Snow Variables for High Mountain Asia

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. Front. Earth Sci. 8:559175. doi: 10.3389/feart.2020.559175 ( https://doi.org/10.3389/feart.2020.559175 )

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:

Annual_SWE_Trend_1987-2016 - Annual average snow-water equivalent trend (1987-2016)
DJF_SWE_Trend_1987-2016  - December-January-February average snow-water equivalent trend (1987-2016)
MAM_SWE_Trend_1987-2016  - March-April-May average snow-water equivalent trend (1987-2016)
JJA_SWE_Trend_1987-2016  - June-July-August average snow-water equivalent trend (1987-2016)
SON_SWE_Trend_1987-2016  - September-October-November average snow-water equivalent trend (1987-2016)

Annual_SWE_Trend_1987-1997 - Annual average snow-water equivalent trend (1987-1997)
Annual_SWE_Trend_1997-2007 - Annual average snow-water equivalent trend (1997-2007)
Annual_SWE_Trend_2006-2016 - Annual average snow-water equivalent trend (2006-2016)

Annual_Average_SWE - Annual average snow-water equivalent (1987-2016)
DJF_Average_SWE  - December-January-February average snow-water equivalent (1987-2016)
MAM_Average_SWE  - March-April-May average snow-water equivalent (1987-2016)
JJA_Average_SWE  - June-July-August average snow-water equivalent (1987-2016)
SON_Average_SWE  - September-October-November average snow-water equivalent (1987-2016)

Annual_Average_SCA - Annual average snow-covered area (2001-2019)
DJF_Average_SCA  - December-January-February average snow-covered area (2001-2019)
MAM_Average_SCA  - March-April-May average snow-covered area (2001-2019)
JJA_Average_SCA  - June-July-August average snow-covered area (2001-2019)
SON_Average_SCA  - September-October-November average snow-covered area (2001-2019)

The NetCDF file also contains projected x/y coordinates in EASEgrid 2.0, as well as relevant geographic and projection parameters.

These data provide high-resolution averages and trends of key snow parameters for analyzing climate change in High Mountain Asia.

The underlying data sources are:

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.

and:

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.

Quick python script for converting to GeoTIFF:

import xarray as xr
import rioxarray

ds = xr.open_dataset('SWE_Variables_HMA.nc')
save_loc = 'Annual_SWE_Trend.tif'
da = ds['Annual_SWE_Trend_1987-2016']
da = da.rio.set_crs(ds.crs)

da.rio.to_raster(save_loc)