"""Mass balance models"""
# Built ins
import logging
# External libs
import cftime
import numpy as np
import pandas as pd
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, clip_min, clip_max, clip_array,
weighted_average_1d)
from oggm.exceptions import InvalidWorkflowError, InvalidParamsError
from oggm import entity_task
# Module logger
log = logging.getLogger(__name__)
[docs]class MassBalanceModel(object, metaclass=SuperclassMeta):
"""Interface and common logic for all mass balance models used in OGGM.
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.
hemisphere : str, {'nh', 'sh'}
Used for time handling when the hydrological year convention is
used (``cfg.PARAMS['hydro_month_nh'] != 1``)
rho : float, default: ``cfg.PARAMS['ice_density']``
Density of ice
"""
[docs] def __init__(self):
""" Initialize."""
self.valid_bounds = None
self.hemisphere = None
self.rho = cfg.PARAMS['ice_density']
def __repr__(self):
"""String Representation of the mass balance model"""
summary = ['<oggm.MassBalanceModel>']
summary += [' Class: ' + self.__class__.__name__]
summary += [' Attributes:']
# Add all scalar attributes
for k, v in self.__dict__.items():
if np.isscalar(v) and not k.startswith('_'):
nbform = ' - {}: {}'
if k == 'mu_star':
nbform = ' - {}: {:.2f}'
summary += [nbform.format(k, v)]
return '\n'.join(summary) + '\n'
[docs] def get_monthly_mb(self, heights, year=None, fl_id=None, fls=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)
fls: list of flowline instances, optional
the flowlines array, in case the MB model implementation needs
to know details about the glacier geometry at the moment the
MB model is called
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, fls=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 altitudes 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)
fls: list of flowline instances, optional
the flowlines array, in case the MB model implementation needs
to know details about the glacier geometry at the moment the
MB model is called
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 altitudes 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, optional
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 = fl.widths
try:
# For rect and parabola don't compute spec mb
_widths = np.where(fl.thick > 0, _widths, 0)
except AttributeError:
pass
widths = np.append(widths, _widths)
mbs = np.append(mbs, self.get_annual_mb(fl.surface_h,
fls=fls, fl_id=i,
year=year))
else:
mbs = self.get_annual_mb(heights, year=year)
return weighted_average_1d(mbs, widths) * SEC_IN_YEAR * self.rho
[docs] def get_ela(self, year=None, **kwargs):
"""Compute the equilibrium line altitude for a given year.
Parameters
----------
year: float, optional
the time (in the "hydrological floating year" convention)
**kwargs: any other keyword argument accepted by self.get_annual_mb
Returns
-------
the equilibrium line altitude (ELA, units: m)
"""
if len(np.atleast_1d(year)) > 1:
return np.asarray([self.get_ela(year=yr, **kwargs) 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, **kwargs))) or
(self.get_annual_mb([b0], year=year, **kwargs)[0] > 0) or
(self.get_annual_mb([b1], year=year, **kwargs)[0] < 0)):
return np.NaN
def to_minimize(x):
return (self.get_annual_mb([x], year=year, **kwargs)[0] *
SEC_IN_YEAR * self.rho)
return optimization.brentq(to_minimize, *self.valid_bounds, xtol=0.1)
[docs]class ScalarMassBalance(MassBalanceModel):
"""Constant mass balance, everywhere."""
[docs] def __init__(self, mb=0.):
""" Initialize.
Parameters
----------
mb : float
Fix the mass balance to a certain value (unit: [mm w.e. yr-1])
"""
super(ScalarMassBalance, self).__init__()
self.hemisphere = 'nh'
self.valid_bounds = [-2e4, 2e4] # in m
self._mb = mb
def get_monthly_mb(self, heights, **kwargs):
mb = np.asarray(heights) * 0 + self._mb
return mb / SEC_IN_YEAR / self.rho
def get_annual_mb(self, heights, **kwargs):
mb = np.asarray(heights) * 0 + self._mb
return mb / SEC_IN_YEAR / self.rho
[docs]class LinearMassBalance(MassBalanceModel):
"""Constant mass balance as a linear function of altitude.
Attributes
----------
ela_h: float
the equilibrium line altitude (units: [m])
grad: float
the 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])
temp_bias
"""
[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])
"""
super(LinearMassBalance, self).__init__()
self.hemisphere = 'nh'
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):
"""Change the ELA following a simple rule: + 1K -> ELA + 150 m
A "temperature bias" doesn't makes much sense in the linear MB
context, but we implemented a simple empirical rule:
+ 1K -> ELA + 150 m
"""
return self._temp_bias
@temp_bias.setter
def temp_bias(self, value):
self.ela_h = self.orig_ela_h + value * 150
self._temp_bias = value
def get_monthly_mb(self, heights, **kwargs):
mb = (np.asarray(heights) - self.ela_h) * self.grad
if self.max_mb is not None:
clip_max(mb, self.max_mb, out=mb)
return mb / SEC_IN_YEAR / self.rho
def get_annual_mb(self, heights, **kwargs):
return self.get_monthly_mb(heights, **kwargs)
[docs]class PastMassBalance(MassBalanceModel):
"""Mass balance during the climate data period.
Attributes
----------
temp_bias
prcp_fac
"""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
filename='climate_historical', 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.
"""
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']:
msg = ('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 prevent this '
'error.')
raise InvalidWorkflowError(msg)
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']
# check if valid prcp_fac is used
if prcp_fac <= 0:
raise InvalidParamsError('prcp_fac has to be above zero!')
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.get_climate_info()['mb_calib_params']
for k, v in mb_calib.items():
if v != cfg.PARAMS[k]:
msg = ('You seem to use different mass balance parameters '
'than used for the calibration: '
f"you use cfg.PARAMS['{k}']={cfg.PARAMS[k]} while "
f"it was calibrated with cfg.PARAMS['{k}']={v}. "
'Set `check_calib_params=False` to ignore this '
'warning.')
raise InvalidWorkflowError(msg)
# Public attrs
self.hemisphere = gdir.hemisphere
self.repeat = repeat
# Private attrs
# to allow prcp_fac to be changed after instantiation
# prescribe the prcp_fac as it is instantiated
self._prcp_fac = prcp_fac
# same for temp bias
self._temp_bias = 0.
# Read file
fpath = gdir.get_filepath(filename, filesuffix=input_filesuffix)
with ncDataset(fpath, mode='r') as nc:
# time
time = nc.variables['time']
try:
time = netCDF4.num2date(time[:], time.units)
except ValueError:
# This is for longer time series
time = cftime.num2date(time[:], time.units, calendar='noleap')
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)
pok = slice(None) # take all is default (optim)
if ys is not None:
pok = self.years >= ys
if ye is not None:
try:
pok = pok & (self.years <= ye)
except TypeError:
pok = self.years <= ye
self.years = self.years[pok]
self.months = np.tile(np.arange(1, 13), ny)[pok]
# Read timeseries and correct it
self.temp = nc.variables['temp'][pok].astype(np.float64) + self._temp_bias
self.prcp = nc.variables['prcp'][pok].astype(np.float64) * self._prcp_fac
if 'gradient' in nc.variables:
grad = nc.variables['gradient'][pok].astype(np.float64)
# 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 = clip_array(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]
self.ye = self.years[-1]
# adds the possibility of changing prcp_fac
# after instantiation with properly changing the prcp time series
@property
def prcp_fac(self):
"""Precipitation factor (default: cfg.PARAMS['prcp_scaling_factor'])
Called factor to make clear that it is a multiplicative factor in
contrast to the additive temperature bias
"""
return self._prcp_fac
@prcp_fac.setter
def prcp_fac(self, new_prcp_fac):
# just to check that no invalid prcp_factors are used
if np.any(np.asarray(new_prcp_fac) <= 0):
raise InvalidParamsError('prcp_fac has to be above zero!')
if len(np.atleast_1d(new_prcp_fac)) == 12:
# OK so that's monthly stuff
# We dirtily assume that user just used calendar month
sm = cfg.PARAMS['hydro_month_' + self.hemisphere]
new_prcp_fac = np.roll(new_prcp_fac, 13 - sm)
new_prcp_fac = np.tile(new_prcp_fac, len(self.prcp) // 12)
self.prcp *= new_prcp_fac / self._prcp_fac
# update old prcp_fac in order that it can be updated again ...
self._prcp_fac = new_prcp_fac
@property
def temp_bias(self):
"""Add a temperature bias to the time series"""
return self._temp_bias
@temp_bias.setter
def temp_bias(self, new_temp_bias):
if len(np.atleast_1d(new_temp_bias)) == 12:
# OK so that's monthly stuff
# We dirtily assume that user just used calendar month
sm = cfg.PARAMS['hydro_month_' + self.hemisphere]
new_temp_bias = np.roll(new_temp_bias, 13 - sm)
new_temp_bias = np.tile(new_temp_bias, len(self.temp) // 12)
self.temp += new_temp_bias - self._temp_bias
# update old temp_bias in order that it can be updated again ...
self._temp_bias = new_temp_bias
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 already (temperature bias and precipitation factor corrected!)
itemp = self.temp[pok]
iprcp = self.prcp[pok]
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
clip_min(tempformelt, 0, out=tempformelt)
# 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 * clip_array(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 already (temperature bias and precipitation factor corrected!)
itemp = self.temp[pok]
iprcp = self.prcp[pok]
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
clip_min(temp2dformelt, 0, out=temp2dformelt)
# 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)
prcpsol = prcp * clip_array(fac, 0, 1)
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, tmelt, prcp, prcpsol = self._get_2d_annual_climate(heights, year)
return (t.mean(axis=1), tmelt.sum(axis=1),
prcp.sum(axis=1), prcpsol.sum(axis=1))
def get_monthly_mb(self, heights, year=None, add_climate=False, **kwargs):
t, tmelt, prcp, 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
if add_climate:
return (mb_month / SEC_IN_MONTH / self.rho, t, tmelt,
prcp, prcpsol)
return mb_month / SEC_IN_MONTH / self.rho
def get_annual_mb(self, heights, year=None, add_climate=False, **kwargs):
t, tmelt, prcp, prcpsol = self._get_2d_annual_climate(heights, year)
mb_annual = np.sum(prcpsol - self.mu_star * tmelt, axis=1)
mb_annual = (mb_annual - self.bias) / SEC_IN_YEAR / self.rho
if add_climate:
return (mb_annual, t.mean(axis=1), tmelt.sum(axis=1),
prcp.sum(axis=1), prcpsol.sum(axis=1))
return mb_annual
[docs]class ConstantMassBalance(MassBalanceModel):
"""Constant mass balance during a chosen period.
This is useful for equilibrium experiments. Note that is is the "correct"
way to represent the average mass balance over a given period.
See: https://oggm.org/2021/08/05/mean-forcing/
Attributes
----------
y0 : int
the center year of the period
halfsize : int
the halfsize of the period
years : ndarray
the years of the period
"""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
y0=None, halfsize=15, filename='climate_historical',
input_filesuffix='', **kwargs):
"""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,
**kwargs)
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:
if np.isfinite(nc.min_h_dem):
# a bug sometimes led to non-finite
zminmax = [nc.min_h_dem-250, nc.max_h_dem+1500]
else:
zminmax = [nc.min_h_glacier-1250, nc.max_h_glacier+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)
self.hemisphere = gdir.hemisphere
@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):
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_fac(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_fac
@prcp_fac.setter
def prcp_fac(self, value):
for attr_name in ['_lazy_interp_yr', '_lazy_interp_m']:
if hasattr(self, attr_name):
delattr(self, attr_name)
self.mbmod.prcp_fac = value
@property
def bias(self):
"""Residual bias to apply to the original series."""
return self.mbmod.bias
@bias.setter
def bias(self, value):
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_monthly_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)
"""
_, m = floatyear_to_date(year)
yrs = [date_to_floatyear(y, m) for y in self.years]
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
return (np.mean(temp, axis=0),
np.mean(tempformelt, axis=0),
np.mean(prcp, axis=0),
np.mean(prcpsol, axis=0))
def get_annual_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:
# this is consistent with OGGM's calendar
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, add_climate=False, **kwargs):
yr, m = floatyear_to_date(year)
if add_climate:
t, tmelt, prcp, prcpsol = self.get_monthly_climate(heights, year=year)
return self.interp_m[m-1](heights), t, tmelt, prcp, prcpsol
return self.interp_m[m-1](heights)
def get_annual_mb(self, heights, year=None, add_climate=False, **kwargs):
mb = self.interp_yr(heights)
if add_climate:
t, tmelt, prcp, prcpsol = self.get_annual_climate(heights)
return mb, t, tmelt, prcp, prcpsol
return mb
[docs]class AvgClimateMassBalance(ConstantMassBalance):
"""Mass balance with the average climate of a selected period (wrong!).
!!!Careful! This is conceptually wrong!!! This is here only to make
a point. See: https://oggm.org/2021/08/05/mean-forcing
"""
[docs] def __init__(self, gdir, mu_star=None, bias=None,
filename='climate_historical', input_filesuffix='',
y0=None, halfsize=15, **kwargs):
"""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
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)
"""
super(AvgClimateMassBalance, self).__init__(gdir, mu_star=mu_star,
bias=bias,
filename=filename,
input_filesuffix=input_filesuffix,
y0=y0, halfsize=halfsize)
if y0 is None:
df = gdir.read_json('local_mustar')
y0 = df['t_star']
self.mbmod = PastMassBalance(gdir, mu_star=mu_star, bias=bias,
filename=filename,
input_filesuffix=input_filesuffix,
ys=y0-halfsize, ye=y0+halfsize,
**kwargs)
tmp = self.mbmod.temp
assert (len(tmp) // 12) == (halfsize * 2 + 1)
self.mbmod.temp = tmp.reshape((len(tmp) // 12, 12)).mean(axis=0)
tmp = self.mbmod.prcp
self.mbmod.prcp = tmp.reshape((len(tmp) // 12, 12)).mean(axis=0)
tmp = self.mbmod.grad
self.mbmod.grad = tmp.reshape((len(tmp) // 12, 12)).mean(axis=0)
self.mbmod.ys = y0
self.mbmod.ye = y0
self.mbmod.months = np.arange(1, 13, dtype=int)
self.mbmod.years = np.asarray([y0]*12)
self.years = np.asarray([y0]*12)
[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_historical', input_filesuffix='',
all_years=False, unique_samples=False, prescribe_years=None,
**kwargs):
"""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
prescribe_years : pandas Series
instead of random samples, take a series of (i, y) pairs where
(i) is the simulation year index and (y) is the year to pick in the
original timeseries. Overrides `y0`, `halfsize`, `all_years`,
`unique_samples` and `seed`.
**kwargs:
kyeword arguments to pass to the PastMassBalance model
"""
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,
**kwargs)
# Climate period
self.prescribe_years = prescribe_years
if self.prescribe_years is None:
# Normal stuff
self.rng = np.random.RandomState(seed)
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)
else:
self.rng = None
self.years = self.prescribe_years.index
self.yr_range = (self.years[0], self.years[-1] + 1)
self.ny = len(self.years)
self.hemisphere = gdir.hemisphere
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_fac(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_fac
@prcp_fac.setter
def prcp_fac(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_fac = 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.prescribe_years is not None:
self._state_yr[year] = self.prescribe_years.loc[year]
else:
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, **kwargs):
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, **kwargs)
def get_annual_mb(self, heights, year=None, **kwargs):
ryr = self.get_state_yr(int(year))
return self.mbmod.get_annual_mb(heights, year=ryr, **kwargs)
[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_fac_seed=None, rdn_prcp_fac_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_fac_seed : int
the seed of the random number generator
rdn_prcp_fac_sigma : float
the standard deviation of the random precipitation error
(to be consistent this should be renamed prcp_fac as well)
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__()
# the aim here is to change temp_bias and prcp_fac so
self.mbmod = basis_model
self.hemisphere = basis_model.hemisphere
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_fac_seed)
self.rng_bias = np.random.RandomState(rdn_bias_seed)
self._temp_sigma = rdn_temp_bias_sigma
self._prcp_sigma = rdn_prcp_fac_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_fac(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_fac
@prcp_fac.setter
def prcp_fac(self, value):
"""Precipitation factor to apply to the original series."""
self.mbmod.prcp_fac = 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, **kwargs):
raise NotImplementedError()
def get_annual_mb(self, heights, year=None, fl_id=None, **kwargs):
# Keep the original biases and add a random error
_t = self.mbmod.temp_bias
_p = self.mbmod.prcp_fac
_b = self.mbmod.bias
self.mbmod.temp_bias = self._get_state_temp(year) + _t
self.mbmod.prcp_fac = 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_fac = _p
self.mbmod.bias = _b
raise
# Back to normal
self.mbmod.temp_bias = _t
self.mbmod.prcp_fac = _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 wrapping 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 InvalidWorkflowError('Need a valid `model_flowlines` '
'file. If you explicitly want to '
'use `inversion_flowlines`, set '
'use_inversion_flowlines=True.')
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
self.hemisphere = gdir.hemisphere
@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_fac(self):
"""Precipitation factor to apply to the original series."""
return self.flowline_mb_models[0].prcp_fac
@prcp_fac.setter
def prcp_fac(self, value):
"""Precipitation factor to apply to the original series."""
for mbmod in self.flowline_mb_models:
mbmod.prcp_fac = 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, **kwargs):
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,
**kwargs)
def get_annual_mb(self, heights, year=None, fl_id=None, **kwargs):
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,
**kwargs)
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 i, (fl, mb_mod) in enumerate(zip(fls, self.flowline_mb_models)):
_widths = fl.widths
try:
# For rect and parabola don't compute spec mb
_widths = np.where(fl.thick > 0, _widths, 0)
except AttributeError:
pass
widths = np.append(widths, _widths)
mb = mb_mod.get_annual_mb(fl.surface_h, year=year, fls=fls, fl_id=i)
mbs = np.append(mbs, mb * SEC_IN_YEAR * mb_mod.rho)
return weighted_average_1d(mbs, widths)
def get_ela(self, year=None, **kwargs):
# 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_id, (fl, mb_mod) in enumerate(zip(self.fls,
self.flowline_mb_models)):
elas = np.append(elas, mb_mod.get_ela(year=year, fl_id=fl_id,
fls=self.fls))
areas = np.append(areas, np.sum(fl.widths))
return weighted_average_1d(elas, areas)
[docs]@entity_task(log)
def fixed_geometry_mass_balance(gdir, ys=None, ye=None, years=None,
monthly_step=False,
use_inversion_flowlines=True,
climate_filename='climate_historical',
climate_input_filesuffix='',
temperature_bias=None,
precipitation_factor=None):
"""Computes the mass balance with climate input from e.g. CRU or a GCM.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
ys : int
start year of the model run (default: from the climate file)
date)
ye : int
end year of the model run (default: from the climate file)
years : array of ints
override ys and ye with the years of your choice
monthly_step : bool
whether to store the diagnostic data at a monthly time step or not
(default is yearly)
use_inversion_flowlines : bool
whether to use the inversion flowlines or the model flowlines
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or
'gcm_data'
climate_input_filesuffix: str
filesuffix for the input climate file
temperature_bias : float
add a bias to the temperature timeseries
precipitation_factor: float
multiply a factor to the precipitation time series
default is None and means that the precipitation factor from the
calibration is applied which is cfg.PARAMS['prcp_scaling_factor']
"""
if monthly_step:
raise NotImplementedError('monthly_step not implemented yet')
mbmod = MultipleFlowlineMassBalance(gdir, mb_model_class=PastMassBalance,
filename=climate_filename,
use_inversion_flowlines=use_inversion_flowlines,
input_filesuffix=climate_input_filesuffix)
if temperature_bias is not None:
mbmod.temp_bias = temperature_bias
if precipitation_factor is not None:
mbmod.prcp_fac = precipitation_factor
if years is None:
if ys is None:
ys = mbmod.flowline_mb_models[0].ys
if ye is None:
ye = mbmod.flowline_mb_models[0].ye
years = np.arange(ys, ye + 1)
odf = pd.Series(data=mbmod.get_specific_mb(year=years),
index=years)
return odf
[docs]@entity_task(log)
def compute_ela(gdir, ys=None, ye=None, years=None, climate_filename='climate_historical',
temperature_bias=None, precipitation_factor=None, climate_input_filesuffix=''):
"""Computes the ELA of a glacier for a for given years and climate.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
ys : int
start year
ye : int
end year
years : array of ints
override ys and ye with the years of your choice
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or
'gcm_data'
climate_input_filesuffix : str
filesuffix for the input climate file
temperature_bias : float
add a bias to the temperature timeseries
precipitation_factor: float
multiply a factor to the precipitation time series
default is None and means that the precipitation factor from the
calibration is applied which is cfg.PARAMS['prcp_scaling_factor']
"""
mbmod = PastMassBalance(gdir, filename=climate_filename,
input_filesuffix=climate_input_filesuffix)
if temperature_bias is not None:
mbmod.temp_bias = temperature_bias
if precipitation_factor is not None:
mbmod.prcp_fac = precipitation_factor
mbmod.valid_bounds = [-10000, 20000]
if years is None:
years = np.arange(ys, ye+1)
ela = []
for yr in years:
ela = np.append(ela, mbmod.get_ela(year=yr))
odf = pd.Series(data=ela, index=years)
return odf
