Open Source Hydrology & Catchment Analysis tools

Deltares' Python framework that builds ready-to-run hydrological models from global and local datasets.

Deep-learning rainfall-runoff modelling — the LSTM framework behind much of ML hydrology research.

Python water-resource network simulator — fast LP-based allocation for reservoir systems and supply networks.

Stochastic storm transposition in Python — probabilistic extreme rainfall scenarios from remote-sensing data.

A flexible hydrological modelling framework — emulate GR4J, HBV, HMETS or build your own model structure.

Python toolbox for calibrating and uncertainty-analysing any environmental model — SCE-UA, DREAM, GLUE and more.

The USDA watershed model for land use, sediment and nutrients — continuous simulation at catchment scale.

The Variable Infiltration Capacity model — macroscale land-surface hydrology for big basins and climate studies.

Deltares' distributed hydrological model in Julia — gridded rainfall-runoff with kinematic-wave routing.

INRAE's GR rainfall-runoff models (GR4J family) plus CemaNeige snow, packaged for R.

Python automation for HEC-HMS — batch and parallel runs, design storms, and DSS handoff to HEC-RAS.

A tiny Python package that computes NSE, KGE and friends correctly — the metrics layer of model evaluation.

Reference and potential evapotranspiration in Python — 20+ published methods, FAO-56 included, on pandas/xarray.

Build RORB, WBNM and URBS model files straight from GIS shapefiles — with companion QGIS plugins.