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Metadata

Name
Shortwave surface albedo of glaciers in the central Chilean Andes
Repository
ZENODO
Identifier
doi:10.5281/zenodo.3937676
Description
Data used to analyse glacier surface albedo change in the central Chilean Andes for the manuscript:

Glacier albedo reduction and drought effects in the extratropical Andes, 1986-2020

Thomas E. Shaw1, Genesis Ulloa2, David Far&iacute;as-Barahona3, Rodrigo Fernandez2, Jose Lattus2, James McPhee1,4

1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile
2 Department of Geology, Universidad de Chile, Santiago, Chile
3 Institute f&uuml;r Geographie, Friedrich-Alexander-Universit&auml;t Erlangen-N&uuml;rnberg, Erlangen, Germany
4 Department of Civil Engineering, Universidad de Chile, Santiago, Chile

Corresponding author: Thomas E. Shaw (thomas.shaw@amtc.uchile.cl)
Keywords: Albedo, Andes, Glacier, Drought, Remote sensing, Climate

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Sub-folders:
&nbsp;&nbsp; &nbsp;[Albedo]:
&nbsp;&nbsp; &nbsp;&#39;Albedo_ChileanGlaciers_DATA.mat&#39; = matlab file that contains a structure of all information for analyses.&nbsp;
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&#39;DATA&#39; structure contains:&nbsp;
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;NAME = Glacier name
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;ALBEDO = 3D albedo matrices for each named glacier
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;DEM = ASTER DEM of same resolution + size
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;DEMtif = as above, but within a georeferenced GRIDobj frame read by TopoToolbox
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;CLASS = classification as 0 (no data), 1 (ice) or 2 (snow)
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;OTSUindex = The histogram separation value per year (per glacier) based upon Otsu inter-class variance
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SHADOW = Shadowed pixels based upon solar geometry
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SOLAR_AZI = Solar Azimuth per year taken from Landsat metadata
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SOLAR_ELE = Solar Elevation per year taken from Landsat metadata
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;NIR = The Near-Infrared band of Landsat images for the Osu classification
&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;SLOPE = The calculated slope angle based upon the DEM (GRIDobj format)


&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;

&nbsp;&nbsp; &nbsp;[Shapefiles]:
&nbsp;&nbsp; &nbsp;&#39;CentralChileGlaciers.shp&#39; = Shapefile of glacier boundaries delineated based upon April 2020 3 m PlanetScope imagery.


&nbsp;&nbsp; &nbsp;[Climate]:
&nbsp;&nbsp; &nbsp;&#39;Ta_Precip_HY.mat&#39; = Mean Monthly Air temperature (&deg;C) and monthly total precipitation (mm) at long term DGA weather stations for each Hydrological year (April-March)
&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
&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.
&nbsp;&nbsp; &nbsp;
&nbsp;&nbsp; &nbsp;


&nbsp;
&nbsp;
Data or Study Types
multiple
Source Organization
Unknown
Access Conditions
available
Year
2020
Access Hyperlink
https://doi.org/10.5281/zenodo.3937676

Distributions

  • Encoding Format: HTML ; URL: https://doi.org/10.5281/zenodo.3937676
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.