Articles | Volume 30, issue 9
https://doi.org/10.5194/hess-30-2637-2026
https://doi.org/10.5194/hess-30-2637-2026
Research article
 | 
06 May 2026
Research article |  | 06 May 2026

Return period analysis of weakly non-stationary processes with trends

Giulio Calvani and Paolo Perona

Cited articles

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Birsan, M.-V., Molnar, P., Burlando, P., and Pfaundler, M.: Streamflow trends in Switzerland, J. Hydrol., 314, 312–329, https://doi.org/10.1016/j.jhydrol.2005.06.008, 2005. a
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Brunetti, M., Buffoni, L., Maugeri, M., and Nanni, T.: Precipitation intensity trends in northern Italy, Int. J. Climatol., 20, 1017–1031, https://doi.org/10.1002/1097-0088(200007)20:9%3C1017::aid-joc515%3E3.0.co;2-s, 2000. a
Calvani, G. and Perona, P.: Splitting probabilities and mean first-passage times across multiple thresholds of jump-and-drift transition paths, Phys. Rev. E, 108, 044105, https://doi.org/10.1103/physreve.108.044105, 2023. a
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Short summary
Traditional analysis of extremes often relies on complex tools when the statistics change over time. We developed a novel, efficient framework based on a maximum return period change over a specific timeframe. Simple formulas are derived to determine the variation in average frequency and magnitude of events. The approach has minor approximations compared to more complex methods, thus providing a reliable tool for practitioners to forecast risk assessment under changing environmental conditions.
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