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 _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_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_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 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 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)
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_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
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)
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)
centerlines.catchment_width_correction(gdir)
# --------------------
# MASS BALANCE TASKS
# --------------------
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 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
