Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1175/BAMS-D-21-0284.1 |
Licence  |
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| Title (Primary) | More complex is not necessarily better in large-scale hydrological modelling: A model complexity experiment across the contiguous United States |
| Author | Merz, R.; Miniussi, A.; Basso, S.; Petersen, K.-J.; Tarasova, L. |
| Source Titel | Bulletin of the American Meteorological Society |
| Year | 2022 |
| Department | CATHYD |
| Volume | 103 |
| Issue | 8 |
| Page From | E1947 |
| Page To | E1967 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Abstract | 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 modelling experiment across 700 catchments in the contiguous US, we analyze the performance of a conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11 to 45 parameters) but with exactly the same inputs, 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 US, 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26311 |
| Merz, R., Miniussi, A., Basso, S., Petersen, K.-J., Tarasova, L. (2022): More complex is not necessarily better in large-scale hydrological modelling: A model complexity experiment across the contiguous United States Bull. Amer. Meteorol. Soc. 103 (8), E1947 - E1967 10.1175/BAMS-D-21-0284.1 |
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