NEOPRENE
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
EPA's Climate Adjustment Tool for SWMM — apply downscaled climate projections to rainfall and evaporation inputs.
by US EPA
SWMM-CAT (Climate Adjustment Tool) answers the question every long-lived drainage design now faces: what do climate projections do to my model inputs? It serves location-specific adjustment factors derived from downscaled CMIP climate scenarios — monthly rainfall multipliers, evaporation changes and modified extreme-event distributions — formatted for direct use in SWMM's climate adjustment panel.
It is deliberately simple: not a downscaling framework, but a defensible, citable bridge between the climate literature and a working SWMM model. For screening how near-term and end-of-century scenarios shift flooding and overflow performance, it is the lowest-friction option going.
Other climate & change analysis tools covering similar workflow stages.
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
The reference R implementation of SPEI and SPI from the index's own authors.
Programmatic access to ERA5 and the Copernicus Climate Data Store — the world's reanalysis workhorse.
Reference Python implementations of SPI, SPEI and Palmer indices for drought monitoring.
Statistical downscaling and bias correction of climate projections in R — part of the climate4R framework.
Climate indices and bias adjustment on xarray — the operational-grade library for climate-data engineering.