def bu_tidewater_bed(gridsize=200, gridlength=6e4, widths_m=600,
b_0=260, alpha=0.017, b_1=350, x_0=4e4, sigma=1e4,
water_level=0, split_flowline_before_water=None):
# Bassis & Ultee bed profile
dx_meter = gridlength / gridsize
x = np.arange(gridsize+1) * dx_meter
bed_h = b_0 - alpha * x + b_1 * np.exp(-((x - x_0) / sigma)**2)
bed_h += water_level
surface_h = bed_h
widths = surface_h * 0. + widths_m / dx_meter
if split_flowline_before_water is not None:
bs = np.min(np.nonzero(bed_h < 0)[0]) - split_flowline_before_water
fls = [RectangularBedFlowline(dx=1, map_dx=dx_meter,
surface_h=surface_h[:bs],
bed_h=bed_h[:bs],
widths=widths[:bs]),
RectangularBedFlowline(dx=1, map_dx=dx_meter,
surface_h=surface_h[bs:],
bed_h=bed_h[bs:],
widths=widths[bs:]),
]
fls[0].set_flows_to(fls[1], check_tail=False, to_head=True)
return fls
else:
return [
RectangularBedFlowline(dx=1, map_dx=dx_meter, surface_h=surface_h,
bed_h=bed_h, widths=widths)]
def objfunc(surface_h):
# glacier bed
# This is the bed rock, linearily decreasing from 3000m altitude to 1000m, in 200 steps
nx = 200
bed_h = np.linspace(3400, 1400, nx)
# At the begining, there is no glacier so our glacier surface is at the bed altitude
# Let's set the model grid spacing to 100m (needed later)
map_dx = 100
# The units of widths is in "grid points", i.e. 3 grid points = 300 m in our case
widths = np.zeros(nx) + 3.
# Define our bed
init_flowline = RectangularBedFlowline(surface_h=rescale(surface_h, nx),
bed_h=bed_h,
widths=widths,
map_dx=map_dx)
# ELA at 3000m a.s.l., gradient 4 mm m-1
mb_model = LinearMassBalance(3000, grad=4)
#annual_mb = mb_model.get_mb(surface_h) * SEC_IN_YEAR
# The model requires the initial glacier bed, a mass-balance model, and an initial time (the year y0)
model = FlowlineModel(init_flowline, mb_model=mb_model, y0=150)
model.run_until(300)
measured = pickle.load(
open('/home/juliaeis/PycharmProjects/find_inital_state/fls_300.pkl',
'rb'))
f = abs(model.fls[-1].surface_h - measured.fls[-1].surface_h)+\
abs(min_length_m(model.fls)-measured.length_m)+\
abs(model.area_km2-measured.area_km2)+\
abs(model.volume_km3-measured.volume_km3)
print(sum(f))
return sum(f)
def _init_flowline(self):
# Initialise a RectangularBedFlowline for the glacier.
return RectangularBedFlowline(
surface_h=self.surface_h,
bed_h=self.bed.bed_h,
widths=self.bed.widths,
map_dx=self.bed.map_dx,
)
def run_model(surface_h):
nx = 200
bed_h = np.linspace(3400, 1400, nx)
# At the begining, there is no glacier so our glacier surface is at the bed altitude
# Let's set the model grid spacing to 100m (needed later)
map_dx = 100
# The units of widths is in "grid points", i.e. 3 grid points = 300 m in our case
widths = np.zeros(nx) + 3.
# Define our bed
init_flowline = RectangularBedFlowline(surface_h=rescale(surface_h, nx),
bed_h=bed_h,
widths=widths,
map_dx=map_dx)
# ELA at 3000m a.s.l., gradient 4 mm m-1
mb_model = LinearMassBalance(3000, grad=4)
# annual_mb = mb_model.get_mb(surface_h) * SEC_IN_YEAR
# The model requires the initial glacier bed, a mass-balance model, and an initial time (the year y0)
model = FlowlineModel(init_flowline, mb_model=mb_model, y0=150)
return model
def surging_glacier(yrs, init_flowline, mb_model, bed, widths, map_dx, glen_a,
fs, fs_surge, model):
"""Function implements surging events in evolution of glacier. 2 different
sliding parameters can be used.
Parameters
----------
yrs : int
years in which glacier evolution should be calculated
init_flowline : flowline
mb_model : mass balance model
bed : ndarray
bed rock
widths : ndarray
width grid
map_dx : int
grid spacing (e.g. 100 m)
glen_a : float
Glen's parameter
fs : float
sliding parameter for slow motion years
fs_surge : float
sliding parameter for the surging period
model : oggm-model
Returns
-------
model
surging_glacier_h : list
outline of glacier
length_s3 : ndarray
length after each time step
vol_s3 : ndarray
volume after each time step
"""
# Array to fill with data
nsteps = len(yrs)
length_s3 = np.zeros(nsteps)
vol_s3 = np.zeros(nsteps)
surging_glacier_h = []
# Loop & glacier evolution
for i, yr in enumerate(yrs):
model.run_until(yr)
length_s3[i] = model.length_m
vol_s3[i] = model.volume_km3
if i == 0 or i == (nsteps - 1):
continue
elif (yr - yrs[i - 1]) == 10 and (yrs[i + 1] - yr) == 1:
# Save glacier geometry before the surge
surging_glacier_h.append(model.fls[-1].surface_h)
# Glacier evolution
surging_glacier_h_ts = model.fls[-1].surface_h
init_flowline = RectangularBedFlowline(
surface_h=surging_glacier_h_ts,
bed_h=bed,
widths=widths,
map_dx=map_dx)
model = FlowlineModel(init_flowline,
mb_model=mb_model,
y0=yr,
glen_a=glen_a,
fs=fs_surge)
elif (yr - yrs[i - 1]) == 1 and (yrs[i + 1] - yr) == 10:
# Save glacier geometry after the surge
surging_glacier_h.append(model.fls[-1].surface_h)
# Glacier evolution
surging_glacier_h_ts = model.fls[-1].surface_h
init_flowline = RectangularBedFlowline(
surface_h=surging_glacier_h_ts,
bed_h=bed,
widths=widths,
map_dx=map_dx)
model = FlowlineModel(init_flowline,
mb_model=mb_model,
y0=yr,
glen_a=glen_a,
fs=fs)
return model, surging_glacier_h, length_s3, vol_s3
def linear_mb_equilibrium(bed,
surface,
accw,
elaw,
nz,
mb_grad,
nx,
map_dx,
idx=None,
advance=None,
retreat=None,
plot=True):
"""Runs the OGGM FluxBasedModel with a linear mass balance
gradient until the glacier reaches equilibrium.
Parameters
----------
bed : ndarray
the glacier bed
surface : ndarray
the initial glacier surface
accw : int
the width of the glacier at the top of the accumulation area
elaw : int
the width of the glacier at the equilibrium line altitude
nz : float
fraction of vertical grid points occupied by accumulation area
mb_grad : int, float
the mass balance altitude gradient in mm w.e. m^-1 yr^-1
nx : int
number of grid points
map_dx : int
grid point spacing
idx : int, optional
number of vertical grid points to shift ELA down/upglacier
advance : bool, optional
move the ELA downglacier by idx grid points to simulate
a glacier advance
retreat : bool, optional
move the ELA upglacier by idx grid points to simulate
a glacier retreat
plot : bool, optional
show a pseudo-3d plot of the glacier (default: True)
Returns
-------
model : oggm.core.flowline.FluxBasedModel
OGGM model class of the glacier in equilibrium
"""
# accumulation area occupies a fraction NZ of the total glacier extent
acc_width = np.linspace(accw, elaw, int(nx * nz))
# ablation area occupies a fraction 1 - NZ of the total glacier extent
abl_width = np.tile(elaw, nx - len(acc_width))
# glacier widths profile
widths = np.hstack([acc_width, abl_width])
# model widths
mwidths = np.zeros(nx) + widths / map_dx
# define the glacier bed
init_flowline = RectangularBedFlowline(surface_h=surface,
bed_h=bed,
widths=mwidths,
map_dx=map_dx)
# equilibrium line altitude
# in case of an advance scenario, move the ELA downglacier by a number of
# vertical grid points idx
if advance and idx:
ela = bed[np.where(widths == elaw)[0][idx]]
# in case of a retreat scenario, move the ELA upglacier by a number of
# vertical grid points idx
elif retreat and idx:
ela = bed[np.where(widths == acc_width[-idx])[0][0]]
# in case of no scenario, the ela is the height where the width of the ela
# is first reached
else:
ela = bed[np.where(widths == elaw)[0][0]]
# linear mass balance model
mb_model = LinearMassBalance(ela, grad=mb_grad)
# flowline model
model = FluxBasedModel(init_flowline,
mb_model=mb_model,
y0=0.,
min_dt=0,
cfl_number=0.01)
# run until the glacier reaches an equilibrium
model.run_until_equilibrium()
# show a pseudo-3d plot of the glacier geometry
if plot:
dis = distance_along_glacier(nx, map_dx)
plot_glacier_3d(dis, bed, widths, nx)
return model
commit_model = FluxBasedModel(fls, mb_model=past_climate,
glen_a=cfg.A, y0=2000, time_stepping='ambitious')
'''
nx = 200
bed_h = np.linspace(3400, 1400, nx)
# At the begining, there is no glacier so our glacier surface is at the bed altitude
surface_h = bed_h
# Let's set the model grid spacing to 100m (needed later)
map_dx = 100
# The units of widths is in "grid points", i.e. 3 grid points = 300 m in our case
widths = np.zeros(nx) + 3.
# Define our bed
init_flowline = RectangularBedFlowline(surface_h=surface_h,
bed_h=bed_h,
widths=widths,
map_dx=map_dx)
# ELA at 3000m a.s.l., gradient 4 mm m-1
mb_model = LinearMassBalance(3000, grad=4)
commit_model = FlowlineModel(init_flowline, mb_model=mb_model, y0=0)
commit_model.run_until(300)
constant_climate = LinearMassBalance(3000, grad=3)
commit_model = FlowlineModel(commit_model.fls,
mb_model=constant_climate,
y0=200)
y_start = copy.deepcopy(commit_model)
x = np.arange(
y_start.fls[-1].nx) * y_start.fls[-1].dx * y_start.fls[-1].map_dx
