Extreme value metastatistical analysis of remotely sensed rainfall in ungauged areas: Spatial downscaling and error modelling

Zorzetto, Enrico; Marani, Marco

doi:10.1016/j.advwatres.2019.103483

Cited by 35 publications

(23 citation statements)

References 62 publications

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“…The MEVD, on the contrary, uses all the daily rainfall values in the analysis window and is thus not very sensitive to a few large or low values, thereby leading to more stable estimates of the 100‐year event. This time coherence of extremes is characteristic of the MEVD and is entirely analogous to the spatial coherence noted when estimating maps of extreme events from rainfall remote sensing estimates (Zorzetto & Marani, 2020). Fluctuations in

h_{100}^{G E V}

are more contained for the 30‐year window (Figure 6b), due to the longer sample used for parameter estimation and because short fluctuation frequencies are filtered out by the use of longer analysis windows (which is also visible in the case of MEVD estimates).…”

Section: Resultssupporting

confidence: 54%

Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection

Miniussi

Marani

2020

Water Resources Research

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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 truen¯= 20–25 year the estimation of distributional/year the estimation of distributional parameters requires samples longer than 1 year; (2) the interannual variability in the distributions of rainfall should be explicitly resolved when truen¯> 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.

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h_{100}^{G E V}

Section: Resultssupporting

confidence: 54%

Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection

Miniussi

Marani

2020

Water Resources Research

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“…So far, MEV methods have been mostly used for the analysis of extreme daily precipitation relying on Weibull distributions to describe the ordinary events (e.g., Marani & Ignaccolo, 2015; Miniussi & Marani, 2020; Zorzetto et al, 2016). Results showed a number of advantages over traditional methods based on the sampled extremes: (i) rare quantiles, corresponding to return periods longer than the available data record, are estimated with significantly reduced errors; (ii) short records are sufficient to obtain robust estimates; (iii) the method is less sensitive to measurement errors typically affecting extremes (Marra et al, 2018; Miniussi & Marani, 2020; Schellander et al, 2019; Zorzetto et al, 2016; Zorzetto & Marani, 2020). However, two knowledge gaps currently restrain the application of MEV to subdaily durations.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Extreme Subdaily Precipitation Frequency Analyses Based on Ordinary Events

Marra

Borga

Morin

2020

Geophysical Research Letters

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The metastatistical extreme value approach proved promising in the frequency analysis of daily precipitation from ordinary events, outperforming traditional methods based on sampled extremes. However, subdaily applications are currently restrained by two knowledge gaps: It is not known if ordinary events can be consistently examined over durations, and it is not clear to what extent their entire distributions represent extremes. We propose here a unified definition of ordinary events across durations and suggest the simplified metastatistical extreme value formulation for dealing with extremes emerging from the tail, rather than the entire distributions, of ordinary events. This unified framework provides robust estimates of extreme quantiles (<10% error on the 100 yr from a 26 yr long record) and allows representations in which ordinary and extreme events share the scaling exponent. Future applications could improve our knowledge of subdaily extreme precipitation and help investigate the impact of local factors and climatic forcing on their frequency. Plain Language Summary We propose here a unified methodology to quantify the intensity of extreme rainfall of short duration, such as events expected to occur on average once every 100 yr. As opposed to alternative methods in literature, we rely on the simultaneous analysis of all everyday rainfall events, which, being much larger in number than extremes, were shown to provide improved estimates for daily rainfall. We show that, under our approach, the hypothesis of everyday and extreme events being similar enough holds also for short-duration rainfall. Application of our method to 26 yr of data from an individual station reproduces analyses based on more than 150 yr of observations from multiple nearby stations, with less than 10% error on the estimation of rain intensities expected to occur on average once every 100 yr, which are not directly quantifiable from the 26 yr of observations. The proposed methodology could help improve our knowledge of short-duration rainfall extremes, with implications for water resources and risk management, and could help investigate the impact of climate change on extreme rainfall events. et al., 2016). The framework can include any class of distributions for F and allows to consider multiple ©2020. American Geophysical Union. All Rights Reserved.

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“…The distribution of extremes arising from mixing different populations of ordinary events is described here using a modified Metastatistical Extreme Value Distribution (MEVD) (Marani & Ignaccolo, 2015), which provides flexibility in incorporating the joint effect of different statistical populations and leverages the added value of incorporating physical mechanisms into statistical analysis (Klemeš, 1974). The MEVD relaxes some of the restrictive assumptions of the traditional Extreme Value Theory (EVT) and has been shown to outperform it in a wide range of applications, from daily and hourly rainfall, to remotely sensed precipitation, to hurricane intensities in the Atlantic Ocean, to peak flood flows (Zorzetto et al, 2016; Marra et al, 2018; Zorzetto & Marani, 2019; Zorzetto & Marani, 2020; Schellander et al, 2019; Hosseini, Scaioni, & Marani, 2020; Miniussi et al, 2020). Here we apply the mixed and original formulations of the MEVD to long series of daily rainfall in several American metropolitan areas, which have a high likelihood of being struck by a TC.…”

Section: Introductionmentioning

confidence: 99%

Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

Miniussi

Villarini

Marani

2020

Geophysical Research Letters

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Tropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty.Here we formulate a mixed-population Metastatistical Extreme Value Distribution explicitly incorporating non-TC and TC-induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC-and non-TC-related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered.Plain Language Summary The accurate estimation of the frequency of extreme rainfall has broad implications in designing mitigation measures, policy making, risk management, geology/geomorphology, insurance and reinsurance, and water-borne disease prevention and management. In many parts of the world tropical cyclones play a significant role in generating heavy rainfall, but the evaluation of their contribution to the frequency of extremes is problematic, due to the low number of tropical cyclones in the measured historical record. Here, we introduce a new statistical tool that explicitly includes tropical cyclone rainfall together with rainfall generated by other physical mechanisms and provides a way to maximize the information that can be extracted from available observations. When applied to locations on the U.S. eastern seaboard, we find that this method significantly improves estimates of extreme rainfall.

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Extreme value metastatistical analysis of remotely sensed rainfall in ungauged areas: Spatial downscaling and error modelling

Cited by 35 publications

References 62 publications

Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection

Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection

A Unified Framework for Extreme Subdaily Precipitation Frequency Analyses Based on Ordinary Events

Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

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