def initialize(self, input_stream=None):
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream)==ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node['topographic__elevation']
except FieldError:
try:
topo_field_name = inputs.read_string('ELEVATION_FIELD_NAME')
except AttributeError:
print('Error: Because your grid does not have a node field called')
print('"topographic__elevation", you need to pass the name of')
print('a text input file or ModelParameterDictionary, and this')
print('file or dictionary needs to include the name of another')
print('field in your grid that contains your elevation data.')
raise AttributeError
except MissingKeyError:
print('Error: Because your grid does not have a node field called')
print('"topographic__elevation", your input file (or')
print('ModelParameterDictionary) must include an entry with the')
print('key "ELEVATION_FIELD_NAME", which gives the name of a')
print('field in your grid that contains your elevation data.')
raise MissingKeyError('ELEVATION_FIELD_NAME')
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print('Your grid does not seem to include a node field called',topo_field_name)
# Create output variables.
#
# Note that we initialize depression depth to -1 (negative values make
# no sense, so this is a clue to non-flooded nodes), and depression
# outlet ID to BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros('node', 'depression__depth') - 1.0
self.depression_outlet = self._grid.add_zeros('node', 'depression__outlet_node_id', dtype=int) + BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = numpy.amax(self._elev)+1
# We'll also need a handy copy of the node neighbor lists
# TODO: presently, this grid method seems to only exist for Raster grids.
# We need it for *all* grids!
self._node_nbrs = self._grid.get_neighbor_list()
if type(self._grid) is landlab.grid.raster.RasterModelGrid:
diag_nbrs = self._grid.get_diagonal_list()
self._node_nbrs = numpy.concatenate((self._node_nbrs, diag_nbrs), 1)
def initialize(self, input_stream=None):
"""
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node["topographic__elevation"]
except FieldError:
try:
self.topo_field_name = inputs.read_string("ELEVATION_" +
"FIELD_NAME")
except AttributeError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", you need to pass the')
print("name of a text input file or ModelParameterDictionary,")
print("and this file or dictionary needs to include the name")
print("of another field in your grid that contains your")
print("elevation data.")
raise AttributeError
except MissingKeyError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", your input file (or')
print("ModelParameterDictionary) must include an entry with")
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print("of a field in your grid that contains your elevation")
print("data.")
raise MissingKeyError("ELEVATION_FIELD_NAME")
try:
self._elev = self._grid.at_node[self.topo_field_name]
except AttributeError:
print(
"Your grid does not seem to have a node field called",
self.topo_field_name,
)
else:
self.topo_field_name = "topographic__elevation"
# create the only new output field:
self.sed_fill_depth = self._grid.add_zeros("node",
"sediment_fill__depth",
noclobber=False)
self._lf = DepressionFinderAndRouter(self._grid, routing=self._routing)
self._fr = FlowAccumulator(self._grid, flow_director=self._routing)
def initialize(self, input_stream=None):
"""
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node["topographic__elevation"]
except FieldError:
try:
self.topo_field_name = inputs.read_string("ELEVATION_" + "FIELD_NAME")
except AttributeError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", you need to pass the')
print("name of a text input file or ModelParameterDictionary,")
print("and this file or dictionary needs to include the name")
print("of another field in your grid that contains your")
print("elevation data.")
raise AttributeError
except MissingKeyError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", your input file (or')
print("ModelParameterDictionary) must include an entry with")
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print("of a field in your grid that contains your elevation")
print("data.")
raise MissingKeyError("ELEVATION_FIELD_NAME")
try:
self._elev = self._grid.at_node[self.topo_field_name]
except AttributeError:
print(
"Your grid does not seem to have a node field called",
self.topo_field_name,
)
else:
self.topo_field_name = "topographic__elevation"
# create the only new output field:
self.sed_fill_depth = self._grid.add_zeros(
"node", "sediment_fill__depth", noclobber=False
)
self._lf = DepressionFinderAndRouter(self._grid, routing=self._routing)
self._fr = FlowAccumulator(self._grid, flow_director=self._routing)
def initialize(self, input_stream=None):
"""Initialize the component from an input file.
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
Parameters
----------
input_stream : str, file_like, or ModelParameterDictionary, optional
ModelParameterDictionary that holds the input parameters.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node["topographic__elevation"]
except FieldError:
try:
topo_field_name = inputs.read_string("ELEVATION_FIELD_NAME")
except AttributeError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", you need to pass the')
print("name of a text input file or ModelParameterDictionary,")
print("and this file or dictionary needs to include the name")
print("of another field in your grid that contains your")
print("elevation data.")
raise AttributeError
except MissingKeyError:
print("Error: Because your grid does not have a node field")
print('called "topographic__elevation", your input file (or')
print("ModelParameterDictionary) must include an entry with")
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print("of a field in your grid that contains your elevation")
print("data.")
raise MissingKeyError("ELEVATION_FIELD_NAME")
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print("Your grid does not seem to have a node field called", topo_field_name)
# Create output variables.
#
# Note that we initialize depression
# outlet ID to BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros("node", "depression__depth")
self.depression_outlet_map = self._grid.add_zeros("node", "depression__outlet_node", dtype=int)
self.depression_outlet_map += BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = np.amax(self._elev) + 1
# We'll also need a handy copy of the node neighbor lists
# TODO: presently, this grid method seems to only exist for Raster
# grids. We need it for *all* grids!
self._node_nbrs = self._grid.get_active_neighbors_at_node()
dx = self._grid.dx
dy = self._grid.dy
if self._D8:
diag_nbrs = self._grid.get_diagonal_list()
self._node_nbrs = np.concatenate((self._node_nbrs, diag_nbrs), 1)
self._link_lengths = np.empty(8, dtype=float)
self._link_lengths[0] = dx
self._link_lengths[2] = dx
self._link_lengths[1] = dy
self._link_lengths[3] = dy
self._link_lengths[4:].fill(np.sqrt(dx * dx + dy * dy))
elif (type(self._grid) is landlab.grid.raster.RasterModelGrid) and (self._routing is "D4"):
self._link_lengths = np.empty(4, dtype=float)
self._link_lengths[0] = dx
self._link_lengths[2] = dx
self._link_lengths[1] = dy
self._link_lengths[3] = dy
else:
self._link_lengths = self._grid.link_length
self._lake_outlets = [] # a list of each unique lake outlet
# ^note this is nlakes-long
self.is_pit = self._grid.add_ones("node", "is_pit", dtype=bool)
self.flood_status = self._grid.add_zeros("node", "flood_status_code", dtype=int)
self._lake_map = np.empty(self._grid.number_of_nodes, dtype=int)
self._lake_map.fill(BAD_INDEX_VALUE)
def initialize(self, grid, params_file):
"""
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', or a string giving the name of the model field
where capacity values are stored on nodes.
At the moment, only 'MPM' is permitted as a way to set the
capacity automatically, but expansion would be trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width and
basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each node,
don't set, and use the Dchar_if_used argument in erode()
instead.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set True,
becomes a weak function of local slope following Lamb et al
(2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation,
An exception will be raised if threshold_shields is
also set.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. NOT YET IMPLEMENTED
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge']. NOT YET IMPLEMENTED
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
"""
# this is the fraction we allow any given slope in the grid to evolve by in one go (suppresses numerical instabilities)
self.fraction_gradient_change = 0.25
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(
grid.number_of_links + 1
) # needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float("g")
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float("rock_density")
except MissingKeyError:
self.rock_density = 2700.0
try:
self.sed_density = inputs.read_float("sediment_density")
except MissingKeyError:
self.sed_density = 2700.0
try:
self.fluid_density = inputs.read_float("fluid_density")
except MissingKeyError:
self.fluid_density = 1000.0
self.rho_g = self.fluid_density * self.g
try:
self.Qc = inputs.read_string("Qc")
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc == "MPM":
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.return_ch_props = inputs.read_bool("return_stream_properties")
except MissingKeyError:
self.return_ch_props = False
try:
self.lamb_flag = inputs.read_bool("slope_sensitive_threshold")
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float("threshold_shields")
self.set_threshold = True # flag for sed_flux_dep_incision to see if the threshold was manually set.
print("Found a threshold to use: ", self.shields_crit)
assert self.lamb_flag == False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.tstep = inputs.read_float("dt")
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool("use_W")
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool("use_Q")
except MissingKeyError:
self.use_Q = False
try:
self.return_capacity = inputs.read_bool("return_capacity")
except MissingKeyError:
self.return_capacity = False
try:
self._b = inputs.read_float("b_sp")
except MissingKeyError:
if self.use_W:
self._b = 0.0
else:
if self.calc_cap_flag:
raise NameError("b was not set")
try:
self._c = inputs.read_float("c_sp")
except MissingKeyError:
if self.use_Q:
self._c = 1.0
else:
if self.calc_cap_flag:
raise NameError("c was not set")
try:
self.Dchar_in = inputs.read_float("Dchar")
except MissingKeyError:
pass
# assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6 * self._c * (1.0 - self._b)
self.k_Q = inputs.read_float("k_Q")
self.k_w = inputs.read_float("k_w")
mannings_n = inputs.read_float("mannings_n")
self.mannings_n = mannings_n
if mannings_n < 0.0 or mannings_n > 0.2:
print(
"***STOP. LOOK. THINK. You appear to have set Manning's n outside its typical range. Did you mean it? Proceeding...***"
)
sleep(2)
try:
self.C_MPM = inputs.read_float("C_MPM")
except MissingKeyError:
self.C_MPM = 1.0
self.diffusivity_power_on_A = 0.9 * self._c * (1.0 - self._b) # i.e., q/D**(1/6)
# new for v3:
# set thresh in shear stress if poss at this stage:
try: # fails if no Dchar provided, or shields crit is being set dynamically from slope
self.thresh = self.shields_crit * (self.sed_density - self.fluid_density) * self.g * self.Dchar_in
except AttributeError:
try:
self.shields_prefactor_to_shear = (self.sed_density - self.fluid_density) * self.g * self.Dchar_in
except AttributeError: # no Dchar
self.shields_prefactor_to_shear_noDchar = (self.sed_density - self.fluid_density) * self.g
twothirds = 2.0 / 3.0
self.Qs_prefactor = (
4.0
* self.C_MPM ** twothirds
* self.fluid_density ** twothirds
/ (self.sed_density - self.fluid_density) ** twothirds
* self.g ** (twothirds / 2.0)
* mannings_n ** 0.6
* self.k_w ** (1.0 / 15.0)
* self.k_Q ** (0.6 + self._b / 15.0)
/ self.sed_density ** twothirds
)
self.Qs_thresh_prefactor = (
4.0
* (
self.C_MPM
* self.k_w
* self.k_Q ** self._b
/ self.fluid_density ** 0.5
/ (self.sed_density - self.fluid_density)
/ self.g
/ self.sed_density
)
** twothirds
)
# both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c * (0.6 + self._b / 15.0)
self.Qs_power_onAthresh = twothirds * self._b * self._c
if RasterModelGrid in inspect.getmro(grid.__class__):
self.cell_areas = grid.dx * grid.dy
else:
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.node_at_cell] = grid.cell_areas
self.bad_neighbor_mask = np.equal(grid.get_neighbor_list(bad_index=-1), -1)
self.routing_code = """
def initialize(self, grid, params_file):
r"""
NOW DEPRECATED, USE __INIT__ DIRECTLY.
params_file is the name of the text file containing the parameters
needed for this stream power component.
Module erodes where channels are, implemented as
E = K * A**m * S**n - sp_crit,
and if E<0, E=0.
If 'use_W' is declared and True, the module instead implements:
E = K * A**m * S**n / W - sp_crit
***Parameters for input file***
OBLIGATORY:
K_sp -> positive float, the prefactor. This is defined per unit
time, not per tstep. Type the string 'array' to cause the
component's erode method to look for an array of values of K
(see documentation for 'erode').
ALTERNATIVES:
*either*
m_sp -> positive float, the power on A
and
n_sp -> positive float, the power on S
*or*
sp_type -> String. Must be one of 'Total', 'Unit', or
'Shear_stress'.
and (following Whipple & Tucker 1999)
a_sp -> +ve float. The power on the SP/shear term to get the
erosion rate.
b_sp -> +ve float. The power on discharge to get width, "hydraulic
geometry". Unnecessary if sp_type='Total'.
c_sp -> +ve float. The power on area to get discharge, "basin
hydology".
... If 'Total', m=a*c, n=a.
... If 'Unit', m=a*c*(1-b), n=a.
... If 'Shear_stress', m=2*a*c*(1-b)/3, n = 2*a/3.
OPTIONS:
threshold_sp -> +ve float; the threshold sp_crit. Defaults to 0.
This threshold is assumed to be in "stream power" units, i.e.,
if 'Shear_stress', the value should be tau**a.
dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. If you set sp_m and sp_n, follows the
equation given above. If you set sp_type, it will be ignored if
'Total', but used directly if you want 'Unit' or
'Shear_stress'.
use_Q -> Bool. If true, the equation becomes E=K*Q**m*S**n.
Effectively sets c=1 in Wh&T's 1999 derivation, if you are
setting m and n through a, b, and c.
"""
self._grid = grid
self.fraction_gradient_change = 1.
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links + 1)
# ^needs to be filled with values in execution
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self._K_unit_time = np.full((grid.status_at_node != 4).sum(),
inputs.read_float('K_sp'))
except ParameterValueError: # it was a string
self.use_K = True
else:
self.use_K = False
try:
self.sp_crit = inputs.read_float('threshold_sp')
self.set_threshold = True
# ^flag for sed_flux_dep_incision to see if the threshold was
# manually set.
# print("Found a threshold to use: ", self.sp_crit)
except MissingKeyError:
self.sp_crit = 0.
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self._m = inputs.read_float('m_sp')
except MissingKeyError:
self._type = inputs.read_string('sp_type')
self._a = inputs.read_float('a_sp')
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
raise NameError('c was not set')
if self._type == 'Total':
self._n = self._a
self._m = self._a * self._c # ==_a if use_Q
elif self._type == 'Unit':
self._n = self._a
self._m = self._a * self._c * (1. - self._b)
# ^ ==_a iff use_Q&use_W etc
elif self._type == 'Shear_stress':
self._m = 2. * self._a * self._c * (1. - self._b) / 3.
self._n = 2. * self._a / 3.
else:
raise MissingKeyError('Not enough information was provided ' +
'on the exponents to use!')
else:
self._n = inputs.read_float('n_sp')
# m and n will always be set, but care needs to be taken to include Q
# and W directly if appropriate
self.stream_power_erosion = grid.zeros(centering='node')
def _initialize(self, input_stream=None):
"""Initialize the component from an input file.
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
Parameters
----------
input_stream : str, file_like, or ModelParameterDictionary, optional
ModelParameterDictionary that holds the input parameters.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node['topographic__elevation']
except FieldError:
try:
topo_field_name = inputs.read_string('ELEVATION_FIELD_NAME')
except AttributeError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", you need to pass the')
print('name of a text input file or ModelParameterDictionary,')
print('and this file or dictionary needs to include the name')
print('of another field in your grid that contains your')
print('elevation data.')
raise AttributeError
except MissingKeyError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", your input file (or')
print('ModelParameterDictionary) must include an entry with')
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print('of a field in your grid that contains your elevation')
print('data.')
raise MissingKeyError('ELEVATION_FIELD_NAME')
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print('Your grid does not seem to have a node field called',
topo_field_name)
# Create output variables.
#
# Note that we initialize depression
# outlet ID to LOCAL_BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros('node',
'depression__depth',
noclobber=False)
self.depression_outlet_map = self._grid.add_zeros(
'node', 'depression__outlet_node', dtype=int, noclobber=False)
self.depression_outlet_map += LOCAL_BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = np.amax(self._elev) + 1
# We'll also need a handy copy of the node neighbor lists
# TODO: presently, this grid method seems to only exist for Raster
# grids. We need it for *all* grids!
self._node_nbrs = self._grid.active_neighbors_at_node()
dx = self._grid.dx
dy = self._grid.dy
if self._D8:
diag_nbrs = self._grid._get_diagonal_list()
self._node_nbrs = np.concatenate((self._node_nbrs, diag_nbrs), 1)
self._link_lengths = np.empty(8, dtype=float)
self._link_lengths[0] = dx
self._link_lengths[2] = dx
self._link_lengths[1] = dy
self._link_lengths[3] = dy
self._link_lengths[4:].fill(np.sqrt(dx * dx + dy * dy))
elif ((type(self._grid) is landlab.grid.raster.RasterModelGrid)
and (self._routing is 'D4')):
self._link_lengths = np.empty(4, dtype=float)
self._link_lengths[0] = dx
self._link_lengths[2] = dx
self._link_lengths[1] = dy
self._link_lengths[3] = dy
else:
self._link_lengths = self._grid._length_of_link_with_diagonals
self._lake_outlets = [] # a list of each unique lake outlet
# ^note this is nlakes-long
self.is_pit = self._grid.add_ones('node',
'is_pit',
dtype=bool,
noclobber=False)
self.flood_status = self._grid.add_zeros('node',
'flood_status_code',
dtype=int,
noclobber=False)
self._lake_map = np.empty(self._grid.number_of_nodes, dtype=int)
self._lake_map.fill(LOCAL_BAD_INDEX_VALUE)
def initialize(self, input_stream):
# Create a ModelParameterDictionary for the inputs
if type(input_stream)==ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Read input/configuration parameters
self.kd = inputs.read_float('linear_diffusivity')
try:
self.uplift_rate = inputs.read_float('uplift_rate')
except MissingKeyError:
self.uplift_rate = 0.
try:
self.values_to_diffuse = inputs.read_string('values_to_diffuse')
except MissingKeyError:
self.values_to_diffuse = 'topographic__elevation'
else:
#take switch in the new field name in the class properties
for mysets in (self._input_var_names, self._output_var_names):
mysets.remove('topographic__elevation')
mysets.add(self.values_to_diffuse)
for mydicts in (self._var_units, self._var_mapping, self._var_doc):
mydicts[self.values_to_diffuse] = mydicts.pop('topographic__elevation')
try:
self.timestep_in = inputs.read_float('dt')
except MissingKeyError:
pass
# Create grid if one doesn't already exist
if self._grid is None:
self._grid = create_and_initialize_grid(input_stream)
# Set internal time step
# ..todo:
# implement mechanism to compute time-steps dynamically if grid is
# adaptive/changing
dx = self._grid.min_active_link_length() # smallest active link length
self.dt = _ALPHA*dx*dx/self.kd # CFL condition
try:
self.tstep_ratio = self.timestep_in/self.dt
except AttributeError:
pass
# Get a list of interior cells
self.interior_cells = self._grid.node_at_core_cell
##DEJH bites the bullet and forces the 2015 style with fields
# # Here we're experimenting with different approaches: with
# # 'make_all_data', we create and manage all the data we need and embed
# # it all in the grid. With 'explicit', we require the caller/user to
# # provide data.
# if _VERSION=='make_all_data':
# #print('creating internal data')
# self.z = self._grid.add_zeros('node', 'landscape_surface__elevation')
# self.g = self._grid.add_zeros('active_link', 'landscape_surface__gradient') # surface gradients
# self.qs = self._grid.add_zeros('active_link','unit_sediment_flux') # unit sediment flux
# self.dqds = self._grid.add_zeros('node', 'sediment_flux_divergence') # sed flux derivative
# elif _VERSION=='explicit':
# pass
# else:
# # Create data arrays for variables that won't (?) be shared with other
# # components
# self.g = self._grid.create_active_link_array_zeros() # surface gradients
# self.qs = self._grid.create_active_link_array_zeros() # unit sediment flux
# self.dqds = self._grid.create_node_array_zeros() # sed flux derivative
self.z = self._grid.at_node[self.values_to_diffuse]
g = self._grid.zeros(centering='link')
qs = self._grid.zeros(centering='link')
try:
self.g = self._grid.add_field('link', 'surface__gradient', g, noclobber=True) #note this will object if this exists already
except FieldError:
pass #field exists, so no problem
try:
self.qs = self._grid.add_field('link', 'unit_flux', qs, noclobber=True)
except FieldError:
pass
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
Module erodes where channels are, implemented as
E = K * A**m * S**n - sp_crit,
and if E<0, E=0.
If 'use_W' is declared and True, the module instead implements:
E = K * A**m * S**n / W - sp_crit
***Parameters for input file***
OBLIGATORY:
K_sp -> positive float, the prefactor. This is defined per unit
time, not per tstep.
ALTERNATIVES:
*either*
m_sp -> positive float, the power on A
and
n_sp -> positive float, the power on S
*or*
sp_type -> String. Must be one of 'Total', 'Unit', or 'Shear_stress'.
and (following Whipple & Tucker 1999)
a_sp -> +ve float. The power on the SP/shear term to get the erosion
rate.
b_sp -> +ve float. The power on discharge to get width, "hydraulic
geometry". Unnecessary if sp_type='Total'.
c_sp -> +ve float. The power on area to get discharge, "basin
hydology".
... If 'Total', m=a*c, n=a.
... If 'Unit', m=a*c*(1-b), n=a.
... If 'Shear_stress', m=2*a*c*(1-b)/3, n = 2*a/3.
OPTIONS:
threshold_sp -> +ve float; the threshold sp_crit. Defaults to 0.
dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. If you set sp_m and sp_n, follows the
equation given above. If you set sp_type, it will be ignored if
'Total', but used directly if you want 'Unit' or 'Shear_stress'.
use_Q -> Bool. If true, the equation becomes E=K*Q**m*S**n.
Effectively sets c=1 in Wh&T's 1999 derivation, if you are
setting m and n through a, b, and c.
prevent_erosion -> Bool. If True, stream powers are calculated and
stored in the grid, but incision is NOT IMPLEMENTED. i.e., values of
elevation are NOT updated. Use if you wish to derive stream power
values for some other purpose, but do not wish to actually model
stream power dependent incision. Defaults to False.
'''
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
self._K_unit_time = inputs.read_float('K_sp')
try:
self.sp_crit = inputs.read_float('threshold_sp')
print "Found a threshold to use: ", self.sp_crit
except MissingKeyError:
self.sp_crit = 0.
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self.no_erode = inputs.read_bool('prevent_erosion')
except MissingKeyError:
self.no_erode = False
try:
self._m = inputs.read_float('m_sp')
except MissingKeyError:
self._type = inputs.read_string('sp_type')
self._a = inputs.read_float('a_sp')
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
raise NameError('c was not set')
if self._type == 'Total':
self._n = self._a
self._m = self._a*self._c #==_a if use_Q
elif self._type == 'Unit':
self._n = self._a
self._m = self._a*self._c*(1.-self._b) #==_a iff use_Q&use_W etc
elif self._type == 'Shear_stress':
self._m = 2.*self._a*self._c*(1.-self._b)/3.
self._n = 2.*self._a/3.
else:
raise MissingKeyError('Not enough information was provided on the exponents to use!')
else:
self._n = inputs.read_float('n_sp')
#m and n will always be set, but care needs to be taken to include Q and W directly if appropriate
self.stream_power_erosion = grid.zeros(centering='node')
def _initialize(self, input_stream=None):
"""Initialize the component from an input file.
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
Parameters
----------
input_stream : str, file_like, or ModelParameterDictionary, optional
ModelParameterDictionary that holds the input parameters.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node['topographic__elevation']
except FieldError:
try:
topo_field_name = inputs.read_string('ELEVATION_FIELD_NAME')
except AttributeError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", you need to pass the')
print('name of a text input file or ModelParameterDictionary,')
print('and this file or dictionary needs to include the name')
print('of another field in your grid that contains your')
print('elevation data.')
raise AttributeError
except MissingKeyError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", your input file (or')
print('ModelParameterDictionary) must include an entry with')
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print('of a field in your grid that contains your elevation')
print('data.')
raise MissingKeyError('ELEVATION_FIELD_NAME')
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print('Your grid does not seem to have a node field called',
topo_field_name)
# Create output variables.
#
# Note that we initialize depression
# outlet ID to LOCAL_BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros('node',
'depression__depth',
noclobber=False)
self.depression_outlet_map = self._grid.add_zeros(
'node', 'depression__outlet_node', dtype=int, noclobber=False)
self.depression_outlet_map += LOCAL_BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = 1.0e99
self.updated_boundary_conditions()
self._lake_outlets = [] # a list of each unique lake outlet
# ^note this is nlakes-long
self.is_pit = self._grid.add_ones('node',
'is_pit',
dtype=bool,
noclobber=False)
self.flood_status = self._grid.add_zeros('node',
'flood_status_code',
dtype=int,
noclobber=False)
self._lake_map = np.empty(self._grid.number_of_nodes, dtype=int)
self._lake_map.fill(LOCAL_BAD_INDEX_VALUE)
def initialize(self, grid, params_file):
"""
This module implements sediment flux dependent channel incision following:
E = f(Qs, Qc) * stream_power - sp_crit,
where stream_power is the stream power (often ==K*A**m*S**n) provided by the
stream_power.py component. Note that under this incision paradigm, sp_crit
is assumed to be controlled exclusively by sediment mobility, i.e., it is
not a function of bedrock resistance. If you want it to represent a bedrock
resistance term, be sure to set Dchar if you use the MPM transport capacity
relation, and do not use the flag 'slope_sensitive_threshold'.
The component currently assumes that the threshold on bed incision is
controlled by the threshold of motion of its sediment cover. This means
there is assumed interplay between the supplied Shields number,
characteristic grain size, and shear stress threshold.
This calculation has a tendency to be slow, and can easily result in
numerical instabilities. These instabilities are suppressed by retaining a
memory of what the sediment flux was in the last time step, and weighting
the next timestep by that value. XXXmore detail needed. Possibilities:
1. weight by last timestep/2timesteps (what about early ones?)
2. do it iteratively; only do incision one the sed flux you are using
stabilises (so the previous iter "seed" becomes less important)
Parameters needed in the initialization file follow those for
stream_power.py. However, we now require additional input terms for the
f(Qs,Qc) term:
REQUIRED:
*Set the stream power terms using a, b, and c NOT m, n.
*...remember, any threshold set is set as tau**a, not just tau.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*sed_dependency_type -> 'None', 'linear_decline', 'parabolic',
'almost_parabolic', 'generalized_humped'. For definitions, see Gasparini
et al., 2006; Hobley et al., 2011.
*Qc -> This input controls the sediment capacity used by the component.
It can calculate sediment carrying capacity for itself if this
parameter is a string 'MPM', which will cause the component to use a
slightly modified version of the Meyer-Peter Muller equation (again, see
Hobley et al., 2011). Alternatively, it can be another string denoting
the grid field name in which a precalculated capacity is stored.
Depending on which options are specified above, further parameters may be
required:
*If sed_dependency_type=='generalized_humped', need the shape parameters
used by Hobley et al:
kappa_hump
nu_hump
phi_hump
c_hump
Note the onus is on the user to ensure that these parameters result
in a viable shape, i.e., one where the maximum is 1 and there is
indeed a hump in the profile. If these parameters are NOT specified,
they will default to the form of the curve for Leh valley as found
in Hobley et al 2011: nu=1.13; phi=4.24; c=0.00181; kappa=13.683.
*If Qc=='MPM', these parameters may optionally be provided:
Dchar -> characteristic grain size (i.e., D50) on the bed, in m.
C_MPM -> the prefactor in the MPM relation. Defaults to 1, as in the
relation sensu stricto, but can be modified to "tune" the
equations to a known point where sediment deposition begins.
In cases where k_Q and k_w are not known from real data, it
is recommended these parameters be tuned in preference to C.
*...if Dchar is NOT provided, the component will attempt to set (and will
report) an appropriate characteristic grain size, such that it is
consistent both with the threshold provided *and* a critical Shields
number of 0.05. (If you really, really want to, you can override this
critical Shields number too; use parameter *threshold_Shields*).
OPTIONAL:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shear_stress -> a float.
If not provided, may be overridden by the following parameters.
If it is not, defaults to 0.
*slope_sensitive_threshold -> a boolean, defaults to FALSE.
In steep mountain environments, the critical Shields number for particle
motion appears to be weakly sensitive to the local slope, as
taustar_c=0.15*S**0.25 (Lamb et al, 2008). If this flag is set to TRUE,
the critical threshold in the landscape is allowed to become slope
sensitive as well, in order to be consistent with this equation.
This modification was used by Hobley et al., 2011.
*set_threshold_from_Dchar -> a boolean, defaults to FALSE.
Use this flag to force an appropriate threshold value from a
provided Dchar. i.e., this is the inverse of the procedure that is
used to find Dchar if it isn't provided. No threshold can be
specified in the parameter file, and Dchar must be specified.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
"""
#this is the fraction we allow any given slope in the grid to evolve by in one go (suppresses numerical instabilities)
self.fraction_gradient_change = 0.25
self.pseudoimplicit_repeats = 5
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(
grid.number_of_links +
1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.thresh = inputs.read_float('threshold_shear_stress')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a shear stress threshold to use: ", self.thresh
except MissingKeyError:
print "Found no incision threshold to use."
self.thresh = 0.
self.set_threshold = False
try:
self._a = inputs.read_float('a_sp')
except:
print "a not supplied. Setting power on shear stress to 1..."
self._a = 1.
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
#if self.use_W:
# self._b = 0.
#else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
#if self.use_Q:
# self._c = 1.
#else:
raise NameError(
'c was not set') #we need to restore this functionality later
#'To use the sed flux dependent model, you must set a,b,c not m,n. Try a=1,b=0.5,c=1...?'
self._K_unit_time = inputs.read_float(
'K_sp') / 31557600. #...because we work with dt in seconds
#set gravity
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.relative_weight = (
self.sed_density - self.fluid_density
) / self.fluid_density * self.g #to accelerate MPM calcs
self.rho_g = self.fluid_density * self.g
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
self.mannings_n = mannings_n
if mannings_n < 0. or mannings_n > 0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside its typical range. Did you mean it? Proceeding...***"
sleep(2)
self.diffusivity_power_on_A = 0.9 * self._c * (1. - self._b
) #i.e., q/D**(1/6)
self.type = inputs.read_string('sed_dependency_type')
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
self.Qc = None
else:
if self.Qc == 'MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.override_threshold = inputs.read_bool(
'set_threshold_from_Dchar')
except MissingKeyError:
self.override_threshold = False
try:
self.shields_crit = inputs.read_float('threshold_Shields')
except MissingKeyError:
self.shields_crit = 0.05
try:
self.return_ch_props = inputs.read_bool('return_stream_properties')
except MissingKeyError:
self.return_ch_props = False
#now conditional inputs
if self.type == 'generalized_humped':
try:
self.kappa = inputs.read_float('kappa_hump')
self.nu = inputs.read_float('nu_hump')
self.phi = inputs.read_float('phi_hump')
self.c = inputs.read_float('c_hump')
except MissingKeyError:
self.kappa = 13.683
self.nu = 1.13
self.phi = 4.24
self.c = 0.00181
print 'Adopting inbuilt parameters for the humped function form...'
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
#this is going to be a nightmare to implement...
except:
self.lamb_flag = False
if self.Qc == 'MPM':
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
assert self.thresh > 0., "Can't automatically set characteristic grain size if threshold is 0 or unset!"
#remember the threshold getting set is already tau**a
self.Dchar_in = self.thresh / self.g / (
self.sed_density - self.fluid_density) / self.shields_crit
print 'Setting characteristic grain size from the Shields criterion...'
print 'Characteristic grain size is: ', self.Dchar_in
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
if self.override_threshold:
print "Overriding any supplied threshold..."
try:
self.thresh = self.shields_crit * self.g * (
self.sed_density - self.fluid_density) * self.Dchar_in
except AttributeError:
self.thresh = self.shields_crit * self.g * (
self.sed_density -
self.fluid_density) * inputs.read_float('Dchar')
print "Threshold derived from grain size and Shields number is: ", self.thresh
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
#new 11/12/14
self.point6onelessb = 0.6 * (1. - self._b)
self.shear_stress_prefactor = self.fluid_density * self.g * (
self.mannings_n / self.k_w)**0.6
if self.set_threshold is False or self.override_threshold:
try:
self.shields_prefactor_to_shear = (
self.sed_density -
self.fluid_density) * self.g * self.Dchar_in
except AttributeError: #no Dchar
self.shields_prefactor_to_shear_noDchar = (
self.sed_density - self.fluid_density) * self.g
twothirds = 2. / 3.
self.Qs_prefactor = 4. * self.C_MPM**twothirds * self.fluid_density**twothirds / (
self.sed_density - self.fluid_density)**twothirds * self.g**(
twothirds /
2.) * mannings_n**0.6 * self.k_w**(1. / 15.) * self.k_Q**(
0.6 + self._b / 15.) / self.sed_density**twothirds
self.Qs_thresh_prefactor = 4. * (
self.C_MPM * self.k_w * self.k_Q**self._b /
self.fluid_density**0.5 / (self.sed_density - self.fluid_density) /
self.g / self.sed_density)**twothirds
#both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c * (0.6 + self._b / 15.)
self.Qs_power_onAthresh = twothirds * self._b * self._c
def initialize(self, input_stream=None):
"""
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node['topographic__elevation']
except FieldError:
try:
topo_field_name = inputs.read_string('ELEVATION_FIELD_NAME')
except AttributeError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", you need to pass the')
print('name of a text input file or ModelParameterDictionary,')
print(' and this file or dictionary needs to include the name')
print(' of another field in your grid that contains your')
print('elevation data.')
raise AttributeError
except MissingKeyError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", your input file (or')
print('ModelParameterDictionary) must include an entry with')
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print('of a field in your grid that contains your elevation')
print('data.')
raise MissingKeyError('ELEVATION_FIELD_NAME')
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print('Your grid does not seem to have a node field called', \
topo_field_name)
# Create output variables.
#
# Note that we initialize depression depth to -1 (negative values make
# no sense, so this is a clue to non-flooded nodes), and depression
# outlet ID to BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros('node', \
'depression__depth') - 1.0
self.depression_outlet = self._grid.add_zeros('node', \
'depression__outlet_node_id', \
dtype=int) + BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = numpy.amax(self._elev) + 1
# We'll also need a handy copy of the node neighbor lists
# TODO: presently, this grid method seems to only exist for Raster grids.
# We need it for *all* grids!
self._node_nbrs = self._grid.get_neighbor_list()
if type(self._grid) is landlab.grid.raster.RasterModelGrid:
diag_nbrs = self._grid.get_diagonal_list()
self._node_nbrs = numpy.concatenate((self._node_nbrs, diag_nbrs),
1)
def _initialize(self, input_stream=None):
"""Initialize the component from an input file.
The BMI-style initialize method takes an optional input_stream
parameter, which may be either a ModelParameterDictionary object or
an input stream from which a ModelParameterDictionary can read values.
Parameters
----------
input_stream : str, file_like, or ModelParameterDictionary, optional
ModelParameterDictionary that holds the input parameters.
"""
# Create a ModelParameterDictionary for the inputs
if input_stream is None:
inputs = None
elif type(input_stream) == ModelParameterDictionary:
inputs = input_stream
else:
inputs = ModelParameterDictionary(input_stream)
# Make sure the grid includes elevation data. This means either:
# 1. The grid has a node field called 'topographic__elevation', or
# 2. The input file has an item called 'ELEVATION_FIELD_NAME' *and*
# a field by this name exists in the grid.
try:
self._elev = self._grid.at_node['topographic__elevation']
except FieldError:
try:
topo_field_name = inputs.read_string('ELEVATION_FIELD_NAME')
except AttributeError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", you need to pass the')
print('name of a text input file or ModelParameterDictionary,')
print('and this file or dictionary needs to include the name')
print('of another field in your grid that contains your')
print('elevation data.')
raise AttributeError
except MissingKeyError:
print('Error: Because your grid does not have a node field')
print('called "topographic__elevation", your input file (or')
print('ModelParameterDictionary) must include an entry with')
print('the key "ELEVATION_FIELD_NAME", which gives the name')
print('of a field in your grid that contains your elevation')
print('data.')
raise MissingKeyError('ELEVATION_FIELD_NAME')
try:
self._elev = self._grid.at_node[topo_field_name]
except AttributeError:
print('Your grid does not seem to have a node field called',
topo_field_name)
# Create output variables.
#
# Note that we initialize depression
# outlet ID to LOCAL_BAD_INDEX_VALUE (which is a major clue!)
self.depression_depth = self._grid.add_zeros('node',
'depression__depth',
noclobber=False)
self.depression_outlet_map = self._grid.add_zeros(
'node', 'depression__outlet_node', dtype=int, noclobber=False)
self.depression_outlet_map += LOCAL_BAD_INDEX_VALUE
# Later on, we'll need a number that's guaranteed to be larger than the
# highest elevation in the grid.
self._BIG_ELEV = 1.0e99
self.updated_boundary_conditions()
self._lake_outlets = [] # a list of each unique lake outlet
# ^note this is nlakes-long
self.is_pit = self._grid.add_ones('node', 'is_pit', dtype=bool,
noclobber=False)
self.flood_status = self._grid.add_zeros('node', 'flood_status_code',
dtype=int, noclobber=False)
self._lake_map = np.empty(self._grid.number_of_nodes, dtype=int)
self._lake_map.fill(LOCAL_BAD_INDEX_VALUE)
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', or a string giving the name of the model field
where capacity values are stored on nodes.
At the moment, only 'MPM' is permitted as a way to set the
capacity automatically, but expansion would be trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width and
basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
The equation for depth used to derive shear stress and hence
carrying capacity contains a prefactor:
mannings_n*(k_Q**(1-b)/K_w)**0.6
(so shear = fluid_density*g*depth_equation_prefactor*A**(0.6*c*(1-b)*S**0.7 !)
Don't know what to set these values to? k_w=0.002, k_Q=1.e-9,
mannings_n=0.03 give vaguely plausible numbers (e.g., for a
drainage area ~400km2, like Boulder Creek at Boulder,
=> depth~2.5m, width~35m, shear stress ~O(1000Pa)).
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each node,
don't set, and use the Dchar_if_used argument in erode()
instead.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set True,
becomes a weak function of local slope following Lamb et al
(2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation. An exception will be raised if threshold_shields is
also set.
*Parker_epsilon -> float, defaults to 0.4. This is Parker's (1978)
epsilon, which is used in the relation
tau - tauc = tau * (epsilon/(epsilon+1))
The 0.4 default is appropriate for coarse (gravelly) channels.
The value approaches infinity as the river banks become more
cohesive.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False.
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge'].
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
*return_capacity -> bool (default False). NOT YET IMPLEMENTED.
If True, this component
will save the calculated capacity in the field
'fluvial_sediment_transport_capacity'. (Requires some additional
math, so is suppressed for speed by default).
'''
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.rho_g = self.fluid_density * self.g
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc=='MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float('threshold_shields')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a threshold to use: ", self.shields_crit
assert self.lamb_flag == False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self.return_capacity = inputs.read_bool('return_capacity')
except MissingKeyError:
self.return_capacity = False
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
if self.calc_cap_flag:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
if self.calc_cap_flag:
raise NameError('c was not set')
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
pass
#assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6*self._c*(1.-self._b)
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
if mannings_n<0. or mannings_n>0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside it's typical range. Did you mean it? Proceeding...***"
sleep(2)
self.depth_prefactor = self.rho_g*mannings_n*(self.k_Q**(1.-self._b)/self.k_w)**0.6
##Note the depth_prefactor we store already holds rho*g
try:
epsilon = inputs.read_float('Parker_epsilon')
except MissingKeyError:
epsilon = 0.4
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
try:
self.shields_prefactor = 1./((self.sed_density-self.fluid_density)*self.g*self.Dchar_in)
self.MPM_prefactor = 8.*self.C_MPM*np.sqrt(self.relative_weight*self.Dchar_in*self.Dchar_in*self.Dchar_in)
self.MPM_prefactor_alt = 4.*self.g**(-2./3.)/self.excess_SG/self.fluid_density/self.sed_density
except AttributeError:
#have to set these manually as needed
self.shields_prefactor_noD = 1./((self.sed_density-self.fluid_density)*self.g)
self.diffusivity_prefactor = 8.*np.sqrt(8.*self.g)/(self.sed_density/self.fluid_density-1.)*(epsilon/(epsilon+1.))**1.5*mannings_n**(5./6.)*self.k_w**-0.9*self.k_Q**(0.9*(1.-self._b)) #...this is multiplied by A**c(1-0.1*(1-b))
#we consciously skip out a factor of S**0.05-->1. in the diffusion prefactor, to avoid delinearizing the diffusion. Only a possible problem at tiny S (20% error @S==0.01; 37% error @S==10**-4)
#we could include this as a static adjustment in the actual looping code (i.e., just multiply by S**0.05, and don't work with it as part of the problem)
#in reality, Manning's n changes downstream too, so... whatever
self.diffusivity_power_on_A = 0.9*self._c*(1.-self._b) #i.e., q/D**(1/6)
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
self.dx2 = grid.node_spacing_horizontal**2
self.dy2 = grid.node_spacing_vertical**2
self.bad_neighbor_mask = np.equal(grid.get_neighbor_list(bad_index=-1),-1)
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
***Parameters for input file***
OBLIGATORY:
* Qc -> String. Controls how to set the carrying capacity.
Either 'MPM', 'power_law', or a string giving the name of the
model field where capacity values are stored on nodes.
At the moment, only 'MPM' and power_law' are permitted as a way
to set the capacity automatically, but expansion would be
trivial.
If 'from_array', the module will attempt to set the capacity
Note capacities must be specified as volume flux.
*
...Then, assuming you set Qc=='MPM':
* b_sp, c_sp -> Floats. These are the powers on discharge and
drainage area in the equations used to control channel width
and basin hydrology, respectively:
W = k_w * Q**b_sp
Q = k_Q * A**c_sp
These parameters are used to constrain flow depth, and may be
omitted if use_W or use_Q are set.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence
carrying capacities from the local slope and drainage area
alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*Dchar -> float. The characteristic grain diameter in meters
(==D50 in most cases) used to calculate Shields numbers
in the channel. If you want to define Dchar values at each
node, don't set, and use the Dchar_if_used argument in erode()
instead.
...or if you set power_law, Qc = K_t*A**m_t*S**n_t,
* m_t, n_t -> Floats. The powers on A and S repectively in this
equation.
* K_t -> float. The prefactor (note time units are years).
Note that Qc is total capacity, not per unit width.
OPTIONS:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults
to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shields -> +ve float; the threshold taustar_crit.
Defaults to 0.047, or if 'slope_sensitive_threshold' is set
True, becomes a weak function of local slope following Lamb et
al (2008):
threshold_shields=0.15*S**0.25
*slope_sensitive_threshold -> bool, defaults to 'False'.
If true, threshold_shields is set according to the Lamb
equation,
An exception will be raised if threshold_shields is
also set.
*dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
*use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. NOT YET IMPLEMENTED
*use_Q -> Bool. Overrides the basin hydrology relation, using an
local water discharge value assumed already calculated and
stored in grid.at_node['discharge']. NOT YET IMPLEMENTED
*C_MPM -> float. Defaults to 1. Allows tuning of the MPM prefactor,
which is calculated as
Qc = 8.*C_MPM*(taustar - taustarcrit)**1.5
In almost all cases, tuning depth_equation_prefactor' is
preferred to tuning this parameter.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
'''
# this is the fraction we allow any given slope in the grid to evolve
# by in one go (suppresses numerical instabilities)
self.capacity_options = ['MPM', 'power_law']
self.fraction_gradient_change = 0.25
self._grid = grid
# needs to be filled with values in execution
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links + 1)
self.count_active_links = np.zeros_like(
self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.rho_g = self.fluid_density * self.g
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
raise MissingKeyError("Qc must be 'MPM' or a grid field name!")
else:
if self.Qc in self.capacity_options:
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.return_ch_props = inputs.read_bool('return_stream_properties')
except MissingKeyError:
self.return_ch_props = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.return_capacity = inputs.read_bool('return_capacity')
except MissingKeyError:
self.return_capacity = False
if self.Qc == 'MPM':
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
except:
self.lamb_flag = False
try:
self.shields_crit = inputs.read_float('threshold_shields')
# flag for sed_flux_dep_incision to see if the threshold was
# manually set.
self.set_threshold = True
# print("Found a threshold to use: ", self.shields_crit)
assert self.lamb_flag is False
except MissingKeyError:
if not self.lamb_flag:
self.shields_crit = 0.047
self.set_threshold = False
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
if self.calc_cap_flag:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
if self.calc_cap_flag:
raise NameError('c was not set')
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
pass
# assume Manning's equation to set the power on A for shear stress:
self.shear_area_power = 0.6 * self._c * (1. - self._b)
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
self.mannings_n = mannings_n
if mannings_n < 0. or mannings_n > 0.2:
print(
"***STOP. LOOK. THINK. You appear to have set Manning's n "
+
"outside its typical range. Did you mean it? Proceeding..."
+ "***")
sleep(2)
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
self.diffusivity_power_on_A = 0.9 * self._c * \
(1. - self._b) # i.e., q/D**(1/6)
# new for v3:
# set thresh in shear stress if poss at this stage:
try: # fails if no Dchar provided, or shields crit is being set
# dynamically from slope
self.thresh = self.shields_crit * (
self.sed_density -
self.fluid_density) * self.g * self.Dchar_in
except AttributeError:
try:
self.shields_prefactor_to_shear = (
(self.sed_density - self.fluid_density) * self.g *
self.Dchar_in)
except AttributeError: # no Dchar
self.shields_prefactor_to_shear_noDchar = (
self.sed_density - self.fluid_density) * self.g
twothirds = 2. / 3.
self.Qs_prefactor = (
4. * self.C_MPM**twothirds * self.fluid_density**twothirds /
(self.sed_density - self.fluid_density)**twothirds *
self.g**(twothirds / 2.) * mannings_n**0.6 *
self.k_w**(1. / 15.) * self.k_Q**(0.6 + self._b / 15.) /
self.sed_density**twothirds)
self.Qs_thresh_prefactor = 4. * (
self.C_MPM * self.k_w * self.k_Q**self._b /
self.fluid_density**0.5 /
(self.sed_density - self.fluid_density) / self.g /
self.sed_density)**twothirds
# both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c * (0.6 + self._b / 15.)
self.Qs_power_onAthresh = twothirds * self._b * self._c
elif self.Qc == 'power_law':
self._Kt = inputs.read_float('K_t') / 31557600. # in sec
self._mt = inputs.read_float('m_t')
self._nt = inputs.read_float('n_t')
self.return_ch_props = False
if RasterModelGrid in inspect.getmro(grid.__class__):
self.cell_areas = grid.dx * grid.dy
else:
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.area_of_cell))
self.cell_areas[grid.node_at_cell] = grid.area_of_cell
self.bad_neighbor_mask = np.equal(
grid.get_active_neighbors_at_node(bad_index=-1), -1)
self.routing_code = """
double sed_flux_into_this_node;
double sed_flux_out_of_this_node;
double flux_excess;
for (int i=len_s_in; i>0; i--) {
sed_flux_into_this_node = sed_into_node[i];
sed_flux_out_of_this_node = transport_capacities[i];
flux_excess = sed_flux_into_this_node - sed_flux_out_of_this_node;
dz[i] = flux_excess/cell_areas*dt_this_step;
sed_into_node[flow_receiver[i]] += sed_flux_out_of_this_node;
}
"""
# set up the necessary fields:
self.initialize_output_fields()
if self.return_ch_props:
self.initialize_optional_output_fields()
def initialize(self, grid, params_file):
'''
params_file is the name of the text file containing the parameters
needed for this stream power component.
Module erodes where channels are, implemented as
E = K * A**m * S**n - sp_crit,
and if E<0, E=0.
If 'use_W' is declared and True, the module instead implements:
E = K * A**m * S**n / W - sp_crit
***Parameters for input file***
OBLIGATORY:
K_sp -> positive float, the prefactor. This is defined per unit
time, not per tstep. Type the string 'array' to cause the
component's erode method to look for an array of values of K
(see documentation for 'erode').
ALTERNATIVES:
*either*
m_sp -> positive float, the power on A
and
n_sp -> positive float, the power on S
*or*
sp_type -> String. Must be one of 'Total', 'Unit', or 'Shear_stress'.
and (following Whipple & Tucker 1999)
a_sp -> +ve float. The power on the SP/shear term to get the erosion
rate.
b_sp -> +ve float. The power on discharge to get width, "hydraulic
geometry". Unnecessary if sp_type='Total'.
c_sp -> +ve float. The power on area to get discharge, "basin
hydology".
... If 'Total', m=a*c, n=a.
... If 'Unit', m=a*c*(1-b), n=a.
... If 'Shear_stress', m=2*a*c*(1-b)/3, n = 2*a/3.
OPTIONS:
threshold_sp -> +ve float; the threshold sp_crit. Defaults to 0.
This threshold is assumed to be in "stream power" units, i.e.,
if 'Shear_stress', the value should be tau**a.
dt -> +ve float. If set, this is the fixed timestep for this
component. Can be overridden easily as a parameter in erode().
If not set (default), this parameter MUST be set in erode().
use_W -> Bool; if True, component will look for node-centered data
describing channel width in grid.at_node['channel_width'], and
use it to implement incision ~ stream power per unit width.
Defaults to False. If you set sp_m and sp_n, follows the
equation given above. If you set sp_type, it will be ignored if
'Total', but used directly if you want 'Unit' or 'Shear_stress'.
use_Q -> Bool. If true, the equation becomes E=K*Q**m*S**n.
Effectively sets c=1 in Wh&T's 1999 derivation, if you are
setting m and n through a, b, and c.
'''
self.grid = grid
self.fraction_gradient_change = 1.
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self._K_unit_time = inputs.read_float('K_sp')
except ParameterValueError: #it was a string
self.use_K = True
else:
self.use_K = False
try:
self.sp_crit = inputs.read_float('threshold_sp')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print("Found a threshold to use: ", self.sp_crit)
except MissingKeyError:
self.sp_crit = 0.
self.set_threshold = False
try:
self.tstep = inputs.read_float('dt')
except MissingKeyError:
pass
try:
self.use_W = inputs.read_bool('use_W')
except MissingKeyError:
self.use_W = False
try:
self.use_Q = inputs.read_bool('use_Q')
except MissingKeyError:
self.use_Q = False
try:
self._m = inputs.read_float('m_sp')
except MissingKeyError:
self._type = inputs.read_string('sp_type')
self._a = inputs.read_float('a_sp')
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
if self.use_W:
self._b = 0.
else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
if self.use_Q:
self._c = 1.
else:
raise NameError('c was not set')
if self._type == 'Total':
self._n = self._a
self._m = self._a*self._c #==_a if use_Q
elif self._type == 'Unit':
self._n = self._a
self._m = self._a*self._c*(1.-self._b) #==_a iff use_Q&use_W etc
elif self._type == 'Shear_stress':
self._m = 2.*self._a*self._c*(1.-self._b)/3.
self._n = 2.*self._a/3.
else:
raise MissingKeyError('Not enough information was provided on the exponents to use!')
else:
self._n = inputs.read_float('n_sp')
#m and n will always be set, but care needs to be taken to include Q and W directly if appropriate
self.stream_power_erosion = grid.zeros(centering='node')
def initialize(self, grid, params_file):
"""
This module implements sediment flux dependent channel incision following:
E = f(Qs, Qc) * stream_power - sp_crit,
where stream_power is the stream power (often ==K*A**m*S**n) provided by the
stream_power.py component. Note that under this incision paradigm, sp_crit
is assumed to be controlled exclusively by sediment mobility, i.e., it is
not a function of bedrock resistance. If you want it to represent a bedrock
resistance term, be sure to set Dchar if you use the MPM transport capacity
relation, and do not use the flag 'slope_sensitive_threshold'.
The component currently assumes that the threshold on bed incision is
controlled by the threshold of motion of its sediment cover. This means
there is assumed interplay between the supplied Shields number,
characteristic grain size, and shear stress threshold.
This calculation has a tendency to be slow, and can easily result in
numerical instabilities. These instabilities are suppressed by retaining a
memory of what the sediment flux was in the last time step, and weighting
the next timestep by that value. XXXmore detail needed. Possibilities:
1. weight by last timestep/2timesteps (what about early ones?)
2. do it iteratively; only do incision one the sed flux you are using
stabilises (so the previous iter "seed" becomes less important)
Parameters needed in the initialization file follow those for
stream_power.py. However, we now require additional input terms for the
f(Qs,Qc) term:
REQUIRED:
*Set the stream power terms using a, b, and c NOT m, n.
*...remember, any threshold set is set as tau**a, not just tau.
*k_Q, k_w, mannings_n -> floats. These are the prefactors on the
basin hydrology and channel width-discharge relations, and n
from the Manning's equation, respectively. These are
needed to allow calculation of shear stresses and hence carrying
capacities from the local slope and drainage area alone.
Don't know what to set these values to? k_w=2.5, k_Q=2.5e-7,
mannings_n=0.05 give vaguely plausible numbers with b=0.5,
c = 1.(e.g., for a drainage area ~350km2, like Boulder Creek
at Boulder, => depth~1.3m, width~23m, shear stress ~O(200Pa)
for an "annual-ish" flood). [If you want to continue playing
with calibration, the ?50yr return time 2013 floods produced
depths ~2.3m with Q~200m3/s]
*sed_dependency_type -> 'None', 'linear_decline', 'parabolic',
'almost_parabolic', 'generalized_humped'. For definitions, see Gasparini
et al., 2006; Hobley et al., 2011.
*Qc -> This input controls the sediment capacity used by the component.
It can calculate sediment carrying capacity for itself if this
parameter is a string 'MPM', which will cause the component to use a
slightly modified version of the Meyer-Peter Muller equation (again, see
Hobley et al., 2011). Alternatively, it can be another string denoting
the grid field name in which a precalculated capacity is stored.
Depending on which options are specified above, further parameters may be
required:
*If sed_dependency_type=='generalized_humped', need the shape parameters
used by Hobley et al:
kappa_hump
nu_hump
phi_hump
c_hump
Note the onus is on the user to ensure that these parameters result
in a viable shape, i.e., one where the maximum is 1 and there is
indeed a hump in the profile. If these parameters are NOT specified,
they will default to the form of the curve for Leh valley as found
in Hobley et al 2011: nu=1.13; phi=4.24; c=0.00181; kappa=13.683.
*If Qc=='MPM', these parameters may optionally be provided:
Dchar -> characteristic grain size (i.e., D50) on the bed, in m.
C_MPM -> the prefactor in the MPM relation. Defaults to 1, as in the
relation sensu stricto, but can be modified to "tune" the
equations to a known point where sediment deposition begins.
In cases where k_Q and k_w are not known from real data, it
is recommended these parameters be tuned in preference to C.
*...if Dchar is NOT provided, the component will attempt to set (and will
report) an appropriate characteristic grain size, such that it is
consistent both with the threshold provided *and* a critical Shields
number of 0.05. (If you really, really want to, you can override this
critical Shields number too; use parameter *threshold_Shields*).
OPTIONAL:
*rock_density -> in kg/m3 (defaults to 2700)
*sediment_density -> in kg/m3 (defaults to 2700)
*fluid_density -> in most cases water density, in kg/m3 (defaults to 1000)
*g -> acceleration due to gravity, in m/s**2 (defaults to 9.81)
*threshold_shear_stress -> a float.
If not provided, may be overridden by the following parameters.
If it is not, defaults to 0.
*slope_sensitive_threshold -> a boolean, defaults to FALSE.
In steep mountain environments, the critical Shields number for particle
motion appears to be weakly sensitive to the local slope, as
taustar_c=0.15*S**0.25 (Lamb et al, 2008). If this flag is set to TRUE,
the critical threshold in the landscape is allowed to become slope
sensitive as well, in order to be consistent with this equation.
This modification was used by Hobley et al., 2011.
*set_threshold_from_Dchar -> a boolean, defaults to FALSE.
Use this flag to force an appropriate threshold value from a
provided Dchar. i.e., this is the inverse of the procedure that is
used to find Dchar if it isn't provided. No threshold can be
specified in the parameter file, and Dchar must be specified.
*return_stream_properties -> bool (default False).
If True, this component will save the calculations for
'channel_width', 'channel_depth', and 'channel_discharge' in
those grid fields. (Requires some
additional math, so is suppressed for speed by default).
"""
#this is the fraction we allow any given slope in the grid to evolve by in one go (suppresses numerical instabilities)
self.fraction_gradient_change = 0.25
self.pseudoimplicit_repeats = 5
self.grid = grid
self.link_S_with_trailing_blank = np.zeros(grid.number_of_links+1) #needs to be filled with values in execution
self.count_active_links = np.zeros_like(self.link_S_with_trailing_blank, dtype=int)
self.count_active_links[:-1] = 1
inputs = ModelParameterDictionary(params_file)
try:
self.thresh = inputs.read_float('threshold_shear_stress')
self.set_threshold = True #flag for sed_flux_dep_incision to see if the threshold was manually set.
print "Found a shear stress threshold to use: ", self.thresh
except MissingKeyError:
print "Found no incision threshold to use."
self.thresh = 0.
self.set_threshold = False
try:
self._a = inputs.read_float('a_sp')
except:
print "a not supplied. Setting power on shear stress to 1..."
self._a = 1.
try:
self._b = inputs.read_float('b_sp')
except MissingKeyError:
#if self.use_W:
# self._b = 0.
#else:
raise NameError('b was not set')
try:
self._c = inputs.read_float('c_sp')
except MissingKeyError:
#if self.use_Q:
# self._c = 1.
#else:
raise NameError('c was not set') #we need to restore this functionality later
#'To use the sed flux dependent model, you must set a,b,c not m,n. Try a=1,b=0.5,c=1...?'
self._K_unit_time = inputs.read_float('K_sp')/31557600. #...because we work with dt in seconds
#set gravity
try:
self.g = inputs.read_float('g')
except MissingKeyError:
self.g = 9.81
try:
self.rock_density = inputs.read_float('rock_density')
except MissingKeyError:
self.rock_density = 2700.
try:
self.sed_density = inputs.read_float('sediment_density')
except MissingKeyError:
self.sed_density = 2700.
try:
self.fluid_density = inputs.read_float('fluid_density')
except MissingKeyError:
self.fluid_density = 1000.
self.relative_weight = (self.sed_density-self.fluid_density)/self.fluid_density*self.g #to accelerate MPM calcs
self.rho_g = self.fluid_density*self.g
self.k_Q = inputs.read_float('k_Q')
self.k_w = inputs.read_float('k_w')
mannings_n = inputs.read_float('mannings_n')
self.mannings_n = mannings_n
if mannings_n<0. or mannings_n>0.2:
print "***STOP. LOOK. THINK. You appear to have set Manning's n outside its typical range. Did you mean it? Proceeding...***"
sleep(2)
self.diffusivity_power_on_A = 0.9*self._c*(1.-self._b) #i.e., q/D**(1/6)
self.type = inputs.read_string('sed_dependency_type')
try:
self.Qc = inputs.read_string('Qc')
except MissingKeyError:
self.Qc = None
else:
if self.Qc=='MPM':
self.calc_cap_flag = True
else:
self.calc_cap_flag = False
try:
self.override_threshold = inputs.read_bool('set_threshold_from_Dchar')
except MissingKeyError:
self.override_threshold = False
try:
self.shields_crit = inputs.read_float('threshold_Shields')
except MissingKeyError:
self.shields_crit = 0.05
try:
self.return_ch_props = inputs.read_bool('return_stream_properties')
except MissingKeyError:
self.return_ch_props = False
#now conditional inputs
if self.type == 'generalized_humped':
try:
self.kappa = inputs.read_float('kappa_hump')
self.nu = inputs.read_float('nu_hump')
self.phi = inputs.read_float('phi_hump')
self.c = inputs.read_float('c_hump')
except MissingKeyError:
self.kappa = 13.683
self.nu = 1.13
self.phi = 4.24
self.c = 0.00181
print 'Adopting inbuilt parameters for the humped function form...'
try:
self.lamb_flag = inputs.read_bool('slope_sensitive_threshold')
#this is going to be a nightmare to implement...
except:
self.lamb_flag = False
if self.Qc == 'MPM':
try:
self.Dchar_in = inputs.read_float('Dchar')
except MissingKeyError:
assert self.thresh > 0., "Can't automatically set characteristic grain size if threshold is 0 or unset!"
#remember the threshold getting set is already tau**a
self.Dchar_in = self.thresh/self.g/(self.sed_density-self.fluid_density)/self.shields_crit
print 'Setting characteristic grain size from the Shields criterion...'
print 'Characteristic grain size is: ', self.Dchar_in
try:
self.C_MPM = inputs.read_float('C_MPM')
except MissingKeyError:
self.C_MPM = 1.
if self.override_threshold:
print "Overriding any supplied threshold..."
try:
self.thresh = self.shields_crit*self.g*(self.sed_density-self.fluid_density)*self.Dchar_in
except AttributeError:
self.thresh = self.shields_crit*self.g*(self.sed_density-self.fluid_density)*inputs.read_float('Dchar')
print "Threshold derived from grain size and Shields number is: ", self.thresh
self.cell_areas = np.empty(grid.number_of_nodes)
self.cell_areas.fill(np.mean(grid.cell_areas))
self.cell_areas[grid.cell_node] = grid.cell_areas
#new 11/12/14
self.point6onelessb = 0.6*(1.-self._b)
self.shear_stress_prefactor = self.fluid_density*self.g*(self.mannings_n/self.k_w)**0.6
if self.set_threshold is False or self.override_threshold:
try:
self.shields_prefactor_to_shear = (self.sed_density-self.fluid_density)*self.g*self.Dchar_in
except AttributeError: #no Dchar
self.shields_prefactor_to_shear_noDchar = (self.sed_density-self.fluid_density)*self.g
twothirds = 2./3.
self.Qs_prefactor = 4.*self.C_MPM**twothirds*self.fluid_density**twothirds/(self.sed_density-self.fluid_density)**twothirds*self.g**(twothirds/2.)*mannings_n**0.6*self.k_w**(1./15.)*self.k_Q**(0.6+self._b/15.)/self.sed_density**twothirds
self.Qs_thresh_prefactor = 4.*(self.C_MPM*self.k_w*self.k_Q**self._b/self.fluid_density**0.5/(self.sed_density-self.fluid_density)/self.g/self.sed_density)**twothirds
#both these are divided by sed density to give a vol flux
self.Qs_power_onA = self._c*(0.6+self._b/15.)
self.Qs_power_onAthresh = twothirds*self._b*self._c
class TestModelParameterDictionary(unittest.TestCase):
def setUp(self):
from StringIO import StringIO
self.param_file = StringIO(_TEST_PARAM_DICT_FILE)
self.param_dict = ModelParameterDictionary()
self.param_dict.read_from_file(self.param_file)
def test_read_file(self):
all_keys = set([
'FLOAT_VAL',
'INT_VAL',
'STRING_VAL',
'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
param_list = set(self.param_dict.params())
self.assertEqual(param_list, all_keys)
def test_read_file_name(self):
(prm_fd, prm_file_name) = tempfile.mkstemp()
prm_file = os.fdopen(prm_fd, 'w')
prm_file.write(_TEST_PARAM_DICT_FILE)
prm_file.close()
param_dict = ModelParameterDictionary()
param_dict.read_from_file(prm_file_name)
os.remove(prm_file_name)
all_keys = set([
'FLOAT_VAL',
'INT_VAL',
'STRING_VAL',
'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
param_list = set(param_dict.params())
self.assertEqual(param_list, all_keys)
def test_read_file_like_twice(self):
from StringIO import StringIO
param_file = StringIO(_TEST_PARAM_DICT_FILE)
param_dict_1 = ModelParameterDictionary()
param_dict_2 = ModelParameterDictionary()
param_dict_1.read_from_file(param_file)
param_dict_2.read_from_file(param_file)
def test_read_int(self):
self.assertEqual(self.param_dict.read_int('INT_VAL'), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.read_int('FLOAT_VAL')
with self.assertRaises(MissingKeyError):
self.param_dict.read_int('MISSING_INT')
self.assertEqual(self.param_dict.read_int('MISSING_INT', 2), 2)
def test_get_int(self):
self.assertEqual(self.param_dict.get('INT_VAL', ptype=int), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.get('FLOAT_VAL', ptype=int)
with self.assertRaises(MissingKeyError):
self.param_dict.get('MISSING_INT', ptype=int)
def test_set_default(self):
self.param_dict.setdefault('MISSING_INT', 2)
self.assertEqual(self.param_dict.read_int('MISSING_INT'), 2)
def test_read_float(self):
self.assertEqual(self.param_dict.read_float('FLOAT_VAL'), 2.2)
self.assertEqual(self.param_dict.read_float('INT_VAL'), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.read_float('STRING_VAL')
with self.assertRaises(MissingKeyError):
self.param_dict.read_float('MISSING_FLOAT')
def test_read_string(self):
self.assertEqual(self.param_dict.read_string('STRING_VAL'),
'The Landlab')
self.assertEqual(self.param_dict.read_string('INT_VAL'), '1')
self.assertEqual(self.param_dict.read_string('FLOAT_VAL'), '2.2')
with self.assertRaises(MissingKeyError):
self.param_dict.read_string('MISSING_STRING')
def test_read_bool(self):
self.assertEqual(self.param_dict.read_bool('TRUE_BOOL_VAL'), True)
self.assertEqual(self.param_dict.read_bool('FALSE_BOOL_VAL'), False)
with self.assertRaises(MissingKeyError):
self.param_dict.read_bool('MISSING_BOOLEAN')
with self.assertRaises(ParameterValueError):
self.param_dict.read_bool('STRING_VAL')
def test_dict_keys(self):
all_keys = set([
'FLOAT_VAL',
'INT_VAL',
'STRING_VAL',
'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
self.assertEqual(set(self.param_dict), all_keys)
for key in all_keys:
self.assertTrue(key in self.param_dict)
def test_dict_index(self):
self.assertEqual(self.param_dict['INT_VAL'], '1')
class TestModelParameterDictionary(unittest.TestCase):
def setUp(self):
from StringIO import StringIO
self.param_file = StringIO(_TEST_PARAM_DICT_FILE)
self.param_dict = ModelParameterDictionary()
self.param_dict.read_from_file(self.param_file)
def test_read_file(self):
all_keys = set([
'FLOAT_VAL', 'INT_VAL', 'STRING_VAL', 'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
param_list = set(self.param_dict.params())
self.assertEqual(param_list, all_keys)
def test_read_file_name(self):
(prm_fd, prm_file_name) = tempfile.mkstemp()
prm_file = os.fdopen(prm_fd, 'w')
prm_file.write(_TEST_PARAM_DICT_FILE)
prm_file.close()
param_dict = ModelParameterDictionary()
param_dict.read_from_file(prm_file_name)
os.remove(prm_file_name)
all_keys = set([
'FLOAT_VAL', 'INT_VAL', 'STRING_VAL', 'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
param_list = set(param_dict.params())
self.assertEqual(param_list, all_keys)
def test_read_file_like_twice(self):
from StringIO import StringIO
param_file = StringIO(_TEST_PARAM_DICT_FILE)
param_dict_1 = ModelParameterDictionary()
param_dict_2 = ModelParameterDictionary()
param_dict_1.read_from_file(param_file)
param_dict_2.read_from_file(param_file)
def test_read_int(self):
self.assertEqual(self.param_dict.read_int('INT_VAL'), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.read_int('FLOAT_VAL')
with self.assertRaises(MissingKeyError):
self.param_dict.read_int('MISSING_INT')
self.assertEqual(self.param_dict.read_int('MISSING_INT', 2), 2)
def test_get_int(self):
self.assertEqual(self.param_dict.get('INT_VAL', ptype=int), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.get('FLOAT_VAL', ptype=int)
with self.assertRaises(MissingKeyError):
self.param_dict.get('MISSING_INT', ptype=int)
def test_set_default(self):
self.param_dict.setdefault('MISSING_INT', 2)
self.assertEqual(self.param_dict.read_int('MISSING_INT'), 2)
def test_read_float(self):
self.assertEqual(self.param_dict.read_float('FLOAT_VAL'), 2.2)
self.assertEqual(self.param_dict.read_float('INT_VAL'), 1)
with self.assertRaises(ParameterValueError):
self.param_dict.read_float('STRING_VAL')
with self.assertRaises(MissingKeyError):
self.param_dict.read_float('MISSING_FLOAT')
def test_read_string(self):
self.assertEqual(self.param_dict.read_string('STRING_VAL'),
'The Landlab')
self.assertEqual(self.param_dict.read_string('INT_VAL'), '1')
self.assertEqual(self.param_dict.read_string('FLOAT_VAL'), '2.2')
with self.assertRaises(MissingKeyError):
self.param_dict.read_string('MISSING_STRING')
def test_read_bool(self):
self.assertEqual(self.param_dict.read_bool('TRUE_BOOL_VAL'), True)
self.assertEqual(self.param_dict.read_bool('FALSE_BOOL_VAL'), False)
with self.assertRaises(MissingKeyError):
self.param_dict.read_bool('MISSING_BOOLEAN')
with self.assertRaises(ParameterValueError):
self.param_dict.read_bool('STRING_VAL')
def test_dict_keys(self):
all_keys = set([
'FLOAT_VAL', 'INT_VAL', 'STRING_VAL', 'TRUE_BOOL_VAL',
'FALSE_BOOL_VAL',
])
self.assertEqual(set(self.param_dict), all_keys)
for key in all_keys:
self.assertTrue(key in self.param_dict)
def test_dict_index(self):
self.assertEqual(self.param_dict['INT_VAL'], '1')
