A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes

Francesco Marra*, Davide Zoccatelli, Moshe Armon, Efrat Morin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

This paper presents a Simplified Metastatistical Extreme Value formulation (SMEV) able to model hydro-meteorological extremes emerging from multiple underlying processes. The formulation explicitly includes the average intensity and probability of occurrence of the processes allowing to parsimoniously model changes in these quantities to quantify changes in the probability of occurrence of extremes. SMEV allows (a) frequency analyses of extremes emerging from multiple underlying processes and (b) computationally efficient analyses of the sensitivity of extreme quantiles to changes in the characteristics of the underlying processes; moreover, (c) it provides a robust framework for explanatory models, nonstationary frequency analyses, and climate projections. The methodology is applied to daily precipitation data from long recording stations in the eastern Mediterranean, using Weibull distributions to model daily precipitation amounts generated by two classes of synoptic systems. At-site application of SMEV provides spatially consistent estimates of extreme quantiles, in line with regional GEV estimates and generally characterized by reduced uncertainties. The sensitivity of extreme quantiles to changes and uncertainty in the intensity and yearly occurrences of events generated by different synoptic classes is examined, and an application of SMEV for the projection of future extremes is provided.

Original languageAmerican English
Pages (from-to)280-290
Number of pages11
JournalAdvances in Water Resources
Volume127
DOIs
StatePublished - May 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

Keywords

  • Climate change
  • Daily precipitation
  • Extreme value analysis
  • Metastatistical extreme value
  • Nonstationary processes

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