More Complex is Not Necessarily Better in Large-Scale Hydrological Modeling A Model Complexity Experiment across the Contiguous United States

Conceptual hydrological models are irreplaceable tools for large-scale (i.e., from -regional to global) hydrological predictions. Large-scale modeling studies typically strive to employ one single model structure regardless of the diversity of catchments under study. However, little is known on the optimal model complexity for large-scale applications. In a modeling experiment across 700 catchments in the contiguous United States, we analyze the performance of a -conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11-45 parameters) but with exactly the same inputs and spatial and temporal resolution and implementing the same regional parameterization approach. The performance of all model versions compares well with those of contemporary large-scale models tested in the United States, suggesting that the applied model structures reasonably account for the dominant hydrological processes. Remarkably, our results favor a simpler model structure where the main hydrological processes of runoff generation and routing through soil, groundwater, and the river network are conceptualized in distinct but parsimonious ways. As long as only observed runoff is used for model validation, including additional soil layers in the model structure to better represent -vertical soil heterogeneity seems not to improve model performance. More complex models tend to have lower model performance and may result in rather large uncertainties in simulating states and fluxes (soil moisture and groundwater recharge) in model ensemble applications. Overall, our results indicate that simpler model structures tend to be a more reliable choice, given the limited validation data available at large scale.

Automated summary

A study on large-scale hydrological models in 700 catchments across the contiguous United States found that simpler model structures tend to be more reliable, while more complex models may have lower performance.