Is the FEFLOW model ready for FePEST?

The model convergence is a very important information to judge if the work in FePEST can be initiated. Since FEFLOW parameters will be automatically adjusted by PEST during any of its operation model (Estimation, Predictive analysis, Regularization, Pareto and Monte Carlo), a numerically "weak" model setup is not recommended. When non-convergence in a FEFLOW model is achieved in a PEST operation, this may understand these situations as improper model outputs, e.g. high penalization in the measurement objective function during a parameter estimation (calibration).

Conceptually, you should have initial idea, what parameters will be employed for the PEST operation. The relevance of one parameter in respect to other depends on the conceptual model and what it will be intended.

Knowing the possible locations for the adjustment of these parameters is an advantage later in the FePEST set-up. Elemental selection sets can be already prepared within the FEFLOW FEM file (*.fem), which subsequently can be used for FePEST Parameter Definitions.

Further details are discussed in section Parameter Definitions.

All the PEST operation modes require certain Observation Definitions. An operation without a single observation is misleading and technically not supported. Observations are used to define the measurement objective function in PEST and these can be standard FEFLOW Observation points, FEFLOW budget groups and/or any other information parsed via a plug-in or script.

In case budget information will be intended to be used in the PEST operation, it is recommended to storage in the FEFLOW FEM file (*.fem) the nodal selection sets containing these budget locations with the option Budget History Charting (context menu of stored selection).

In case transient information will be used in FePEST, it is recommended to have already stored the different time series in the FEFLOW Time Series Editor and to connect these to their corresponding Observation Point.

Further details are discussed in section Observation Definitions.

Certain knowledge about parameter uncertainty and variation within the model domain is always very beneficial, for example for parameter estimation, Monte Carlo analysis, etc in PEST. You can get an initial idea what information is relevant from the list below:

• Parameter bounds : Allowed parameter variation (upper and lower bounds).

• How is the variability of the parameters? Is there any information about the spatial correlation (e.g. variogram), interpolation method, etc.

• Has anisotropy to be considered in the system?

In a general situation, there are some observation sets more important (or relevant) than others. In these circumstances a good practice is to identify possible observation weighting strategy. Weights can define the observation definitions more dominant on the measurement objective function.

In case there is no prior-knowledge about observation relevance, a first PEST run in FePEST with equal observation weights can be also used to understand the contribution of each observation (or observation group) to the measurement objective function, thus the parameter estimation (calibration process).

Further information can be found in our discussion on the section Observation Definitions.

Depending on the conceptual model, there may be certain "rules" to be respect for some parameters used in the PEST operation, strictly speaking on the Estimation mode. Such rules can be for example zones of homogeneity, anisotropy ratios (e.g. horizontal and vertical conductivity), spatial parameter variability, etc.

It is always good to write down these "rules", since they can be later implemented  as the prior knowledge to guide PEST during its operation.

More information is available in section Prior Information.