Pyraingen: A python package for constrained continuous rainfall generation

• Published in: Environmental modelling & software : with environment data news Vol. 175; p. 105984
• Main Authors: Dykman, Caleb, Sharma, Ashish, Wasko, Conrad, Nathan, Rory
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Australia; climate; climate change; computer software; Continuous timeseries; Non-parametric; rain; Rainfall; Stochastic; temperature; time series analysis; Australia; Continuous timeseries; Rainfall; Non-parametric; Stochastic
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2024.105984

Continuous rainfall is often required for flood estimation and water resources assessment. However, stochastically generated continuous rainfall records are typically inconsistent with intensity-frequency-durations (IFDs) used in design, and do not simulate the rainfall behaviour we can expect in a future warmer climate. Here, we present the python package pyraingen to generate ensembles of continuous subdaily rainfalls at a single location that preserve IFD relationships. Rainfall generation can also be conditioned on a climate covariate, in this case temperature, to produce rainfall timeseries reflective of a future climate. We present an implementation at five climatologically unique sites across Australia and demonstrate its ability to produce rainfall timeseries that reflect both present day and future changes in rainfall persistence, temporal patterns, means, and extremes. This package will enable studies using continuous simulation to assess the impacts of climate change using the current guidance on changes to both IFDs and mean annual rainfalls. •Pyraingen stochastically generates continuous subdaily rainfall sequences.•Ensembles of continuous rainfall are constrained to preserve IFD relationships.•Timeseries reflect rainfall persistence, temporal patterns, means and extremes.•Future scenarios can be generated by conditioning on climate covariate(s).