Note

Go to the end to download the full example code.

Model time discretization#

iMOD Python provides nice functionality to discretize your models into stress periods, depending on the timesteps you assigned your boundary conditions. This functionality is activated with the create_time_discretization() method.

Basics#

To demonstrate the create_time_discretization() method, we first have to create a Model object. In this case we’ll use a Modflow 6 simulation, but the imod.wq.SeawatModel and imod.flow.ImodflowModel also support this.

Note

The simulation created in this example misses some mandatory packes, so is not able to write a functional simulation. It is purely intended to describe the workings of the time_discretization method. For a fully functional Modflow 6 model, see the examples in MODFLOW6.

Wel’ll start off with the usual imports:

import numpy as np
import pandas as pd
import xarray as xr

import imod

We can discretize a simulation as follows, this creates a TimeDiscretization object under the key "time_discretization".

simulation = imod.mf6.Modflow6Simulation("example")
simulation.create_time_discretization(
    additional_times=["2000-01-01", "2000-01-02", "2000-01-04"]
)

simulation["time_discretization"]
TimeDiscretization
<xarray.Dataset> Size: 48B
Dimensions:              (time: 2)
Coordinates:
  * time                 (time) datetime64[ns] 16B 2000-01-01 2000-01-02
Data variables:
    timestep_duration    (time) float64 16B 1.0 2.0
    n_timesteps          int64 8B 1
    timestep_multiplier  float64 8B 1.0


To view the data inside TimeDiscretization object:

simulation["time_discretization"].dataset

<xarray.Dataset> Size: 48B
Dimensions:              (time: 2)
Coordinates:
  * time                 (time) datetime64[ns] 16B 2000-01-01 2000-01-02
Data variables:
    timestep_duration    (time) float64 16B 1.0 2.0
    n_timesteps          int64 8B 1
    timestep_multiplier  float64 8B 1.0


Notice that even though we specified three points in time, only two timesteps are included in the time coordinate, this is because Modflow requires a start time and a duration of each stress period. iMOD Python therefore uses three points in time to compute two stress periods with a duration.

simulation["time_discretization"].dataset["timestep_duration"]

<xarray.DataArray 'timestep_duration' (time: 2)> Size: 16B
array([1., 2.])
Coordinates:
  * time     (time) datetime64[ns] 16B 2000-01-01 2000-01-02


These two stress periods use their respective start time in their time coordinate.

Boundary Conditions#

The create_time_discretization method becomes especially useful if we add boundary conditions to our groundwater model. We’ll first still have to initialize a groundwater flow model though:

gwf_model = imod.mf6.GroundwaterFlowModel()

We’ll create a dummy grid, which is used to provide an appropriate grid to the boundary condition packages.

template_data = np.ones((2, 1, 2, 2))

layer = [1]
y = [1.5, 0.5]
x = [0.5, 1.5]

dims = ("time", "layer", "y", "x")

Next, we can assign a Constant Head boundary with two timesteps:

chd_times = pd.to_datetime(["2000-01-01", "2000-01-04"])
chd_data = xr.DataArray(
    data=template_data,
    dims=dims,
    coords={"time": chd_times, "layer": layer, "y": y, "x": x},
)

gwf_model["chd"] = imod.mf6.ConstantHead(head=chd_data)

gwf_model["chd"].dataset

<xarray.Dataset> Size: 139B
Dimensions:        (time: 2, layer: 1, y: 2, x: 2)
Coordinates:
  * time           (time) datetime64[ns] 16B 2000-01-01 2000-01-04
  * layer          (layer) int64 8B 1
  * y              (y) float64 16B 1.5 0.5
  * x              (x) float64 16B 0.5 1.5
Data variables:
    head           (time, layer, y, x) float64 64B 1.0 1.0 1.0 ... 1.0 1.0 1.0
    print_input    bool 1B False
    print_flows    bool 1B False
    save_flows     bool 1B False
    observations   object 8B None
    repeat_stress  object 8B None


We’ll also assign a Recharge boundary with two timesteps, which differ from the ConstantHead boundary:

rch_times = pd.to_datetime(["2000-01-01", "2000-01-02"])
rch_data = xr.DataArray(
    data=template_data,
    dims=dims,
    coords={"time": rch_times, "layer": layer, "y": y, "x": x},
)

gwf_model["rch"] = imod.mf6.Recharge(rate=rch_data)

gwf_model["rch"].dataset

<xarray.Dataset> Size: 140B
Dimensions:        (time: 2, layer: 1, y: 2, x: 2)
Coordinates:
  * time           (time) datetime64[ns] 16B 2000-01-01 2000-01-02
  * layer          (layer) int64 8B 1
  * y              (y) float64 16B 1.5 0.5
  * x              (x) float64 16B 0.5 1.5
Data variables:
    rate           (time, layer, y, x) float64 64B 1.0 1.0 1.0 ... 1.0 1.0 1.0
    print_input    bool 1B False
    print_flows    bool 1B False
    save_flows     bool 1B False
    observations   object 8B None
    repeat_stress  object 8B None
    fixed_cell     bool 1B False


We can now let iMOD Python figure out how the simulation’s time should be discretized. It is important that we provide an endtime, otherwise the duration of the last stress period cannot be determined:

endtime = pd.to_datetime(["2000-01-06"])

simulation_bc = imod.mf6.Modflow6Simulation("example_bc")
simulation_bc["gwf_1"] = gwf_model

simulation_bc.create_time_discretization(additional_times=endtime)

simulation_bc["time_discretization"].dataset

<xarray.Dataset> Size: 64B
Dimensions:              (time: 3)
Coordinates:
  * time                 (time) datetime64[ns] 24B 2000-01-01 ... 2000-01-04
Data variables:
    timestep_duration    (time) float64 24B 1.0 2.0 2.0
    n_timesteps          int64 8B 1
    timestep_multiplier  float64 8B 1.0


Notice that iMOD Python figured out that the two boundary conditions, both with two timesteps, should lead to three stress periods!

Specifying extra settings#

The TimeDiscretization package also supports other settings, like the amount of timesteps which Modflow should use within a stress period, as well as a timestep multiplier, to gradually increase the timesteps modflow uses within a stress period. This can be useful when boundary conditions change very abruptly between stress periods. These settings are set by accessing their respective variables in the dataset.

times = simulation_bc["time_discretization"].dataset.coords["time"]

simulation_bc["time_discretization"].dataset["timestep_multiplier"] = 1.5
simulation_bc["time_discretization"].dataset["n_timesteps"] = xr.DataArray(
    data=[2, 4, 4], dims=("time",), coords={"time": times}
)

simulation_bc["time_discretization"].dataset

<xarray.Dataset> Size: 80B
Dimensions:              (time: 3)
Coordinates:
  * time                 (time) datetime64[ns] 24B 2000-01-01 ... 2000-01-04
Data variables:
    timestep_duration    (time) float64 24B 1.0 2.0 2.0
    n_timesteps          (time) int64 24B 2 4 4
    timestep_multiplier  float64 8B 1.5


Total running time of the script: (0 minutes 0.141 seconds)

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