ALBERT

Description: Himalayan glaciers are a focus of public and scientific debate. Prevailing uncertainties are of major concern because some projections of their future have serious implications for water resources. Most Himalayan glaciers are losing mass at rates similar to glaciers elsewhere, except for emerging indications of stability or mass gain in the Karakoram. A poor understanding of the processes affecting them, combined with the diversity of climatic conditions and the extremes of topographical relief within the region, makes projections speculative. Nevertheless, it is unlikely that dramatic changes in total runoff will occur soon, although continuing shrinkage outside the Karakoram will increase the seasonality of runoff, affect irrigation and hydropower, and alter hazards.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Bolch, T -- Kulkarni, A -- Kaab, A -- Huggel, C -- Paul, F -- Cogley, J G -- Frey, H -- Kargel, J S -- Fujita, K -- Scheel, M -- Bajracharya, S -- Stoffel, M -- New York, N.Y. -- Science. 2012 Apr 20;336(6079):310-4. doi: 10.1126/science.1215828.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Geography, University of Zurich, 8057 Zurich, Switzerland. tobias.bolch@geo.uzh.ch〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22517852" target="_blank"〉PubMed〈/a〉

Description: The Gorkha earthquake (magnitude 7.8) on 25 April 2015 and later aftershocks struck South Asia, killing ~9000 people and damaging a large region. Supported by a large campaign of responsive satellite data acquisitions over the earthquake disaster zone, our team undertook a satellite image survey of the earthquakes' induced geohazards in Nepal and China and an assessment of the geomorphic, tectonic, and lithologic controls on quake-induced landslides. Timely analysis and communication aided response and recovery and informed decision-makers. We mapped 4312 coseismic and postseismic landslides. We also surveyed 491 glacier lakes for earthquake damage but found only nine landslide-impacted lakes and no visible satellite evidence of outbursts. Landslide densities correlate with slope, peak ground acceleration, surface downdrop, and specific metamorphic lithologies and large plutonic intrusions.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Kargel, J S -- Leonard, G J -- Shugar, D H -- Haritashya, U K -- Bevington, A -- Fielding, E J -- Fujita, K -- Geertsema, M -- Miles, E S -- Steiner, J -- Anderson, E -- Bajracharya, S -- Bawden, G W -- Breashears, D F -- Byers, A -- Collins, B -- Dhital, M R -- Donnellan, A -- Evans, T L -- Geai, M L -- Glasscoe, M T -- Green, D -- Gurung, D R -- Heijenk, R -- Hilborn, A -- Hudnut, K -- Huyck, C -- Immerzeel, W W -- Liming, Jiang -- Jibson, R -- Kaab, A -- Khanal, N R -- Kirschbaum, D -- Kraaijenbrink, P D A -- Lamsal, D -- Shiyin, Liu -- Mingyang, Lv -- McKinney, D -- Nahirnick, N K -- Zhuotong, Nan -- Ojha, S -- Olsenholler, J -- Painter, T H -- Pleasants, M -- Pratima, K C -- Yuan, Q I -- Raup, B H -- Regmi, D -- Rounce, D R -- Sakai, A -- Donghui, Shangguan -- Shea, J M -- Shrestha, A B -- Shukla, A -- Stumm, D -- van der Kooij, M -- Voss, K -- Xin, Wang -- Weihs, B -- Wolfe, D -- Lizong, Wu -- Xiaojun, Yao -- Yoder, M R -- Young, N -- New York, N.Y. -- Science. 2016 Jan 8;351(6269):aac8353. doi: 10.1126/science.aac8353. Epub 2015 Dec 16.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA. ; School of Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, WA, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Department of Geology, University of Dayton, Dayton, OH, USA. kargel@hwr.arizona.edu dshugar@uw.edu uharitashya1@udayton.edu. ; Ministry of Forests, Lands and Natural Resource Operations, Prince George, BC, Canada. ; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. ; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan. ; Scott Polar Research Institute, University of Cambridge, Cambridge, UK. ; Institute of Environmental Engineering, Federal Institute of Technology-ETH, Zurich, Switzerland. ; NASA Marshall Space Flight Center, Huntsville, AL, USA. ; International Centre for Integrated Mountain Development, Kathmandu, Nepal. ; NASA Headquarters, Washington, DC, USA. ; GlacierWorks, Marblehead, MA, USA. ; The Mountain Institute, Elkins, WV, USA. ; U.S. Geological Survey, Menlo Park, CA, USA. ; Central Department of Geology, Tribhuvan University, Kirtipur, Kathmandu, Nepal. ; Department of Geography, University of Victoria, Victoria, BC, Canada. ; CVA Engineering, Suresnes, France. ; Earthquake Science Center, U.S. Geological Survey, Pasadena, CA, USA. ; ImageCat, Long Beach, CA, USA. ; Faculty of Geosciences, Utrecht University, Utrecht, Netherlands. ; State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, Hubei Province, China. ; U.S. Geological Survey, Golden, CO, USA. ; Department of Geosciences, University of Oslo, Blindern, Oslo, Norway. ; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA. ; Cold and Arid Regions of Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China. ; School of Earth Sciences and Engineering, Nanjing University, Nanjing, China. ; Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX, USA. ; School of Geography Science, Nanjing Normal University, Nanjing, China. ; Department of Geography, Texas A&M University, College Station, TX, USA. ; Department of Geology, University of Dayton, Dayton, OH, USA. ; Arizona Remote Sensing Center, School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA. ; National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA. ; Himalayan Research Center, Kathmandu, Nepal. ; Environmental and Water Resources Engineering, University of Texas at Austin, Austin, TX, USA. ; Wadia Institute of Himalayan Geology, Dehradun, India. ; MacDonald Dettwiler and Associates-GSI, Ottawa, Ontario, Canada. ; Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA. ; College of Architecture and Urban Planning, Hunan University of Science and Technology, Xiangtan, China. ; Geography Department, Kansas State University, Manhattan, KS, USA. ; Global Land Ice Measurements from Space (GLIMS) Steward, Alaska Region, Anchorage, AK, USA. ; College of Geographical Science and Environment, Northwest Normal University, China. ; Department of Physics, University of California, Davis, Davis, CA, USA. ; Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, TAS, Australia.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26676355" target="_blank"〉PubMed〈/a〉

Description: According to Liu et al. (2014), borrowing, substituting and generating (BSG) are the main methods people used to acquire the discharge at ungauged stations. Two of the substitution (modelling and disaggregation) methods in combination with the borrowing idea are compared for simulating discharge for the Upper Salween and Mekong River Basin (USMRB). It is seen that with a simple borrowing/ disaggregating method, the Nash-Sutcliffe Efficiency (NSE) can reach 0.82. The similarity in the seasonal variation pattern is a more important requirement to identify if the two stations are to be considered as having hydrological similarity. From the experience obtained for the USMRB, an upstream station with shorter geographical distance may be more in hydrological similarity than a station in the far downstream. The NSE is quite low when borrowing occurs within the low altitude downstream region. The efficiency will be decreased when we borrow information from several stations which may be not in hydrological similarity.

Description: In this study, we apply a glacier mass balance and ice redistribution model to simulate historical and future glacier change in the Everest region of Nepal. High-resolution temperature and precipitation fields derived from gridded APHRODITE data, and validated against independent station observations from the EVK2CNR network, are used to drive the historical model from 1961 to 2007. The model is calibrated against geodetically derived estimates of net glacier mass change from 1992 to 2008, termini position of four large glaciers at the end of the calibration period, average velocities observed on selected debris-covered glaciers, and total glacierized area. We integrate field-based observations of glacier mass balance and ice thickness with remotely-sensed observations of decadal glacier change to validate the model. Between 1961 and 2007, the mean modelled volume change over the Dudh Kosi basin is −6.4 ± 1.5 km3, a decrease of 15.6% from the original estimated ice volume in 1961. Modelled glacier area change between 1961 and 2007 is −101.0 ± 11.4 km2, a decrease of approximately 20% from the initial extent. Scenarios of future climate change, based on CMIP5 RCP4.5 and RCP8.5 end members, suggest that glaciers in the Everest region will continue to lose mass through the 21st century. Glaciers in the basin are concentrated between 5000 and 6000 m of elevation, and are thus expected to be sensitive to changes in temperature and equilibrium line altitude (ELA). Glacier volume reductions between −35 to −62% are possible by 2050, and sustained temperature increases to 2100 may result in total glacier volume losses of between −73 and −96%.

Description: Ice volume estimates are crucial for assessing water reserves stored in glaciers. A variety of different methodologies exist but there is a lack of systematic comparative analysis thereof. Due to its large glacier coverage, such estimates are of particular interest for the Himalayan-Karakoram (HK) region. Here, three volume–area (V–A) relations, a slope-dependent estimation method, and two ice-thickness distribution models are applied to a complete glacier inventory of the HK region. An uncertainty and sensitivity assessment is performed to investigate the influence of the input glacier areas, and model approaches and parameters on the resulting total ice volumes. Results of the two ice-thickness distribution models are validated with local ice-thickness measurements at six glaciers. The resulting ice volumes for the entire HK region range from 2955 km3 to 6455 km3, depending on the approach. Results from the ice thickness distribution models and the slope-dependent thickness estimations agree well with measured local ice thicknesses while V–A relations show stronger deviations. The study provides evidence on the significant effect of the selected method on results and underlines the importance of a careful and critical evaluation. More ice-thickness measurements are needed to improve models and results in the future.

Description: In this study, we apply a glacier mass balance and ice redistribution model to examine the sensitivity of glaciers in the Everest region of Nepal to climate change. High-resolution temperature and precipitation fields derived from gridded station data, and bias-corrected with independent station observations, are used to drive the historical model from 1961 to 2007. The model is calibrated against geodetically derived estimates of net glacier mass change from 1992 to 2008, termini position of four large glaciers at the end of the calibration period, average velocities observed on selected debris-covered glaciers, and total glacierized area. We integrate field-based observations of glacier mass balance and ice thickness with remotely sensed observations of decadal glacier change to validate the model. Between 1961 and 2007, the mean modelled volume change over the Dudh Koshi basin is −6.4 ± 1.5 km3, a decrease of 15.6% from the original estimated ice volume in 1961. Modelled glacier area change between 1961 and 2007 is −101.0 ± 11.4 km2, a decrease of approximately 20% from the initial extent. The modelled glacier sensitivity to future climate change is high. Application of temperature and precipitation anomalies from warm/dry and wet/cold end-members of the CMIP5 RCP4.5 and RCP8.5 ensemble results in sustained mass loss from glaciers in the Everest region through the 21st century.

Description: Ice volume estimates are crucial for assessing water reserves stored in glaciers. Due to its large glacier coverage, such estimates are of particular interest for the Himalayan–Karakoram (HK) region. In this study, different existing methodologies are used to estimate the ice reserves: three area–volume relations, one slope-dependent volume estimation method, and two ice-thickness distribution models are applied to a recent, detailed, and complete glacier inventory of the HK region, spanning over the period 2000–2010 and revealing an ice coverage of 40 775 km2. An uncertainty and sensitivity assessment is performed to investigate the influence of the observed glacier area and important model parameters on the resulting total ice volume. Results of the two ice-thickness distribution models are validated with local ice-thickness measurements at six glaciers. The resulting ice volumes for the entire HK region range from 2955 to 4737 km3, depending on the approach. This range is lower than most previous estimates. Results from the ice thickness distribution models and the slope-dependent thickness estimations agree well with measured local ice thicknesses. However, total volume estimates from area-related relations are larger than those from other approaches. The study provides evidence on the significant effect of the selected method on results and underlines the importance of a careful and critical evaluation.

Description: Failures of glacial lake dams can cause outburst floods and represents a serious hazard. The potential danger of outburst floods depends on various factors like the lake's area and volume, glacier change, morphometry of the glacier and its surrounding moraines and valley, and glacier velocity. Remote sensing offers an efficient tool for displacement calculations and risk assessment of the identification of potentially dangerous glacial lakes (PDGLs) and is especially helpful for remote mountainous areas. Not all important parameters can, however, be obtained using spaceborne imagery. Additional interpretation by an expert is required. ASTER data has a suitable accuracy to calculate surface velocity. Ikonos data offers more detail but requires more effort for rectification. All investigated debris-covered glacier tongues show areas with no or very slow movement rates. From 1962 to 2003 the number and area of glacial lakes increased, dominated by the occurrence and almost linear areal expansion of the moraine-dammed lakes, like the Imja Lake. Although the Imja Lake will probably still grow in the near future, the risk of an outburst flood (GLOF) is considered not higher than for other glacial lakes in the area. Potentially dangerous lakes and areas of lake development are identified. There is a high probability of further lake development at Khumbu Glacier, but a low one at Lhotse Glacier.

Description: Glaciers are one of the important natural resources of freshwater and sources of water for hydropower, agriculture and drinking whenever the water is scarce. This mapping and change analysis helps to understand the status and decadal changes of glaciers in Hidden Valley, Mustang district, Nepal. The investigation is carried out using Landsat images of the years 1977 (~1980s), 1990, 2000 and 2010. We mapped 10 glaciers of the Hidden Valley covering an area of 19.79 km2 based on the object-based image classification method using an automatic method and manual delineation by a Geographic Information System (GIS), separately. The glacier outlines for 2010, 2000, 1990 and 1980s in both methods are delineated from the multispectral Landsat images of the respective years. The total area losses of the glaciers from the automatic method are 1.713 and 0.625 km2 between 1990−2000 and 2000−2010 and from manual delineation are 2.021, 1.264, 1.041 km2 between ~1980s−1990, 1990−2000 and 2000−2010. The amount of average estimated glacier ice reserves lost is 0.326 km3 (26.26 %) and the total glacier area loss is 4.33 km2 (21.87 %) from the 1980s to 2010 based on manual delineation. The glaciers of Hidden Valley are shrinking and fragmented due to decrease in glacier area and ice reserves.