Global climate change and permafrost degradation have significantly heightened the risk of geological hazards in high-altitude cold regions, resulting in severe casualties and property damage, particularly in the Qinghai-Tibet Plateau of China. To mitigate the risk of geological disasters, it is crucial to identify the primary disaster-inducing factors. Therefore, to address this issue more effectively, this study proposes a spatiotemporal-scale approach for detecting disaster-inducing factors and investigates the disaster-inducing factors of geological hazards in high-altitude cold regions, using the Kanchenjunga Basin as a case study. As the world's third-highest peak, Kanchenjunga is highly sensitive to climate fluctuations. This study first integrates the frost heave model and multitemporal interferometric synthetic aperture radar techniques to monitor ascending and descending track line-of-sight deformation of the frozen active layer in the study area. Subsequently, the surface parallel flow constrained model is employed to decompose the 3-D time-series deformation of geological hazards in the basin, with remote sensing imagery and field surveys used to identify a total of 94 disaster sites. In parallel, a database of potential conditioning factors is constructed by leveraging Google Earth Engine remote sensing inversion technology and relevant data provided by the China Geological Survey. Finally, by integrating monitoring results with a database of potential geological conditioning factors, the spatiotemporal-scale approach for detecting disaster-inducing factors proposed in this study is applied to investigate the disaster-inducing factors in the Kanchenjunga Basin. The research results highlight that surface temperature is the primary driving factor of geological hazards in the Kanchenjunga Basin. This research helps bridge the data gap in the region and offers critical support for local government decision-making in disaster prevention, risk assessment, and related areas.

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

The Circumpolar Active Layer Monitoring (CALM) network is an ongoing international effort to collect and disseminate standardized measurements of active-layer dynamics to monitor the response of near-surface permafrost parameters to climate change. This work presents a distillation of 25 years (1995-2019) of observations from three north-south transects of CALM sites in tundra environments of Alaska. Transects examined in this work bisect tundra regions of discontinuous permafrost on the Seward Peninsula, and the continuous permafrost zone on the western and eastern sections of the Arctic Foothills and Arctic Coastal Plain. These transects represent regional climatic gradients, several physiographic provinces, and regionally characteristic landcover associations. Total active-layer thickening at observed sites ranged from 7 to 26 cm; more significant thaw occurred in the foothills despite less pronounced warming air temperature trends. This summary highlights several regional active layer responses to climate warming, complicated by distinct thermal landscape sensitivities, landscape variability, and documented thaw subsidence. Data summarized in this report are publicly available and represent an important validation resource for earth-system models that include regions in the continuous and discontinuous permafrost zones of northern and western Alaska.

To better understand the ecological and hydrological responses to climatic and cryospheric changes, the spatiotemporal variations in the active layer thickness (ALT) need to be scrupulously studied. Based on more than 230 sites from the circumpolar active layer monitoring network, the spatiotemporal characteristics of the ALT across the northern hemisphere during 1990-2015 were investigated. Results indicate that the ALT exhibits substantial spatial variations across the northern hemisphere, ranging from approximately 30 cm in the arctic and subarctic regions to greater than 10 m in the mountainous permafrost regions at mid-latitudes. Regional averages of ALT are 48 cm in Alaska, 93 cm in Canada, 164 cm in the Nordic countries (including Greenland and Svalbard) and Switzerland, 330 cm in Mongolia, 476 cm in Kazakhstan, and 230 cm on the Qinghai-Tibetan Plateau (QTP), respectively. In Russia, the regional averages of ALT in European North, West Siberia, Central Siberia, Northeast Siberia, Chukotka, and Kamchatka are 110, 92, 69, 61, 53 and 60 cm, respectively. Increasing trends of ALT were not uniformly present in the observational records. Significant changes in the ALT were observed at 73 sites, approximately 43.2 % of the investigated 169 sites that are available for statistical analysis. Less than 25 % Alaskan sites and approximately 33 % Canadian sites showed significant increase in the ALT. On the QTP, almost all the sites showed significant ALT increases. Insignificant increase and even decrease in the ALT were observed in some parts of the northern hemisphere, e.g., Mongolia, parts of Alaska and Canada. The air and ground temperatures, vegetation, substrate, microreliefs, and soil moisture in particular, play decisive roles in the spatiotemporal variations in the ALT, but the relationships among each other are complicated and await further studies.

The Arctic is experiencing an unprecedented rate of environmental and climate change. The active layer ( the uppermost layer of soil between the atmosphere and permafrost that freezes in winter and thaws in summer) is sensitive to both climatic and environmental changes, and plays an important role in the functioning, planning, and economic activities of Arctic human and natural ecosystems. This study develops a methodology for modeling and estimating spatial-temporal variations in active layer thickness ( ALT) using data from several sites of the Circumpolar Active Layer Monitoring network, and demonstrates its use in spatial-temporal interpolation. The simplest model's stochastic component exhibits no spatial or spatio-temporal dependency and is referred to as the naive model, against which we evaluate the performance of the other models, which assume that the stochastic component exhibits either spatial or spatio-temporal dependency. The methods used to fit the models are then discussed, along with point forecasting. We compare the predicted fit of the various models at key study sites located in the North Slope of Alaska and demonstrate the advantages of space-time models through a series of error statistics such as mean squared error, mean absolute and percent deviance from observed data. We find the difference in performance between the spatio-temporal and remaining models is significant for all three error statistics. The best stochastic spatio-temporal model increases predictive accuracy, compared to the naive model, of 33.3%, 36.2% and 32.5% on average across the three error metrics at the key sites for a one-year hold out period.