def _init_model(self, solver):
if solver == 'numba':
solver = PegasusNumba()
num_app = ImplicitEulerNumba(root_finder=solver)
elif solver == 'python':
solver = PegasusPython()
num_app = ImplicitEulerPython(root_finder=solver)
fr = PowerReservoir(parameters = {'k' : 0.01,
'alpha' : 2.5},
states = {'S0' : 0.0},
approximation=num_app,
id = 'FR')
ur = UnsaturatedReservoir(parameters = {'Smax' : 50.0,
'Ce' : 1.5,
'm' : 0.01,
'beta' : 1.5,
},
states = {'S0' : 0.2*50.0, 'PET' : None},
approximation=num_app,
id = 'UR')
hru = Unit(layers = [
[ur],
[fr]
],
id = 'H1')
hru.set_timestep(1.0)
self._model = hru
def _init_model(self, solver):
if solver == 'numba':
solver = PegasusNumba()
num_app = ImplicitEulerNumba(root_finder=solver)
elif solver == 'python':
solver = PegasusPython()
num_app = ImplicitEulerPython(root_finder=solver)
# Define HRU 1 (40%)
fr = PowerReservoir(parameters = {'k' : 0.01,
'alpha' : 2.5},
states = {'S0' : 0.0},
approximation=num_app,
id = 'FR')
h1 = Unit(layers = [[fr]], id = 'H1')
# Define HRU 2 (60%)
fr = PowerReservoir(parameters = {'k' : 0.01,
'alpha' : 2.5},
states = {'S0' : 0.0},
approximation=num_app,
id = 'FR')
sr = PowerReservoir(parameters = {'k' : 1e-4,
'alpha' : 1.0},
states = {'S0' : 0.0},
approximation=num_app,
id = 'SR')
ur = UnsaturatedReservoir(parameters = {'Smax' : 50.0,
'Ce' : 1.5,
'm' : 0.01,
'beta' : 1.5,
},
states = {'S0' : 0.2*50.0, 'PET' : None},
approximation=num_app,
id = 'UR')
s = Splitter(weight = [[0.3], [0.7]],
direction = [[0], [0]],
id = 'S')
j = Junction(direction = [[0, 0]],
id = 'J')
h2 = Unit(layers = [
[ur],
[s],
[fr, sr],
[j]
],
id = 'H2')
# Define the catchment
cat = Node(units = [h1, h2], weights = [0.4, 0.6], area = 1.0, id = 'Cat')
cat.set_timestep(1.0)
self._model = cat
def _init_model(self, solver):
if solver == 'numba':
solver = PegasusNumba()
num_app = ImplicitEulerNumba(root_finder=solver)
elif solver == 'python':
solver = PegasusPython()
num_app = ImplicitEulerPython(root_finder=solver)
fr = PowerReservoir(parameters={'k': 0.01,
'alpha': 2.5},
states={'S0': 0.0},
approximation=num_app,
id='FR')
fr.set_timestep(2.0)
self._model = fr
def _init_model(self, solver):
if solver == 'numba':
solver = PegasusNumba()
num_app = ImplicitEulerNumba(root_finder=solver)
elif solver == 'python':
solver = PegasusPython()
num_app = ImplicitEulerPython(root_finder=solver)
ur = UnsaturatedReservoir(parameters={
'Smax': 50.0,
'Ce': 1.5,
'm': 0.01,
'beta': 1.5
},
states={'S0': 0.2 * 50.0},
approximation=num_app,
id='UR')
ur.set_timestep(1.0)
self._model = ur
'm04_SR_k': 0.000916739466627,
'm04_split_split-par': 0.0,
}
PARAMETERS_WEIERBACH_M04 = {
'm04_FR_k': 0.003454773102112,
'm04_FR_alpha': 2.06272140965805,
'm04_UR_Ce': 0.771711960349972,
'm04_UR_Smax': 117.195201845032,
'm04_UR_beta': 10.0,
'm04_SR_k': 0.00157523385576,
'm04_split_split-par': 0.0,
}
root_finder = PegasusPython(iter_max=1000)
num_app = ImplicitEulerPython(root_finder=root_finder)
unsaturated_reservoir = UnsaturatedReservoir(parameters={
'Smax': 200.0,
'Ce': 1.0,
'm': 0.01,
'beta': 0.02
},
states={'S0': 100.0},
approximation=num_app,
id='UR')
splitter = ParameterizedSingleFluxSplitter(parameters={'split-par': 0.3},
id='split')
fast_reservoir = PowerReservoir(parameters={
'k': 0.1,
visit the page https://superflexpy.readthedocs.io
CODED BY: Marco Dal Molin
DESIGNED BY: Marco Dal Molin, Fabrizio Fenicia, Dmitri Kavetski
This file implements a version of the model Hymod
"""
from superflexpy.implementation.computation.pegasus_root_finding import PegasusPython
from superflexpy.implementation.computation.implicit_euler import ImplicitEulerPython
from superflexpy.implementation.elements.hymod import UpperZone, LinearReservoir
from superflexpy.implementation.elements.structure_elements import Junction, Splitter, Transparent
from superflexpy.framework.unit import Unit
root_finder = PegasusPython() # Use the default parameters
numerical_approximation = ImplicitEulerPython(root_finder)
upper_zone = UpperZone(parameters={
'Smax': 50.0,
'm': 0.01,
'beta': 2.0
},
states={'S0': 10.0},
approximation=numerical_approximation,
id='uz')
splitter = Splitter(weight=[[0.6], [0.4]], direction=[[0], [0]], id='spl')
channel_routing_1 = LinearReservoir(parameters={'k': 0.1},
states={'S0': 10.0},
approximation=numerical_approximation,
Reference
---------
Kavetski, D., and F. Fenicia (2011), Elements of a flexible approach
for conceptual hydrological modeling: 2. Application and experimental insights,
Water Resour. Res., 47, W11511, doi:10.1029/2011WR010748
"""
from superflexpy.implementation.computation.pegasus_root_finding import PegasusPython
from superflexpy.implementation.computation.implicit_euler import ImplicitEulerPython
from superflexpy.implementation.elements.hbv import UnsaturatedReservoir, PowerReservoir
from superflexpy.framework.unit import Unit
root_finder = PegasusPython()
numeric_approximator = ImplicitEulerPython(root_finder=root_finder)
ur = UnsaturatedReservoir(parameters={
'Smax': 50.0,
'Ce': 1.0,
'm': 0.01,
'beta': 2.0
},
states={'S0': 25.0},
approximation=numeric_approximator,
id='UR')
fr = PowerReservoir(parameters={
'k': 0.1,
'alpha': 1.0
},
import sys
import pathlib
REPO_FOLDER = pathlib.Path(__file__).absolute().parents[2]
sys.path.insert(0, str(REPO_FOLDER))
from superflexpy.implementation.elements.thur_model_hess import SnowReservoir, UnsaturatedReservoir, HalfTriangularLag, PowerReservoir
from superflexpy.implementation.elements.structure_elements import Transparent, Junction, Splitter
from superflexpy.framework.unit import Unit
from superflexpy.framework.node import Node
from superflexpy.framework.network import Network
from superflexpy.implementation.computation.pegasus_root_finding import PegasusPython
from superflexpy.implementation.computation.implicit_euler import ImplicitEulerPython
solver = PegasusPython()
approximator = ImplicitEulerPython(root_finder=solver)
# Fluxes in the order P, T, PET
upper_splitter = Splitter(
direction=[
[0, 1, None], # P and T go to the snow reservoir
[2, None, None] # PET goes to the transparent element
],
weight=[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
id='upper-splitter')
snow = SnowReservoir(parameters={
't0': 0.0,
'k': 0.01,
'm': 2.0
},
states={'S0': 0.0},
approximation=approximator,