IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i15p11656-d1204787.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/15/11656/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/15/11656/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Demetris Koutsoyiannis & George Baloutsos, 2000. "Analysis of a Long Record of Annual Maximum Rainfall in Athens, Greece, and Design Rainfall Inferences," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 22(1), pages 29-48, July.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    2. Carlin C. F. Chu & Simon S. W. Li, 2024. "A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series," Computational Management Science, Springer, vol. 21(1), pages 1-14, June.
    3. Pawel Siarka, 2012. "Implementation of the Stress Test Methods in the Retail Portfolio," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(6), pages 1-2.
    4. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    5. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    6. Ghosh Indranil, 2019. "On the Reliability for Some Bivariate Dependent Beta and Kumaraswamy Distributions: A Brief Survey," Stochastics and Quality Control, De Gruyter, vol. 34(2), pages 115-121, December.
    7. Lillo, Fabrizio & Livieri, Giulia & Marmi, Stefano & Solomko, Anton & Vaienti, Sandro, 2023. "Unimodal maps perturbed by heteroscedastic noise: an application to a financial systems," LSE Research Online Documents on Economics 120290, London School of Economics and Political Science, LSE Library.
    8. Zhang, Zhengjun & Zhu, Bin, 2016. "Copula structured M4 processes with application to high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 231-241.
    9. Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.
    10. Lan-Fen Chu & Michael McAleer & Szu-Hua Wang, 2012. "Statistical Modelling of Recent Changes in Extreme Rainfall in Taiwan," Documentos de Trabajo del ICAE 2012-29, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    11. Jose Fernandes & Augusto Hasman & Juan Ignacio Pena, 2007. "Risk premium: insights over the threshold," Applied Financial Economics, Taylor & Francis Journals, vol. 18(1), pages 41-59.
    12. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    13. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.
    14. Jonas Smit Andersen & Sara Maria Lerer & Antje Backhaus & Marina Bergen Jensen & Hjalte Jomo Danielsen Sørup, 2017. "Characteristic Rain Events: A Methodology for Improving the Amenity Value of Stormwater Control Measures," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    15. Torsten Heinrich & Juan Sabuco & J. Doyne Farmer, 2022. "A simulation of the insurance industry: the problem of risk model homogeneity," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 535-576, April.
    16. Apostolos Kiohos & Maria Paspati, 2021. "Alternative to Insurance Risk Transfer: Creating a catastrophe bond for Romanian earthquakes," Bulletin of Applied Economics, Risk Market Journals, vol. 8(1), pages 1-17.
    17. Saiful Izzuan Hussain & Steven Li, 2022. "Dependence structure between oil and other commodity futures in China based on extreme value theory and copulas," The World Economy, Wiley Blackwell, vol. 45(1), pages 317-335, January.
    18. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    19. Martins-Filho Carlos & Yao Feng, 2006. "Estimation of Value-at-Risk and Expected Shortfall based on Nonlinear Models of Return Dynamics and Extreme Value Theory," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-43, May.
    20. Goran Andjelic & Ivana Milosev & Vladimir Djakovic, 2010. "Extreme Value Theory In Emerging Markets," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 55(185), pages 63-106, April - J.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11656-:d:1204787. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.