Conceptual runoff models - fiction or representation of reality?

Abstract: Available observations are often not sufficient as a basis for decision making in water management. Conceptual runoff models are frequently used as tools for a wide range of tasks to compensate the lack of measurements, e.g., to extend runoff series, compute design floods and predict the leakage of nutrients or the effects of a climatic change. Conceptual runoff models are practical tools, especially if the reliability in their predictions can be assessed. Testing of these models is usually based solely on comparison of simulated and observed runoff, although most models also simulate other fluxes and states. Such tests do not allow thorough assessment ofmodel-prediction reliability. In this thesis, two widespread conceptual models, the HBV modeland TOPMODEL, were tested using a catalogue of methods for model validation (defined as estimation of confidence in model simulations). The worth of multi-criteria validation forevaluating model consistency was emphasised. Both models were capable to simulate runoffadequately after calibration, whereas the performance for some of the other validation tests wasless satisfactory. The impossibility to identify unique parameter values caused large uncertainties in model predictions for the HBV model. The parameter uncertainty was reducedwhen groundwater levels were included into the calibration, whereas groundwater-levelsimulations were in weak agreement with observations when the model was calibrated againstonly runoff. The agreement of TOP-MODEL simulations with spatially distributed data was weak for both groundwater levels and the distribution of saturated areas. Furthermore, validation against hydrological common sense revealed weaknesses in the TOPMODEL approach. In summary these results indicated limitations of conceptual runoff models and highlighted the need for powarful validation methods. The use of such methods enables assessment of the reliability of model predictions. It also supports the further development of models by identification of weak parts and evalution of improvements.