NEOPRENE
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
6 tools · Topic
Bringing CMIP-class projections into a catchment study means data access, bias correction and downscaling. This page indexes the packages that handle each step, so future-climate scenarios can enter your hydrology with methods you can cite.
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.
The Variable Infiltration Capacity model — macroscale land-surface hydrology for big basins and climate studies.
Programmatic access to ERA5 and the Copernicus Climate Data Store — the world's reanalysis workhorse.
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.