardly be discriminated using conventional optical remote sensing sensors and C-band polarimetric SAR. In this study, the dominant and medium-scattering phases generated by the Touzi decomposition are investigated for discontinuous permafrost mapping in peatland regions. Polarimetric ALOS2, LiDAR, and field data were collected in the middle of August 2014, at the maximum permafrost thaw conditions, over discontinuous permafrost distributed within wooded palsa bogs and peat plateaus near the Namur Lake (Northern Alberta). The ALOS2 image, which was miscellaneously calibrated with antenna cross talk (−33 dB) much higher than the actual ones, was recalibrated. This led to a reduction of the residual calibration error (down to −43 dB) and permitted a significant improvement of the dominant and medium-scattering-type phase (20∘ to −30∘) over peatlands underlain by discontinuous permafrost. The Touzi decomposition, Cloude–Pottier α-H incoherent target scattering decomposition, and the HH-VV phase difference were investigated, in addition to the conventional multipolarization (HH, HV, and VV) channels, for discontinuous permafrost mapping using the recalibrated ALOS2 image. A LiDAR-based permafrost classification developed by the Alberta Geological Survey (AGS) was used in conjunction with the field data collected during the ALOS2 image acquisition for the validation of the results. It is shown that the dominant- and scattering-type phases are the only polarimetric parameters which can detect peatland subsurface discontinuous permafrost. The medium-scattering-type phase, ϕs2, performs better than the dominant-scattering-type phase, ϕs1, and permits a better detection of subsurface discontinuous permafrost in peatland regions. ϕs2 also allows for a better discrimination of areas underlain by permafrost from the nonpermafrost areas. The medium Huynen maximum polarization return (m2) and the minimum degree of polarization (DoP), pmin, can be used to remove the scattering-type phase ambiguities that might occur in areas with deep permafrost (more than 50 cm in depth). The excellent performance of polarimetric PALSAR2 in term of NESZ (−37 dB) permits the demonstration of the very promising L-band long-penetration SAR capabilities for enhanced detection and mapping of relatively deep (up to 50 cm) discontinuous permafrost in peatland regions.

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

The land surface temperature obtained from remote sensing was widely used in the simulation of permafrost mapping instead of air temperature with the rapid development of remote sensing technology. The land surface freezing and thawing index (LFI and LTI), which is commonly regarded as the ground surface freezing and thawing index (GFI and GTI), can produce certain errors in the simulation of permafrost distribution on the Qinghai–Tibet Plateau. This paper improved the accuracy of the thermal condition of the surface soil in the Qinghai–Tibet Engineering Corridor (QTEC) by calculating the LFI (or LTI) and N-factors. The environmental factors affecting the spatial distribution of the GFI and GTI were detected by the GeoDetector model. Finally, the multiple linear relationships between the GFI (or GTI) and the environmental factors were established. The results from 25 monitoring sites in the QTEC show that the Nf (ratio of GFI to LFI) is 1.088, and the Nt (ratio of GTI to LTI) is 0.554. The explanatory power of the interaction between elevation and latitude for the GFI and GTI is 79.3% and 85.6%, respectively. The multiple linear regression model with six explanatory variables established by GFI (or GTI) has good accuracy. This study can provide relatively accurate upper boundary conditions for the simulation of permafrost distribution in the QTEC region.

Spatial distribution mapping of permafrost in Mongolia using TTOP

AbstractThis study presents the results of permafrost mapping in Mongolia based on the TTOP (temperature‐on‐top‐of‐permafrost) approach, which were validated against in situ measurements at various locations. In situ measurements indicated that the mean annual ground temperature (MAGT) ranged from 0.6 to 2.2°C interannually, showing the greatest variability when furthest from 0°C. The differences between the modeled and measured MAGTs exceeded ±1°C in locations where permafrost was in a nonequilibrium state and was controlled predominantly by local factors. It was estimated that permafrost occupies one‐third of Mongolia. We divided the extent of the permafrost into five zones: continuous, discontinuous, sporadic, isolated, and seasonally frozen ground. In total, the permafrost zones cover ~462.8 × 103 km2, accounting for 29.3% of Mongolia. Of this total area, continuous permafrost accounted for 118.3 × 103 km2 (7.5%), discontinuous permafrost 127.7 × 103 km2 (8.1%), sporadic permafrost 112.4 × 103 km2 (7.1%), and isolated permafrost 104.4 × 103 km2 (6.6%).

Evaluation of MERRA-2 land surface temperature dataset and its application in permafrost mapping over China

The second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) land surface temperature (LST) dataset has been widely used for permafrost mapping in specific areas; however, its accuracy and application need to be evaluated over China. In this study, the MERRA-2 LST was evaluated against meteorological observations and three other reanalysis datasets including the first-generation MERRA, Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), using multiple statistical methods over the period from 1980 to 2018. The results revealed that the MERRA-2 LST generally exhibited cold bias compared to meteorological observations while performing better than the JRA55, ERA-Interim, and MERRA datasets in China, particularly in high-altitude permafrost regions. The comparison indicated that the time series trends for the MERRA-2 LST was consistent with that observed until 2000, and noticeably amplified cold bias, particularly for the period after 2005, was observed. Moreover, two correction methods were proposed and compared to reduce the error range for the MERRA-2 dataset, which was caused by the difference in elevation and land cover types. Calibrated results demonstrated that the linear regression method (Method1) between the elevation difference and mean bias error (MBE) for the LST performed well with root mean square error (RMSE) ranged from 2.15 to 5.97 °C to 1.09–2.53 °C. Moreover, in comparison with the MODIS LST dataset, the results showed that the adjusted MERRA-2 LST was in good agreement with smaller RMSEs against the observations. The surface frost number model was used for mapping the permafrost distribution over China based on the daily adjusted MERRA-2 LST dataset. According to the simulation results, the permafrost extent had a slightly continued degradation trend with a rate of 3–5% per decade over the past 39 years. The simulated permafrost area over China for the years 2010–2018 was approximately 1.63 × 106 km2, which accounts for 16.9% of mainland China. Thus, the adjusted MERRA-2 LST with high spatial–temporal consistency is the optimal choice to investigate permafrost distribution on a large scale.

Influence of ecosystem and disturbance on near‐surface permafrost distribution, Whatì, Northwest Territories, Canada

AbstractFor 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 Whatì, 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.

Standoff High-Frequency Electromagnetic Induction Response of Unsaturated Sands: A Tank-Scale Feasibility Study

Standoff electromagnetic induction (EMI) measurements of complex conductivity and complex permittivity for engineering soil properties have the potential to revolutionize the way the US Army handles route planning and infrastructure assessment. An unmanned aerial system (UAS) based EM platform for soil interrogation would have wide reaching impact in a variety of applications including: civil infrastructure inspection, in-theater ingress and egress routing, reduction of false positives in IED detection, and permafrost mapping, among many others. Traditional frequency domain EMI instruments assess conductivity at low-frequencies, generally in the range of 1–20 kHz; however, recent advancements have resulted in instrumentation targeting a broadband range of frequencies, from 10 kHz through 20 MHz. This advancement, known as high-frequency electromagnetic induction (HFEMI) allows the potential to evaluate frequency domain relaxation effects in soils by acquiring both the in phase and quadrature response of the secondary field from the soil. Relaxation phenomena such as induced polarization and dielectric permittivity are related to important soil properties that can potentially be exploited using this HFEMI system. While conductivity measurements using the quadrature component of the EMI response are well established in EMI instrumentation, understanding of the relationship between direct electrical measurements and standoff HFEMI measurements is lacking. In an effort to illuminate this relationship between various electrical and electromagnetic methods at a scale suitable for soil property estimation, we perform side-by-side measurements using galvanic geoelectrical methods (ERT, IP), electromagnetics, time-domain reflectometry (TDR) and ground penetrating radar (GPR). We compare HFEMI obtained quadrature and in-phase responses to ERT, IP, TDR and GPR measurements. A tank-scale test cell was developed for comparison of the above methods and allowed assessment of sand at varying saturation levels. Further, the HFEMI response at varying heights above the sand surface was also assessed. Qualitative observations are reported in an initial attempt to relate the HFEMI response to important soil parameters.

Evaluation of Merra-2 Land Surface Temperature Dataset and its Application in Permafrost Mapping Over China

Evaluation of Merra-2 Land Surface Temperature Dataset and its Application in Permafrost Mapping Over China

Land Surface Freeze/Thaw Detection Over the Qinghai–Tibet Plateau Using FY-3/MWRI Data

The spatial extent and duration of soil freeze/thaw (F/T) control water and heat exchange, the energy cycle, and climate change. Global warming causes permafrost thawing, which increases carbon emissions and in turn exacerbates climate change. Passive microwave remote sensing has been proven to be effective in monitoring land surface F/T. However, it was found that the applicability of existing passive microwave remote sensing-retrieved F/T products in large-scale areas (such as the Qinghai-Tibetan Plateau (QTP)) was influenced by some landscape factors, such as the arid climate type and terrain elevation gradient. FY-3 series satellites have accumulated nearly 10 years of passive microwave data, but there is little work based on FY-3 passive microwave data to see its potential in land surface F/T status monitoring. In this work, we proposed a dynamic method to determine the surface F/T status by combining the edge detection method and discriminant function algorithm from FY-3B X-and Ka-band microwave radiation imager (MWRI) data. Comparing the results against three F/T products based on in situ 5 cm soil temperature, we demonstrate that this algorithm performs best over different validation areas with an overall accuracy of 86.5%. More specifically, the new algorithm improved the accuracy of current F/T products in arid and semiarid regions from 73% to 90%. Additionally, the spatial distribution of frozen days over the QTP of 2018 based on the new algorithm has good consistency with the permafrost map. However, the accuracy is influenced by snowmelt and appears to be overestimated for thaw soil during the day. This algorithm performs well in QTP areas with complex topography and climate types and holds the promise of providing users with highly accurate F/T products on larger and even global scales.

Spatial Distribution of Permafrost in the Xing’an Mountains of Northeast China from 2001 to 2018

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km2), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

Landscape‐scale variations in near‐surface soil temperature and active‐layer thickness: Implications for high‐resolution permafrost mapping

AbstractSoil temperature observations in permafrost regions are sparse, which limits our understanding and ability to map permafrost conditions at high spatial resolutions. In this study, we measured near‐surface soil temperatures (Tnss) at 107 sites from August 2016 to August 2017 in northern boreal and tundra areas in northwestern Canada. Active‐layer thickness (ALT), soil and vegetation conditions were also measured at these sites. Our observations show large variations in Tnss and ALT across an area with a similar climate. This high degree of spatial heterogeneity illustrates the importance of high‐resolution mapping of permafrost for infrastructure planning and understanding the impacts of permafrost thaw. Annual mean Tnss varied by 5–6°C among observation sites, which was mainly due to differences in Tnss in winter and spring, indicating the importance of snow conditions on determining landscape‐scale variation in near‐surface ground temperatures. ALT varied from about 30 cm to more than 120 cm. The variation in ALT among sites did not correlate with thawing season Tnss, but was associated with variation in soil conditions, especially the surface organic layer thickness. Freezing n‐factors varied significantly from site to site and among ecotypes, while thawing n‐factors were similar among sites, except bare soils. This study shows that ecotypes can be used to map ALT and Tnss at landscape scales in tundra areas, but the method is not as effective in the northern boreal region.

Dynamics of freezing/thawing indices and frozen ground from 1900 to 2017 in the upper Brahmaputra River Basin, Tibetan Plateau

Dynamics of the frozen ground are key to understand the changes of eco-environment in cold regions, especially for areas with limited field observations. In this study, we analyzed the spatial and temporal variations of the ground surface freezing and thawing indices from 1900 to 2017 for the upper Brahmaputra River (also called the Yarlung Zangbo River in China) Basin (UBRB), southwestern Tibetan Plateau, with the air freezing and thawing indices using the University of Delaware (UDEL) monthly gridded air temperature dataset. The abrupt change years for air freezing index (AFI) and ground surface freezing index (GFI) were detected in 1999 and 2002, respectively, and for both air thawing index (ATI) and ground surface thawing index (GTI) were 2009. With the air temperature rising at a rate of 0.006 °C per year over 1900–2017, the AFI and GFI decreased at a rate of −0.1 °C d per year, while the ATI and GTI increased at rates of 0.3 and 0.5 °C d per year before the abrupt change year, respectively; all changing trends of freezing/thawing indices increased after the abrupt year, which was −2.9, −0.8, 7.3, and 21.7 °C d per year for the AFI, GFI, ATI, and GTI, respectively. We utilized the surface frost number model to obtain the dynamics of the frozen ground over the UBRB. When the empirical coefficient of E was assigned to 1.2, the simulated frozen ground occupied about 53.2% of the whole UBRB in the 1990s, which agreed well with the existing permafrost map published in 1996. The area of frozen ground accounts for 51.5%–54.5% of the UBRB during 1900–2017. This result may facilitate further studies of the multi-interactions among the frozen ground and relevant eco-environment, such as the air-ground surface energy exchange, hydrological cycles, and changes of the active layer thickness over the UBRB.

Mapping the permafrost stability on the Tibetan Plateau for 2005–2015

Data scarcity is a major obstacle for high-resolution mapping of permafrost on the Tibetan Plateau (TP). This study produces a new permafrost stability distribution map for the 2010s (2005–2015) derived from the predicted mean annual ground temperature (MAGT) at a depth of zero annual amplitude (10–25 m) by integrating remotely sensed freezing degree-days and thawing degree-days, snow cover days, leaf area index, soil bulk density, high-accuracy soil moisture data, and in situ MAGT measurements from 237 boreholes on the TP by using an ensemble learning method that employs a support vector regression model based on distance-blocked resampled training data with 200 repetitions. Validation of the new permafrost map indicates that it is probably the most accurate of all currently available maps. This map shows that the total area of permafrost on the TP, excluding glaciers and lakes, is approximately 115.02 (105.47–129.59) ´ 104 km2. The areas corresponding to the very stable, stable, semi-stable, transitional, and unstable types are 0.86 ´ 104, 9.62 ´ 104, 38.45 ´ 104, 42.29 ´ 104, and 23.80 ´ 104 km2, respectively. This new map is of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future on the TP as a baseline.

Spatiotemporal freeze–thaw variations over the Qinghai‐Tibet Plateau 1981–2017 from reanalysis

AbstractThe thawing and freezing conditions on the Qinghai‐Tibet Plateau (QTP) are considered effective indicators that are widely used in ecology, climate change, cold‐region engineering design, and permafrost mapping. The purpose of this study is to utilize reanalysis to detect spatial variations in freeze–thaw conditions across the QTP from 1981–2017. From a comparison of five recent reanalysis models (MERRA2: National Aeronautics and Space Administration Modern‐Era Reanalysis for Research and Applications, Version 2; ERA: European Center for Medium‐Range Weather Forecasts Interim Reanalysis; GLDAS: Global Land Data Assimilation System NOAH; CFS: National Centers for Environmental Prediction Climate Forecast System Reanalysis, Version 2; and CMFD: China Meteorological Forcing Dataset) against existing sparse observations of 2‐m surface air temperature (SAT), we find that NASA MERRA2 dataset is the most applicable to the QTP. Then, the MERRA2 SAT dataset was selected for this study and was corrected and validated by utilizing calibration models built from the observed data. The results revealed that the correlation coefficients between the corrected MERRA2 SAT and meteorological station observed data increased from 0.52 to 0.93, 0.49 to 0.90, 0.56 to 0.93, and 0.69 to 0.93 in spring, summer, autumn, and winter, respectively. The corrected parameters performed better in the southern and southeastern portions of the QTP. Finally, we utilize the corrected MERRA2 dataset to develop freeze–thaw indices and evaluate statistical trends. We find that relatively high air freezing indices are one of the important factors for the presence of permafrost in the central and northeastern portions of the QTP. Trends in the air thawing and freezing indices in the permafrost and seasonally frozen ground regions indicate that, from 1981 to 2017 in the permafrost regions, the warming was more significant in the summer than in the other seasons. However, the winter warming rates in permafrost and seasonally frozen ground regions are almost the same.