Preprints
https://doi.org/10.5194/hess-2023-293
https://doi.org/10.5194/hess-2023-293
18 Dec 2023
 | 18 Dec 2023
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Impact of spatio-temporal dependence on the frequency of precipitation extremes: Negligible or neglected?

Francesco Serinaldi

Abstract. Statistics is often abused and misused in hydro-climatology, thus causing research to get stuck around unscientific concepts that hinder scientific advances. In this study, we focus on the iterated underestimation and misinterpretation of the role of spatio-temporal dependence in statistical analysis of hydro-climatological processes. To this aim, we analyze the occurrence process of extreme precipitation (P) derived from 100-year daily time series recorded at 1,106 worldwide gauges of the Global Historical Climatology Network. The analysis relies on a model-based approach involving first-order Poisson integer autoregressive processes (Poisson-INAR(1)), nonhomogeneous Poisson processes (NHP), and Iterative Amplitude Adjusted Fourier Transform (IAAFT), which are used to describe and simulate the frequency of P events. This approach allows us to highlight the actual impact of spatio-temporal dependence and finite sample size on statistical inference, resulting in over-dispersed marginal distributions and biased estimates of dependence properties, such as autocorrelation and power spectrum density. These issues also affect the outcome and interpretation of statistical tests for trend detection. Overall, stationary stochastic processes incorporating the empirical spatio-temporal correlation and its effects provide a faithful description of the occurrence process of extreme P at various spatio-temporal scales. Therefore, accounting for the effect of dependence in the analysis of the frequency of extreme P has huge impact that cannot be ignored.

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Francesco Serinaldi

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-293', Giuseppe Mascaro, 18 Jan 2024
    • AC1: 'Preliminary reply on RC1', Francesco Serinaldi, 15 Feb 2024
  • RC2: 'Can the author define what a trend is? (Comment on hess-2023-293)', Demetris Koutsoyiannis, 07 Feb 2024
    • AC2: 'Preliminary reply on RC2', Francesco Serinaldi, 15 Feb 2024
  • RC3: 'Comment on hess-2023-293', Anonymous Referee #3, 15 Feb 2024
    • AC3: 'Preliminary reply on RC3', Francesco Serinaldi, 15 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-293', Giuseppe Mascaro, 18 Jan 2024
    • AC1: 'Preliminary reply on RC1', Francesco Serinaldi, 15 Feb 2024
  • RC2: 'Can the author define what a trend is? (Comment on hess-2023-293)', Demetris Koutsoyiannis, 07 Feb 2024
    • AC2: 'Preliminary reply on RC2', Francesco Serinaldi, 15 Feb 2024
  • RC3: 'Comment on hess-2023-293', Anonymous Referee #3, 15 Feb 2024
    • AC3: 'Preliminary reply on RC3', Francesco Serinaldi, 15 Feb 2024
Francesco Serinaldi
Francesco Serinaldi

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Short summary
Although spatio-temporal dependence (STD) is an acknowledged characteristic of hydro-climatological records, its magnitude and its effects on statistical analysis are commonly underestimated. Using a worldwide precipitation data set, we show that the appropriate assessment of STD reveals that the increasing/decreasing frequency of extreme events along the past century over various geographic regions can be consistent with the stationary assumption.