Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1029/2019WR026535 |
| Title (Primary) | Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection |
| Author | Miniussi, A.; Marani, M. |
| Source Titel | Water Resources Research |
| Year | 2020 |
| Department | CATHYD |
| Volume | 56 |
| Issue | 7 |
| Page From | e2019WR026535 |
| Language | englisch |
| Keywords | Metastatistical Extreme Value Distribution; Rainfall extremes; Optimized MEVD formulation; Inter‐annual variability; Trend detection |
| Abstract | The accurate estimation of hydrologic extremes is central to planning
and engineering mitigation and adaptation measures. The traditional
Extreme Value Theory is based on often‐overlooked assumptions that
preclude the use of all available observations and negatively affect
estimation uncertainty. The Metastatistical Extreme Value Distribution
(MEVD) was introduced to make full use of available data, and was shown
to significantly improve estimation uncertainty for large extremes.
However, no systematic understanding existed as to how to optimally
apply the MEVD depending on the statistical properties of the observed
variables. With reference to daily rainfall, we identify here the local
climatic factors that define the optimal MEVD formulation. We analyze a
large set of long daily rainfall records, as well as synthetic time
series with prescribed statistical characteristics, and find that 1) in
most climates the MEVD should be based on yearly estimates of the
ordinary rainfall distributions, and only in climates with less than
 =20‐25
rainy days/year the estimation of distributional parameters requires
samples longer than 1 year; 2) the inter‐annual variability in the
distributions of rainfall should be explicitly resolved when
 >20‐25
rainy days/year. Finally, we use the optimized MEVD to study the
variability of daily rainfall extremes over 294 years in Padova (Italy)
and compare it to traditional extreme‐value estimates. We find that,
through its improved accuracy for short observations, MEVD better
resolves high‐quantile fluctuations and allows the emergence of
long‐term trends over estimation noise. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23212 |
| Miniussi, A., Marani, M. (2020): Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection Water Resour. Res. 56 (7), e2019WR026535 10.1029/2019WR026535 |
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