Ambient seismic noise and microseismicity analyses are increasingly applied for the monitoring of landslides and natural hazards. These methodologies can offer a valuable monitoring tool also for glacial and periglacial bodies, to understand the internal processes driven by external modifications in air temperature and rainfall/snowfall regimes and to forecast possible melting-related hazards in the light of climate change adaptation. We applied the methods to an almost continuous year of data recorded by a network of four passive seismic stations deployed in the frontal portion of the Gran Sometta rock glacier (Aosta Valley, NW Italian Alps). The spectral analysis of ambient seismic noise revealed frequency peaks related to stratigraphic resonances inside the rock glacier. Although the resonance frequency related to the bedrock interface was constant over time, a second higher resonance frequency was identified as the effect of variations in the active layer thickness driven by external air temperature modifications at the daily and seasonal scales. Ambient seismic noise cross-correlation highlighted coherent shear wave velocity modifications inside the periglacial body. The microseismicity dataset extracted from the continuous ambient noise recordings was analyzed and clustered to further investigate the ongoing internal processes and gain insight into their source mechanism and location. The first cluster of events was found to be likely related to the basal movements of the rock glacier and to falls and slides of the debris material. The second cluster was possibly related to shallow ice and rock fracturing processes. The validation of the seismic results through simple models of the rock glacier physical and mechanical layering, the internal thermal regime and the surface displacements allowed for a comprehensive understanding of the rock glacier's reaction to the external conditions.

Periglacial processes and permafrost-related landforms, such as rock glaciers, are particularly vulnerable to climate change because of their reliance on sustained low temperatures to maintain permafrost integrity. Rising temperatures lead to permafrost thawing, increased active layer thickness, and ground instability, which disrupt the structural and ecological stability of these environments. Rock glaciers, which are ubiquitous in high mountain systems, are especially sensitive to these changes and serve as key geo-indicators of current or past alpine permafrost conditions, reflecting the multifaceted impacts of warming on both ecological and abiotic components. In this review, we synthesize current scientific knowledge on the complex and divergent responses of alpine rock glaciers to climate change, highlighting a wide range of methodologies employed to study the complex interactions between climatic drivers and rock glacier dynamics. We first explore ecological impacts, focusing on how climatic changes influence vegetation patterns, species composition, and overall biodiversity associated with rock glaciers. Subsequently, we examine the dynamic behavior of rock glaciers, including their structural integrity, movement patterns, and hydrological roles within high mountain ecosystems. By integrating findings from various disciplines, this review underscores the importance of multidisciplinary approaches and long-term monitoring to advance our understanding of rock glacier ecosystem dynamics and their role in periglacial processes under climate change. Our synthesis identifies critical knowledge gaps, such as the uncertain drivers of divergent rock glacier responses and the limited integration of ecological and abiotic data in existing studies. We highlight research priorities, including the establishment of regional monitoring networks and the development of predictive models that incorporate vegetation and permafrost interactions. These insights provide actionable guidance for adaptive management strategies to mitigate the ecological and geological impacts of climate change on these unique and sensitive environments.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.

Study area: Urumqi Glacier No.1 Catchment in central Asia. Study focus: Chemical weathering at the basin scale is important process for understanding the feedback mechanism of the carbon cycle and climate change. This study mainly used the actual sampling data in 2013, 2014, and 2016, and the first collection from the literature in same catchment to analyze the seasonal and interannual characteristics of meltwater runoff, as well as cation denudation rate (CDR). New hydrological insights for the study region: The dominant ions of meltwater runoff are Ca2 +, HCO3- , and SO42-, which are mainly derived from calcite dissolution, feldspar weathering and sulfide oxidation. Meltwater runoff at Urumqi Glacier No.1 has higher concentrations of Ca2+ and lower concentrations of HCO3- than that from glaciers in Asia. Compared to 2006 and 2007, cation concentrations increased in 2013 and 2014, while SO42- concentration decreased. The daily ion concentration has seasonality and exhibits a negative relationship with discharge. Daily CDR is positively related to discharge and temperature. Annual CDR values range from 12.34 to 19.04 t/ km2/yr in 2013, 2014, and 2016, which are 1-1.7 times higher than those in 2006 and 2007 and higher than some glaciers in Asia. These results indicate that chemical weathering rate in the Urumqi Glacier No.1 catchment has increased with climate warming, and it is stronger than that of some glaciers in the Tibetan Plateau and surroundings.

Glaciers provide multiple ecosystem services (ES) to human society. Due to the continued global warming, the valuation of glacier ES is of urgent importance because this knowledge can support the protection of glaciers. However, a systematic valuation of glacier ES is still lacking, particularly from the perspective of ES contributors. In this study, we introduce the concept of emergy to establish a methodological framework for accounting glacier ES values, and take the Tibetan Plateau (TP) as a case study to comprehensively evaluate the spatiotemporal characteristics of glacier ES during the early 21st century. The results show that the total glacier ES values on the TP increased from 2.36E+24 sej/yr in the 2000s to 2.40E+24 sej/yr in the 2010s, with an overall growth rate of 1.6%. The values of the various services in the 2010s are ranked in descending order: climate regulation (1.59E+24 sej/yr, 66.1%), runoff regulation (4.40E+23 sej/yr, 18.4%), hydropower generation (1.88E+23 sej/ yr, 7.8%). Significantly higher glacier ES values were recorded in the marginal TP than in the endorheic area. With the exception of climate regulation and carbon sequestration, all other service values increased during the study period, partially cultural services, which have experienced rapid growth in tandem with social development. The results of this study will help establish the methodological basis for the assessment of regional and global glacier ES, as well as a scientific basis for the regional protection of glacier resources.

As a key component of the cryosphere, permafrost is sensitive to climate change, but mapping permafrost, especially in the Tibetan Plateau, has been challenging due to the heterogeneous mountainous landscape and limited representativeness of ground observations. Using 155 compiled ground observations and more than 20,000 rock glacier records, we developed a machine learning model to map the distribution of permafrost and produce an improved permafrost zonation index (PZI) map. The model was applied by incorporating several control variables, including terrain (elevation and relief), soil (bulk density, clay, coarse fragments, sand, and silt), and temperature (MAAT, FDD, and TDDT) to estimate the PZI at a 1-km resolution in the southern Tibetan Plateau. Excluding glaciers and lakes, the area of permafrost estimated by the new map is approximately 103.5 x 103 km2, accounting for 47.8% of the total area of the region. The result was assessed with various datasets and compared with existing permafrost maps and achieved higher accuracy compared with previous studies. The overall classification accuracy was 96.1% in high plain areas and 84.4% in mountain areas. The results demonstrated the substantial potential for improving mapping permafrost and understanding the periglacial environment with rock glacier inventories and machine learning, especially in complex terrain and climate.

This study investigates black carbon (BC) concentrations in the seasonal snowpack on the Godwin-Austen Glacier and in surface snow at K2 Camps 1 and 2 (Karakoram Range), assessing their impact on snowmelt during the 2019 ablation season. Potential BC and moisture sources were identified through back-trajectory analysis and atmospheric reanalyses. Variations in water stable isotopes (delta 1(8)O and delta 2H) in the snowpack were analysed to confirm its representativeness as a climatic record for the 2018-19 accumulation season. The average BC concentration in the snow pits (12 ng g-1) generated 66 mm w.e. (or 53 mm w.e. excluding the basal zone) of meltwater. Surface snow at K2 Camp 1 showed BC concentrations of 7 ng g-1, consistent with those on the snowpack surface, suggesting it may reflect local BC levels in late February 2019. In contrast, higher concentrations at K2 Camp 2 (26 ng g-1) were potentially linked to expedition activities.

Rationale. Glaciers in the Tibetan Plateau (TP), especially in the Himalayas, are retreating rapidly due to rising air temperature and increasing anthropogenic emissions from nearby regions. Traditionally, pollutants deposited on the glaciers have been assumed to originate from long-range transport from its outside. Methodology. This study investigated the concentrations of black carbon (BC) and major ions in snowpit samples collected from two glaciers in the south-eastern TP (Demula and Palongzangbu) and one glacier in the west Himalayas (Jiemayangzong). The radiative forcing of BC was calculated based on BC concentration and glacier characteristics. Results. The results revealed that the BC/Ca2+ concentration ratio in snowpit samples from Palongzangbu, located near residential villages, is similar to 2.05 times higher than that of Demula, which is mainly influenced by long-range transported pollutants. Furthermore, on Jiemayangzong glacier, snowpit samples collected with 100-m vertical resolution exhibited that BC-induced radiative forcings at low altitude are similar to 2.37 +/- 0.16 times greater than those at high altitude. Discussion. These findings demonstrated that in addition to long-range transport, emissions from local residents also make substantial contributions to BC and certain major ions (e.g. SO42-). To accurately assess the sources and radiative forcing of BC and other light-absorbing impurities on glaciers of the TP, it is necessary to consider the impact of local populations and altitude-dependent variations.

With the global climate change, glaciers on the Qinghai-Tibet Plateau (QTP) and its adjacent mountainous regions are retreating rapidly, leading to an increase in active rock glaciers (ARGs) in front of glaciers. As crucial components of water resources in alpine regions and indicators of permafrost boundaries, ARGs reflect climatic and environmental changes on the QTP and its adjacent mountainous regions. However, the extensive scale of rock glacier development poses a challenge to field investigations and sampling, and manual visual interpretation requires substantial effort. Consequently, research on rock glacier cataloging and distribution characteristics across the entire area is scarce. This study statistically analyzed the geometric characteristics of ARGs using high- resolution GF-2 satellite images. It examined their spatial distribution and relationship with local factors. The findings reveal that 34,717 ARGs, covering an area of approximately 6873.54 km2, with an average area of 0.19 +/- 0.24 km2, a maximum of 0.0012 km2, and a minimum of 4.6086 km2, were identified primarily in north-facing areas at elevations of 4300-5300 m and slopes of 9 degrees-25 degrees, predominantly in the Karakoram Mountains and the Himalayas. Notably, the largest concentration of ARGs was found on north-facing shady slopes, constituting about 42 % of the total amount, due to less solar radiation and lower near-surface temperatures favorable for interstitial ice preservation. This research enriches the foundational data on ARG distribution across the QTP and its adjacent mountainous regions, offering significant insights into the response mechanisms of rock glacier evolution to environmental changes and their environmental and engineering impacts.

Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance-a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier anomalies. However, the dynamics of the Karakoram anomaly, particularly its evolution and persistence, remain insufficiently explored. In this study, we employed Landsat reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 albedo products to developed high-resolution albedo retrieval models using two machine learning (ML) regressions--random forest regression (RFR) and back-propagation neural network regression (BPNNR). The optimal BPNNR model (Pearson correlation coefficient [r] = 0.77-0.97, unbiased root mean squared error [ubRMSE] = 0.056-0.077, RMSE = 0.055-0.168, Bias = -0.149 similar to -0.001) was implemented on the Google Earth Engine cloud-based platform to estimate summer albedo at a 30-m resolution for the Karakoram region from 1990 to 2021. Validation against in-situ albedo measurements on three glaciers (Batura, Mulungutti and Yala Glacier) demonstrated that the model achieved an average ubRMSE of 0.069 (p < 0.001), with RMSE and ubRMSE improvements of 0.027 compared to MODIS albedo products. The high-resolution data was then used to identify firn/snow extents using a 0.37 threshold, facilitating the extraction of long-term firn-line altitudes (FLA) to indicate the glacier dynamics. Our findings revealed that a consistent decline in summer albedo across the Karakoram over the past three decades, signifying a darkening of glacier surfaces that increased solar radiation absorption and intensified melting. The reduction in albedo showed spatial heterogeneity, with slower reductions in the western and central Karakoram (-0.0005-0.0005 yr(-1)) compared to the eastern Karakoram (-0.006 similar to -0.01 yr(-1)). Notably, surge- or advance-type glaciers, avalanche-fed glaciers and debris-covered glaciers exhibited slower albedo reduction rates, which decreased further with increasing glacier size. Additionally, albedo reduction accelerated with altitude, peaking near the equilibrium-line altitude. Fluctuations in the albedo-derived FLAs suggest a transition in the dynamics of Karakoram glaciers from anomalous behavior to retreat. Most glaciers exhibited anomalous behavior from 1995 to 2010, peaking in 2003, but they have shown signs of retreat since the 2010s, marking the end of the Karakoram anomaly. These insights deepen our understanding of the Karakoram anomaly and provide a theoretical basis for assessing the effect of glacier anomaly to retreat dynamics on the water resources and adaptation strategies for the Indus and Tarim Rivers.