Calibration of parameters of mathematical models is still a tough task in several engineering problems. Many of the models adopted for the numerical simulations of real phenomena, in fact, are of empirical derivation. Therefore, they include parameters which have to be calibrated in order to correctly reproduce the physical evidence. Thus, the success of a numerical model application depends on the quality of the performed calibration, which can be of great complexity, especially if the number of parameters is higher than one. Calibration is traditionally performed by engineers and researchers through manual trial-and-error procedures. However, since models themselves are increasingly sophisticated, it seems more proper to look at more advanced calibration procedures. In this work, in particular, an optimization technique for a multi-parameter calibration is applied to a two-phase depth-averaged model, already adopted in previous works to simulate morphodynamic processes, such as, for example, the dike erosion by overtopping.
We examined the reliability of laboratory-derived calibration curves for age determination of field individuals of the common vole, Microtus arvalis. The sex-specific calibration curves for age determination based on the relationship between eye lens mass and age derived in the laboratory were applied to a live-trapped field population of common vole. When comparing the individual’s age to the length of its trapping history, we found a slight tendency for underestimation of real age. These errors were observed slightly more in females than in males and in individuals captured over a longer time. This could mean that growth rates in captive animals, especially older ones, and in females are greater than those from the field. The month of first trapping has no effect on the presence of the error. We suggest that, in population studies with a special concern for ageing individuals over the whole life span, other methods should be examined, such as those measuring insoluble eye lens proteins or calibration curves based on more than one predictor.