def get_monthly_mb(self, heights, year=None, **kwargs):
"""Calculate the monthly mass balance for the glacier.
Parameters
----------
heights : np.ndarray
altitudes at which to compute the MB
year : float, optional
The current hydrological year. Used to evaluate if mass balance model should be updated.
Returns
-------
The monthly mass-balances for each height.
"""
# A year will always be provided during a model run, the only time we do this evaluation.
# In ohter cases i.e. plotting annual mb, we don't evaluate this.
# We check if its a float instead of simply if year since this would miss the 0th year.
if isinstance(year, float):
# Since we are progressing the glacier we want the next year of the climate to update
# the mass balance.
year = year + 1
# If we have a future climate scenario essentially.
if year <= self.temp_bias_series.year.iloc[-1]:
# We get a temp bias from the series, update the ela of our mb.
# Uses the temp_bias setter.
self.temp_bias = self.temp_bias_series.bias[
self.temp_bias_series.year == year].values[0]
# If there is no future climate scenario, we simply append the current bias to the history.
# e.g. when climate remains constant.
else:
# Add the current temperature bias (unchanged) to the history.
self.temp_bias_series = [self.temp_bias]
# Compute the mb
# We use the _gradient_lookup to get an array of gradients matching the length of heights.
# _breakpoint_diff computes the mb difference at the breakpoint height for the two different
# gradients. Used to adjust the intercept of the new gradient curve.
mb = (np.asarray(heights) - self.ela_h) * self._gradient_lookup(
heights) + self._breakpoint_diff(heights)
# Adjust intercept for the case when breakpoint is above ELA.
# Essentially making sure that the mb profile is zero at the ELA.
ela_idx = np.argmin(np.abs(np.asarray(heights) - self.ela_h))
mb = mb - mb[ela_idx]
# Should we cap the mb?
if self.max_mb:
clip_max(mb, self.max_mb, out=mb)
return mb / SEC_IN_YEAR / self.rho
def _vol_below_water(surface_h, bed_h, bed_shape, thick, widths,
is_rectangular, fac, dx, water_level):
bsl = (bed_h < water_level) & (thick > 0)
n_thick = np.copy(thick)
n_thick[~bsl] = 0
n_thick[bsl] = utils.clip_max(surface_h[bsl], water_level) - bed_h[bsl]
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
n_w = np.sqrt(4 * n_thick / bed_shape)
n_w[is_rectangular] = widths[is_rectangular]
return fac * n_thick * n_w * dx
def _vol_below_water(surface_h, bed_h, bed_shape, thick, widths,
is_rectangular, is_trapezoid, fac, t_lambda,
dx, water_level):
bsl = (bed_h < water_level) & (thick > 0)
n_thick = np.copy(thick)
n_thick[~bsl] = 0
n_thick[bsl] = utils.clip_max(surface_h[bsl], water_level) - bed_h[bsl]
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
n_w = np.sqrt(4 * n_thick / bed_shape)
n_w[is_rectangular] = widths[is_rectangular]
out = fac * n_thick * n_w * dx
# Trap
it = is_trapezoid
out[it] = (n_w[it] + n_w[it] - t_lambda*n_thick[it]) / 2*n_thick[it]*dx
return out
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