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

2026-06-28 23:02:58: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-06-28 23:02:58: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-06-28 23:02:58: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-06-28 23:02:58: 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]

2026-06-28 23:02:59: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-06-28 23:02:59: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-06-28 23:02:59: 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: ['COPDEM90', 'TANDEM', 'DEM3', 'COPDEM30', 'SRTM', 'NASADEM', 'MAPZEN', 'AW3D30', '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()

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

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

df.corr()

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

df_diff.describe()

	COPDEM90-TANDEM	COPDEM90-DEM3	COPDEM90-COPDEM30	COPDEM90-SRTM	COPDEM90-NASADEM	COPDEM90-MAPZEN	COPDEM90-AW3D30	COPDEM90-ASTER	TANDEM-DEM3	TANDEM-COPDEM30	...	SRTM-NASADEM	SRTM-MAPZEN	SRTM-AW3D30	SRTM-ASTER	NASADEM-MAPZEN	NASADEM-AW3D30	NASADEM-ASTER	MAPZEN-AW3D30	MAPZEN-ASTER	AW3D30-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	-52.288639	-37.630648	0.394885	-17.773239	-13.863640	-8.698371	-9.349505	-16.768890	14.657989	52.683521	...	3.909599	9.074868	8.423734	1.004349	5.165269	4.514135	-2.905250	-0.651134	-8.070519	-7.419385
std	1.918336	26.316103	2.296632	19.143038	14.565806	8.053156	8.700743	12.362052	26.184944	3.006184	...	13.306268	16.760635	16.492592	14.211992	9.311976	9.496460	10.862782	4.841786	8.942135	9.677552
min	-76.923584	-125.127686	-16.592773	-74.127686	-68.478271	-38.233398	-41.233398	-52.431641	-72.623047	17.101074	...	-63.000000	-72.000000	-73.000000	-59.000000	-41.000000	-44.000000	-51.000000	-31.000000	-47.000000	-46.000000
25%	-52.462891	-49.807861	-0.518311	-29.323608	-18.536743	-12.022217	-14.035889	-24.107666	2.590820	51.883301	...	-1.000000	0.000000	-0.500000	-7.000000	-1.000000	-2.000000	-9.000000	-3.000000	-13.000000	-13.000000
50%	-52.427734	-28.607910	0.166992	-17.155762	-9.619873	-7.265137	-9.158936	-14.818359	23.722900	52.589600	...	4.000000	10.000000	9.000000	0.000000	3.000000	3.000000	-3.000000	-1.000000	-8.000000	-7.000000
75%	-52.394165	-19.852539	1.276245	-6.779907	-4.275879	-4.063965	-4.221680	-8.283325	32.422241	53.701782	...	10.000000	21.000000	20.000000	10.000000	10.000000	8.000000	3.000000	1.000000	-2.000000	-1.000000
max	-20.051270	4.963135	10.730957	53.959717	18.129395	30.612549	29.469238	32.612549	58.887939	79.928711	...	71.000000	84.000000	82.000000	68.000000	39.000000	37.000000	40.000000	34.000000	36.000000	29.000000

8 rows × 36 columns

df_diff.abs().describe()

	COPDEM90-TANDEM	COPDEM90-DEM3	COPDEM90-COPDEM30	COPDEM90-SRTM	COPDEM90-NASADEM	COPDEM90-MAPZEN	COPDEM90-AW3D30	COPDEM90-ASTER	TANDEM-DEM3	TANDEM-COPDEM30	...	SRTM-NASADEM	SRTM-MAPZEN	SRTM-AW3D30	SRTM-ASTER	NASADEM-MAPZEN	NASADEM-AW3D30	NASADEM-ASTER	MAPZEN-AW3D30	MAPZEN-ASTER	AW3D30-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	52.288639	37.664915	1.499186	21.490858	14.493817	9.357302	10.491179	17.410026	26.621369	52.683521	...	10.039453	15.263747	14.641193	10.919851	7.433054	7.345449	8.727555	3.359428	9.759863	9.676608
std	1.918336	26.267020	1.783883	14.847274	13.938685	7.276826	7.283151	11.441161	13.844030	3.006184	...	9.566846	11.412421	11.338245	9.149292	7.624547	7.522906	7.088670	3.546510	7.059090	7.419808
min	20.051270	0.172363	0.001465	0.002197	0.040283	0.004150	0.001709	0.004639	0.021484	17.101074	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	52.394165	19.852539	0.335815	10.327148	4.831055	4.442871	5.152466	8.570312	16.813110	51.883301	...	3.000000	6.000000	5.000000	4.000000	2.000000	2.000000	3.000000	1.000000	4.000000	4.000000
50%	52.427734	28.607910	0.867920	18.616943	9.785400	7.405518	9.456299	14.985840	27.969238	52.589600	...	7.000000	13.000000	12.000000	9.000000	5.000000	5.000000	7.000000	2.000000	9.000000	8.000000
75%	52.462891	49.807861	1.980347	29.641357	18.536743	12.271118	14.206177	24.158325	35.264526	53.701782	...	15.000000	23.000000	22.000000	16.000000	10.000000	10.000000	12.000000	4.000000	14.000000	14.000000
max	76.923584	125.127686	16.592773	74.127686	68.478271	38.233398	41.233398	52.431641	72.623047	79.928711	...	71.000000	84.000000	82.000000	68.000000	41.000000	44.000000	51.000000	34.000000	47.000000	46.000000

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents