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

2026-04-16 11:17:42: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-04-16 11:17:42: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-04-16 11:17:42: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-04-16 11:17:42: 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]

2026-04-16 11:17:43: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-04-16 11:17:43: 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: ['NASADEM', 'AW3D30', 'TANDEM', 'ASTER', 'MAPZEN', 'COPDEM90', 'COPDEM30', 'SRTM', 'DEM3']
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()

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

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

[-50, -30, -15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15, 30, 50]

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()

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

df.corr()

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

df_diff.describe()

	NASADEM-AW3D30	NASADEM-TANDEM	NASADEM-ASTER	NASADEM-MAPZEN	NASADEM-COPDEM90	NASADEM-COPDEM30	NASADEM-SRTM	NASADEM-DEM3	AW3D30-TANDEM	AW3D30-ASTER	...	MAPZEN-COPDEM90	MAPZEN-COPDEM30	MAPZEN-SRTM	MAPZEN-DEM3	COPDEM90-COPDEM30	COPDEM90-SRTM	COPDEM90-DEM3	COPDEM30-SRTM	COPDEM30-DEM3	SRTM-DEM3
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	4.514135	-38.424998	-2.905250	5.165269	13.863640	14.258524	-3.909599	-23.767008	-42.939132	-7.419385	...	8.698371	9.093256	-9.074868	-28.932277	0.394885	-17.773239	-37.630648	-18.168124	-38.025533	-19.857409
std	9.496460	14.470125	10.862782	9.311976	14.565806	14.798913	13.306268	13.827482	8.697157	9.677552	...	8.053156	7.771080	16.760635	20.821993	2.296632	19.143038	26.316103	19.636149	26.544060	18.295258
min	-44.000000	-75.664795	-51.000000	-41.000000	-18.129395	-18.503906	-71.000000	-85.000000	-83.232178	-46.000000	...	-30.612549	-31.396973	-84.000000	-116.000000	-16.592773	-74.127686	-125.127686	-80.028076	-131.028076	-74.000000
25%	-2.000000	-47.947998	-9.000000	-1.000000	4.275879	4.520020	-10.000000	-31.000000	-48.114868	-13.000000	...	4.063965	4.445435	-21.000000	-37.500000	-0.518311	-29.323608	-49.807861	-30.036133	-49.742188	-32.000000
50%	3.000000	-42.638916	-3.000000	3.000000	9.619873	10.160156	-4.000000	-21.000000	-43.225586	-7.000000	...	7.265137	7.469482	-10.000000	-23.000000	0.166992	-17.155762	-28.607910	-17.448486	-29.390381	-21.000000
75%	8.000000	-33.795532	3.000000	10.000000	18.536743	19.309448	1.000000	-15.000000	-38.299927	-1.000000	...	12.022217	12.000854	0.000000	-15.000000	1.276245	-6.779907	-19.852539	-7.017456	-20.157104	-9.000000
max	37.000000	15.975098	40.000000	39.000000	68.478271	70.410156	63.000000	12.000000	3.142090	29.000000	...	38.233398	39.028076	72.000000	21.000000	10.730957	53.959717	4.963135	63.182861	9.135010	58.000000

8 rows × 36 columns

df_diff.abs().describe()

	NASADEM-AW3D30	NASADEM-TANDEM	NASADEM-ASTER	NASADEM-MAPZEN	NASADEM-COPDEM90	NASADEM-COPDEM30	NASADEM-SRTM	NASADEM-DEM3	AW3D30-TANDEM	AW3D30-ASTER	...	MAPZEN-COPDEM90	MAPZEN-COPDEM30	MAPZEN-SRTM	MAPZEN-DEM3	COPDEM90-COPDEM30	COPDEM90-SRTM	COPDEM90-DEM3	COPDEM30-SRTM	COPDEM30-DEM3	SRTM-DEM3
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	7.345449	38.691837	8.727555	7.433054	14.493817	14.973715	10.039453	23.908046	42.941478	9.676608	...	9.357302	9.485731	15.263747	29.158434	1.499186	21.490858	37.664915	22.003013	38.091287	23.011805
std	7.522906	13.740463	7.088670	7.624547	13.938685	14.074612	9.566846	13.582091	8.685566	7.419808	...	7.276826	7.286675	11.412421	20.503992	1.783883	14.847274	26.267020	15.214420	26.449586	14.122605
min	0.000000	0.058594	0.000000	0.000000	0.040283	0.003418	0.000000	0.000000	0.632812	0.000000	...	0.004150	0.005371	0.000000	0.000000	0.001465	0.002197	0.172363	0.000244	0.022705	0.000000
25%	2.000000	33.795532	3.000000	2.000000	4.831055	5.109253	3.000000	15.000000	38.299927	4.000000	...	4.442871	4.655762	6.000000	15.000000	0.335815	10.327148	19.852539	10.488037	20.157104	12.000000
50%	5.000000	42.638916	7.000000	5.000000	9.785400	10.431396	7.000000	21.000000	43.225586	8.000000	...	7.405518	7.558350	13.000000	23.000000	0.867920	18.616943	28.607910	19.133545	29.390381	22.000000
75%	10.000000	47.947998	12.000000	10.000000	18.536743	19.309448	15.000000	31.000000	48.114868	14.000000	...	12.271118	12.152710	23.000000	37.500000	1.980347	29.641357	49.807861	30.468506	49.742188	32.000000
max	44.000000	75.664795	51.000000	41.000000	68.478271	70.410156	71.000000	85.000000	83.232178	46.000000	...	38.233398	39.028076	84.000000	116.000000	16.592773	74.127686	125.127686	80.028076	131.028076	74.000000

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents