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293 results for “Svalbard”
Research in Svalbard international projects edgelist
<p>This dataset lists all the country to country ties derived from the <a href="https://www.researchinsvalbard.no/">Research in Svalbard</a> (RIS) database using the country of origin of the organisations with joint research projects in Svalbard and the projects year. This edgelist is broken down into two time periods: 1972-2004 ; 2005-2022. Per each pair of countries, it gives the number of joint research projects registered in the RIS database per period of time. It can be used for network analysis purposes. It has been created and analysed using a core-periphery approach within the publication: Strouk, M. & Maisonobe, M. (2024). "Field science and scientific collaboration in the Svalbard Archipelago: beyond science diplomacy<em>". Science and Public Policy.</em> DOI: <a href="https://doi.org/10.1093/scipol/scae012">https://doi.org/10.1093/scipol/scae012/</a></p>
EISCAT Svalbard radar Common Program data from February 26 to February 28 2023, which is processed by GUISDAP
<p>This is two-dimensional (time and altitude) ionospheric parameter data that contains electron density, electron temperature and ion temperature. It is estimated based on EISCAT Svalbard radar measurement implemeted as common program from February 26 to Feburuary 28, 2023 (https://portal.eiscat.se/) and processed by a software for incoherent scatter radar analysis, GUISDAP (https://gitlab.com/eiscat/guisdap9). The more detailed descriptions can be found as metadata in the uploaded netCDF file.</p>
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </p>
DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring
<p> </p> <p> </p> <p>This dataset aims to support the work presented in</p> <blockquote> <p>Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>.</p> </blockquote> <p>It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices. </p> <p>The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m).</p> <p>The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format <em>YYYYMMDD</em>, UTC time at the beginning of the file, channels, whale_raw, duration of the file L<em>xx</em>s, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example </p> <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a> <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <p><strong>Each file contains the following variables</strong></p> <ul> <li><em>data: </em>The DAS-recorded nano strain data</li> <li><em>info_GL_m:</em> Used gauge length (m)</li> <li><em>info_nsamples</em>: Number of temporal samples in the file</li> <li><em>info_ntraces</em>: Number of spatial samples (channels) in the file</li> <li><em>info_sample_interval_s</em>: Sampling period (s)</li> <li><em>info_sampling_frequency_Hz</em>: Sampling frequency (Hz)</li> <li><em>info_SSI_m</em>: Spatial sampling interval (m)</li> <li><em>info_timestamp</em>: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li><em>x1_absolute_channel</em>: Vector containing the absolute channel number</li> <li><em>x1_distance_from_shore_m</em>: Vector containing the distance along the FO cable from shore (m)</li> <li><em>x1_position_m</em>: Vector containing the distance along the FO cable from the interrogator (m)</li> <li><em>x1_recwdepthz_m</em>: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li><em>x1_relative_channel</em>: Vector containing the channel number</li> <li><em>x2_time_s</em>: Time vector (s)</li> </ul> <p> </p> <p><strong>List of the files and related manuscript examples</strong></p> <p>Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC</p> <ul> <li><em>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat</em> </li> </ul> <p>Example of<strong> </strong>series of blue whale calls recorded with a move out on the Svalbard DAS array - Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC</p> <ul> <li><em>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</em></li> </ul> <p>Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC</p> <ul> <li><em>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat </em></li> <li><em>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </em></li> </ul> <p><strong>--------------</strong></p> <p><strong>Analysis tools </strong></p> <p>To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>:</p> <blockquote> <p>Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a></p> </blockquote> <p>Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a></p> <p><strong>--------------</strong></p> <p><strong>Please cite as </strong></p> <blockquote> <p>Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>.</p> </blockquote> <p><strong>--------------</strong></p> <p><strong>Contact</strong></p> <p><a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a></p>
* Arctic specimens in the NHMO Insect collection 2022 - Svalbard
<p>Arctic specimens from Svalbard in the NHMO Insect collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Near Pan-Svalbard cryospheric hazards inventory (SvalCryo)
<p>We present a comprehensive inventory of thaw slumps (TS) and thermo-erosion gullies (TEG) on the Svalbard Archipelago. We used the most recent orthophotos (0.5 x 0.5 m pixel size) acquired in 2009-2011 from the Web Map Services (WMS) of the Norwegian Polar Institute. TS and TEG were identified and digitised on-screen as polygons in the ETRS_1989_UTM_Zone_33N coordinate reference system. <span>TS and TEG were identified based on their morphology, digitised on-screen (maximum zoom was 1:1000) as polygons, and then individually quality checked in the GIS environment. This process was repeated twice, to avoid any bias in feature(s) mapping, first by a geomorphologist (first author) and then by an Arctic geologist (second author). The cryospheric inventory of the 14 regions (Andre<span>é</span> Land, Dickson Land, James I Land, Nordenski<span>ö</span>ld Land, Bünsow Land, Olav V Land, Sabine Land, Nathorst Land, Heer Land, Wedel Jarlsberg Land, Torell Land, S<span>ørkapp Land, </span>Barents<span>øya and Edgeøya) </span>totalises 8491 polygons, out of which 3679 are TS and 4812 are TEG. Within the attribute tables, there are eight columns comprising details about each polygon/feature, as follows: FID (ID showing the total number of polygons), Shape (Polygon), ID (each polygon from each region has associated an ID for both TS and TEG), Area (sq. m), Perimeter (m), MaxDistanc (calculated between two points along the polygon perimeter), Elongation (calculated as the maximum distance divided by the square root of the area), Region (the name of the region that the feature belongs to).</span></p>
High-frequency, year-round time series of the carbonate chemistry in a high-Arctic fjord (Svalbard)
<p>The Arctic Ocean is subject to high rates of ocean warming and acidification, with critical implications for marine organisms as well as ecosystems and the services they provide. Carbonate system data in the Arctic realm are spotty in space and time and, until recently, there was no time-series station measuring the carbonate chemistry at high frequency in this region, particularly in coastal waters. We report here on the first high-frequency (1 h), multi-year (6 years) dataset of salinity, temperature, dissolved inorganic carbon, total alkalinity, CO2 partial pressure (pCO2) and pH at a coastal site (12 m) in Kongsfjorden, Svalbard. We show that the choice of formulations for calculating the dissociation constants of the carbonic acid remains unsettled, (2) the water column is generally somewhat stratified despite the shallow depth, (3) the saturation state of calcium carbonate is subject to large seasonal changes but never reaches undersaturation (Oa ranges between 1.4 and 3.0) and (4) pCO2 is lower than atmospheric CO2 at all seasons, making this site a sink for atmospheric CO2.</p> <p>In addition to the sources of funding findable within the Zenodo interface, this work has been supported by the Coastal Observing System for Northern and Arctic Seas (COSYNA), the two Helmholtz large-scale infrastructure projects ACROSS and MOSES, the French Polar Institute (IPEV) as well as the European Union's Horizon 2020 research and innovation programme Jericho-Next (No 871153 and 951799). <br> <br> ------ <br> <br> Column descriptions are as follows: <br> <br> date/time [UTC+0]: The date and time of sampling at UTC <br> pressure [dbar]: hydrostatic pressure (profiler) <br> s_insitu [unit]: salinity in situ (profiler) <br> s_fb [unit], salinity (FerryBox) <br> t_11m [°C]: temperature in situ (static at 11 m) <br> t_ctd [°C]: temperature in situ (profiler) <br> t_fb [°C]: temperature (FerryBox) <br> t_sf [°C]: temperature SeaFET (profiler) <br> pco2 [uatm]: Partial pressure of CO2 (FerryBox) <br> pH_sensor [total scale]: pH in situ at in situ temperature (profiler) <br> at [umol kg-1] at, total alkalinity in situ(discrete) <br> ct [umol kg-1]: dissolved inorganic carbon in situ (discrete) <br> pH_discrete [total scale]: spectrophotometric pH in situ (total scale) at in situ temperature (discrete)</p>
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Svalbard Surge Database 2024 (RGI2000-v7.0-G-07)
<p>We have developed a new database of surge-type glaciers in Svalbard by combining existing compilations and reviewing studies examining their dynamics. Our database is based upon the Global Land and Ice Measurements from Space (GLIMS) database (König et al. 2014), which is now incorporated into RGI 7.0 (RGI 7.0 Consortium 2023) and consists of 1,583 glaciers in Svalbard. Therefore, the first five fields come from the RGI 7.0 database:</p> <ul> <li><strong>rgi_id</strong>: Glacier ID from RGI database.</li> <li><strong>glims_id</strong>: Glacier ID from GLIMS database.</li> <li><strong>cenlon</strong>: Longitude of glacier centre point.</li> <li><strong>cenlat</strong>: Latitude of glacier centre point.</li> <li><strong>glac_name</strong>: Name of glacier.</li> </ul> <p>Our compilation of existing Svalbard-wide glacier surge databases is sourced from several studies: Lefauconnier and Hagen (1991) [LH1991]; Hagen et al. (1993) [H1993]; Sevestre and Benn (2015) [SB2015]; Farnsworth et al. (2016) [F2016]; Kääb et al. (2023) [KA2023]; and Koch et al. (2023) [KO2023]. The compilation of LH1991 only covers eastern Svalbard and is focused on marine-terminating glaciers but is included as it contains several important details on surge characteristics. H1993 is the original database of glaciers across Svalbard and similarly contains details of historical surges. The current RGI 7.0 database defines the “surge status” of each glacier according to Sevestre and Benn (2015): no evidence of surging (0); possible surge (1), probably surge (2), and observed surge (3). Where the SB2015 database does not have corresponding evidence from one of the other compilations, we determine the glaciers surge status to be ‘undefined’ and do not include it in the S_All field. The F2016 compilation was manually translated into the RGI 7.0 database. The glacier names described in F2016 often referred to tributaries which are now combined into single glacier catchments (e.g., Nuddbreen / Strongbreen), hence we manually combined these entries. The recent compilations from KA2023 and KO2023 were manually transcribed from tables in PDF files. The subsequent eight fields document each compilation:</p> <ul> <li><strong>SB2015</strong>: Surge database from Sevestre and Benn (2015). [0-3]</li> <li><strong>F2016</strong>: Surge database from Farnsworth et al. (2016). [0-1]</li> <li><strong>H1993</strong>: Surge database from Hagen et al. (1993). [0-1]</li> <li><strong>LH1991</strong>: Surge database from Lefauconnier and Hagen (1991). [0-1]</li> <li><strong>KA2023</strong>: Surge observations from Kääb et al. (2023). This data set is based on manual surge identification in annual Sentinel-1 interferometric wide-swath (IW) satellite radar backscatter differences between 2017 and 2022 (Kääb et al. 2023). This has been updated in this database (version 3) by mapping more recent surges from winter-to-winter differences 2022-2023, 2023-2024, and 2024-2025 using new IW data. Before 2017, no Sentinel-1 IW data are available over Svalbard, and we use 2015-2016 and 2016-2017 extended wide-swath data (EW) instead, acknowledging that these coarser data (compared to IW) might lead to less detailed surge identification, or overlooking of surges of small glaciers or surges accompanied by only limited backscatter changes. Based on these additional data, we are also able to update some surge information contained in the original KA2023, for instance concerning surge start and end years, and by adding the last year of strongly enhanced backscatter (before backscatter reduction). The new 2015-2025 backscatter-derived surge inventory over Svalbard contains now 40 surging glaciers (the 2017-2022 KA2023 contained 26 surging glaciers). [0-1]</li> <li><strong>KO2023</strong>: Surge observations from Koch et al. (2023). [0-1]</li> <li><strong>Other</strong>: Surge observations from other literature sources. [0-1]</li> <li><strong>S_Direct</strong>: All surges that have been directly observed. [0-1]</li> <li><strong>S_Indirect</strong>: All surges that have been indirectly observed e.g. from palaeo-glaciological analysis. [0-1]</li> <li><strong>S_All</strong>: All surges that have been either directly observed or inferred from the palaeo-glaciological record. [0-1]</li> </ul> <p>Contemporary and palaeo-glaciological evidence of surges is generally limited to the period ~1850–present, which broadly corresponds to the end of the LIA through to the modern-day. Where multiple surges have been recorded, we separate these using “;” in the database, and use “n/a” where the details of the surge have not been recorded. Surges have been classified as a binary 0 (not surge-type) or 1 (surge-type), with the exception of the Sevestre and Benn (2015) database as described above.</p> <p>The subsequent eight fields document (if known) the following characteristics for each glacier in Svalbard:</p> <ul> <li><strong>S_Onset</strong>: Surge Onset (Year)</li> <li><strong>S_Term</strong>: Surge Termination (Year)</li> <li><strong>S_Act_Vel</strong>: Max Active-Phase Velocity (m/d)</li> <li><strong>S_Qui_Vel</strong>: Mean Quiescence Velocity (m/d)</li> <li><strong>S_Term_Ch</strong>: Terminus Change (m)</li> </ul> <p>Here, we use 'n/a' for glaciers with no evidence of surging, whilst 'Not observed' is used where we have not observed any of the above characteristics for a glacier with evidence of surging.</p> <p>The final column contains references to where surges have been reported.</p> <p>Included in this version is also a version of the RGI for Svalvard with the new database included.</p> <div> </div>
Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023
<p>Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions.</p> <p>The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, fjord masks and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2.</p> <p>Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder.</p> <p>Table 1. The layer structure of the Svalbard calving front data product.</p> <table> <tbody> <tr> <td> <p><strong>Layer Name</strong></p> </td> <td> <p><strong>Details</strong></p> </td> </tr> <tr> <td> <p>traces</p> </td> <td> <p>Line geometries recording the terminal traces of all the glaciers (EPSG:3995).</p> </td> </tr> <tr> <td> <p>centrelines</p> </td> <td> <p>Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995).</p> </td> </tr> <tr> <td> <p>domains</p> </td> <td> <p>Polygon geometries recording the glacier domains (EPSG:3995).</p> </td> </tr> <tr> <td> <p>fjord_masks</p> </td> <td> <p>Polygon geometries recording the fjord masks (EPSG:3995).</p> </td> </tr> <tr> <td> <p>front_change_time_series</p> </td> <td> <p>Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326).</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 2. The feature attribute table of the data layer.</p> <table> <tbody> <tr> <td> <p><strong>Data Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Glacier</p> </td> <td> <p>The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id.</p> </td> </tr> <tr> <td> <p>Sensor</p> </td> <td> <p>The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”.</p> </td> </tr> <tr> <td> <p>ImageId</p> </td> <td> <p>The image id of the satellite image used in mapping the glacier calving front.</p> </td> </tr> <tr> <td> <p>DateString</p> </td> <td> <p>The datetime string of the satellite image in the format of “YYYYMMDD”.</p> </td> </tr> <tr> <td> <p>CFL_Change</p> </td> <td> <p>The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series.</p> </td> </tr> <tr> <td> <p>glacier_lat</p> </td> <td> <p>The latitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> <tr> <td> <p>glacier_lon</p> </td> <td> <p>The longitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> </tbody> </table>
A new repository of electrical resistivity tomography and ground penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard.
<p>We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (Western Svalbard, Brøggerhalvøya peninsula, Norway) as part of the project ICEtoFLUX (MUR/PRA2021 project-0027). The data set is composed of Electrical Resistivity Tomography (ERT) and GroundPenetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These examples can support the identification of the active layer and the occurrence of spatial variation of soil conditions at depth. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set is of major relevance because scant attention has been paid to the publication of geophysical data from the Ny-Ålesund area so far. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate changes studies on the Ny-Ålesund area. </p>
Digital Elevation Model (DEM) of northern Brøggerhalvøya (Svalbard, Norway) with Ground Sampling Distance (GSD) of 50 cm
<p>HRSC is a multisensor pushbroom instrument with 9 CCD line sensors mounted in parallel that has been in orbit around Mars since January 2004 on ESA’s Mars Express spacecraft (Gwinner et al., 2016). It simultaneously obtains high-resolution stereo, multicolor, and multiphase images. Digital photogrammetric techniques are used to reconstruct the topography on the basis of five stereo channels, which provide five different views of the ground.</p> <p>An airborne version of the HRSC was used for the acquisition of stereo and color images in Svalbard. Since 1997, different airborne versions of HRSC have been developed. The principles of HRSC-AX data processing are described by Gwinner et al. (2006). The orientation data of the camera are reconstructed from a global positioning system inertial navigation system (GPS INS). HRSC-AX has been applied in diverse technical and scientific applications (e.g., Gwinner et al., 1999, 2000; Hauber et al., 2001; Otto et al., 2007) and has also been successfully used to investigate rock glacier activity (Roer and Nyenhuis, 2007). The flight campaign in July–August 2008 covered a total of seven regions in Svalbard: (1) Longyearbyen and the surroundings of Adventfjorden, (2) large parts of Adventdalen, (3) large parts of the Brøggerhalvøya (halvøya = peninsula) in western Spitsbergen (this dataset), (4) the Bockfjorden area in northern Spitsbergen, (5) the northeastern shore of the Palanderbukta and the margin of the adjacent ice cap in Nordaustlandet, (6) an area on Prins Karls Forland, and (7) the area of the abandoned Russian mining settlement of Pyramiden together with the nearby Ebbedalen. </p> <p>This dataset is a Digital Elevation Model (DEM) derived from HRSC-AX stereo images. The elevations recorded in the DEM are ellipsoid heights; i.e., they are not computed with respect to a geoid but to a mathematically defined reference surface, which is a<br>rotational ellipsoid with the equatorial A and B axes both having a radius of 6378.14 km and the polar<br>C axis having a radius of 6356.75 km. This results in an offset of about 36.5m with respect to geoid<br>heights; i.e., sea level in the HRSC-AX DEM is not at 0 m, but at ~36.5 m.</p> <p><strong>References</strong></p> <p>Gwinner, K., Hauber, E., Hoffmann, H., Scholten, F., Jaumann, R., Neukum, G.,<br>Coltelli, M., and Puglisi, G., 1999, The HRSC-A experiment on high reso-<br>lution imaging and DEM generation at the Aeolian Islands, in Proceedings<br>of the 13th International Conference on Applied Geologic Remote Sens-<br>ing: Ann Arbor, Michigan, ERIM International, v. I, p. 560–569.</p> <p>Gwinner, K., Hauber, E., Jaumann, R., and Neukum, G., 2000, High-resolution,<br>digital photogrammetric mapping: A tool for earth science: Eos<br>(Transactions, American Geophysical Union), v. 81, no. 44, p. 513–520,<br>doi:10.1029/00EO00364.</p> <p>Gwinner, K., Coltelli, M., Flohrer, J., Jaumann, R., Matz, K.-D., Marsella, M.,<br>Roatsch, T., Scholten, F., and Trauthan, F., 2006, The HRSC-AX Mt.<br>Etna Project: High-Resolution Orthoimages and 1 m DEM at Regional<br>Scale: International Archives of Photogrammetry and Remote Sensing,<br>v. XXXVI, Part 1, http://isprs.free.fr/documents/Papers/T05-23.pdf.</p> <p>Gwinner, K., Scholten, F., Spiegel, M., Schmidt, R., Giese, B., Oberst,<br>J., Heipke, C., Jaumann, R., and Neukum, G., 2009, Derivation and<br>validation of high-resolution digital elevation models from Mars Express<br>HRSC data: Photogrammetric Engineering and Remote Sensing, v. 75,<br>no. 9, p. 1127–1142.</p> <p>Gwinner, K., Jaumann, R., Hauber, E., et al., 2016, The High Resolution Stereo Camera (HRSC) of Mars Express and its<br>approach to science analysis and mapping for Mars and its satellites: Planetary and Space Science, v. 126, p. 93–138. http://dx.doi.org/10.1016/j.pss.2016.02.014</p> <p>Hauber, E., Slupetzky, H., Jaumann, R., Wewel, F., Gwinner, K., and Neukum,<br>G., 2001, Digital and automated high resolution stereo mapping of the<br>Sonnblick glacier: EARSeL eProceedings, v. 1, no. 1, p. 246–254.</p> <p>Jaumann, R., Neukum, G., Behnke, T., Duxbury, T.C., Eichentopf, K., Flohrer,<br>J., van Gasselt, S., Giese, B., Gwinner, K., Hauber, E., Hoffmann, H., Hoff-<br>meister, A., Köhler, U., Matz, K.-D., McCord, T.B., Mertens, V., Oberst,<br>J., Pischel, R., Reiss, D., Ress, E., Roatsch, T., Saiger, P., Scholten, F.,<br>Schwarz, G., Stephan, K., Wählisch, M., and the HRSC Co-Investigator<br>Team, 2007, The high-resolution stereo camera (HRSC) experiment on<br>Mars Express: instrument aspects and experiment conduct from interplan-<br>etary cruise through the nominal mission: Planetary and Space Science, v.<br>55, p. 928–952, doi:10.1016/j.pss.2006.12.003.</p> <p>Otto, J.-C., Kleinod, K., König, O., Krautblatter, M., Nyenhuis, M., Roer,<br>I., Schneider, M., Schreiner, B., and Dikau, R., 2007, HRSC-A data:<br>A new high-resolution data set with multipurpose applications in physi-<br>cal geography: Progress in Physical Geography, v. 31, no. 2, p. 179–197,<br>doi:10.1177/0309133307076479.</p> <p>Roer, I., and Nyenhuis, M., 2007, Rockglacier activity studies on a regional<br>scale: Comparison of geomorphological mapping and photogrammetric<br>monitoring: Earth Surface Processes and Landforms, v. 32, p. 1747–1758,<br>doi:10.1002/esp.1496.</p>
Satellite NDSI in the Hornsund area (Svalbard, Norway)
<p>The gridded datasets is a cropped area obtained by the use of Google Earth Engine and selecting Sentinel-2 and Landsat 8 data for estimating the Normalised Difference Snow Index between 2014 and 2020. The considered region is the Hornsund area.</p>
Svalbard Rock Vault - liberating vintage geoscience data from Svalbard
<p>The Svalbard Rock Vault project (www.svalbox.no/srv) has the main objective to recover vintage geoscientific data from Svalbard's geoscientific exploration, in particular the overlooked petroleum exploration onshore Svalbard. Eighteen boreholes were drilled from 1961 to 1994 but data are only available as fragments. These data are systematically collected, digitized and made available for research purposes by the Svalbox team at the University Centre in Svalbard. </p> <p>This initial data set publication includes three overview spreadsheets:</p> <p>1) An overview of the archive material of Norsk Polar Navigasjon (NPN), a private company that was involved in many of the exploration boreholes. The physical archive is available at UNIS and scans are available upon request. </p> <p>2) An overview of the archive material related to the Tromsøbreen II exploration borehole, drilled in 1988. The physical archive is in Lund, Sweden, and most of it has been digitized by UNIS and scans are available upon request. </p> <p>3) An overview of the data available from the eighteen exploration boreholes, including wireline log data, reports and analyses. The data are also integrated in a Petrel project available at UNIS. </p> <p>More data will be added as they are (re-)discovered. </p>
New glacier thickness and bed topography maps for Svalbard - Dataset
<p>The dataset includes three Geotiff files that include:</p> <p>1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff]</p> <p>2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff]</p> <p>3) A mask file that with values from 0-3 indicating non-glacier areas (0), glaciers modelled with the Parallel Ice Sheet model (1), glaciers modelled with the Instructed Glacier Model (2), and surging glaciers (3). </p> <p>For a description of the methods used to generate the datasets, we refer to the manuscript "A new glacier thickness and bed map for Svalbard", to be submitted to The Cryosphere Discussions.</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
Macrobenthic data of Rijpfjorden, Svalbard
<p>The aim of this study was to monitor the temporal changes of the macrobenthos in Rijpfjorden (Svalbard, Arctic Ocean). This dataset contains abundance data of benthic macrofauna collected during five cruises to Rijpfjorden between late July and early September in 2003, 2007, 2010, 2013 and 2017. During each cruise, the seafloor of four stations (‘Inner Rijpfjorden’ (IR), ‘Middle Rijpfjorden’ (MR), ‘Outer Rijpfjorden’ (OR) and ‘Rijpfjorden North’ (RN)) was sampled with three deployments of a Van Veen grab (0.1 m<sup>2</sup>). The sediment was sieved over a 0.5 mm mesh and fauna was preserved in 4% buffered formaldehyde solution. Identifcation was carried out to the lowest possible taxonomic level.<br><br>This dataset is in a multi-sheet format and quality-controlled by CRITTERBASE (https://critterbase.awi.de/#qc).</p>
25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks
<p><strong>Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis.</strong></p> <p><strong>The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard.</strong></p> <p><strong>This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).</strong></p>
Svalbard time-lapse cameras
<p>Time-lapse cameras are important data sources enabling us to observe changes in the Svalbard environment in an efficient and economically favorable way. Focusing on snow cover monitoring using cameras, it is important to identify potential image providers, archived imagery, and processed datasets.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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