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GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

Author

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  • Matteo Gentilucci

    (School of Science and Technology, Geology Division, University of Camerino, 62036 Camerino, Italy)

  • Alessandro Rossi

    (School of Science and Technology, Geology Division, University of Camerino, 62036 Camerino, Italy)

  • Niccolò Pelagagge

    (School of Science and Technology, Geology Division, University of Camerino, 62036 Camerino, Italy)

  • Domenico Aringoli

    (School of Science and Technology, Geology Division, University of Camerino, 62036 Camerino, Italy)

  • Maurizio Barbieri

    (Department of Chemical Engineering Materials Environment, University of Rome “La Sapienza”, 00185 Roma, Italy)

  • Gilberto Pambianchi

    (School of Science and Technology, Geology Division, University of Camerino, 62036 Camerino, Italy)

Abstract

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

Suggested Citation

  • Matteo Gentilucci & Alessandro Rossi & Niccolò Pelagagge & Domenico Aringoli & Maurizio Barbieri & Gilberto Pambianchi, 2023. "GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy," Sustainability, MDPI, vol. 15(15), pages 1-25, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11656-:d:1204787
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    References listed on IDEAS

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    1. Y. Malevergne & V. Pisarenko & D. Sornette, 2006. "On the power of generalized extreme value (GEV) and generalized Pareto distribution (GPD) estimators for empirical distributions of stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(3), pages 271-289.
    2. Ahmed M. Aggag & Abdulaziz Alharbi, 2022. "Spatial Analysis of Soil Properties and Site-Specific Management Zone Delineation for the South Hail Region, Saudi Arabia," Sustainability, MDPI, vol. 14(23), pages 1-19, December.
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    4. Paul Embrechts & Sidney Resnick & Gennady Samorodnitsky, 1999. "Extreme Value Theory as a Risk Management Tool," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 30-41.
    5. Guido Antonetti & Matteo Gentilucci & Domenico Aringoli & Gilberto Pambianchi, 2022. "Analysis of landslide Susceptibility and Tree Felling Due to an Extreme Event at Mid-Latitudes: Case Study of Storm Vaia, Italy," Land, MDPI, vol. 11(10), pages 1-21, October.
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