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

2026-11-06 12:38:34: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-11-06 12:38:34: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-11-06 12:38:34: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-11-06 12:38:34: 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]

2026-11-06 12:38:34: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-11-06 12:38:34: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-11-06 12:38:34: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar...

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: ['MAPZEN', 'COPDEM90', 'DEM3', 'TANDEM', 'NASADEM', 'SRTM', 'ASTER', 'COPDEM30', 'AW3D30']
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()

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

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()

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

df.corr()

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

df_diff.describe()

	MAPZEN-COPDEM90	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-NASADEM	MAPZEN-SRTM	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-AW3D30	COPDEM90-DEM3	COPDEM90-TANDEM	...	NASADEM-SRTM	NASADEM-ASTER	NASADEM-COPDEM30	NASADEM-AW3D30	SRTM-ASTER	SRTM-COPDEM30	SRTM-AW3D30	ASTER-COPDEM30	ASTER-AW3D30	COPDEM30-AW3D30
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	8.698371	-28.932277	-43.590266	-5.165269	-9.074868	-8.070519	9.093256	-0.651134	-37.630648	-52.288639	...	-3.909599	-2.905250	14.258524	4.514135	1.004349	18.168124	8.423734	17.163774	7.419385	-9.744389
std	8.053156	20.821993	7.957814	9.311976	16.760635	8.942135	7.771080	4.841786	26.316103	1.918336	...	13.306268	10.862782	14.798913	9.496460	14.211992	19.636149	16.492592	12.606325	9.677552	8.719621
min	-30.612549	-116.000000	-83.664795	-39.000000	-84.000000	-47.000000	-31.396973	-31.000000	-125.127686	-76.923584	...	-71.000000	-51.000000	-18.503906	-44.000000	-59.000000	-63.182861	-73.000000	-32.396973	-29.000000	-40.435303
25%	4.063965	-37.500000	-48.326782	-10.000000	-21.000000	-13.000000	4.445435	-3.000000	-49.807861	-52.462891	...	-10.000000	-9.000000	4.520020	-2.000000	-7.000000	7.017456	-0.500000	8.674438	1.000000	-14.411499
50%	7.265137	-23.000000	-45.169434	-3.000000	-10.000000	-8.000000	7.469482	-1.000000	-28.607910	-52.427734	...	-4.000000	-3.000000	10.160156	3.000000	0.000000	17.448486	9.000000	15.394531	7.000000	-9.586670
75%	12.022217	-15.000000	-40.418091	1.000000	0.000000	-2.000000	12.000854	1.000000	-19.852539	-52.394165	...	1.000000	3.000000	19.309448	8.000000	10.000000	30.036133	20.000000	24.747314	13.000000	-4.713745
max	38.233398	21.000000	0.142090	41.000000	72.000000	36.000000	39.028076	34.000000	4.963135	-20.051270	...	63.000000	40.000000	70.410156	37.000000	68.000000	80.028076	82.000000	52.590332	46.000000	26.607422

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-COPDEM90	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-NASADEM	MAPZEN-SRTM	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-AW3D30	COPDEM90-DEM3	COPDEM90-TANDEM	...	NASADEM-SRTM	NASADEM-ASTER	NASADEM-COPDEM30	NASADEM-AW3D30	SRTM-ASTER	SRTM-COPDEM30	SRTM-AW3D30	ASTER-COPDEM30	ASTER-AW3D30	COPDEM30-AW3D30
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	9.357302	29.158434	43.590355	7.433054	15.263747	9.759863	9.485731	3.359428	37.664915	52.288639	...	10.039453	8.727555	14.973715	7.345449	10.919851	22.003013	14.641193	17.911928	9.676608	10.836687
std	7.276826	20.503992	7.957330	7.624547	11.412421	7.059090	7.286675	3.546510	26.267020	1.918336	...	9.566846	7.088670	14.074612	7.522906	9.149292	15.214420	11.338245	11.518214	7.419808	7.317387
min	0.004150	0.000000	0.142090	0.000000	0.000000	0.000000	0.005371	0.000000	0.172363	20.051270	...	0.000000	0.000000	0.003418	0.000000	0.000000	0.000244	0.000000	0.014893	0.000000	0.002197
25%	4.442871	15.000000	40.418091	2.000000	6.000000	4.000000	4.655762	1.000000	19.852539	52.394165	...	3.000000	3.000000	5.109253	2.000000	4.000000	10.488037	5.000000	9.050293	4.000000	5.573120
50%	7.405518	23.000000	45.169434	5.000000	13.000000	9.000000	7.558350	2.000000	28.607910	52.427734	...	7.000000	7.000000	10.431396	5.000000	9.000000	19.133545	12.000000	15.547363	8.000000	9.769287
75%	12.271118	37.500000	48.326782	10.000000	23.000000	14.000000	12.152710	4.000000	49.807861	52.462891	...	15.000000	12.000000	19.309448	10.000000	16.000000	30.468506	22.000000	24.809326	14.000000	14.518677
max	38.233398	116.000000	83.664795	41.000000	84.000000	47.000000	39.028076	34.000000	125.127686	76.923584	...	71.000000	51.000000	70.410156	44.000000	68.000000	80.028076	82.000000	52.590332	46.000000	40.435303

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents