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774 results for “glacier”
Simulated glacier runoff to 75 global major river basins (2000-2100, 3 glacier models)
<p>This dataset accompanies our study examining projected, century-scale, glacier runoff simulated by three global glacier evolution models (GloGEM, OGGM, and PyGEM). The dataset includes glacier model projections for all 75 major river basins of interest and includes four Shared Socioeconomic Pathways (SSPs) and twelve Global Climate Models (GCMs). Projections, originally provided as single glacier simulations for RGI (Randolph Glacier Inventory) regions of interest were aggregated at the basin scale using Jupyter notebooks. The dataset gives glacier runoff projections, for all three models, for each combination of GCM, SSP, and basin, with monthly resolution.</p>
Regional Assessments of Glacier Mass Change (RAGMAC) experiment dataset
<p>This repository contains the dataset that was distributed to the participants of the Regional Assessments of Glacier Mass Change (RAGMAC) working<br>group of the International Association of Cryospheric Science (IACS, 2023).</p> <p>In short, the dataset is divided in 5 study sites: Hintereisferner (Austrian Alps - HEF_AT), Grosser Aletschgletscher (Swiss Alps - ALE_CH), Vestre Svartisen ice cap (Scandes, Norway - VES_NO), Baltoro glacier (Karakoram, Pakistan - BAL_PK), and the Northern Patagonian Icefield (Andes, Chile - NPI_CL).</p> <p>For each site, we provide series of Digital Elevation Models (DEMs) derived from SRTM, ASTER and TanDEM-X sensors. Additionally, we provide glacier outlines extracted from the Randolph Glacier Inventory version 6, for all glaciers in the study area, and selected glaciers for which participants were required to calculate the geodetic mass balance.</p> <p>Additionally, we provide the airborne DEMs that were used as reference data to evaluate the spaceborne DEMs. These are available for the cases HEF_AT, ALE_CH and VES_NO, all included in the single zip file “validation_data.zip”.</p> <p>For details on the dataset, experiment and any reference to this dataset, please refer to the following publication: Piermattei et al. (2024) "Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data" The Cryosphere, DOI: <a href="https://doi.org/10.5194/egusphere-2023-2309">10.5194/egusphere-2023-2309</a> (to be updated upon final acceptance).</p> <p><strong>NOTE: </strong>As of July 2024, the TanDEM-X DEMs cannot be publicly shared due to license restrictions. We are discussing future opportunities to share the data in a future version of this repository.</p> <p> </p>
Supraglacial debris thickness data from Ngozumpa Glacier, Nepal
<p>This repository contains:</p> <ul> <li>2 files of measurements of supraglacial debris thickness at two sites (Gokyo and Margin) on the surface of the Ngozumpa Glacier (27°57′N, 85°42′E), Nepal, made using ground penetrating radar (GPR)</li> <li>1 file of supplementary supraglacial thickness measurements from additional glacier sites using various methods</li> <li>All files are comma separated text files</li> </ul> <p>Description of Ngozumpa GPR data:</p> <ul> <li>GPR measurements were made between 31<sup>st</sup> March and 20<sup>th</sup> April 2016.</li> <li>Debris thickness was sampled in 36 individual radar transects, covering sloping and level terrain with coarse and fine surface material. The GPR system was a dual frequency 200/600MHz IDS RIS One, mounted on a small plastic sled and drawn along the surface.</li> <li>Data were collected to a Lenovo Thinkpad using the IDS K2 FastWave software. </li> <li>The 200 and 600 MHz antennas have separation distances of 0.230 m and 0.096 m respectively.</li> <li>Data acquisition used a continuous step size, a time window of 100 ms and a digitization interval of 0.024 ns.</li> <li>The location of the GPR system was recorded simultaneously at 1 s intervals by a low precision GPS integrated with the IDS which assigns a GPS location and time directly to every twelfth GPR trace, and by a more accurate differential GPS (dGPS) system consisting of a Trimble XH and Tornado antenna mounted on the GPR and a local base station of a Trimble Geo7X and Zephyr antenna.</li> <li>Radargrams were processed in REFLEXW (Sandmeier software)</li> <li>The reflection at the ice surface was picked manually wherever it was clearly identifiable and was not picked if it was indistinct.</li> <li>The appropriate signal velocity for the supraglacial debris was obtained by burying a 1.5 m long steel bar to a known depth and then passing the GPR over the buried target and picking the two-way travel time to its reflection. Both fine and coarse material gave similar wave speeds (0.15 and 0.16 m ns<sup>-1</sup>), the average of which was used for all the radar lines measured</li> </ul> <p>Description of supplementary data:</p> <ul> <li>C1: Ngozumpa glacier (Nepal) about 1km from the terminus, measured using a theodolite survey (Nicholson and Benn, 2012)</li> <li>C2: Ngozumpa glacier (Nepal) about 7km from the terminus, measured using a theodolite survey (Nicholson and Benn, 2012)</li> <li>C3: Ngozumpa glacier (Nepal) about 3km from the terminus, measured using a photogrammetric survey (Nicholson and Mertes, 2017)</li> <li>C4: Lirung glacier (Nepal), measured with GPR (McCarthy and others 2016)</li> <li>C5: Suldenferner (Italy), measured with GPR (del Gobbo, 2017)</li> <li>C6: Suldenferner (Italy), measured by excavation of debris (del Gobbo, 2017)</li> <li>C7: Arolla glacier, (Switzerland), measured by excavation of debris (Reid and others, 2012)</li> </ul> <p>Details of these datasets can be found in the following publications:</p> <p>Nicholson, L. I. and Benn, D. I.: Properties of natural supraglacial debris in relation to modelling sub-debris ice ablation, Earth Surf. Process. Landforms, 38(5), 409–501, doi:10.1002/esp.3299, 2012.</p> <p>Nicholson, L. I. and Mertes, J. R.: Thickness estimation of supraglacial debris above ice cliff exposures using a high-resolution digital surface model derived from terrestrial photography, J. Glaciol., 1–10, doi:10.1017/jog.2017.68, 2017</p> <p>McCarthy, M., Pritchard, H. D., Willis, I. and King, E.: Ground-penetrating radar measurements of debris thickness on Lirung Glacier, Nepal, J. Glaciol., 63(239), 534–555, doi:10.1017/jog.2017.18, 2017.</p> <p>del Gobbo, C.: Debris thickness investigation of Solda glacier, southern Rhaetian Alps, Italy: Methodological considerations about the use of ground penetrating radar over a debris-covered glacier. MSc Thesis, University of Innsbruck, 2017.</p> <p>Reid, T. D., Carenzo, M., Pellicciotti, F. and Brock, B. W.: Including debris cover effects in a distributed model of glacier ablation, J. Geophys. Res., 117(D18), 1–15, doi:10.1029/2012JD017795, 2012.</p> <p> </p> <p> </p>
Brewster Glacier AWS data 2008-2009
<p>Meteorological data collected over Brewster Glacier, New Zealand for the period November 2008-February 2009.</p> <p>The data sets has undergone quality control consistent with later Brewster Glacier AWS data sets as described in:</p> <p>Cullen, N. J. and Conway, J. P.:(2015) A 22 month record of surface meteorology and energy balance from the ablation zone of Brewster Glacier, New Zealand, Journal of Glaciology, 61, 931-946. doi:10.3189/2015JoG15J004</p> <p>The readme file gives a brief description of the data in each column.</p> <p>Please get in touch if you would like more details - jono.conway@niwa.co.nz or nicolas.cullen@otago.ac.nz</p>
Mapping of the calving front of Eqip Sermia Glacier, West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2018 at Eqip Sermia Glacier. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>For each, processed and raw, data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM or GLACIER_YYYYMMDD_HHMM_N.</p> <p>For instance : eqip_20180708_1225 contains the Data of the LARGE-SCALE survey of Eqip glacier that started on July 8, 2018 at 12:25 UTC time. eqip_20180707_1245_1 contains the Data of the FIRST repeat survey of the calving front of Eqip glacier that started on July 7, 2018 at 12:45 UTC time, eqip_20180707_1245_2 is the second repeat survey, eqip_20180707_1245_3 is the third and eqip_20180707_1245_4 is the last.</p> <p>Coordinate system used is UTM Zone 22W based on WGS84</p> <p>Take-off and landing site latitude and longitude was ( 69.757767 , - 50.228172 )</p> <p>This dataset is related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. V. Ryan, Frontiers in Earth Sciences.</p> <p> </p> <p> </p>
Mapping of the calving front of Hart, Sharp, Melville and Farquhar glaciers, Northwest Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : farquhar_20170705_1914 contains the Data of the survey of Farquhar glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>This paper is related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. V. Ryan, Frontiers in Earth Sciences</p> <p> </p>
Mapping of the calving front of Heilprin Glacier, North West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : heilprin_20170705_1914 contains the Data of the survey of Heilprin glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>The data are related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. Ryan, Frontiers in Earth Sciences</p>
Mapping of the calving front of Tracy Glacier, North West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : tracy_20170705_1914 contains the Data of the survey of Tracy glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>The data are related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. Ryan, Frontiers in Earth Sciences</p>
Simulation output of Lirung glacier
<p>This is the data used for <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>by PNJ Bonekamp, CC van Heerwaarden, JF Steiner and WW Immerzeel<br> <br> For each experiment one summary file is present.</p> <p>Description of experiments:<br> HOM<sub>flat</sub> Homogeneous glacier (constant DEM, surface specific moisture and temperature)<br> HOM<sub>1/2DEM</sub> ½ DEM, homogeneous surface specific moisture and temperature<br> HOM<sub>DEM </sub> With the real DEM, homogeneous surface specific moisture and temperature<br> HET<sub>T</sub> Spatially heterogeneous surface temperature, the real DEM, homogeneous surface specific moisture<br> HET<sub>qdry</sub> Surface specific humidity is spatially RH=70-75%, surface temperature constant, real DEM is included<br> HET<sub>qmoist</sub> Spatially RH=70-85%, surface temperature constant, real DEM is included<br> <br> In each file various variables are present;<br> u_xz, v_xz, qt_xz, thl_xz: which are the wind vectors, total specific humidity and liquid potential temperature for the xz cross section respectively<br> u_yz, v_yz, qt_yz, thl_yz: which are the wind vectors, total specific humidity and liquid potential temperature for the yz cross section respectively<br> qtfluxbot_ib, thlfluxbot_ib: the total specific moisture and temperature flux at the surface respectively. </p> <p>See for more specific information:<br> <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>PNJ Bonekamp, CC van Heerwaarden, JF Steinerand WW Immerzeel, The Cryosphere, submitted</p> <p> </p> <p> </p>
Simulation output of Lirung glacier (REAL EXP)
<p>This is the data used for <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>by PNJ Bonekamp, CC van Heerwaarden, JF Steiner and WW Immerzeel. In this netcdf file only the data of the REAL experiment can be found. See for data of the other experiments 10.5281/zenodo.3375325. <br> <br> These variables are present;<br> u_xz, v_xz, qt_xz, thl_xz: which are the wind vectors, total specific humidity and liquid potential temperature for the xz cross section respectively<br> u_yz, v_yz, qt_yz, thl_yz: which are the wind vectors, total specific humidity and liquid potential temperature for the yz cross section respectively<br> qtfluxbot_ib, thlfluxbot_ib: the total specific moisture and temperature flux at the surface respectively. </p> <p>See for more specific information:<br> <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>PNJ Bonekamp, CC van Heerwaarden, JF Steiner and WW Immerzeel, The Cryosphere, submitted</p> <p> </p>
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</p>
Helicopter-based GPR survey of Rutor glacier and nearby glaciers, Aosta Valley, Italy, in May 2012
<p>This dataset contains Ground Penetrating Radar (GPR) data collected on May 2012, from helicopter, with a 70 Mhz antenna, on the Rutor Glacier (files numbered from 6 to 10), and nearby glaciers situated in Aosta Valley (European Alps). They were collected for ARPA Val d'Aosta, the local environmental protection agency.</p> <p>The Rutor glacier data and their interpretation, at August 2024, are also being submitted to The Cryosphere scientific journal, in a possible future publication entitled Ground penetrating radar on Rutor temperate glacier supported by ice-thickness modeling algorithms for bedrock detection, by Andrea Vergnano, Diego Franco and Alberto Godio.</p> <p><br>The .DZT files are the data files. They can be opened with specialistic geophysical software. In particular, a free and open access package is available, called RGPR and installable in the R environment. (https://cran.r-project.org/) (https://emanuelhuber.github.io/RGPR/). After loading the RGPR library with the R command</p> <p>library(RGPR)</p> <p>a file can be read and stored in the "data" variable by the command</p> <p>data=readGPR("FILE____001.DZT")</p> <p> </p> <p>The .PLT files are simple text files that contain the geolocation information in standard NMEA format (https://aprs.gids.nl/nmea/#gga).<br>Each row is a measurement, and there is a measurement every second. To assign the correct geolocation to each GPR trace contained in the .DZT files there are two options:</p> <p>1) follow your specialistic software manual</p> <p>2) do it manually by considering that the GPR sample rate of traces is constant in time. The first .PLT line refers to the first GPR trace, and the last .PLT line refers to the last GPR trace.</p> <p> </p> <p> </p> <p> </p>
Glacier Inventory and Elevation Change Data for the Shule River, Heihe River, and Shiyang River Basins in the Qilian Mountains (1990-2020)
<p>This dataset contains comprehensive glacier inventory data and elevation change measurements for the Qilian Mountains, specifically covering the Shule River, Heihe River, and Shiyang River basins. The dataset includes:</p> <ol> <li> <p>Glacier inventory data for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The inventory provides detailed information on glacier attributes such as area, length, orientation, and other essential characteristics for each of the target years.</p> </li> <li> <p>Elevation change data for glaciers within the Qilian Mountains during the periods 2000-2010 and 2010-2020. This data tracks changes in glacier surface elevation, providing valuable insights into glacier mass balance and regional climate change impacts.</p> </li> </ol> <p>The data is critical for understanding the response of glaciers to climate change over the past three decades and will support ongoing research in glaciology, hydrology, and environmental monitoring.</p>
Table 4 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
<p><i>Table 4.</i> Summary of harbor seal mother-pup pairs (MP) and seals without pups (nMP) counted during pupping, numbers of seals counted during the molt, and proportions of pups counted relative to total seals (including pups) observed during pupping and during the molt. Counts are presented as means, maximums, and sample sizes (in parentheses) for each region in each year and summarized across years by arithmetic mean, standard deviation and geometric mean.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>Geo</th></tr></tbody><tbody><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>Mother-pup pairs during pupping</th></tr><tr><th>AB</th><td>Mean</td><td>20.3</td><td>33.5</td><td>20.0</td><td>22.0</td><td>26.5</td><td>24.3</td><td>87.0</td><td>72.0</td><td>64.0</td><td>41.1</td><td>24.5</td><td>34.9</td></tr><tr><th></th><td>Max</td><td>28</td><td>39</td><td>20</td><td>23</td><td>38</td><td>31</td><td>87</td><td>77</td><td>64</td><td>45.2</td><td>23.2</td><td>39.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>38.3</td><td>57.5</td><td>39.0</td><td>52.0</td><td>41.5</td><td>38.7</td><td>116.0</td><td>83.0</td><td></td><td>58.2</td><td>26.0</td><td>53.7</td></tr><tr><th></th><td>Max</td><td>54</td><td>79</td><td>39</td><td>54</td><td>42</td><td>45</td><td>116</td><td>84</td><td></td><td>64.1</td><td>25.0</td><td>59.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>22.8</td><td>38.0</td><td>32.0</td><td>48.5</td><td>35.0</td><td>14.0</td><td></td><td></td><td></td><td>31.7</td><td>11.0</td><td>29.5</td></tr><tr><th></th><td>Max</td><td>26</td><td>38</td><td>32</td><td>58</td><td>35</td><td>14</td><td></td><td></td><td></td><td>33.8</td><td>13.3</td><td>31.1</td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>19.5</td><td>11.0</td><td>33.5</td><td>40.0</td><td></td><td>36.3</td><td>8.0</td><td>29.0</td><td></td><td>25.3</td><td>11.7</td><td></td></tr><tr><th></th><td>Max</td><td>33</td><td>21</td><td>33.5</td><td>40</td><td></td><td>70</td><td>8</td><td>38</td><td></td><td>34.8</td><td>17.7</td><td></td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Seals without pups during pupping</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>124.8</td><td>103.0</td><td>86.0</td><td>178.0</td><td>179.0</td><td>206.7</td><td>178.0</td><td>406.5</td><td>140.0</td><td>178.0</td><td>89.1</td><td>161.3</td></tr><tr><th></th><td>Max</td><td>188</td><td>144</td><td>86</td><td>211</td><td>211</td><td>253</td><td>178</td><td>439</td><td>140</td><td>205.6</td><td>94.4</td><td>187.5</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>86.5</td><td>163.5</td><td>80.0</td><td>179.5</td><td>179.5</td><td>243.3</td><td>182.0</td><td>250.0</td><td></td><td>170.5</td><td>58.3</td><td>158.8</td></tr><tr><th></th><td>Max</td><td>96</td><td>179</td><td>80</td><td>205</td><td>202</td><td>342</td><td>182</td><td>257</td><td></td><td>192.9</td><td>78.3</td><td>175.8</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>50.5</td><td>145.0</td><td>106.0</td><td>119.5</td><td>173.0</td><td>89.0</td><td></td><td></td><td></td><td>113.8</td><td>39.1</td><td>106.1</td></tr><tr><th></th><td>Max</td><td>77</td><td>145</td><td>106</td><td>155</td><td>173</td><td>89</td><td></td><td></td><td></td><td>124.2</td><td>35.5</td><td>118.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>RB-JB</th><td>Mean</td><td>148.8</td><td>143.0</td><td>275.5</td><td>353.0</td><td></td><td>284.0</td><td>211.0</td><td>251.3</td><td></td><td>238.1</td><td>70.4</td><td>227.1</td></tr><tr><th></th><td>Max</td><td>174</td><td>233</td><td>275.5</td><td>353</td><td></td><td>448</td><td>211</td><td>329</td><td></td><td>289.1</td><td>87.5</td><td>276.2</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Total seals during molt</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>253.5</td><td>391.7</td><td>267.0</td><td>437.0</td><td>649.0</td><td></td><td></td><td>738.0</td><td></td><td>456.0</td><td>181.7</td><td>420.8</td></tr><tr><th></th><td>Max</td><td>303</td><td>503</td><td>267</td><td>437</td><td>649</td><td></td><td></td><td>738</td><td></td><td>482.8</td><td>170.4</td><td>451.9</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>49.7</td><td>224.3</td><td>236.0</td><td>342.0</td><td>140.0</td><td></td><td></td><td>650.0</td><td></td><td>273.7</td><td>190.7</td><td>208.4</td></tr><tr><th></th><td>Max</td><td>85</td><td>306</td><td>236</td><td>342</td><td>140</td><td></td><td></td><td>650</td><td></td><td>293.2</td><td>182.5</td><td>240.0</td></tr><tr><th></th><td></td><td>(3)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>252.5</td><td>333.7</td><td></td><td>220.0</td><td>312.0</td><td></td><td></td><td></td><td></td><td>279.5</td><td>45.4</td><td>275.8</td></tr><tr><th></th><td>Max</td><td>552</td><td>380</td><td></td><td>220</td><td>312</td><td></td><td></td><td></td><td></td><td>366.0</td><td>121.5</td><td>346.4</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(0)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>146.8</td><td>308.7</td><td>165.5</td><td>513.5</td><td>564.0</td><td></td><td></td><td>477.0</td><td></td><td>362.6</td><td>165.7</td><td>318.1</td></tr><tr><th></th><td>Max</td><td>226</td><td>569</td><td>225</td><td>546</td><td>564</td><td></td><td></td><td>477</td><td></td><td>434.5</td><td>150.8</td><td>402.5</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(2)</td><td>(2)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Proportion pups relative to total seal including pups</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>11.8%</td><td>20.6%</td><td>15.9%</td><td>10.1%</td><td>10.9%</td><td>9.6%</td><td>24.7%</td><td>13.1%</td><td>23.9%</td><td>15.6%</td><td>6%</td><td>14.7%</td></tr><tr><th></th><td>Max</td><td>15.0%</td><td>23.7%</td><td>15.9%</td><td>11.2%</td><td>13.2%</td><td>13.3%</td><td>24.7%</td><td>14.6%</td><td>23.9%</td><td>17.3%</td><td>5%</td><td>16.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(1)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>22.3%</td><td>20.1%</td><td>24.7%</td><td>18.5%</td><td>15.9%</td><td>12.7%</td><td>28.0%</td><td>20.0%</td><td></td><td>20.3%</td><td>5%</td><td>19.8%</td></tr><tr><th></th><td>Max</td><td>29.6%</td><td>25.8%</td><td>24.7%</td><td>19.7%</td><td>17.2%</td><td>15.7%</td><td>28.0%</td><td>20.4%</td><td></td><td>22.6%</td><td>5%</td><td>22.1%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(1)</td><td>(2)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>26.3%</td><td>17.2%</td><td>18.8%</td><td>22.7%</td><td>14.4%</td><td>12.0%</td><td></td><td></td><td></td><td>18.6%</td><td>5%</td><td>17.9%</td></tr><tr><th></th><td>Max</td><td>42.0%</td><td>17.2%</td><td>18.8%</td><td>24.1%</td><td>14.4%</td><td>12.0%</td><td></td><td></td><td></td><td>21.4%10%</td><td>19.6%</td><td></td></tr><tr><th></th><td></td><td>(4)</td><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Region</th><td></td><td>2004</td><td>2005</td><td>2006</td><td>2007</td><td>2008</td><td>2009</td><td>2011</td><td>2012</td><td>2013</td><td>Mean</td><td>SD</td><td>Mean</td></tr><tr><th>RB-JB</th><td>Mean</td><td>10%</td><td>5%</td><td>10%</td><td>9%</td><td></td><td>11%</td><td>4%</td><td>10%</td><td></td><td>8.3%</td><td>3%</td><td>7.8%</td></tr><tr><th></th><td>Max</td><td>15%</td><td>8%</td><td>10%</td><td>9%</td><td></td><td>14%</td><td>4%</td><td>15%</td><td></td><td>10.6%</td><td>4%</td><td>9.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(2)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(3)</td><td>(1)</td><td>(3)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>Proportion pups relative to molting seals</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>AB</th><td>Mean</td><td>8.0%</td><td>8.6%</td><td>7.5%</td><td>5.0%</td><td>4.1%</td><td></td><td></td><td>9.8%</td><td></td><td>7.2%</td><td>2%</td><td>6.8%</td></tr><tr><th></th><td>Max</td><td>9.2%</td><td>7.8%</td><td>7.5%</td><td>5.3%</td><td>5.9%</td><td></td><td></td><td>10.4%</td><td></td><td>7.7%</td><td>2%</td><td>7.5%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>NWF</th><td>Mean</td><td>77.0%</td><td>25.6%</td><td>16.5%</td><td>15.2%</td><td>29.6%</td><td></td><td></td><td>12.8%</td><td></td><td>29.5%</td><td>22%</td><td>23.9%</td></tr><tr><th></th><td>Max</td><td>63.5%</td><td>25.8%</td><td>16.5%</td><td>15.8%</td><td>30.0%</td><td></td><td></td><td>12.9%</td><td></td><td>27.4%</td><td>17%</td><td>23.4%</td></tr><tr><th></th><td></td><td>(3)</td><td>(3)</td><td>(1)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>MF</th><td>Mean</td><td>9.0%</td><td>11.4%</td><td></td><td>22.0%</td><td>11.2%</td><td></td><td></td><td></td><td></td><td>13.4%</td><td>5%</td><td>12.6%</td></tr><tr><th></th><td>Max</td><td>4.7%</td><td>10.0%</td><td></td><td>26.4%</td><td>11.2%</td><td></td><td></td><td></td><td></td><td>13.1%</td><td>8%</td><td>10.9%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(0)</td><td>(1)</td><td>(1)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(0)</td><td></td><td></td><td></td></tr><tr><th>RB-JB</th><td>Mean</td><td>13.3%</td><td>3.6%</td><td>20.2%</td><td>7.8%</td><td></td><td></td><td></td><td>6.1%</td><td></td><td>10.2%</td><td>6%</td><td>8.5%</td></tr><tr><th></th><td>Max</td><td>14.6%</td><td>3.7%</td><td>14.9%</td><td>7.3%</td><td></td><td></td><td></td><td>8.0%</td><td></td><td>9.7%</td><td>4%</td><td>8.6%</td></tr><tr><th></th><td></td><td>(4)</td><td>(3)</td><td>(2)</td><td>(2)</td><td>(0)</td><td>(0)</td><td>(0)</td><td>(1)</td><td>(0)</td><td></td><td></td><td></td></tr></tbody></table><p>(Continued)</p><p>(Continued)</p>
Table 3 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
<p><i>Table 3.</i> Location-habitat categories, regions, and associated acronyms used in this study.</p><table><tbody><tr><th>Acronyms</th></tr><tr><th>Fjord-Habitat</th></tr></tbody><tbody><tr><th>Johnstone Bay Lake Ice</th><td>JBI</td></tr><tr><th>Johnstone Bay Land</th><td>JBL</td></tr><tr><th>Whidbey Bay Land</th><td>WBL</td></tr><tr><th>Day Harbor Land</th><td>DHL</td></tr><tr><th>Resurrection Bay Ice</th><td>RBI</td></tr><tr><th>Resurrection Bay Land</th><td>RBL</td></tr><tr><th>Aialik Bay Glacier Ice</th><td>AGI</td></tr><tr><th>Aialik Bay Land</th><td>AGL</td></tr><tr><th>Pedersen Glacier Ice</th><td>PGI</td></tr><tr><th>Pedersen Glacier Land</th><td>PGL</td></tr><tr><th>Northwestern Fjord Ice</th><td>NWFI</td></tr><tr><th>Northwestern Fjord Land</th><td>NWFL</td></tr><tr><th>McCarty Fjord Ice</th><td>MFI</td></tr><tr><th>McCarty Fjord Land</th><td>MFL</td></tr><tr><th>Regions</th><td></td></tr><tr><th>Resurrection Bay through Johnstone Bay</th><td>RB-JB</td></tr><tr><th>Aialik Bay</th><td>AB</td></tr><tr><th>Northwestern Fjord</th><td>NWF</td></tr><tr><th>McCarty Fjord</th><td>MCF</td></tr><tr><th>Southern Coast (McCarty Fjord-Harris Bay)</th><td>SC</td></tr></tbody></table>
Table 5 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
<p><i>Table 5.</i> Summary of mean percent change, annually, in numbers of harbor seals counted from Johnstone Bay through Northwestern Fjord (JB-NWF) during the molt and of pups and nonpups counted during pupping, as determined from aerial surveys. For comparison, Aialik Bay trends, based on mean estimates adjusted for favorable standard conditions from 2004 to 2011, are included.</p><table><tbody><tr><th>Trend</th></tr></tbody><tbody><tr><th>Location/Time Period</th><td>Glacier</td><td>Terrestrial</td><td>Total</td></tr><tr><th>Molt</th><td></td><td></td><td></td></tr><tr><th>JB-NWF</th><td>5.40%</td><td>9.00%</td><td>6.10%</td></tr><tr><th>Aialik Bay</th><td>6.70%</td><td></td><td></td></tr><tr><th>Pupping</th><td></td><td></td><td></td></tr><tr><th>JB-NWF pups</th><td>5.00%</td><td>1.50%</td><td>6.90%</td></tr><tr><th>Aialik Bay pups</th><td>8.00%</td><td></td><td></td></tr><tr><th>JB-NWF seals without pups</th><td>7.96%</td><td>5.10%</td><td>8.15%</td></tr></tbody></table>
Table 2 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
<p><i>Table 2.</i> Habitat haul-out categories.</p><table><tbody><tr><th>Tidewater</th><th>Tidewater glacier ice habitats include ice calved from Aialik Glacier in</th></tr></tbody><tbody><tr><th>glacier ice</th><td>Aialik Bay; Northwestern, Ogive, and Anchor Glaciers in Northwestern</td></tr><tr><th></th><td>Fjord; and McCarty Glacier in McCarty Fjord and comprise primary</td></tr><tr><th></th><td>habitats for seals on the Kenai Peninsula. Harbor seals rarely hauled out on</td></tr><tr><th></th><td>ice calved from Holgate Glacier in Aialik Bay.</td></tr><tr><th>Glacial</th><td>Glacial Lake ice habitats, including Johnstone Lake (terminus of Excelsior</td></tr><tr><th>lake ice</th><td>Glacier in Johnstone Bay), Bear Glacier Lake (terminus of Bear Glacier in</td></tr><tr><th></th><td>Resurrection Bay), and Pedersen Glacier Lake (terminus of Pedersen Glacier</td></tr><tr><th></th><td>in Aialik Bay), are highly estuarine and have restricted access <i>via</i> tidally</td></tr><tr><th>influenced streams. During the winter, surface waters freeze. In this paper,</th></tr><tr><th></th><td>seals using lake-ice habitats were associated with adjoining habitats.</td></tr><tr><th></th><td>Pedersen Lake was associated with tidewater glacial habitats while lakes at</td></tr><tr><th></th><td>the terminus of Excelsior and Bear glaciers were associated with terrestrial</td></tr><tr><th></th><td>habitats.</td></tr><tr><th>Traditional</th><td>Traditional terrestrial haul-outs along the southeastern Kenai Peninsula are</td></tr><tr><th>terrestrial</th><td>primarily rocky shorelines and include all terrestrial haul-outs from</td></tr><tr><th></th><td>Johnstone Bay through Resurrection Bay.</td></tr><tr><th>Incidental</th><td>Incidental terrestrial haul-outs are those associated with tidewater glacier</td></tr><tr><th>terrestrial</th><td>systems in Aialik Bay, Northwestern Fjord and McCarty Fjord. Primary</td></tr><tr><th></th><td>haul-out activity occurs on the glacial ice and occupancy at the terrestrial</td></tr><tr><th></th><td>haul-outs is irregular and involved small numbers of seals (<20 seals).</td></tr><tr><th></th><td>In this study, survey times in the tidewater glacier fjords often occurred</td></tr><tr><th></th><td>late in the low tide cycle which, in conjunction with the irregularity in</td></tr><tr><th></th><td>the seal’s use of these terrestrial haul-outs, resulted in suboptimal</td></tr><tr><th></th><td>assessments of habitat use.</td></tr></tbody></table>
Table 1 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
<p><i>Table 1.</i> Summary of the location, terminus habitat and status of glaciers that calve ice used by harbor seals in this study.</p><table><tbody><tr><th>Glacier</th><th>Status</th><th>Habitat</th><th>Location</th></tr></tbody><tbody><tr><th>Excelsior Glacier</th><td>Retreating</td><td>Tidally influenced Lake</td><td>Johnstone Bay</td></tr><tr><th>Bear Glacier</th><td>Retreating</td><td>Tidally influenced Lake</td><td>Resurrection Bay</td></tr><tr><th>Holgate Glacier Pedersen Glacier</th><td>Stable Retracted Retreating a</td><td>Tidewater Tidally Influenced Lake</td><td>Aialik Bay Aialik Bay</td></tr><tr><th>Aialik Glacier</th><td>Stable Retracted/</td><td>Tidewater</td><td>Aialik Bay</td></tr><tr><th>Anchor Glacier Ogive Glacier Northwestern Glacier</th><td>Thinning Retracted a Retracted a Retracted a,b</td><td>Tidewater Tidewater Tidewater</td><td>Northwestern Fjord Northwestern Fjord Northwestern Fjord</td></tr><tr><th>McCarty Glacier</th><td>Retreating</td><td>Tidewater</td><td>McCarty Fjord</td></tr></tbody></table><p><sup>a</sup> Detaching from icefield. <sup>b</sup> Partially grounded.</p>
A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers
<p>KazmierczakGregov24_data.zip contains the Matlab scripts and data necessary to reproduce the results and figures of the article "A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers" by Kazmierczak, Gregov, Coulon, and Pattyn. For more details, please, open the README.txt file or contact elise (dot) kazmierczak (at) ulb (dot) be or thomas (dot) gregov (at) uliege (dot) be.</p>
Dataset for 'Warmer Oceans may Accelerate the Marine-to-Land Transition of a West Greenland Outlet Glacier'
<p>Monthly hydrographic records from 2008-2023, collected from the standard station GF10 (64°36.6N, 51°57.5 W; maximum depth: 576 m) as part of Greenland Climate Research Centre “Long-term standard hydrographic fjord research program”. Note that the date values reflect the last day of each month, not necessarily the date that the measurements were obtained.</p> <p>Please cite this reference if dataset were used in any way.</p> <p>Correspondence to John Mortensen at jomo@natur.gl</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.