Compare different DEMs for individual glaciers

For most glaciers in the world there are several digital elevation models (DEM) which cover the respective glacier. In OGGM we have currently implemented 10 different open access DEMs to choose from. Some are regional and only available in certain areas (e.g. Greenland or Antarctica) and some cover almost the entire globe. For more information, visit the rgitools documentation about DEMs.

This notebook allows to see which of the DEMs are available for a selected glacier and how they compare to each other. That way it is easy to spot systematic differences and also invalid points in the DEMs.

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 = 'RGI60-11.00897'

# 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)
import pandas as pd
import numpy as np
from oggm import cfg, utils, workflow, tasks, graphics, GlacierDirectory
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-DEMS', reset=True)
cfg.PARAMS['use_intersects'] = False
2022-10-07 12:45:48: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2022-10-07 12:45:48: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2022-10-07 12:45:48: oggm.cfg: Multiprocessing: using all available processors (N=2)
2022-10-07 12:45:48: 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 https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/

# URL of the preprocessed GDirs
gdir_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/'
# We use OGGM to download the data
gdir = init_glacier_directories([rgi_id], from_prepro_level=1, prepro_border=10, 
                                 prepro_rgi_version='62', prepro_base_url=gdir_url)[0]
2022-10-07 12:45:48: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2022-10-07 12:45:48: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers
2022-10-07 12:45:48: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar...
2022-10-07 12:45:55: oggm.utils: No known hash for cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar

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: RGI60-11.00897
Available DEM sources: ['MAPZEN', 'TANDEM', 'ASTER', 'NASADEM', 'SRTM', 'DEM3', 'AW3D30']
Plotting directory: /__w/tutorials/tutorials/notebooks/outputs/RGI60-11.00897
# 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]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_color.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_19_0.png

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_mode='none',
                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]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_shade.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_21_0.png

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]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_slope.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_23_0.png

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]
df.describe()
MAPZEN TANDEM ASTER NASADEM SRTM DEM3 AW3D30
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3023.202610 3066.819092 3031.316346 3028.332505 3032.272219 3061.494096 3023.871970
std 254.144292 258.874115 250.883864 247.615467 246.447678 241.860064 253.597569
min 2413.000000 2469.287598 2428.000000 2430.000000 2450.000000 2501.000000 2417.000000
25% 2850.000000 2889.410828 2865.000000 2855.000000 2857.000000 2886.000000 2851.000000
50% 3051.000000 3097.242310 3058.000000 3054.000000 3060.000000 3076.000000 3052.500000
75% 3201.750000 3249.972717 3203.000000 3202.000000 3203.000000 3233.750000 3201.750000
max 3723.000000 3738.977051 3693.000000 3691.000000 3684.000000 3706.000000 3720.000000

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
[-80, -50, -30, -15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15, 30, 50, 80]
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')
../_images/dem_comparison_30_0.png

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(xdata, ydata, **kwargs):
    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')
../_images/dem_comparison_32_0.png

Table statistics

df.describe()
MAPZEN TANDEM ASTER NASADEM SRTM DEM3 AW3D30
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3023.202610 3066.819092 3031.316346 3028.332505 3032.272219 3061.494096 3023.871970
std 254.144292 258.874115 250.883864 247.615467 246.447678 241.860064 253.597569
min 2413.000000 2469.287598 2428.000000 2430.000000 2450.000000 2501.000000 2417.000000
25% 2850.000000 2889.410828 2865.000000 2855.000000 2857.000000 2886.000000 2851.000000
50% 3051.000000 3097.242310 3058.000000 3054.000000 3060.000000 3076.000000 3052.500000
75% 3201.750000 3249.972717 3203.000000 3202.000000 3203.000000 3233.750000 3201.750000
max 3723.000000 3738.977051 3693.000000 3691.000000 3684.000000 3706.000000 3720.000000
df.corr()
MAPZEN TANDEM ASTER NASADEM SRTM DEM3 AW3D30
MAPZEN 1.000000 0.999687 0.999455 0.999643 0.998217 0.996358 0.999819
TANDEM 0.999687 1.000000 0.999344 0.999342 0.998315 0.996175 0.999633
ASTER 0.999455 0.999344 1.000000 0.999131 0.998518 0.995739 0.999315
NASADEM 0.999643 0.999342 0.999131 1.000000 0.998544 0.997409 0.999553
SRTM 0.998217 0.998315 0.998518 0.998544 1.000000 0.997038 0.998216
DEM3 0.996358 0.996175 0.995739 0.997409 0.997038 1.000000 0.996423
AW3D30 0.999819 0.999633 0.999315 0.999553 0.998216 0.996423 1.000000
df_diff.describe()
MAPZEN-TANDEM MAPZEN-ASTER MAPZEN-NASADEM MAPZEN-SRTM MAPZEN-DEM3 MAPZEN-AW3D30 TANDEM-ASTER TANDEM-NASADEM TANDEM-SRTM TANDEM-DEM3 ... ASTER-NASADEM ASTER-SRTM ASTER-DEM3 ASTER-AW3D30 NASADEM-SRTM NASADEM-DEM3 NASADEM-AW3D30 SRTM-DEM3 SRTM-AW3D30 DEM3-AW3D30
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 ... 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean -43.617270 -8.113735 -5.129894 -9.069608 -38.291485 -0.669360 35.503534 38.487375 34.547661 5.325784 ... 2.983841 -0.955873 -30.177750 7.444375 -3.939714 -33.161591 4.460534 -29.221877 8.400249 37.622126
std 7.972539 8.953622 9.360127 16.809936 24.467068 4.859039 12.211383 14.527865 19.220421 27.721822 ... 10.892745 14.247553 24.464741 9.720664 13.380009 18.533220 9.588316 19.342194 16.555147 24.010492
min -85.858154 -46.000000 -40.000000 -84.000000 -123.000000 -35.000000 -0.389160 -16.636475 -21.226807 -79.397461 ... -40.000000 -69.000000 -112.000000 -33.000000 -69.000000 -92.000000 -48.000000 -84.000000 -75.000000 -109.000000
25% -48.300476 -14.000000 -10.000000 -20.750000 -50.000000 -3.000000 28.404724 33.745605 22.963257 -7.438965 ... -3.000000 -10.000000 -42.000000 1.000000 -10.000000 -43.000000 -2.000000 -40.000000 -1.000000 25.000000
50% -45.182861 -8.000000 -3.000000 -10.000000 -38.000000 -1.000000 37.429810 42.692627 35.271484 6.978149 ... 4.000000 0.000000 -29.000000 7.000000 -4.000000 -34.000000 3.000000 -32.000000 9.000000 36.000000
75% -40.532715 -2.000000 1.000000 -1.000000 -25.000000 1.000000 43.959106 48.055481 45.801941 21.998291 ... 9.000000 7.000000 -20.000000 13.000000 1.000000 -25.000000 8.000000 -22.000000 20.000000 49.000000
max -1.023926 30.000000 51.000000 69.000000 100.000000 28.000000 79.663330 80.386475 106.757324 131.650635 ... 55.000000 59.000000 94.000000 44.000000 59.000000 91.000000 38.000000 47.000000 79.000000 121.000000

8 rows × 21 columns

df_diff.abs().describe()
MAPZEN-TANDEM MAPZEN-ASTER MAPZEN-NASADEM MAPZEN-SRTM MAPZEN-DEM3 MAPZEN-AW3D30 TANDEM-ASTER TANDEM-NASADEM TANDEM-SRTM TANDEM-DEM3 ... ASTER-NASADEM ASTER-SRTM ASTER-DEM3 ASTER-AW3D30 NASADEM-SRTM NASADEM-DEM3 NASADEM-AW3D30 SRTM-DEM3 SRTM-AW3D30 DEM3-AW3D30
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 ... 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 43.617270 9.781231 7.451212 15.292107 40.133623 3.354257 35.503776 38.762453 35.206708 21.395372 ... 8.758856 10.924798 33.658173 9.678682 10.136109 34.688626 7.379117 32.021753 14.691734 39.443754
std 7.972539 7.093540 7.641868 11.442731 21.310154 3.578254 12.210679 13.776885 17.984406 18.411083 ... 7.128573 9.193391 19.397900 7.498083 9.580093 15.486716 7.574302 14.233504 11.346579 20.882402
min 1.023926 0.000000 0.000000 0.000000 0.000000 0.000000 0.389160 0.005859 0.166016 0.001709 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 40.532715 4.000000 2.000000 6.000000 26.000000 1.000000 28.404724 33.745605 22.963257 7.188965 ... 3.000000 4.000000 21.000000 4.000000 3.000000 25.000000 2.000000 24.000000 5.000000 26.000000
50% 45.182861 9.000000 4.000000 13.000000 38.000000 2.000000 37.429810 42.692627 35.271484 16.839111 ... 7.000000 9.000000 30.000000 8.000000 7.000000 34.000000 5.000000 32.000000 13.000000 36.000000
75% 48.300476 14.000000 10.000000 23.000000 50.000000 4.000000 43.959106 48.055481 45.801941 30.157959 ... 12.000000 16.000000 43.000000 14.000000 15.000000 43.000000 10.000000 41.000000 22.000000 49.000000
max 85.858154 46.000000 51.000000 84.000000 123.000000 35.000000 79.663330 80.386475 106.757324 131.650635 ... 55.000000 69.000000 112.000000 44.000000 69.000000 92.000000 48.000000 84.000000 79.000000 121.000000

8 rows × 21 columns

What’s next?