Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain and can cause dynamic changes to the landscape. Their occurrence in the Arctic has become increasingly frequent. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial distribution of RTS is critical to understanding and modelling global warming factors from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 as the vegetation feature and 2-m ArcticDEM as the elevation feature. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. Our experiments shed light on the impact of within-class and between-class variances of RTS in different regions on the model performance and provided critical implications for our follow-up study. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.

How to cite: Yang, Y., M. Rogers, B., Fiske, G., Watts, J., Potter, S., Windholz, T., Mullen, A., Nitze, I., and Natali, S.: Mapping Retrogressive Thaw Slumps Using Satellite Data With Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1675, https://doi.org/10.5194/egusphere-egu23-1675, 2023.

Permafrost soils in boreal forests and tundra store more than two atmospheres worth of carbon, yet the vigorous permafrost-carbon-climate feedback loop remains poorly understood. In addition to ongoing strong warming, fires can further accelerate permafrost degradation and trigger the release of ancient carbon into the atmosphere. Despite the urgency after the recent Arctic fire seasons of 2019, 2020 and 2021, fire-permafrost interactions are currently not included in Earth system models from the sixth assessment of the Intergovernmental Panel on Climate Change (IPCC). This is because large-scale observations of fire-induced permafrost degradation are lacking. Therefore, we studied fire-induced permafrost degradation using the Interferometric Synthetic Aperture Radar (InSAR) technique and time series of Sentinel-1 (S-1) imagery. In this pilot study, we tested our approach on fires from 2019 and 2020 in the Chokurdakh area, Northeastern Siberia. We processed time series S-1 SAR data from a snow-free season (June-October) where S-1 SAR image selection was automated by using the Moderate Resolution Imaging Spectroradiometer snow cover products. To understand the drivers of InSAR-derived subsidence, we applied the XGBoost regression algorithm using subsidence as a response variable and ten other environmental variables as predictor variables. First, we found that the time series InSAR technique is suitable for deriving subsidence over fire-affected permafrost terrain. Second, the fire-affected permafrost terrain exhibited four to five times greater subsidence compared to the surrounding unburned area. Third, the XGBoost regression model revealed land surface temperature (LST) and albedo (derived from Landsat data) as the primary predictor variables  of surface subsidence, accounting for more than 50% of the predictive power. The permafrost degradation in many tundra areas is likely dominated by fire-induced changes in the surface energy balance.  From this pilot study, we conclude that our approach has the potential to study fire-permafrost interaction and environmental drivers of surface subsidence at the northern circumpolar scale. Models can also use our data to parameterize subsidence and thermokarst processes associated with permafrost degradation due to fire.

How to cite: Wangchuk, S., Schaefer, K., Michaelides, R., Vonk, J., and Veraverbeke, S.: Unraveling fire-permafrost interactions in Northeastern Siberian tundra using InSAR and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9035, https://doi.org/10.5194/egusphere-egu23-9035, 2023.

Rock glaciers are geomorphologically valuable indicators of permafrost distribution and form potentially important hydrological resources in the context of future climate change. Despite the widespread distribution of permafrost on the Tibetan Plateau and its reputation as the "water tower of Asia", this region lacks a complete inventory and systematic investigation of rock glaciers. In this study, we develop a deep-learning-based approach for mapping rock glaciers on the Tibetan Plateau. A powerful deep learning network, DeepLabv3+, is trained using Planet Basemaps as training imagery and multi-source rock glacier inventories as training labels. The well-trained model is then used to map new rock glaciers. The visually consistent and cloud-free properties of Planet Basemaps are crucial for developing comprehensive maps of rock glacier distribution; and the rock glacier inventories from multiple regions can improve the volume and diversity of the training dataset. The deep learning mapped results present strong identification and acceptable boundary delineation of rock glaciers, indicating that the deep learning model could serve as a useful tool for facilitating the inventory of rock glaciers over vast regions. Based on the deep learning outputs, we compile 4233 rock glaciers on eight subregions of the Tibetan Plateau, which are widespread in the surrounding regions while being scarcely distributed in inner areas. Talus- and glacier-connected rock glaciers are two major classes, which are dominant on the southeastern and densely distributed on the northwestern Tibetan Plateau, respectively. The regions with steep slopes are favored by rock glacier clusters with high density, and glacier-abundant regions tend to breed large rock glaciers. The proposed rock glacier mapping method effectively speeds up inventorying efforts, which will be used to map and inventory rock glaciers on the entire Tibetan Plateau. The complete inventory will offer a significant contribution to the global catalog and serves as a benchmark dataset for modeling and monitoring the state of permafrost in a changing climate.

How to cite: Sun, Z., Hu, Y., Liu, L., Racoviteanu, A., and Harrison, S.: Mapping and inventorying rock glaciers on the Tibetan Plateau from Planet Basemaps using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6816, https://doi.org/10.5194/egusphere-egu23-6816, 2023.

Rock glaciers are common landforms in many alpine permaforst landscapes that play an important role in alpine hydrology and landscape evolution, principally through the release of seasonal meltwater and the downslope transport of coarse material. Here, we use satellite-based interferometric synthetic aperture radar (InSAR) to identify and monitor rock glaciers in the Western USA. We focus on the movement of active and transitional rock glaciers in Utah (Uinta, Wasatch, and La Sal Mountains), and Wyoming (Wind River Mountains) between 2015 and 2022. Using the new framework established by the International Permafrost Association (IPA) Action Group, we identified 255 active and transitional rock glaciers in the ~3500 km² Uinta Mountains, 45 rock glaciers in the ~200 km² La Sal Mountains, 55 rock glaciers in the ~135 km² Wasatch Mountains, and 120 rock glaciers in the ~3000 km² Wind River Mountains. These rock glaciers currently occur under different climatic regimes based on data from the 30 year (1991-2020) normal Parameter-elevation Relationships on Independent Slopes Model (PRISM). The La Sals and Wasatch are warmer and wetter with a mean annual air temperature (MAAT) of ~3.0± 1.9 ˚C and  2.7 ± 1.1 ˚C and a mean annual precipitation (MAP) of ~92 ± 13 cm and ~130 ± 17 cm, respectively, whereas the Uintas and Wind Rivers are cooler and drier with a MAAT of ~0.24 ± 1.4 ˚C and  -0.87 ± 1.4 ˚C and a MAP of ~87 ± 11 cm and ~81 ± 10 cm. The mean line-of-sight (LOS) velocities for individual rock glaciers range from ~1 to 10 cm/yr. We also examined the time-dependent relationship between the motion of the rock glaciers and local climatic drivers such as temperature and precipitation. We found that rock glaciers exhibit seasonal and annual velocity changes, likely driven by liquid water availability (from snowmelt and rainfall), with accelerated motion during summers and during wetter years. Our findings demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.

How to cite: Handwerger, A. L., Kluetmeier, C., Brencher, G., and Munroe, J. S.: Seasonal and annual kinematics of active rock glaciers under different climate regimes in the Western USA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9092, https://doi.org/10.5194/egusphere-egu23-9092, 2023.

Permafrost is an important component of sub-Arctic environments and is extremely vulnerable to the impact of climate change. During the last decades, permafrost regions in northern high latitudes have been exposed to greater temperature changes than other regions worldwide. Increased temperatures cause rapid thawing of permafrost which can lead to changes in hydrological processes. Therefore, capturing dynamics of land surface temperature (LST) as one of the key factors affecting the thermal regime of permafrost landscapes at high spatial resolution is crucial for better monitoring these areas under drastic warming projected due to climate change. 
Landsat imagery at 30 m resolution offers the potential to obtain a consistent coverage of near-surface temperature values. In this study LST values from Landsat were compared with in-situ based soil freeze/thaw (F/T) index, air and soil temperature measurements obtained at the Abisko peatland site in the permafrost areas of northern Sweden. The soil F/T index is an important proxy that describes the relationship between the unfrozen soil water content and the soil temperature in freezing soils.  From 2017 to 2022, comparisons between Landsat LST and soil F/T index show high similarity between them in identifying frozen state, thawed state, and transition periods. In addition, Landsat LST values were found to be better correlated with air temperature (R² > 90%) than with soil temperature (R² > 80%) measurements. Overall, it is concluded that Landsat LST offers great potential for monitoring surface temperature changes in high-latitude permafrost regions and provides a promising source of input data for developing models to determine the spatial heterogeneity of freezing and thawing cycles.

How to cite: Taghavi Bayat, A., Gerke, M., and Riedel, B.: Comparison of land surface temperatures from Landsat with soil freeze/thaw measurements in permafrost peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9630, https://doi.org/10.5194/egusphere-egu23-9630, 2023.

Lake ice plays a critical role in local energy balances and provides crucial socio-economic services such as travel between communities and transportation of goods during winter months. However, over the past 40 years, the number of in situ observations has declined. Additionally, increasing temperatures lead to an increasing number of melt events throughout the season, resulting in the formation of more snow ice and slush layers. The increase in wet ice conditions poses a challenge in monitoring lake ice using active microwave technologies (e.g., synthetic aperture radar) and can be a risk to those who use ice cover as an essential travel route. This study focuses on Lake Oulujärvi in Finland during the 2020-2021 ice season. Using the snow microwave radiative transfer (SMRT) model, backscatter was modelled using observations of dry and wet conditions from the field. Snow density, snow depth, microstructure data, and ice thickness data collected during the field campaign helped parameterize the Snow Microwave Radiative Transfer (SMRT) model. Simulations under dry conditions showed that increasing roughness at the ice-water interface had the largest increase in backscatter. However, when the water content of the overlying snow layers increased, the roughness of the interface with the highest moisture content became the dominant interface impacting backscatter. Melt-freeze events throughout the ice season had a prolonged impact on backscatter resulting in increases of >3.69 dB. Larger increases in backscatter due to higher moisture were a result of larger dielectric contrasts created between overlying dry snow on slush layers. Improved understanding of the impact of wet conditions on backscatter can help to improve the monitoring of hazardous lake ice conditions and aid in the further development of inversion models for lake ice properties.

How to cite: Murfitt, J., Duguay, C., Picard, G., and juha.lemmetyinen@fmi.fi, J.: Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10182, https://doi.org/10.5194/egusphere-egu23-10182, 2023.

Aufeis is a common phenomenon in permafrost and cold regions of the Northern Hemisphere that develops during winter by successive water overflow and freezing on ice-covered surfaces. Most studies on the occurrence and hydrological importance of aufeis focus on North America and Siberia, while research in High Mountain Asia is still in an early phase. However, its widespread occurrence in the Upper Indus Basin, especially in the cold-arid regions of the Trans-Himalaya and the Tibetan Plateau indicates a need for comprehensive analysis.

Two endorheic basins, located at an elevation above 4500 m a.s.l. were selected for an in depth study: Pangong Tso and Tso Moriri covering an area of ~33500 km² and 2350 km², respectively. Based on a time-series analysis of Landsat and Sentinel-2 data for the period 2008–2021, aufeis fields were mapped and their spatial occurrence and temporal patterns were characterized. Derived parameters include the number, maximum area, and topographical parameters, such as elevation and slope. In addition, high altitude wetland areas were classified for both basins in order to explore potential interactions between hydrology and vegetation cover.

More than 1000 aufeis fields covering an area of 88 km² were detected in the Pangong basin. The largest individual aufeis field reached an area of 14 km². The size increases from south to north towards the Tibetan plateau. 50 % are located at an elevational range from 4800 and 5000 m a.s.l.. In the Tso Moriri basin 27 aufeis fields covering a maximum area of 9 km² spreading over an elevational range from 4600 up to 5000 m a.s.l. were detected. Here, the largest individual aufeis spreads over 1.7 km². The accumulation of aufeis fields starts with regular overflow of water between November until April, while aufeis is usually completely melted by the end of July. However, in the Pangong basin 28 aufeis fields remain until the onset of the next accumulation cycle. All of them are located in elevations above 5000 m a.s.l.. In contrast to the Pangong basin, aufeis fields in the Tso Moriri basin are mostly found in close proximity to wetlands, on areas with frequent aufeis accumulation vegetation is almost completely absent. Potential water sources for overflow events are often located close or within the wetland areas, suggesting close hydrological interactions. The study contributes to an improved understanding of aufeis development and distribution in cold-arid environments and will help further comprehensive cryosphere studies in High Mountain Asia and beyond.

How to cite: Brombierstäudl, D., Schmitt, T., Schmidt, S., and Nüsser, M.: Aufeis in High Mountain Asia: Evidence from two endorheic basins (Tso Moriri and Pangong Tso), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13288, https://doi.org/10.5194/egusphere-egu23-13288, 2023.

Known as “Water Tower of Asia”, the Tibetan Plateau (TP) is widely distributed with numerous inflow lakes. Lakes on the TP are less affected by human actives and can be considered as a sensitive indicator of climate change, changes of lakes can well reflect the change of regional climate. However, due to the harsh environment, data acquisition is not easy, studies on the response of lake change to climate mainly focus on several typical lakes (Nam Co, Selin Co Ngoring lake, etc.), but less attention is paid to variation characteristics of mesoscale lake (~100km²). To compensate for this deficiency, we selected three typical mesoscale lakes (Bamu Co, Langa Co and Longmu Co) in different climate zones and studied the lake changes and their responses to climate change using in-situ observations data and remote sensing data. By using multisource remote sensing and water level observation data, this study systematically analyzed inter-annual changes from 1970 to 2021 and monthly changes from 2019 to 2021. The main conclusions are as follows: (1) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (2) The monthly variation of lake water volume showed a periodical trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (3) Temperature and precipitation are dominant meteorological elements affecting the variation of lakes, and with the warming of the TP, temperature plays an increasingly important role. Combining observational data and remote sensing data, the study of mesoscale lakes changes can increase the understanding of relationship between lake change and climate change, provide help for further study of lake - atmosphere interaction and climate effect and climate change in the TP.

Key words: Tibetan Plateau; mesoscale lakes; change of lake water volume; multisource altimetry data; in-situ observation; climate zones

How to cite: Ma, W., Bai, L., Ma, W., Hu, W., Xie, Z., Su, R., Wang, B., and Ma, Y.: Variation characteristics of mesoscale lakes in the Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4024, https://doi.org/10.5194/egusphere-egu23-4024, 2023.