Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026535 |
Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection | |
Arianna Miniussi; Marco Marani | |
2020-06-05 | |
发表期刊 | Water Resources Research |
出版年 | 2020 |
英文摘要 | 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. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/273327 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Arianna Miniussi,Marco Marani. Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection[J]. Water Resources Research,2020. |
APA | Arianna Miniussi,&Marco Marani.(2020).Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection.Water Resources Research. |
MLA | Arianna Miniussi,et al."Estimation of daily rainfall extremes through the Metastatistical Extreme Value Distribution: uncertainty minimization and implications for trend detection".Water Resources Research (2020). |
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