Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors

Francesco Marra*, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Efrat Morin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (<30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets.

Original languageAmerican English
Pages (from-to)27-39
Number of pages13
JournalAdvances in Water Resources
Volume117
DOIs
StatePublished - Jul 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Dependence on data record and measurement errors
  • Long return period quantile estimation uncertainty
  • Metastatistical extreme value
  • Short records
  • Sub-daily rainfall frequency

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