SynHydro

Tests

SynHydro is a Python library for generating synthetic hydrologic timeseries using a unified, scikit-learn-style API. All generators share a common fit() and generate() workflow, and the library includes validation, drought analysis, plotting, and ensemble data management tools.

Installation

pip install synhydro

To install a specific tagged release directly from GitHub instead:

pip install git+https://github.com/TrevorJA/SynHydro.git@v0.1.0

Quick example

import synhydro

Q_daily = synhydro.load_example_data()
Q_monthly = Q_daily.resample("MS").sum()

gen = synhydro.KirschGenerator()
gen.fit(Q_monthly)
ensemble = gen.generate(n_realizations=50, n_years=30, seed=42)

Supported generators

Generators are classified into three bins by the mathematical character of their generative mechanism: parametric (sample from a fitted probability model), hybrid (combine a parametric structure with a non-parametric resampling step), and non-parametric (resample the historical record directly). See the Algorithms page for full descriptions.

Generator Class Frequency Sites Reference
ThomasFieringGenerator Parametric Monthly Single Thomas & Fiering (1962)
MatalasGenerator Parametric Monthly Multi Matalas (1967)
ARFIMAGenerator Parametric Monthly/Annual Single Hosking (1984)
SPARTAGenerator Parametric Monthly Multi Tsoukalas et al. (2018)
SMARTAGenerator Parametric Annual Multi Tsoukalas et al. (2018)
MultiSiteHMMGenerator Parametric Annual Multi Gold et al. (2024)
KirschGenerator Hybrid Weekly/Monthly Multi Kirsch et al. (2013)
WARMGenerator Hybrid Annual Single Nowak et al. (2011)
PhaseRandomizationGenerator Hybrid Daily Single Brunner et al. (2019)
MultisitePhaseRandomizationGenerator Hybrid Daily Multi Brunner & Gilleland (2020)
KNNBootstrapGenerator Non-parametric Monthly/Annual Multi Lall & Sharma (1996); Prairie et al. (2006, 2008)

Supported disaggregators

Disaggregator Direction Reference
NowakDisaggregator {Annual, Monthly, Weekly} to {Monthly, Weekly, Daily} Nowak et al. (2010)
ValenciaSchaakeDisaggregator Annual to Monthly Valencia & Schaake (1973)

Pre-built pipelines (KirschNowakPipeline, ThomasFieringNowakPipeline) chain generation and disaggregation in a single interface.

Contributing

SynHydro is under active development, and contributions are welcome. For bug reports, feature requests, or discussion of new methods, please open an issue or pull request on GitHub. See CONTRIBUTING.md for guidelines on adding new generators and the project's development practices.

Documentation

Full documentation including tutorials, algorithm descriptions, and API reference is available at the project website.

Citing SynHydro

DOI

The DOI badge and the version DOI below are filled in after the first release is archived on Zenodo. If you use SynHydro in your research, please cite it as:

Amestoy, T. (2026). SynHydro: Synthetic hydrologic timeseries generation
in Python (Version 0.1.0) [Computer software]. Zenodo.
https://doi.org/10.5281/zenodo.YYYYYYY

@software{amestoy_synhydro_2026,
  author    = {Amestoy, Trevor},
  title     = {{SynHydro}: Synthetic hydrologic timeseries generation in Python},
  version   = {0.1.0},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.YYYYYYY},
  url       = {https://github.com/TrevorJA/SynHydro}
}

Project links

Data verified by PyPI on Sep 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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Avatar for tjamestoy from gravatar.com tjamestoy

Credits

Author: Trevor Amestoy

GitHub Statistics

Data verified by PyPI on Sep 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev docs lint test

Tags

hydrology stochastic streamflow synthetic generation time series

Classifiers

Report project as malware

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