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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.;
Journal Water Resources Research
Year 2020
Department CATHYD;
Volume 56
Issue 7
Language englisch;
POF III (all) T31;
Data links https://www.ncdc.noaa.gov/cdo-web
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 urn:x-wiley:00431397:media:wrcr24694:wrcr24694-math-0001=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 urn:x-wiley:00431397:media:wrcr24694:wrcr24694-math-0002>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.
ID 23212
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