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

2026-04-11 17:38:08: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2026-04-11 17:38:08: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2026-04-11 17:38:08: oggm.cfg: Multiprocessing: using all available processors (N=4)
2026-04-11 17:38:08: 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]

2026-04-11 17:38:08: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2026-04-11 17:38:08: 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', 'AW3D30', 'TANDEM', 'ASTER', 'MAPZEN', 'COPDEM90', 'COPDEM30', 'ALASKA', 'DEM3']
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	AW3D30	TANDEM	ASTER	MAPZEN	COPDEM90	COPDEM30	ALASKA	DEM3
count	767.000000	0.0	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000
mean	5529.413086	NaN	5153.806152	5295.587672	5319.226212	5277.694824	5277.514160	5316.735352	5328.549372
std	393.222046	NaN	388.357605	351.229245	350.840381	370.293671	370.142792	351.459656	351.724101
min	4871.422852	NaN	4497.636719	4565.000000	4622.000000	4584.852539	4585.657715	4610.906738	4644.000000
25%	5111.490234	NaN	4837.172607	5026.500000	5050.000000	4980.320312	4982.135010	5047.515381	5058.000000
50%	5613.539551	NaN	5044.266113	5250.000000	5258.000000	5231.637207	5231.779785	5255.337402	5259.000000
75%	5869.918945	NaN	5441.289551	5543.000000	5578.500000	5564.726562	5562.813232	5578.190430	5590.000000
max	6126.963379	NaN	5975.219727	6113.000000	6111.000000	6073.276855	6073.898438	6116.865234	6120.000000
range	1255.540527	NaN	1477.583008	1548.000000	1489.000000	1488.424316	1488.240723	1505.958496	1476.000000

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/3.13.12/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()

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

df.corr()

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

df_diff.describe()

	ARCTICDEM-AW3D30	ARCTICDEM-TANDEM	ARCTICDEM-ASTER	ARCTICDEM-MAPZEN	ARCTICDEM-COPDEM90	ARCTICDEM-COPDEM30	ARCTICDEM-ALASKA	ARCTICDEM-DEM3	AW3D30-TANDEM	AW3D30-ASTER	...	MAPZEN-COPDEM90	MAPZEN-COPDEM30	MAPZEN-ALASKA	MAPZEN-DEM3	COPDEM90-COPDEM30	COPDEM90-ALASKA	COPDEM90-DEM3	COPDEM30-ALASKA	COPDEM30-DEM3	ALASKA-DEM3
count	0.0	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	0.0	0.0	...	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000
mean	NaN	251.653717	102.185113	83.946521	110.184074	110.319916	85.975136	76.316795	NaN	NaN	...	41.531287	41.711812	2.490369	-9.323160	0.180525	-39.040920	-50.854447	-39.221443	-51.034972	-11.813529
std	NaN	224.423676	108.948818	113.273900	113.430321	114.278862	113.401772	114.381777	NaN	NaN	...	37.537576	38.776733	4.234587	16.420294	5.457573	37.098961	38.750864	38.322636	40.056016	18.159255
min	NaN	-48.799805	-121.276367	-146.276367	-131.313965	-136.359863	-147.653809	-151.276367	NaN	NaN	...	-33.757324	-38.260254	-14.705078	-60.000000	-17.572266	-130.026367	-163.009766	-135.432617	-173.853027	-73.005371
25%	NaN	111.032227	28.157715	4.010010	36.502441	35.619141	5.880127	-1.839355	NaN	NaN	...	12.118164	13.209717	0.054199	-20.000000	-2.751221	-65.058350	-74.026611	-65.968750	-73.800049	-23.316650
50%	NaN	170.058594	71.489746	56.379883	81.454102	82.882324	58.945312	38.955078	NaN	NaN	...	34.726074	34.193848	2.204590	-10.000000	0.013672	-33.313477	-39.517090	-32.329102	-39.264160	-11.001953
75%	NaN	358.547607	171.116211	143.172607	158.221924	158.155029	143.909668	139.544922	NaN	NaN	...	68.542236	69.063965	5.209473	0.000000	3.210938	-10.340332	-23.536377	-10.385498	-22.958496	-1.023926
max	NaN	1085.924805	428.646973	396.646973	452.215332	459.774902	401.538574	399.646973	NaN	NaN	...	135.499023	137.803711	20.434082	47.000000	18.569824	42.941895	17.004395	46.030762	26.395508	44.825195

8 rows × 36 columns

df_diff.abs().describe()

	ARCTICDEM-AW3D30	ARCTICDEM-TANDEM	ARCTICDEM-ASTER	ARCTICDEM-MAPZEN	ARCTICDEM-COPDEM90	ARCTICDEM-COPDEM30	ARCTICDEM-ALASKA	ARCTICDEM-DEM3	AW3D30-TANDEM	AW3D30-ASTER	...	MAPZEN-COPDEM90	MAPZEN-COPDEM30	MAPZEN-ALASKA	MAPZEN-DEM3	COPDEM90-COPDEM30	COPDEM90-ALASKA	COPDEM90-DEM3	COPDEM30-ALASKA	COPDEM30-DEM3	ALASKA-DEM3
count	0.0	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	767.000000	0.0	0.0	...	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000	1671.000000
mean	NaN	253.502304	112.711556	103.447394	117.711647	118.163139	105.086456	95.275925	NaN	NaN	...	44.107472	44.739207	3.799071	15.171155	4.060298	42.339176	51.431971	42.954536	52.023959	17.200776
std	NaN	222.330780	98.004051	95.769833	105.587395	106.137970	95.936180	99.124644	NaN	NaN	...	34.471963	35.238571	3.113807	11.238222	3.649907	33.283024	37.980538	34.083324	38.762088	13.166538
min	NaN	0.293457	0.155762	0.289062	0.624512	0.650879	0.155273	0.184570	NaN	NaN	...	0.069336	0.117188	0.002930	0.000000	0.000488	0.000000	0.002441	0.007324	0.012207	0.002441
25%	NaN	111.032227	41.791260	33.356445	41.026611	41.187012	36.557861	21.433594	NaN	NaN	...	14.985596	16.361816	1.328125	6.000000	1.359131	14.249756	23.536377	14.802979	23.231689	6.530273
50%	NaN	170.058594	73.289062	64.257812	82.970215	84.257812	65.577148	52.738770	NaN	NaN	...	34.726074	34.610840	3.072266	12.000000	2.911621	33.496582	39.517090	32.889648	39.264160	13.911133
75%	NaN	358.547607	171.116211	144.176514	158.221924	158.155029	144.205811	141.093506	NaN	NaN	...	68.542236	69.063965	5.534912	22.000000	5.748779	65.058350	74.026611	65.968750	73.800049	25.122559
max	NaN	1085.924805	428.646973	396.646973	452.215332	459.774902	401.538574	399.646973	NaN	NaN	...	135.499023	137.803711	20.434082	60.000000	18.569824	130.026367	163.009766	135.432617	173.853027	73.005371

8 rows × 36 columns

What’s next?

• return to the OGGM documentation

• back to the table of contents