Dismissing return periods!

Serinaldi, Francesco

doi:10.1007/s00477-014-0916-1

Cited by 230 publications

(196 citation statements)

References 37 publications

(44 reference statements)

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“…The aim was to transfer concepts well-known in univariate analyses to the multivariate framework, where they seems to be overlooked or forgot (see also Serinaldi 2014).…”

Section: Introductionmentioning

confidence: 99%

Upper tail dependence in rainfall extremes: would we know it if we saw it?

Serinaldi

Bàrdossy

Kilsby

2014

Stoch Environ Res Risk Assess

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The simultaneous occurrence of extreme events, such as simultaneous storms and floods at different locations, has a serious impact on risk assessment and mitigation strategies. The joint occurrence of extreme events can be measured by the so-called upper tail dependence (UTD) coefficient k U . In this study, we reconsider the properties of the most popular k U estimators and show that their strong bias and uncertainty make most of the empirical results reported in the hydrological literature questionable. In order to overcome the limits of k U analysis, we test several alternative tools such as a pool of formal statistical tests devised for recognizing upper tail independence and graphical diagnostics based on binary correlation and binary entropy. The reliability of all the methods is preliminarily checked by Monte Carlo experiments. Statistical tests and graphical diagnostics are therefore applied to three different rainfall data sets that allow us to explore the properties of the spatial dependence structure of rainfall extremes over a wide range of spatio-temporal scales ranging from 30 min and 1 km to 30 days and %3000 km. Results highlight that (1) classical estimators provide non zero tail dependence even for cases where it should be zero; (2) formal tests and binary correlation highlight that the pairwise spatial dependence structure can be weaker than Gaussian, thus excluding UTD calculated in a pairwise manner; (3) the binary entropy computed on triples of locations shows that the pairwise UTD is not enough to explain the spatial dependence structure of extreme rainfall, whose complexity becomes evident only after resorting to higher order correlation measures. The results concerning the bias and uncertainty of k U estimators are fully general and suggest avoiding their use especially for the short time series usually available in hydrology.

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“…The aim was to transfer concepts well-known in univariate analyses to the multivariate framework, where they seems to be overlooked or forgot (see also Serinaldi 2014).…”

Section: Introductionmentioning

confidence: 99%

Upper tail dependence in rainfall extremes: would we know it if we saw it?

Serinaldi

Bàrdossy

Kilsby

2014

Stoch Environ Res Risk Assess

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“…As stated in Shiau [11] and Serinaldi [12], the choice for the expression of T depends on which event is critical for the investigated system. If both variables must exceed specific values to achieve critical conditions, then T AND (.)…”

Section: Bivariate Return Period Definitionmentioning

confidence: 99%

“…T KEN (.) should be used when critical conditions are induced by all the events (h, w n ) with a joint CDF greater than an assigned threshold (see Figure 1 in Serinaldi [12] and Figure 2 in Gräler et al [22] for further graphical details).…”

Section: Bivariate Return Period Definitionmentioning

confidence: 99%

“…In fact, different expressions can be obtained depending on which event is deemed as critical for the investigated system [11,12]; the most important cases are: (i) all the variables must exceed a certain magnitude to achieve critical conditions; or (ii) at least one variable must be greater than a threshold; or (iii) critical conditions are induced by all the events with a joint Cumulative Density Function (CDF) overcoming an assigned probability threshold. Once the specific expression for JRP was chosen for the selected case study, the authors provided as a final result a summary diagram, which allows for: (1) the identification of the main characteristics of T-year DHs; and (2) the analysis of the relationship among T-year DHs and T-year design flood peaks, obtained through a simple lumped Rainfall-Runoff (RR) model.…”

Section: Introductionmentioning

confidence: 99%

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Bivariate Return Period for Design Hyetograph and Relationship with T-Year Design Flood Peak

Luca

Biondi

2017

Water

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This study focuses on the return period evaluation for design hyetographs, which is usually estimated by adopting a univariate statistical approach. Joint Return Period (JRP) and copula-based multivariate analysis are used in this work to better define T-year synthetic rainfall patterns which can be used as input for design flood peak estimation by means of hydrological simulation involving rainfall-runoff (RR) models. Specifically, a T-year Design Hyetograph (DH) is assumed to be characterized by its peak H, at the chosen time resolution ∆t, and by the total rainfall height W, cumulated on its critical duration d Crit , which has been a priori fixed. As stated in technical literature, the choice of the expression for JRP depends on which event is deemed as critical for the investigated system; the most important cases are: (i) all the variables must exceed a certain magnitude to achieve critical conditions; or (ii) at least one variable must be greater than a threshold; or (iii) critical conditions are induced by all the events with a joint Cumulative Density Function (CDF) overcoming an assigned probability threshold. Once the expression for JRP was chosen, the relationship among multivariate T-year design hyetographs and T-year design flood peak was investigated for a basin located in Calabria region (southern Italy). Specifically, for the selected case study, a summary diagram was obtained as final result, which allows the main characteristics of T-year DHs to be estimated, considering both the univariate and the copula based multivariate analysis, and the associated T-year design flood peaks obtained through the simulation with a RR model.

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“…Coulibaly and Baldwin (2005) proposed an optimal dynamic recurrent neural network to directly forecast different nonstationary hydrological time series. Several investigators have recently discussed nonstationarity and the resulting uncertainty (Cohn and Lins 2005;Shao and Li 2011;Serinaldi 2015). However, how to judge the nonstationarity of hydrologic time series and whether a rebuilt series is stationary have not been concluded.…”

Section: Introductionmentioning

confidence: 99%

Assumption-Simulation-Feedback-Adjustment (ASFA) Framework for Real-Time Correction of Water Resources Allocation: a Case Study of Longgang River Basin in Southern China

Chen

Singh

et al. 2018

Water Resour Manage

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Water resources allocation is subject to uncertain future conditions and therefore needs real-time correction. This study develops a framework of Bassumption-simulationfeedback-adjustment^(ASFA) for real-time correction of water resources allocation. The assumption component constructs a water resources allocation model and generates initial allocation solution (IAS); the simulation component applies IAS in a real-time hydrological scenario; the performance information is input into the feedback component. Three feedback functions, including gain function, correlation function, and least square function, are employed to deal with the information, and the value of output gain is determined for the adjustment component. The result then is a feedback allocation solution (FAS). This study applied ASFA to Longgang River basin, China, as a case study, compared FASs generated by three different feedback functions as well as IAS. Results showed that FAS generated by the gain function (FAS_GF) performed better with a higher assurance rate and less risk of continuous water shortage. Results also showed that to achieve the same management requirement, FAS_GF had a lower requirement of the amount of diverted water, indicating that the ASFA framework can make better use of water resources and reduce the pressure of diverted water. The ASFA framework builds a feedback mechanism for real-time correction of Water Resour Manage (2018) water resources allocation, provides a novel perspective for addressing the challenge of future uncertainty, which significantly improves the solutions of water allocation.

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Dismissing return periods!

Cited by 230 publications

References 37 publications

Upper tail dependence in rainfall extremes: would we know it if we saw it?

Upper tail dependence in rainfall extremes: would we know it if we saw it?

Bivariate Return Period for Design Hyetograph and Relationship with T-Year Design Flood Peak

Assumption-Simulation-Feedback-Adjustment (ASFA) Framework for Real-Time Correction of Water Resources Allocation: a Case Study of Longgang River Basin in Southern China

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