Ingest gridded products such as ice velocity into OGGM

After running our OGGM experiments we often want to compare the model output to other gridded observations or maybe we want to use additional data sets that are not currently in the OGGM shop to calibrate parameters in the model (e.g. Glen A creep parameter, sliding parameter or the calving constant of proportionality). If you are looking on ways or ideas on how to do this, you are in the right tutorial!

In OGGM, a local map projection is defined for each glacier entity in the RGI inventory following the methods described in Maussion and others (2019). The model uses a Transverse Mercator projection centred on the glacier. A lot of data sets, especially those from Polar regions can have a different projections and if we are not careful, we would be making mistakes when we compare them with our model output or when we use such data sets to constrain our model experiments.

New in OGGM 1.6! We now offer preprocess directories where the data is available already. Visit OGGM as an accelerator for modelling and machine learning for more info.

First lets import the modules we need:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import salem

from oggm import cfg, utils, workflow, tasks, graphics
cfg.initialize(logging_level='WARNING')

2023-03-10 19:18:54: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2023-03-10 19:18:54: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2023-03-10 19:18:54: oggm.cfg: Multiprocessing: using all available processors (N=2)

cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-shop-on-Flowlines', reset=True)

Lets define the glaciers for the run

rgi_ids = ['RGI60-14.06794']  # Baltoro

# The RGI version to use
# Size of the map around the glacier.
prepro_border = 80
# Degree of processing level. This is OGGM specific and for the shop 1 is the one you want
from_prepro_level = 3
# URL of the preprocessed gdirs
# we use elevation bands flowlines here
base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/L3-L5_files/2023.1/elev_bands/W5E5'
gdirs = workflow.init_glacier_directories(rgi_ids,
                                          from_prepro_level=from_prepro_level,
                                          prepro_base_url=base_url,
                                          prepro_border=prepro_border)

2023-03-10 19:18:54: oggm.workflow: init_glacier_directories from prepro level 3 on 1 glaciers.
2023-03-10 19:18:54: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers

gdir = gdirs[0]

graphics.plot_googlemap(gdir, figsize=(8, 7))

The gridded_data file in the glacier directory

A lot of the data that the model use and produce for a glacier is stored under the glaciers directories in a NetCDF file called gridded_data:

fpath = gdir.get_filepath('gridded_data')
fpath

'/tmp/OGGM/OGGM-shop-on-Flowlines/per_glacier/RGI60-14/RGI60-14.06/RGI60-14.06794/gridded_data.nc'

with xr.open_dataset(fpath) as ds:
    ds = ds.load()

ds

<xarray.Dataset>
Dimensions:          (x: 479, y: 346)
Coordinates:
  * x                (x) float32 -4.764e+04 -4.744e+04 ... 4.776e+04 4.796e+04
  * y                (y) float32 3.992e+06 3.992e+06 ... 3.923e+06 3.923e+06
Data variables:
    topo             (y, x) float32 5.128e+03 4.999e+03 ... 5.291e+03 5.3e+03
    topo_smoothed    (y, x) float32 5.113e+03 5.019e+03 ... 5.292e+03 5.296e+03
    topo_valid_mask  (y, x) int8 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1
    glacier_mask     (y, x) int8 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
    glacier_ext      (y, x) int8 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
Attributes:
    author:         OGGM
    author_info:    Open Global Glacier Model
    pyproj_srs:     +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0...
    max_h_dem:      8437.0
    min_h_dem:      2996.0
    max_h_glacier:  8437.0
    min_h_glacier:  3397.0

xarray.Dataset

• Dimensions:
  • x: 479
  • y: 346
• Coordinates: (2)
  • x
    (x)
    float32
    -4.764e+04 -4.744e+04 ... 4.796e+04

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-47638.055, -47438.055, -47238.055, ...,  47561.945,  47761.945,
            47961.945], dtype=float32)

  • y
    (y)
    float32
    3.992e+06 3.992e+06 ... 3.923e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([3991927., 3991727., 3991527., ..., 3923327., 3923127., 3922927.],
          dtype=float32)

• Data variables: (5)
  • topo
    (y, x)
    float32
    5.128e+03 4.999e+03 ... 5.3e+03

    units :

    m

    long_name :

    DEM topography

    array([[5128., 4999., 4920., ..., 5500., 5563., 5641.],
           [5192., 5036., 4973., ..., 5423., 5449., 5520.],
           [5145., 5041., 5025., ..., 5260., 5313., 5396.],
           ...,
           [3985., 3874., 3787., ..., 5258., 5265., 5268.],
           [4032., 3927., 3822., ..., 5282., 5287., 5289.],
           [4113., 4021., 3934., ..., 5293., 5291., 5300.]], dtype=float32)

  • topo_smoothed
    (y, x)
    float32
    5.113e+03 5.019e+03 ... 5.296e+03

    units :

    m

    long_name :

    DEM topography smoothed with radius: 3e+02 m

    array([[5113.0107, 5019.37  , 4943.0776, ..., 5468.213 , 5543.3647,
            5599.1377],
           [5138.959 , 5046.7715, 4977.54  , ..., 5396.912 , 5451.8525,
            5503.462 ],
           [5128.378 , 5063.569 , 5022.3706, ..., 5261.8364, 5320.352 ,
            5378.3755],
           ...,
           [3953.9526, 3874.4246, 3787.1133, ..., 5262.384 , 5262.0576,
            5266.156 ],
           [4017.2664, 3936.9917, 3842.895 , ..., 5291.61  , 5283.133 ,
            5286.0977],
           [4075.7495, 4001.9124, 3910.3765, ..., 5304.3345, 5291.8545,
            5296.075 ]], dtype=float32)

  • topo_valid_mask
    (y, x)
    int8
    1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1

    units :

    -

    long_name :

    DEM validity mask according to geotiff input (1-0)

    array([[1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           ...,
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1]], dtype=int8)

  • glacier_mask
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier mask

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

  • glacier_ext
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier external boundaries

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Float64Index([-47638.0546875, -47438.0546875, -47238.0546875, -47038.0546875,
                  -46838.0546875, -46638.0546875, -46438.0546875, -46238.0546875,
                  -46038.0546875, -45838.0546875,
                  ...
                   46161.9453125,  46361.9453125,  46561.9453125,  46761.9453125,
                   46961.9453125,  47161.9453125,  47361.9453125,  47561.9453125,
                   47761.9453125,  47961.9453125],
                 dtype='float64', name='x', length=479))

  • y
    PandasIndex

    PandasIndex(Float64Index([3991927.0, 3991727.0, 3991527.0, 3991327.0, 3991127.0, 3990927.0,
                  3990727.0, 3990527.0, 3990327.0, 3990127.0,
                  ...
                  3924727.0, 3924527.0, 3924327.0, 3924127.0, 3923927.0, 3923727.0,
                  3923527.0, 3923327.0, 3923127.0, 3922927.0],
                 dtype='float64', name='y', length=346))

• Attributes: (7)

  author :

  OGGM

  author_info :

  Open Global Glacier Model

  pyproj_srs :

  +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs

  max_h_dem :

  8437.0

  min_h_dem :

  2996.0

  max_h_glacier :

  8437.0

  min_h_glacier :

  3397.0

cmap=salem.get_cmap('topo')
ds.topo.plot(figsize=(7, 4), cmap=cmap);

ds.glacier_mask.plot();

Add data from OGGM-Shop: bed topography data

Additionally to the data produced by the model, the OGGM-Shop counts with routines that will automatically download and reproject other useful data sets into the glacier projection (For more information also check out this notebook). This data will be stored under the file described above.

OGGM can now download data from the Farinotti et al., (2019) consensus estimate and reproject it to the glacier directories map:

from oggm.shop import bedtopo

workflow.execute_entity_task(bedtopo.add_consensus_thickness, gdirs);

2023-03-10 19:19:06: oggm.workflow: Execute entity tasks [add_consensus_thickness] on 1 glaciers

with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
    ds = ds.load()

the cell below might take a while… be patient

# plot the salem map background, make countries in grey
smap = ds.salem.get_map(countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
smap.set_topography(ds.topo.data);

f, ax = plt.subplots(figsize=(9, 9))
smap.set_data(ds.consensus_ice_thickness)
smap.set_cmap('Blues')
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label='ice thickness (m)');

OGGM-Shop: velocities

We download data from Millan 2022 (see the shop).

If you want more velocity products, feel free to open a new topic on the OGGM issue tracker!

this will download severals large datasets depending on your connection, it might take some time …

# attention downloads data!!!
from oggm.shop import millan22
workflow.execute_entity_task(millan22.velocity_to_gdir, gdirs);

2023-03-10 19:19:19: oggm.workflow: Execute entity tasks [velocity_to_gdir] on 1 glaciers
/usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/salem/datasets.py:173: RuntimeWarning: y0 out of bounds
  warnings.warn('y0 out of bounds', RuntimeWarning)

By applying the entity task velocity to gdir OGGM downloads and reprojects the ITS_live files to a given glacier map.

The velocity components (vx, vy) are added to the gridded_data nc file.

Now we can read in all the gridded data that comes with OGGM, including the velocity components.

with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
    ds = ds.load()
ds

<xarray.Dataset>
Dimensions:                  (x: 479, y: 346)
Coordinates:
  * x                        (x) float32 -4.764e+04 -4.744e+04 ... 4.796e+04
  * y                        (y) float32 3.992e+06 3.992e+06 ... 3.923e+06
Data variables:
    topo                     (y, x) float32 5.128e+03 4.999e+03 ... 5.3e+03
    topo_smoothed            (y, x) float32 5.113e+03 5.019e+03 ... 5.296e+03
    topo_valid_mask          (y, x) int8 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    glacier_mask             (y, x) int8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    glacier_ext              (y, x) int8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    consensus_ice_thickness  (y, x) float32 nan nan nan nan ... nan nan nan nan
    millan_v                 (y, x) float32 0.009646 0.0 8.727 ... 46.07 61.48
    millan_vx                (y, x) float32 nan nan 0.5949 ... -17.15 -28.61
    millan_vy                (y, x) float32 nan nan 8.707 ... 7.233 42.76 54.42
Attributes:
    author:         OGGM
    author_info:    Open Global Glacier Model
    pyproj_srs:     +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0...
    max_h_dem:      8437.0
    min_h_dem:      2996.0
    max_h_glacier:  8437.0
    min_h_glacier:  3397.0

xarray.Dataset

• Dimensions:
  • x: 479
  • y: 346
• Coordinates: (2)
  • x
    (x)
    float32
    -4.764e+04 -4.744e+04 ... 4.796e+04

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-47638.055, -47438.055, -47238.055, ...,  47561.945,  47761.945,
            47961.945], dtype=float32)

  • y
    (y)
    float32
    3.992e+06 3.992e+06 ... 3.923e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([3991927., 3991727., 3991527., ..., 3923327., 3923127., 3922927.],
          dtype=float32)

• Data variables: (9)
  • topo
    (y, x)
    float32
    5.128e+03 4.999e+03 ... 5.3e+03

    units :

    m

    long_name :

    DEM topography

    array([[5128., 4999., 4920., ..., 5500., 5563., 5641.],
           [5192., 5036., 4973., ..., 5423., 5449., 5520.],
           [5145., 5041., 5025., ..., 5260., 5313., 5396.],
           ...,
           [3985., 3874., 3787., ..., 5258., 5265., 5268.],
           [4032., 3927., 3822., ..., 5282., 5287., 5289.],
           [4113., 4021., 3934., ..., 5293., 5291., 5300.]], dtype=float32)

  • topo_smoothed
    (y, x)
    float32
    5.113e+03 5.019e+03 ... 5.296e+03

    units :

    m

    long_name :

    DEM topography smoothed with radius: 3e+02 m

    array([[5113.0107, 5019.37  , 4943.0776, ..., 5468.213 , 5543.3647,
            5599.1377],
           [5138.959 , 5046.7715, 4977.54  , ..., 5396.912 , 5451.8525,
            5503.462 ],
           [5128.378 , 5063.569 , 5022.3706, ..., 5261.8364, 5320.352 ,
            5378.3755],
           ...,
           [3953.9526, 3874.4246, 3787.1133, ..., 5262.384 , 5262.0576,
            5266.156 ],
           [4017.2664, 3936.9917, 3842.895 , ..., 5291.61  , 5283.133 ,
            5286.0977],
           [4075.7495, 4001.9124, 3910.3765, ..., 5304.3345, 5291.8545,
            5296.075 ]], dtype=float32)

  • topo_valid_mask
    (y, x)
    int8
    1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1

    units :

    -

    long_name :

    DEM validity mask according to geotiff input (1-0)

    array([[1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           ...,
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1]], dtype=int8)

  • glacier_mask
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier mask

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

  • glacier_ext
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier external boundaries

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

  • consensus_ice_thickness
    (y, x)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Ice thickness from the consensus estimate

    base_url :

    https://cluster.klima.uni-bremen.de/~fmaussion/icevol/composite/

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

  • millan_v
    (y, x)
    float32
    0.009646 0.0 8.727 ... 46.07 61.48

    units :

    m

    long_name :

    Ice velocity from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[9.6464921e-03, 0.0000000e+00, 8.7274847e+00, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           [0.0000000e+00, 3.3396182e+00, 1.9843628e+01, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           [0.0000000e+00, 4.4036991e+01, 1.9893900e+01, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           ...,
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 4.4023067e+01,
            5.2307713e+01, 5.8931698e+01],
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.1996721e+01,
            5.2488129e+01, 5.7668842e+01],
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.2152705e+01,
            4.6074623e+01, 6.1482475e+01]], dtype=float32)

  • millan_vx
    (y, x)
    float32
    nan nan 0.5949 ... -17.15 -28.61

    units :

    m

    long_name :

    Ice velocity in map x direction from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[        nan,         nan,   0.5949099, ...,         nan,
                    nan,         nan],
           [        nan,         nan,  11.420494 , ...,         nan,
                    nan,         nan],
           [        nan,  43.813683 ,   4.029796 , ...,         nan,
                    nan,         nan],
           ...,
           [        nan,         nan,         nan, ..., -11.01701  ,
             -5.947851 , -21.181208 ],
           [        nan,         nan,         nan, ...,  -9.892379 ,
            -17.386967 , -22.882816 ],
           [        nan,         nan,         nan, ...,  -9.765955 ,
            -17.154419 , -28.61151  ]], dtype=float32)

  • millan_vy
    (y, x)
    float32
    nan nan 8.707 ... 7.233 42.76 54.42

    units :

    m

    long_name :

    Ice velocity in map y direction from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[       nan,        nan,  8.707185 , ...,        nan,        nan,
                   nan],
           [       nan,        nan, 16.227812 , ...,        nan,        nan,
                   nan],
           [       nan, -4.4291787, 19.481478 , ...,        nan,        nan,
                   nan],
           ...,
           [       nan,        nan,        nan, ..., 42.622246 , 51.968452 ,
            54.99365  ],
           [       nan,        nan,        nan, ..., 30.429115 , 49.52471  ,
            52.934605 ],
           [       nan,        nan,        nan, ...,  7.2328672, 42.762093 ,
            54.41945  ]], dtype=float32)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Float64Index([-47638.0546875, -47438.0546875, -47238.0546875, -47038.0546875,
                  -46838.0546875, -46638.0546875, -46438.0546875, -46238.0546875,
                  -46038.0546875, -45838.0546875,
                  ...
                   46161.9453125,  46361.9453125,  46561.9453125,  46761.9453125,
                   46961.9453125,  47161.9453125,  47361.9453125,  47561.9453125,
                   47761.9453125,  47961.9453125],
                 dtype='float64', name='x', length=479))

  • y
    PandasIndex

    PandasIndex(Float64Index([3991927.0, 3991727.0, 3991527.0, 3991327.0, 3991127.0, 3990927.0,
                  3990727.0, 3990527.0, 3990327.0, 3990127.0,
                  ...
                  3924727.0, 3924527.0, 3924327.0, 3924127.0, 3923927.0, 3923727.0,
                  3923527.0, 3923327.0, 3923127.0, 3922927.0],
                 dtype='float64', name='y', length=346))

• Attributes: (7)

  author :

  OGGM

  author_info :

  Open Global Glacier Model

  pyproj_srs :

  +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs

  max_h_dem :

  8437.0

  min_h_dem :

  2996.0

  max_h_glacier :

  8437.0

  min_h_glacier :

  3397.0

# plot the salem map background, make countries in grey
smap = ds.salem.get_map(countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
smap.set_topography(ds.topo.data);

ds

<xarray.Dataset>
Dimensions:                  (x: 479, y: 346)
Coordinates:
  * x                        (x) float32 -4.764e+04 -4.744e+04 ... 4.796e+04
  * y                        (y) float32 3.992e+06 3.992e+06 ... 3.923e+06
Data variables:
    topo                     (y, x) float32 5.128e+03 4.999e+03 ... 5.3e+03
    topo_smoothed            (y, x) float32 5.113e+03 5.019e+03 ... 5.296e+03
    topo_valid_mask          (y, x) int8 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    glacier_mask             (y, x) int8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    glacier_ext              (y, x) int8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
    consensus_ice_thickness  (y, x) float32 nan nan nan nan ... nan nan nan nan
    millan_v                 (y, x) float32 0.009646 0.0 8.727 ... 46.07 61.48
    millan_vx                (y, x) float32 nan nan 0.5949 ... -17.15 -28.61
    millan_vy                (y, x) float32 nan nan 8.707 ... 7.233 42.76 54.42
Attributes:
    author:         OGGM
    author_info:    Open Global Glacier Model
    pyproj_srs:     +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0...
    max_h_dem:      8437.0
    min_h_dem:      2996.0
    max_h_glacier:  8437.0
    min_h_glacier:  3397.0

xarray.Dataset

• Dimensions:
  • x: 479
  • y: 346
• Coordinates: (2)
  • x
    (x)
    float32
    -4.764e+04 -4.744e+04 ... 4.796e+04

    units :

    m

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([-47638.055, -47438.055, -47238.055, ...,  47561.945,  47761.945,
            47961.945], dtype=float32)

  • y
    (y)
    float32
    3.992e+06 3.992e+06 ... 3.923e+06

    units :

    m

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([3991927., 3991727., 3991527., ..., 3923327., 3923127., 3922927.],
          dtype=float32)

• Data variables: (9)
  • topo
    (y, x)
    float32
    5.128e+03 4.999e+03 ... 5.3e+03

    units :

    m

    long_name :

    DEM topography

    array([[5128., 4999., 4920., ..., 5500., 5563., 5641.],
           [5192., 5036., 4973., ..., 5423., 5449., 5520.],
           [5145., 5041., 5025., ..., 5260., 5313., 5396.],
           ...,
           [3985., 3874., 3787., ..., 5258., 5265., 5268.],
           [4032., 3927., 3822., ..., 5282., 5287., 5289.],
           [4113., 4021., 3934., ..., 5293., 5291., 5300.]], dtype=float32)

  • topo_smoothed
    (y, x)
    float32
    5.113e+03 5.019e+03 ... 5.296e+03

    units :

    m

    long_name :

    DEM topography smoothed with radius: 3e+02 m

    array([[5113.0107, 5019.37  , 4943.0776, ..., 5468.213 , 5543.3647,
            5599.1377],
           [5138.959 , 5046.7715, 4977.54  , ..., 5396.912 , 5451.8525,
            5503.462 ],
           [5128.378 , 5063.569 , 5022.3706, ..., 5261.8364, 5320.352 ,
            5378.3755],
           ...,
           [3953.9526, 3874.4246, 3787.1133, ..., 5262.384 , 5262.0576,
            5266.156 ],
           [4017.2664, 3936.9917, 3842.895 , ..., 5291.61  , 5283.133 ,
            5286.0977],
           [4075.7495, 4001.9124, 3910.3765, ..., 5304.3345, 5291.8545,
            5296.075 ]], dtype=float32)

  • topo_valid_mask
    (y, x)
    int8
    1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1

    units :

    -

    long_name :

    DEM validity mask according to geotiff input (1-0)

    array([[1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           ...,
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1],
           [1, 1, 1, ..., 1, 1, 1]], dtype=int8)

  • glacier_mask
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier mask

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

  • glacier_ext
    (y, x)
    int8
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    long_name :

    Glacier external boundaries

    array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=int8)

  • consensus_ice_thickness
    (y, x)
    float32
    nan nan nan nan ... nan nan nan nan

    units :

    m

    long_name :

    Ice thickness from the consensus estimate

    base_url :

    https://cluster.klima.uni-bremen.de/~fmaussion/icevol/composite/

    array([[nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

  • millan_v
    (y, x)
    float32
    0.009646 0.0 8.727 ... 46.07 61.48

    units :

    m

    long_name :

    Ice velocity from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[9.6464921e-03, 0.0000000e+00, 8.7274847e+00, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           [0.0000000e+00, 3.3396182e+00, 1.9843628e+01, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           [0.0000000e+00, 4.4036991e+01, 1.9893900e+01, ..., 0.0000000e+00,
            0.0000000e+00, 0.0000000e+00],
           ...,
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 4.4023067e+01,
            5.2307713e+01, 5.8931698e+01],
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.1996721e+01,
            5.2488129e+01, 5.7668842e+01],
           [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.2152705e+01,
            4.6074623e+01, 6.1482475e+01]], dtype=float32)

  • millan_vx
    (y, x)
    float32
    nan nan 0.5949 ... -17.15 -28.61

    units :

    m

    long_name :

    Ice velocity in map x direction from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[        nan,         nan,   0.5949099, ...,         nan,
                    nan,         nan],
           [        nan,         nan,  11.420494 , ...,         nan,
                    nan,         nan],
           [        nan,  43.813683 ,   4.029796 , ...,         nan,
                    nan,         nan],
           ...,
           [        nan,         nan,         nan, ..., -11.01701  ,
             -5.947851 , -21.181208 ],
           [        nan,         nan,         nan, ...,  -9.892379 ,
            -17.386967 , -22.882816 ],
           [        nan,         nan,         nan, ...,  -9.765955 ,
            -17.154419 , -28.61151  ]], dtype=float32)

  • millan_vy
    (y, x)
    float32
    nan nan 8.707 ... 7.233 42.76 54.42

    units :

    m

    long_name :

    Ice velocity in map y direction from Millan et al. 2022

    data_source :

    RGI-13-15.6_2022February09

    array([[       nan,        nan,  8.707185 , ...,        nan,        nan,
                   nan],
           [       nan,        nan, 16.227812 , ...,        nan,        nan,
                   nan],
           [       nan, -4.4291787, 19.481478 , ...,        nan,        nan,
                   nan],
           ...,
           [       nan,        nan,        nan, ..., 42.622246 , 51.968452 ,
            54.99365  ],
           [       nan,        nan,        nan, ..., 30.429115 , 49.52471  ,
            52.934605 ],
           [       nan,        nan,        nan, ...,  7.2328672, 42.762093 ,
            54.41945  ]], dtype=float32)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Float64Index([-47638.0546875, -47438.0546875, -47238.0546875, -47038.0546875,
                  -46838.0546875, -46638.0546875, -46438.0546875, -46238.0546875,
                  -46038.0546875, -45838.0546875,
                  ...
                   46161.9453125,  46361.9453125,  46561.9453125,  46761.9453125,
                   46961.9453125,  47161.9453125,  47361.9453125,  47561.9453125,
                   47761.9453125,  47961.9453125],
                 dtype='float64', name='x', length=479))

  • y
    PandasIndex

    PandasIndex(Float64Index([3991927.0, 3991727.0, 3991527.0, 3991327.0, 3991127.0, 3990927.0,
                  3990727.0, 3990527.0, 3990327.0, 3990127.0,
                  ...
                  3924727.0, 3924527.0, 3924327.0, 3924127.0, 3923927.0, 3923727.0,
                  3923527.0, 3923327.0, 3923127.0, 3922927.0],
                 dtype='float64', name='y', length=346))

• Attributes: (7)

  author :

  OGGM

  author_info :

  Open Global Glacier Model

  pyproj_srs :

  +proj=tmerc +lat_0=0 +lon_0=76.4047 +k=0.9996 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs

  max_h_dem :

  8437.0

  min_h_dem :

  2996.0

  max_h_glacier :

  8437.0

  min_h_glacier :

  3397.0

# get the velocity data
u = ds.millan_vx.where(ds.glacier_mask)
v = ds.millan_vy.where(ds.glacier_mask)
ws = (u**2 + v**2)**0.5

The .where(ds.glacier_mask) command will remove the data outside of the glacier outline.

# get the axes ready
f, ax = plt.subplots(figsize=(12, 12))

# Quiver only every 3rd grid point
us = u[1::5, 1::5]
vs = v[1::5, 1::5]

smap.set_data(ws)
smap.set_cmap('Blues')
smap.plot(ax=ax)
smap.append_colorbar(ax=ax, label = 'ice velocity (m yr$^{-1}$)')

# transform their coordinates to the map reference system and plot the arrows
xx, yy = smap.grid.transform(us.x.values, us.y.values, crs=gdir.grid.proj)
xx, yy = np.meshgrid(xx, yy)
qu = ax.quiver(xx, yy, us.values, vs.values)
qk = ax.quiverkey(qu, 0.82, 0.97, 200, '200 m yr$^{-1}$',
                   labelpos='E', coordinates='axes')

Bin the data in gridded_data into OGGM elevation bands

Now that we have added new data to the gridded_data file, we can bin the data sets into the same elevation bands as OGGM by recomputing the elevation bands flowlines:

tasks.elevation_band_flowline(gdir,
                              bin_variables=['consensus_ice_thickness', 
                                             'millan_vx',
                                             'millan_vy'],
                              preserve_totals=[True, False, False]  # I"m actually not sure if preserving totals is meaningful with velocities - likely not
                              # NOTE: we could bin variables according to max() as well!
                              )

/usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/oggm/core/centerlines.py:2301: RuntimeWarning: Mean of empty slice
  df.loc[bi, var] = np.nanmean(data[bin_coords])
/usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/oggm/core/centerlines.py:2301: RuntimeWarning: Mean of empty slice
  df.loc[bi, var] = np.nanmean(data[bin_coords])

This created a csv in the glacier directory folder with the data binned to it:

import pandas as pd
df = pd.read_csv(gdir.get_filepath('elevation_band_flowline'), index_col=0)
df

	area	mean_elevation	slope	consensus_ice_thickness	millan_vx	millan_vy	bin_elevation	dx	width
dis_along_flowline
60.171679	40000.0	8345.080078	0.244307	18.147938	4.305162	-1.510011	8355.0	120.343358	332.382282
140.402840	80000.0	8235.237305	0.642076	13.891281	2.600823	1.027055	8235.0	40.118964	1994.069438
173.939055	40000.0	8197.490234	0.838839	21.082452	10.003611	-6.313942	8205.0	26.953465	1484.039223
202.715842	40000.0	8150.462402	0.775496	25.654684	8.991845	3.440062	8145.0	30.600109	1307.184865
232.061569	40000.0	8062.645020	0.818242	31.144651	2.866110	-8.948615	8055.0	28.091343	1423.926246
...	...	...	...	...	...	...	...	...	...
42248.427355	760000.0	3525.279541	0.104130	139.482501	-4.504803	-6.100419	3525.0	287.058262	2647.546162
42523.397659	400000.0	3495.227295	0.113628	101.917223	-3.805607	-2.713950	3495.0	262.882346	1521.593236
42754.481988	200000.0	3472.789551	0.149415	98.673988	-0.934793	-5.369528	3465.0	199.286311	1003.581227
42949.869900	200000.0	3441.432861	0.155403	59.133849	-3.215284	-4.582561	3435.0	191.489514	1044.443613
43164.524282	120000.0	3407.177490	0.125483	32.997104	1.515356	-5.287875	3405.0	237.819249	504.584891

155 rows × 9 columns

df['obs_velocity'] = (df['millan_vx']**2 + df['millan_vy']**2)**0.5
df['bed_h'] = df['mean_elevation'] - df['consensus_ice_thickness']

df[['mean_elevation', 'bed_h', 'obs_velocity']].plot(figsize=(10, 4), secondary_y='obs_velocity');

The problem with this file is that it does not have a regular spacing. The numerical model needs regular spacing, which is why OGGM does this:

# This takes the csv file and prepares new 'inversion_flowlines.pkl' and created a new csv file with regular spacing
tasks.fixed_dx_elevation_band_flowline(gdir,
                                       bin_variables=['consensus_ice_thickness',
                                                      'millan_vx', 
                                                      'millan_vy'],
                                       preserve_totals=[True, False, False]
                                      )

df_regular = pd.read_csv(gdir.get_filepath('elevation_band_flowline', filesuffix='_fixed_dx'), index_col=0)
df_regular

	widths_m	area_m2	consensus_ice_thickness	millan_vx	millan_vy
dis_along_flowline
200.0	1336.837353	5.347349e+05	24.955179	9.087332	2.519516
600.0	2539.126601	1.015651e+06	23.557878	2.265657	-4.975729
1000.0	7460.016058	2.984006e+06	23.410327	3.112515	-3.995712
1400.0	15133.068131	6.053227e+06	30.843423	2.909817	-1.774169
1800.0	30934.847236	1.237394e+07	40.123951	-2.660904	3.120775
...	...	...	...	...	...
41400.0	2107.883243	8.431533e+05	182.959468	-8.061361	-23.767049
41800.0	1790.637265	7.162549e+05	164.778888	-8.665738	-12.170673
42200.0	2535.499611	1.014200e+06	142.729076	-4.752502	-6.729841
42600.0	1363.093218	5.452373e+05	99.770677	-2.853958	-3.594250
43000.0	927.357155	3.709429e+05	52.466467	-2.110497	-4.747279

108 rows × 5 columns

The other variables have disappeared for saving space, but I think it would be nicer to have them here as well. We can grab them from the inversion flowlines (this could be better handled in OGGM):

fl = gdir.read_pickle('inversion_flowlines')[0]
df_regular['mean_elevation'] = fl.surface_h

df_regular['obs_velocity'] = (df_regular['millan_vx']**2 + df_regular['millan_vy']**2)**0.5
df_regular['consensus_bed_h'] = df_regular['mean_elevation'] - df_regular['consensus_ice_thickness']

df_regular[['mean_elevation', 'consensus_bed_h', 'obs_velocity']].plot(figsize=(10, 4),
                                                                       secondary_y='obs_velocity');

OK so more or less the same but this time on a regular grid. Note that these now have the same length as the OGGM inversion flowlines, i.e. one can do stuff such as comparing what OGGM has done for the inversion:

inv = gdir.read_pickle('inversion_output')[0]

df_regular['OGGM_bed'] = df_regular['mean_elevation'] - inv['thick']

df_regular[['mean_elevation', 'consensus_bed_h', 'OGGM_bed']].plot();

Compare velocities: at inversion

OGGM already inverted the ice thickness based on some optimisation such as matching the regional totals. Which parameters did we use?

d = gdir.get_diagnostics()
d

{'dem_source': 'NASADEM',
 'flowline_type': 'elevation_band',
 'apparent_mb_from_any_mb_residual': 56.10000000000031,
 'inversion_glen_a': 6.428035316419423e-24,
 'inversion_fs': 0}

# OK set them so that we are consistent
cfg.PARAMS['inversion_glen_a'] = d['inversion_glen_a']
cfg.PARAMS['inversion_fs'] = d['inversion_fs']

2023-03-10 19:21:17: oggm.cfg: PARAMS['inversion_glen_a'] changed from `2.4e-24` to `6.428035316419423e-24`.

# Since we overwrote the inversion flowlines we have to do stuff again
tasks.mb_calibration_from_geodetic_mb(gdir,
                                      informed_threestep=True,
                                      overwrite_gdir=True)
tasks.apparent_mb_from_any_mb(gdir)
tasks.prepare_for_inversion(gdir)
tasks.mass_conservation_inversion(gdir)
tasks.compute_inversion_velocities(gdir)

inv = gdir.read_pickle('inversion_output')[0]

df_regular['OGGM_inversion_velocity'] = inv['u_surface']

df_regular[['obs_velocity', 'OGGM_inversion_velocity']].plot();

The velocities are higher, because:

• OGGM is at equilibrium

• the comparison between the bulk velocities of OGGM and the gridded observed ones is not straightforward.

Velocities during the run

Inversion velocities are for a glacier at equilibrium - this is not always meaningful. Lets do a run and store the velocities with time:

cfg.PARAMS['store_fl_diagnostics'] = True

2023-03-10 19:21:18: oggm.cfg: PARAMS['store_fl_diagnostics'] changed from `False` to `True`.

tasks.run_from_climate_data(gdir);

with xr.open_dataset(gdir.get_filepath('model_diagnostics')) as ds_diag:
    ds_diag = ds_diag.load()

ds_diag.volume_m3.plot();

with xr.open_dataset(gdir.get_filepath('fl_diagnostics'), group='fl_0') as ds_fl:
    ds_fl = ds_fl.load()

ds_fl

<xarray.Dataset>
Dimensions:             (dis_along_flowline: 179, time: 19)
Coordinates:
  * dis_along_flowline  (dis_along_flowline) float64 0.0 400.0 ... 7.12e+04
  * time                (time) float64 2.002e+03 2.003e+03 ... 2.02e+03
Data variables:
    point_lons          (dis_along_flowline) float64 75.88 75.88 ... 76.66 76.67
    point_lats          (dis_along_flowline) float64 36.07 36.07 ... 36.07 36.07
    bed_h               (dis_along_flowline) float64 8.131e+03 ... 3.007e+03
    volume_m3           (time, dis_along_flowline) float64 1.28e+07 ... 0.0
    volume_bsl_m3       (time, dis_along_flowline) float64 0.0 0.0 ... 0.0 0.0
    volume_bwl_m3       (time, dis_along_flowline) float64 0.0 0.0 ... 0.0 0.0
    area_m2             (time, dis_along_flowline) float64 5.402e+05 ... 0.0
    thickness_m         (time, dis_along_flowline) float64 24.13 33.73 ... 0.0
    ice_velocity_myr    (time, dis_along_flowline) float64 nan nan ... 0.0 0.0
    calving_bucket_m3   (time) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Attributes:
    class:                MixedBedFlowline
    map_dx:               200.0
    dx:                   2.0
    description:          OGGM model output
    oggm_version:         1.5.4.dev78+g6d305a4
    calendar:             365-day no leap
    creation_date:        2023-03-10 19:21:18
    water_level:          0
    glen_a:               6.428035316419423e-24
    fs:                   0
    mb_model_class:       MultipleFlowlineMassBalance
    mb_model_hemisphere:  nh

xarray.Dataset

• Dimensions:
  • dis_along_flowline: 179
  • time: 19
• Coordinates: (2)
  • dis_along_flowline
    (dis_along_flowline)
    float64
    0.0 400.0 ... 7.08e+04 7.12e+04

    array([    0.,   400.,   800.,  1200.,  1600.,  2000.,  2400.,  2800.,  3200.,
            3600.,  4000.,  4400.,  4800.,  5200.,  5600.,  6000.,  6400.,  6800.,
            7200.,  7600.,  8000.,  8400.,  8800.,  9200.,  9600., 10000., 10400.,
           10800., 11200., 11600., 12000., 12400., 12800., 13200., 13600., 14000.,
           14400., 14800., 15200., 15600., 16000., 16400., 16800., 17200., 17600.,
           18000., 18400., 18800., 19200., 19600., 20000., 20400., 20800., 21200.,
           21600., 22000., 22400., 22800., 23200., 23600., 24000., 24400., 24800.,
           25200., 25600., 26000., 26400., 26800., 27200., 27600., 28000., 28400.,
           28800., 29200., 29600., 30000., 30400., 30800., 31200., 31600., 32000.,
           32400., 32800., 33200., 33600., 34000., 34400., 34800., 35200., 35600.,
           36000., 36400., 36800., 37200., 37600., 38000., 38400., 38800., 39200.,
           39600., 40000., 40400., 40800., 41200., 41600., 42000., 42400., 42800.,
           43200., 43600., 44000., 44400., 44800., 45200., 45600., 46000., 46400.,
           46800., 47200., 47600., 48000., 48400., 48800., 49200., 49600., 50000.,
           50400., 50800., 51200., 51600., 52000., 52400., 52800., 53200., 53600.,
           54000., 54400., 54800., 55200., 55600., 56000., 56400., 56800., 57200.,
           57600., 58000., 58400., 58800., 59200., 59600., 60000., 60400., 60800.,
           61200., 61600., 62000., 62400., 62800., 63200., 63600., 64000., 64400.,
           64800., 65200., 65600., 66000., 66400., 66800., 67200., 67600., 68000.,
           68400., 68800., 69200., 69600., 70000., 70400., 70800., 71200.])

  • time
    (time)
    float64
    2.002e+03 2.003e+03 ... 2.02e+03

    description :

    Floating hydrological year

    array([2002., 2003., 2004., 2005., 2006., 2007., 2008., 2009., 2010., 2011.,
           2012., 2013., 2014., 2015., 2016., 2017., 2018., 2019., 2020.])

• Data variables: (10)
  • point_lons
    (dis_along_flowline)
    float64
    75.88 75.88 75.88 ... 76.66 76.67

    description :

    Longitude along the flowline

    unit :

    deg

    array([75.87566708, 75.88010902, 75.88455096, 75.8889929 , 75.89343485,
           75.8978768 , 75.90231876, 75.90676072, 75.91120268, 75.91564465,
           75.92008662, 75.9245286 , 75.92897058, 75.93341256, 75.93785455,
           75.94229654, 75.94673854, 75.95118054, 75.95562254, 75.96006455,
           75.96450656, 75.96894857, 75.97339059, 75.97783261, 75.98227463,
           75.98671666, 75.99115869, 75.99560073, 76.00004276, 76.0044848 ,
           76.00892685, 76.0133689 , 76.01781095, 76.022253  , 76.02669506,
           76.03113712, 76.03557918, 76.04002125, 76.04446331, 76.04890539,
           76.05334746, 76.05778954, 76.06223162, 76.0666737 , 76.07111579,
           76.07555788, 76.07999997, 76.08444206, 76.08888416, 76.09332626,
           76.09776836, 76.10221046, 76.10665257, 76.11109468, 76.11553679,
           76.1199789 , 76.12442102, 76.12886314, 76.13330526, 76.13774738,
           76.1421895 , 76.14663163, 76.15107376, 76.15551589, 76.15995802,
           76.16440016, 76.1688423 , 76.17328444, 76.17772658, 76.18216872,
           76.18661086, 76.19105301, 76.19549516, 76.19993731, 76.20437946,
           76.20882161, 76.21326377, 76.21770592, 76.22214808, 76.22659024,
           76.2310324 , 76.23547456, 76.23991672, 76.24435889, 76.24880106,
           76.25324322, 76.25768539, 76.26212756, 76.26656973, 76.2710119 ,
           76.27545408, 76.27989625, 76.28433843, 76.2887806 , 76.29322278,
           76.29766496, 76.30210713, 76.30654931, 76.31099149, 76.31543368,
           76.31987586, 76.32431804, 76.32876022, 76.33320241, 76.33764459,
           76.34208678, 76.34652896, 76.35097115, 76.35541333, 76.35985552,
           76.36429771, 76.3687399 , 76.37318208, 76.37762427, 76.38206646,
           76.38650865, 76.39095084, 76.39539303, 76.39983521, 76.4042774 ,
           76.40871959, 76.41316178, 76.41760397, 76.42204616, 76.42648835,
           76.43093053, 76.43537272, 76.43981491, 76.4442571 , 76.44869928,
           76.45314147, 76.45758366, 76.46202584, 76.46646803, 76.47091021,
           76.4753524 , 76.47979458, 76.48423677, 76.48867895, 76.49312113,
           76.49756331, 76.50200549, 76.50644767, 76.51088985, 76.51533203,
           76.51977421, 76.52421638, 76.52865856, 76.53310073, 76.53754291,
           76.54198508, 76.54642725, 76.55086942, 76.55531159, 76.55975375,
           76.56419592, 76.56863809, 76.57308025, 76.57752241, 76.58196457,
           76.58640673, 76.59084889, 76.59529105, 76.5997332 , 76.60417535,
           76.60861751, 76.61305966, 76.6175018 , 76.62194395, 76.6263861 ,
           76.63082824, 76.63527038, 76.63971252, 76.64415466, 76.64859679,
           76.65303893, 76.65748106, 76.66192319, 76.66636531])

  • point_lats
    (dis_along_flowline)
    float64
    36.07 36.07 36.07 ... 36.07 36.07

    description :

    Latitude along the flowline

    unit :

    deg

    array([36.07076715, 36.07078668, 36.07080604, 36.07082523, 36.07084426,
           36.07086312, 36.07088182, 36.07090036, 36.07091873, 36.07093694,
           36.07095498, 36.07097285, 36.07099057, 36.07100812, 36.0710255 ,
           36.07104272, 36.07105977, 36.07107666, 36.07109339, 36.07110995,
           36.07112634, 36.07114257, 36.07115864, 36.07117454, 36.07119028,
           36.07120585, 36.07122126, 36.0712365 , 36.07125158, 36.07126649,
           36.07128124, 36.07129583, 36.07131025, 36.07132451, 36.0713386 ,
           36.07135252, 36.07136628, 36.07137988, 36.07139331, 36.07140658,
           36.07141969, 36.07143262, 36.0714454 , 36.07145801, 36.07147045,
           36.07148273, 36.07149485, 36.0715068 , 36.07151859, 36.07153021,
           36.07154167, 36.07155296, 36.07156409, 36.07157505, 36.07158585,
           36.07159648, 36.07160695, 36.07161726, 36.0716274 , 36.07163737,
           36.07164718, 36.07165683, 36.07166631, 36.07167563, 36.07168478,
           36.07169377, 36.07170259, 36.07171125, 36.07171974, 36.07172807,
           36.07173624, 36.07174424, 36.07175207, 36.07175975, 36.07176725,
           36.07177459, 36.07178177, 36.07178878, 36.07179563, 36.07180231,
           36.07180883, 36.07181519, 36.07182138, 36.0718274 , 36.07183326,
           36.07183896, 36.07184449, 36.07184985, 36.07185505, 36.07186009,
           36.07186496, 36.07186967, 36.07187421, 36.07187859, 36.07188281,
           36.07188685, 36.07189074, 36.07189446, 36.07189801, 36.0719014 ,
           36.07190463, 36.07190769, 36.07191059, 36.07191332, 36.07191589,
           36.07191829, 36.07192053, 36.0719226 , 36.07192451, 36.07192626,
           36.07192783, 36.07192925, 36.0719305 , 36.07193159, 36.07193251,
           36.07193326, 36.07193386, 36.07193428, 36.07193455, 36.07193464,
           36.07193458, 36.07193435, 36.07193395, 36.07193339, 36.07193266,
           36.07193177, 36.07193072, 36.0719295 , 36.07192812, 36.07192657,
           36.07192486, 36.07192298, 36.07192094, 36.07191873, 36.07191636,
           36.07191382, 36.07191112, 36.07190826, 36.07190523, 36.07190203,
           36.07189867, 36.07189515, 36.07189146, 36.07188761, 36.07188359,
           36.07187941, 36.07187506, 36.07187055, 36.07186587, 36.07186103,
           36.07185602, 36.07185085, 36.07184552, 36.07184002, 36.07183436,
           36.07182853, 36.07182253, 36.07181638, 36.07181005, 36.07180357,
           36.07179691, 36.0717901 , 36.07178312, 36.07177597, 36.07176866,
           36.07176119, 36.07175355, 36.07174574, 36.07173777, 36.07172964,
           36.07172134, 36.07171288, 36.07170425, 36.07169546, 36.0716865 ,
           36.07167738, 36.0716681 , 36.07165865, 36.07164903])

  • bed_h
    (dis_along_flowline)
    float64
    8.131e+03 7.694e+03 ... 3.007e+03

    description :

    Bed elevation along the flowline

    unit :

    m

    array([8130.77490707, 7693.7374126 , 7513.53452754, 7249.84803344,
           7045.74694662, 6799.4500911 , 6561.62253898, 6361.77538765,
           6101.12250008, 5881.21027426, 5685.72766872, 5520.64535476,
           5401.14602808, 5286.17546702, 5178.46021071, 5073.63569118,
           4996.08490442, 4928.63346962, 4840.84020888, 4777.00996387,
           4734.06031947, 4668.12729078, 4629.66289808, 4604.0262977 ,
           4571.55668579, 4521.29845318, 4460.32234919, 4402.70696421,
           4344.36484298, 4292.04301319, 4266.35835591, 4268.02655902,
           4276.02778088, 4271.02632906, 4247.88205676, 4210.49167331,
           4181.07779366, 4168.34609401, 4158.74631941, 4175.64077296,
           4196.5265999 , 4205.68568481, 4187.57128083, 4132.14006133,
           4076.55271641, 4033.4199807 , 4003.09583105, 3997.63253101,
           4005.46950493, 3997.13421127, 3980.77708307, 3952.46398681,
           3913.3147302 , 3862.61854393, 3821.85277871, 3829.94068037,
           3841.58291807, 3813.53064955, 3780.08037656, 3758.78196943,
           3726.77144742, 3701.59802558, 3686.55867899, 3676.68245158,
           3669.53897471, 3681.42255077, 3694.81516229, 3680.10808071,
           3670.75985896, 3673.14463797, 3659.35576187, 3630.99078928,
           3633.37367624, 3628.70125673, 3589.67718909, 3550.18078477,
           3525.55446113, 3495.24960385, 3478.73574293, 3498.30198792,
    ...
           3395.65087308, 3415.00527012, 3418.20586297, 3431.48128707,
           3425.29064447, 3422.41755998, 3414.04335292, 3403.52559503,
           3396.53084763, 3387.73173195, 3379.12629815, 3373.38474319,
           3368.55185801, 3364.20044409, 3360.59398   , 3357.64902036,
           3355.09285256, 3353.93538731, 3356.47294464, 3360.37523379,
           3356.86800034, 3341.71626479, 3325.03030682, 3317.00541302,
           3312.00824581, 3299.51168769, 3282.16075266, 3267.2439886 ,
           3257.46847502, 3251.07899002, 3244.64913997, 3239.08900448,
           3234.07062534, 3227.70680978, 3221.27410175, 3214.79795146,
           3209.00781414, 3205.79052014, 3205.75369839, 3207.12839079,
           3204.59770874, 3198.71960138, 3191.07876062, 3182.38339337,
           3174.67817133, 3167.42442929, 3159.30884536, 3151.59404511,
           3147.94278595, 3150.36958969, 3155.35499178, 3154.28407399,
           3143.40365889, 3129.0661275 , 3118.65805078, 3112.63258967,
           3106.30132836, 3099.02010849, 3092.35968023, 3086.83573424,
           3082.68191238, 3080.29880375, 3079.9235029 , 3079.43926301,
           3075.27276504, 3069.42936718, 3068.52037495, 3071.65714631,
           3068.17614263, 3055.97110076, 3045.13693956, 3039.46151169,
           3035.60589776, 3030.59836492, 3025.37443607, 3021.0475862 ,
           3016.18948108, 3010.36748166, 3006.57562897])

  • volume_m3
    (time, dis_along_flowline)
    float64
    1.28e+07 2.056e+07 ... 0.0 0.0

    array([[1.27999892e+07, 2.05569508e+07, 1.14762389e+08, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           [1.12942683e+07, 2.15132625e+07, 1.04344120e+08, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           [1.03206443e+07, 2.27918250e+07, 9.85292533e+07, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           ...,
           [3.65604836e+06, 2.82700764e+07, 8.32381089e+07, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           [3.33803031e+06, 2.79748801e+07, 8.24464361e+07, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           [3.50505064e+06, 2.82132366e+07, 8.42619111e+07, ...,
            0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])

  • volume_bsl_m3
    (time, dis_along_flowline)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    description :

    Section volume below sea level

    unit :

    m 3

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]])

  • volume_bwl_m3
    (time, dis_along_flowline)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    description :

    Section volume below water level

    unit :

    m 3

    array([[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]])

  • area_m2
    (time, dis_along_flowline)
    float64
    5.402e+05 6.23e+05 ... 0.0 0.0

    array([[ 540202.03506994,  623009.21167764, 3011891.10349764, ...,
                  0.        ,       0.        ,       0.        ],
           [ 537967.55042448,  624235.99429482, 3009122.59461442, ...,
                  0.        ,       0.        ,       0.        ],
           [ 536517.74147941,  625872.4124787 , 3007576.26719582, ...,
                  0.        ,       0.        ,       0.        ],
           ...,
           [ 526486.40380222,  632836.06003615, 3003506.14646426, ...,
                  0.        ,       0.        ,       0.        ],
           [ 526002.9510468 ,  632462.77739675, 3003295.27276522, ...,
                  0.        ,       0.        ,       0.        ],
           [ 526256.91163091,  632764.20187121, 3003778.82932183, ...,
                  0.        ,       0.        ,       0.        ]])

  • thickness_m
    (time, dis_along_flowline)
    float64
    24.13 33.73 38.3 ... 0.0 0.0 0.0

    description :

    Section thickness

    unit :

    m

    array([[24.12580885, 33.72653573, 38.29789192, ...,  0.        ,
             0.        ,  0.        ],
           [21.33270304, 35.260014  , 34.83725581, ...,  0.        ,
             0.        ,  0.        ],
           [19.52044186, 37.30553673, 32.90434654, ...,  0.        ,
             0.        ,  0.        ],
           ...,
           [ 6.98126976, 46.01009618, 27.81669562, ...,  0.        ,
             0.        ,  0.        ],
           [ 6.37695382, 45.54349288, 27.5531035 , ...,  0.        ,
             0.        ,  0.        ],
           [ 6.69440455, 45.92027347, 28.1575492 , ...,  0.        ,
             0.        ,  0.        ]])

  • ice_velocity_myr
    (time, dis_along_flowline)
    float64
    nan nan nan nan ... 0.0 0.0 0.0 0.0

    description :

    Ice velocity at the surface

    unit :

    m yr-1

    array([[        nan,         nan,         nan, ...,         nan,
                    nan,         nan],
           [26.43729901, 31.27854445, 22.1096087 , ...,  0.        ,
             0.        ,  0.        ],
           [26.14741938, 31.34895197, 20.81437929, ...,  0.        ,
             0.        ,  0.        ],
           ...,
           [16.91657805, 24.81010442, 18.72488458, ...,  0.        ,
             0.        ,  0.        ],
           [15.57380955, 23.13638687, 17.93015562, ...,  0.        ,
             0.        ,  0.        ],
           [16.41627234, 24.36571273, 18.97666985, ...,  0.        ,
             0.        ,  0.        ]])

  • calving_bucket_m3
    (time)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    description :

    Flowline calving bucket (volume not yet calved)

    unit :

    m 3

    array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
           0., 0.])

• Indexes: (2)
  • dis_along_flowline
    PandasIndex

    PandasIndex(Float64Index([    0.0,   400.0,   800.0,  1200.0,  1600.0,  2000.0,  2400.0,
                   2800.0,  3200.0,  3600.0,
                  ...
                  67600.0, 68000.0, 68400.0, 68800.0, 69200.0, 69600.0, 70000.0,
                  70400.0, 70800.0, 71200.0],
                 dtype='float64', name='dis_along_flowline', length=179))

  • time
    PandasIndex

    PandasIndex(Float64Index([2002.0, 2003.0, 2004.0, 2005.0, 2006.0, 2007.0, 2008.0, 2009.0,
                  2010.0, 2011.0, 2012.0, 2013.0, 2014.0, 2015.0, 2016.0, 2017.0,
                  2018.0, 2019.0, 2020.0],
                 dtype='float64', name='time'))

• Attributes: (12)

  class :

  MixedBedFlowline

  map_dx :

  200.0

  dx :

  2.0

  description :

  OGGM model output

  oggm_version :

  1.5.4.dev78+g6d305a4

  calendar :

  365-day no leap

  creation_date :

  2023-03-10 19:21:18

  water_level :

  0

  glen_a :

  6.428035316419423e-24

  fs :

  0

  mb_model_class :

  MultipleFlowlineMassBalance

  mb_model_hemisphere :

  nh

ds_fl.sel(time=[2003, 2010, 2020]).ice_velocity_myr.plot(hue='time');

# The OGGM model flowlines also have the downstream lines
df_regular['OGGM_velocity_run_begin'] = ds_fl.sel(time=2003).ice_velocity_myr.data[:len(df_regular)]
df_regular['OGGM_velocity_run_end'] = ds_fl.sel(time=2020).ice_velocity_myr.data[:len(df_regular)]

df_regular[['obs_velocity', 'OGGM_inversion_velocity',
            'OGGM_velocity_run_begin', 'OGGM_velocity_run_end']].plot();

What’s next?

• return to the OGGM documentation

• back to the table of contents