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High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
Results for 'Health and sustainability of glaciers in High Mountain Asia'
<p>Summary table updated relative to prior version to provide more useful outputs and units according to the description below.</p> <p>Contains 1 .csv file including the glacier health metrics used in Miles and others (2021) for all RGI glacier outlines larger than 2km2 in High Mountain Asia (regions 13/14/15). The following attributes are provided in the table:</p> <ul> <li>RGIID: unique identifier from the RGI6.0</li> <li>VALID: flag to indicate if the data quality of the inputs and results was acceptable (see study Supplementary Material)</li> <li>CenLat: Latitude of glacier outline centroid from the RGI6.0</li> <li>CenLon: Longitude of glacier outline centroid from the RGI6.0</li> <li>meanSMB: Glacier mean mass balance (m w.e./year) derived by this study</li> <li>ELA: Equilibrium Line Altitude (m a.s.l.) estimated by this study</li> <li>ELAsig: Uncertainty of ELA based on 1000 Monte Carlo simulations using the derived surface mass balance uncertainty</li> <li>AAR: Accumulation Area Ration (unitless) estimated by this study</li> <li>AARsig: Uncertainty of AAR as for ELA</li> <li>totAbl: Volume of annual ablation, glacier-wide (m3/year)</li> <li>totAblsig: Uncertainty of total ablation, glacier-wide (m3/year)</li> <li>balAbl: Volume of 'balance' annual ablation, ie that compensated by net annual accumulation, glacier-wide (m3/year)</li> <li>balAblsig: Uncertainty of 'balanced' ablation, glacier-wide (m3/year)</li> <li>imbalAbl: Volume of 'imbalance' annual ablation, glacier-wide (m3/year)</li> <li>imbalAblsig: Uncertainty of imbalance ablation, glacier-wide (m3/year)</li> <li>balAblPct: Portion of annual ablation balanced by accumulation (unitless)</li> <li>balAblPctsig: Uncertainty of balance portion of ablation (unitless)</li> <li>Vol2100: Simulated glacier volume in the year 2100 under repeated application of current mass balace (m3)</li> <li>Vol2100sig: Uncertainty in Vol2100 based on current mass balance uncertainty (m3)</li> <li>PctVol2100: Simulated volume at 2100 expressed as a fraction of volume at 2000 (unitless)</li> <li>PctVol2100sig: Uncertainty in PctVol2100 based on current mass balance uncertainty (unitless)</li> </ul> <p>Also contains 1 .zip file with principal regridded inputs and results for continuity-derived glacier specific mass balances of High Mountain Asia, 2000-2016. A subdirectory contains the following for each glacier, identified by its Randolph Glacier Inventory identification number (RGIID), all in geotiff format and at the same resolution:</p> <ul> <li>'*_AW3D.tif': regridded digital elevation model based on the ASTER GDEM3 (apologies for misleading name)</li> <li>'*_debris.tif': binary rasterized debris-cover map based on the results of Scherler et al (2018)</li> <li>'*_dH.tif': regridded elevation change rate from Brun et al (2017), in m per year</li> <li>'*_dHe.tif': regridded elevation change rate uncertainty from Brun et al (2017), in m per year</li> <li>'*_FDIV.tif': raster of flux divergence, in m per year</li> <li>'*_FDIVe.tif': raster of flux divergence uncertainty, in m per year</li> <li>'*_Hdensity.tif': raster of estimated density of dH signal, in 1000 kg per m3</li> <li>'*_SMB.tif': raster of specific mass balance, in m w.e. per year</li> <li>'*_SMBe.tif': raster of specific mass balance uncertainty, in m w.e. per year</li> <li>'*_Smean.tif': raster of column-average surface speed based on regridded data from ITS_LIVE (Gardner et al, 2019), in m per year</li> <li>'*_THX.tif': raster of glacier thickness from consensus estimate of Farinotti et al (2019), in m </li> <li>'*_zFDIV.tif': raster of zonally-aggregated flux divergence, in m per year</li> <li>'*_zFDIVe.tif': raster of zonally-aggregated flux divergence uncertainty, in m per year</li> <li>'*_zones.tif': raster of elevation-based zonal segmentation for each glacier</li> <li>'*_zSMB.tif': raster of zonally-aggregated specific mass balance, in m w.e. per year</li> <li>'*_zSMBe.tif': raster of zonally-aggregated specific mass balance uncertainty, in m w.e. per year</li> </ul>
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</p>
Temperature sensitivity of mountain glaciers
<p><strong>Distributed summer air temperatures across mountain glaciers in the south-east Tibetan Plateau: temperature sensitivity and </strong><strong>comparison with existing glacier datasets</strong></p> <p>Thomas E. Shaw<sup>1</sup>, Wei Yang<sup>2,3</sup>, Álvaro Ayala<sup>4</sup>, Claudio Bravo<sup>5</sup>, Chuanxi Zhao<sup>2</sup>, Francesca Pellicciotti<sup>1,6</sup></p> <p> </p> <p><sup>1</sup> Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</p> <p><sup>2</sup> Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China</p> <p><sup>3</sup> CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China</p> <p><sup>4 </sup>Centre for Advanced Studies in Arid Zones (CEAZA), La Serena, Chile</p> <p><sup>5</sup> School of Geography, University of Leeds, Leeds, UK</p> <p><sup>6 </sup>Department of Geography, Northumbria University, Newcastle, UK</p> <p><em>Corresponding author: Thomas E. Shaw (</em><a href="mailto:thomas.shaw@wsl.ch"><em>thomas.shaw@wsl.ch</em></a><em>)</em></p> <p>Keywords: Air Temperature, Glaciers, Tibetan Plateau, Temperature Sensitivity</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>Dataset provided:</strong></p> <p>'<strong>Climatic_Sensitivity_Mountain_Glaciers.mat'</strong> = Matlab file with data structures for each glacier site. The following sites are:</p> <p>% Parameter set calculated from data on Parlung Glaciers (this study)<br> % Parameter set from Shea and Moore (2010) for Rockies- Canada (Published parameters)<br> % Parameter set from Carturan et al. (2015) for Ortles Cevedale, Italy (k1/k2 data from author)<br> % Parameter set from Shaw et al. (2017) on Tsanteleina Glacier, Italy (Reassesed parameters)<br> % Parameter set calculated from data of Bravo et al., (2019) on South Patagonian Icefield (SPI), Chile<br> % Parameter set calculated from data of Bravo et al., (2017) on Universidad Glacier, Chile<br> % Parameter set calculated from data of Ayala et al., (2015) on Arolla Glacier, Switzerland<br> % Parameter set calculated from data of Ayala et al., (2015) on JuncalNorte Glacier, Chile<br> % Parameter set calculated from data of Troxler et al., (2020) on McCall Glacier, Alaska<br> % Parameter set calculated from data of Greuell and Böhm (1998) on Pasterze Glacier, Austria<br> % Parameter set calculated from data of Rets et al., (2019) on Djankuat Glacier, Russia<br> % Parameter set calculated from data of Pradhananga et al., (2020 In prep) on Peyto Glacier, Canada</p> <p><strong>Variables include:</strong></p> <p>'Name' = name of individual observation station (AWS or Temp/RH 'T-logger')<br> 'Elevation' = Elevation (m a.s.l.) of given observation station<br> 'Flowline' = The distance along the glacier flowline from an upslope summit or crest (m)<br> 'k1' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature below the onset of katabatic onset (following Shea and Moore, 2010)<br> 'k2' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature above the onset of katabatic onset (following Shea and Moore, 2010)<br> 'Tst' = The T* parameter that defines the threshold (off-glacier) temperature for katabatic conditions parameterised following Carturan et al. (2015)<br> 'T1' = The equivalent on-glacier threshold temeprature derived from k1 and Tst<br> 'DataSource' = The citation readout</p> <p><br> <strong>Cited literature</strong><br> Ayala, A., Pellicciotti, F., & Shea, J. (2015). Modeling 2m air temperatures over mountain glaciers: Exploring the influence of katabatic cooling and external warming. Journal of Geophysical Research: Atmospheres, 120, 1–19. https://doi.org/10.1002/2015JD023137.</p> <p><br> Bravo, C., Quincey, D. J., Ross, A. N., Rivera, A., Brock, B. W., Miles, E., & Silva, A. (2019). Air Temperature Characteristics , Distribution , and Impact on Modeled Ablation for the South Patagonia Ice field. Journal of Geophysical Research : Atmospheres, 124, 907–925. https://doi.org/10.1029/2018JD028857</p> <p><br> Bravo, C., Lorlaux, T., Rivera, A., & Brock, B. W. (2017). Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile. Hydrology and Earth System Sciences, 21, 3249–3266. https://doi.org/10.5194/hess-21-3249-2017</p> <p><br> Carturan, L., Cazorzi, F., De Blasi, F., & Dalla Fontana, G. (2015). Air temperature variability over three glaciers in the Ortles–Cevedale (Italian Alps): effects of glacier fragmentation, comparison of calculation methods, and impacts on mass balance modeling. The Cryosphere, 9(3), 1129–1146. https://doi.org/10.5194/tc-9-1129-2015</p> <p><br> Greuell, W., & Böhm, R. (1998). 2 m temperatures along melting mid-latitude glaciers , and implications for the sensitivity of the mass balance to variations in temperature. Journal of Glaciology, 44(146), 9–20.</p> <p><br> Rets, E. P., Popovnin, V. V, Toropov, P. A., Smirnov, A. M., Tokarev, I. V, Chizhova, J. N., … Kireeva, M. B. (2019). Djankuat glacier station in the North Caucasus , Russia : a database of glaciological , hydrological , and meteorological observations and stable isotope sampling results during 2007 – 2017. Earth System Science Data, ||, 1463–1481. https://doi.org/https://doi.org/10.5194/essd-11-1463-2019</p> <p><br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p><br> Shea, J. M., & Moore, R. D. (2010). Prediction of spatially distributed regional-scale fields of air temperature and vapor pressure over mountain glaciers. Journal of Geophysical Research, 115(D23), D23107. https://doi.org/10.1029/2010JD014351</p> <p><br> Troxler, P., Ayala, Á., Shaw, T. E., Nolan, M., Brock, B. W., & Pellicciotti, F. (2020). Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier , Alaska. Journal of Glaciology, 1–15. https://doi.org/https://doi.org/10.1017/jog.2020.12</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
RTK-GPS measurements on 6 rock glaciers in the La Sal and Uinta Mountains, Utah between 2021 and 2023
<p>RTK-GPS surveying of points marked on the surfaces of 6 rock glaciers in the La Sal and Uinta Mountains, Utah</p> <p>All<span> </span>measurements made with a pair of Emlid Reach RS2 RTK-GPS receivers connected in FIX mode</p> <p>Comparison of the x/y coordinates for points in subsequent surveys reveals planimetric motion of the rock glacier</p> <p>Error on measurements in the z direction (vertical) is large enough that up/down changes in the rock glaicer surface cannot be quantified from these data alone</p>
FIG. 2 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 2.— Morphology of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt: A, B, thalli; C, D, vegetative cells; E, F, cells in the lower part of the blade; G, cells in the upper portion of the thallus; H, uniseriate branches. Scale bars: A, B, 1 cm; C, D, 100 µm; E-H, 20 µm.
FIG. 1 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 1. — Habitat of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt: A, streams under a glacier; B, population of P. fluviatilis.
FIG. 5 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 5.— Global distribution of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt (blue: glacier area [GLIMS and NSIDC 2005, updated 2019]; Ϙ, occurrence records based on morphology; Δ, occurrence records based on morphology and molecular evidence)
High Mountain Asia glacier velocities 2013-2015 (Landsat 8)
<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 2013-2015 (Landsat 8 only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude 'vel' (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities 'std' (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p> </p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya. Remote Sensing of Environment 162, 55–66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></p>
High Mountain Asia glacier velocities 1999-2003 (Landsat 7)
<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 1999-2003 (Landsat 7 SLC-ON only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude 'vel' (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities 'std' (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p> </p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya. Remote Sensing of Environment 162, 55–66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></p>
Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On"
<p>Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On" Data" </p>
Datafiles resulting from work at the RG-2 rock glacier in the Uinta Mountains, 2021-2022
<p>Datafiles resulting from work at the RG-2 rock glacier in the Uinta Mountains, 2021-2022</p> <ul> <li>2023JF007453-Table S1 – list of interferograms processed</li> <li>2023JF007453-Table S2 – cosmogenic surface-exposure dating results</li> <li>RG-2 Surveying – RTK-GPS surveying results for 32 points on the rock glacier surface. Used to calculate displacement between September 2021 and September 2022</li> <li>GPR – exported ground penetrating radar survey transects at the head of the rock glacier.Files are provided in .sgv form and the locations of each transect are provided in .kml form</li> <li>UAV – orthophotomosaic and digital elevation model for the RG-2 rock glacier produced from images collected by a drone mapping mission flown on 9/22/2021</li> </ul>
Subsurface Temperature Data from Seven Rock Glaciers in Rocky Mountain National Park and Niwot Ridge LTER, Colorado, USA, 2018-2019
HOBO temperature-sensing data loggers (hereafter, sensors) were placed under the surface of seven rock glaciers predicted to have the presence (n=4) or absence (n=3) of sub-surface ice in Rocky Mountain National Park and near Niwot Ridge Long-term Ecological Research site in Colorado. Sub-surface temperatures were recorded to evaluate habitat differences among American pika populations. All sensors (model H08 pro, H08, or H02; Onset Computer Corporation, onsetcomp.com) recorded temperature (±0.2° Celsius) every one or two hours for one year starting in September 2018. Each rock glacier received at least one sensor, positioned near the center and near pika sign. Three rock glaciers received an additional sensor positioned near the edge of the rock glacier near pika sign. Sensors were placed at similar depths (~0.75 m) and similar aspects.
Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
<p>rbcL and tufA alignments used for the phylogenetic analyses.</p> <p>Genbank accession numbers of new sequences of Prasiola fluviatilis voucher XJ20170806: MT846163 (rbcL), MT846164 (<em>tuf</em>A).</p> <p>Samples deposited in the herbarium of the Laboratory of Algae and Environment, Biology Department, Shanghai Normal University (SHTU), Shanghai, China.</p> <p>Abstract of publication: The green alga <em>Prasiola fluviatilis</em> (Sommerfelt) Areschoug ex Lagerstedt occurs in cold lotic environments. The species has a mainly circumarctic distribution, but has also been reported from glacier areas in lower latitude regions in both hemispheres. It was reported from China on a single occasion in the first half of the 20<sup>th</sup> century, but without description, illustrations or voucher specimens the identity of this record cannot be verified. Here we confirm the presence of <em>P. fluviatilis</em> in the Tianshan Mountains, Xinjiang Province, China based on morphological features, habitat characteristics, and plastid <em>rbc</em>L and <em>tuf</em>A gene sequences. The biogeographic distribution of <em>P. fluviatilis</em> and its phylogenetic relationship with other terrestrial and freshwater <em>Prasiola</em> species are discussed.</p>
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Reconstructed, Long-Term Meteorological Forcing and Mass Balance over Glaciers in High Mountain Asia
<p>%%%% Reconstructed_WRF_Forcing_HMA.mat %%%%%%%%<br> A reconstructed, long-term data series of meteorological data for three glaciers in distinct regions of High Mountain Asia. The meteorological forcing consists of hourly Weather Research and Forecasting (WRF) data bias-corrected by high elevation, off-glacier automatic weather station (AWS) data. The variables of the forcing data consist of 2m air temperature ('T' °C), relative humidity ('RH' %), air pressure ('PRESS' hPa), incoming shortwave ('SWIN' Wm-2) and longwave ('LWIN' Wm-2) radiation, precipitation ('PP', mm hr) and wind speed ('FF' m s-1). The period of the timeseries ranges from January 1981 to December 2019. <br> <br> Data are available for the following glaciers (bias-corrected to the given coordinates / elevation of the off-glacier AWS)<br> Yala Glacier, Nepal (28.237°N 85.619°E, 5090 m a.s.l.) - 'AWS Yala Basecamp' (AWS Data available on the ICIMOD RDS)<br> Parlung Glacier Number 4 (29.245°N 96.928°E, 4600 m a.s.l.) - 'AWS4600' (Contact author Wei Yang for AWS data requests)</p> <p>Mugagangqiong Glacier (32.234°N, 87.485°E, 5850 m a.s.l.) - 'AWS5850' (Contact author Wei Yang for AWS data requests)</p> <p> </p> <p>Data are stored as matlab tables in .mat files. Data were created using Matlab version 2020b. </p> <p> </p> <p>%%%% Reconstructed_Mass_Balance_HMA.mat %%%%%%%%<br> <br> Reconstructed cumulative mass balances for the aforementioned glaciers (values in m w.e.)</p> <p> </p> <p> </p> <p> </p> <p> </p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
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