Compare different DEMs for individual glaciers: RGI-TOPO for RGI v6.0

For most glaciers in the world there are several digital elevation models (DEM) which cover the respective glacier. In OGGM we have currently implemented more than 10 different open access DEMs to choose from. Some are regional and only available in certain areas (e.g. Greenland or Antarctica) and some cover almost the entire globe.

This notebook allows to see which of the DEMs are available for a selected glacier and how they compare to each other. That way it is easy to spot systematic differences and also invalid points in the DEMs.

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 = 'RGI60-11.00897'

# 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)

import pandas as pd
import numpy as np
from oggm import cfg, utils, workflow, tasks, graphics, GlacierDirectory
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-DEMS', reset=True)
cfg.PARAMS['use_intersects'] = False

2026-08-07 07:52:58: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-08-07 07:52:58: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-08-07 07:52:58: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-08-07 07:52:58: 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 https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/ (version with the selected DEM: https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/selected_dem/)

# 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_base_url=gdir_url)[0]

2026-08-07 07:52:59: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-08-07 07:52:59: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers

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: RGI60-11.00897
Available DEM sources: ['SRTM', 'COPDEM90', 'DEM3', 'MAPZEN', 'TANDEM', 'COPDEM30', 'AW3D30', 'NASADEM', 'ASTER']
Plotting directory: /__w/tutorials/tutorials/notebooks/tutorials/outputs/RGI60-11.00897

# 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')

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]

df.describe()

	SRTM	COPDEM90	DEM3	MAPZEN	TANDEM	COPDEM30	AW3D30	NASADEM	ASTER
count	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000
mean	3031.629388	3013.856201	3051.486797	3022.554520	3066.144775	3013.461426	3023.205654	3027.719789	3030.625039
std	247.360252	260.099152	238.175459	255.076764	259.755615	260.305847	254.510913	248.589552	251.752913
min	2450.000000	2417.131104	2488.000000	2412.000000	2469.645020	2413.272217	2416.000000	2431.000000	2428.000000
25%	2856.000000	2834.996582	2882.000000	2848.500000	2887.448853	2835.018066	2850.000000	2853.500000	2864.000000
50%	3059.000000	3043.559570	3067.000000	3051.000000	3095.948730	3042.966797	3052.000000	3052.000000	3056.000000
75%	3204.500000	3196.595459	3217.000000	3200.500000	3248.545532	3195.463745	3203.000000	3203.000000	3205.000000
max	3684.000000	3688.516113	3703.000000	3716.000000	3737.720215	3694.617676	3719.000000	3692.000000	3691.000000

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

[-100, -50, -20, -10, 0, 10, 20, 50, 100]

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,  marker=None,
                   linestyle=':', linewidth=3.0, color='k')

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[19], line 13
      9     plt.gca().plot(points, points,  marker=None,
     10                    linestyle=':', linewidth=3.0, color='k')
     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()

	SRTM	COPDEM90	DEM3	MAPZEN	TANDEM	COPDEM30	AW3D30	NASADEM	ASTER
count	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000
mean	3031.629388	3013.856201	3051.486797	3022.554520	3066.144775	3013.461426	3023.205654	3027.719789	3030.625039
std	247.360252	260.099152	238.175459	255.076764	259.755615	260.305847	254.510913	248.589552	251.752913
min	2450.000000	2417.131104	2488.000000	2412.000000	2469.645020	2413.272217	2416.000000	2431.000000	2428.000000
25%	2856.000000	2834.996582	2882.000000	2848.500000	2887.448853	2835.018066	2850.000000	2853.500000	2864.000000
50%	3059.000000	3043.559570	3067.000000	3051.000000	3095.948730	3042.966797	3052.000000	3052.000000	3056.000000
75%	3204.500000	3196.595459	3217.000000	3200.500000	3248.545532	3195.463745	3203.000000	3203.000000	3205.000000
max	3684.000000	3688.516113	3703.000000	3716.000000	3737.720215	3694.617676	3719.000000	3692.000000	3691.000000

df.corr()

	SRTM	COPDEM90	DEM3	MAPZEN	TANDEM	COPDEM30	AW3D30	NASADEM	ASTER
SRTM	1.000000	0.998413	0.997875	0.998246	0.998330	0.998307	0.998246	0.998573	0.998533
COPDEM90	0.998413	1.000000	0.998290	0.999701	0.999974	0.999961	0.999664	0.999384	0.999365
DEM3	0.997875	0.998290	1.000000	0.998783	0.998222	0.998267	0.998646	0.999301	0.998130
MAPZEN	0.998246	0.999701	0.998783	1.000000	0.999687	0.999751	0.999822	0.999648	0.999463
TANDEM	0.998330	0.999974	0.998222	0.999687	1.000000	0.999935	0.999636	0.999344	0.999343
COPDEM30	0.998307	0.999961	0.998267	0.999751	0.999935	1.000000	0.999680	0.999368	0.999346
AW3D30	0.998246	0.999664	0.998646	0.999822	0.999636	0.999680	1.000000	0.999564	0.999329
NASADEM	0.998573	0.999384	0.999301	0.999648	0.999344	0.999368	0.999564	1.000000	0.999137
ASTER	0.998533	0.999365	0.998130	0.999463	0.999343	0.999346	0.999329	0.999137	1.000000

df_diff.describe()

	SRTM-COPDEM90	SRTM-DEM3	SRTM-MAPZEN	SRTM-TANDEM	SRTM-COPDEM30	SRTM-AW3D30	SRTM-NASADEM	SRTM-ASTER	COPDEM90-DEM3	COPDEM90-MAPZEN	...	TANDEM-COPDEM30	TANDEM-AW3D30	TANDEM-NASADEM	TANDEM-ASTER	COPDEM30-AW3D30	COPDEM30-NASADEM	COPDEM30-ASTER	AW3D30-NASADEM	AW3D30-ASTER	NASADEM-ASTER
count	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	...	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000
mean	17.773239	-19.857409	9.074868	-34.515398	18.168124	8.423734	3.909599	1.004349	-37.630648	-8.698371	...	52.683521	42.939132	38.424998	35.519748	-9.744389	-14.258524	-17.163774	-4.514135	-7.419385	-2.905250
std	19.143038	18.295258	16.760635	19.189930	19.636149	16.492592	13.306268	14.211992	26.316103	8.053156	...	3.006184	8.697157	14.470125	12.246588	8.719621	14.798913	12.606325	9.496460	9.677552	10.862782
min	-53.959717	-74.000000	-72.000000	-107.228760	-63.182861	-73.000000	-63.000000	-59.000000	-125.127686	-38.233398	...	17.101074	-3.142090	-15.975098	0.063477	-40.435303	-70.410156	-52.590332	-37.000000	-46.000000	-51.000000
25%	6.779907	-32.000000	0.000000	-45.617188	7.017456	-0.500000	-1.000000	-7.000000	-49.807861	-12.022217	...	51.883301	38.299927	33.795532	28.249023	-14.411499	-19.309448	-24.747314	-8.000000	-13.000000	-9.000000
50%	17.155762	-21.000000	10.000000	-35.234131	17.448486	9.000000	4.000000	0.000000	-28.607910	-7.265137	...	52.589600	43.225586	42.638916	37.508057	-9.586670	-10.160156	-15.394531	-3.000000	-7.000000	-3.000000
75%	29.323608	-9.000000	21.000000	-22.934814	30.036133	20.000000	10.000000	10.000000	-19.852539	-4.063965	...	53.701782	48.114868	47.947998	44.028198	-4.713745	-4.520020	-8.674438	2.000000	-1.000000	3.000000
max	74.127686	58.000000	84.000000	21.623047	80.028076	82.000000	71.000000	68.000000	4.963135	30.612549	...	79.928711	83.232178	75.664795	74.897705	26.607422	18.503906	32.396973	44.000000	29.000000	40.000000

8 rows × 36 columns

df_diff.abs().describe()

	SRTM-COPDEM90	SRTM-DEM3	SRTM-MAPZEN	SRTM-TANDEM	SRTM-COPDEM30	SRTM-AW3D30	SRTM-NASADEM	SRTM-ASTER	COPDEM90-DEM3	COPDEM90-MAPZEN	...	TANDEM-COPDEM30	TANDEM-AW3D30	TANDEM-NASADEM	TANDEM-ASTER	COPDEM30-AW3D30	COPDEM30-NASADEM	COPDEM30-ASTER	AW3D30-NASADEM	AW3D30-ASTER	NASADEM-ASTER
count	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	...	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000
mean	21.490858	23.011805	15.263747	35.187296	22.003013	14.641193	10.039453	10.919851	37.664915	9.357302	...	52.683521	42.941478	38.691837	35.519748	10.836687	14.973715	17.911928	7.345449	9.676608	8.727555
std	14.847274	14.122605	11.412421	17.927794	15.214420	11.338245	9.566846	9.149292	26.267020	7.276826	...	3.006184	8.685566	13.740463	12.246588	7.317387	14.074612	11.518214	7.522906	7.419808	7.088670
min	0.002197	0.000000	0.000000	0.001465	0.000244	0.000000	0.000000	0.000000	0.172363	0.004150	...	17.101074	0.632812	0.058594	0.063477	0.002197	0.003418	0.014893	0.000000	0.000000	0.000000
25%	10.327148	12.000000	6.000000	22.934814	10.488037	5.000000	3.000000	4.000000	19.852539	4.442871	...	51.883301	38.299927	33.795532	28.249023	5.573120	5.109253	9.050293	2.000000	4.000000	3.000000
50%	18.616943	22.000000	13.000000	35.234131	19.133545	12.000000	7.000000	9.000000	28.607910	7.405518	...	52.589600	43.225586	42.638916	37.508057	9.769287	10.431396	15.547363	5.000000	8.000000	7.000000
75%	29.641357	32.000000	23.000000	45.617188	30.468506	22.000000	15.000000	16.000000	49.807861	12.271118	...	53.701782	48.114868	47.947998	44.028198	14.518677	19.309448	24.809326	10.000000	14.000000	12.000000
max	74.127686	74.000000	84.000000	107.228760	80.028076	82.000000	71.000000	68.000000	125.127686	38.233398	...	79.928711	83.232178	75.664795	74.897705	40.435303	70.410156	52.590332	44.000000	46.000000	51.000000

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents