English Abstract In this report two techniques for regional calibration
of mathematical models are discussed. Regional calibration is concerned
with rescaling the model from a local (site) level to a regional level.
This is typically done by assigning probability distributions to the unknown
parameters, which reflect their (regional) spatial variability in an
adequate way. Due to insufficient availability of data on the local level,
the calibration of the parameters is performed on the regional level by
matching the (simulated) distribution of the model outputs with the
distribution of the measurement data. The discussed techniques, Bin Filling
(BF) and Weighted Frequency Matching, are based on Monte Carlo sampling and
simulation in combination with a reweighing of the sampled values to
accomplish an optimal match between the distributions of the model results
and the measurement data. The characteristic features of the presented
techniques are discussed and their utility is indicated. In addition some
guidelines are presented for an appropriate use of the methods which have
been implemented aas software for general use.
