SynHydro¶

Synthetic Generation Library - stochastic streamflow generation for hydrologic analysis.

SynHydro provides parametric, nonparametric, and machine-learning stochastic generation methods under a unified API. All generators share the same fit() then generate() workflow.

Generators¶

Class	Type	Frequency	Sites	Reference
`KirschGenerator`	Nonparametric	Monthly	Multi	Kirsch et al. (2013)
`PhaseRandomizationGenerator`	Nonparametric	Daily	Single	Brunner et al. (2019)
`ThomasFieringGenerator`	Parametric AR(1)	Monthly	Single	Thomas & Fiering (1962)
`MATALASGenerator`	Parametric MAR(1)	Monthly	Multi	Matalas (1967)
`MultiSiteHMMGenerator`	Hidden Markov Model	Annual	Multi	Gold et al. (2024)
`WARMGenerator`	Wavelet AR	Annual	Single	Nowak et al. (2011)

Quick Example¶

import synhydro

Q_obs = synhydro.load_example_data()                       # daily DataFrame
Q_monthly = Q_obs.resample("MS").mean()                 # resample to monthly

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

Installation¶

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

See Getting Started for full setup and data format details.