Author(s): Manuel Del Jesus; Salvador Navas; Javier Diez-Sierra

Linked Author(s): Salvador Navas, Manuel del Jesus

Keywords: Extremes; Open-source; Python; Rainfall; Synthetic generation

Abstract: Extensive time series data on precipitation at various temporal resolutions (typically daily or hourly) serve as fundamental input for studies pertaining to hydrology, hydraulics, and climatology. However, frequently, the available records lack the requisite length, completeness, temporal precision, or spatial extent to facilitate robust analysis. In this context, we present NEOPRENE, a Python library designed to generate synthetic precipitation time series. NEOPRENE facilitates the simulation of multisite synthetic precipitation, mirroring observed statistical characteristics across varying temporal scales. Through three illustrative case studies, we showcase the utility of this library, emphasizing its application in modeling extreme precipitation events and disaggregating daily rainfall observations into hourly increments. NEOPRENE is openly available on GitHub under the GPLv3 license, permitting unrestricted usage for both research and commercial endeavors. Additionally, we offer Jupyter notebooks featuring practical examples to encourage adoption among researchers and practitioners engaged in studies related to vulnerability, impact assessment, and adaptation.

DOI: https://doi.org/10.3850/iahr-hic2483430201-121

Year: 2024