Effect of model structure on the accuracy and uncertainty of results from water quality models

Eight one-dimensional steady-state models with different complexity, which describe the phosphate concentration as a function of the distance along a river, were examined with respect to accuracy and uncertainty of the model results and identifiability of the model parameters by means of combined calibration and sensitivity analysis using Monte Carlo simulations. In addition, the models were evaluated by the Akaike information criterion (AIC). All eight models were calibrated on the same data set from the Biebrza River, Poland. Although the accuracy increases with model complexity, the percentage of explained variance is not significantly improved in comparison with the model that describes the phosphate concentration by means of three parameters. This model also yields the minimum value of the AIC and the parameters could be well identified. Identification of the model parameters becomes poorer with increasing model complexity; in other words the parameters become increasingly correlated. This scarcely affects the uncertainty of the model results if correlation is taken into account. If correlation is not taken into account, the uncertainty of model results increases with model complexity.