pastas.stressmodels.TarsoModel
==============================

.. toctree::
   :hidden:

   /api/pastas/stressmodels/TarsoModel.set_init_parameters
   /api/pastas/stressmodels/TarsoModel.simulate
   /api/pastas/stressmodels/TarsoModel.tarso
   /api/pastas/stressmodels/TarsoModel.to_dict

.. py:class:: pastas.stressmodels.TarsoModel(prec: pandas.Series, evap: pandas.Series, rfunc: pastas.rfunc.Exponential | None = None, name: str = 'tarso', oseries: pandas.Series | None = None, dmin: float | None = None, dmax: float | None = None, **kwargs)



   
   Stressmodel simulating the effect of recharge using the Tarso method.

   :param prec: pandas.Series with pandas.DatetimeIndex containing the precipitation series.
   :type prec: pandas.Series
   :param evap: pandas.Series with pandas.DatetimeIndex containing the potential evaporation
                series.
   :type evap: pandas.Series
   :param rfunc: this model only works with the Exponential response function.
   :type rfunc: pastas.rfunc instance
   :param name: Name of the stressmodel. Default is "tarso".
   :type name: str, optional
   :param oseries: A pandas.Series with pandas.DatetimeIndex of observations to which the model
                   will be calibrated. It is used to determine the initial values of the
                   drainage levels and the boundaries of the upper drainage level. Specify
                   either oseries or dmin and dmax.
   :type oseries: pandas.Series, optional
   :param dmin: The minimum drainage level. It is used to determine the initial values of the
                drainage levels and the lower boundary of the upper drainage level. Specify
                either oseries or dmin and dmax.
   :type dmin: float, optional
   :param dmax: The maximum drainage level. It is used to determine the initial values of the
                drainage levels and the upper boundary of the upper drainage level. Specify
                either oseries or dmin and dmax.
   :type dmax: float, optional
   :param max_cache_size: Maximum size of the cache (in number of entries). Only used when cachetools is
                          installed and caching is enabled (see ps.set_use_cache()).
   :type max_cache_size: int, optional

   .. seealso:: :py:obj:`pastas.recharge`

   .. rubric:: Notes

   The Threshold autoregressive self-exciting open-loop (Tarso) model
   :cite:t:`knotters_tarso_1999` is nonlinear in structure because it incorporates
   two regimes which are separated by a threshold. This model method can be used to
   simulate a groundwater system where the groundwater head reaches the surface or
   drainage level in wet conditions. TarsoModel uses two drainage levels, with two
   exponential response functions. When the simulation reaches the second drainage
   level, the second response function becomes active. Because of its structure,
   TarsoModel cannot be combined with other stress models, a constant or a transform.
   TarsoModel inherits from RechargeModel. Only parameters specific to the child
   class are named above.















   ..
       !! processed by numpydoc !!

   .. py:property:: nsplit
      :type: int


      
      Tarso has a single effective contribution.
















      ..
          !! processed by numpydoc !!

Methods
-------

.. autoapisummary::

   pastas.stressmodels.TarsoModel.set_init_parameters
   pastas.stressmodels.TarsoModel.simulate
   pastas.stressmodels.TarsoModel.tarso
   pastas.stressmodels.TarsoModel.to_dict