WMS:GSSHA Manual Calibration

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The GSSHA model allows for both automated and manual calibration. There is a WMS tutorial that describes how to set up a basic calibration model.

Manual calibration is the process of changing simulation input so that the simulation output matches observed values. Manual calibration takes a lot of experience and a lot of patience, but it is possible to achieve a good fit between the simulation and the observed data. Being able to successfully manually calibrate a simulation is a necessary skill to successfully set up and run an automatic calibration program.

Set Up and Run a Successful Simulation

The first step to calibrating a simulation is to set up and successfully run a reasonable simulation. All known parameters, such as precipitation, should be defined; all unknown parameters should be set to physically realistic values.

For example, if actual roughness values are unknown, set all of the roughness values to "0.035" or some other reasonable number. Setting the roughness values to "0.00" or another unreasonable default value will not allow the simulation to proceed. If the spatial or temporal resolution is too coarse, then the simulation will be unduly influenced by numerical issues related to the implementation of the partial differential equations. A temporal or spatial resolution that is too course will result in delayed flows.

Identify the Calibration Variables

The calibration variables are the simulation parameters whose exact quantities are unknown. The number of these variables may range from a small handful to several dozen. At this stage, it is often necessary to identify which parameters to which the simulation is sensitive and which can be left at good approximations without unduly affecting the model. The number of calibration variables must be pared down to a manageable number, as attempting to manually calibrate a simulation with dozens of unknown parameters will prove extremely difficult instead of providing a good, robust simulation. Calibration cannot overcome a general lack of data.

Occasionally, lab tests for such parameters as hydraulic conductivity will be available. Such data is very valuable, but it still may be necessary to calibrate on that specific parameter. A simulation parameter represents a uniform parameter over a specific area, while lab results generate the parameter for a specific point. The lab data is a very good starting value, but the data may need some modification before becoming applicable to a general area.

Decide on a Valid Range for Each Variable

Knowing a range for each variable is very important. To accurately simulate what is actually present in the watershed requires knowledge of the physical meaning of all of the numerical parameters for the watershed. Without this understanding, a simulation that does not accurately reflect reality will be created, and the simulation will be worthless in a predictive capacity. It is highly recommended to consult published works that describe the formulas used in GSSHA and detail the values and physical meaning of the formula parameters.

Set Initial Values of Variables and Run the Model

Once the calibration variables have been decided upon and the valid range for each has been identified, set an initial value for each variable. The usual process is to begin with the middle value. Later on, these values will be modified little by little, either up or down. Beginning with the middle value of the range gives a good comparison point against higher or lower values in later simulations to determine simulation trends.

Compare Model Results to Observed Values

This is the key step to calibrating a simulation. Click on the button in the Solution Results column of the Feature Point/Node Properties dialog to display the Solution Analysis dialog, which allows both visual inspection of the solution result as well as numerical evaluation of the “fitness” of a solution. Using these criteria judge how well the simulation output fits the observed.

Change Variables and Re-run

If the simulation output is not sufficiently close to the observed data, the next step is to adjust one or more of the model parameters to try to get a better fit. This step takes practice, experience, and patience. If a better fit is obtained by adjusting the variables outside of the predefined range, then either the simulation is set up poorly or the data on which the model is based may be in question. The interdependence of variables may also require adjusting the other variables in the model before adjusting the one outside the parameter bounds. After adjusting the variables and running the simulation, check the new output and judge the results of the new variable setting.

Simulation Non-uniqueness

One important facet of calibrating a simulation is that changing more than one variable can often have very similar results on the simulation output. Calibrating a simulation attempts to extract spatial simulation parameters from observed data through a process called inverse modeling. Problems arise in calibration in situations where modifying more than one variable produces only one type of result in the simulation. The question then arises as to which variable values should be the actual variable values. This problem is not solvable and the simulation is said to be non-unique or over-specified. The only way to overcome this problem is by utilizing more data that is of a different type than that already being used. For example, using a stream-flow hydrograph as well as a set of observed groundwater elevations would help eliminate simulation non-uniqueness.