On the value of experimental data to reduce the prediction uncertainty of a process-oriented catchment model

Predicting hydrological response to rainfall or snowmelt including an estimation of prediction uncertainty is a major challenge in current hydrological research. The process-based catchment model TAC(D) (tracer aided catchment model, distributed) was applied to the mountainous Brugga basin (40 km(2)), located in the Black Forest Mountains, southwest Germany. The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) framework was used to analyse the uncertainty of discharge predictions. The model input parameter sets were generated using the Latin Hypercube sampling method, which is an efficient way to sample the parameter space representatively. It was shown that the number of investigated parameters should exceed the number of varied parameters by at least a factor of 10. Even if the process basis and suitability of the model could be proven, relatively large uncertainty ranges of the discharge predictions still occurred during the simulation of floods. Prediction uncertainty varied both temporally and spatially. Incorporating additional data, i.e. sub-basin runoff and observed tracer concentrations, reduced the prediction uncertainty. However, the potential restriction of the uncertainty clearly depends on the goodness of the simulation of the additional data set. Knowledge of the uncertainty of model predictions and of the potential for experimental data to reduce it are crucial to sustainable environmental management, and should be considered more thoroughly during the planning of future field studies. (C) 2004 Elsevier Ltd. All rights reserved.