def test_monthly_specific_mb(self):
"""Test the monthly specific mass balance against the
corresponding yearly mass balance.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance mb models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# get all month of that year in the
# floating (hydrological) year convention
year = 1803
months = np.linspace(year, year + 1, num=12, endpoint=False)
# compute monthly specific mass balance for
# all month of given year and store in array
spec_mb_month = np.empty(months.size)
for i, month in enumerate(months):
spec_mb_month[i] = vas_mbmod.get_monthly_specific_mb(min_hgt,
max_hgt,
month)
# compute yearly specific mass balance
spec_mb_year = vas_mbmod.get_specific_mb(min_hgt, max_hgt, year)
# compare
np.testing.assert_allclose(spec_mb_month.sum(), spec_mb_year,
rtol=1e-3)
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_annual_mb(self):
"""Test the routine computing the annual mass balance."""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# define temporal range
year = 1975
years = np.array([year, year])
# get mass balance relevant climate data
_, temp, prcp = vascaling.get_yearly_mb_temp_prcp(gdir,
year_range=years)
temp = temp[0]
prcp = prcp[0]
# read mu* and bias from vascaling_mustar
vascaling_mustar = gdir.read_json('vascaling_mustar')
mu_star = vascaling_mustar['mu_star']
bias = vascaling_mustar['bias']
# specify scaling factor for SI units [kg s-1]
fac_SI = cfg.SEC_IN_YEAR * cfg.PARAMS['ice_density']
# compute mass balance 'by hand'
mb_ref = (prcp - mu_star * temp - bias) / fac_SI
# compute mb using the VAS mass balance model
mb_mod = vascaling.VAScalingMassBalance(gdir).get_annual_mb(min_hgt,
max_hgt,
year)
# compare mass balances with bias
np.testing.assert_allclose(mb_ref, mb_mod, rtol=1e-3)
# compute mass balance 'by hand'
mb_ref = (prcp - mu_star * temp) / fac_SI
# compute mb 'by model'
mb_mod = vascaling.VAScalingMassBalance(gdir, bias=0). \
get_annual_mb(min_hgt, max_hgt, year)
# compare mass balances without bias
np.testing.assert_allclose(mb_ref, mb_mod, rtol=1e-3)
def test_specific_mb(self):
"""Compare the specific mass balance to the one computed
using the OGGM function of the PastMassBalance model.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance mb models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
past_mbmod = massbalance.PastMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# define temporal range
ys = 1802
ye = 2003
years = np.arange(ys, ye + 1)
# get flow lines
fls = gdir.read_pickle('inversion_flowlines')
# create empty container
past_mb = np.empty(years.size)
vas_mb = np.empty(years.size)
# get specific mass balance for all years
for i, year in enumerate(years):
past_mb[i] = past_mbmod.get_specific_mb(fls=fls, year=year)
vas_mb[i] = vas_mbmod.get_specific_mb(min_hgt, max_hgt, year)
# compute and check correlation
assert corrcoef(past_mb, vas_mb) >= 0.94
# relative error of average spec mb
# TODO: does this even make any sense?!
assert np.abs(rel_err(past_mb.mean(), vas_mb.mean())) <= 0.36
# check correlation of positive and negative mb years
assert corrcoef(np.sign(past_mb), np.sign(vas_mb)) >= 0.72
# compare to reference mb measurements
mbs = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
assert corrcoef(vas_mb[np.in1d(years, mbs.index)], mbs) >= 0.79
def test_annual_climate(self):
"""Test my routine against the corresponding OGGM routine from
the `PastMassBalance()` model.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance the mass balance models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
past_mbmod = massbalance.PastMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
heights = np.array([min_hgt, (min_hgt + max_hgt) / 2, max_hgt])
# specify an (arbitray) year
year = 1975
# get mass balance relevant climate information
temp_for_melt_vas, prcp_solid_vas = \
vas_mbmod.get_annual_climate(min_hgt, max_hgt, year)
_, temp_for_melt_oggm, _, prcp_solid_oggm = \
past_mbmod.get_annual_climate(heights, year)
# prepare my (monthly) values for comparison
temp_for_melt_vas = temp_for_melt_vas.sum()
prcp_solid_vas = prcp_solid_vas.sum()
# computed positive terminus melting temperature must be equal for both
# used methods, i.e. temp_VAS == temp_OGGM
np.testing.assert_allclose(temp_for_melt_vas,
temp_for_melt_oggm[0],
rtol=1e-3)
# glacier averaged solid precipitation amount must be greater than (or
# equal to) solid precipitation amount at glacier terminus elevation
assert md(prcp_solid_oggm[0], prcp_solid_vas) >= 0
# glacier averaged solid precipitation amount must be comparable to the
# solid precipitation amount at average glacier surface elevation
assert rel_err(prcp_solid_oggm[1], prcp_solid_vas) <= 0.15
# glacier averaged solid precipitation amount must be less than (or
# equal to) solid precipitation amount at maximum glacier elevation
assert md(prcp_solid_oggm[2], prcp_solid_vas) <= 0
def test_monthly_climate(self):
"""Test the routine getting the monthly climate against
the routine getting annual climate.
"""
# run all needed prepro tasks
gdir = self._setup_mb_test()
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# get relevant glacier surface elevation
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
# get all month of the year in the
# floating (hydrological) year convention
year = 1975
months = np.linspace(year, year + 1, num=12, endpoint=False)
# create containers
temp_month = np.empty(12)
prcp_month = np.empty(12)
# get mb relevant climate data for every month
for i, month in enumerate(months):
_temp, _prcp = mbmod.get_monthly_climate(min_hgt, max_hgt, month)
temp_month[i] = _temp
prcp_month[i] = _prcp
# melting temperature and precipitation amount cannot be negative
assert temp_month.all() >= 0.
assert prcp_month.all() >= 0.
# get climate data for the whole year
temp_year, prcp_year = mbmod.get_annual_climate(min_hgt, max_hgt, year)
# compare
np.testing.assert_array_almost_equal(temp_month, temp_year, decimal=2)
np.testing.assert_array_almost_equal(prcp_month, prcp_year, decimal=2)
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
# 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)
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area (from RGI entry)
a0 = gdir.rgi_area_m2
# get reference year (start of climate records)
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation (based on RGI outline)
h0, h1 = vascaling.get_min_max_elevation(gdir)
# initialize iteration counter variable
k = 1
# start iteration process of "finding the start area"
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)
