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Shortwave surface albedo of glaciers in the central Chilean Andes

<p>Data used to analyse glacier surface albedo change in the central Chilean Andes for the manuscript:</p> <p>Glacier albedo reduction and drought effects in the extratropical Andes, 1986-2020</p> <p>Thomas E. Shaw1, Genesis Ulloa2, David Far&iacute;as-Barahona3, Rodrigo Fernandez2, Jose Lattus2, James McPhee1,4</p> <p>1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile<br> 2 Department of Geology, Universidad de Chile, Santiago, Chile<br> 3 Institute f&uuml;r Geographie, Friedrich-Alexander-Universit&auml;t Erlangen-N&uuml;rnberg, Erlangen, Germany<br> 4 Department of Civil Engineering, Universidad de Chile, Santiago, Chile</p> <p>Corresponding author: Thomas E. Shaw (thomas.shaw@amtc.uchile.cl)<br> Keywords: Albedo, Andes, Glacier, Drought, Remote sensing, Climate</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Sub-folders:<br> &nbsp;&nbsp; <strong>&nbsp;[Albedo]:</strong><br> &nbsp;&nbsp; &nbsp;&#39;<strong>Albedo_ChileanGlaciers_DATA.mat&#39;</strong> = matlab file that contains a structure of all information for analyses.&nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&#39;DATA&#39; structure contains:&nbsp;<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;NAME = Glacier name<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;ALBEDO = 3D albedo matrices for each named glacier<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;DEM = ASTER DEM of same resolution + size<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;DEMtif = as above, but within a georeferenced GRIDobj frame read by TopoToolbox<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;CLASS = classification as 0 (no data), 1 (ice) or 2 (snow)<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;OTSUindex = The histogram separation value per year (per glacier) based upon Otsu inter-class variance<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SHADOW = Shadowed pixels based upon solar geometry<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SOLAR_AZI = Solar Azimuth per year taken from Landsat metadata<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SOLAR_ELE = Solar Elevation per year taken from Landsat metadata<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;NIR = The Near-Infrared band of Landsat images for the Osu classification<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SLOPE = The calculated slope angle based upon the DEM (GRIDobj format)</p> <p><br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&#39;SHAPE&#39; is an 18*1 structure of the imported shapefiles in matlab format. Can be plotted using &#39;mapshow&#39;</p> <p>&nbsp;&nbsp; &nbsp;<strong>[Shapefiles]:</strong><br> &nbsp;&nbsp; &nbsp;&#39;<strong>CentralChileGlaciers.shp</strong>&#39; = Shapefile of glacier boundaries delineated based upon April 2020 3 m PlanetScope imagery.</p> <p><br> &nbsp;&nbsp; &nbsp;<strong>[Climate]:</strong><br> &nbsp;&nbsp; &nbsp;&#39;<strong>Ta_Precip_HY.mat</strong>&#39; = Mean Monthly Air temperature (&deg;C) and monthly total precipitation (mm) at long term DGA weather stations for each Hydrological year (April-March)<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&#39;TAmonth_HY&#39; = A matrix of 35 x 12 mean month air temperatures (&deg;C) where rows (x35) = the hydrological year starting 1985-1986 and columns (x12) = the months of the hydrological year so that the first column is April and the final column is March of the following year<br> &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&#39;PPmonth_HY&#39; = As above but a 3D matrix of 35 x 12 x 3 for monthly sums of precipitation (mm). The rows and columns are defined above and the third dimension are the stations Riecillos (32.92&deg;S, 70.35&deg;W ,1290 m a.s.l.), Embalse Yeso (33.67&deg;S, 70.08&deg;W, 2475 m a.s.l.) and Rengo (34.19&deg;S, 70.75&deg;W, 515 m a.s.l.), respectively.<br> &nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;</p> <p><br> &nbsp;<br> &nbsp;</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

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