Hy2DL: Hydrological modeling using Deep Learning methods

Hy²DL is a python library to create hydrological models for rainfall-runoff prediction using deep learning methods. The repository includes implementations with Large-Sample Hydrology datasets such as CAMELS-GB, CAMELS-US, CAMELS-DE, CAMELS-CH and CARAVAN.

The folder structure and some of the code logic presented here is based on NeuralHydrology.

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. Following is a quick overview of the repository structure:

• benchmarks: Comparison of our library against other studies from scientific literature.
• data: Folder where the different datasets (e.g CAMELS-GB, CAMELS-US...) should be added. This information should be independently downloaded by the user.
• docs: Library documentation
• examples: Codes (.py) and configuration files to run multiple examples.
• notebooks: Jupyter notebooks showing implementation examples, for different cases.
• results: Folder where the results generated by the codes will be stored.
• src/hy2dl: Code of the library.

Documentation:

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

Installation

A release version is available on PyPI and can be installed using:

uv

uv add hy2dl

or pip.

pip install hy2dl

The pyproject.toml file includes the package requirements. If you want to install an editable version of the package (e.g. for development), please refer to the documentation.

Citation:

If you find Hy²DL useful in your research or applications, please cite it as:

@software{acuna2025,
  author       = {Eduardo Acuna and
                  Manuel Álvarez Chaves and
                  Alexander Dolich and
                  Benedikt Heudorfer and
                  Ashish Manoj J and
                  Wiktoria Brzezińska and
                  Mirko Mälicke},
  title        = {Hy2DL: Hydrological modeling using Deep Learning methods},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17251944},
  url          = {https://doi.org/10.5281/zenodo.17251944},
}