RGI-TOPO for RGI 7.0

OGGM was used to generate the topography data used to compute the topographical attributes and the centerlines products for RGI v7.0.

Here we show how to access this data from OGGM.

Input parameters

This notebook can be run as a script with parameters using papermill, but it is not necessary. The following cell contains the parameters you can choose from:

# The RGI-id of the glaciers you want to look for
# Use the original shapefiles or the GLIMS viewer to check for the ID: https://www.glims.org/maps/glims
rgi_id = 'RGI2000-v7.0-G-01-06486'  # Denali

# The default is to test for all sources available for this glacier
# Set to a list of source names to override this
sources = None
# Where to write the plots. Default is in the current working directory
plot_dir = f'outputs/{rgi_id}'
# The RGI version to use
# V62 is an unofficial modification of V6 with only minor, backwards compatible modifications
prepro_rgi_version = 62
# Size of the map around the glacier. Currently only 10 and 40 are available
prepro_border = 10
# Degree of processing level.  Currently only 1 is available.
from_prepro_level = 1

Check input and set up

# The sources can be given as parameters
if sources is not None and isinstance(sources, str):
    sources = sources.split(',')

# Plotting directory as well
if not plot_dir:
    plot_dir = './' + rgi_id
import os
plot_dir = os.path.abspath(plot_dir)

from oggm import cfg, utils, workflow, tasks, graphics, GlacierDirectory
import pandas as pd
import numpy as np
import xarray as xr
import rioxarray as rioxr
import geopandas as gpd
import salem
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import itertools

from oggm.utils import DEM_SOURCES
from oggm.workflow import init_glacier_directories

# Make sure the plot directory exists
utils.mkdir(plot_dir);
# Use OGGM to download the data
cfg.initialize()
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-RGITOPO-RGI7', reset=True)
cfg.PARAMS['use_intersects'] = False

2026-07-20 13:38:49: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-07-20 13:38:49: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-07-20 13:38:49: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-07-20 13:38:49: oggm.cfg: PARAMS['use_intersects'] changed from `True` to `False`.

Download the data using OGGM utility functions

Note that you could reach the same goal by downloading the data manually from

# URL of the preprocessed GDirs
gdir_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/'
# We use OGGM to download the data
gdir = init_glacier_directories([rgi_id], from_prepro_level=1, prepro_border=10,  prepro_rgi_version='70G', prepro_base_url=gdir_url)[0]

2026-07-20 13:38:50: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-07-20 13:38:50: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-07-20 13:38:50: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01.064.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01.064.tar...
2026-07-20 13:38:50: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01-06.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01-06.tar...

gdir

<oggm.GlacierDirectory>
  RGI id: RGI2000-v7.0-G-01-06486
  Region: 01: Alaska
  Subregion: 01-02: Alaska Range (Wrangell/Kilbuck)
  Glacier type: Glacier
  Terminus type: Not assigned
  Status: Glacier
  Area: 0.961122833004822 km2
  Lon, Lat: (-151.0094740399913, 63.061062)
  Grid (nx, ny): (70, 88)
  Grid (dx, dy): (24.0, -24.0)

Read the DEMs and store them all in a dataset

if sources is None:
    sources = [src for src in os.listdir(gdir.dir) if src in utils.DEM_SOURCES]

print('RGI ID:', rgi_id)
print('Available DEM sources:', sources)
print('Plotting directory:', plot_dir)

RGI ID: RGI2000-v7.0-G-01-06486
Available DEM sources: ['MAPZEN', 'TANDEM', 'ASTER', 'DEM3', 'COPDEM30', 'ALASKA', 'AW3D30', 'ARCTICDEM', 'COPDEM90']
Plotting directory: /__w/tutorials/tutorials/notebooks/tutorials/outputs/RGI2000-v7.0-G-01-06486

# We use xarray to store the data
ods = xr.Dataset()
for src in sources:
    demfile = os.path.join(gdir.dir, src) + '/dem.tif'
    with rioxr.open_rasterio(demfile) as ds:
        data = ds.sel(band=1).load() * 1.
        ods[src] = data.where(data > -100, np.nan)

    sy, sx = np.gradient(ods[src], gdir.grid.dx, gdir.grid.dx)
    ods[src + '_slope'] = ('y', 'x'),  np.arctan(np.sqrt(sy**2 + sx**2))

with rioxr.open_rasterio(gdir.get_filepath('glacier_mask')) as ds:
    ods['mask'] = ds.sel(band=1).load()

# Decide on the number of plots and figure size
ns = len(sources)
x_size = 12
n_cols = 3
n_rows = -(-ns // n_cols)
y_size = x_size / n_cols * n_rows

Raw topography data

smap = salem.graphics.Map(gdir.grid, countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
smap.set_plot_params(cmap='topo')
smap.set_lonlat_contours(add_tick_labels=False)
smap.set_plot_params(vmin=np.nanquantile([ods[s].min() for s in sources], 0.25),
                     vmax=np.nanquantile([ods[s].max() for s in sources], 0.75))

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_mode='each',
                cbar_location='right',
                cbar_pad=0.1
                )

for i, s in enumerate(sources):
    data = ods[s]
    smap.set_data(data)
    ax = grid[i]
    smap.visualize(ax=ax, addcbar=False, title=s)
    if np.isnan(data).all():
        grid[i].cax.remove()
        continue
    cax = grid.cbar_axes[i]
    smap.colorbarbase(cax)

# take care of uneven grids
if ax != grid[-1] and not grid[-1].title.get_text():
    grid[-1].remove()
    grid[-1].cax.remove()
if ax != grid[-2] and not grid[-2].title.get_text():
    grid[-2].remove()
    grid[-2].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_color.png'), dpi=150, bbox_inches='tight')

Shaded relief

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_location='right',
                cbar_pad=0.1
                )
smap.set_plot_params(cmap='Blues')
smap.set_shapefile()
for i, s in enumerate(sources):
    data = ods[s].copy().where(np.isfinite(ods[s]), 0)
    smap.set_data(data * 0)
    ax = grid[i]
    smap.set_topography(data)
    smap.visualize(ax=ax, addcbar=False, title=s)

# take care of uneven grids
if ax != grid[-1] and not grid[-1].title.get_text():
    grid[-1].remove()
    grid[-1].cax.remove()
if ax != grid[-2] and not grid[-2].title.get_text():
    grid[-2].remove()
    grid[-2].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_shade.png'), dpi=150, bbox_inches='tight')

Slope

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_mode='each',
                cbar_location='right',
                cbar_pad=0.1
                )

smap.set_topography()
smap.set_plot_params(vmin=0, vmax=0.7, cmap='Blues')

for i, s in enumerate(sources):
    data = ods[s + '_slope']
    smap.set_data(data)
    ax = grid[i]
    smap.visualize(ax=ax, addcbar=False, title=s + ' (slope)')
    cax = grid.cbar_axes[i]
    smap.colorbarbase(cax)

# take care of uneven grids
if ax != grid[-1] and not grid[-1].title.get_text():
    grid[-1].remove()
    grid[-1].cax.remove()
if ax != grid[-2] and not grid[-2].title.get_text():
    grid[-2].remove()
    grid[-2].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_slope.png'), dpi=150, bbox_inches='tight'):

  Cell In[14], line 29
    plt.savefig(os.path.join(plot_dir, 'dem_slope.png'), dpi=150, bbox_inches='tight'):
                                                                                      ^
SyntaxError: invalid syntax

Some simple statistics about the DEMs

df = pd.DataFrame()
for s in sources:
    df[s] = ods[s].data.flatten()[ods.mask.data.flatten() == 1]

dfs = pd.DataFrame()
for s in sources:
    dfs[s] = ods[s + '_slope'].data.flatten()[ods.mask.data.flatten() == 1]

dfs = df.describe()
dfs.loc['range'] = dfs.loc['max'] - dfs.loc['min']
dfs

	MAPZEN	TANDEM	ASTER	DEM3	COPDEM30	ALASKA	AW3D30	ARCTICDEM	COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	1671.000000
mean	5319.226212	5153.806152	5295.587672	5328.549372	5277.514160	5316.735352	NaN	5529.413086	5277.694824
std	350.840381	388.357605	351.229245	351.724101	370.142792	351.459656	NaN	393.222046	370.293671
min	4622.000000	4497.636719	4565.000000	4644.000000	4585.657715	4610.906738	NaN	4871.422852	4584.852539
25%	5050.000000	4837.172607	5026.500000	5058.000000	4982.135010	5047.515381	NaN	5111.490234	4980.320312
50%	5258.000000	5044.266113	5250.000000	5259.000000	5231.779785	5255.337402	NaN	5613.539551	5231.637207
75%	5578.500000	5441.289551	5543.000000	5590.000000	5562.813232	5578.190430	NaN	5869.918945	5564.726562
max	6111.000000	5975.219727	6113.000000	6120.000000	6073.898438	6116.865234	NaN	6126.963379	6073.276855
range	1489.000000	1477.583008	1548.000000	1476.000000	1488.240723	1505.958496	NaN	1255.540527	1488.424316

Comparison matrix plot

# Table of differences between DEMS
df_diff = pd.DataFrame()
done = []
for s1, s2 in itertools.product(sources, sources):
    if s1 == s2:
        continue
    if (s2, s1) in done:
        continue
    df_diff[s1 + '-' + s2] = df[s1] - df[s2]
    done.append((s1, s2))

# Decide on plot levels
max_diff = df_diff.quantile(0.99).max()
base_levels = np.array([-8, -5, -3, -1.5, -1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1, 1.5, 3, 5, 8])
if max_diff < 10:
    levels = base_levels
elif max_diff < 100:
    levels = base_levels * 10
elif max_diff < 1000:
    levels = base_levels * 100
else:
    levels = base_levels * 1000
levels = [l for l in levels if abs(l) < max_diff]
if max_diff > 10:
    levels = [int(l) for l in levels]
levels

[-500, -300, -150, -100, -50, -20, -10, 0, 10, 20, 50, 100, 150, 300, 500]

smap.set_plot_params(levels=levels, cmap='PuOr', extend='both')
smap.set_shapefile(gdir.read_shapefile('outlines'))

fig = plt.figure(figsize=(14, 14))
grid = AxesGrid(fig, 111,
                nrows_ncols=(ns - 1, ns - 1),
                axes_pad=0.3,
                cbar_mode='single',
                cbar_location='right',
                cbar_pad=0.1
                )
done = []
for ax in grid:
    ax.set_axis_off()
for s1, s2 in itertools.product(sources, sources):
    if s1 == s2:
        continue
    if (s2, s1) in done:
        continue
    data = ods[s1] - ods[s2]
    ax = grid[sources.index(s1) * (ns - 1) + sources[1:].index(s2)]
    ax.set_axis_on()
    smap.set_data(data)
    smap.visualize(ax=ax, addcbar=False)
    done.append((s1, s2))
    ax.set_title(s1 + '-' + s2, fontsize=8)

cax = grid.cbar_axes[0]
smap.colorbarbase(cax);

plt.savefig(os.path.join(plot_dir, 'dem_diffs.png'), dpi=150, bbox_inches='tight')

Comparison scatter plot

import seaborn as sns
sns.set(style="ticks")

l1, l2 = (utils.nicenumber(df.min().min(), binsize=50, lower=True),
          utils.nicenumber(df.max().max(), binsize=50, lower=False))

def plot_unity():
    points = np.linspace(l1, l2, 100)
    plt.gca().plot(points, points, color='k', marker=None,
                   linestyle=':', linewidth=3.0)

g = sns.pairplot(df.dropna(how='all', axis=1).dropna(), plot_kws=dict(s=50, edgecolor="C0", linewidth=1));
g.map_offdiag(plot_unity)
for asx in g.axes:
    for ax in asx:
        ax.set_xlim((l1, l2))
        ax.set_ylim((l1, l2))

plt.savefig(os.path.join(plot_dir, 'dem_scatter.png'), dpi=150, bbox_inches='tight')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[20], line 13
      9     plt.gca().plot(points, points, color='k', marker=None,
     10                    linestyle=':', linewidth=3.0)
     11 
     12 g = sns.pairplot(df.dropna(how='all', axis=1).dropna(), plot_kws=dict(s=50, edgecolor="C0", linewidth=1));
---> 13 g.map_offdiag(plot_unity)
     14 for asx in g.axes:
     15     for ax in asx:
     16         ax.set_xlim((l1, l2))

File /usr/local/pyenv/versions/3.13.13/lib/python3.13/site-packages/seaborn/axisgrid.py:1425, in PairGrid.map_offdiag(self, func, **kwargs)
   1414 """Plot with a bivariate function on the off-diagonal subplots.
   1415 
   1416 Parameters
   (...)   1422 
   1423 """
   1424 if self.square_grid:
-> 1425     self.map_lower(func, **kwargs)
   1426     if not self._corner:
   1427         self.map_upper(func, **kwargs)

File /usr/local/pyenv/versions/3.13.13/lib/python3.13/site-packages/seaborn/axisgrid.py:1395, in PairGrid.map_lower(self, func, **kwargs)
   1384 """Plot with a bivariate function on the lower diagonal subplots.
   1385 
   1386 Parameters
   (...)   1392 
   1393 """
   1394 indices = zip(*np.tril_indices_from(self.axes, -1))
-> 1395 self._map_bivariate(func, indices, **kwargs)
   1396 return self

File /usr/local/pyenv/versions/3.13.13/lib/python3.13/site-packages/seaborn/axisgrid.py:1574, in PairGrid._map_bivariate(self, func, indices, **kwargs)
   1572     if ax is None:  # i.e. we are in corner mode
   1573         continue
-> 1574     self._plot_bivariate(x_var, y_var, ax, func, **kws)
   1575 self._add_axis_labels()
   1577 if "hue" in signature(func).parameters:

File /usr/local/pyenv/versions/3.13.13/lib/python3.13/site-packages/seaborn/axisgrid.py:1583, in PairGrid._plot_bivariate(self, x_var, y_var, ax, func, **kwargs)
   1581 """Draw a bivariate plot on the specified axes."""
   1582 if "hue" not in signature(func).parameters:
-> 1583     self._plot_bivariate_iter_hue(x_var, y_var, ax, func, **kwargs)
   1584     return
   1586 kwargs = kwargs.copy()

File /usr/local/pyenv/versions/3.13.13/lib/python3.13/site-packages/seaborn/axisgrid.py:1659, in PairGrid._plot_bivariate_iter_hue(self, x_var, y_var, ax, func, **kwargs)
   1657         func(x=x, y=y, **kws)
   1658     else:
-> 1659         func(x, y, **kws)
   1661 self._update_legend_data(ax)

TypeError: plot_unity() got an unexpected keyword argument 'color'

Table statistics

df.describe()

	MAPZEN	TANDEM	ASTER	DEM3	COPDEM30	ALASKA	AW3D30	ARCTICDEM	COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	1671.000000
mean	5319.226212	5153.806152	5295.587672	5328.549372	5277.514160	5316.735352	NaN	5529.413086	5277.694824
std	350.840381	388.357605	351.229245	351.724101	370.142792	351.459656	NaN	393.222046	370.293671
min	4622.000000	4497.636719	4565.000000	4644.000000	4585.657715	4610.906738	NaN	4871.422852	4584.852539
25%	5050.000000	4837.172607	5026.500000	5058.000000	4982.135010	5047.515381	NaN	5111.490234	4980.320312
50%	5258.000000	5044.266113	5250.000000	5259.000000	5231.779785	5255.337402	NaN	5613.539551	5231.637207
75%	5578.500000	5441.289551	5543.000000	5590.000000	5562.813232	5578.190430	NaN	5869.918945	5564.726562
max	6111.000000	5975.219727	6113.000000	6120.000000	6073.898438	6116.865234	NaN	6126.963379	6073.276855

df.corr()

	MAPZEN	TANDEM	ASTER	DEM3	COPDEM30	ALASKA	AW3D30	ARCTICDEM	COPDEM90
MAPZEN	1.000000	0.930963	0.997390	0.998911	0.995645	0.999929	NaN	0.957611	0.996033
TANDEM	0.930963	1.000000	0.923299	0.934220	0.928303	0.930687	NaN	0.858754	0.928387
ASTER	0.997390	0.923299	1.000000	0.997277	0.995283	0.997280	NaN	0.960869	0.995702
DEM3	0.998911	0.934220	0.997277	1.000000	0.995141	0.998666	NaN	0.957046	0.995559
COPDEM30	0.995645	0.928303	0.995283	0.995141	1.000000	0.995697	NaN	0.957143	0.999891
ALASKA	0.999929	0.930687	0.997280	0.998666	0.995697	1.000000	NaN	0.957513	0.996075
AW3D30	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
ARCTICDEM	0.957611	0.858754	0.960869	0.957046	0.957143	0.957513	NaN	1.000000	0.957821
COPDEM90	0.996033	0.928387	0.995702	0.995559	0.999891	0.996075	NaN	0.957821	1.000000

df_diff.describe()

	MAPZEN-TANDEM	MAPZEN-ASTER	MAPZEN-DEM3	MAPZEN-COPDEM30	MAPZEN-ALASKA	MAPZEN-AW3D30	MAPZEN-ARCTICDEM	MAPZEN-COPDEM90	TANDEM-ASTER	TANDEM-DEM3	...	COPDEM30-ALASKA	COPDEM30-AW3D30	COPDEM30-ARCTICDEM	COPDEM30-COPDEM90	ALASKA-AW3D30	ALASKA-ARCTICDEM	ALASKA-COPDEM90	AW3D30-ARCTICDEM	AW3D30-COPDEM90	ARCTICDEM-COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	1671.000000	1671.000000	1671.000000	...	1671.000000	0.0	767.000000	1671.000000	0.0	767.000000	1671.000000	0.0	0.0	767.000000
mean	165.419989	23.638540	-9.323160	41.711812	2.490369	NaN	-83.946521	41.531287	-141.781450	-174.743149	...	-39.221443	NaN	-110.319916	-0.180525	NaN	-85.975136	39.040920	NaN	NaN	110.184074
std	142.198023	25.367381	16.420294	38.776733	4.234587	NaN	113.273900	37.537576	149.341491	138.969170	...	38.322636	NaN	114.278862	5.457573	NaN	113.401772	37.098961	NaN	NaN	113.430321
min	-16.507324	-54.000000	-60.000000	-38.260254	-14.705078	NaN	-396.646973	-33.757324	-829.932129	-872.888672	...	-135.432617	NaN	-459.774902	-18.569824	NaN	-401.538574	-42.941895	NaN	NaN	-131.313965
25%	71.849121	6.000000	-20.000000	13.209717	0.054199	NaN	-143.172607	12.118164	-193.438232	-217.682861	...	-65.968750	NaN	-158.155029	-3.210938	NaN	-143.909668	10.340332	NaN	NaN	36.502441
50%	123.718750	22.000000	-10.000000	34.193848	2.204590	NaN	-56.379883	34.726074	-100.195801	-133.516602	...	-32.329102	NaN	-82.882324	-0.013672	NaN	-58.945312	33.313477	NaN	NaN	81.454102
75%	214.740479	40.000000	0.000000	69.063965	5.209473	NaN	-4.010010	68.542236	-44.948730	-84.589355	...	-10.385498	NaN	-35.619141	2.751221	NaN	-5.880127	65.058350	NaN	NaN	158.221924
max	851.888672	130.000000	47.000000	137.803711	20.434082	NaN	146.276367	135.499023	85.662109	14.181152	...	46.030762	NaN	136.359863	17.572266	NaN	147.653809	130.026367	NaN	NaN	452.215332

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-TANDEM	MAPZEN-ASTER	MAPZEN-DEM3	MAPZEN-COPDEM30	MAPZEN-ALASKA	MAPZEN-AW3D30	MAPZEN-ARCTICDEM	MAPZEN-COPDEM90	TANDEM-ASTER	TANDEM-DEM3	...	COPDEM30-ALASKA	COPDEM30-AW3D30	COPDEM30-ARCTICDEM	COPDEM30-COPDEM90	ALASKA-AW3D30	ALASKA-ARCTICDEM	ALASKA-COPDEM90	AW3D30-ARCTICDEM	AW3D30-COPDEM90	ARCTICDEM-COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	1671.000000	1671.000000	1671.000000	...	1671.000000	0.0	767.000000	1671.000000	0.0	767.000000	1671.000000	0.0	0.0	767.000000
mean	165.650691	27.788151	15.171155	44.739207	3.799071	NaN	103.447394	44.107472	147.605664	174.815853	...	42.954536	NaN	118.163139	4.060298	NaN	105.086456	42.339176	NaN	NaN	117.711647
std	141.929044	20.735849	11.238222	35.238571	3.113807	NaN	95.769833	34.471963	143.584187	138.877648	...	34.083324	NaN	106.137970	3.649907	NaN	95.936180	33.283024	NaN	NaN	105.587395
min	0.151367	0.000000	0.000000	0.117188	0.002930	NaN	0.289062	0.069336	0.175293	0.848633	...	0.007324	NaN	0.650879	0.000488	NaN	0.155273	0.000000	NaN	NaN	0.624512
25%	71.849121	12.000000	6.000000	16.361816	1.328125	NaN	33.356445	14.985596	49.170898	84.589355	...	14.802979	NaN	41.187012	1.359131	NaN	36.557861	14.249756	NaN	NaN	41.026611
50%	123.718750	24.000000	12.000000	34.610840	3.072266	NaN	64.257812	34.726074	100.195801	133.516602	...	32.889648	NaN	84.257812	2.911621	NaN	65.577148	33.496582	NaN	NaN	82.970215
75%	214.740479	40.000000	22.000000	69.063965	5.534912	NaN	144.176514	68.542236	193.438232	217.682861	...	65.968750	NaN	158.155029	5.748779	NaN	144.205811	65.058350	NaN	NaN	158.221924
max	851.888672	130.000000	60.000000	137.803711	20.434082	NaN	396.646973	135.499023	829.932129	872.888672	...	135.432617	NaN	459.774902	18.569824	NaN	401.538574	130.026367	NaN	NaN	452.215332

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents