Difference between revisions of "Building a Model:Calibration and Verification"

Latest revision as of 17:54, 20 May 2008

The complete model should be calibrated and verified to an extended period of data while operating in the LONG_TERM mode. For flow models, the model should be calibrated to observed discharges at the outlet and any interior points. The OPTIMIZE project card can be used to provide peak discharge and discharge volume for individual events and the entire simulation at the watershed outlet and at any desired internal locations. These can be used to calculate a cost function, and parameter sets that produce the smallest cost function, minimum error as defined by the cost function, can be determined either manually or preferably with an automated method, such as the SCE method. Typical calibration parameters listed in order of importance for each process include:

Overland Flow

Surface roughness

Retention depth

Infiltration

Saturated hydraulic conductivity (all methods)

Suction head (GA, multi-layer GA, GAR) or bubbling pressure (RE)

Initial moisture

Porosity

Channel Flow

Roughness coefficient

Evapo-transpiration

Root depth

Canopy resistance

Soil Moisture

Pore distribution index

Wilting-point soil moisture

Groundwater Flow

Saturated hydraulic conductivity

Porosity

To use the SCE method, the number of parameters to be calibrated should be kept to a minimum, typically less than 16 (Senarath et al., 2000). To reduce the number of parameters, the proportions of initial estimates of parameters for different index types can be adjusted as a set, i.e. adjust all values of some parameter, saturated hydraulic conductivity for example, by the same fractional amount.

The model with the calibrated parameter set should be tested against independent verification period, such as a split-sample test (Klemes, 1986). Once the model demonstrates the ability to predict discharge, or other variables of interest, for the verification period, it can be used with confidence to analyze model scenarios and make predictions under varying hydrologic conditions.