
class
BasicStVs
¶
Model BasicStVs¶
terrainbento Model BasicStVs program.
Erosion model program using linear diffusion and stream power. Precipitation is modeled as a stochastic process. Discharge is calculated from precipitation using a simple variable sourcearea formulation.
 Landlab components used:

class
BasicStVs
(clock, grid, m_sp=0.5, n_sp=1.0, water_erodibility=0.0001, regolith_transport_parameter=0.1, hydraulic_conductivity=0.1, **kwargs)[source]¶ Bases:
terrainbento.base_class.stochastic_erosion_model.StochasticErosionModel
BasicStVs model program.
This model program uses a stochastic treatment of runoff and discharge, using a variable source area runoff generation model. It combines
BasicSt
andBasicVs
. The model evolves a topographic surface, \(\eta (x,y,t)\), with the following governing equation:\[\frac{\partial \eta}{\partial t} = K_{q}\hat{Q}^{m}S^{n} + D\nabla^2 \eta\]where \(\hat{Q}\) is the local stream discharge (the hat symbol indicates that it is a randomintime variable) and \(S\) is the local slope gradient. \(m\) and \(n\) are the discharge and slope exponent, respectively, \(K\) is the erodibility by water, and \(D\) is the regolith transport parameter.
This model iterates through a sequence of storm and interstorm periods. Given a storm precipitation intensity \(P\), the discharge \(Q\). is calculated using:
\[Q = PA  T\lambda S [1  \exp (PA/T\lambda S) ]\]where \(T = K_sH\) is the soil transmissivity, \(H\) is soil thickness, \(K_s\) is hydraulic conductivity, and \(\lambda\) is cell width.
Refer to Barnhart et al. (2019) Table 5 for full list of parameter symbols, names, and dimensions.
 The following atnode fields must be specified in the grid:
topographic__elevation

__init__
(clock, grid, m_sp=0.5, n_sp=1.0, water_erodibility=0.0001, regolith_transport_parameter=0.1, hydraulic_conductivity=0.1, **kwargs)[source]¶  Parameters
clock (terrainbento Clock instance) –
grid (landlab model grid instance) – The grid must have all required fields.
m_sp (float, optional) – Drainage area exponent (\(m\)). Default is 0.5.
n_sp (float, optional) – Slope exponent (\(n\)). Default is 1.0.
water_erodibility (float, optional) – Water erodibility (\(K\)). Default is 0.0001.
regolith_transport_parameter (float, optional) – Regolith transport efficiency (\(D\)). Default is 0.1.
infiltration_capacity (float, optional) – Infiltration capacity (\(I_m\)). Default is 1.0.
hydraulic_conductivity (float, optional) – Hydraulic conductivity (\(K_{sat}\)). Default is 0.1.
**kwargs – Keyword arguments to pass to
StochasticErosionModel
. These arguments control the discharge \(\hat{Q}\).
 Returns
BasicStVs
 Return type
model object
Examples
This is a minimal example to demonstrate how to construct an instance of model BasicStVs. For more detailed examples, including steadystate test examples, see the terrainbento tutorials.
To begin, import the model class.
>>> from landlab import RasterModelGrid >>> from landlab.values import random >>> from terrainbento import Clock, BasicStVs >>> clock = Clock(start=0, stop=100, step=1) >>> grid = RasterModelGrid((5,5)) >>> _ = random(grid, "topographic__elevation") >>> _ = random(grid, "soil__depth")
Construct the model.
>>> model = BasicStVs(clock, grid)
Running the model with
model.run()
would create output, so here we will just run it one step.>>> model.run_one_step(1.) >>> model.model_time 1.0

run_one_step
(step)[source]¶ Advance model BasicStVs for one timestep of duration step.
The run_one_step method does the following:
Directs flow, accumulates drainage area, and calculates effective drainage area.
Assesses the location, if any, of flooded nodes where erosion should not occur.
Assesses if a
PrecipChanger
is an active boundary handler and if so, uses it to modify the erodibility by water.Calculates detachmentlimited erosion by water.
Calculates topographic change by linear diffusion.
Finalizes the step using the
ErosionModel
base class function finalize__run_one_step. This function updates all boundary handlers handlers bystep
and increments model time bystep
.
 Parameters
step (float) – Increment of time for which the model is run.