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

2026-03-09 23:43:33: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-03-09 23:43:33: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-03-09 23:43:33: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-03-09 23:43:33: 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/2025.4'
# 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]

2026-03-09 23:43:34: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-03-09 23:43:34: 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: ['COPDEM90', 'ARCTICDEM', 'TANDEM', 'COPDEM30', 'DEM3', 'ASTER', 'MAPZEN', 'ALASKA', '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

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

File /usr/local/pyenv/versions/3.13.12/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/3.13.12/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/3.13.12/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/3.13.12/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/3.13.12/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	ARCTICDEM	TANDEM	COPDEM30	DEM3	ASTER	MAPZEN	ALASKA	AW3D30
count	1671.000000	767.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	0.0
mean	5277.694824	5529.413086	5153.806152	5277.514160	5328.549372	5295.587672	5319.226212	5316.735352	NaN
std	370.293671	393.222046	388.357605	370.142792	351.724101	351.229245	350.840381	351.459656	NaN
min	4584.852539	4871.422852	4497.636719	4585.657715	4644.000000	4565.000000	4622.000000	4610.906738	NaN
25%	4980.320312	5111.490234	4837.172607	4982.135010	5058.000000	5026.500000	5050.000000	5047.515381	NaN
50%	5231.637207	5613.539551	5044.266113	5231.779785	5259.000000	5250.000000	5258.000000	5255.337402	NaN
75%	5564.726562	5869.918945	5441.289551	5562.813232	5590.000000	5543.000000	5578.500000	5578.190430	NaN
max	6073.276855	6126.963379	5975.219727	6073.898438	6120.000000	6113.000000	6111.000000	6116.865234	NaN

df.corr()

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

df_diff.describe()

	COPDEM90-ARCTICDEM	COPDEM90-TANDEM	COPDEM90-COPDEM30	COPDEM90-DEM3	COPDEM90-ASTER	COPDEM90-MAPZEN	COPDEM90-ALASKA	COPDEM90-AW3D30	ARCTICDEM-TANDEM	ARCTICDEM-COPDEM30	...	DEM3-ASTER	DEM3-MAPZEN	DEM3-ALASKA	DEM3-AW3D30	ASTER-MAPZEN	ASTER-ALASKA	ASTER-AW3D30	MAPZEN-ALASKA	MAPZEN-AW3D30	ALASKA-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	-110.184074	123.888710	0.180525	-50.854447	-17.892747	-41.531287	-39.040920	NaN	251.653717	110.319916	...	32.961700	9.323160	11.813529	NaN	-23.638540	-21.148171	NaN	2.490369	NaN	NaN
std	113.430321	144.647827	5.457573	38.750864	38.488241	37.537576	37.098961	NaN	224.423676	114.278862	...	25.941984	16.420294	18.159255	NaN	25.367381	25.915689	NaN	4.234587	NaN	NaN
min	-452.215332	-78.665039	-17.572266	-163.009766	-124.032227	-135.499023	-130.026367	NaN	-48.799805	-136.359863	...	-52.000000	-47.000000	-44.825195	NaN	-130.000000	-137.751953	NaN	-14.705078	NaN	NaN
25%	-158.221924	17.190430	-2.751221	-74.026611	-40.641357	-68.542236	-65.058350	NaN	111.032227	35.619141	...	19.000000	0.000000	1.023926	NaN	-40.000000	-36.852539	NaN	0.054199	NaN	NaN
50%	-81.454102	95.284180	0.013672	-39.517090	-12.738770	-34.726074	-33.313477	NaN	170.058594	82.882324	...	29.000000	10.000000	11.001953	NaN	-22.000000	-19.398438	NaN	2.204590	NaN	NaN
75%	-36.502441	179.401855	3.210938	-23.536377	7.423828	-12.118164	-10.340332	NaN	358.547607	158.155029	...	48.000000	20.000000	23.316650	NaN	-6.000000	-1.874023	NaN	5.209473	NaN	NaN
max	131.313965	812.890137	18.569824	17.004395	111.338867	33.757324	42.941895	NaN	1085.924805	459.774902	...	128.000000	60.000000	73.005371	NaN	54.000000	55.279785	NaN	20.434082	NaN	NaN

8 rows × 36 columns

df_diff.abs().describe()

	COPDEM90-ARCTICDEM	COPDEM90-TANDEM	COPDEM90-COPDEM30	COPDEM90-DEM3	COPDEM90-ASTER	COPDEM90-MAPZEN	COPDEM90-ALASKA	COPDEM90-AW3D30	ARCTICDEM-TANDEM	ARCTICDEM-COPDEM30	...	DEM3-ASTER	DEM3-MAPZEN	DEM3-ALASKA	DEM3-AW3D30	ASTER-MAPZEN	ASTER-ALASKA	ASTER-AW3D30	MAPZEN-ALASKA	MAPZEN-AW3D30	ALASKA-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	117.711647	134.962799	4.060298	51.431971	31.869296	44.107472	42.339176	NaN	253.502304	118.163139	...	35.226212	15.171155	17.200776	NaN	27.788151	26.224301	NaN	3.799071	NaN	NaN
std	105.587395	134.367798	3.649907	37.980538	28.025463	34.471963	33.283024	NaN	222.330780	106.137970	...	22.770194	11.238222	13.166538	NaN	20.735849	20.760783	NaN	3.113807	NaN	NaN
min	0.624512	0.050781	0.000488	0.002441	0.000000	0.069336	0.000000	NaN	0.293457	0.650879	...	0.000000	0.000000	0.002441	NaN	0.000000	0.001953	NaN	0.002930	NaN	NaN
25%	41.026611	38.032227	1.359131	23.536377	10.204102	14.985596	14.249756	NaN	111.032227	41.187012	...	20.000000	6.000000	6.530273	NaN	12.000000	11.052246	NaN	1.328125	NaN	NaN
50%	82.970215	95.284180	2.911621	39.517090	22.945801	34.726074	33.496582	NaN	170.058594	84.257812	...	29.000000	12.000000	13.911133	NaN	24.000000	21.200195	NaN	3.072266	NaN	NaN
75%	158.221924	179.401855	5.748779	74.026611	46.310059	68.542236	65.058350	NaN	358.547607	158.155029	...	48.000000	22.000000	25.122559	NaN	40.000000	37.241211	NaN	5.534912	NaN	NaN
max	452.215332	812.890137	18.569824	163.009766	124.032227	135.499023	130.026367	NaN	1085.924805	459.774902	...	128.000000	60.000000	73.005371	NaN	130.000000	137.751953	NaN	20.434082	NaN	NaN

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents