Last in this batch drawing on Tony Ladson’s blog — and the one entry in the series that isn’t a translation of his work, because there’s nothing to translate. His R Packages for Hydrology (30 December 2017) is a reference list, not a method. The useful companion isn’t a Python port of his list — it’s an independently-researched Python-side equivalent, checked against each package’s actual current status rather than assumed from memory.
Flood frequency and extreme value analysis
- pyextremes — block-maxima and peaks-over-threshold extreme value analysis. My own fork adds LP3, Multiple Grubbs-Beck, TCEV and LH-moments for ARR 2019 compliance.
- lmoments3 (PyPI, v1.0.8) — L-moment parameter estimation for statistical distributions, a Python port of Hosking’s original
lmoments.f. Directly relevant to LP3/GEV fitting.
Model performance and calibration
- hydroeval (PyPI) — vectorised NSE, KGE (and its α/β/γ or r/α/β components), and non-parametric KGE. Covers the same ground as the metrics I built from scratch earlier in this series, if you’d rather use a maintained package than your own implementation.
- HydroErr (PyPI) — a broader library of 70+ error metrics, with explicit handling of NaN/Inf/negative/zero values.
- SPOTPY (PyPI; Houska et al., PLOS ONE, 2015) — calibration, sensitivity and uncertainty analysis for environmental models generally, not hydrology-specific but heavily used in the field. Eight sampling algorithms, eleven objective functions, MPI-parallel.
Groundwater
- Pastas (PyPI, v1.13.2 as of Feb 2026) — time series analysis of groundwater levels: response functions for rainfall, pumping and other stresses, trend and outlier detection. Actively maintained at TU Delft.
Terrain and catchment analysis
- pysheds (PyPI) — DEM-based catchment delineation and flow accumulation, D8 routing by default. Built on the standard geospatial Python stack (numpy, rasterio, scikit-image).
General scientific stack
Not hydrology-specific, but where most of the actual work happens: numpy/scipy for the numerical core, pandas for time series handling, xarray (plus rioxarray for georeferencing) for gridded/NetCDF climate and model output — the same stack every notebook in this series has been built on.
For more
raoulcollenteur/Python-Hydrology-Tools is a maintained, community-curated list that goes considerably wider than what’s above — worth bookmarking rather than something I’d try to duplicate here.
Source: Ladson, A.R. (2017). R Packages for Hydrology — the R-side original this post is a companion to, not a translation of.