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Hy<sup>2</sup>DL: Hybrid Hydrological modeling using Deep Learning methods

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<p align="justify"> Hy<sup>2</sup>DL is a python library to create hydrological models for rainfall-runoff prediction, which make use of deep learning methods. The main idea of this repository is to provide models that are 'easy' to understand, interpret and implement. This 'ease', naturally, comes at the cost of code modularity and, to some extent flexibility. The logic of the codes presented here are heavily based on 'NeuralHydrology --- A Python library for Deep Learning research in hydrology' (https://github.com/neuralhydrology/neuralhydrology.git). For a more flexible, robust and modular implementation of deep learning method in hydrological modeling we advice the use of Neural Hydrology.

In addition to Long Short Term Memory (LSTM) network architectures, the repository also features hybrid hydrological models which use an LSTM network combined with a process based rainfall-runoff model and transformer based hydrological model. Regional hydrological models for several datasets namely the CAMELS_GB, CAMELS_US, CAMELS_CH, CAMELS_DE and the CARAVAN are at the user's easy disposal.

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Structure of the repository:

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. Detailed documentation for the repository can be found at Hy2DL.readthedocs.io.

Following is a quick overview of the repository structure:

Dependencies

The packages used to run the codes are indicated at the beginning of each notebook. It must be considered that the codes for the data-driven models run better in GPU, therefore a PyTorch version that supports GPU should be installed!

Citation:

This code is part of our study

Acuña Espinoza, E., Loritz, R., Álvarez Chaves, M., Bäuerle, N., and Ehret, U.: To Bucket or not to Bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization, (https://doi.org/10.5194/hess-28-2705-2024, 2024)

Authors:

Disclaimer:

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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