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DATA of 'Quantification of seasonal and diurnal dynamics of subglacial channels using seismic observations on an Alpine Glacier.' from Nanni et al. 2020,
<p>This dataset belongs to the study of <strong>Nanni et al., 2020</strong> "Quantification of seasonal and diurnal dynamics of subglacial channels using seismic observations on an Alpine Glacier." accepted for publication in The Cryosphere on March 9th 2020.</p> <p>You can find additional information on the "<strong>README_data_NANNI_2020_glacier</strong>"</p>
Surface elevation change for Khumbu Glacier, 1984–2015
<p>Digital Elevation Model (DEMs) of Difference showing the change in surface elevation of Khumbu Glacier, Nepal, between 1984–2015 and 2000–2015 derived through the differencing of high-resolution DEMs generated from 0.5 m ground resolution aerial photographs (1984 CE) and WorldView-1 and WorldView-2 imagery with a spatial resolution of 0.46 m (2015 CE).</p>
Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice: Datasets
<p>This repository contains data of near surface data of turbulence conditions measured simultaneously over exposed ice and a 0.08 m thick supraglacial debris cover on Suldenferner, a small glacier in the Italian Alps. It is related to the following publication:</p> <p>Nicholson, L. and Stiperski, I. (2020) Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice. Journal of Glaciology.</p> <p><strong>The repository contains the following files</strong></p> <p>(1) Overview figure of the locations of the installed weather stations collecting data used in the analysis, and images of the eddy covariance installations </p> <ul> <li><strong>Filename:</strong> overview.tif</li> </ul> <p>(2) 30 minute average meteorological data from the automatic weather station (AWS) on the debris-covered glacier surface with ca. 0.09 m thick debris cover.</p> <ul> <li><strong>Filename:</strong> aws.csv</li> <li><strong>Location:</strong> 46.496 °N / 10.569 °E / ~2625 m</li> <li><strong>Time period:</strong> 11.08.2015 13:30 – 14.08.2015 21:00</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(3) 5 minute data from the eddy covariance station on the clean ice glacier surface.</p> <ul> <li><strong>Filename:</strong> ecci.csv</li> <li><strong>Location:</strong> 46.498°N /10.560°E / ~ 2780 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(4) 5 minute data from the eddy covariance station on the debris-covered glacier surface with ca. 0.08 m thick debris cover.</p> <ul> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> <li><strong>Location:</strong> 46.495 °N / 10.572 °E / ~ 2600 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(5) A list of the variables and units used in the datafiles.</p> <ul> <li><strong>Filename:</strong> variables&units.pdf</li> </ul> <p> </p> <p>The<strong> instrumentation locations</strong> can be seen in the overview figure. </p> <p>The <strong>automatic weather station </strong>consists of a Kipp and Zonen CNR1 4-way radiation sensor, a shielded Vaisala HMP45c temperature and relative humidity sensor, and a Young 05103 anemometer. 30-minute averages and standard deviations of variables were recorded by a Campbell C3000 datalogger. Temperature and relative humidity are also sampled at 30-minute intervals allowing the vapor pressure to be calculated at this interval. </p> <p>The <strong>eddy covariance</strong><strong> instrumentation</strong> was identical at both stations (ecci and ecdc) and consisted of two segmented masts drilled into the ice with sensors mounted at a height of 1.6 m on a cross arm spanning the vertical masts. A CSAT 3D sonic anemometer and KH20 hygrometer sampling data at a frequency of 20Hz were mounted parallel to the surface and facing obliquely across-glacier at a bearing of 255° so as to capture both up and downglacier winds. A shielded Vaisala HMP45 was installed on the EC mast to record 1-minute averages of air temperature, relative humidity and vapor pressure. Data were recorded using Campbell Scientific CR1000 data loggers with compact flash card storage modules. Power was provided by 60Ah deep cycle batteries connected to 20 W solar panels. </p> <p>The <strong>data provided</strong> here is gap-filled data. The eddy covariance station over debris-covered ice was installed on 10 August 2015, and the one over clean ice was installed on 11 August 2015. At ecdc, a faulty solar panel regulator resulted in this station losing power on 15 August. At ecci two instrument failures occurred; the Vaisala instrument on the afternoon of 12 August and the KH20 at the end of 14 August. Air pressure was not recorded at any of the three stations. Missing data was filled on the basis of multiple regression transformation of data measured at nearby stations, as described fully in the publication. Pressure data were filled in with data from the nearby Madritsch weather station, operated by the Autonomous Province of Bozen. Missing temperature and vapor pressure data spanning 12-14 August at the eddy station over clean ice was filled in with data from the glacier automatic weather station.</p> <p><strong>Thanks</strong> are due to the Gutgsell family at the Hintergrathütte for continued support of our research activities and (in alphabetical order) Michael Adamer, Federico Covi, Costanza del Gobbo, Lukas Hammerer, Irmgard Juen, Marius Massimo, Kristin Richter, Reto Stauffer and Anna Wirbel for assistance in the field. Permission to work on Suldenferner is granted by Stelvio National Park. This research was funded by the Austrian Science Fund Grant numbers V309, P28521 and T781-N32.</p> <p> </p>
Supraglacial debris thickness data from Khumbu Glacier, Nepal
<p>One dataset containing 153 point measurements of supraglacial debris thickness on Khumbu Glacier, Nepal. Measurements were made by manual excavation in 2014 and 2015 by Morgan Gibson and Ann Rowan. </p> <p>The dataset is included here in two formats; (1) a delimited text file, and (2) a .kmz file for Google Earth. The data in each file are identical. A Google Earth map showing an overview of the data coverage is also included as a jpg.</p> <p>The debris thickness to the ice surface was measured as the distance to the ice from a horizontal reference placed on the unmodified surrounding surface bridging the excavation. Debris thickness data are reported to the nearest 0.1 m. Where debris thickness is given as 1.0 m this indicates that this is the minimum value and it was not possible to excavate to the debris–ice interface. Latitude and longitude were recorded with a handheld Garmin GPS.</p> <p><br> <br> </p> <div class="wayback1996-RTmodal"> <div> </div> <div> </div> × <div> </div> <div> </div> <div> </div> </div>
Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier
<p>Abstract:</p> <p>These data products accompany the paper "Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier" (MacKie et al., in review). In this study, geostatistical techniques were used to generate an ensemble of topographic realizations that retain the spatial statistics of radar bed elevation measurements. The simulation was conditioned to local radar data and mass conservation bed estimates. This repository contains the radar and mass conservation conditioning data, the ensemble of topographic realizations, and coordinate data.</p> <p> </p> <p>Content and processing steps:</p> <p>The study area is 75.15 x 48.90 km^2. The grid cell resolution is 150 meters. Each digital elevation model (DEM) has 501 x 326 grid cells. The mass conservation DEM was obtained from BedMachine Greenland (Morlighem and others, 2017). The radar data were acquired from the Center for Remote Sensing of Ice Sheets (CReSIS) 2009 flights (Gogineni, 2012; Gogineni and others, 2014). A probabilistic modeling technique called sequential Gaussian co-simulation (Verly, 1993; Almeida and Journel, 1994; Journel, 1999; Remy, 2005) was used to generate the topographic realizations. The datasets are as follows:</p> <p> </p> <p>1) Jakobshavn_mass_conservation.txt - Mass conservation conditioning data</p> <p>2) Jakobshavn_radar_data.txt - Radar conditioning data</p> <p>3) Jakobshavn_simulation.txt - 250 topographic realizations. The shape of this file is 250 x 163326, where each column corresponds to one topographic realization. Each column should be reshaped to 501 x 326 to view the DEM.</p> <p>4) Jakobshavn_x_data.txt - Polar stereographic X coordinates in meters</p> <p>5) Jakobshavn_y_data.txt - Polar stereographic Y coordinates in meters</p> <p> </p> <p>References:</p> <p>Almeida, A. S., & Journel, A. G. (1994). Joint simulation of multiple variables with a Markov-type coregionalization model. <em>Mathematical Geology</em>, <em>26</em>(5), 565-588.</p> <p>Gogineni, P. (2012). CReSIS radar depth sounder data. <em>Center for Remote Sensing of Ice Sheets, Lawrence, KS https://data. cresis.-ku. edu</em>.</p> <p>Gogineni, S., Yan, J. B., Paden, J., Leuschen, C., Li, J., Rodriguez-Morales, F., ... & Gauch, J. (2014). Bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica. <em>Journal of Glaciology</em>, <em>60</em>(223), 813-833.</p> <p>Journel, A. G. (1999). Markov models for cross-covariances. <em>Mathematical Geology</em>, <em>31</em>(8), 955-964.</p> <p>Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., ... & Fenty, I. (2017). BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation. <em>Geophysical research letters</em>, <em>44</em>(21), 11-051.</p> <p>Remy, N. (2005). S-GeMS: the stanford geostatistical modeling software: a tool for new algorithms development. In <em>Geostatistics banff 2004</em> (pp. 865-871). Springer, Dordrecht.</p> <p>Verly, G. W. (1993). Sequential Gaussian cosimulation: a simulation method integrating several types of information. In <em>Geostatistics Troia’92</em> (pp. 543-554). Springer, Dordrecht.</p>
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier mass balance (MB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct MB observations and remote sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed through an extensive cross-validation against a dataset of 32 glaciers , with an estimated average error (RMSE) of 0.55 m.w.e. a<sup>-1</sup>, an explained variance (r2) of 75% and an average bias of -0.021 m.w.e. a<sup>-1</sup>. We estimate an average regional area-weighted glacier-wide MB of -0.71±0.21 (1 sigma) m.w.e. a<sup>-1</sup> for the 1967-2015 period, with negative mass balances in the 1970s (-0.44 m.w.e. a<sup>-1</sup>), moderately negative in the 1980s (-0.16 m.w.e. a<sup>-1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.34 m.w.e. a<sup>-1</sup> in the 2010s. A comparison with ASTER-derived geodetic MB for the 2000-2015 period showed important differences with the photogrammetric geodetic MB used to train our model. When recalibrating our reconstructions with the new ASTER-derived geodetic MB, the estimated average regional area-weighted glacier-wide MB (1967-2015) is reduced to -0.64±0.21 (1 sigma) m.w.e. a<sup>-1</sup>. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967-2015 period are the Chablais (-0.93 m.w.e. a<sup>-1</sup>), Champsaur and Haute-Maurienne (-0.86 m.w.e. a<sup>-1</sup> both) and Ubaye ranges (-0.83 m.w.e. a<sup>-1</sup>), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.69 m.w.e. a<sup>-1</sup>), Oisans and Haute-Tarentaise ranges (-0.75 m.w.e. a<sup>-1</sup> both). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.</p> <p>The MB dataset is presented in two different formats: (a) A single netCDF file containing the MB reconstructions, the glacier RGI and GLIMS IDs and the glacier names. This file contains all the necessary information to correctly interact with the data, including some metadata with the authorship and data units. (b) A dataset comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide MB time series. Glaciers with remote sensing-derived estimates (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;).</p>
Meteorological records in Laohugou Glacier over the Qilian Mountains, northeastern Tibetan Plateau
<p>Meteorological records in Laohugou Glacier over the Qilian Mountains, northeastern Tibetan Plateau</p>
Iceberg melting substantially modifies oceanic heat flux towards a major Greenlandic tidewater glacier - data and code
<p>This repository contains code and data required to reproduce the analysis presented in the manuscript:</p> <p>Davison, B. J., Cowton, T. R., Cottier, F. R., and Sole, A. J. 2020. Iceberg melting substantially<br>modifies oceanic heat flux towards a major Greenlandic tidewater glacier". Nature Communications. <a href="https://www.nature.com/articles/s41467-020-19805-7">Iceberg melting substantially modifies oceanic heat flux towards a major Greenlandic tidewater glacier | Nature Communications</a></p> <p> </p> <p>NOTE: it has recently come to my attention that the ICEBERG package for MITgcm does not provide a physical blocking effect to the ocean (with thanks to Paul Summers for alerting me to this issue). </p>
Predicting hydrologic responses to climate changes in highly glacierized and mountainous region Upper Indus Basin
<p><span>The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and</span><span> is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation, and snowmelt </span><span>could be reasons for further escalation of the problem. </span><span>Therefore, estimation of hydrological processes </span><span>is </span><span>critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using Soil and Water Assessment Tool (SWAT). The methodology includes</span><span>:</span><span> (i) development of SWAT model using </span><span>land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from </span><span>1978-1993; (iii) model validation for the time 1994-2003; (iv) bias correction of Regional Climate Model (v) </span><span>utilization of </span><span>bias corrected RCM for future assessment under RCP4.5 and RCP8.5 </span><span>for mid (2041-2070) and late century (2071-2100). The results of the study revealed a strong correlation between simulated and observed flow </span><span>with R<sup>2</sup> and NSE equals 0.85 each for daily flow</span><span>. For validation, R<sup>2</sup> and NSE were found to be 0.84 and 0.80 respectively. Compared to baseline period (1976-2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of 21<sup>st</sup> century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP 4.5 and 8.5 respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model </span><span>could </span><span>be used as effective tool for climate change valuation and for sustainable management of water resources in future.</span></p>
Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers
<p>This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 files and each file contains data for an individual outlet glacier or a branch of an outlet glacier. Shapefiles and NetCDF files are provided with data for 6 primary flowlines, and up to 18 iterated flowlines, spanning the width of each glacier. The iterated flowlines are used to find the furthest inland thinning limits (see the associated publication for more details). Files are named <em>glacierXXXX </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a separate branch of an outlet glacier. Shapefiles filenames have an additional <em>_iterNN </em>suffix, where <em>NN</em> identifies the iteration number of that set of flowlines.</p> <p>The shapefiles contain multiple features, each of which is one flowline. Each flowline has a "flowline" attribute, a two-digit identifier that corresponds to the same identifier in the NetCDF files. The NetCDF files contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints, extracted and calculated along the primary and iterated flowlines. The flowlines in the NetCDF files have a regular 50-meter spacing between nodes, whereas the flowlines in the shapefiles have a coarser and irregular spacing.</p> <p>More information on how the flowlines were derived and how data was extracted and calculated is in the associated publication (Felikson et al., 2020), with a DOI to be provided. Example code that can be used to read and plot the data can be obtained at http://doi.org/10.5281/zenodo.4284715.</p>
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) - Data Set
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the paper "Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – Towards an improved representation of mountain water resources in global assessments"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p><strong>Hanus, S., Schuster, L., Burek, P., Maussion, F., Wada, Y., and Viviroli, D.: Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments, Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, 2024.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Thwaites and Pine Island Glacier change 2016-2021
<p>Visualization of <a href="https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1">Sentinel</a> 1 radar images using <a href="https://matplotlib.org/cmocean/">cmocean</a> ice colourmap, similar to the approach used by <a href="https://www.pnas.org/content/117/40/24735/tab-figures-data">Lhermitte</a>. Data from <a href="https://www.nasa.gov/mission_pages/Grace/index.html">GRACE</a> was processed by <a href="https://data1.geo.tu-dresden.de/ais_gmb/index.html#grid">TU Dresden</a>. Funded by Rijkswaterstaat KPP. </p>
Caucasus glacier mass balance and thickness changes in 2000-2019
<p>In this study, we used glacier mass balance and thickness data obtained from geodetic method by Hugonnet et al. (2021) to clarify the response of region wide glacier mass balance to climate change in the Greater Caucasus over the last two decades (2000‒2019). We also analyze the long-term spatio-temporal glacier surface elevation and specific mass change for the regional, river basin, and individual glaciers. Besides, we provide a key dataset (shp file) that can be useful for further studies, including for example, hydrological modeling of runoff changes, as well as for the implementation of water management plans in this region.</p>
Mapped glacier region in BH during LIA
<p>Mapped glacier region in BH during LIA </p>
The Glaciers of the Dolomites: last 40 years of melting
<p><strong>This dataset refers to: </strong></p> <p>Securo, A., Del Gobbo, C., Baccolo, G., Barbante, C., Citterio, M., De Blasi, F., Marcer, M., Valt, M., and Colucci, R. R.: The Glaciers of the Dolomites: last 40 years of melting, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1357, 2024.</p> <p>Read more here: <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1357/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1357/</a></p> <p>The study presents a multi-decadal (1980s-2023) estimation of surface elevation change and geodetic mass balance of the current mountain glaciers present in the area. Calculations are based on geodetic data: high resolution and accuracy is obtained with unmanned aerial vehicle (UAV) Structure from Motion (SfM) and airborne Light Detection and Ranging (LiDAR), from 2010 to 2023. SfM on historical aerial imagery is used for previous decades.</p> <p><strong>The dataset contains:</strong></p> <ul> <li>1-band raster files (.tif) with the M3C2 (Multi Scale Model to Model Cloud Comparison) distance calculations from all mountain glaciers of the Dolomites, computed on common glacier area between the two periods. File names are as following: "AreaID_glaciername_year1_year2.tif"<br>SR<em> Monte Mario / Italy zone 2 - EPSG:3004</em></li> <li>Geopackage (.gpkg) files with glaciers area during different periods. Polygon have the following attributes: Area ID, Glacier name, CGI-ID (Comitato Glaciologico Italiano), RGI-ID (Randoph Glacier Inventory), Area, Year.<br>SR <em>WGS84 - EPSG: 4326</em></li> </ul> <p><strong>Additional 3D models of the Dolomites Glaciers in 2023 can be found here: <a title="Sketchfab Collection" href="https://skfb.ly/oRtOW" target="_blank" rel="noopener">Sketchfab</a></strong></p> <p><strong>Full file list:</strong></p> <p>"1_Popera_2010_2014.tif"<br>"1_Popera_Alto_1992_2010.tif"<br>"1_Popera_Alto_2010_2023.tif"<br>"1_Popera_Alto_2014_2023.tif"<br>"1_Popera_Pensile_1992_2010.tif"<br>"1_Popera_Pensile_2010_2023.tif"<br>"1_Popera_Pensile_2014_2023.tif"<br>"2_Cristallo_1992_2010.tif"<br>"2_Cristallo_2010_2014.tif"<br>"2_Cristallo_2010_2023.tif"<br>"2_Cristallo_2014_2023.tif"<br>"3_Sorapiss_Occidentale_1980_2010.tif"<br>"3_Sorapiss_Occidentale_2010_2014.tif"<br>"3_Sorapiss_Occidentale_2010_2023.tif"<br>"3_Sorapiss_Occidentale_2014_2023.tif"<br>"4_Antelao_2010_2014.tif"<br>"4_Antelao_Inferiore_1982_2010.tif"<br>"4_Antelao_Inferiore_2010_2023.tif"<br>"4_Antelao_Inferiore_2014_2023.tif"<br>"4_Antelao_Superiore_1982_2010.tif"<br>"4_Antelao_Superiore_2010_2023.tif"<br>"4_Antelao_Superiore_2014_2023.tif"<br>"5_Marmolada_1982_2010.tif"<br>"5_Marmolada_2010_2014.tif"<br>"5_Marmolada_2010_2023.tif"<br>"5_Marmolada_2014_2023.tif"<br>"6_Fradusta_1982_2010.tif"<br>"6_Fradusta_2010_2014.tif"<br>"6_Fradusta_2010_2023.tif"<br>"6_Fradusta_2014_2023.tif"<br>"6_Travignolo_1982_2010.tif"<br>"6_Travignolo_2010_2014.tif"<br>"6_Travignolo_2010_2023.tif"<br>"6_Travignolo_2014_2023.tif"</p> <p>"area_dolomites_glaciers.gpkg"<br>"area_dolomites_glaciers_1980s_1990s.gpkg"<br>"area_dolomites_glaciers_2010.gpkg"<br>"area_dolomites_glaciers_2023.gpkg"</p>
Perlin glacier data
Open the record for dataset details and reuse information.
Figure 2 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 2. Mean numbers of total harbor seals counted during the molt (Panel A) and pups during pupping season (Panel B) in Aialik Bay. Estimated means were adjusted for standard environmental conditions using Generalized Linear Model (Hoover-Miller et al. 2011). Error bars represent 95% confidence intervals of means.
FIGURE 7. Andiperla morenensis n in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 7. Andiperla morenensis n. sp. male, habitus.
Seismic Deformation of Himalayan Glaciers Using Synthetic Aperture Radar Interferometry, Supplementary Material
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Table 1 in DNA barcoding and morphology reveal exceptional species diversity of Scoparia (Lepidoptera: Crambidae) from the Hailuogou Glacier area, China
<p><b>Table 1.</b> Percentage of divergence in the cytochrome <i>c</i> oxidase subunit I (<i>COI</i>) gene sequences of the Scoparia species with out-groups</p><table><tbody><tr><th></th><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th></tr></tbody><tbody><tr><th>1</th><td><i>Eudonia hexamera</i></td><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>2</th><td><i>Eudonia puellaris</i></td><td>6.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>3</th><td><b><i>Scoparia simplicissima</i> sp. nov.</b></td><td>7.9–8.2</td><td>7.9–8.1</td><td><b>0–0.3</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>4</th><td><b><i>Scoparia tribulosa</i> sp. nov.</b></td><td>8.7–9.3</td><td>10.5–11.0</td><td>6.2–6.9</td><td><b>0–0.5</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>5</th><td><b><i>Scoparia longispina</i> sp. nov.</b></td><td>8.4–8.7</td><td>8.9–9.2</td><td>5.1–6.2</td><td>6.4–7.3</td><td><b>0–1.5</b></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>6</th><td><b><i>Scoparia gibbosa</i> sp. nov.</b></td><td>8.0–9.3</td><td>8.7–10.0</td><td>6.2–7.4</td><td>7.3–8.8</td><td>5.6–7.5</td><td><b>0–1.7</b></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>7</th><td><i>Scoparia metaleucalis</i></td><td>9.7–10.8</td><td>10.8–11.7</td><td>8.2–9.6</td><td>10.8–12.4</td><td>9.1–9.9</td><td>10.1–11.8</td><td><b>0–1.5</b></td><td></td><td></td><td></td><td></td></tr><tr><th>8</th><td><i>Scoparia jiuzhaiensis</i></td><td>9.2–9.7</td><td>9.1–9.6</td><td>8.4–8.9</td><td>9.4–10.5</td><td>10.5–10.8</td><td>9.1–10.2</td><td>13.0–14.0</td><td><b>0–0.8</b></td><td></td><td></td><td></td></tr><tr><th>9</th><td><i>Scoparia brevituba</i></td><td>9.9–10.1</td><td>9.9–10.1</td><td>9.8–10.0</td><td>10.3–11.0</td><td>11.0–11.9</td><td>9.8–11.2</td><td>12.1–13.3</td><td>7.2–7.9</td><td><b>0–0.2</b></td><td></td><td></td></tr><tr><th>10</th><td><b><i>Scoparia globosa</i> sp. nov.</b></td><td>7.9–8.5</td><td>7.7–8.2</td><td>7.7–8.7</td><td>9.9–11.2</td><td>9.3–9.9</td><td>7.4–8.6</td><td>10.5–11.5</td><td>7.5–8.2</td><td>9.6–10.3</td><td><b>0–0.6</b></td><td></td></tr><tr><th>11</th><td><b><i>Scoparia annulata</i> sp. nov.</b></td><td>8.0–9.2</td><td>8.7–9.8</td><td>7.5–8.9</td><td>9.6–11.2</td><td>8.7–10.1</td><td>7.5–10.1</td><td>11.0–12.4</td><td>6.9–8.2</td><td>8.4–9.8</td><td>6.4–7.9</td><td>0–1.7</td></tr></tbody></table><p>All genetic distances (%) were corrected with the Kimura two-parameter (K2P) substitution model using MEGA 5; extreme values of intraspecific and interspecific distances are given (the numbers in bold are the intraspecific distances).</p>
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