RGI-TOPO for RGI 7.0

OGGM was used to generate the topography data used to compute the topographical attributes and the centerlines products for RGI v7.0.

Here we show how to access this data from OGGM.

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 = 'RGI2000-v7.0-G-01-06486'  # Denali

# 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)

from oggm import cfg, utils, workflow, tasks, graphics, GlacierDirectory
import pandas as pd
import numpy as np
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-RGITOPO-RGI7', reset=True)
cfg.PARAMS['use_intersects'] = False

2026-05-28 14:46:30: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-05-28 14:46:30: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-05-28 14:46:30: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-05-28 14:46:30: 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

# 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_rgi_version='70G', prepro_base_url=gdir_url)[0]

2026-05-28 14:46:30: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-05-28 14:46:30: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-05-28 14:46:30: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01-06.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6/rgitopo/2026.1/all_dems/RGI70G/b_010/L1/RGI2000-v7.0-G-01/RGI2000-v7.0-G-01-06.tar...

gdir

<oggm.GlacierDirectory>
  RGI id: RGI2000-v7.0-G-01-06486
  Region: 01: Alaska
  Subregion: 01-02: Alaska Range (Wrangell/Kilbuck)
  Glacier type: Glacier
  Terminus type: Not assigned
  Status: Glacier
  Area: 0.961122833004822 km2
  Lon, Lat: (-151.0094740399913, 63.061062)
  Grid (nx, ny): (70, 88)
  Grid (dx, dy): (24.0, -24.0)

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: RGI2000-v7.0-G-01-06486
Available DEM sources: ['MAPZEN', 'ASTER', 'COPDEM30', 'TANDEM', 'ALASKA', 'DEM3', 'ARCTICDEM', 'AW3D30', 'COPDEM90']
Plotting directory: /__w/tutorials/tutorials/notebooks/tutorials/outputs/RGI2000-v7.0-G-01-06486

# 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'):

  Cell In[14], line 29
    plt.savefig(os.path.join(plot_dir, 'dem_slope.png'), dpi=150, bbox_inches='tight'):
                                                                                      ^
SyntaxError: invalid syntax

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]

dfs = df.describe()
dfs.loc['range'] = dfs.loc['max'] - dfs.loc['min']
dfs

	MAPZEN	ASTER	COPDEM30	TANDEM	ALASKA	DEM3	ARCTICDEM	AW3D30	COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	0.0	1671.000000
mean	5319.226212	5295.587672	5277.514160	5153.806152	5316.735352	5328.549372	5529.413086	NaN	5277.694824
std	350.840381	351.229245	370.142792	388.357605	351.459656	351.724101	393.222046	NaN	370.293671
min	4622.000000	4565.000000	4585.657715	4497.636719	4610.906738	4644.000000	4871.422852	NaN	4584.852539
25%	5050.000000	5026.500000	4982.135010	4837.172607	5047.515381	5058.000000	5111.490234	NaN	4980.320312
50%	5258.000000	5250.000000	5231.779785	5044.266113	5255.337402	5259.000000	5613.539551	NaN	5231.637207
75%	5578.500000	5543.000000	5562.813232	5441.289551	5578.190430	5590.000000	5869.918945	NaN	5564.726562
max	6111.000000	6113.000000	6073.898438	5975.219727	6116.865234	6120.000000	6126.963379	NaN	6073.276855
range	1489.000000	1548.000000	1488.240723	1477.583008	1505.958496	1476.000000	1255.540527	NaN	1488.424316

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

[-500, -300, -150, -100, -50, -20, -10, 0, 10, 20, 50, 100, 150, 300, 500]

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, 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')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[20], line 13
      9     plt.gca().plot(points, points, color='k', marker=None,
     10                    linestyle=':', linewidth=3.0)
     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	COPDEM30	TANDEM	ALASKA	DEM3	ARCTICDEM	AW3D30	COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	0.0	1671.000000
mean	5319.226212	5295.587672	5277.514160	5153.806152	5316.735352	5328.549372	5529.413086	NaN	5277.694824
std	350.840381	351.229245	370.142792	388.357605	351.459656	351.724101	393.222046	NaN	370.293671
min	4622.000000	4565.000000	4585.657715	4497.636719	4610.906738	4644.000000	4871.422852	NaN	4584.852539
25%	5050.000000	5026.500000	4982.135010	4837.172607	5047.515381	5058.000000	5111.490234	NaN	4980.320312
50%	5258.000000	5250.000000	5231.779785	5044.266113	5255.337402	5259.000000	5613.539551	NaN	5231.637207
75%	5578.500000	5543.000000	5562.813232	5441.289551	5578.190430	5590.000000	5869.918945	NaN	5564.726562
max	6111.000000	6113.000000	6073.898438	5975.219727	6116.865234	6120.000000	6126.963379	NaN	6073.276855

df.corr()

	MAPZEN	ASTER	COPDEM30	TANDEM	ALASKA	DEM3	ARCTICDEM	AW3D30	COPDEM90
MAPZEN	1.000000	0.997390	0.995645	0.930963	0.999929	0.998911	0.957611	NaN	0.996033
ASTER	0.997390	1.000000	0.995283	0.923299	0.997280	0.997277	0.960869	NaN	0.995702
COPDEM30	0.995645	0.995283	1.000000	0.928303	0.995697	0.995141	0.957143	NaN	0.999891
TANDEM	0.930963	0.923299	0.928303	1.000000	0.930687	0.934220	0.858754	NaN	0.928387
ALASKA	0.999929	0.997280	0.995697	0.930687	1.000000	0.998666	0.957513	NaN	0.996075
DEM3	0.998911	0.997277	0.995141	0.934220	0.998666	1.000000	0.957046	NaN	0.995559
ARCTICDEM	0.957611	0.960869	0.957143	0.858754	0.957513	0.957046	1.000000	NaN	0.957821
AW3D30	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
COPDEM90	0.996033	0.995702	0.999891	0.928387	0.996075	0.995559	0.957821	NaN	1.000000

df_diff.describe()

	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-TANDEM	MAPZEN-ALASKA	MAPZEN-DEM3	MAPZEN-ARCTICDEM	MAPZEN-AW3D30	MAPZEN-COPDEM90	ASTER-COPDEM30	ASTER-TANDEM	...	ALASKA-DEM3	ALASKA-ARCTICDEM	ALASKA-AW3D30	ALASKA-COPDEM90	DEM3-ARCTICDEM	DEM3-AW3D30	DEM3-COPDEM90	ARCTICDEM-AW3D30	ARCTICDEM-COPDEM90	AW3D30-COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	0.0	1671.000000	1671.000000	1671.000000	...	1671.000000	767.000000	0.0	1671.000000	767.000000	0.0	1671.000000	0.0	767.000000	0.0
mean	23.638540	41.711812	165.419989	2.490369	-9.323160	-83.946521	NaN	41.531287	18.073272	141.781450	...	-11.813529	-85.975136	NaN	39.040920	-76.316795	NaN	50.854447	NaN	110.184074	NaN
std	25.367381	38.776733	142.198023	4.234587	16.420294	113.273900	NaN	37.537576	39.801988	149.341491	...	18.159255	113.401772	NaN	37.098961	114.381777	NaN	38.750864	NaN	113.430321	NaN
min	-54.000000	-38.260254	-16.507324	-14.705078	-60.000000	-396.646973	NaN	-33.757324	-123.904297	-85.662109	...	-73.005371	-401.538574	NaN	-42.941895	-399.646973	NaN	-17.004395	NaN	-131.313965	NaN
25%	6.000000	13.209717	71.849121	0.054199	-20.000000	-143.172607	NaN	12.118164	-8.449463	44.948730	...	-23.316650	-143.909668	NaN	10.340332	-139.544922	NaN	23.536377	NaN	36.502441	NaN
50%	22.000000	34.193848	123.718750	2.204590	-10.000000	-56.379883	NaN	34.726074	13.353027	100.195801	...	-11.001953	-58.945312	NaN	33.313477	-38.955078	NaN	39.517090	NaN	81.454102	NaN
75%	40.000000	69.063965	214.740479	5.209473	0.000000	-4.010010	NaN	68.542236	42.359863	193.438232	...	-1.023926	-5.880127	NaN	65.058350	1.839355	NaN	74.026611	NaN	158.221924	NaN
max	130.000000	137.803711	851.888672	20.434082	47.000000	146.276367	NaN	135.499023	128.154297	829.932129	...	44.825195	147.653809	NaN	130.026367	151.276367	NaN	163.009766	NaN	452.215332	NaN

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-TANDEM	MAPZEN-ALASKA	MAPZEN-DEM3	MAPZEN-ARCTICDEM	MAPZEN-AW3D30	MAPZEN-COPDEM90	ASTER-COPDEM30	ASTER-TANDEM	...	ALASKA-DEM3	ALASKA-ARCTICDEM	ALASKA-AW3D30	ALASKA-COPDEM90	DEM3-ARCTICDEM	DEM3-AW3D30	DEM3-COPDEM90	ARCTICDEM-AW3D30	ARCTICDEM-COPDEM90	AW3D30-COPDEM90
count	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	0.0	1671.000000	1671.000000	1671.000000	...	1671.000000	767.000000	0.0	1671.000000	767.000000	0.0	1671.000000	0.0	767.000000	0.0
mean	27.788151	44.739207	165.650691	3.799071	15.171155	103.447394	NaN	44.107472	32.801881	147.605664	...	17.200776	105.086456	NaN	42.339176	95.275925	NaN	51.431971	NaN	117.711647	NaN
std	20.735849	35.238571	141.929044	3.113807	11.238222	95.769833	NaN	34.471963	28.886490	143.584187	...	13.166538	95.936180	NaN	33.283024	99.124644	NaN	37.980538	NaN	105.587395	NaN
min	0.000000	0.117188	0.151367	0.002930	0.000000	0.289062	NaN	0.069336	0.005371	0.175293	...	0.002441	0.155273	NaN	0.000000	0.184570	NaN	0.002441	NaN	0.624512	NaN
25%	12.000000	16.361816	71.849121	1.328125	6.000000	33.356445	NaN	14.985596	11.155762	49.170898	...	6.530273	36.557861	NaN	14.249756	21.433594	NaN	23.536377	NaN	41.026611	NaN
50%	24.000000	34.610840	123.718750	3.072266	12.000000	64.257812	NaN	34.726074	23.466309	100.195801	...	13.911133	65.577148	NaN	33.496582	52.738770	NaN	39.517090	NaN	82.970215	NaN
75%	40.000000	69.063965	214.740479	5.534912	22.000000	144.176514	NaN	68.542236	47.615723	193.438232	...	25.122559	144.205811	NaN	65.058350	141.093506	NaN	74.026611	NaN	158.221924	NaN
max	130.000000	137.803711	851.888672	20.434082	60.000000	396.646973	NaN	135.499023	128.154297	829.932129	...	73.005371	401.538574	NaN	130.026367	399.646973	NaN	163.009766	NaN	452.215332	NaN

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents