Comparison of model structural uncertainty using a multi-objective optimisation method

This study aims to propose a method for effectively recognising and evaluating model structural uncertainty. It began with a comparative assessment of various model structures that have differing features regarding the rainfall-runoff mechanism and DEM spatial resolution. The assessment applied a multi-objective optimisation method (MOSCEM-UA) with two objective functions (simple least-squares and the heteroscedastic maximum likelihood estimator), and focused on five historical flood events. The study was based on the assumptions that a structurally sound model assures improved prediction results (either minimized or maximized model performance measure), allows constant model performance with regard to objective functions (a small Pareto solution set), and yields good applicability of a calibrated parameter set to various events (good parameter stability). The results indicated that KWMSS, a distributed model, was superior to SFM, a simple lumped model, when estimating a Pareto solution set and assessing parameter stability for the applied events. In addition, three different spatial resolutions (250 m, 500 m, and 1 km) were compared to assess the structural uncertainty due to changes in the topographical representation in distributed rainfall-runoff modelling. The results indicated that the 250 and 500 m models were Pareto-equivalent, containing similar Pareto fronts, and both produced Pareto results superior to the 1 km model. Both models also yielded parameter stability values that were much more superior to the model based on a 1 km DEM. As the topographic representation became more detailed, the model showed a tendency to have less structural uncertainty in terms of guarantying better performance, better parameter stability, and a smaller Pareto solution set. On the other hand, the output of a spatially detailed model was likely to be insensitive to the variation of model parameters (i.e. equifinality).