def track_mb_1980_avg(self, gdir):
self.cfg_init()
mb = massbalance.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
mb_ts = mb.get_specific_mb(heights=h, widths=w,
year=np.arange(31)+1970)
return np.mean(mb_ts)
def iterative_initial_glacier_search(gdir, y0=None, init_bias=0., rtol=0.005,
write_steps=True):
"""Iterative search for the glacier in year y0.
this is outdated and doesn't really work.
"""
fs = cfg.PARAMS['fs']
glen_a = cfg.PARAMS['glen_a']
if y0 is None:
y0 = cfg.PARAMS['y0']
y1 = gdir.rgi_date.year
mb = mbmods.PastMassBalance(gdir)
fls = gdir.read_pickle('model_flowlines')
model = FluxBasedModel(fls, mb_model=mb, y0=0., fs=fs, glen_a=glen_a)
assert np.isclose(model.area_km2, gdir.rgi_area_km2, rtol=0.05)
mb = mbmods.BackwardsMassBalanceModel(gdir)
ref_area = gdir.rgi_area_m2
ite, bias, past_model = _find_inital_glacier(model, mb, y0, y1,
rtol=rtol,
init_bias=init_bias,
ref_area=ref_area)
path = gdir.get_filepath('model_run', delete=True)
if write_steps:
past_model.run_until_and_store(y1, path=path)
else:
past_model.to_netcdf(path)
def test_mb_modules_monthly(gdir):
# check if massbalance.PastMassBalance equal to mb_modules with cte
# gradient and mb_monthly as options for lapse rate mb_type
mu_star_opt_cte_var = 195.5484547754791
# if I use ERA5dr in PastMassBalance, it applies automatically the
# gradient that changes with time and location
mu_opts = [mu_star_opt_cte['mb_monthly'], mu_star_opt_cte_var]
grads = ['cte', 'var']
for k, clim in zip([0, 1], ['ERA5', 'ERA5dr']):
mu_opt = mu_opts[k]
grad_type = grads[k]
cfg.PARAMS['baseline_climate'] = clim
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset=clim)
mb_mod = mb_modules(gdir,
mu_opt,
mb_type='mb_monthly',
prcp_fac=2.5,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
hgts, widths = gdir.get_inversion_flowline_hw()
mbdf = gdir.get_ref_mb_data()
tot_mb = mb_mod.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
cfg.PARAMS['temp_default_gradient'] = -0.0065
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_all_solid'] = 0
cfg.PARAMS['temp_all_liq'] = 2
cfg.PARAMS['temp_melt'] = 0
# check if the default OGGM monthly mass balance with cte gradient
# gives the same result as the new mb_modules with the options
# mb_monthly and constant lapse rate gradient!
mb_mod_default = massbalance.PastMassBalance(gdir,
mu_star=mu_opt,
bias=0,
check_calib_params=False)
tot_mb_default = mb_mod_default.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
assert_allclose(tot_mb, tot_mb_default, rtol=1e-4)
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_hydro_month_changes(self, hef_gdir):
# test for HEF if applying different hydro_months does the right thing
# check if mb of neighbouring hydro_months correlate
# do this for different climate scenarios
# maybe there is already somewhere an overview or a better way to get
# these dates, but I did not find it
base_data_time = {
'CRU': {
'start_year': 1901,
'end_year': 2014
},
'ERA5': {
'start_year': 1979,
'end_year': 2018
},
'ERA5dr': {
'start_year': 1979,
'end_year': 2019
},
'HISTALP': {
'start_year': 1850,
'end_year': 2014
},
'CERA': {
'start_year': 1901,
'end_year': 2010
},
'ERA5L': {
'start_year': 1981,
'end_year': 2018
}
}
gdir = hef_gdir
oggm.core.flowline.init_present_time_glacier(gdir)
mb_mod = oggm.core.massbalance.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
exps = ['ERA5dr', 'CRU', 'HISTALP', 'ERA5', 'ERA5L', 'CERA']
for base in exps:
# this does not need to be the best one,
# just for comparison between different hydro months
mu_opt = 213.54
files = []
ref_hgts = []
dft = []
dfp = []
tot_mbs = []
cfg.PARAMS['baseline_climate'] = base
for m in np.arange(1, 13):
cfg.PARAMS['hydro_month_nh'] = m
fsuff = '_{}_{}'.format(base, m)
tasks.process_climate_data(gdir, output_filesuffix=fsuff)
files.append(
gdir.get_filepath('climate_historical', filesuffix=fsuff))
with xr.open_dataset(files[-1]) as ds:
ref_hgts.append(ds.ref_hgt)
dft.append(ds.temp.to_series())
dfp.append(ds.prcp.to_series())
ci = gdir.get_climate_info(input_filesuffix=fsuff)
# check if the right climate source is used
assert base in ci['baseline_climate_source']
mm = str(m) if m > 9 else str(0) + str(m)
mm_e = str(m - 1) if (m - 1) > 9 else str(0) + str(m - 1)
b_s_y = base_data_time[base]['start_year']
b_e_y = base_data_time[base]['end_year']
stime = '{}-{}-01'.format(b_s_y, mm)
assert ds.time[0] == np.datetime64(stime)
if m == 1:
assert ci['baseline_hydro_yr_0'] == b_s_y
if base == 'ERA5dr':
# do not have full 2019
assert ci['baseline_hydro_yr_1'] == b_e_y - 1
else:
assert ci['baseline_hydro_yr_1'] == b_e_y
elif m < 7 and base == 'ERA5dr':
# have data till 2019-05 for ERA5dr
stime = '{}-{}-01'.format(b_e_y, mm_e)
assert ds.time[-1] == np.datetime64(stime)
assert ci['baseline_hydro_yr_0'] == b_s_y + 1
assert ci['baseline_hydro_yr_1'] == b_e_y
else:
assert ci['baseline_hydro_yr_0'] == b_s_y + 1
if base == 'ERA5dr':
# do not have full 2019
stime = '{}-{}-01'.format(b_e_y - 1, mm_e)
assert ds.time[-1] == np.datetime64(stime)
assert ci['baseline_hydro_yr_1'] == b_e_y - 1
else:
assert ci['baseline_hydro_yr_1'] == b_e_y
stime = '{}-{}-01'.format(b_e_y, mm_e)
assert ds.time[-1] == np.datetime64(stime)
mb_mod = massbalance.PastMassBalance(
gdir,
mu_star=mu_opt,
input_filesuffix=fsuff,
bias=0,
check_calib_params=False)
years = np.arange(ds.hydro_yr_0, ds.hydro_yr_1 + 1)
mb_ts = mb_mod.get_specific_mb(heights=h,
widths=w,
year=years)
tot_mbs.append(pd.Series(mb_ts))
# check if all ref_hgts are equal
# means that we likely compare same glacier and climate dataset
assert len(np.unique(ref_hgts)) == 1
# concatenate temperature and prcp from different hydromonths
dft = pd.concat(dft, axis=1, keys=np.arange(1, 13))
dfp = pd.concat(dfp, axis=1, keys=np.arange(1, 13))
# Common period
dft_na = dft.dropna().iloc[1:]
dfp_na = dfp.dropna().iloc[1:]
# check if the common period of temperature prcp
# series is equal for all starting hydromonth dates
assert np.all(dft_na.eq(dft_na.iloc[:, 0], axis=0).all(1))
assert np.all(dfp_na.eq(dfp_na.iloc[:, 0], axis=0).all(1))
# mass balance of different years
pd_tot_mbs = pd.concat(tot_mbs, axis=1, keys=np.arange(1, 13))
pd_tot_mbs = pd_tot_mbs.dropna()
# compute correlations
corrs = []
for m in np.arange(1, 12):
# check if correlation between time series of hydro_month =1,
# is high to hydro_month = 2 and so on
corrs.append(pd_tot_mbs.corr().loc[m, m + 1])
# would be better if for hydro_month=12,
# correlation is tested to next year
assert np.mean(corrs) > 0.9
def time_get_ela():
mb_mod = massbalance.PastMassBalance(gdir, bias=0)
mb_mod.get_ela(year=years)
def time_PastMassBalance():
mb_mod = massbalance.PastMassBalance(gdir, bias=0)
for yr in years:
mb_mod.get_annual_mb(heights, year=yr)
def track_mb_1870_sigma(self, gdir):
self.cfg_init()
mb = massbalance.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
mb_ts = mb.get_specific_mb(h, w, year=np.arange(31) + 1860)
return np.std(mb_ts)
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
# -------------------- # 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'] # compute local t* and the corresponding mu* climate.local_t_star(gdir, tstar=t_star, bias=bias) climate.mu_star_calibration(gdir) from oggm.core import massbalance # instance the mass balance models mb_mod = massbalance.PastMassBalance(gdir) # ----------- # INVERSION # ----------- from oggm.core import inversion inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) # ---------------- # DYNAMICAL PART # ---------------- from oggm.core import flowline # initialize present time glacier
def run_uncertain_random_climate(gdir,
nyears=700,
output_filesuffix='',
sigma_t=None,
sigma_p=None,
sigma_smb=None,
rdn_temp_bias_seed=None,
rdn_prcp_bias_seed=None,
rdn_bias_seed=None,
orig_mb_tbias=0,
orig_mb_pbias=1,
orig_mb_sbias=0,
**kwargs):
"""Test stuff
Parameters
----------
gdir
nyears
output_filesuffix
rdn_temp_bias_seed
rdn_prcp_bias_seed
rdn_bias_seed
kwargs
Returns
-------
"""
# Find the optimal bias for a balanced MB
mbref = mbmods.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
cyrs = np.unique(mbref.years)
def to_minimize(x):
mbref.bias = x
return np.mean(mbref.get_specific_mb(h, w, cyrs))
opti_bias = optimization.brentq(to_minimize, -10000, 10000, xtol=0.01)
mbref.bias = opti_bias
assert np.allclose(np.mean(mbref.get_specific_mb(h, w, cyrs)),
0,
atol=0.01)
rdf = pd.DataFrame(index=cyrs)
for y in cyrs:
rdf.loc[y, 'SMB'] = mbref.get_specific_mb(h, w, year=y)
t, _, p, _ = mbref.get_annual_climate([np.mean(h)], year=y)
rdf.loc[y, 'TEMP'] = t
rdf.loc[y, 'PRCP'] = p
if sigma_smb is None:
sigma_smb = rdf.std()['SMB'] / 2
if sigma_t is None:
sigma_t = rdf.std()['TEMP'] / 2
if sigma_p is None:
sigma_p = (rdf.std() / rdf.mean())['PRCP'] / 2
mb = mbmods.RandomMassBalance(gdir, all_years=True, seed=0)
mb.mbmod.temp_bias = orig_mb_tbias
mb.mbmod.prcp_bias = orig_mb_pbias
mb.mbmod.bias = orig_mb_sbias + opti_bias
rmb = mbmods.UncertainMassBalance(mb,
rdn_temp_bias_seed=rdn_temp_bias_seed,
rdn_temp_bias_sigma=sigma_t,
rdn_prcp_bias_seed=rdn_prcp_bias_seed,
rdn_prcp_bias_sigma=sigma_p,
rdn_bias_seed=rdn_bias_seed,
rdn_bias_sigma=sigma_smb)
return robust_model_run(gdir,
output_filesuffix=output_filesuffix,
mb_model=rmb,
ys=0,
ye=nyears,
**kwargs)
def test_varying_bias_mb_model(self, case_dir):
cfg.initialize()
cfg.PARAMS['prcp_scaling_factor'] = 1.6
cfg.PATHS['working_dir'] = case_dir
cfg.PARAMS['use_multiprocessing'] = True
# Go - get the pre-processed glacier directories
for rid in ['RGI60-03.04384', 'RGI60-11.00897', 'RGI60-16.02207']:
gdirs = workflow.init_glacier_directories(
[rid],
from_prepro_level=5,
prepro_base_url=prepro_base_url,
prepro_border=80,
prepro_rgi_version='62')
workflow.execute_entity_task(parse_dt_per_dt, gdirs)
gdir = gdirs[0]
exp = 'netzero_py2020_fac1.0_decr0.3'
magicc_file = magicc_dir + exp + '.nc'
with xr.open_dataset(utils.file_downloader(magicc_file),
decode_times=False) as ds:
ds = ds.load()
df = ds['ens_avg'].to_dataframe()
dt_per_dt = gdir.get_diagnostics()['magicc_dt_per_dt']
dp_per_dt = gdir.get_diagnostics()['magicc_dp_per_dt']
fls = gdir.read_pickle('model_flowlines')
y0 = 2014
hs = 5
mbc = massbalance.ConstantMassBalance(gdir, y0=y0, halfsize=hs)
mb_ref = massbalance.PastMassBalance(gdir)
years = np.arange(1980, 2020, dtype=np.float64)
df['mb_ref'] = pd.Series(index=years,
data=mb_ref.get_specific_mb(fls=fls,
year=years))
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'],
dt_per_dt=dt_per_dt)
df['mb'] = mb.get_specific_mb(fls=fls, year=df.index)
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'],
dt_per_dt=dt_per_dt,
dp_per_dt=dp_per_dt)
df['mb_wp'] = mb.get_specific_mb(fls=fls, year=df.index)
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'])
df['mb_nodt'] = mb.get_specific_mb(fls=fls, year=df.index)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb'].mean(),
mbc.get_specific_mb(fls=fls),
rtol=5e-3)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb'].mean(),
atol=1e-2)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb_wp'].mean(),
atol=1e-2)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb_nodt'].mean(),
atol=1e-2)
assert_allclose(df[['ens_avg', 'mb']].corr().min().min(),
-1,
rtol=1e-2)
assert_allclose(df[['ens_avg', 'mb_nodt']].corr().min().min(),
-1,
rtol=1e-2)
assert_allclose(df[['mb_wp', 'mb']].corr().min().min(),
1,
rtol=1e-2)
assert_allclose(df['mb'], df['mb_wp'], atol=115)
if DO_PLOT:
plt.figure()
df[['mb_ref', 'mb', 'mb_wp',
'mb_nodt']].loc[2000:2025].plot(title=rid)
plt.figure()
df[['mb_ref', 'mb', 'mb_wp',
'mb_nodt']].loc[2000:].plot(title=rid)
plt.show()
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)
