Hydrological models must be reliable and robust as these qualities influence all applications based on model output. Previous studies on conceptual rainfall-runoff models have shown that one of the root causes of their output uncertainty is model over-parameterisation. The problem of poorly defined parameters has attracted much attention but has not yet been satisfactorily solved. We believe that the most fruitful way forward is to improve the structures where these parameters act. The main objective of this paper is to examine the role of complexity in hydrological models by studying the relation between the number of optimised parameters and model performance. An extensive comparative performance assessment of the structures of 19 daily lumped models was carried out on 429 catchments, mostly in France but also in the United States, Australia, the Ivory Coast and Brazil. Bulk treatment of the data showed that the complex models outperform the simple ones in calibration mode but not in verification mode. We argue that the main reason why complex models lack stability is that the structure, i.e. the way components are organised, is not suited to extracting information available in hydrological time-series. An inadequate complexity typically results in model over-parameterisation and parameter uncertainty. Although complexity has been used as a response to the challenge of predicting the hydrological effects of environmental changes, this study suggests that such models may have been developed with excessive confidence and that they could face difficulties of parameter estimation and structure validation when confronted with hydro-meteorological time-series. This comparative study indicates that some parsimonious models can yield promising results and should be further developed, although they are not able to tackle all types of problems, which would be the case if their complexity were ideally adapted. (C) 2001 Elsevier Science B.V. All rights reserved.
