PyCoSMoS: An advanced toolbox for simulating real-world hydroclimatic data

• Published in: Environmental modelling & software : with environment data news 2024-07, Vol.178, p.106076, Article 106076
• Main Authors: Francesco, Cappelli, Papalexiou, Simon Michael, Markonis, Yannis, Grimaldi, Salvatore
• Format: Article
• Language: English
• Subjects: Hydroclimatic simulations; Python toolbox; Stochastic modeling; Weather generator
• ISSN: 1364-8152
• DOI: 10.1016/j.envsoft.2024.106076

Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a Python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed-Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user’s input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case. •Python toolbox (PyCoSMoS) that simplifies hydroclimatic process simulation while preserving distribution and autocorrelation functions.•Minimal user input to provide visual and quantitative comparisons between observed and simulated time series.•Accurate simulation of real-world data for precipitation, temperature, and relative humidity.