Search Results (10)

The Daxing’anling forest region represents a crucial forestry hub in China and confronts some of the nation’s most severe freezing–thawing hazards. This study delved into the temporal trends and spatial distributions of various parameters related to freezing and thawing, including air temperature, ground surface temperature, freezing index, thawing index, and freezing–thawing frequency. Furthermore, this study assessed and delineated freezing–thawing hazards within the research area. The findings revealed a rapid increase in air temperature and ground surface temperature within the Daxing’anling forest region yet a lower rate of increase in ground surface temperature compared to Northeast China. Latitude had the strongest influence on mean annual air temperature, mean annual ground surface temperature, air freezing index, air thawing index, ground surface freezing index, ground surface thawing index, air freezing–thawing frequency, and ground surface freezing–thawing frequency, followed by longitude and elevation. Overall, freezing index, and air freezing–thawing frequency increased from south to north, whereas mean annual air temperature, mean annual ground surface temperature, air thawing index, ground surface thawing index, and ground surface freezing–thawing frequency decreased from south to north. The assessment outcomes underscore the importance of closely monitoring freezing–thawing hazards in regions north of the 50th parallel. Full article

Freezing/thawing indices are important indicators of the dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially in areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP after presenting changes in frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60 × 10⁶ km² of permafrost in the QTP degraded to SFG in the 1960s–2000s, and the primary shrinkage period occurred in the 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at rates of 71.00 °C·d/decade and 34.33 °C·d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly, with values of 48.13 °C·d/decade and 40.37 °C·d/decade in the past five decades, respectively. Permafrost showed more pronounced changes in freezing/thawing indices since the 1990s compared to SFG. The changes in thermal regimes in frozen ground showed close relations to air warming until the late 1990s, especially in 1998, when the QTP underwent the most progressive warming. However, a sharp increase in the annual precipitation from 1998 began to play a more controlling role in thermal degradation in frozen ground than the air warming in the 2000s. Meanwhile, the following vegetation expansion hiatus further promotes the thermal instability of frozen ground in this highly wet period. Full article

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 N_f (ratio of GFI to LFI) is 1.088, and the N_t (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. Full article

Under the trend of climate warming, the high-latitude permafrost in Heilongjiang Province is becoming seriously degraded. The question of how to quantitatively analyze the spatial and temporal trends of multi-year permafrost has become fundamental for current permafrost research. In this study, the temporal and spatial variations of annual mean air temperature (MAAT), annual mean ground temperature (MAGST) and freezing/thawing index based on air and surface temperature data from 34 meteorological stations in Heilongjiang Province from 1971–2019, as well as the variation characteristics of permafrost distribution, were analyzed based on the freezing index model. The results showed that both MAAT and MAGST in Heilongjiang Province tended to decrease with the increase of altitude and latitude. For interannual variation, the MAAT and MAGST warming rates tended to be consistent across Heilongjiang Province, with multi-year variation from −8.64 to 5.60 °C and from −6.52 to 7.58 °C, respectively. From 1971–2019, the mean annual air freezing index (AFI) and ground surface freezing index (GFI) declined at −5.07 °C·d·a⁻¹ and −5.04 °C·d·a⁻¹, respectively, whereas the mean annual air thawing index (ATI) and ground surface thawing index (GTI) were elevated at 7.63 °C·d·a⁻¹ and 11.89 °C·d·a⁻¹, respectively. The spatial distribution of the multiyear mean AFI, ATI, GFI and GTI exhibited a latitudinal trend, whereas the effect of altitude in the northern mountainous areas was greater than that of latitude. Permafrost was primarily discovered in the Daxing’an and Xiaoxing’an Mountains in the north, and sporadically in the central mountainous regions. The southern boundary of permafrost shifted nearly 2° to the north from 1970 to 2010s, while the southern boundary of permafrost in Heilongjiang Province was stable at nearly 51° N. The total area of permafrost narrowed from 1.11 × 10⁵ km² in the 1970s to 6.53 × 10⁴ km² in the 2010s. The results of this study take on a critical significance for the analysis of the trend of perennial permafrost degradation at high latitudes in Heilongjiang Province and the whole northeastern China, as well as for mapping the distribution of large areas of permafrost using the freezing index model. This study provides a reference for natural cold resource development, ecological protection, climate change and engineering construction and maintenance in permafrost areas. Full article

As an important parameter in permafrost research, the annual ground surface freezing/thawing index is widely used in the variation of permafrost. In addition, it is also an important indicator in climatology, providing a large amount of theoretical basis for the assessment of climate change. Based on the ground surface temperature data recorded at 707 meteorological stations from 1960 to 2020, the ground surface freezing/thawing index in China were calculated. The results showed that over the past six decades, the thawing index has shown an upward trend, whereas the freezing index has shown a downward trend, and the trend is stronger around 2000. The results of the R/S-based analysis indicate that the freezing/thawing index will remain on a decreasing/increasing trend for some time to come. Based on the five warming scenarios published by Coupled Model Intercomparison Project Phase 6 (CMIP6), the spatial–temporal variation characteristics of the ground surface freezing/thawing index in China during 2020–2100 was simulated. It was found that under SSP3-7.0 and SSP5-8.5 scenarios, the freezing/thawing index may be 0 °C-days in 2080 and 2070, respectively, which may imply that the ground surface freezing process in some regions of China may disappear. Full article

Frozen soil is an important environmental factor in cold regions. Warming climate will increase the risk of permafrost thawing, i.e., accelerated carbon release, reduced super-frozen soil water, intensified desertification and destruction of infrastructure. Based on MOD11A2 and MYD11A2 products of MODIS Terra/Aqua, the distribution and change of surface frost number under the influence of normalized difference vegetation index and forest canopy closure in Northeast China from 2003 to 2019 were produced. From 2012 to 2015, the area of the regions where the surface frost number was higher than 0.5 continued to decrease in Northeast China. Taking 2013 as the time turning point, two periods of changes in the distribution of surface frost number in Northeast China were divided, namely, into 2003–2013 and 2014–2014. The spatial distribution of permafrost temperature is simulated by establishing the numerical relationship between the surface frost number and the annual average ground temperature of permafrost. From 2003 to 2019, the area of permafrost changed from 32.77 × 10⁴ to 27.10 × 10⁴ km². The distribution characteristics show that the area with permafrost temperature below −4 °C accounts for 0.1%, and below −3.0 °C accounts for 3.45%. The permafrost with lower temperature is mainly distributed in the Greater Khingan Mountains, from the northernmost Mohe to the Aershan in the middle of the ridge. The area where the permafrost temperature ranges from −2 to 0 °C is the largest, accounting for 73.81% of the total area. The distribution of permafrost temperatures in the Greater Khingan Mountains is mainly between −1.5 and −3 °C, while that in the Lesser Khingan Mountains is mainly between −2.0 and 0 °C. The altitude is the main factor controlling the permafrost temperature distributed at high latitudes in Northeast China. This work will provide more detailed basic data for regional research on frozen soil and the environment in Northeast China. Full article

In permafrost areas, the active layer undergoes seasonal frost heave and thaw subsidence caused by ice formation and melting. The amplitude and timing of the ground displacement cycles depend on the climatic and ground conditions. Here we used Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to document the seasonal displacement progression in three regions of Svalbard. We retrieved June–November 2017 time series and identified thaw subsidence maxima and their timing. InSAR measurements were compared with a composite index model based on ground surface temperature. Cyclic seasonal patterns are identified in all areas, but the timing of the displacement progression varies. The subsidence maxima occurred later on the warm western coast (Kapp Linné and Ny-Ålesund) compared to the colder interior (Adventdalen). The composite index model is generally able to explain the observed patterns. In Adventdalen, the model matches the InSAR time series at the location of the borehole. In Kapp Linné and Ny-Ålesund, larger deviations are found at the pixel-scale, but km or regional averaging improves the fit. The study highlights the potential for further development of regional InSAR products to represent the cyclic displacements in permafrost areas and infer the active layer thermal dynamics. Full article

Ground surface changes caused by freeze-thaw action affect agriculture and forestry, as well as artificial structures such as roads. In this study, an area is examined in which reforestation is urgently needed but the growth of naturally restored seedlings and planted trees is impaired by freeze-thaw action. Thus, a method of measuring freeze-thaw induced ground surface changes and mitigating their negative impacts is needed. Real-time kinematic unmanned aerial vehicle and structure-from-motion multiview stereophotogrammetry are used on slope-failure sites in forest areas to observe the ground surface changes caused by freeze-thaw action over a wide area, in a nondestructive manner. The slope characteristics influencing the ground-surface changes were examined, and it was confirmed that it is possible to observe minute topographical changes of less than ±5 cm resulting from freeze-thaw action. Statistical models show that the amount of freeze-thaw action is mostly linked to the cumulative solar radiation, daily ground-surface temperature range, and topographic-wetness index, which influence the microscale dynamics of the ground surface. The proposed method will be useful for future quantitative assessments of ground-surface conditions. Further, efficient reforestation could be implemented by considering the effects of the factors identified on the amount of freeze-thaw action. Full article

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF ≥ 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF < 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km² over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics. Full article

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 10⁴ km² (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km⁻¹, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 10⁴ km²·yr⁻¹. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr⁻¹, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs. Full article