SynHydro¶

Synthetic Generation Library - stochastic streamflow generation for hydrologic analysis.

SynHydro provides parametric, hybrid, and non-parametric stochastic generation methods under a unified API. All generators share the same fit() and generate() workflow. See the Algorithms overview for the classification and plain-language descriptions of each class.

Generators¶

Matrix of SynHydro generators arranged by method family (Parametric, Hybrid, Non-parametric) on the vertical axis and timescale (Daily, Weekly, Monthly, Annual) on the horizontal axis, with capability bars indicating single-site vs. multi-site support

See the Algorithms overview for definitions of the method-family groups.

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)

Quick Example¶

import synhydro

Q_obs = synhydro.load_example_data()                       # daily DataFrame
Q_monthly = Q_obs.resample("MS").sum()                  # 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.