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

2026-06-28 23:03:27: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-06-28 23:03:27: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-06-28 23:03:27: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-06-28 23:03:27: 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]

2026-06-28 23:03:27: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-06-28 23:03:27: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2026-06-28 23:03:27: 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: ['COPDEM90', 'TANDEM', 'DEM3', 'COPDEM30', 'ALASKA', 'ARCTICDEM', 'MAPZEN', 'AW3D30', 'ASTER']
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

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

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

[-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()

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

df.corr()

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

df_diff.describe()

	COPDEM90-TANDEM	COPDEM90-DEM3	COPDEM90-COPDEM30	COPDEM90-ALASKA	COPDEM90-ARCTICDEM	COPDEM90-MAPZEN	COPDEM90-AW3D30	COPDEM90-ASTER	TANDEM-DEM3	TANDEM-COPDEM30	...	ALASKA-ARCTICDEM	ALASKA-MAPZEN	ALASKA-AW3D30	ALASKA-ASTER	ARCTICDEM-MAPZEN	ARCTICDEM-AW3D30	ARCTICDEM-ASTER	MAPZEN-AW3D30	MAPZEN-ASTER	AW3D30-ASTER
count	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	1671.000000	0.0	1671.000000	1671.000000	1671.000000	...	767.000000	1671.000000	0.0	1671.000000	767.000000	0.0	767.000000	0.0	1671.000000	0.0
mean	123.888710	-50.854447	0.180525	-39.040920	-110.184074	-41.531287	NaN	-17.892747	-174.743149	-123.708183	...	-85.975136	-2.490369	NaN	21.148171	83.946521	NaN	102.185113	NaN	23.638540	NaN
std	144.647827	38.750864	5.457573	37.098961	113.430321	37.537576	NaN	38.488241	138.969170	144.721756	...	113.401772	4.234587	NaN	25.915689	113.273900	NaN	108.948818	NaN	25.367381	NaN
min	-78.665039	-163.009766	-17.572266	-130.026367	-452.215332	-135.499023	NaN	-124.032227	-872.888672	-812.099121	...	-401.538574	-20.434082	NaN	-55.279785	-146.276367	NaN	-121.276367	NaN	-54.000000	NaN
25%	17.190430	-74.026611	-2.751221	-65.058350	-158.221924	-68.542236	NaN	-40.641357	-217.682861	-178.104980	...	-143.909668	-5.209473	NaN	1.874023	4.010010	NaN	28.157715	NaN	6.000000	NaN
50%	95.284180	-39.517090	0.013672	-33.313477	-81.454102	-34.726074	NaN	-12.738770	-133.516602	-95.392578	...	-58.945312	-2.204590	NaN	19.398438	56.379883	NaN	71.489746	NaN	22.000000	NaN
75%	179.401855	-23.536377	3.210938	-10.340332	-36.502441	-12.118164	NaN	7.423828	-84.589355	-18.154541	...	-5.880127	-0.054199	NaN	36.852539	143.172607	NaN	171.116211	NaN	40.000000	NaN
max	812.890137	17.004395	18.569824	42.941895	131.313965	33.757324	NaN	111.338867	14.181152	84.786133	...	147.653809	14.705078	NaN	137.751953	396.646973	NaN	428.646973	NaN	130.000000	NaN

8 rows × 36 columns

df_diff.abs().describe()

	COPDEM90-TANDEM	COPDEM90-DEM3	COPDEM90-COPDEM30	COPDEM90-ALASKA	COPDEM90-ARCTICDEM	COPDEM90-MAPZEN	COPDEM90-AW3D30	COPDEM90-ASTER	TANDEM-DEM3	TANDEM-COPDEM30	...	ALASKA-ARCTICDEM	ALASKA-MAPZEN	ALASKA-AW3D30	ALASKA-ASTER	ARCTICDEM-MAPZEN	ARCTICDEM-AW3D30	ARCTICDEM-ASTER	MAPZEN-AW3D30	MAPZEN-ASTER	AW3D30-ASTER
count	1671.000000	1671.000000	1671.000000	1671.000000	767.000000	1671.000000	0.0	1671.000000	1671.000000	1671.000000	...	767.000000	1671.000000	0.0	1671.000000	767.000000	0.0	767.000000	0.0	1671.000000	0.0
mean	134.962799	51.431971	4.060298	42.339176	117.711647	44.107472	NaN	31.869296	174.815853	135.184494	...	105.086456	3.799071	NaN	26.224301	103.447394	NaN	112.711556	NaN	27.788151	NaN
std	134.367798	37.980538	3.649907	33.283024	105.587395	34.471963	NaN	28.025463	138.877648	134.057739	...	95.936180	3.113807	NaN	20.760783	95.769833	NaN	98.004051	NaN	20.735849	NaN
min	0.050781	0.002441	0.000488	0.000000	0.624512	0.069336	NaN	0.000000	0.848633	0.002441	...	0.155273	0.002930	NaN	0.001953	0.289062	NaN	0.155762	NaN	0.000000	NaN
25%	38.032227	23.536377	1.359131	14.249756	41.026611	14.985596	NaN	10.204102	84.589355	38.805664	...	36.557861	1.328125	NaN	11.052246	33.356445	NaN	41.791260	NaN	12.000000	NaN
50%	95.284180	39.517090	2.911621	33.496582	82.970215	34.726074	NaN	22.945801	133.516602	95.392578	...	65.577148	3.072266	NaN	21.200195	64.257812	NaN	73.289062	NaN	24.000000	NaN
75%	179.401855	74.026611	5.748779	65.058350	158.221924	68.542236	NaN	46.310059	217.682861	178.104980	...	144.205811	5.534912	NaN	37.241211	144.176514	NaN	171.116211	NaN	40.000000	NaN
max	812.890137	163.009766	18.569824	130.026367	452.215332	135.499023	NaN	124.032227	872.888672	812.099121	...	401.538574	20.434082	NaN	137.751953	396.646973	NaN	428.646973	NaN	130.000000	NaN

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents