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

2026-08-07 07:53:19: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-08-07 07:53:19: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-08-07 07:53:19: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-08-07 07:53:19: 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]

2026-08-07 07:53:20: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-08-07 07:53:20: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers

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: ['ARCTICDEM', 'COPDEM90', 'ALASKA', 'DEM3', 'MAPZEN', 'TANDEM', 'COPDEM30', '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

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

[-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()

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

df.corr()

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

df_diff.describe()

	ARCTICDEM-COPDEM90	ARCTICDEM-ALASKA	ARCTICDEM-DEM3	ARCTICDEM-MAPZEN	ARCTICDEM-TANDEM	ARCTICDEM-COPDEM30	ARCTICDEM-AW3D30	ARCTICDEM-ASTER	COPDEM90-ALASKA	COPDEM90-DEM3	...	MAPZEN-TANDEM	MAPZEN-COPDEM30	MAPZEN-AW3D30	MAPZEN-ASTER	TANDEM-COPDEM30	TANDEM-AW3D30	TANDEM-ASTER	COPDEM30-AW3D30	COPDEM30-ASTER	AW3D30-ASTER
count	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	0.0	767.000000	1671.000000	1671.000000	...	1671.000000	1671.000000	0.0	1671.000000	1671.000000	0.0	1671.000000	0.0	1671.000000	0.0
mean	110.184074	85.975136	76.316795	83.946521	251.653717	110.319916	NaN	102.185113	-39.040920	-50.854447	...	165.419989	41.711812	NaN	23.638540	-123.708183	NaN	-141.781450	NaN	-18.073272	NaN
std	113.430321	113.401772	114.381777	113.273900	224.423676	114.278862	NaN	108.948818	37.098961	38.750864	...	142.198023	38.776733	NaN	25.367381	144.721756	NaN	149.341491	NaN	39.801988	NaN
min	-131.313965	-147.653809	-151.276367	-146.276367	-48.799805	-136.359863	NaN	-121.276367	-130.026367	-163.009766	...	-16.507324	-38.260254	NaN	-54.000000	-812.099121	NaN	-829.932129	NaN	-128.154297	NaN
25%	36.502441	5.880127	-1.839355	4.010010	111.032227	35.619141	NaN	28.157715	-65.058350	-74.026611	...	71.849121	13.209717	NaN	6.000000	-178.104980	NaN	-193.438232	NaN	-42.359863	NaN
50%	81.454102	58.945312	38.955078	56.379883	170.058594	82.882324	NaN	71.489746	-33.313477	-39.517090	...	123.718750	34.193848	NaN	22.000000	-95.392578	NaN	-100.195801	NaN	-13.353027	NaN
75%	158.221924	143.909668	139.544922	143.172607	358.547607	158.155029	NaN	171.116211	-10.340332	-23.536377	...	214.740479	69.063965	NaN	40.000000	-18.154541	NaN	-44.948730	NaN	8.449463	NaN
max	452.215332	401.538574	399.646973	396.646973	1085.924805	459.774902	NaN	428.646973	42.941895	17.004395	...	851.888672	137.803711	NaN	130.000000	84.786133	NaN	85.662109	NaN	123.904297	NaN

8 rows × 36 columns

df_diff.abs().describe()

	ARCTICDEM-COPDEM90	ARCTICDEM-ALASKA	ARCTICDEM-DEM3	ARCTICDEM-MAPZEN	ARCTICDEM-TANDEM	ARCTICDEM-COPDEM30	ARCTICDEM-AW3D30	ARCTICDEM-ASTER	COPDEM90-ALASKA	COPDEM90-DEM3	...	MAPZEN-TANDEM	MAPZEN-COPDEM30	MAPZEN-AW3D30	MAPZEN-ASTER	TANDEM-COPDEM30	TANDEM-AW3D30	TANDEM-ASTER	COPDEM30-AW3D30	COPDEM30-ASTER	AW3D30-ASTER
count	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	0.0	767.000000	1671.000000	1671.000000	...	1671.000000	1671.000000	0.0	1671.000000	1671.000000	0.0	1671.000000	0.0	1671.000000	0.0
mean	117.711647	105.086456	95.275925	103.447394	253.502304	118.163139	NaN	112.711556	42.339176	51.431971	...	165.650691	44.739207	NaN	27.788151	135.184494	NaN	147.605664	NaN	32.801881	NaN
std	105.587395	95.936180	99.124644	95.769833	222.330780	106.137970	NaN	98.004051	33.283024	37.980538	...	141.929044	35.238571	NaN	20.735849	134.057739	NaN	143.584187	NaN	28.886490	NaN
min	0.624512	0.155273	0.184570	0.289062	0.293457	0.650879	NaN	0.155762	0.000000	0.002441	...	0.151367	0.117188	NaN	0.000000	0.002441	NaN	0.175293	NaN	0.005371	NaN
25%	41.026611	36.557861	21.433594	33.356445	111.032227	41.187012	NaN	41.791260	14.249756	23.536377	...	71.849121	16.361816	NaN	12.000000	38.805664	NaN	49.170898	NaN	11.155762	NaN
50%	82.970215	65.577148	52.738770	64.257812	170.058594	84.257812	NaN	73.289062	33.496582	39.517090	...	123.718750	34.610840	NaN	24.000000	95.392578	NaN	100.195801	NaN	23.466309	NaN
75%	158.221924	144.205811	141.093506	144.176514	358.547607	158.155029	NaN	171.116211	65.058350	74.026611	...	214.740479	69.063965	NaN	40.000000	178.104980	NaN	193.438232	NaN	47.615723	NaN
max	452.215332	401.538574	399.646973	396.646973	1085.924805	459.774902	NaN	428.646973	130.026367	163.009766	...	851.888672	137.803711	NaN	130.000000	812.099121	NaN	829.932129	NaN	128.154297	NaN

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents