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66 results for “High Mountain Asia”
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>
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>
The dataset on structural connectivity in high-mountain Asia
<p>This is the inaugural version of the dataset, comprising 10 slices in GeoTIFF format at a resolution of 30 meters, provided for reviewers to assess our manuscript. We will release the full version of the dataset upon acceptance of the article. For any inquiries, please contact the following email address. jinlongli@stu.scu.edu.cn.</p>
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>
Datasets for "The hydro-climate and freshwater supply of High-Mountain Asia under warming climate conditions"
<p>This repository contains ensemble-mean fields and corresponding standard deviations of post-processed outputs of CMIP5 models for three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5) described in a manuscript "The hydro-climate and freshwater supply of High-Mountain Asia under warming climate conditions".</p> <p>Each netcdf file contains one field (either an ensemble mean of a standard deviation). The fields are the following: Positive Degree Days (PDD), Total Precipitation (Ptotal), Liquid Precipitation (PLiquiq), Frozen Precipitation (Pfrozen) and Glaciers Mass Balance (MB).</p>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</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>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</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>
Data and code for the paper titled "Changing Northern Hemisphere Weather Linked to Warming Amplification in High Mountain Asia"
<p>This is the data and code for the paper titled "Changing Northern Hemisphere Weather Linked to Warming Amplification in High Mountain Asia", which has published in</p> <div> <div><em>Communications Earth & Environment, </em>DOI : 10.1038/s43247-025-02883-0.</div> </div>
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>
A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach
<p>This file is the .csv database compiling surge-type glaciers automatically identified in Guillet et al (2022).</p> <p>File format is compliant with the Randolph Glacier Inventory (RGI) V6.0.</p> <p>If you have questions about the dataset - please refer to the following reference or contact Dr. Guillet.</p> <table> <tbody> <tr> <td>Guillet, G., King, O., Lv, M., Ghuffar, S., Benn, D., Quincey, D., & Bolch, T. (2022). A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach. <em>The Cryosphere</em>, <em>16</em>(2), 603-623.</td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Sentinel-1 RTC imagery processed by ASF over central Himalaya in High Mountain Asia
<p>This is a dataset of Sentinel-1 radiometric terrain corrected (RTC) imagery processed by the Alaska Satellite Facility covering a region within the Central Himalaya. It accompanies a tutorial demonstrating accessing and working with Sentinel-1 RTC imagery using xarray and other open source python packages.</p>
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Snow Variables for High Mountain Asia
<p>Data associated with the paper: Smith T and Bookhagen B (2020) Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data. <em>Front. Earth Sci.</em> 8:559175. doi: 10.3389/feart.2020.559175 ( <a href="https://doi.org/10.3389/feart.2020.559175">https://doi.org/10.3389/feart.2020.559175</a> )</p> <p>This data resource contains one NetCDF file containing high-resolution (3.125km) snow-water equivalent and snow-cover parameters. The named datasets within the NetCDF file are:</p> <p>Annual_SWE_Trend_1987-2016 - Annual average snow-water equivalent trend (1987-2016)<br> DJF_SWE_Trend_1987-2016 - December-January-February average snow-water equivalent trend (1987-2016)<br> MAM_SWE_Trend_1987-2016 - March-April-May average snow-water equivalent trend (1987-2016)<br> JJA_SWE_Trend_1987-2016 - June-July-August average snow-water equivalent trend (1987-2016)<br> SON_SWE_Trend_1987-2016 - September-October-November average snow-water equivalent trend (1987-2016)</p> <p>Annual_SWE_Trend_1987-1997 - Annual average snow-water equivalent trend (1987-1997)<br> Annual_SWE_Trend_1997-2007 - Annual average snow-water equivalent trend (1997-2007)<br> Annual_SWE_Trend_2006-2016 - Annual average snow-water equivalent trend (2006-2016)</p> <p>Annual_Average_SWE - Annual average snow-water equivalent (1987-2016)<br> DJF_Average_SWE - December-January-February average snow-water equivalent (1987-2016)<br> MAM_Average_SWE - March-April-May average snow-water equivalent (1987-2016)<br> JJA_Average_SWE - June-July-August average snow-water equivalent (1987-2016)<br> SON_Average_SWE - September-October-November average snow-water equivalent (1987-2016)</p> <p>Annual_Average_SCA - Annual average snow-covered area (2001-2019)<br> DJF_Average_SCA - December-January-February average snow-covered area (2001-2019)<br> MAM_Average_SCA - March-April-May average snow-covered area (2001-2019)<br> JJA_Average_SCA - June-July-August average snow-covered area (2001-2019)<br> SON_Average_SCA - September-October-November average snow-covered area (2001-2019)</p> <p>The NetCDF file also contains projected x/y coordinates in EASEgrid 2.0, as well as relevant geographic and projection parameters.</p> <p>These data provide high-resolution averages and trends of key snow parameters for analyzing climate change in High Mountain Asia.</p> <p>The underlying data sources are:</p> <p>Brodzik, M. J., D. G. Long, M. A. Hardman, A. Paget, and R. Armstrong. 2016, Updated 2020. MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001.</p> <p>and:</p> <p>Hall, D. K. and G. A. Riggs. 2016. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MODIS/MOD10A1.006.</p> <p> </p> <p>Quick python script for converting to GeoTIFF:</p> <p>import xarray as xr<br> import rioxarray</p> <p>ds = xr.open_dataset('SWE_Variables_HMA.nc')<br> save_loc = 'Annual_SWE_Trend.tif'<br> da = ds['Annual_SWE_Trend_1987-2016']<br> da = da.rio.set_crs(ds.crs)</p> <p>da.rio.to_raster(save_loc)</p>
Annual 30-meter Dataset for Glacial Lakes in High Mountain Asia from 2008 to 2017
<p>We developed a High Mountain Asia (HMA) Glacial Lake Inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution. For the development of the Hi-MAG database, a total of 40,481 satellite images including Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were used, and a systematic glacial lake detection method that comprised the automated processing using GEE and subsequent manual refinement of these lake mapping results were applied. This is the first glacial lake inventory across the HMA with annual temporal resolution, it can provide details for different types of glacial lakes and evolution patterns. It can be used for studies of the complex interactions between glaciers, climate, and glacial lakes, and GLOFs, potential downstream risks, and water resources.</p>
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