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774 results for “glacier”
Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region from 1986 to 2021 Based on Deep Learning
<p>The glacier outlines in the Three-Rivers Headwater Region from 1986–2021 in a total of 12 periods were obtained based on Landsat-5 and 8 images using the M-LandsNet and through manual adjustments. The comparison with previous research results indicated that this dataset has high accuracy. This dataset can provide support for the study of the glaciers mass balance in the Three-Rivers Headwater Region.</p>
Glacier retreat creating new Pacific salmon habitat in western North America
<p>Glacier retreat poses risks and benefits for species of cultural and economic importance. One example is Pacific salmon (<em>Oncorhynchus</em> spp.), which support subsistence harvests, and commercial and recreational fisheries worth billions of dollars annually. Although decreases in summer streamflow and warming freshwater is reducing Pacific salmon habitat in parts of their range, glacier retreat is creating new streams and lakes that salmon can colonize. However, potential gains in future salmon habitat associated with glacier loss have yet to be quantified across the range of Pacific salmon. Here we project future gains in Pacific salmon freshwater habitat by linking a model of glacier mass change for 315 glaciers, forced by five different Global Climate Models, with a simple model of salmon stream habitat potential throughout the Pacific Mountain ranges of western North America. We project that by the year 2100 glacier retreat will create 6,146 (+1,619) km of new streams accessible for colonization by Pacific salmon, of which 1,930 (+569) km have the potential to be used for spawning and juvenile rearing, representing 0 to 27% gains within the 18 sub-regions we studied. These findings can inform proactive management and conservation of Pacific salmon in this era of rapid climate change.</p>
Data from: Effect of subshelf melt variability on sea level rise contribution from Thwaites Glacier, Antarctica
<p>This archive includes output from the ice sheet model MALI used in the paper:<br> Effect of subshelf melt variability on sea level rise contribution from<br> Thwaites Glacier, Antarctica by Matthew Hoffman, et al.<br> submitted to Journal of Geophysical Research-Earth Surface in May 2019.</p> <p>The contents of this archive are:</p> <p>* control_run: annual output file for the control run</p> <p>* main_ensembles: global statistics files for the 72 runs making up the main<br> ensembles described in the paper and shown in Figures 4-6.</p> <p>* scaled_melt_ensemble: global statistics files for the scaled-melt ensemble<br> shown in Figures 9 and 10.</p> <p>* linear_perturbation_runs: global statistics files for each of the linear<br> perturbation runs used in Figure 11.</p> <p>The model code used to to generate the output and the scripts used to process<br> the data are available here:<br> https://github.com/matthewhoffman/MPAS-Model<br> or<br> https://github.com/MPAS-Dev/MPAS-Model<br> in the commit 93359c339b6d06726f8e3fbbfadc19337c20c0c7</p> <p>The processing scripts are in the directory:<br> testing_and_setup/compass/landice/Thwaites_variability</p>
Data: Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces
<p>Hydrology, flow cytometry and upscaling data for the manuscript "Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces". Each .zip file includes an individual 'readme' document.</p>
Long-term snow chemical composition monitoring - Ariebreen glacier (Hornsund) - raw data
<p>Since 2020, snow samples have been taken from the Ariebreen glacier several times a season during the accumulation season. The snow samples are collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, they are analysed in the chemical laboratory of the Polish Polar Station for pH, conductivity and chemical composition (major ions).<br> <br> Site Information Ariebreen - 0.5 km long glacier between Skoddefjellet and the northern part of Ariekammen, southernmost in Wedel Jarlsberg Land.</p> <p>Presented data from 2020 to 2022</p> <p>The data has not been checked, which means that it is raw data.</p> <p>Principal investigator (PI) Adam Nawrot</p>
Rock glacier inventory from multi-temporal InSAR measurement and optical image interpretation in southern Daxue Shan
<p>Based on the Sentinel-1A ascending SAR images acquired between June 2019 and June 2022, we derived a five-year-long annual average velocity map in southern Daxue Shan. Kinematic characteristics from InSAR measurement and geomorphic characteristics from optical image interpretation are then considered comprehensively for rock glaciers inventorying. A total of 860 rock glaciers are compiled and their geomorphic and kinematic parameters are calculated.</p>
Glacier Bay National Park mammal and plant community data
<p>1.<span> </span>Disturbance is a key driver of community assembly and patterns of diversity. Whereas successional changes in vegetation have been well studied, post-disturbance successional patterns of wildlife communities remain poorly understood.</p> <p>2.<span> </span>Here, we investigated the roles of site age and habitat in shaping community assembly and the diversity of terrestrial mammals in Glacier Bay National Park, Alaska (GBNP), which has undergone the most rapid and extensive deglaciation in the world since the Little Ice Age. Deglaciation has extensively altered the landscape, opening up new habitat for recolonization by plants and animals. </p> <p>3.<span> </span>We used camera traps, small mammal trapping and vegetation surveys to investigate the patterns of mammalian succession and beta diversity following deglaciation, using a space-for-time substitution across 10 sites during summers 2017 and 2018. Site age and habitat characteristics were not strongly correlated (r < 0.46), allowing the influences of time since disturbance and habitat changes to be distinguished.</p> <p>4.<span> </span>PERMANOVA analyses indicated that mammal community assembly was more strongly influenced by site age than habitat, whereas habitat and age had similar effects on beta (between site) diversity. Beta diversity was higher for smaller, less mobile mammals than larger, more mobile mammals and was primarily driven by species turnover among sites, whereas relative turnover was much lower for larger mammals. A comprehensive review of historic distributions of mammals in GBNP supported our findings that species turnover is a driving influence of community assembly for smaller mammals. </p> <p>5.<span> </span>Our results indicate that body size of mammals may play an important role in shaping colonization patterns post-disturbance, likely via size-related differences in mobility. Patterns of wildlife community assembly may therefore not track vegetation succession following disturbances if there are barriers to movement or if dispersal ability is limited, highlighting the importance of incorporating landscape connectivity and species traits into wildlife conservation efforts following disturbances. This knowledge may improve predictions of mammalian community assembly following major disturbance events. </p>
Plant succession at Mueller Glacier (New Zealand) UAV imagery and reference data (raw)
<p>This dataset includes a drone (Uncrewed Aerial Vehicles, UAV) orthomosaic (RGB) of plant communities acquired at the Mueller Glacier (New Zealand) in Februrary 2018. The resolution (ground sampling distance) of the orthomosaic amounts to approx. 3-4 cm. The orthomosaic is partially labelled (polygon shapefiles) in terms of plant community cover. The plant communities have been defined according to a field survey (see reference below). The orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI plant communities are assumed to be completely delineated (by visual interpretation).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publications:</p> <p>Kattenborn, T., Eichel, J., & Fassnacht, F. E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. <em>Scientific reports</em>, <em>9</em>(1), 1-9.</p> <p><a href="https://doi.org/10.1038/s41598-019-53797-9">https://doi.org/10.1038/s41598-019-53797-9</a></p> <p><a href="https://www.nature.com/articles/s41598-019-53797-9">https://www.nature.com/articles/s41598-019-53797-9</a><br><br><br>Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., & Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. <em>Remote Sensing in Ecology and Conservation</em>, <em>6</em>(4), 472-486.</p> <p><a href="https://doi.org/10.1002/rse2.146">https://doi.org/10.1002/rse2.146</a></p> <p><a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146</a></p>
Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier
<p>This Zenodo repository contains all datasets (IPSL CMIP6 data, glaciological data, SAFRAN data), ElmerIce codes and Python Jupyter Notebook used in the study reported in the article <strong><em>"Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier"</em></strong></p>
Glacier slowdown and rapid ice loss in the Tinguiririca and Cachapoal Basin, Central Andes of Chile
<p>Ice thickness and velocity data for the paper "Glacier slowdown and rapid ice loss in the Tinguiririca and Cachapoal Basin, Central Andes of Chile".</p> <p>The data is available for both annual medians and multi-year medians.</p> <p>Raw Sentinel-2 imagery used to reproduce these results can be freely downloaded from a variety of online sources, we do not upload it here to avoid excessive storage. The GIV toolbox may be accessed here: https://github.com/MaxVWDV/glacier-image-velocimetry.</p>
Hydrological Response of Andean Catchments to Recent Glacier Mass Loss (data)
<p>Data related to the article in review (The Cryosphere journal)</p>
Using a Web Map Service to map Little Ice Age glacier extents at regional scales
<p>Extending the record of glacier area changes into the past improves our understanding of climate change impacts. Although analogue maps showing historic glacier extents are abundant, digital outlines from before the satellite era are sparse as the digitisation of moraines and trimlines on freely available satellite images is challenging. With the now available very high-resolution images provided by Web Map Services (WMS), new doors are open for the precise digitisation. Here, we used the ESRI WMS to digitise Little Ice Age (LIA) glacier extents and present area changes since the LIA in four selected regions along with a detailed uncertainty analysis. We used modern glacier outlines as a starting point and additionally consulted Sentinel-2 images, the ArcticDEM and historic maps for interpretation. Dating records from the literature allowed calculating area change rates. In total, 493 LIA glaciers (4640 km2, now 891 ice bodies with 3590 km2) were digitised, yielding relative area changes of −20% (−0.14% a−1), −15% (−0.10% a−1), −26% (−0.16% a−1) and −61% (−0.19% a−1) for Alaska, Baffin Island, Novaya Zemlya and the tropics, respectively. The ESRI WMS images are a great asset to precisely map moraines and trimlines, but information about the timing of the related extents requires further sources.</p>
Direct observation and measurements of subglacial deformation in a tunnel beneath Wright Lower Glacier, Antarctica
<p>Photographs and video of a tunnel excavated in the margin of Wright Lower Glacier, Antarctica</p>
Processed ApRES displacement of internal reflectors at Helheim Glacier in August, 2016.
<p>This is the processed ApRES product using dataset collected in August, 2016.</p> <p>A readme file is included to extract displacement time series at a given point. </p>
Seismic waveform data collected at Helheim Glacier between Aug, 2012 and Jun, 2017 (part 1)
<p> Raw seismic waveforms (in miniSEED format) at HEL1 station collected at Helheim Glacier between 2012 and 2017 (part 1)</p>
Line of Sight (LOS) velocity obtained from terrestrial radar interferometer at Helheim Glacier
<p>1h-interval LOS ice flow speed processed using TRI dataset collected at Helheim Glacier in August, 2016.</p> <p> </p>
Seismic waveform data collected at Helheim Glacier between Aug, 2012 and Jun, 2017 (part 2)
<p>Raw seismic waveforms (in miniSEED format) at HEL1 station collected at Helheim Glacier between 2012 and 2017 (part 2)</p>
Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer
<p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> GLACIER METEOROLOGICAL DATA<br> SWISS ALPS -2022<br>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br>Data gathered and structured by Thomas Shaw (WSL, Switzerland (until Oct 2022)).</p><p>On and off-glacier meteorological data were gathered and analysed as part of a Marie-Curie project 'TEMPEST' (tempestglacier.com).<br>The dataset consists of hourly low-cost AWS (Davis Vantage Pro2) and simple temperature ('T-')logger (Onset TidBitv2) sensor records on three glaciers in the Swiss Alps (Canton Valais).</p><p>The glaciers are:<br>Haut Glacier d'Arolla (45.967°N, 7.526°E)<br>Glacier d'Otemma (45.956°N, 7.454°E)<br>Glacier du Corbassière (45.975°N, 7.303°E)</p><p>Data are provided in individual Excel files per glacier that contain all hourly data for the sub-period of comparison (11 August-18 September, 2022).<br>Data are quality controlled and checked for obvious errors. Any uncertain values are set to NaN.<br>Air temperature data at 'T-Logger' stations were corrected for heating errors using the comparison of measurements in artificially (AWS) and naturally ventilated (T-Logger) radiation shields on Arolla and Corbassiere glaciers.<br>A multiple linear regression model was applied to estimate these differences at all T-Loggers on all glaciers as a function of incoming shortwave radiation (MeteoSwiss station-derived) and wind speed (measured at AWS).</p><p>Each Excel file contains a 'META' tab for simple metadata related to station locations (latitude 'LAT' (°), longitude 'LON' (°), elevation 'ELE' (m a.s.l.) and flowpath length 'FPL' (m)) and a 'DATA' tab for the hourly data. <br>Suffixes to the station names in each column provide the variable measured at that site:<br>'TA' - 2m air temperature (°C)<br>'TA_Hi' - Maximum air temperature for timestep (°C)<br>'TA_Lo' - Minimum air temperature for timestep (°C)<br>'RH' - 2m relative humiditiy (%)<br>'FF' - Wind speed (m s^-1)<br>'FF_Hi' - Maximum wind speed for timestep (m s^-1)<br>'FF_Lo' - Minimum wind speed for timestep (m s^-1)<br>'DIR' - Wind direction (°)<br>'DEW' - Dewpoint temperature (°C)<br>'PRESS' - Air pressure (mbar)<br>'CHILL' - Calculated wind chill temperature (°C)<br>'Heat_idx' - Calculated heat index (°C)<br>'THSW' - A calculated index that uses humidity and temperature like for the Heat Index, but also includes the heating effects of sunshine and the cooling effects of wind (like Wind Chill) to calculate an apparent temperature of what it "feels" like out in the shade</p><p>Wind speeds and direction measured at off-glacier sites 'OG' are for the lower off-glacier station ('OG_Low'). </p><p>-------------------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.<br> </p>
Data for "A thermal-hydro-mechanical model for evaluating the stability of mountain glaciers"
<p>This dataset details the data produced in our study "A thermal-hydro-mechanical model for evaluating the stability of mountain glaciers".</p>
Glacier D´ Arcouzan. 24-09-2023
<p>Glacier D´ Arcouzan. 24-09-2023</p>
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