The Betami Method - in practice
Let's contrast the last page facsimile explanation with our case study database scenario.
Betami acts as visual back and front-end platform to PEST demands and responses.
It first matches all the correspondent variables in a way to rapidly pinpoint what has been done between one realization and another.
Then, creates the usually needed new sets of “observations” for subsequent PEST runs.
Further on, Betami suggest minor changes for new distributions of the former list of parameters currently used. Including a analise of these parameters previous variations, and also the correlated spectrum of alowed new amplitudes to keep the modflow solution stable.
So, in addition to a few other roles that still need to be revealed, it establishes the needed new indices of what is now more accurately understood as a composite objective function.
" Observations "
There are quotation marks around this title because here the “observations” assumes a dual functionality. It may have to do with the information effectively collected in the field, or more accurately, it may have to do with the actual goals that need to be met. Or alternatively, it may be interpreted as supplementary, yet equally significant, observation objectives that aids the pursuit of a more balanced simulated solution. The role of the modeller (as well as the customer) is to distinguish amongst the better options.
Going back to the present model, we can anticipate that its Hydraulic Heads did not pose much difficulties to the calibration process. The estimation General Heads (GHB) regional flows was the greater challenge indeed. Because to be in accordance with the initial conceptual model proposition, we needed positive influxes uphill, in conjunction with negative ones (discharges) at the lower portions of the domain.
You can see all the stimatives in the chats below.
" Parameters "
The accountability for each of the parameters used follows the predicted observations.
This data is provided here not only to enable this solution replication, but also because in this Bayesian metier (and almost any other kind of scenarios), expectations might affect outcomes.
So, first share if River (RIV) and General Head (GHB) conductances, comes five hydraulic conductivities guided by 15 pilot points each, plus the lateral and vertical anisotropy terms (HANI and VANI).
