Making (selected) model parameters time-dependent was proposed long ago as a means for supporting the identification of causes of model structure deficits. I will present recent results of implementing this idea in a continuous-time framework that is based on describing the time-dependent parameter(s) by a mean-reverting Ornstein-Uhlenbeck process. I will present this approach in a notation that emphasizes the similarty of underlying ideas with the approach we were discussing over the past weeks at SAMSI (e.g. Bayarri et al. 2005). In some aspects, our approach is a special case of that approach as it focuses on time series output. On the other hand, it makes a step further in trying to track back the cause of the bias to mechanisms in the model and/or input error. This is intended to support model improvement and bias reduction (instead of just bias description). I will try to present preliminary results of the application of this approach to a simple hydrologic model described in Kuczera et al. 2006.

My talk is based on Tomassini, Reichert, Kuensch, Buser and Borsuk 2006 (with a new application) which extends earlier work by Brun (and Kuensch and Reichert) and Buser (and Kuensch).

The Papers by Bayarri et al. and Tomassini et al. are available on the home page of the methodology group. The one by Kuczera can be provided on request (we only have the journal version of the paper in press which we are not allowed to put on the web).