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Sonam Wangchuk acknowledges ESKAS - Swiss Government Excellence Scholarship for Foreign Scholars, Swiss Polar Institute, and University of Zurich for supporting the research. Tobias Bolch thanks the Swiss National Science Foundation [IZLCZ2_169979/1]. ; Glacial lakes pose a serious threat to downstream areas and significantly impact glacier melt. The number and area of lakes has grown in most regions during the last decades due to the ongoing atmospheric warming and retreating glaciers. It is therefore important to identify and monitor these lakes. However, mapping of glacial lakes in alpine regions is challenged by many factors. These factors include small size of glacial lakes, cloud cover in optical satellite images, cast shadows from mountains and clouds, seasonal snow in satellite images, varying degrees of turbidity amongst glacial lakes, and frozen glacial lake surface. In our study, we have developed a fully automated method for mapping glacial lake across alpine regions including the Python package called "GLakeMap". The method uses multi-source data such as Sentinel-1 Synthetic Aperture Radar and Sentinel-2 Multi-spectral Instrument data, digital elevation model, and a random forest classifier model. We use multi-source datasets as inputs for rule-based segmentation of images, mainly aiming at extracting glacial lake objects from satellite images using a set of rules. Segmented objects are then classified either as glacial lake or non-glacial lake objects by the random forest classifier model. The method was tested in eight sites across alpine regions mainly located in High Mountain Asia but also in the Alps and the Andes. We show that the proposed method overcomes a majority of the aforementioned challenges to detect and delineate glacial lakes. The method performs efficiently irrespective of geographic, geologic, and climatic conditions of glacial lakes. ; Publisher PDF ; Peer reviewed

Tarim River basin is the largest endorheic river basin in China. Due to the extremely arid climate the water supply solely depends on water originating from the glacierised mountains with about 75% stemming from the transboundary Aksu River. The water demand is linked to anthropogenic (specifically agriculture) and natural ecosystems, both competing for water. Ongoing climate change significantly impacts the cryosphere. The mass balance of the glaciers in Aksu River basin was clearly negative since 1975. The discharge of the Aksu headwaters has been increasing over the last decades mainly due to the glacier contribution. The average glacier melt contribution to total runoff is 30–37% with an estimated glacier imbalance contribution of 8–16%. Modelling using future climate scenarios indicate a glacier area loss of at least 50% until 2100. River discharge will first increase concomitant with glacier shrinkage until about 2050, but likely decline thereafter. The irrigated area doubled in the Aksu region between the early 1990s and 2020, causing at least a doubling of water demand. The current water surplus is comparable to the glacial runoff. Hence, even if the water demand will not grow further in the future a significant water shortage can be expected with declining glacial runoff. However, with the further expansion of irrigated agriculture and related industries, the water demand is expected to even further increase. Both improved discharge projections and planning of efficient and sustainable water use are necessary for further socioeconomic development in the region along with the preservation of natural ecosystems.

The formation and breaching of natural dammed lakes have formed the landscapes, especially in seismically active high-mountain regions. Dammed lakes pose both, potential water resources, and hazard in case of dam breaching. Central Asia has mostly arid and semi-arid climates. Rock glaciers already store more water than ice-glaciers in some semi-arid regions of the world, but their distribution and advance mechanisms are still under debate in recent research. Their impact on the water availability in Central Asia will likely increase as temperatures rise and glaciers diminish. This thesis provides insight to the relative age distribution of selected Kyrgyz and Kazakh rock glaciers and their single lobes derived from lichenometric dating. The size of roughly 8000 different lichen specimens was used to approximate an exposure age of the underlying debris surface. We showed that rock-glacier movement differs signifcantly on small scales. This has several implications for climatic inferences from rock glaciers. [...]

The formation and breaching of natural dammed lakes have formed the landscapes, especially in seismically active high-mountain regions. Dammed lakes pose both, potential water resources, and hazard in case of dam breaching. Central Asia has mostly arid and semi-arid climates. Rock glaciers already store more water than ice-glaciers in some semi-arid regions of the world, but their distribution and advance mechanisms are still under debate in recent research. Their impact on the water availability in Central Asia will likely increase as temperatures rise and glaciers diminish. This thesis provides insight to the relative age distribution of selected Kyrgyz and Kazakh rock glaciers and their single lobes derived from lichenometric dating. The size of roughly 8000 different lichen specimens was used to approximate an exposure age of the underlying debris surface. We showed that rock-glacier movement differs signifcantly on small scales. This has several implications for climatic inferences from rock glaciers. [...]

B Robson was supported by the Meltzer foundation and a University of Bergen grant. S MacDonell was supported by CONICYT-Programa Regional (R16A10003) and the Coquimbo Regional Government via FIC-R(2016)BIP 40000343. D. Hölbling has been supported by the Austrian Science Fund through the project MORPH (Mapping, Monitoring and Modeling the Spatio-Temporal Dynamics of Land Surface Morphology; FWF-P29461-N29). N Schaffer was financed by CONICYT-FONDECYT (3180417) and P Rastner by the ESA Dragon 4 programme (4000121469/17/I-NB). ; Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories. ; Publisher PDF ; Peer reviewed

Abstract. Glacial lake outburst floods (GLOFs) are a major concern throughout High Mountain Asia, where societal impacts can extend far downstream. This is
particularly true for transboundary Himalayan basins, where risks are expected to further increase as new lakes develop. Given the need for
anticipatory approaches to disaster risk reduction, this study aims to demonstrate how the threat from a future lake can be feasibly assessed
alongside that of worst-case scenarios from current lakes, as well as how this information is relevant for disaster risk management. We have focused on two previously identified dangerous lakes (Galongco and Jialongco), comparing the timing and magnitude of simulated worst-case outburst events from these lakes both in the Tibetan town of Nyalam and downstream at the border with Nepal. In addition, a future scenario has been assessed, whereby an avalanche-triggered GLOF was simulated for a potential large new lake forming upstream of Nyalam. Results show that large (>20×106 m3) rock and/or ice avalanches could generate GLOF discharges at the border with Nepal that are more than 15 times larger than what has been observed previously or anticipated based on more gradual breach simulations. For all assessed lakes, warning times in Nyalam would be only 5–11 min and 30 min at the border. Recent remedial measures undertaken to lower the water level at Jialongco would have little influence on downstream impacts resulting from a very large-magnitude GLOF, particularly in Nyalam where there has been significant development of infrastructure directly within the high-intensity flood zone. Based on these findings, a comprehensive approach to disaster risk management is called for, combining early warning systems with effective land use zoning and programmes to build local response capacities. Such approaches would address the current drivers of GLOF risk in the basin while remaining robust in the face of worst-case, catastrophic outburst events that become more likely under a warming climate.

Funding: This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300) and the Swiss National Science Foundation (200021E_177652/1). NN received funding from the European Union's Horizon 2020 programme (No. 689443). ; Knowledge about the long-term response of High Mountain Asian glaciers to climatic variations is paramount because of their important role in sustaining Asian river flow. Here, a satellite-based time series of glacier mass balance for seven climatically different regions across High Mountain Asia since the 1960s shows that glacier mass loss rates have persistently increased at most sites. Regional glacier mass budgets ranged from −0.40 ± 0.07 m w.e.a−1 in Central and Northern Tien Shan to −0.06 ± 0.07 m w.e.a−1 in Eastern Pamir, with considerable temporal and spatial variability. Highest rates of mass loss occurred in Central Himalaya and Northern Tien Shan after 2015 and even in regions where glaciers were previously in balance with climate, such as Eastern Pamir, mass losses prevailed in recent years. An increase in summer temperature explains the long-term trend in mass loss and now appears to drive mass loss even in regions formerly sensitive to both temperature and precipitation. ; Publisher PDF ; Peer reviewed

This study was partially supported by core funds of ICIMOD contributed by the governments of Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, Sweden, and Switzerland. AO was supported by funding from the National Environmental Research Council (NERC) National Capability Overseas Development Assistance under the grant 'Polar expertise –Supporting development' (NE/R000107/1). ; River systems originating from the Upper Indus Basin (UIB) are dominated by runoff from snow and glacier melt and summer monsoonal rainfall. These water resources are highly stressed as huge populations of people living in this region depend on them, including for agriculture, domestic use, and energy production. Projections suggest that the UIB region will be affected by considerable (yet poorly quantified) changes to the seasonality and composition of runoff in the future, which are likely to have considerable impacts on these supplies. Given how directly and indirectly communities and ecosystems are dependent on these resources and the growing pressure on them due to ever-increasing demands, the impacts of climate change pose considerable adaptation challenges. The strong linkages between hydroclimate, cryosphere, water resources, and human activities within the UIB suggest that a multi- and inter-disciplinary research approach integrating the social and natural/environmental sciences is critical for successful adaptation to ongoing and future hydrological and climate change. Here we use a horizon scanning technique to identify the Top 100 questions related to the most pressing knowledge gaps and research priorities in social and natural sciences on climate change and water in the UIB. These questions are on the margins of current thinking and investigation and are clustered into 14 themes, covering three overarching topics of 'governance, policy, and sustainable solutions', 'socioeconomic processes and livelihoods', and 'integrated Earth System processes'. Raising awareness of these ...