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

2025-06-05 13:07:57: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2025-06-05 13:07:57: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2025-06-05 13:07:57: oggm.cfg: Multiprocessing: using all available processors (N=4)
2025-06-05 13:07:58: 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/data/gdirs/dems_v2/default (high resolution version: https://cluster.klima.uni-bremen.de/data/gdirs/dems_v1/highres)

# URL of the preprocessed GDirs
gdir_url = 'https://cluster.klima.uni-bremen.de/data/gdirs/dems_v2/default'
# 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]

2025-06-05 13:07:58: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2025-06-05 13:07:58: 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: ['MAPZEN', 'ASTER', 'DEM3', 'TANDEM', 'NASADEM', 'SRTM', 'COPDEM90', 'AW3D30', 'COPDEM30']
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	DEM3	TANDEM	NASADEM	SRTM	COPDEM90	AW3D30	COPDEM30
count	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000
mean	3023.202610	3031.316346	3052.045681	3066.820068	3028.332505	3032.272219	3014.512695	3023.871970	3014.124512
std	254.144292	250.883864	237.228101	258.874115	247.615467	246.447678	259.186768	253.597569	259.403259
min	2413.000000	2428.000000	2490.000000	2469.287598	2430.000000	2450.000000	2416.773438	2417.000000	2413.545654
25%	2850.000000	2865.000000	2883.500000	2889.410828	2855.000000	2857.000000	2836.975037	2851.000000	2837.251770
50%	3051.000000	3058.000000	3068.500000	3097.242310	3054.000000	3060.000000	3044.824097	3052.500000	3044.196289
75%	3201.750000	3203.000000	3217.000000	3249.972717	3202.000000	3203.000000	3196.834839	3201.750000	3196.260803
max	3723.000000	3693.000000	3701.000000	3738.977051	3691.000000	3684.000000	3688.330078	3720.000000	3694.253174

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(xdata, ydata, **kwargs):
    points = np.linspace(l1, l2, 100)
    plt.gca().plot(points, points, color='k', marker=None,
                   linestyle=':', linewidth=3.0)

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')

Table statistics

df.describe()

	MAPZEN	ASTER	DEM3	TANDEM	NASADEM	SRTM	COPDEM90	AW3D30	COPDEM30
count	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000
mean	3023.202610	3031.316346	3052.045681	3066.820068	3028.332505	3032.272219	3014.512695	3023.871970	3014.124512
std	254.144292	250.883864	237.228101	258.874115	247.615467	246.447678	259.186768	253.597569	259.403259
min	2413.000000	2428.000000	2490.000000	2469.287598	2430.000000	2450.000000	2416.773438	2417.000000	2413.545654
25%	2850.000000	2865.000000	2883.500000	2889.410828	2855.000000	2857.000000	2836.975037	2851.000000	2837.251770
50%	3051.000000	3058.000000	3068.500000	3097.242310	3054.000000	3060.000000	3044.824097	3052.500000	3044.196289
75%	3201.750000	3203.000000	3217.000000	3249.972717	3202.000000	3203.000000	3196.834839	3201.750000	3196.260803
max	3723.000000	3693.000000	3701.000000	3738.977051	3691.000000	3684.000000	3688.330078	3720.000000	3694.253174

df.corr()

	MAPZEN	ASTER	DEM3	TANDEM	NASADEM	SRTM	COPDEM90	AW3D30	COPDEM30
MAPZEN	1.000000	0.999455	0.998774	0.999687	0.999643	0.998217	0.999700	0.999819	0.999754
ASTER	0.999455	1.000000	0.998119	0.999344	0.999131	0.998518	0.999365	0.999315	0.999343
DEM3	0.998774	0.998119	1.000000	0.998220	0.999294	0.997864	0.998282	0.998638	0.998258
TANDEM	0.999687	0.999344	0.998220	1.000000	0.999342	0.998315	0.999974	0.999633	0.999932
NASADEM	0.999643	0.999131	0.999294	0.999342	1.000000	0.998544	0.999379	0.999553	0.999358
SRTM	0.998217	0.998518	0.997864	0.998315	0.998544	1.000000	0.998394	0.998216	0.998275
COPDEM90	0.999700	0.999365	0.998282	0.999974	0.999379	0.998394	1.000000	0.999660	0.999958
AW3D30	0.999819	0.999315	0.998638	0.999633	0.999553	0.998216	0.999660	1.000000	0.999679
COPDEM30	0.999754	0.999343	0.998258	0.999932	0.999358	0.998275	0.999958	0.999679	1.000000

df_diff.describe()

	MAPZEN-ASTER	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-NASADEM	MAPZEN-SRTM	MAPZEN-COPDEM90	MAPZEN-AW3D30	MAPZEN-COPDEM30	ASTER-DEM3	ASTER-TANDEM	...	NASADEM-SRTM	NASADEM-COPDEM90	NASADEM-AW3D30	NASADEM-COPDEM30	SRTM-COPDEM90	SRTM-AW3D30	SRTM-COPDEM30	COPDEM90-AW3D30	COPDEM90-COPDEM30	AW3D30-COPDEM30
count	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	...	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000
mean	-8.113735	-28.843070	-43.617270	-5.129894	-9.069608	8.689804	-0.669360	9.078125	-20.729335	-35.503534	...	-3.939714	13.819699	4.460534	14.208019	17.759413	8.400249	18.147733	-9.359164	0.388321	9.747485
std	8.953622	20.831143	7.972539	9.360127	16.809936	8.057372	4.859039	7.750302	20.258841	12.211383	...	13.380009	14.616837	9.588316	14.878318	19.169901	16.555147	19.706512	8.710851	2.387619	8.717534
min	-46.000000	-116.000000	-85.858154	-40.000000	-84.000000	-32.682129	-35.000000	-31.172363	-105.000000	-79.663330	...	-69.000000	-27.849121	-48.000000	-27.088867	-53.235596	-75.000000	-55.908691	-41.972168	-15.591797	-23.341553
25%	-14.000000	-37.000000	-48.300476	-10.000000	-20.750000	4.038086	-3.000000	4.448425	-27.000000	-43.959106	...	-10.000000	4.234375	-2.000000	4.427490	6.643860	-1.000000	6.655701	-14.219543	-0.533203	4.652039
50%	-8.000000	-23.000000	-45.182861	-3.000000	-10.000000	7.221680	-1.000000	7.459595	-18.000000	-37.429810	...	-4.000000	9.529785	3.000000	10.123413	17.123169	9.000000	17.336670	-9.151611	0.146362	9.645752
75%	-2.000000	-15.000000	-40.532715	1.000000	-1.000000	11.912292	1.000000	11.907959	-10.000000	-28.404724	...	1.000000	18.632935	8.000000	19.370178	29.375549	20.000000	30.145203	-4.334778	1.260254	14.405151
max	30.000000	22.000000	-1.023926	51.000000	69.000000	44.972168	28.000000	46.652588	42.000000	0.389160	...	59.000000	69.138428	38.000000	71.462646	73.730957	79.000000	79.334717	29.402100	15.551025	43.652588

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-ASTER	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-NASADEM	MAPZEN-SRTM	MAPZEN-COPDEM90	MAPZEN-AW3D30	MAPZEN-COPDEM30	ASTER-DEM3	ASTER-TANDEM	...	NASADEM-SRTM	NASADEM-COPDEM90	NASADEM-AW3D30	NASADEM-COPDEM30	SRTM-COPDEM90	SRTM-AW3D30	SRTM-COPDEM30	COPDEM90-AW3D30	COPDEM90-COPDEM30	AW3D30-COPDEM30
count	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	...	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000	3218.000000
mean	9.781231	29.093536	43.617270	7.451212	15.292107	9.364936	3.354257	9.462017	22.658484	35.503776	...	10.136109	14.487043	7.379117	14.978851	21.517830	14.691734	22.048134	10.511262	1.539755	10.837928
std	7.093540	20.479767	7.972539	7.641868	11.442731	7.261413	3.578254	7.276508	18.074653	12.210679	...	9.580093	13.955490	7.574302	14.101744	14.826293	11.346579	15.215713	7.278676	1.865470	7.317186
min	0.000000	0.000000	1.023926	0.000000	0.000000	0.000488	0.000000	0.007568	0.000000	0.389160	...	0.000000	0.001953	0.000000	0.002197	0.012451	0.000000	0.003906	0.000977	0.000732	0.003418
25%	4.000000	15.000000	40.532715	2.000000	6.000000	4.394409	1.000000	4.625854	11.000000	28.404724	...	3.000000	4.906738	2.000000	5.027039	10.496765	5.000000	10.525879	5.184937	0.342957	5.617004
50%	9.000000	23.000000	45.182861	4.000000	13.000000	7.369751	2.000000	7.555664	19.000000	37.429810	...	7.000000	9.772339	5.000000	10.341919	18.754150	13.000000	19.279541	9.402832	0.886719	9.821289
75%	14.000000	37.000000	48.300476	10.000000	23.000000	12.140320	4.000000	12.061157	27.000000	43.959106	...	15.000000	18.664856	10.000000	19.433960	29.730896	22.000000	30.630066	14.364990	2.049927	14.540771
max	46.000000	116.000000	85.858154	51.000000	84.000000	44.972168	35.000000	46.652588	105.000000	79.663330	...	69.000000	69.138428	48.000000	71.462646	73.730957	79.000000	79.334717	41.972168	15.591797	43.652588

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents