One of the tools provided in GMS for model calibration is automated parameter estimation. With automated parameter estimation, an external utility is used to iteratively adjust a set of parameters and repeatedly launch the model until the computed output matches field-observed values. Parameter estimation is used in conjunction with the point observations and the flow observations.
Automated parameter estimation is supported in GMS for the MODFLOW simulations using MODFLOW PES, PEST, and UCODE. These are sometimes called "inverse models". Most of the steps involved in setting up an inverse model in GMS are the same regardless of the selected inverse model.
Inverse models should only be used carefully and with a full understanding of the assumptions, equations, and methods involved. It is suggested that the user read the available documentation on the inverse model being used. Only the steps involved in setting up an inverse model are described in this document.
The basic steps involved in using an inverse model for parameter estimation are follows:
The first step is to create your MODFLOW model and run a simulation. Before launching the inverse model, you need to have a MODFLOW model that successfully converges and you need to determine a good set of starting values for your parameters. Once you have a solution it is also a good idea to copy the computed heads from your solution to your starting heads array. This ensures that as the inverse model modifies the parameters and runs MODFLOW repeatedly, it more likely that MODFLOW will quickly converge each time it is launched.
Once you have a working MODFLOW model, you should enter your head and flux observations. Head observations are entered as points using an observation coverage in the Map module. Flow observations are assigned directly to arcs and polygons in source/sink coverages. Each of the observations is assigned a weight that is saved to the inverse input files.
You must select an inverse model. Bring up the Global Options dialog and select either the Parameter Estimation or Stochastic Inverse Model button depending on whether a stochastic simulation is being run. Next, select the Packages button. In the selection box below the Parameter Estimation toggle select the appropriate inverse model.
The next step is to parameterize your model. This is accomplished by assigning key values to zones. In general, the number of parameters should be less than the number of observations. However, if the user chooses to use pilot points with PEST in regularization mode, then the number of parameters does not have to be less than the number of observations.
Once the key values are assigned, the next step is to create a Parameter List with the Parameters dialog in the MODFLOW menu.
Once the parameter list is set up, you may wish to edit the general Parameter Estimation options. These options include the output control and convergence criteria.
The group weight multipliers can be edited to adjust the relative weight of the head and flux observations.
Edit the MODFLOW Convergence Options if necessary in order to ensure a stable solution.
Once all of the inverse model options have been set, the next step is to save the MODFLOW model using the Save/Save As command in the File menu. Next, run MODFLOW and the inverse model will run with MODFLOW. The inverse model will then be launched in a separate window or the model wrapper in which you will see information relating to the MODFLOW runs and the status of the objective function. Depending on the problem, the inverse model may take anywhere from several minutes to several hours (or days) to run to completion. When the inverse process is completed successfully, GMS automatically launches a MODFLOW forward run with the optimal values computed by the inverse model. Thus, the solution will reflect the optimal values computed by the inverse model.
When the inverse model is finished, it writes out a text file containing the set of parameter values corresponding to the minimum calibration error. These values can be viewed with the Import Optimal Values button. This copies the optimal parameter values to the Starting Value field in the Parameter List.
Related Links:
Model Calibration