# Load the WSIM-GLDAS dataset (12-month composite netCDF file)
wsim_gldas = xr.open_dataset("composite_12mo.nc", engine="netcdf4")
wsim_gldas

<xarray.Dataset> Size: 14GB
Dimensions:                       (lon: 1440, lat: 600, time: 793)
Coordinates: (3)
Data variables: (7)
Attributes: (5)

xarray.Dataset

• Dimensions:
  • lon: 1440
  • lat: 600
  • time: 793
• Coordinates: (3)
  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    units :

    degrees_east

    long_name :

    Longitude

    axis :

    X

    standard_name :

    longitude

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • lat
    (lat)
    float64
    89.88 89.62 89.38 ... -59.62 -59.88

    units :

    degrees_north

    long_name :

    Latitude

    axis :

    Y

    standard_name :

    latitude

    array([ 89.875,  89.625,  89.375, ..., -59.375, -59.625, -59.875])

  • time
    (time)
    datetime64[ns]
    1948-12-01 ... 2014-12-01

    array(['1948-12-01T00:00:00.000000000', '1949-01-01T00:00:00.000000000',
           '1949-02-01T00:00:00.000000000', ..., '2014-10-01T00:00:00.000000000',
           '2014-11-01T00:00:00.000000000', '2014-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (7)
  • deficit
    (time, lat, lon)
    float32
    ...

    long_name :

    Composite Deficit Index

    grid_mapping :

    crs

    [685152000 values with dtype=float32]

  • deficit_cause
    (time, lat, lon)
    float32
    ...

    long_name :

    Cause of Deficit

    flag_values :

    [1 2 3]

    grid_mapping :

    crs

    flag_meanings :

    neg_petme soil_moisture runoff_accum

    [685152000 values with dtype=float32]

  • surplus
    (time, lat, lon)
    float32
    ...

    long_name :

    Composite Surplus Index

    grid_mapping :

    crs

    [685152000 values with dtype=float32]

  • surplus_cause
    (time, lat, lon)
    float32
    ...

    long_name :

    Cause of Surplus

    flag_values :

    [1 2]

    grid_mapping :

    crs

    flag_meanings :

    runoff runoff_accum

    [685152000 values with dtype=float32]

  • both
    (time, lat, lon)
    float32
    ...

    long_name :

    Composite Combined Surplus & Deficit Index

    threshold :

    3

    grid_mapping :

    crs

    [685152000 values with dtype=float32]

  • crs
    (time)
    int32
    ...

    grid_mapping_name :

    latitude_longitude

    longitude_of_prime_meridian :

    0

    semi_major_axis :

    6378137

    inverse_flattening :

    298.257223563

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    [793 values with dtype=int32]

  • integration_period_end_month
    (time)
    <U6
    ...

    units :

    YYYYMM

    [793 values with dtype=<U6]

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • lat
    PandasIndex

    PandasIndex(Index([ 89.875,  89.625,  89.375,  89.125,  88.875,  88.625,  88.375,  88.125,
            87.875,  87.625,
           ...
           -57.625, -57.875, -58.125, -58.375, -58.625, -58.875, -59.125, -59.375,
           -59.625, -59.875],
          dtype='float64', name='lat', length=600))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1948-12-01', '1949-01-01', '1949-02-01', '1949-03-01',
                   '1949-04-01', '1949-05-01', '1949-06-01', '1949-07-01',
                   '1949-08-01', '1949-09-01',
                   ...
                   '2014-03-01', '2014-04-01', '2014-05-01', '2014-06-01',
                   '2014-07-01', '2014-08-01', '2014-09-01', '2014-10-01',
                   '2014-11-01', '2014-12-01'],
                  dtype='datetime64[ns]', name='time', length=793, freq=None))

• Attributes: (5)

  date_created :

  2021-03-24T22:39:08+0000

  Conventions :

  CF-1.6

  Title :

  Water Security Indicator Model -- Global Land Data Assimilation System Data Set (WSIM-GLDAS), version 1.0: Monthly Grids

  Institution :

  NASA Socioeconomic Data and Applications Center (SEDAC), Center for International Earth Science Information Network (CIESIN) Columbia University

  References :

  Crowley, C., Baston, D., Brinks, J. 2020. Water Security Indicator Model -- Global Land Data Assimilation System Data Set (WSIM-GLDAS), version 1.0: Monthly Grids. Palisades, NY: NASA Socioeconomic Data and Applications Center.