The choice of hydrological model structure, i.e. a model’s selection of states and ﬂuxes and the equations used to describe them, strongly controls model performance and realism. This work investigates diﬀerences in performance of 36 lumped conceptual model structures calibrated to and evaluated on daily streamﬂow data in 559 catchments across the United States. Model performance is compared against a benchmark that accounts for the seasonality of ﬂows in each catchment. We ﬁnd that our model ensemble struggles to beat the benchmark in snow-dominated catchments. In most other catchments model structure equiﬁnality (i.e. cases where diﬀerent models achieve similar high eﬃciency scores) can be very high. We ﬁnd no relation between the number of model parameters and performance during either calibration or evaluation periods, nor evidence of increased risk of overﬁtting for models with more parameters. Instead, the choice of model parametrization (i.e. which equations are used and how parameters are used within them) dictates the model’s strengths and weaknesses. Results suggest that certain model structures are inherently better suited for certain objective functions and thus for certain study purposes. We ﬁnd no clear relationships between the catchments where any model performs well and descriptors of those catchments’ geology, topography, soil and vegetation characteristics. Instead, model suitability seems to relate strongest to the streamﬂow regime each catchment generates and we have formulated several tentative hypotheses that relate commonalities in model structure to similarities in model performance. Modeling results are made publicly available for further investigation.
- conceptual model comparison
- model structure uncertainty
- catchment modeling
Data from "A brief analysis of conceptual model structure uncertainty using 36 models and 559 catchments"