Metastatistical Extreme Value Distribution applied to floods across the continental United States

Miniussi, Arianna; Marani, Marco; Villarini, Gabriele

doi:10.1016/j.advwatres.2019.103498

Cited by 51 publications

(43 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several studies on the hydroclimatic nature of flood events in the United States (e.g., Barth et al, 2019; Hirschboeck, 1987b; Smith et al, 2011, 2018; Villarini & Smith, 2010) showed that the nature of upper tail events is often different from that of annual floods. On the other hand, recently developed approaches that utilize the whole range of observed streamflows to derive flood frequency curves (Basso et al, 2016; Claps & Laio, 2003; Miniussi et al, 2020) delivered more accurate predictions of upper tails, thus supporting the hypothesis that the largest floods (statistically) might indeed originate from ordinary runoff events.…”

Section: Introductionmentioning

confidence: 86%

Transformation of Generation Processes From Small Runoff Events to Large Floods

Tarasova

Basso

Merz

2020

Geophysical Research Letters

View full text Add to dashboard Cite

Mixture of runoff generation processes poses a challenge for predicting upper flood quantiles. We examined transformations of generation processes from all identifiable runoff events to frequent and upper tail floods for a large set of mesoscale catchments and observed a substantial change of the dominant processes. Two trajectories of transformation were detected. In regions where floods occur almost exclusively in winter the dominance of processes related to snowmelt consistently increases from small events to frequent and upper tail floods. In catchments characterized by frequent winter-spring floods and occasional summer-autumn flood events triggered by rare meteorological phenomena (e.g., Vb cyclones), processes that dominate upper tails are not adequately represented in the sample of frequent floods. Predictions of extremes and projections of flood changes might remain highly uncertain in the latter cases. Plain Language Summary Prediction of floods remains a challenging task for the engineering practice. Floods triggered by different physical mechanisms have contrasting statistical attributes. Mixture of these processes hinders reliable prediction of the largest floods. In this study we classified a large number of streamflow events observed in a wide range of German river basins according to their generation processes. We analyzed changes in the frequency of occurrence of different generation processes, from the smallest identifiable runoff events to annual floods to rarer events corresponding to larger river flows. Interestingly, for some river basins certain processes tend to consistently increase their frequency from small streamflow events to common and larger floods. In other cases, we observed an opposite tendency. Certain processes become less important for the generation of annual floods compared to small streamflow events but then dominate generation of the largest floods. This has important implications for our ability to predict extreme floods and their possible changes.

show abstract

Section: Introductionmentioning

confidence: 86%

Transformation of Generation Processes From Small Runoff Events to Large Floods

Tarasova

Basso

Merz

2020

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…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%

“…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. We then quantify the benefit of considering mixed distributions in the estimation of high‐return period quantiles with respect to a single‐component MEVD using a cross‐validation approach (e.g., Miniussi et al, 2020).…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Based on our analyses, we conclude that the Weibull distribution suitably models the observational records in our data set and, since it constitutes a robust assumption across a wide range of rainfall regimes, we will continue to use it here. Practical applications of the MEVD may find that alternative choices for the ordinary value probability distribution (e.g., see Papalexiou & Koutsoyiannis, 2012) may provide advantages, on a site‐dependent basis, with no loss of generality for the overall MEVD framework (e.g., see Hosseini et al., 2020, and Miniussi, Marani, & Villarini, 2020, for other hydrometeorological processes).…”

Section: Methodsmentioning

confidence: 99%

“…Interannual variabilities, and dependence, can still be allowed at interannual time scales. Hence, issues associated with dependence need to be resolved, for example, when dealing with subdaily rainfall (Marra et al., 2018) or peak flows (Miniussi et al., 2020), which are, however, allowed to show dependence on long time scales. Zorzetto et al.…”

Section: Introductionmentioning

confidence: 99%

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

Miniussi

Marani

2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Metastatistical Extreme Value Distribution applied to floods across the continental United States

Cited by 51 publications

References 32 publications

Transformation of Generation Processes From Small Runoff Events to Large Floods

Transformation of Generation Processes From Small Runoff Events to Large Floods

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

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

Contact Info

Product

Resources

About