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The cryosphere reacts sensitively to climate change, as evidenced by the widespread retreat of mountain glaciers
. Subsurface ice contained in permafrost is similarly affected by climate change, causing persistent impacts on natural and human systems. In contrast to glaciers, permafrost is not observable spatially and therefore its presence and possible changes are frequently overlooked. Correspondingly, little is known about permafrost in the mountains of the Hindu Kush Himalaya (HKH) region, despite permafrost area exceeding that of glaciers in nearly all countries. Based on evidence and insight gained mostly in other permafrost areas globally, this review provides a synopsis on what is known or can be inferred about permafrost in the mountains of the HKH region. Given the extreme nature of the environment concerned, it is to be expected that the diversity of conditions and phenomena encountered in permafrost exceed what has previously been described and investigated. We further argue that climate change in concert with increasing development will bring about diverse permafrost-related impacts on vegetation, water quality, geohazards, and livelihoods. To better anticipate and mitigate these effects, a deepened understanding of high-elevation permafrost in subtropical latitudes as well as the pathways interconnecting environmental changes and human livelihoods are needed
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The manual aims to provide a detailed and comprehensive methodology for mapping rock glaciers in Google Earth
. The method was developed to support an assessment of permafrost distribution in the Hindu Kush Himalayan (HKH) region and the examples are based on this study. The objectives are - to provide a step-by-step manual on how to map rock glaciers in Google Earth,
- to provide examples of the diversity of rock glaciers encountered, of permafrost related features, and of how to classify rock glaciers, and
- to provide a step-by-step guide on how to extract spatial attributes from the mapped rock glaciers.
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The extent and distribution of permafrost in the mountainous parts of the Hindu Kush Himalayan (HKH) region are largely unknown
. A long tradition of permafrost research, predominantly on rather gentle relief, exists only on the Tibetan Plateau. Two permafrost maps are available digitally that cover the HKH and provide estimates of permafrost extent, i.e., the areal proportion of permafrost: the manually delineated Circum-Arctic Map of Permafrost and Ground Ice Conditions (Brown et al., 1998) and the Global Permafrost Zonation Index, based on a computer model (Gruber, 2012). This article provides a first-order assessment of these permafrost maps in the HKH region based on the mapping of rock glaciers. Rock glaciers were used as a proxy, because they are visual indicators of permafrost, can occur near the lowermost regional occurrence of permafrost in mountains, and can be delineated based on high-resolution remote sensing imagery freely available on Google Earth. For the mapping, 4000 square samples (~ 30 km2) were randomly distributed over the HKH region. Every sample was investigated and rock glaciers were mapped by two independent researchers following precise mapping instructions. Samples with insufficient image quality were recorded but not mapped. We use the mapping of rock glaciers in Google Earth as first-order evidence for permafrost in mountain areas with severely limited ground truth. The minimum elevation of rock glaciers varies between 3500 and 5500 m a.s.l. within the region. The Circum-Arctic Map of Permafrost and Ground Ice Conditions does not reproduce mapped conditions in the HKH region adequately, whereas the Global Permafrost Zonation Index does so with more success. Based on this study, the Permafrost Zonation Index is inferred to be a reasonable first-order prediction of permafrost in the HKH. In the central part of the region a considerable deviation exists that needs further investigations.
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Wagnon, P.; Vincent, C.; Arnaud, Y.; Berthier, E.; Vuillermoz, E.; Gruber, S.; Ménégoz, M.; Gilbert, A.; Dumont, M.; Shea, J. M.; Stumm, D.; Pokhrel, B. K.
In the Everest region, Nepal, ground-based monitoring programmes were started on the debris-free Mera Glacier (27
.7° N, 86.9° E; 5.1 km2, 6420 to 4940 m a.s.l.) in 2007 and on the small Pokalde Glacier (27.9° N, 86.8° E; 0.1 km2, 5690 to 5430 m a.s.l., ~ 25 km north of Mera Glacier) in 2009. These glaciers lie on the southern flank of the central Himalaya under the direct influence of the Indian monsoon and receive more than 80% of their annual precipitation in summer (June to September). Despite a large inter-annual variability with glacier-wide mass balances ranging from −0.67 ± 0.28 m w.e. in 2011–2012 (Equilibrium-line altitude (ELA) at ~ 5800 m a.s.l.) to +0.46 ± 0.28 m w.e. in 2010–2011 (ELA at ~ 5340 m a.s.l.), Mera Glacier has been shrinking at a moderate mass balance rate of −0.08 ± 0.28 m w.e. yr−1 since 2007. Ice fluxes measured at two distinct transverse cross sections at ~ 5350 m a.s.l. and ~ 5520 m a.s.l. confirm that the mean state of this glacier over the last one or two decades corresponds to a limited mass loss, in agreement with remotely-sensed region-wide mass balances of the Everest area. Seasonal mass balance measurements show that ablation and accumulation are concomitant in summer which in turn is the key season controlling the annual glacier-wide mass balance. Unexpectedly, ablation occurs at all elevations in winter due to wind erosion and sublimation, with remobilised snow potentially being sublimated in the atmosphere. Between 2009 and 2012, the small Pokalde Glacier lost mass more rapidly than Mera Glacier with respective mean glacier-wide mass balances of −0.72 and −0.23 ± 0.28 m w.e. yr−1. Low-elevation glaciers, such as Pokalde Glacier, have been usually preferred for in-situ observations in Nepal and more generally in the Himalayas, which may explain why compilations of ground-based mass balances are biased toward negative values compared with the regional mean under the present-day climate
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In high mountain areas, permafrost is important because it influences the occurrence of natural hazards, because it has to be considered in construction practices, and because it is sensitive to climate change
. The assessment of its distribution and evolution is challenging because of highly variable conditions at and below the surface, steep topography and varying climatic conditions. This paper presents a systematic investigation of effects of topography and climate variability that are important for subsurface temperatures in Alpine bedrock permafrost. The authors studied the effects of both, past and projected future ground surface temperature variations on the basis of numerical experimentation with simplified mountain topography in order to demonstrate the principal effects. The modeling approach applied combines a distributed surface energy balance model and a three-dimensional subsurface heat conduction scheme. Results show that the past climate variations that essentially influence present-day permafrost temperatures at depth of the idealized mountains are the last glacial period and the major fluctuations in the past millennium. Transient effects from projected future warming, however, are likely larger than those from past climate conditions because larger temperature changes at the surface occur in shorter time periods. We further demonstrate the accelerating influence of multi-lateral warming in steep and complex topography for a temperature signal entering the subsurface as compared to the situation in flat areas. The effects of varying and uncertain material properties (i.e., thermal properties, porosity, and freezing characteristics) on the subsurface temperature field were examined in sensitivity studies. A considerable influence of latent heat due to water in low-porosity bedrock was only shown for simulations over time periods of decades to centuries. At the end, the model was applied to the topographic setting of the Matterhorn (Switzerland). Results from idealized geometries are compared to this first example of real topography, and possibilities as well as limitations of the model application are discussed.
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