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

2026-11-06 12:38:52: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-11-06 12:38:52: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-11-06 12:38:52: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-11-06 12:38:52: 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]

2026-11-06 12:38:52: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-11-06 12:38:52: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-11-06 12:38:52: 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', 'ARCTICDEM', 'COPDEM90', 'DEM3', 'TANDEM', 'ALASKA', 'ASTER', 'COPDEM30', 'AW3D30']
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	ARCTICDEM	COPDEM90	DEM3	TANDEM	ALASKA	ASTER	COPDEM30	AW3D30
count	1671.000000	767.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0
mean	5319.226212	5529.413086	5277.694824	5328.549372	5153.806152	5316.735352	5295.587672	5277.514160	NaN
std	350.840381	393.222046	370.293671	351.724101	388.357605	351.459656	351.229245	370.142792	NaN
min	4622.000000	4871.422852	4584.852539	4644.000000	4497.636719	4610.906738	4565.000000	4585.657715	NaN
25%	5050.000000	5111.490234	4980.320312	5058.000000	4837.172607	5047.515381	5026.500000	4982.135010	NaN
50%	5258.000000	5613.539551	5231.637207	5259.000000	5044.266113	5255.337402	5250.000000	5231.779785	NaN
75%	5578.500000	5869.918945	5564.726562	5590.000000	5441.289551	5578.190430	5543.000000	5562.813232	NaN
max	6111.000000	6126.963379	6073.276855	6120.000000	5975.219727	6116.865234	6113.000000	6073.898438	NaN
range	1489.000000	1255.540527	1488.424316	1476.000000	1477.583008	1505.958496	1548.000000	1488.240723	NaN

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

[-800,
 -500,
 -300,
 -150,
 -100,
 -50,
 -20,
 -10,
 0,
 10,
 20,
 50,
 100,
 150,
 300,
 500,
 800]

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

df.corr()

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

df_diff.describe()

	MAPZEN-ARCTICDEM	MAPZEN-COPDEM90	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-ALASKA	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-AW3D30	ARCTICDEM-COPDEM90	ARCTICDEM-DEM3	...	TANDEM-ALASKA	TANDEM-ASTER	TANDEM-COPDEM30	TANDEM-AW3D30	ALASKA-ASTER	ALASKA-COPDEM30	ALASKA-AW3D30	ASTER-COPDEM30	ASTER-AW3D30	COPDEM30-AW3D30
count	767.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	767.000000	...	1671.000000	1671.000000	1671.000000	0.0	1671.000000	1671.000000	0.0	1671.000000	0.0	0.0
mean	-83.946521	41.531287	-9.323160	165.419989	2.490369	23.638540	41.711812	NaN	110.184074	76.316795	...	-162.929626	-141.781450	-123.708183	NaN	21.148171	39.221443	NaN	18.073272	NaN	NaN
std	113.273900	37.537576	16.420294	142.198023	4.234587	25.367381	38.776733	NaN	113.430321	114.381777	...	142.417313	149.341491	144.721756	NaN	25.915689	38.322636	NaN	39.801988	NaN	NaN
min	-396.646973	-33.757324	-60.000000	-16.507324	-14.705078	-54.000000	-38.260254	NaN	-131.313965	-151.276367	...	-848.312500	-829.932129	-812.099121	NaN	-55.279785	-46.030762	NaN	-123.904297	NaN	NaN
25%	-143.172607	12.118164	-20.000000	71.849121	0.054199	6.000000	13.209717	NaN	36.502441	-1.839355	...	-212.847412	-193.438232	-178.104980	NaN	1.874023	10.385498	NaN	-8.449463	NaN	NaN
50%	-56.379883	34.726074	-10.000000	123.718750	2.204590	22.000000	34.193848	NaN	81.454102	38.955078	...	-120.674805	-100.195801	-95.392578	NaN	19.398438	32.329102	NaN	13.353027	NaN	NaN
75%	-4.010010	68.542236	0.000000	214.740479	5.209473	40.000000	69.063965	NaN	158.221924	139.544922	...	-67.788818	-44.948730	-18.154541	NaN	36.852539	65.968750	NaN	42.359863	NaN	NaN
max	146.276367	135.499023	47.000000	851.888672	20.434082	130.000000	137.803711	NaN	452.215332	399.646973	...	15.732910	85.662109	84.786133	NaN	137.751953	135.432617	NaN	128.154297	NaN	NaN

8 rows × 36 columns

df_diff.abs().describe()

	MAPZEN-ARCTICDEM	MAPZEN-COPDEM90	MAPZEN-DEM3	MAPZEN-TANDEM	MAPZEN-ALASKA	MAPZEN-ASTER	MAPZEN-COPDEM30	MAPZEN-AW3D30	ARCTICDEM-COPDEM90	ARCTICDEM-DEM3	...	TANDEM-ALASKA	TANDEM-ASTER	TANDEM-COPDEM30	TANDEM-AW3D30	ALASKA-ASTER	ALASKA-COPDEM30	ALASKA-AW3D30	ASTER-COPDEM30	ASTER-AW3D30	COPDEM30-AW3D30
count	767.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0	767.000000	767.000000	...	1671.000000	1671.000000	1671.000000	0.0	1671.000000	1671.000000	0.0	1671.000000	0.0	0.0
mean	103.447394	44.107472	15.171155	165.650691	3.799071	27.788151	44.739207	NaN	117.711647	95.275925	...	163.309280	147.605664	135.184494	NaN	26.224301	42.954536	NaN	32.801881	NaN	NaN
std	95.769833	34.471963	11.238222	141.929044	3.113807	20.735849	35.238571	NaN	105.587395	99.124644	...	141.981552	143.584187	134.057739	NaN	20.760783	34.083324	NaN	28.886490	NaN	NaN
min	0.289062	0.069336	0.000000	0.151367	0.002930	0.000000	0.117188	NaN	0.624512	0.184570	...	0.070801	0.175293	0.002441	NaN	0.001953	0.007324	NaN	0.005371	NaN	NaN
25%	33.356445	14.985596	6.000000	71.849121	1.328125	12.000000	16.361816	NaN	41.026611	21.433594	...	67.788818	49.170898	38.805664	NaN	11.052246	14.802979	NaN	11.155762	NaN	NaN
50%	64.257812	34.726074	12.000000	123.718750	3.072266	24.000000	34.610840	NaN	82.970215	52.738770	...	120.674805	100.195801	95.392578	NaN	21.200195	32.889648	NaN	23.466309	NaN	NaN
75%	144.176514	68.542236	22.000000	214.740479	5.534912	40.000000	69.063965	NaN	158.221924	141.093506	...	212.847412	193.438232	178.104980	NaN	37.241211	65.968750	NaN	47.615723	NaN	NaN
max	396.646973	135.499023	60.000000	851.888672	20.434082	130.000000	137.803711	NaN	452.215332	399.646973	...	848.312500	829.932129	812.099121	NaN	137.751953	135.432617	NaN	128.154297	NaN	NaN

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents