NEOPRENE
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
Stochastic storm transposition in Python — probabilistic extreme rainfall scenarios from remote-sensing data.
by University of Wisconsin-Madison
RainyDay, from Daniel Wright's Hydroclimate Extremes Group (University of Wisconsin-Madison), implements stochastic storm transposition: it resamples and spatially transposes observed storms from gridded rainfall archives to synthesise thousands of plausible extreme-rainfall realisations over your watershed — rainfall frequency analysis built from real storm structures rather than point statistics and area-reduction factors.
The approach shines exactly where design-storm convention is weakest: large watersheds, rare recurrence intervals and physically realistic space-time storm structure for 2D models. It underpins a growing body of probabilistic flood work, including FEMA-adjacent probabilistic frameworks, and remains the reference open implementation of SST.
Ranked by the topics, categories and workflow stages they share with RainyDay.
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
Browser-based hydrology and hydraulics calculators for practising engineers — no install, report-ready output.
Dewberry's open tool for building STAC catalogs of storms and gauge data — inputs for stochastic storm transposition.
The open global large-sample hydrology dataset — thousands of catchments with forcing, attributes and streamflow.
ECMWF's hourly global reanalysis — the default gap-free climate forcing record for ungauged catchments worldwide.
Browser-based rainfall frequency analysis — fit and rank 11 distributions with confidence intervals.