Identification of a river water quality model and assessment of data information content

Substantial uncertainties exist in the identification of river water quality models, which partially depend on the information content of the calibration data. To evaluate the dependencies between available calibration data and model predictions investigations were conducted based on a 536 km free-flowing reach of the German part of the River Elbe. Five extensive flowtime related longitudinal surveys with 14 sampling locations were used. The multi-objective calibration of the deterministic river water quality model QSIM of the BfG (Germany) was carried out with the nonlinear parameter estimator PEST. The Elbe case study showed that calibration with less than two survey data sets leads to substantial errors if these parameters are applied to deviating boundary conditions. These uncertainties can be reduced with an increased calibration database. The results of this study will help model users to define appropriate data collections and monitoring schemes.