CastaliaR: An R package for multivariate stochastic simulation at multiple temporal scales

I. Tsoukalas, P. Kossieris, A. Efstratiadis, C. Makropoulos, and D. Koutsoyiannis, CastaliaR: An R package for multivariate stochastic simulation at multiple temporal scales, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18433, doi:10.13140/RG.2.2.20978.81605, European Geosciences Union, 2018.



In contrast to great advances on stochastic simulation techniques in hydrology and their importance on water management and uncertainty assessment studies, operational software packages for generating synthetic data are limited and hardly accessible. This limits their adoption to a narrow audience, excluding the vast majority of researchers and practitioners. In an effort to bridge this gap, we introduce CastaliaR package that constitutes the R-based, open-source implementation of a state-of-the art methodology for multivariate stochastic simulation. Its background builds upon the works of Koutsoyiannis and Manetas (1996), Koutsoyiannis (1999, 2000) and Efstratiadis et al. (2014). Briefly, the overall scheme reproduces the statistical characteristics of the historical data at three temporal scales (annual, monthly and daily). The generation procedure lies upon a symmetric moving average process for the annual scale and a periodic autoregressive process for the finer scales, while a Monte Carlo disaggregation approach re-establishes consistency across the three temporal scales.

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Our works that reference this work:

1. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.