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  • 1
    Publication Date: 2023-06-16
    Description: Supraglacial deposits of tephra or volcaniclastics have the potential to cause significant anomalies of glacier ablation and runoff. The intensity of these anomalies is governed by the thermal resistivity of the covering layer and hence the thermal conductivity of the deposited grains. This study concentrates on causal and quantitative relationships between density, geochemical composition and thermal conductivity of volcanic materials based on the analysis of 43 samples from locations across Iceland. Thermal conductivity is primarily influenced by density, whereas geochemical composition has been proved to be of subsidiary importance. Four different multiple regression models were calibrated that calculate the grain thermal conductivity of a volcanic material based on rock properties and geochemical composition. In a subsequent step, the bulk thermal conductivity of the respective deposit is calculated as a function of porosity and degree of water saturation. Examples using volcanic material from the Eyjafjallajökull 2010 and Grímsvötn 2011 eruptions confirm that the presented calculation scheme can be executed using only limited geochemical data as input. This facilitates an easy application of the modeling scheme immediately after a volcanic eruption.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:552.2 ; Volcanic tephra ; Thermal conductivity ; Major element oxides ; Iceland ; Modeling
    Language: English
    Type: doc-type:article
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  • 2
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca (2019): Snow cover variability across glaciers in Nordenskiöldland (Svalbard) from point measurements in 2014–2016. Earth System Science Data Discussions, https://doi.org/10.5194/essd-2018-158
    Publication Date: 2023-01-13
    Description: Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.
    Keywords: A-01; A-02; A-03; A-04; A-05; A-06; B-01; B-02; B-03; B-04; B-05; B-06; B-07; B-08; B-09; B-10; B-11; C-01; C-02; C-03; C-04; C-05; D-01; D-02; D-03; D-04; D-05; D-06; D-07; DATE/TIME; Density, snow; Density, snow, uncertainty; E-01; E-02; E-03; E-04; E-05; Elevation of event; Event label; F-01; F-02; F-03; F-04; F-05; F-06; F-07; F-08; F-09; F-10; F-11; F-12; Flag; G-01; G-02; G-03; G-04; H-01; H-02; H-03; H-04; H-05; H-06; H-07; H-08; H-09; I-01; I-02; I-03; I-04; I-05; I-06; I-07; I-08; I-09; I-10; ICEM; Ice measurement; Latitude of event; Longitude of event; Number; Randolph Glacier Inventory 6.0, glacier ID; Snow thickness; Snow thickness, uncertainty; Snow water equivalent; Snow water equivalent, uncertainty; Svalbard; Svalbard_A-01; Svalbard_A-02; Svalbard_A-03; Svalbard_A-04; Svalbard_A-05; Svalbard_A-06; Svalbard_B-01; Svalbard_B-02; Svalbard_B-03; Svalbard_B-04; Svalbard_B-05; Svalbard_B-06; Svalbard_B-07; Svalbard_B-08; Svalbard_B-09; Svalbard_B-10; Svalbard_B-11; Svalbard_C-01; Svalbard_C-02; Svalbard_C-03; Svalbard_C-04; Svalbard_C-05; Svalbard_D-01; Svalbard_D-02; Svalbard_D-03; Svalbard_D-04; Svalbard_D-05; Svalbard_D-06; Svalbard_D-07; Svalbard_E-01; Svalbard_E-02; Svalbard_E-03; Svalbard_E-04; Svalbard_E-05; Svalbard_F-01; Svalbard_F-02; Svalbard_F-03; Svalbard_F-04; Svalbard_F-05; Svalbard_F-06; Svalbard_F-07; Svalbard_F-08; Svalbard_F-09; Svalbard_F-10; Svalbard_F-11; Svalbard_F-12; Svalbard_G-01; Svalbard_G-02; Svalbard_G-03; Svalbard_G-04; Svalbard_H-01; Svalbard_H-02; Svalbard_H-03; Svalbard_H-04; Svalbard_H-05; Svalbard_H-06; Svalbard_H-07; Svalbard_H-08; Svalbard_H-09; Svalbard_I-01; Svalbard_I-02; Svalbard_I-03; Svalbard_I-04; Svalbard_I-05; Svalbard_I-06; Svalbard_I-07; Svalbard_I-08; Svalbard_I-09; Svalbard_I-10
    Type: Dataset
    Format: text/tab-separated-values, 981 data points
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  • 3
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca (2017): Modeling glacier-surface albedo across Svalbard for the 1979-2015 period: The HiRSvaC500-alpha data set. Journal of Advances in Modeling Earth Systems, 9, 19 pp, https://doi.org/10.1002/2016MS000752
    Publication Date: 2023-01-13
    Description: Albedo is an important quantity for determining the energy balance of snow and ice surfaces and thus also for the mass balance of glaciers. It is especially important in polar regions where shortwave radiation fluxes typically provide most of the energy input to a glacier. In order to use albedo data in any spatially distributed glaciological modeling, it is vital that the albedo fields are not only of high accuracy, but also available on sufficiently high spatial resolution and in a manner that is consistent over time. This article presents the newly developed data set HiRSvaC500-alpha which provides daily updated, gapless albedo fields for all glacierized areas of the Arctic archipelago Svalbard on a 500 m resolution over the period 1979–2015. Albedo modeling for creation of the data set is done using a multi-step geostatistical approach on the basis of remotely-sensed Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data and gridded ERA-Interim climate data. Validation of the modeled HiRSvaC500-alpha albedo fields against in situ albedo measurements at automatic weather stations operated on two different glaciers suggests that the accuracy of the newly developed data set lies close to that of remotely-sensed MODIS albedo data. An analysis of the HiRSvaC500-alpha albedo data set yields a mean annual-average albedo of 0.754 across all glaciers of Svalbard over 1979–2015. A decrease of albedo with time is found, following a highly significant (95% level) trend of -0.010 per decade. For certain subregions, this trend even reaches up to -0.014 per decade.
    Keywords: Date/time end; Date/time start; File format; File name; File size; MULT; Multiple investigations; Svalbard; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 216 data points
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  • 4
    Publication Date: 2023-01-13
    Keywords: Ablation; Automatic weather station; AWS; Comment; DATE/TIME; HEIGHT above ground; Humidity, relative; Iceland_AWS; Precipitation; Short-wave downward (GLOBAL) radiation; Short-wave upward (REFLEX) radiation; Surface temperature; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 43854 data points
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  • 5
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    PANGAEA
    In:  Supplement to: Möller, Marco; Möller, Rebecca; Beaudon, Emilie; Mattila, Olli-Pekka; Finkelnburg, Roman; Braun, Matthias Holger; Grabiec, Mariusz; Jonsell, Ulf; Luks, Bartłomiej; Puczko, Dariusz; Scherer, Dieter; Schneider, Christoph (2011): Snowpack characteristics of Vestfonna and De Geerfonna (Nordaustlandet, Svalbard) - a spatiotemporal analysis based on multiyear snow-pit data. Geografiska Annaler Series A-Physical Geography, 93(4), 273-285, https://doi.org/10.1111/j.1468-0459.2011.00440.x
    Publication Date: 2023-12-13
    Description: Extensive glaciological field measurements were carried out on the ice cap Vestfonna as well as on the minor ice body De Geerfonna (Nordaustlandet, Svalbard) within the framework of IPY Kinnvika. Field campaigns were conducted during the period 2007-2010 in spring (April/May) and summer (August). In this study we compile and present snow cover information obtained from 22 snow pits that were dug on Vestfonna during this period. Locations are along two transects on the northwestern, land terminating slope of the ice cap, on its central summit, Ahlmann Summit, and at a set of several other locations in the eastern and northern part of the ice cap. Snow-cover information acquired from four snow pits on adjacent De Geerfonna is also incorporated in this study. Field data are analysed regarding snow stratigraphy, snow density, snow hardness and snow temperature. Results reveal mean snow densities of around 400 kg/m**3 for the snowpack of Vestfonna with no apparent spatial or interannual variability. A distinctly higher value of more than 450 kg/m**3 was obtained for De Geerfonna. A spatial comparison of snow water equivalents above the previous end-of-summer surface serves for obtaining insights into the spatial distribution of snow accumulation across Vestfonna. Altitude was found to be the only significant spatial parameter for controlling snow accumulation across the ice cap.
    Keywords: DATE/TIME; DeGeerfonna_DG; Density, snow; Density, standard deviation; Depth, relative; Depth water equivalent; ELEVATION; Event label; Glacier; Hardness description; Identification; International Polar Year (2007-2008); International Polar Year 2007-2008; IPY; IPY-4; Latitude of event; Longitude of event; Nordaustlandet, Svalbard; SNOWPIT; Snow pit; Standard deviation; Temperature, air; Temperature, ice/snow; Temperature, ice/snow, maximum; Temperature, ice/snow, minimum; Temperature, ice/snow, standard deviation; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Vestfonna_V1; Vestfonna_V10; Vestfonna_V11; Vestfonna_V12; Vestfonna_V13; Vestfonna_V14; Vestfonna_V15; Vestfonna_V16; Vestfonna_V17; Vestfonna_V18; Vestfonna_V2; Vestfonna_V3; Vestfonna_V4; Vestfonna_V5; Vestfonna_V6; Vestfonna_V7; Vestfonna_V8; Vestfonna_V9
    Type: Dataset
    Format: text/tab-separated-values, 454 data points
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  • 6
    Publication Date: 2015-11-20
    Print ISSN: 0022-1430
    Electronic ISSN: 1727-5652
    Topics: Geography , Geosciences
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  • 7
    Publication Date: 2017-01-27
    Description: Albedo is an important quantity for determining the energy balance of snow and ice surfaces and thus also for the mass balance of glaciers. It is especially important in polar regions where shortwave radiation fluxes typically provide most of the energy input to a glacier. In order to use albedo data in any spatially distributed glaciological modeling, it is vital that the albedo fields are not only of high accuracy, but also available on sufficiently high spatial resolution and in a manner that is consistent over time. This article presents the newly developed data set HiRSvaC500-α which provides daily updated, gapless albedo fields for all glacierized areas of the Arctic archipelago Svalbard on a 500 m resolution over the period 1979–2015. Albedo modeling for creation of the data set is done using a multi-step geostatistical approach on the basis of remotely-sensed Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data and gridded ERA-Interim climate data. Validation of the modeled HiRSvaC500-α albedo fields against in situ albedo measurements at automatic weather stations operated on two different glaciers suggests that the accuracy of the newly developed data set lies close to that of remotely-sensed MODIS albedo data. An analysis of the HiRSvaC500-α albedo data set yields a mean annual-average albedo of 0.754 across all glaciers of Svalbard over 1979–2015. A decrease of albedo with time is found, following a highly significant (95% level) trend of -0.010 per decade. For certain subregions, this trend even reaches up to −0.014 per decade. This article is protected by copyright. All rights reserved.
    Electronic ISSN: 1942-2466
    Topics: Geography , Geosciences
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 8
    Publication Date: 2016-07-18
    Description: Supraglacial deposits are known for their influence on glacier ablation. The magnitude of this influence depends on the thickness and the type of the deposited material. The effects of thin layers of atmospheric black carbon and of thick moraine debris have been intensively studied. Studies related to regional-scale deposits of volcanic tephra with thicknesses varying between millimetres and metres and thus over several orders of magnitude are scarce. We present results of a field experiment in which we investigated the influence of supraglacial deposits of tephra from Grimsvotn volcano on bare-ice ablation at Svinafelsjokull, Iceland. We observed that the effective thickness at which ablation is maximized ranges from 1.0 to 2.0 mm. At similar to 10 mm a critical thickness is reached where sub-tephra ablation equals bare-ice ablation. We calibrated two empirical ablation models and a semi-physicsbased ablation model that all account for varying tephra-layer thicknesses. A comparison of the three models indicates that for tephra deposits in the lower-millimetre scale the temperature/radiationindex model performs best, but that a semi-physics-based approach could be expected to yield superior results for tephra deposits of the order of decimetres.
    Print ISSN: 0022-1430
    Electronic ISSN: 1727-5652
    Topics: Geography , Geosciences
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  • 9
    Publication Date: 2016-09-19
    Print ISSN: 0018-2222
    Electronic ISSN: 1432-119X
    Topics: Biology , Medicine
    Published by Springer
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  • 10
    Publication Date: 2006-08-01
    Print ISSN: 0378-1127
    Electronic ISSN: 1872-7042
    Topics: Biology , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Elsevier
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