Pyraingen: A python package for constrained continuous rainfall generation

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...

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Published inEnvironmental modelling & software : with environment data news Vol. 175; p. 105984
Main Authors Dykman, Caleb, Sharma, Ashish, Wasko, Conrad, Nathan, Rory
Format Journal Article
LanguageEnglish
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
Online AccessGet full text
ISSN1364-8152
1873-6726
DOI10.1016/j.envsoft.2024.105984

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Abstract 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).
AbstractList 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).
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.
ArticleNumber 105984
Author Dykman, Caleb
Wasko, Conrad
Nathan, Rory
Sharma, Ashish
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SubjectTerms Australia
climate
climate change
computer software
Continuous timeseries
Non-parametric
rain
Rainfall
Stochastic
temperature
time series analysis
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