A multisignal automatic calibration methodology for hydrochemical models: A case study of the Birkenes Model

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Abstract
Most hydrochemical models employ immeasurable or “artificial” parameters that need to be estimated from calibration data. A methodology is presented here for the automatic calibration of artificial parameters, which improves parameter identifiability through increasing the information available for determining the parameter values. Instead of calibrating the model using only one signal (e.g., the hydrograph), multiple signals (e.g., chemical signals as well as the hydrograph) are considered simultaneously. Either a simple least squares or a weighted least squares objective function may be used. The methodology is applied to the hydrologic module of the Birkenes hydrochemical model using artificial data. From a wide range of starting points, a gradient search optimization technique is able to consistently locate the correct parameter values uniformly better when using two signals (the hydrograph and a conservative tracer) than when only one signal (the hydrograph) is used.