NEOPRENE
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
7 tools · Category
Design assumptions built on a stationary climate no longer hold, and a new generation of tools has emerged to deal with it: climate-projection access layers, statistical downscaling packages, non-stationary frequency analysis and drought/heat indices. This category indexes the tools engineers and hydrologists use to bring climate change into practical design — not climate science for its own sake, but change analysis you can defend in a design report.
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.
EPA's Climate Adjustment Tool for SWMM — apply downscaled climate projections to rainfall and evaporation inputs.
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.