def test_min_max_elevation(self):
"""Test the helper method which computes the minimal and maximal
glacier surface elevation in meters asl, from the given DEM and glacier
outline.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# set targets from RGI
min_target = 2430.0
max_target = 3674.0
# get values from method
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# test with one percentage relative tolerance
np.testing.assert_allclose(min_hgt, min_target, rtol=1e-2)
np.testing.assert_allclose(max_hgt, max_target, rtol=1e-2)
def init_columbia(reset=False):
from oggm.core import gis, climate, centerlines
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_columbia')
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_Columbia.tif')
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, reset=reset)
if gdir.has_file('climate_historical'):
return gdir
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
tasks.process_dummy_cru_file(gdir, seed=0)
return gdir
def init_columbia_eb(dir_name, reset=False):
from oggm.core import gis, centerlines
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), dir_name)
mkdir(testdir, reset=reset)
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_Columbia.tif')
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity)
if gdir.has_file('climate_historical'):
return gdir
gis.define_glacier_region(gdir)
gis.simple_glacier_masks(gdir)
centerlines.elevation_band_flowline(gdir)
centerlines.fixed_dx_elevation_band_flowline(gdir)
centerlines.compute_downstream_line(gdir)
tasks.process_dummy_cru_file(gdir, seed=0)
return gdir
def test_run_random_climate(self):
""" Test the run_random_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
Returns
-------
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the random climate model
nyears = 300
seed = 1
temp_bias = 0.5
# read the equilibirum year used for the mass balance calibration
t_star = gdir.read_json('vascaling_mustar')['t_star']
# run model with random climate
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed)
# run model with positive temperature bias
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed, temperature_bias=temp_bias,
output_filesuffix='_bias_p')
# run model with negative temperature bias
_ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star,
seed=seed,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change much under a random climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 0.015
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
def test_run_constant_climate(self):
""" Test the run_constant_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the constant climate model
nyears = 500
temp_bias = 0.5
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
output_filesuffix='')
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
temperature_bias=+temp_bias,
output_filesuffix='_bias_p')
_ = vascaling.run_constant_climate(gdir, nyears=nyears,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change under a constant climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 1e-7
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
# compute volume change from one year to the next
dV_p = (ds_p.volume[1:].values - ds_p.volume[:-1].values).flatten()
dV_n = (ds_n.volume[1:].values - ds_n.volume[:-1].values).flatten()
# compute relative volume change, with respect to the final volume
rate_p = abs(dV_p / float(ds_p.volume.values[-1]))
rate_n = abs(dV_n / float(ds_n.volume.values[-1]))
# the glacier should be in a new equilibirum for last 300 years
assert max(rate_p[-300:]) < 0.001
assert max(rate_n[-300:]) < 0.001
def test_run(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Climate tasks -- only data IO and tstar interpolation!
tasks.process_dummy_cru_file(gdir, seed=0)
tasks.local_t_star(gdir)
tasks.mu_star_calibration(gdir)
# Inversion tasks
tasks.find_inversion_calving(gdir)
# Final preparation for the run
tasks.init_present_time_glacier(gdir)
# check that calving happens in the real context as well
tasks.run_constant_climate(gdir,
bias=0,
nyears=200,
temperature_bias=-0.5)
with xr.open_dataset(gdir.get_filepath('model_diagnostics')) as ds:
assert ds.calving_m3[-1] > 10
def _set_up_VAS_model(self):
"""Avoiding a chunk of code duplicate. Set's up a running volume/area
scaling model, including all needed prepo tasks.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=y0,
area_m2_0=a0,
min_hgt=h0,
max_hgt=h1,
mb_model=mbmod)
return gdir, model
def test_to_tiff(self):
from oggm import tasks
from oggm.workflow import execute_entity_task
from oggm import GlacierDirectory
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = GlacierDirectory(entity, base_dir=self.testdir)
gdir.rgi_id = gdir.rgi_id.replace('50-', '60-')
gis.define_glacier_region(gdir, entity=entity)
gdirs = [gdir]
# Preprocessing tasks
task_list = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.catchment_area,
tasks.catchment_intersections,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
]
for task in task_list:
execute_entity_task(task, gdirs)
# Climate tasks -- only data IO and tstar interpolation!
execute_entity_task(tasks.process_cru_data, gdirs)
tasks.distribute_t_stars(gdirs)
execute_entity_task(tasks.apparent_mb, gdirs)
# Inversion tasks
execute_entity_task(tasks.prepare_for_inversion, gdirs)
execute_entity_task(tasks.volume_inversion,
gdirs,
glen_a=cfg.A * 3,
fs=0)
execute_entity_task(tasks.distribute_thickness_per_altitude, gdirs)
g2task.oggm_to_g2ti(gdir)
ft = os.path.join(cfg.PATHS['working_dir'], 'final',
'RGI60-{}'.format(gdir.rgi_region))
ft = os.path.join(ft, 'thickness_{}.tif'.format(gdir.rgi_id))
ds = xr.open_rasterio(ft)
tpl_f = os.path.join(g2ti.geometry_dir, gdir.rgi_id[:8], gdir.rgi_id,
'mask.tif')
da = xr.open_rasterio(tpl_f)
np.testing.assert_allclose(ds.sum(), (da * ds).sum(), rtol=0.05)
if do_plot:
import matplotlib.pyplot as plt
ds.plot()
plt.figure()
da.plot()
plt.show()
def test_coxe():
testdir = os.path.join(get_test_dir(), 'tmp_coxe')
utils.mkdir(testdir, reset=True)
# Init
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif')
cfg.PARAMS['border'] = 40
cfg.PARAMS['clip_tidewater_border'] = False
cfg.PARAMS['use_multiple_flowlines'] = False
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
cfg.PARAMS['trapezoid_lambdas'] = 1
hef_file = get_demo_file('rgi_RGI50-01.10299.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
p = gdir.read_pickle('linear_mb_params')
mb_mod = massbalance.LinearMassBalance(ela_h=p['ela_h'], grad=p['grad'])
mb_mod.temp_bias = -0.3
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0,
inplace=True,
is_tidewater=True)
# run
model.run_until(200)
assert model.calving_m3_since_y0 > 0
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def test_local_t_star(self):
# set parameters for climate file and mass balance calibration
cfg.PARAMS['baseline_climate'] = 'CUSTOM'
cfg.PARAMS['baseline_y0'] = 1850
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['run_mb_calibration'] = False
# read the Hintereisferner
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir)
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# read calibration results
vas_mustar_refmb = gdir.read_json('vascaling_mustar')
# get reference t* list
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, ref_df=ref_df)
# read calibration results
vas_mustar_refdf = gdir.read_json('vascaling_mustar')
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# read calibration results
vas_mustar = gdir.read_json('vascaling_mustar')
# compare with each other
assert vas_mustar_refdf == vas_mustar
# TODO: this test is failing currently
# np.testing.assert_allclose(vas_mustar_refmb['bias'],
# vas_mustar_refdf['bias'], atol=1)
vas_mustar_refdf.pop('bias')
vas_mustar_refmb.pop('bias')
# end of workaround
assert vas_mustar_refdf == vas_mustar_refmb
def test_set_width(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Test that area and area-altitude elev is fine
with utils.ncDataset(gdir.get_filepath('gridded_data')) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo_smoothed'][:]
rhgt = topo[np.where(mask)][:]
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
bs = 100
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
centerlines.terminus_width_correction(gdir, new_width=714)
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
# Check that the width is ok
np.testing.assert_allclose(fls[-1].widths[-1] * gdir.grid.dx, 714)
# Check for area distrib
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
def test_set_width(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Test that area and area-altitude elev is fine
with utils.ncDataset(gdir.get_filepath('gridded_data')) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo_smoothed'][:]
rhgt = topo[np.where(mask)][:]
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
bs = 100
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
centerlines.terminus_width_correction(gdir, new_width=714)
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
# Check that the width is ok
np.testing.assert_allclose(fls[-1].widths[-1] * gdir.grid.dx, 714)
# Check for area distrib
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
def test_run_until_equilibrium(self):
""""""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# instance a constant mass balance model, centred around t*
mb_model = vascaling.ConstantVASMassBalance(gdir)
# add a positive temperature bias
mb_model.temp_bias = 0.5
# create a VAS model: start with year 0 since we are using a constant
# massbalance model, other values are read from RGI
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=0,
area_m2_0=gdir.rgi_area_m2,
min_hgt=min_hgt,
max_hgt=max_hgt,
mb_model=mb_model)
# run glacier with new mass balance model
model.run_until_equilibrium(rate=1e-4)
# equilibrium should be reached after a couple of 100 years
assert model.year <= 300
# new equilibrium glacier should be smaller (positive temperature bias)
assert model.volume_m3 < model.volume_m3_0
# run glacier for another 100 years and check volume again
v_eq = model.volume_m3
model.run_until(model.year + 100)
assert abs(1 - (model.volume_m3 / v_eq)) < 0.01
def _setup_mb_test(self):
"""Avoiding a chunk of code duplicate. Performs needed prepo tasks and
returns the oggm.GlacierDirectory.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# run OGGM mu* calibration
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# pass the GlacierDirectory
return gdir
def seek_start_area(rgi_id,
name,
show=False,
path='',
ref=np.NaN,
adjust_term_elev=False,
legend=True,
instant_geometry_change=False):
""" Set up an VAS model from scratch and run/test the start area seeking
tasks. The result is a plot showing the modeled glacier area evolution for
different start values. The plots can be displayed and/or stored to file.
Parameters
----------
rgi_id: string
RGI ID denoting the glacier on which to perform the tasks
name: string
Name og glacier, since it is not always given (or correct) in RGI
show: bool, optional, default=False
Flag deciding whether or not to show the created plots.
path: string, optional, default=''
Path under which the modeled area plot should be stored.
ref: float, optional, default=np.NaN
Historic (1851) reference area with which a reference model run is
performed.
"""
# Initialization and load default parameter file
vascaling.initialize()
# compute RGI region and version from RGI IDs
# assuming they all are all the same
rgi_region = (rgi_id.split('-')[-1]).split('.')[0]
rgi_version = (rgi_id.split('-')[0])[-2:-1]
# specify working directory and output directory
working_dir = os.path.abspath('../working_directories/start_area/')
# output_dir = os.path.abspath('./vas_run_output')
output_dir = os.path.abspath('../data/vas_run_output')
# create working directory
utils.mkdir(working_dir, reset=False)
utils.mkdir(output_dir)
# set path to working directory
cfg.PATHS['working_dir'] = working_dir
# set RGI version and region
cfg.PARAMS['rgi_version'] = rgi_version
# define how many grid points to use around the glacier,
# if you expect the glacier to grow large use a larger border
cfg.PARAMS['border'] = 20
# we use HistAlp climate data
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# set the mb hyper parameters accordingly
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['run_mb_calibration'] = False
# the bias is defined to be zero during the calibration process,
# which is why we don't use it here to reproduce the results
cfg.PARAMS['use_bias_for_run'] = True
# get/downlaod the rgi entity including the outline shapefile
rgi_df = utils.get_rgi_glacier_entities([rgi_id])
# set name, if not delivered with RGI
if rgi_df.loc[int(rgi_id[-5:]) - 1, 'Name'] is None:
rgi_df.loc[int(rgi_id[-5:]) - 1, 'Name'] = name
# get and set path to intersect shapefile
intersects_db = utils.get_rgi_intersects_region_file(region=rgi_region)
cfg.set_intersects_db(intersects_db)
# initialize the GlacierDirectory
gdir = workflow.init_glacier_directories(rgi_df)[0]
# # DEM and GIS tasks
# # get the path to the DEM file (will download if necessary)
# dem = utils.get_topo_file(gdir.cenlon, gdir.cenlat)
# print('DEM source: {}, path to DEM file: {}'.format(dem[1], dem[0][0]))
# # set path in config file
# cfg.PATHS['dem_file'] = dem[0][0]
# cfg.PARAMS['border'] = 10
# cfg.PARAMS['use_intersects'] = False
# run GIS tasks
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
# process climate data
climate.process_climate_data(gdir)
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# create mass balance model
mb_mod = vascaling.VAScalingMassBalance(gdir)
# look at specific mass balance over climate data period
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
y0 = 1851
y1 = 2014
# run scalar minimization
minimize_res = vascaling.find_start_area(
gdir,
adjust_term_elev=adjust_term_elev,
instant_geometry_change=instant_geometry_change)
# print(minimize_res)
# stop script if minimization was not successful
if minimize_res.status and False:
sys.exit(minimize_res.status)
# instance glacier with today's values
model_ref = vascaling.VAScalingModel(year_0=gdir.rgi_date,
area_m2_0=gdir.rgi_area_m2,
min_hgt=min_hgt,
max_hgt=max_hgt,
mb_model=mb_mod)
# instance guessed starting areas
num = 9
area_guess = np.linspace(1e6,
np.floor(gdir.rgi_area_m2 * 2),
num,
endpoint=True)
# create empty containers
area_list = list()
volume_list = list()
spec_mb_list = list()
# iterate over all starting areas
for area_ in area_guess:
# instance iteration model
model_guess = vascaling.VAScalingModel(year_0=gdir.rgi_date,
area_m2_0=gdir.rgi_area_m2,
min_hgt=min_hgt,
max_hgt=max_hgt,
mb_model=mb_mod)
# set new starting values
model_guess.create_start_glacier(area_,
y0,
adjust_term_elev=adjust_term_elev)
# run model and save years and area
best_guess_ds = model_guess.run_until_and_store(
year_end=model_ref.year,
instant_geometry_change=instant_geometry_change)
# create series and store in container
area_list.append(best_guess_ds.area_m2.to_dataframe()['area_m2'])
volume_list.append(best_guess_ds.volume_m3.to_dataframe()['volume_m3'])
spec_mb_list.append(best_guess_ds.spec_mb.to_dataframe()['spec_mb'])
# create DataFrame
area_df = pd.DataFrame(
area_list, index=['{:.2f}'.format(a / 1e6) for a in area_guess])
area_df.index.name = 'Start Area [km$^2$]'
volume_df = pd.DataFrame(
volume_list, index=['{:.2f}'.format(a / 1e6) for a in area_guess])
volume_df.index.name = 'Start Area [km$^2$]'
# set up model with resulted starting area
model = vascaling.VAScalingModel(year_0=model_ref.year_0,
area_m2_0=model_ref.area_m2_0,
min_hgt=model_ref.min_hgt_0,
max_hgt=model_ref.max_hgt,
mb_model=model_ref.mb_model)
model.create_start_glacier(minimize_res.x,
y0,
adjust_term_elev=adjust_term_elev)
# run model with best guess initial area
best_guess_ds = model.run_until_and_store(
year_end=model_ref.year,
instant_geometry_change=instant_geometry_change)
# run model with historic reference area
if ref:
model.reset()
model.create_start_glacier(ref * 1e6,
y0,
adjust_term_elev=adjust_term_elev)
ref_ds = model.run_until_and_store(
year_end=model_ref.year,
instant_geometry_change=instant_geometry_change)
# create figure and add axes
fig = plt.figure(figsize=[5, 5])
ax = fig.add_axes([0.125, 0.075, 0.85, 0.9])
# plot model output
ax = (area_df / 1e6).T.plot(legend=False, colormap='Spectral', ax=ax)
# plot best guess
ax.plot(
best_guess_ds.time,
best_guess_ds.area_m2 / 1e6,
color='k',
ls='--',
lw=1.2,
label=
f'{best_guess_ds.area_m2.isel(time=0).values/1e6:.2f} km$^2$ (best result)'
)
# plot reference
if ref:
ax.plot(
ref_ds.time,
ref_ds.area_m2 / 1e6,
color='k',
ls='-.',
lw=1.2,
label=
f'{ref_ds.area_m2.isel(time=0).values/1e6:.2f} km$^2$ (1850 ref.)')
# plot 2003 reference line
ax.axhline(
model_ref.area_m2_0 / 1e6,
c='k',
ls=':',
label=f'{model_ref.area_m2_0/1e6:.2f} km$^2$ ({gdir.rgi_date} obs.)')
# add legend
if legend:
handels, labels = ax.get_legend_handles_labels()
labels[:-3] = [r'{} km$^2$'.format(l) for l in labels[:-3]]
leg = ax.legend(handels, labels, loc='upper right', ncol=2)
# leg.set_title('Start area $A_0$', prop={'size': 12})
# replot best guess estimate and reference (in case it lies below another
# guess)
ax.plot(best_guess_ds.time,
best_guess_ds.area_m2 / 1e6,
color='k',
ls='--',
lw=1.2)
if ref:
ax.plot(ref_ds.time, ref_ds.area_m2 / 1e6, color='k', ls='-.', lw=1.2)
# labels, title
ax.set_xlim([best_guess_ds.time.values[0], best_guess_ds.time.values[-1]])
ax.set_xlabel('')
ax.set_ylabel('Glacier area [km$^2$]')
# save figure to file
if path:
fig.savefig(path)
# show plot
if show:
plt.show()
plt.clf()
# plot and store volume evolution
(volume_df / 1e9).T.plot(legend=False, colormap='viridis')
plt.gcf().savefig(path[:-4] + '_volume.pdf')
def init_hef(reset=False, border=40):
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border))
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['baseline_climate'] = ''
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['border'] = border
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(centerlines.compute_centerlines, [gdir])
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.process_custom_climate_data(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf=mbdf)
climate.local_t_star(gdir, tstar=res['t_star'], bias=res['bias'])
climate.mu_star_calibration(gdir)
inversion.prepare_for_inversion(gdir, add_debug_var=True)
ref_v = 0.573 * 1e9
glen_n = cfg.PARAMS['glen_n']
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (glen_n+2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir, fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (glen_n+2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir, fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
d['factor_fs'] = out[1]
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir
def init_hef(reset=False,
border=40,
invert_with_sliding=True,
invert_with_rectangular=True):
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border))
if not invert_with_sliding:
testdir += '_withoutslide'
if not invert_with_rectangular:
testdir += '_withoutrectangular'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
cfg.PARAMS['use_optimized_inversion_params'] = True
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(centerlines.compute_centerlines, [gdir])
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.process_custom_climate_data(gdir)
climate.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
climate.local_mustar(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
climate.apparent_mb(gdir)
inversion.prepare_for_inversion(
gdir,
add_debug_var=True,
invert_with_rectangular=invert_with_rectangular)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10), ),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir
def test_terminus_temp(self):
"""Testing the subroutine which computes the terminus temperature
from the given climate file and glacier DEM. Pretty straight forward
and somewhat useless, but nice finger exercise.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid
gis.define_glacier_region(gdir, entity=entity)
# process the given climate file
climate.process_custom_climate_data(gdir)
# read the following variable from the center pixel (46.83N 10.75E)
# of the Hintereisferner HistAlp climate file for the
# entire time period from October 1801 until September 2003
# - surface height in m asl.
# - total precipitation amount in kg/m2
# - 2m air temperature in °C
with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r:
ref_h = nc_r.variables['hgt'][1, 1]
ref_t = nc_r.variables['temp'][:, 1, 1]
# define a temperature anomaly
temp_anomaly = 0
# specify temperature gradient
temp_grad = -0.0065
# the terminus temperature must equal the input temperature
# if terminus elevation equals reference elevation
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad, ref_hgt=ref_h,
terminus_hgt=ref_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly)
# the terminus temperature must equal the input terperature
# if the gradient is zero
for term_h in np.array([-100, 0, 100]) + ref_h:
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad=0,
ref_hgt=ref_h,
terminus_hgt=term_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly)
# now test the routine with actual elevation differences
# and a non zero temperature gradient
for h_diff in np.array([-100, 0, 100]):
term_h = ref_h + h_diff
temp_diff = temp_grad * h_diff
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad,
ref_hgt=ref_h,
terminus_hgt=term_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus,
ref_t + temp_anomaly + temp_diff)
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = 10
cfg.PARAMS['run_mb_calibration'] = True
cfg.PARAMS['baseline_climate'] = ''
cfg.PARAMS['use_multiprocessing'] = True
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
from oggm.core import gis
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
from oggm.core import climate
# process the given climate file
climate.process_custom_climate_data(gdir)
from oggm.core import centerlines
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
def test_run_until_and_store(self):
"""Test the volume/area scaling model against the oggm.FluxBasedModel.
Both models run the Hintereisferner over the entire HistAlp climate
period, initialized with the 2003 RGI outline without spin up.
The following two parameters for length, area and volume are tested:
- correlation coefficient
- relative RMSE, i.e. RMSE/mean(OGGM). Whereby the results from the
VAS model are offset with the average differences to the OGGM
results.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mbmod)
# let model run over entire HistAlp climate period
vas_ds = vas_model.run_until_and_store(2003)
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# perform ice thickness inversion
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(2003)
# temporal indices must be equal
assert (vas_ds.time == oggm_ds.time).all()
# specify which parameters to compare and their respective correlation
# coefficients and rmsd values
params = ['length_m', 'area_m2', 'volume_m3']
corr_coeffs = np.array([0.96, 0.90, 0.93])
rmsds = np.array([0.43e3, 0.14e6, 0.03e9])
# compare given parameters
for param, cc, rmsd in zip(params, corr_coeffs, rmsds):
# correlation coefficient
assert corrcoef(oggm_ds[param].values, vas_ds[param].values) >= cc
# root mean squared deviation
rmsd_an = rmsd_bc(oggm_ds[param].values, vas_ds[param].values)
assert rmsd_an <= rmsd
def test_solid_prcp(self):
"""Tests the subroutine which computes solid precipitation amount from
given total precipitation and temperature.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid
gis.define_glacier_region(gdir, entity=entity)
# process the given climate file
climate.process_custom_climate_data(gdir)
# read the following variable from the center pixel (46.83N 10.75E)
# of the Hintereisferner HistAlp climate file for the
# entire time period from October 1801 until September 2003
# - surface height in m asl.
# - total precipitation amount in kg/m2
# - 2m air temperature in °C
with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r:
ref_h = nc_r.variables['hgt'][1, 1]
ref_p = nc_r.variables['prcp'][:, 1, 1]
ref_t = nc_r.variables['temp'][:, 1, 1]
# define needed parameters
prcp_factor = 1
temp_all_solid = 0
temp_grad = -0.0065
# define elevation levels
ref_hgt = ref_h
min_hgt = ref_h - 100
max_hgt = ref_h + 100
# if the terminus temperature is below the threshold for
# solid precipitation all fallen precipitation must be solid
temp_terminus = ref_t * 0 + temp_all_solid
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
min_hgt, max_hgt,
temp_terminus,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, ref_p)
# if the temperature at the maximal elevation is above the threshold
# for solid precipitation all fallen precipitation must be liquid
temp_terminus = ref_t + 100
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
min_hgt, max_hgt,
temp_terminus,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, 0)
# test extreme case if max_hgt equals min_hgt
test_p = ref_p * (ref_t <= temp_all_solid).astype(int)
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
ref_hgt, ref_hgt, ref_t,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, test_p)
def init_hef(reset=False, border=40, logging_level='INFO'):
from oggm.core import gis, inversion, climate, centerlines, flowline
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border))
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize(logging_level=logging_level)
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['baseline_climate'] = ''
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['border'] = border
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(centerlines.compute_centerlines, [gdir])
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.process_custom_climate_data(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf=mbdf)
climate.local_t_star(gdir, tstar=res['t_star'], bias=res['bias'])
climate.mu_star_calibration(gdir)
inversion.prepare_for_inversion(gdir, add_debug_var=True)
ref_v = 0.573 * 1e9
glen_n = cfg.PARAMS['glen_n']
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (glen_n + 2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (glen_n + 2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
d['factor_fs'] = out[1]
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir
def compare(rgi_id, glacier_name):
"""
:param rgi_id:
:param glacier_name:
:return:
"""
# ---------------------
# PREPROCESSING TASKS
# ---------------------
# create test directory
wdir = os.path.join(os.path.abspath('.'), 'comparison_wdir')
if not os.path.exists(wdir):
os.makedirs(wdir)
shutil.rmtree(wdir)
os.makedirs(wdir)
# load default parameter file
cfg.initialize()
# RGI entity
# get/downlaod the rgi entity including the outline shapefile
rgi_df = utils.get_rgi_glacier_entities([rgi_id])
# set name, since not delivered with RGI
if rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] is None:
rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] = glacier_name
# select single entry
rgi_entity = rgi_df.iloc[0]
# GlacierDirectory
# specify the working directory and define the glacier directory
cfg.PATHS['working_dir'] = wdir
gdir = oggm.GlacierDirectory(rgi_entity)
# DEM and GIS tasks
# get the path to the DEM file (will download if necessary)
dem = utils.get_topo_file(gdir.cenlon, gdir.cenlat)
# set path in config file
cfg.PATHS['dem_file'] = dem[0][0]
cfg.PARAMS['border'] = 10
cfg.PARAMS['use_intersects'] = False
# run GIS tasks
gis.define_glacier_region(gdir, entity=rgi_entity)
gis.glacier_masks(gdir)
# Climate data
# using HistAlp
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# climate records before 1850 are hardly reliable, which is not so drastic for
# qualitative experiments (could be driven with random climate anyway)
# cfg.PARAMS['baseline_y0'] = 1850
# change hyper parameters for HistAlp
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# run climate task
climate.process_histalp_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# instance the mass balance models
vas_mb_mod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
# instance VAS model
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mb_mod)
# run model over all HistAlp climate period
vas_df = vas_model.run_and_store(y1, reset=True)
# get relevant parameters
years_vas = vas_df.index.values
length_m_vas = vas_df.length_m.values
area_m2_vas = vas_df.area_m2.values
volume_m3_vas = vas_df.volume_m3.values
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# run inversion tasks
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(y1)
years_oggm = oggm_ds.hydro_year.values
# annual index must be equal
np.testing.assert_array_equal(years_oggm, years_vas)
length_m_oggm = oggm_ds.length_m.values
area_m2_oggm = oggm_ds.area_m2.values
volume_m3_oggm = oggm_ds.volume_m3.values
# define column names for DataFrame
names = ['length_vas', 'length_oggm',
'area_vas', 'area_oggm',
'volume_vas', 'volume_oggm']
# combine glacier geometries into DataFrame
df = pd.DataFrame(np.array([length_m_vas, length_m_oggm,
area_m2_vas, area_m2_oggm,
volume_m3_vas, volume_m3_oggm]).T,
index=years_vas, columns=names)
# save to file
store = True
if store:
# define path and file names
folder = '/Users/oberrauch/work/master/data/'
df.to_csv(folder+'run_comparison.csv')
def plot_both(vas_df, oggm_df, ref=None, correct_bias=False,
title='', ylabel='', file_path='', exp=0):
""" Plot geometric parameters of both models.
If a `file_path` is given, the figure will be saved.
:param vas_df: (pandas.Series) geometric glacier parameter of the VAS model
:param oggm_df: (pandas.Series) geometric glacier parameter of the OGGM
:param ref: (pandas.Series) measured glacier parameter, optional
:param title: (string) figure title, optional
:param ylabel: (string) label for y-axis, optional
:param file_path: (string) where to store the figure, optional
:param exp: (int) exponent for labels in scientific notation, optional
"""
beamer = True
if beamer:
mpl.rc('axes', titlesize=18)
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=14)
mpl.rc('ytick', labelsize=14)
mpl.rc('legend', fontsize=10)
# create figure and first axes
fig = plt.figure(figsize=[6, 4])
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8])
# define colors
c1 = 'C0'
c2 = 'C1'
c3 = 'C3'
# plot vas and OGGM parameters
ax.plot(oggm_df.index, oggm_df.values, c=c2, label='OGGM')
ax.plot(vas_df.index, vas_df.values, c=c1, label='VAS')
if ref:
# plot reference parameter if given
ax.plot(ref.index, ref.values, c=c3, label='measurements')
if correct_bias:
# plot bias corrected vas
df_ = pd.DataFrame([oggm_df, vas_df]).T
bias = vas_df.values - df_.mean().diff().iloc[1]
ax.plot(vas_df.index, bias, c=c1, ls='--',
label='VAS, bias corrected')
# add RMSD as text
ax.text(0.05, 0.05,
'RMSD: {:.1e}'.format(utils.rmsd(oggm_df, bias)),
transform=plt.gca().transAxes)
# add correlation coefficient as text
ax.text(0.05, 0.11, 'Corr. Coef.: {:.2f}'.format(
utils.corrcoef(oggm_df, vas_df)),
transform=plt.gca().transAxes)
# add title, labels, legend
ax.set_title(title)
ax.set_ylabel(ylabel)
ax.legend()
import matplotlib.ticker
class OOMFormatter(matplotlib.ticker.ScalarFormatter):
def __init__(self, order=0, fformat="%1.1f", offset=False, mathText=False):
self.oom = order
self.fformat = fformat
matplotlib.ticker.ScalarFormatter.__init__(self, useOffset=offset, useMathText=mathText)
def _set_orderOfMagnitude(self, nothing):
self.orderOfMagnitude = self.oom
def _set_format(self, vmin, vmax):
self.format = self.fformat
if self._useMathText:
self.format = '$%s$' % matplotlib.ticker._mathdefault(self.format)
# use scientific notation with fixed exponent according
ax.yaxis.set_major_formatter(OOMFormatter(exp, "%1.2f"))
# store to file
if file_path:
plt.savefig(file_path, bbox_inches='tight',
format=file_path.split('.')[-1])
# specify plot directory
folder = '/Users/oberrauch/work/master/plots/'
# plot length
plot_both(df.length_vas, df.length_oggm, correct_bias=True,
title='Glacier length - {}'.format(glacier_name),
ylabel=r'Length [m]',
file_path=os.path.join(folder, '{}_length.pdf'.format(rgi_id)),
exp=3)
# plot area
plot_both(df.area_vas, df.area_oggm, correct_bias=True,
title='Surface area - {}'.format(glacier_name),
ylabel=r'Area [m$^2$]',
file_path=os.path.join(folder, '{}_area.pdf'.format(rgi_id)),
exp=6)
# plot volume
plot_both(df.volume_vas, df.volume_oggm, correct_bias=True,
title='Glacier volume - {}'.format(glacier_name),
ylabel=r'Volume [m$^3$]',
file_path=os.path.join(folder, '{}_volume.pdf'.format(rgi_id)),
exp=9)
def test_yearly_mb_temp_prcp(self):
"""Test the routine which returns the yearly mass balance relevant
climate parameters, i.e. positive melting temperature and solid
precipitation. The testing target is the output of the corresponding
OGGM routine `get_yearly_mb_climate_on_glacier(gdir)`.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# get yearly sums of terminus temperature and solid precipitation
years, temp, prcp = vascaling.get_yearly_mb_temp_prcp(gdir)
# use the OGGM methode to get the mass balance
# relevant climate parameters
years_oggm, temp_oggm, prcp_oggm = \
climate.mb_yearly_climate_on_glacier(gdir)
# the energy input at the glacier terminus must be greater than (or
# equal to) the glacier wide average, since the air temperature drops
# with elevation, i.e. the mean deviation must be positive, using the
# OGGM data as reference
assert md(temp_oggm, temp) >= 0
# consequentially, the average mass input must be less than (or equal
# to) the mass input integrated over the whole glacier surface, i.e.
# the mean deviation must be negative, using the OGGM data as reference
# TODO: does it actually?! And if so, why?! @ASK
assert md(prcp_oggm, prcp) <= 0
# correlation must be higher than set threshold
assert corrcoef(temp, temp_oggm) >= 0.94
assert corrcoef(prcp, prcp_oggm) >= 0.98
# get terminus temperature using the OGGM routine
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo'][:]
heights = np.array([np.min(topo[np.where(mask == 1)])])
years_height, temp_height, _ = \
climate.mb_yearly_climate_on_height(gdir, heights, flatten=False)
temp_height = temp_height[0]
# both time series must be equal
np.testing.assert_array_equal(temp, temp_height)
# get solid precipitation averaged over the glacier
# (not weighted with widths)
fls = gdir.read_pickle('inversion_flowlines')
heights = np.array([])
for fl in fls:
heights = np.append(heights, fl.surface_h)
years_height, _, prcp_height = \
climate.mb_yearly_climate_on_height(gdir, heights, flatten=True)
# correlation must be higher than set threshold
assert corrcoef(prcp, prcp_height) >= 0.99
# TODO: assert absolute values (or differences) of precipitation @ASK
# test exception handling of out of bounds time/year range
with self.assertRaises(climate.MassBalanceCalibrationError):
# start year out of bounds
year_range = [1500, 1980]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
year_range=year_range)
with self.assertRaises(climate.MassBalanceCalibrationError):
# end year oud of bounds
year_range = [1980, 3000]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
year_range=year_range)
with self.assertRaises(ValueError):
# get not N full years
t0 = datetime.datetime(1980, 1, 1)
t1 = datetime.datetime(1980, 3, 1)
time_range = [t0, t1]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
time_range=time_range)
# TODO: assert gradient in climate file?!
pass
