HyMC is a Python library designed to facilitate the calibration of hydrological models for rainfall-runoff predictions. Built as a wrapper around the Spotpy library, it leverages Spotpy's robust calibration methods (see Spotpy repository: https://github.com/thouska/spotpy) while integrating large-sample hydrology datasets such as CAMELS_GB, CAMELS_US and CAMELS_DE
Key features of HyMC include:
- Efficient Multi-Basin Calibration: Calibrate models across multiple basins simultaneously. By running each basin on a separate CPU core, HyMC significantly reduces computation time, making large-scale calibration tasks more manageable and efficient.
- Direct integration with large-sample hydrological datasets.
The codes presented in the repository are in the form of python scripts. Additionally several experiments are in the form of JupyterNotebooks for easy reproduction and execution. Following is a quick overview of the repository structure:
- data: Folder where the different datasets (e.g CAMELS-GB, CAMELS-US...) should be added. This information should be independently downloaded by the user.
- hymc: Code of the library.
- experiments: Implementation examples for different cases.
- results: Folder where the results generated by the codes will be stored.
This code started as part of our study:
Acuña Espinoza, E., Loritz, R., Álvarez Chaves, M., Bäuerle, N., & Ehret, U. (2023). To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization. EGUsphere, 2023, 1–22. https://doi.org/10.5194/egusphere-2023-1980
- Eduardo Acuña Espinoza (eduardo.espinoza@kit.edu)
No warranty is expressed or implied regarding the usefulness or completeness of the information and documentation provided. References to commercial products do not imply endorsement by the Authors. The concepts, materials, and methods used in the algorithms and described in the documentation are for informational purposes only. The Authors has made substantial effort to ensure the accuracy of the algorithms and the documentation, but the Authors shall not be held liable, nor his employer or funding sponsors, for calculations and/or decisions made on the basis of application of the scripts and documentation. The information is provided "as is" and anyone who chooses to use the information is responsible for her or his own choices as to what to do with the data. The individual is responsible for the results that follow from their decisions.
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