EPANET
Gov-PublicVerified
The public-domain standard for water distribution modelling — hydraulics and water quality in pressurised networks.
WNTR
DHS VerifiedActiveSandia/EPA's Python toolkit for water network resilience — EPANET simulation plus disaster scenarios and metrics.
by Sandia National Laboratories / US EPA
- Pricing
- Open Source
- License
- Revised BSD
- Commercial use
- Permitted
- Ecosystem
- Python, EPANET
- Platform
- Windows, macOS, Linux
Install
pip install wntrAbout WNTR
WNTR (Water Network Tool for Resilience) wraps water-distribution modelling in Python with a resilience focus: run EPANET-compatible hydraulics (or its own pressure-dependent solver), then subject the network to earthquakes, pipe breaks, power outages and contamination events, and quantify the consequences with built-in resilience metrics.
Developed by Sandia National Laboratories and the EPA, it has become the de facto research platform for network resilience and a very capable general Python interface to EPANET models — network graphs come out as NetworkX objects, results as pandas, and the whole scientific Python stack is available around it.
Where it fits in the workflow
TerrainCatchmentRainfallRunoffRoutingHydraulicsMappingReporting
Appears in collections
- The EPANET Ecosystem
The public-domain engine and everything built on it — Python, MATLAB, QGIS and the browser.
Alternatives to WNTR
Ranked by the topics, categories and workflow stages they share with WNTR.
EPANET-MATLAB Toolkit
Open Source
Full MATLAB control of the EPANET engine — the standard bridge for control and optimisation research.
InfoWater Pro
Commercial
Autodesk's ArcGIS Pro-native distribution modelling — for utilities whose network data lives in Esri.
OpenFlows WaterGEMS
Commercial
Bentley's flagship distribution suite — design, optimisation and criticality on EPANET-compatible hydraulics.
QGISRed
Open Source
A QGIS plugin that turns the free GIS into a water-network modelling environment for EPANET.
epanet-js
Freemium
EPANET in the browser — build, import and run distribution models on a map with zero installation.