RechargeModel

class RechargeModel(prec, evap, rfunc=None, name='recharge', recharge=None, temp=None, settings=('prec', 'evap', 'evap'), metadata=(None, None, None))

Stressmodel simulating the effect of groundwater recharge on the head.

Parameters

• prec (pandas.Series) – pandas.Series with pandas.DatetimeIndex containing the precipitation series. The precipitation series should be provided in mm/day when a nonlinear model is used.

• evap (pandas.Series) – pandas.Series with pandas.DatetimeIndex containing the potential evaporation series. The evaporation series should be provided in mm/day when a nonlinear model is used.

• rfunc (pastas.rfunc instance, optional) – Instance of the response function used in the convolution with the stress. Default is ps.Exponential().

• name (str, optional) – Name of the stress. Default is “recharge”.

• recharge (pastas.recharge instance, optional) – Instance of a recharge model. Options are: Linear, FlexModel and Berendrecht. These can be accessed through ps.rch. Default is ps.rch.Linear().

• temp (pandas.Series, optional) – pandas.Series with pandas.DatetimeIndex containing the temperature series. It depends on the recharge model if this argument is required or not. The temperature series should be provided in degrees Celsius.

• settings (list of dicts or str, optional) – The settings of the precipitation, evaporation and optionally temperature time series, in this order. By default (“prec”, “evap”, “evap”). This can be a string referring to a predefined settings dict (defined in ps.rcParams[“timeseries”]), or a dict with the settings to apply. For more information refer to Time Series Settings section below for more information.

• metadata (tuple of dicts or list of dicts, optional) – dictionary containing metadata about the stress. This is passed onto the TimeSeries object.

Examples

>>> sm = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(),
>>>                       recharge=ps.rch.FlexModel(), name="rch")
>>> ml.add_stressmodel(sm)

Time series settings

• fill_nan ({“drop”, “mean”, “interpolate”} or float) –

  Method for filling NaNs.

  • drop: drop NaNs from time series

  • mean: fill NaNs with mean value of time series

  • interpolate: fill NaNs by interpolating between finite values

  • float: fill NaN with provided value, e.g. 0.0

• fill_before ({“mean”, “bfill”} or float) –

  Method for extending time series into past.

  • mean: extend time series into past with mean value of time series

  • bfill: extend time series into past by back-filling first value

  • float: extend time series into past with provided value, e.g. 0.0

• fill_after ({“mean”, “ffill”} or float) –

  Method for extending time series into future.

  • mean: extend time series into future with mean value of time series

  • ffill: extend time series into future by forward-filling last value

  • float: extend time series into future with provided value, e.g. 0.0

• sample_up ({“mean”, “interpolate”, “divide”} or float) – Method for up-sampling time series (increasing frequency, e.g. going from weekly to daily values).

  • bfill or backfill: fill up-sampled time steps by back-filling current values

  • ffill or pad: fill up-sampled time steps by forward-filling current values

  • mean: fill up-sampled time steps with mean of timeseries

  • interpolate: fill up-sampled time steps by interpolating between current values

  • divide: fill up-sampled steps with current value divided by length of current time steps (i.e. spread value over new time steps).

• sample_down ({“mean”, “drop”, “sum”, “min”, “max”}) – Method for down-sampling time series (decreasing frequency, e.g. going from daily to weekly values).

  • mean: resample time series by taking the mean

  • drop: resample the time series by taking the mean, dropping any NaN-values

  • sum: resample time series by summing values

  • max: resample time series with maximum value

  • min: resample time series with minimum value

Parameters

• prec (Series) –

• evap (Series) –

• rfunc (Optional[RFunc]) –

• name (str) –

• recharge (Optional[Recharge]) –

• temp (Optional[Series]) –

• settings (Tuple[Union[str, StressSettingsDict], Union[str, StressSettingsDict], Union[str, StressSettingsDict]]) –

• metadata (Optional[Tuple[dict, dict, dict]]) –

Notes

This stress model computes the contribution of precipitation and potential evaporation in two steps. In the first step a recharge flux is computed by a model determined by the input argument recharge. In the second step this recharge flux is convolved with a response function to obtain the contribution of recharge to the groundwater levels. If a nonlinear recharge model is used, the precipitation should be in mm/d.

Warning

We recommend not to store a RechargeModel is a variable named rm. This name is already reserved in IPython to remove files and will cause problems later.

Raises

• A warning if the the maximum annual precipitation is smaller than 12 and a –

• nonlinear recharge model is applied. This is likely an indication that the units of –

• the precipitation series are in m/d instead of mm/d. Please check the units of the –

• precipitation series. –

Parameters

• prec (Series) –

• evap (Series) –

• rfunc (Optional[RFunc]) –

• name (str) –

• recharge (Optional[Recharge]) –

• temp (Optional[Series]) –

• settings (Tuple[Union[str, StressSettingsDict], Union[str, StressSettingsDict], Union[str, StressSettingsDict]]) –

• metadata (Optional[Tuple[dict, dict, dict]]) –

See also

pastas.rfunc, pastas.timeseries.TimeSeries, pastas.recharge

Attributes

nparam

Methods

__init__
get_nsplit	Determine in how many time series the contribution can be split.
get_parameters	Get parameters and return as array.
get_settings	Method to obtain the settings of the stresses.
get_stress	Method to obtain the recharge stress calculated by the model.
get_water_balance	Method to obtain the water balance components.
set_init_parameters	Internal method to set the initial parameters.
simulate	Method to simulate the contribution of recharge to the head.
to_dict	Method to export the RechargeModel object.
update_stress	Method to update the settings of the all stresses in the stress model.