Heuberger PSCSanders RJanssen PHM2012-12-122012-12-121992-09-30http://hdl.handle.net/10029/256691Abstract niet beschikbaarIn 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.35 pnl2092-3wiskundige modellenmodelcalibratiesysteemidentificatie; regionale calibratiekansverdelingenbin fillingweighted frequency matchingmonte-carlo samplingReport2012-12-12