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

2026-05-28 14:45:26: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-05-28 14:45:26: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-05-28 14:45:26: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-05-28 14:45:26: 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]

2026-05-28 14:45:27: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-05-28 14:45:27: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-05-28 14:45:27: 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', 'ASTER', 'SRTM', 'NASADEM', 'COPDEM30', 'TANDEM', 'DEM3', 'AW3D30', 'COPDEM90']
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	ASTER	SRTM	NASADEM	COPDEM30	TANDEM	DEM3	AW3D30	COPDEM90
count	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000	3219.000000
mean	3022.554520	3030.625039	3031.629388	3027.719789	3013.461426	3066.144775	3051.486797	3023.205654	3013.856201
std	255.076764	251.752913	247.360252	248.589552	260.305847	259.755615	238.175459	254.510913	260.099152
min	2412.000000	2428.000000	2450.000000	2431.000000	2413.272217	2469.645020	2488.000000	2416.000000	2417.131104
25%	2848.500000	2864.000000	2856.000000	2853.500000	2835.018066	2887.448853	2882.000000	2850.000000	2834.996582
50%	3051.000000	3056.000000	3059.000000	3052.000000	3042.966797	3095.948730	3067.000000	3052.000000	3043.559570
75%	3200.500000	3205.000000	3204.500000	3203.000000	3195.463745	3248.545532	3217.000000	3203.000000	3196.595459
max	3716.000000	3691.000000	3684.000000	3692.000000	3694.617676	3737.720215	3703.000000	3719.000000	3688.516113

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

df.corr()

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

df_diff.describe()

	MAPZEN-ASTER	MAPZEN-SRTM	MAPZEN-NASADEM	MAPZEN-COPDEM30	MAPZEN-TANDEM	MAPZEN-DEM3	MAPZEN-AW3D30	MAPZEN-COPDEM90	ASTER-SRTM	ASTER-NASADEM	...	COPDEM30-TANDEM	COPDEM30-DEM3	COPDEM30-AW3D30	COPDEM30-COPDEM90	TANDEM-DEM3	TANDEM-AW3D30	TANDEM-COPDEM90	DEM3-AW3D30	DEM3-COPDEM90	AW3D30-COPDEM90
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	-8.070519	-9.074868	-5.165269	9.093256	-43.590266	-28.932277	-0.651134	8.698371	-1.004349	2.905250	...	-52.683521	-38.025533	-9.744389	-0.394885	14.657989	42.939132	52.288639	28.281143	37.630648	9.349505
std	8.942135	16.760635	9.311976	7.771080	7.957814	20.821993	4.841786	8.053156	14.211992	10.862782	...	3.006184	26.544060	8.719621	2.296632	26.184944	8.697157	1.918336	20.759226	26.316103	8.700743
min	-47.000000	-84.000000	-39.000000	-31.396973	-83.664795	-116.000000	-31.000000	-30.612549	-68.000000	-40.000000	...	-79.928711	-131.028076	-40.435303	-10.730957	-72.623047	-3.142090	20.051270	-24.000000	-4.963135	-29.469238
25%	-13.000000	-21.000000	-10.000000	4.445435	-48.326782	-37.500000	-3.000000	4.063965	-10.000000	-3.000000	...	-53.701782	-49.742188	-14.411499	-1.276245	2.590820	38.299927	52.394165	15.000000	19.852539	4.221680
50%	-8.000000	-10.000000	-3.000000	7.469482	-45.169434	-23.000000	-1.000000	7.265137	0.000000	3.000000	...	-52.589600	-29.390381	-9.586670	-0.166992	23.722900	43.225586	52.427734	22.000000	28.607910	9.158936
75%	-2.000000	0.000000	1.000000	12.000854	-40.418091	-15.000000	1.000000	12.022217	7.000000	9.000000	...	-51.883301	-20.157104	-4.713745	0.518311	32.422241	48.114868	52.462891	36.000000	49.807861	14.035889
max	36.000000	72.000000	41.000000	39.028076	0.142090	21.000000	34.000000	38.233398	59.000000	51.000000	...	-17.101074	9.135010	26.607422	16.592773	58.887939	83.232178	76.923584	114.000000	125.127686	41.233398

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-ASTER	MAPZEN-SRTM	MAPZEN-NASADEM	MAPZEN-COPDEM30	MAPZEN-TANDEM	MAPZEN-DEM3	MAPZEN-AW3D30	MAPZEN-COPDEM90	ASTER-SRTM	ASTER-NASADEM	...	COPDEM30-TANDEM	COPDEM30-DEM3	COPDEM30-AW3D30	COPDEM30-COPDEM90	TANDEM-DEM3	TANDEM-AW3D30	TANDEM-COPDEM90	DEM3-AW3D30	DEM3-COPDEM90	AW3D30-COPDEM90
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	9.759863	15.263747	7.433054	9.485731	43.590355	29.158434	3.359428	9.357302	10.919851	8.727555	...	52.683521	38.091287	10.836687	1.499186	26.621369	42.941478	52.288639	28.555763	37.664915	10.491179
std	7.059090	11.412421	7.624547	7.286675	7.957330	20.503992	3.546510	7.276826	9.149292	7.088670	...	3.006184	26.449586	7.317387	1.783883	13.844030	8.685566	1.918336	20.379699	26.267020	7.283151
min	0.000000	0.000000	0.000000	0.005371	0.142090	0.000000	0.000000	0.004150	0.000000	0.000000	...	17.101074	0.022705	0.002197	0.001465	0.021484	0.632812	20.051270	0.000000	0.172363	0.001709
25%	4.000000	6.000000	2.000000	4.655762	40.418091	15.000000	1.000000	4.442871	4.000000	3.000000	...	51.883301	20.157104	5.573120	0.335815	16.813110	38.299927	52.394165	15.000000	19.852539	5.152466
50%	9.000000	13.000000	5.000000	7.558350	45.169434	23.000000	2.000000	7.405518	9.000000	7.000000	...	52.589600	29.390381	9.769287	0.867920	27.969238	43.225586	52.427734	22.000000	28.607910	9.456299
75%	14.000000	23.000000	10.000000	12.152710	48.326782	37.500000	4.000000	12.271118	16.000000	12.000000	...	53.701782	49.742188	14.518677	1.980347	35.264526	48.114868	52.462891	36.000000	49.807861	14.206177
max	47.000000	84.000000	41.000000	39.028076	83.664795	116.000000	34.000000	38.233398	68.000000	51.000000	...	79.928711	131.028076	40.435303	16.592773	72.623047	83.232178	76.923584	114.000000	125.127686	41.233398

8 rows × 36 columns

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