[1] A method of transfer function-noise (TFN) modeling is presented that operates in continuous time and uses a predefined family of impulse response (IR) functions. The resulting class of models is referred to as predefined IR function in continuous time (PIRFICT). It provides a useful tool for standardized analysis of time series, as it can be calibrated using irregularly spaced data and does not require a model identification phase prior to calibration. In section 2, the discrete Box-Jenkins (BJ) model is presented and transformed into continuous time to obtain the PIRFICT model. The discrete transfer function of a BJ model, which is made up of a variable number of parameters, is replaced by a simple analytical expression that defines the IR function. From the IR function, block response functions are derived that enable the model to handle irregularly spaced data. In the example application, the parameter estimates and performance of the BJ and PIRFICT model are compared using a data set of 15 piezometers and a simulated series. It was found that the estimated transfer and BR functions of both models follow the same general pattern, although the BJ transfer functions are partly irregular. The performance of both models proves to be highly comparable for all piezometers.
The methods behind the predefined impulse response function in continuous time (PIRFICT) time series model are extended to cover more complex situations where multiple stresses influence ground water head fluctuations simultaneously. In comparison to autoregressive moving average (ARMA) time series models, the PIRFICT model is optimized for use on hydrologic problems. The objective of the paper is twofold. First, an approach is presented for handling multiple stresses in the model. Each stress has a specific parametric impulse response function. Appropriate impulse response functions for other stresses than precipitation are derived from analytical solutions of elementary hydrogeological problems. Furthermore, different stresses do not need to be connected in parallel in the model, as is the standard procedure in ARMA models. Second, general procedures are presented for modeling and interpretation of the results. The multiple-input PIRFICT model is applied to two real cases. In the first one, it is shown that this model can effectively decompose series of ground water head fluctuations into partial series, each representing the influence of an individual stress. The second application handles multiple observation wells. It is shown that elementary physical knowledge and the spatial coherence in the results of multiple wells in an area may be used to interpret and check the plausibility of the results. The methods presented can be used regardless of the hydrogeological setting. They are implemented in a computer package named Menyanthes (www.menyanthes.nl).
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