To bucket or not to bucket? Analyzing the performance  and interpretability of hybrid hydrological  models with dynamic parameterization

Acuña Espinoza, Eduardo; Loritz, Ralf; Álvarez Chaves, Manuel; Bäuerle, Nicole; Ehret, Uwe

doi:https://doi.org/10.5194/hess-28-2705-2024

Articles | Volume 28, issue 12

https://doi.org/10.5194/hess-28-2705-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-2705-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 12

Research article

|

27 Jun 2024

Research article |

| 27 Jun 2024

To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization

Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret

Data sets

CAMELS: Catchment Attributes and MEteorology for Large-sample Studies A. Newman et al. https://doi.org/10.5065/D6MW2F4D

Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB) G. Coxon et al. https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9

KIT-HYD/Hy2DL: Preview release for submission (1.0) Eduardo Acuna Espinoza et al. https://doi.org/10.5281/zenodo.11103634

Model code and software

KIT-HYD/Hy2DL: Preview release for submission (1.0) Eduardo Acuna Espinoza et al. https://doi.org/10.5281/zenodo.11103634

Short summary

Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.