IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i10d10.1007_s11269-020-02609-1.html
   My bibliography  Save this article

Classification of Extreme Rainfall for a Mediterranean Region by Means of Atmospheric Circulation Patterns and Reanalysis Data

Author

Listed:
  • Giuseppe Cipolla

    (University of Palermo)

  • Antonio Francipane

    (University of Palermo)

  • Leonardo Valerio Noto

    (University of Palermo)

Abstract

The atmospheric circulation can be recognized as one of the causes of severe rainfall events occurrence. Such events, especially when are characterized by short durations and high intensities, result in flood events in the Mediterranean area. It is very important to understand how these heavy rainfall events, which can be usually identified with convective rainfall, are related to the different types of atmospheric circulation. In order to do this, some weather circulation patterns (WPs), which have been derived for the Europe, have been first connected with the rainfall annual maxima (AMAX) recorded over the Sicily. The analyses allowed to identify those WPs that are more likely to result into the occurrence of the AMAX. Secondly, two ERA-Interim reanalysis indexes have been used to define a criterion to distinguish those AMAX mainly due to a convective component from those more related to a stratiform precipitation, also detecting a transient zone between these two types of events. Finally, the main results have been connected together with the aim to define a set of triggering factors of extreme rainfall events.

Suggested Citation

  • Giuseppe Cipolla & Antonio Francipane & Leonardo Valerio Noto, 2020. "Classification of Extreme Rainfall for a Mediterranean Region by Means of Atmospheric Circulation Patterns and Reanalysis Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3219-3235, August.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:10:d:10.1007_s11269-020-02609-1
    DOI: 10.1007/s11269-020-02609-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-020-02609-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-020-02609-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leonardo Noto & Goffredo La Loggia, 2009. "Use of L-Moments Approach for Regional Flood Frequency Analysis in Sicily, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2207-2229, September.
    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. T. K. Drissia & V. Jothiprakash & A. B. Anitha, 2019. "Flood Frequency Analysis Using L Moments: a Comparison between At-Site and Regional Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1013-1037, February.
    2. Andrea Gioia & Maria Francesca Bruno & Vincenzo Totaro & Vito Iacobellis, 2020. "Parametric Assessment of Trend Test Power in a Changing Environment," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    3. Pezhman Allahbakhshian-Farsani & Mehdi Vafakhah & Hadi Khosravi-Farsani & Elke Hertig, 2020. "Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2887-2909, July.
    4. Rakesh Kumar & Narendra Goel & Chandranath Chatterjee & Purna Nayak, 2015. "Regional Flood Frequency Analysis using Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1965-1978, April.
    5. Brunella Bonaccorso & Giuseppe T. Aronica, 2016. "Estimating Temporal Changes in Extreme Rainfall in Sicily Region (Italy)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5651-5670, December.
    6. Neslihan Seckin & Murat Cobaner & Recep Yurtal & Tefaruk Haktanir, 2013. "Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2103-2124, May.
    7. S. Baidya & Ajay Singh & Sudhindra N. Panda, 2020. "Flood frequency analysis," 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. 100(3), pages 1137-1158, February.
    8. Chang-Shian Chen & Frederick Chou & Boris Chen, 2010. "Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4175-4197, November.
    9. Felício Cassalho & Samuel Beskow & Carlos Rogério Mello & Maíra Martim Moura & Laura Kerstner & Leo Fernandes Ávila, 2018. "At-Site Flood Frequency Analysis Coupled with Multiparameter Probability Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 285-300, January.
    10. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2022. "Statistical Modeling on the Severity of Unhealthy Air Pollution Events in Malaysia," Mathematics, MDPI, vol. 10(16), pages 1-15, August.

    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:spr:waterr:v:34:y:2020:i:10:d:10.1007_s11269-020-02609-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.