We present an innovative approach to understanding permafrost degradation processes through the application of new environment-based particle image velocimetry (E-PIV) to time-lapse imagery and correlation with synchronous temperature and rainfall measurements. Our new approach to extracting quantitative vector movement from dynamic environmental conditions that can change both the position and the color balance of each image has optimized the trade-off between noise reduction and preserving the authenticity of movement data. Despite the dynamic polar environments and continuous landscape movements, the E-PIV provides the first quantitative real-time associations between environmental drivers and the responses of permafrost degradation mechanism. We analyze four event-based datasets from an island southwest of Tuktoyaktuk, named locally as Imnaqpaaluk or Peninsula Point near Tuktoyaktuk, NWT, Canada, spanning a 5-year period from 2017 to 2022. The 2017 dataset focuses on the interaction during a hot dry summer between slope movement and temperature changes, laying the foundation for subsequent analyses. In 2018, two datasets significantly expand our understanding of typical failure mechanisms in permafrost slopes: one investigates the relationship between slope movement and rainfall, while the other captures an overhang collapse, providing a rare quantitative observation of an acute landscape change event. The 2022 dataset revisits the combination of potential rain and air temperature-related forcing to explore the environment-slope response relationship around an ice wedge, a common feature of ice-rich permafrost coasts. These analyses reveal both a direct but muted association with air temperatures and a detectable delayed slope response to the occurrence of rainfall, potentially reflective of the time taken for the warm rainwater to infiltrate through the active layer and affect the frozen ground. Whilst these findings also indicate that other factors are likely to influence permafrost degradation processes, the associations have significant implications given the projections for a warmer, wetter Arctic. The ability to directly measure permafrost slope responses offers exciting new potential to quantitatively assess the sensitivity of different processes of degradation for the first time, improving the vulnerability components of hazard risk assessments, guiding mitigation efforts, and better constraining future projections of erosion rates and the mobilization of carbon-rich material.

The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

Permafrost degradation is a growing direct impact of climate change. Detecting permafrost shrinkage, in terms of extension, depth reduction and active layer shift is fundamental to capture the magnitude of trends and address actions and warnings. Temperature profiles in permafrost allow direct understanding of the status of the frozen ground layer and its evolution in time. The Sommeiller Pass permafrost monitoring station, at about 3000 m of elevation, is the key site of the regional network installed in 2009 during the European Project PermaNET in the Piedmont Alps (NW Italy). The station consists of three vertical boreholes with different characteristics, equipped with a total of 36 thermistors distributed in three different chains. The collected raw data shows a degradation of the permafrost base at approximately 60 m of depth since 2014, corresponding to about 0.03 degrees C/yr. In order to verify and better quantify this potential degradation, three on-site sensor calibration campaigns were carried out to understand the reliability of these measurements. By repeating calibrations in different years, two key results have been achieved: the profiles have been corrected for errors and the re-calibration allowed to distinguish the effective change of permafrost temperatures during the years, from possible drifts of the sensors, which can be of the same order of magnitude of the investigated thermal change. The warming of permafrost base at a depth of similar to 60 m has been confirmed, with a rate of (4.2 +/- 0.5)center dot 10(-2) degrees C/yr. This paper reports the implementation and installation of the on-site metrology laboratory, the dedicated calibration procedure adopted, the calibration results and the resulting adjusted data, profiles and their evolution with time. It is intended as a further contribution to the ongoing studies and definition of best practices, to improve data traceability and comparability, as prescribed by the World Meteorological Organization Global Cryosphere Watch programme.

The Qinghai-Tibet Plateau (QTP) is the largest permafrost region in the world at low and middle latitudes and high elevation. Permafrost is being degraded on the QTP due to global warming, which has a significant effect on regional climate, hydrological, and ecological processes. This paper provides a summary of recent progress in methods used in permafrost research, the permafrost distribution, and basic data relevant to permafrost research on the QTP. The area of permafrost was 1.32 x 106 km2 over the QTP, which accounts for approximately 46% of the QTP. Moreover, simulation studies of the hydrothermal process and permafrost change were reviewed and evaluated the effect of permafrost degradation on hydrological and ecological processes. The results revealed that the effects of permafrost on runoff were closely related to soil temperature, and the effect of permafrost degradation on the carbon cycle requires further study. Finally, current challenges in simulation of permafrost change processes on the QTP were discussed, emphasizing that permafrost degradation under climate change is a slow and non-linear process. This review will aid future studies examining the mechanism underlying the interaction between permafrost and climate change, and environmental protection in permafrost regions on the QTP.

Permafrost is a widespread phenomenon in the cold regions of the globe and is under-represented in global monitoring networks. This study presents a novel low-cost, low-power, and robust Autonomous Electrical Resistivity Tomography (A-ERT) monitoring system and open-source processing tools for permafrost monitoring. The processing workflow incorporates diagnostic and filtering tools and utilizes open-source software, ResIPy, for data inversion. The workflow facilitates quick and efficient extraction of key information from large data sets. Field experiments conducted in Antarctica demonstrated the system's capability to operate in harsh and remote environments and provided high-temporal-resolution imaging of ground freezing and thawing dynamics. This data set and processing workflow allow for a detailed investigation of how meteorological conditions impact subsurface processes. The A-ERT setup can complement existing monitoring networks on permafrost and is suitable for continuous monitoring in polar and mountainous regions, contributing to cryosphere research and gaining deeper insights into permafrost and active layer dynamics. Permafrost, frozen ground in cold regions, has significant impacts on the global environment. Monitoring of permafrost is crucial because it influences the global carbon cycle, hydrology, contaminant movement, and ecosystem stability. However, current monitoring systems have limitations, particularly in remote regions like Antarctica. To tackle this challenge, a new monitoring system, Autonomous Electrical Resistivity Tomography (A-ERT), was introduced. A-ERT is a geophysical technique that employs electrical signals to study ground freezes and thaws with high precision over time. Alongside this, open-source processing tools were developed to process obtained A-ERT data and efficiently extract essential information from large data sets. The developed A-ERT system is robust, low-cost, low-power, and designed to operate in harsh conditions. Tested in Antarctica, our findings show that A-ERT data combined with processing pipelines offers a valuable tool for examining freezing and thawing processes in extreme environments. The proposed setup can contribute to a network of autonomous permafrost monitoring systems, important for cryosphere research and advancing our understanding of climate change's impact on permafrost dynamics. We present a robust low-cost Autonomous Electrical Resistivity Tomography system for permafrost monitoring in polar and mountainous regions We introduce an open-source tool for processing and inverting large data sets, enabling quick and efficient extraction of key information Field experiments conducted in Antarctica show high-temporal-resolution imaging of ground freezing and thawing dynamics

Due to the effects of global climate change, the permafrost temperature in the Qinghai-Tibet Plateau (QTP) has rapidly increased over the past decades. The development of thermokarst landforms is one distinctive indicator of permafrost degradation, while the change of the rate of permafrost degradation in recent 10 years has not been systematically investigated in QTP. In this paper, the annual average growth rate (AAGR) of ground deformation, the change of thaw slump areas, and the change of active layer thickness (ALT) of thermokarst landforms are monitored integrating SAR (synthetic aperture radar) and optical images for years 2007 to 2020 in Qilian Mountain, northern QTP. The ground deformation rate and seasonal amplitude were estimated by InSAR method, and the descending and ascending InSAR data are compared the validate the results. Based on the deformation results, AAGR was introduced to evaluate the permafrost degradation degree. Moreover, the ALT were estimated based on the seasonal deformation amplitude and Stefan model. The spatio-temporal characteristics of ground deformation and its relationship with thaw slump and temperature are explored. Experimental results show that the deformation rate increased about 150 % from 2007 to 10 to 2017-20. The maximum AAGR of deformation rate in the study area can reach 20.6 %. The thaw slump area has an obvious trend of expansion from 2009 to 2015, and its distribution agreed well with the deformation map. The ALT results ranged from 0.5 m to 2.8 m, indicating an obvious increase trend from 2007 to 2020. Based on the estimated increased ground deformation, thaw slump area, and ALT, it is inferred that frozen ground was undergoing serious degradation in the last 10 years. This study demonstrates the capability of multi-temporal InSAR in observing the accelerated permafrost thaw-freezing process and monitoring the permafrost parameters.

Extreme heat events in the summer of 2022 were observed in Eurasia, North America and China. Glaciers are a unique indicator of climate change, and the European Alps experienced substantial glacier mass loss as a result of the conditions in 2022, which prompted a wide range of community concerns. However, relevant findings for glaciers in China have not been currently reported. Here, we document the response of Urumqi Glacier No. 1 in the eastern Tien Shan to the extreme heat observed in 2022 based on in situ measurements that span more than 60 years. In 2022, Urumqi Glacier No. 1 exhibited the second largest annual mass loss on record, and the summer mass balance was the most negative on record. The hottest summer on record and relatively lower solid precipitation ratio contributed to the exceptional mass losses at Urumqi Glacier No. 1 in 2022, demonstrating the significant influence of heatwaves on extreme glacier melt in China.

Extreme heat events in the summer of 2022 were observed in Eurasia, North America and China. Glaciers are a unique indicator of climate change, and the European Alps experienced substantial glacier mass loss as a result of the conditions in 2022, which prompted a wide range of community concerns. However, relevant findings for glaciers in China have not been currently reported. Here, we document the response of Urumqi Glacier No. 1 in the eastern Tien Shan to the extreme heat observed in 2022 based on in situ measurements that span more than 60 years. In 2022, Urumqi Glacier No. 1 exhibited the second largest annual mass loss on record, and the summer mass balance was the most negative on record. The hottest summer on record and relatively lower solid precipitation ratio contributed to the exceptional mass losses at Urumqi Glacier No. 1 in 2022, demonstrating the significant influence of heatwaves on extreme glacier melt in China.

This study presents data from the first years of permafrost monitoring in boreholes in the French Alps that started at the end of 2009 in the framework of the PermaFrance network. Nine boreholes are instrumented, among which six monitored permafrost temperature and active layer thickness (ALT) over >10 years. Ice-poor and cold permafrost in high-elevation north-facing rock walls has warmed by up to >1(degrees)C at 10 m depth over the reference decade (2011-2020), whereas ice-rich permafrost (rock glacier) temperatures remained stable. ALT has increased at four of the five boreholes for which decadal data are available. Summer 2015 marks a turning point in ALT regime and greatest ALT values were observed in 2022 (available for six boreholes), but thawing intensity did not show an obvious change. At one site with a layer of coarse blocks about 2 m thick, ALT was stable over 2018-2022 and response to the hottest years was dampened. Linear trends suggest an ALT increase of 2 m per decade for some ice-poor rock walls, independently of their thermal state. The data reveal a variety of permafrost patterns and evolution with significant intraregional and local differences. Snow modulates the response to air temperature signal in various ways, with an important effect on near-surface temperature trends and ALT: early snow melting in spring favors an ALT increase in rock walls. Maintaining these monitoring systems and understanding the physical processes controlling heterogeneous responses to climate signals is crucial to better assess permafrost dynamics and to adapt to its consequences.

Ground temperature measurements are crucial for a better understanding of changes in the natural environment, especially in the Arctic. Previous measurement systems provided accurate measurements; however, their most significant disadvantage was the relatively low spatial resolution, including in the vertical profile. The aim of this work was to develop and initially validate a new, original temperature measurement system based on the photonic sensing technique of optical frequency-domain reflectometry (OFDR). The system consists of a fibre-optic sensor, an interrogator, and an automatic data acquisition system. Such fibre-optic sensors allow a significant increase in spatial resolution. Data on precise temperature distribution in the ground profile will allow for a detailed determination of the changes in the thickness of the permafrost active layer (PAL) and, as a consequence, a better description of the current state of the permafrost and the layers above it in relation to their progressive degradation. In the longer term, it will make a better prediction of the pace of possible changes in the polar environment and will open up previously unavailable opportunities in the field of climate change monitoring and forecasting.