"""Mass-balance models"""
# Built ins
# External libs
import numpy as np
import netCDF4
from scipy.interpolate import interp1d
from scipy import optimize as optimization
# Locals
import oggm.cfg as cfg
from oggm.cfg import SEC_IN_YEAR, SEC_IN_MONTH
from oggm.utils import (SuperclassMeta, lazy_property, floatyear_to_date,
date_to_floatyear, monthly_timeseries, ncDataset,
tolist)
[docs]class MassBalanceModel(object, metaclass=SuperclassMeta):
"""Common logic for the mass balance models.
All mass-balance models should implement this interface.
Attributes
----------
valid_bounds : [float, float]
The altitudinal bounds where the MassBalanceModel is valid. This is
necessary for automated ELA search.
"""
[docs] def __init__(self):
""" Initialize."""
self.valid_bounds = None
self.rho = cfg.PARAMS['ice_density']
[docs] def get_monthly_mb(self, heights, year=None, fl_id=None):
"""Monthly mass-balance at given altitude(s) for a moment in time.
Units: [m s-1], or meters of ice per second
Note: `year` is optional because some simpler models have no time
component.
Parameters
----------
heights: ndarray
the atitudes at which the mass-balance will be computed
year: float, optional
the time (in the "hydrological floating year" convention)
fl_id: float, optional
the index of the flowline in the fls array (might be ignored
by some MB models)
Returns
-------
the mass-balance (same dim as `heights`) (units: [m s-1])
"""
raise NotImplementedError()
[docs] def get_annual_mb(self, heights, year=None, fl_id=None):
"""Like `self.get_monthly_mb()`, but for annual MB.
For some simpler mass-balance models ``get_monthly_mb()` and
`get_annual_mb()`` can be equivalent.
Units: [m s-1], or meters of ice per second
Note: `year` is optional because some simpler models have no time
component.
Parameters
----------
heights: ndarray
the atitudes at which the mass-balance will be computed
year: float, optional
the time (in the "floating year" convention)
fl_id: float, optional
the index of the flowline in the fls array (might be ignored
by some MB models)
Returns
-------
the mass-balance (same dim as `heights`) (units: [m s-1])
"""
raise NotImplementedError()
[docs] def get_specific_mb(self, heights=None, widths=None, fls=None,
year=None):
"""Specific mb for this year and a specific glacier geometry.
Units: [mm w.e. yr-1], or millimeter water equivalent per year
Parameters
----------
heights: ndarray
the atitudes at which the mass-balance will be computed.
Overridden by ``fls`` if provided
widths: ndarray
the widths of the flowline (necessary for the weighted average).
Overridden by ``fls`` if provided
fls: list of flowline instances
Another way to get heights and widths - overrides them if
provided.
year: float, optional
the time (in the "hydrological floating year" convention)
Returns
-------
the specific mass-balance (units: mm w.e. yr-1)
"""
if len(np.atleast_1d(year)) > 1:
out = [self.get_specific_mb(heights=heights, widths=widths,
fls=fls, year=yr)
for yr in year]
return np.asarray(out)
if fls is not None:
mbs = []
widths = []
for i, fl in enumerate(fls):
widths = np.append(widths, fl.widths)
mbs = np.append(mbs, self.get_annual_mb(fl.surface_h,
year=year, fl_id=i))
else:
mbs = self.get_annual_mb(heights, year=year)
return np.average(mbs, weights=widths) * SEC_IN_YEAR * self.rho
[docs] def get_ela(self, year=None):
"""Compute the equilibrium line altitude for this year
Parameters
----------
year: float, optional
the time (in the "hydrological floating year" convention)
Returns
-------
the equilibrium line altitude (ELA, units: m)
"""
if len(np.atleast_1d(year)) > 1:
return np.asarray([self.get_ela(year=yr) for yr in year])
if self.valid_bounds is None:
raise ValueError('attribute `valid_bounds` needs to be '
'set for the ELA computation.')
# Check for invalid ELAs
b0, b1 = self.valid_bounds
if (np.any(~np.isfinite(self.get_annual_mb([b0, b1], year=year))) or
(self.get_annual_mb([b0], year=year)[0] > 0) or
(self.get_annual_mb([b1], year=year)[0] < 0)):
return np.NaN
def to_minimize(x):
o = self.get_annual_mb([x], year=year)[0] * SEC_IN_YEAR * self.rho
return o
return optimization.brentq(to_minimize, *self.valid_bounds, xtol=0.1)
[docs]class LinearMassBalance(MassBalanceModel):
"""Constant mass-balance as a linear function of altitude.
"""
[docs] def __init__(self, ela_h, grad=3., max_mb=None):
""" Initialize.
Parameters
----------
ela_h: float
Equilibrium line altitude (units: [m])
grad: float
Mass-balance gradient (unit: [mm w.e. yr-1 m-1])
max_mb: float
Cap the mass balance to a certain value (unit: [mm w.e. yr-1])
Attributes
----------
temp_bias : float, default 0
A "temperature bias" doesn't makes much sense in the linear MB
context, but we implemented a simple empirical rule:
+ 1K -> ELA + 150 m
"""
super(LinearMassBalance, self).__init__()
self.valid_bounds = [-1e4, 2e4] # in m
self.orig_ela_h = ela_h
self.ela_h = ela_h
self.grad = grad
self.max_mb = max_mb
self._temp_bias = 0
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self._temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to change the ELA."""
self.ela_h = self.orig_ela_h + value * 150
self._temp_bias = value
def get_monthly_mb(self, heights, year=None, fl_id=None):
mb = (np.asarray(heights) - self.ela_h) * self.grad
if self.max_mb is not None:
mb = mb.clip(None, self.max_mb)
return mb / SEC_IN_YEAR / self.rho
def get_annual_mb(self, heights, year=None, fl_id=None):
return self.get_monthly_mb(heights, year=year)
[docs]class PastMassBalance(MassBalanceModel):
"""Mass balance during the climate data period."""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
filename='climate_monthly', input_filesuffix='',
repeat=False, ys=None, ye=None, check_calib_params=True):
"""Initialize.
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value).
bias : float, optional
set to the alternative value of the calibration bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
Note that this bias is *substracted* from the computed MB. Indeed:
BIAS = MODEL_MB - REFERENCE_MB.
filename : str, optional
set to a different BASENAME if you want to use alternative climate
data.
input_filesuffix : str
the file suffix of the input climate file
repeat : bool
Whether the climate period given by [ys, ye] should be repeated
indefinitely in a circular way
ys : int
The start of the climate period where the MB model is valid
(default: the period with available data)
ye : int
The end of the climate period where the MB model is valid
(default: the period with available data)
check_calib_params : bool
OGGM will try hard not to use wrongly calibrated mu* by checking
the parameters used during calibration and the ones you are
using at run time. If they don't match, it will raise an error.
Set to False to suppress this check.
Attributes
----------
temp_bias : float, default 0
Add a temperature bias to the time series
prcp_bias : float, default 1
Precipitation factor to the time series (called bias for
consistency with `temp_bias`)
"""
super(PastMassBalance, self).__init__()
self.valid_bounds = [-1e4, 2e4] # in m
if mu_star is None:
df = gdir.read_json('local_mustar')
mu_star = df['mu_star_glacierwide']
if check_calib_params:
if not df['mu_star_allsame']:
raise RuntimeError('You seem to use the glacier-wide mu* '
'to compute the mass-balance although '
'this glacier has different mu* for '
'its flowlines. '
'Set `check_calib_params=False` '
'to ignore this warning.')
if bias is None:
if cfg.PARAMS['use_bias_for_run']:
df = gdir.read_json('local_mustar')
bias = df['bias']
else:
bias = 0.
self.mu_star = mu_star
self.bias = bias
# Parameters
self.t_solid = cfg.PARAMS['temp_all_solid']
self.t_liq = cfg.PARAMS['temp_all_liq']
self.t_melt = cfg.PARAMS['temp_melt']
prcp_fac = cfg.PARAMS['prcp_scaling_factor']
default_grad = cfg.PARAMS['temp_default_gradient']
# Check the climate related params to the GlacierDir to make sure
if check_calib_params:
mb_calib = gdir.read_pickle('climate_info')['mb_calib_params']
for k, v in mb_calib.items():
if v != cfg.PARAMS[k]:
raise RuntimeError('You seem to use different mass-'
'balance parameters than used for the '
'calibration. '
'Set `check_calib_params=False` '
'to ignore this warning.')
# Public attrs
self.temp_bias = 0.
self.prcp_bias = 1.
self.repeat = repeat
# Read file
fpath = gdir.get_filepath(filename, filesuffix=input_filesuffix)
with ncDataset(fpath, mode='r') as nc:
# time
time = nc.variables['time']
time = netCDF4.num2date(time[:], time.units)
ny, r = divmod(len(time), 12)
if r != 0:
raise ValueError('Climate data should be N full years')
# This is where we switch to hydro float year format
# Last year gives the tone of the hydro year
self.years = np.repeat(np.arange(time[-1].year-ny+1,
time[-1].year+1), 12)
self.months = np.tile(np.arange(1, 13), ny)
# Read timeseries
self.temp = nc.variables['temp'][:]
self.prcp = nc.variables['prcp'][:] * prcp_fac
if 'gradient' in nc.variables:
grad = nc.variables['gradient'][:]
# Security for stuff that can happen with local gradients
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
grad = np.where(~np.isfinite(grad), default_grad, grad)
grad = np.clip(grad, g_minmax[0], g_minmax[1])
else:
grad = self.prcp * 0 + default_grad
self.grad = grad
self.ref_hgt = nc.ref_hgt
self.ys = self.years[0] if ys is None else ys
self.ye = self.years[-1] if ye is None else ye
def get_monthly_climate(self, heights, year=None):
"""Monthly climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other model biases (temp and prcp) are applied.
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
y, m = floatyear_to_date(year)
if self.repeat:
y = self.ys + (y - self.ys) % (self.ye - self.ys + 1)
if y < self.ys or y > self.ye:
raise ValueError('year {} out of the valid time bounds: '
'[{}, {}]'.format(y, self.ys, self.ye))
pok = np.where((self.years == y) & (self.months == m))[0][0]
# Read timeseries
itemp = self.temp[pok] + self.temp_bias
iprcp = self.prcp[pok] * self.prcp_bias
igrad = self.grad[pok]
# For each height pixel:
# Compute temp and tempformelt (temperature above melting threshold)
npix = len(heights)
temp = np.ones(npix) * itemp + igrad * (heights - self.ref_hgt)
tempformelt = temp - self.t_melt
tempformelt[:] = np.clip(tempformelt, 0, tempformelt.max())
# Compute solid precipitation from total precipitation
prcp = np.ones(npix) * iprcp
fac = 1 - (temp - self.t_solid) / (self.t_liq - self.t_solid)
prcpsol = prcp * np.clip(fac, 0, 1)
return temp, tempformelt, prcp, prcpsol
def _get_2d_annual_climate(self, heights, year):
# Avoid code duplication with a getter routine
year = np.floor(year)
if self.repeat:
year = self.ys + (year - self.ys) % (self.ye - self.ys + 1)
if year < self.ys or year > self.ye:
raise ValueError('year {} out of the valid time bounds: '
'[{}, {}]'.format(year, self.ys, self.ye))
pok = np.where(self.years == year)[0]
if len(pok) < 1:
raise ValueError('Year {} not in record'.format(int(year)))
# Read timeseries
itemp = self.temp[pok] + self.temp_bias
iprcp = self.prcp[pok] * self.prcp_bias
igrad = self.grad[pok]
# For each height pixel:
# Compute temp and tempformelt (temperature above melting threshold)
heights = np.asarray(heights)
npix = len(heights)
grad_temp = np.atleast_2d(igrad).repeat(npix, 0)
grad_temp *= (heights.repeat(12).reshape(grad_temp.shape) -
self.ref_hgt)
temp2d = np.atleast_2d(itemp).repeat(npix, 0) + grad_temp
temp2dformelt = temp2d - self.t_melt
temp2dformelt[:] = np.clip(temp2dformelt, 0, temp2dformelt.max())
# Compute solid precipitation from total precipitation
prcp = np.atleast_2d(iprcp).repeat(npix, 0)
fac = 1 - (temp2d - self.t_solid) / (self.t_liq - self.t_solid)
fac = np.clip(fac, 0, 1)
prcpsol = prcp * fac
return temp2d, temp2dformelt, prcp, prcpsol
def get_annual_climate(self, heights, year=None):
"""Annual climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other model biases (temp and prcp) are applied.
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
t, tfmelt, prcp, prcpsol = self._get_2d_annual_climate(heights, year)
return (t.mean(axis=1), tfmelt.sum(axis=1),
prcp.sum(axis=1), prcpsol.sum(axis=1))
def get_monthly_mb(self, heights, year=None, fl_id=None):
_, tmelt, _, prcpsol = self.get_monthly_climate(heights, year=year)
mb_month = prcpsol - self.mu_star * tmelt
mb_month -= self.bias * SEC_IN_MONTH / SEC_IN_YEAR
return mb_month / SEC_IN_MONTH / self.rho
def get_annual_mb(self, heights, year=None, fl_id=None):
_, temp2dformelt, _, prcpsol = self._get_2d_annual_climate(heights,
year)
mb_annual = np.sum(prcpsol - self.mu_star * temp2dformelt, axis=1)
return (mb_annual - self.bias) / SEC_IN_YEAR / self.rho
[docs]class ConstantMassBalance(MassBalanceModel):
"""Constant mass-balance during a chosen period.
This is useful for equilibrium experiments.
"""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
y0=None, halfsize=15, filename='climate_monthly',
input_filesuffix=''):
"""Initialize
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value)
bias : float, optional
set to the alternative value of the annual bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
y0 : int, optional, default: tstar
the year at the center of the period of interest. The default
is to use tstar as center.
halfsize : int, optional
the half-size of the time window (window size = 2 * halfsize + 1)
filename : str, optional
set to a different BASENAME if you want to use alternative climate
data.
input_filesuffix : str
the file suffix of the input climate file
"""
super(ConstantMassBalance, self).__init__()
self.mbmod = PastMassBalance(gdir, mu_star=mu_star, bias=bias,
filename=filename,
input_filesuffix=input_filesuffix)
if y0 is None:
df = gdir.read_json('local_mustar')
y0 = df['t_star']
# This is a quick'n dirty optimisation
try:
fls = gdir.read_pickle('model_flowlines')
h = []
for fl in fls:
# We use bed because of overdeepenings
h = np.append(h, fl.bed_h)
h = np.append(h, fl.surface_h)
zminmax = np.round([np.min(h)-50, np.max(h)+2000])
except FileNotFoundError:
# in case we don't have them
with ncDataset(gdir.get_filepath('gridded_data')) as nc:
zminmax = [nc.min_h_dem-250, nc.max_h_dem+1500]
self.hbins = np.arange(*zminmax, step=10)
self.valid_bounds = self.hbins[[0, -1]]
self.y0 = y0
self.halfsize = halfsize
self.years = np.arange(y0-halfsize, y0+halfsize+1)
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self.mbmod.temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to add to the original series."""
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.temp_bias = value
@property
def prcp_bias(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_bias
@prcp_bias.setter
def prcp_bias(self, value):
"""Precipitation factor to apply to the original series."""
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.prcp_bias = value
@property
def bias(self):
"""Residual bias to apply to the original series."""
return self.mbmod.bias
@bias.setter
def bias(self, value):
"""Residual bias to apply to the original series."""
self.mbmod.bias = value
@lazy_property
def interp_yr(self):
# annual MB
mb_on_h = self.hbins*0.
for yr in self.years:
mb_on_h += self.mbmod.get_annual_mb(self.hbins, year=yr)
return interp1d(self.hbins, mb_on_h / len(self.years))
@lazy_property
def interp_m(self):
# monthly MB
months = np.arange(12)+1
interp_m = []
for m in months:
mb_on_h = self.hbins*0.
for yr in self.years:
yr = date_to_floatyear(yr, m)
mb_on_h += self.mbmod.get_monthly_mb(self.hbins, year=yr)
interp_m.append(interp1d(self.hbins, mb_on_h / len(self.years)))
return interp_m
def get_climate(self, heights, year=None):
"""Average climate information at given heights.
Note that prcp is corrected with the precipitation factor and that
all other biases (precipitation, temp) are applied
Returns
-------
(temp, tempformelt, prcp, prcpsol)
"""
yrs = monthly_timeseries(self.years[0], self.years[-1],
include_last_year=True)
heights = np.atleast_1d(heights)
nh = len(heights)
shape = (len(yrs), nh)
temp = np.zeros(shape)
tempformelt = np.zeros(shape)
prcp = np.zeros(shape)
prcpsol = np.zeros(shape)
for i, yr in enumerate(yrs):
t, tm, p, ps = self.mbmod.get_monthly_climate(heights, year=yr)
temp[i, :] = t
tempformelt[i, :] = tm
prcp[i, :] = p
prcpsol[i, :] = ps
# Note that we do not weight for number of days per month - bad
return (np.mean(temp, axis=0),
np.mean(tempformelt, axis=0) * 12,
np.mean(prcp, axis=0) * 12,
np.mean(prcpsol, axis=0) * 12)
def get_monthly_mb(self, heights, year=None, fl_id=None):
yr, m = floatyear_to_date(year)
return self.interp_m[m-1](heights)
def get_annual_mb(self, heights, year=None, fl_id=None):
return self.interp_yr(heights)
[docs]class RandomMassBalance(MassBalanceModel):
"""Random shuffle of all MB years within a given time period.
This is useful for finding a possible past glacier state or for sensitivity
experiments.
Note that this is going to be sensitive to extreme years in certain
periods, but it is by far more physically reasonable than other
approaches based on gaussian assumptions.
"""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
y0=None, halfsize=15, seed=None, filename='climate_monthly',
input_filesuffix='', all_years=False,
unique_samples=False):
"""Initialize.
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value)
bias : float, optional
set to the alternative value of the calibration bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
Note that this bias is *substracted* from the computed MB. Indeed:
BIAS = MODEL_MB - REFERENCE_MB.
y0 : int, optional, default: tstar
the year at the center of the period of interest. The default
is to use tstar as center.
halfsize : int, optional
the half-size of the time window (window size = 2 * halfsize + 1)
seed : int, optional
Random seed used to initialize the pseudo-random number generator.
filename : str, optional
set to a different BASENAME if you want to use alternative climate
data.
input_filesuffix : str
the file suffix of the input climate file
all_years : bool
if True, overwrites ``y0`` and ``halfsize`` to use all available
years.
unique_samples: bool
if true, chosen random mass-balance years will only be available
once per random climate period-length
if false, every model year will be chosen from the random climate
period with the same probability
"""
super(RandomMassBalance, self).__init__()
self.valid_bounds = [-1e4, 2e4] # in m
self.mbmod = PastMassBalance(gdir, mu_star=mu_star, bias=bias,
filename=filename,
input_filesuffix=input_filesuffix)
# Climate period
if all_years:
self.years = self.mbmod.years
else:
if y0 is None:
df = gdir.read_json('local_mustar')
y0 = df['t_star']
self.years = np.arange(y0-halfsize, y0+halfsize+1)
self.yr_range = (self.years[0], self.years[-1]+1)
self.ny = len(self.years)
# RandomState
self.rng = np.random.RandomState(seed)
self._state_yr = dict()
# Sampling without replacement
self.unique_samples = unique_samples
if self.unique_samples:
self.sampling_years = self.years
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self.mbmod.temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to add to the original series."""
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.temp_bias = value
@property
def prcp_bias(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_bias
@prcp_bias.setter
def prcp_bias(self, value):
"""Precipitation factor to apply to the original series."""
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.prcp_bias = value
@property
def bias(self):
"""Residual bias to apply to the original series."""
return self.mbmod.bias
@bias.setter
def bias(self, value):
"""Residual bias to apply to the original series."""
self.mbmod.bias = value
def get_state_yr(self, year=None):
"""For a given year, get the random year associated to it."""
year = int(year)
if year not in self._state_yr:
if self.unique_samples:
# --- Sampling without replacement ---
if self.sampling_years.size == 0:
# refill sample pool when all years were picked once
self.sampling_years = self.years
# choose one year which was not used in the current period
_sample = self.rng.choice(self.sampling_years)
# write chosen year to dictionary
self._state_yr[year] = _sample
# update sample pool: remove the chosen year from it
self.sampling_years = np.delete(
self.sampling_years,
np.where(self.sampling_years == _sample))
else:
# --- Sampling with replacement ---
self._state_yr[year] = self.rng.randint(*self.yr_range)
return self._state_yr[year]
def get_monthly_mb(self, heights, year=None, fl_id=None):
ryr, m = floatyear_to_date(year)
ryr = date_to_floatyear(self.get_state_yr(ryr), m)
return self.mbmod.get_monthly_mb(heights, year=ryr)
def get_annual_mb(self, heights, year=None, fl_id=None):
ryr = self.get_state_yr(int(year))
return self.mbmod.get_annual_mb(heights, year=ryr)
[docs]class UncertainMassBalance(MassBalanceModel):
"""Adding uncertainty to a mass balance model.
There are three variables for which you can add uncertainty:
- temperature (additive bias)
- precipitation (multiplicative factor)
- residual (a bias in units of MB)
"""
[docs] def __init__(self, basis_model,
rdn_temp_bias_seed=None, rdn_temp_bias_sigma=0.1,
rdn_prcp_bias_seed=None, rdn_prcp_bias_sigma=0.1,
rdn_bias_seed=None, rdn_bias_sigma=100):
"""Initialize.
Parameters
----------
basis_model : MassBalanceModel
the model to which you want to add the uncertainty to
rdn_temp_bias_seed : int
the seed of the random number generator
rdn_temp_bias_sigma : float
the standard deviation of the random temperature error
rdn_prcp_bias_seed : int
the seed of the random number generator
rdn_prcp_bias_sigma : float
the standard deviation of the random precipitation error
rdn_bias_seed : int
the seed of the random number generator
rdn_bias_sigma : float
the standard deviation of the random MB error
"""
super(UncertainMassBalance, self).__init__()
self.mbmod = basis_model
self.valid_bounds = self.mbmod.valid_bounds
self.rng_temp = np.random.RandomState(rdn_temp_bias_seed)
self.rng_prcp = np.random.RandomState(rdn_prcp_bias_seed)
self.rng_bias = np.random.RandomState(rdn_bias_seed)
self._temp_sigma = rdn_temp_bias_sigma
self._prcp_sigma = rdn_prcp_bias_sigma
self._bias_sigma = rdn_bias_sigma
self._state_temp = dict()
self._state_prcp = dict()
self._state_bias = dict()
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self.mbmod.temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to add to the original series."""
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.temp_bias = value
@property
def prcp_bias(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_bias
@prcp_bias.setter
def prcp_bias(self, value):
"""Precipitation factor to apply to the original series."""
self.mbmod.prcp_bias = value
def _get_state_temp(self, year):
year = int(year)
if year not in self._state_temp:
self._state_temp[year] = self.rng_temp.randn() * self._temp_sigma
return self._state_temp[year]
def _get_state_prcp(self, year):
year = int(year)
if year not in self._state_prcp:
self._state_prcp[year] = self.rng_prcp.randn() * self._prcp_sigma
return self._state_prcp[year]
def _get_state_bias(self, year):
year = int(year)
if year not in self._state_bias:
self._state_bias[year] = self.rng_bias.randn() * self._bias_sigma
return self._state_bias[year]
def get_monthly_mb(self, heights, year=None, fl_id=None):
raise NotImplementedError()
def get_annual_mb(self, heights, year=None, fl_id=None):
# Keep the original biases and add a random error
_t = self.mbmod.temp_bias
_p = self.mbmod.prcp_bias
_b = self.mbmod.bias
self.mbmod.temp_bias = self._get_state_temp(year) + _t
self.mbmod.prcp_bias = self._get_state_prcp(year) + _p
self.mbmod.bias = self._get_state_bias(year) + _b
try:
out = self.mbmod.get_annual_mb(heights, year=year, fl_id=fl_id)
except BaseException:
self.mbmod.temp_bias = _t
self.mbmod.prcp_bias = _p
self.mbmod.bias = _b
raise
# Back to normal
self.mbmod.temp_bias = _t
self.mbmod.prcp_bias = _p
self.mbmod.bias = _b
return out
[docs]class MultipleFlowlineMassBalance(MassBalanceModel):
"""Handle mass-balance at the glacier level instead of flowline level.
Convenience class doing not much more than wraping a list of mass-balance
models, one for each flowline.
This is useful for real-case studies, where each flowline might have a
different mu*.
Attributes
----------
fls : list
list of flowline objects
mb_models : list
list of mass-balance objects
"""
[docs] def __init__(self, gdir, fls=None, mu_star=None,
mb_model_class=PastMassBalance, use_inversion_flowlines=False,
input_filesuffix='', bias=None, **kwargs):
"""Initialize.
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float or list of floats, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value). Give a list of values
for flowline-specific mu*
fls : list, optional
list of flowline objects to use (defaults to 'model_flowlines',
and if not available, to 'inversion_flowlines')
mb_model_class : class, optional
the mass-balance model to use (e.g. PastMassBalance,
ConstantMassBalance...)
use_inversion_flowlines: bool, optional
if True 'inversion_flowlines' instead of 'model_flowlines' will be
used.
input_filesuffix : str
the file suffix of the input climate file
bias : float, optional
set to the alternative value of the calibration bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
Note that this bias is *substracted* from the computed MB. Indeed:
BIAS = MODEL_MB - REFERENCE_MB.
kwargs : kwargs to pass to mb_model_class
"""
# Read in the flowlines
if use_inversion_flowlines:
fls = gdir.read_pickle('inversion_flowlines')
if fls is None:
try:
fls = gdir.read_pickle('model_flowlines')
except FileNotFoundError:
raise RuntimeError('Need a valid `model_flowlines` file. '
'If you explicitly want to use '
'`inversion_flowlines`, set '
'use_inversion_flowlines=True.') from None
self.fls = fls
_y0 = kwargs.get('y0', None)
# User mu*?
if mu_star is not None:
mu_star = tolist(mu_star, length=len(fls))
for fl, mu in zip(self.fls, mu_star):
fl.mu_star = mu
# Initialise the mb models
self.flowline_mb_models = []
for fl in self.fls:
# Merged glaciers will need different climate files, use filesuffix
if (fl.rgi_id is not None) and (fl.rgi_id != gdir.rgi_id):
rgi_filesuffix = '_' + fl.rgi_id + input_filesuffix
else:
rgi_filesuffix = input_filesuffix
# merged glaciers also have a different MB bias from calibration
if ((bias is None) and cfg.PARAMS['use_bias_for_run'] and
(fl.rgi_id != gdir.rgi_id)):
df = gdir.read_json('local_mustar', filesuffix='_' + fl.rgi_id)
fl_bias = df['bias']
else:
fl_bias = bias
# Constant and RandomMassBalance need y0 if not provided
if (issubclass(mb_model_class, RandomMassBalance) or
issubclass(mb_model_class, ConstantMassBalance)) and (
fl.rgi_id != gdir.rgi_id) and (_y0 is None):
df = gdir.read_json('local_mustar', filesuffix='_' + fl.rgi_id)
kwargs['y0'] = df['t_star']
self.flowline_mb_models.append(
mb_model_class(gdir, mu_star=fl.mu_star, bias=fl_bias,
input_filesuffix=rgi_filesuffix, **kwargs))
self.valid_bounds = self.flowline_mb_models[-1].valid_bounds
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self.flowline_mb_models[0].temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to add to the original series."""
for mbmod in self.flowline_mb_models:
mbmod.temp_bias = value
@property
def prcp_bias(self):
"""Precipitation factor to apply to the original series."""
return self.flowline_mb_models[0].prcp_bias
@prcp_bias.setter
def prcp_bias(self, value):
"""Precipitation factor to apply to the original series."""
for mbmod in self.flowline_mb_models:
mbmod.prcp_bias = value
@property
def bias(self):
"""Residual bias to apply to the original series."""
return self.flowline_mb_models[0].bias
@bias.setter
def bias(self, value):
"""Residual bias to apply to the original series."""
for mbmod in self.flowline_mb_models:
mbmod.bias = value
def get_monthly_mb(self, heights, year=None, fl_id=None):
if fl_id is None:
raise ValueError('`fl_id` is required for '
'MultipleFlowlineMassBalance!')
return self.flowline_mb_models[fl_id].get_monthly_mb(heights,
year=year)
def get_annual_mb(self, heights, year=None, fl_id=None):
if fl_id is None:
raise ValueError('`fl_id` is required for '
'MultipleFlowlineMassBalance!')
return self.flowline_mb_models[fl_id].get_annual_mb(heights,
year=year)
def get_annual_mb_on_flowlines(self, fls=None, year=None):
"""Get the MB on all points of the glacier at once.
Parameters
----------
fls: list, optional
the list of flowlines to get the mass-balance from. Defaults
to self.fls
year: float, optional
the time (in the "floating year" convention)
Returns
-------
Tuple of (heights, widths, mass_balance) 1D arrays
"""
if fls is None:
fls = self.fls
heights = []
widths = []
mbs = []
for i, fl in enumerate(fls):
h = fl.surface_h
heights = np.append(heights, h)
widths = np.append(widths, fl.widths)
mbs = np.append(mbs, self.get_annual_mb(h, year=year, fl_id=i))
return heights, widths, mbs
def get_specific_mb(self, heights=None, widths=None, fls=None,
year=None):
if heights is not None or widths is not None:
raise ValueError('`heights` and `widths` kwargs do not work with '
'MultipleFlowlineMassBalance!')
if fls is None:
fls = self.fls
if len(np.atleast_1d(year)) > 1:
out = [self.get_specific_mb(fls=fls, year=yr) for yr in year]
return np.asarray(out)
mbs = []
widths = []
for fl, mb_mod in zip(self.fls, self.flowline_mb_models):
mb = mb_mod.get_annual_mb(fl.surface_h, year=year)
mbs = np.append(mbs, mb * SEC_IN_YEAR * mb_mod.rho)
widths = np.append(widths, fl.widths)
return np.average(mbs, weights=widths)
def get_ela(self, year=None):
# ELA here is not without ambiguity.
# We compute a mean weighted by area.
if len(np.atleast_1d(year)) > 1:
return np.asarray([self.get_ela(year=yr) for yr in year])
elas = []
areas = []
for fl, mb_mod in zip(self.fls, self.flowline_mb_models):
elas = np.append(elas, mb_mod.get_ela(year=year))
areas = np.append(areas, np.sum(fl.widths))
return np.average(elas, weights=areas)