Effects of disregarding seasonality on the distribution of hydrological extremes

Allamano, Paola; Laio, Francesco; Claps, Pierluigi

doi:10.5194/hess-15-3207-2011

Cited by 35 publications

(20 citation statements)

References 28 publications

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“…assumption in the GEV approach. A similar remark was also made by Allamano et al (). However, for intra‐annual hydrological design and management, it is crucial to take seasonal variability into account.…”

Section: Discussionsupporting

confidence: 86%

Characterizing and Modeling Seasonality in Extreme Rainfall

Iliopoulou

Koutsoyiannis

Montanari

2018

Water Resources Research

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A comprehensive understanding of seasonality in extreme rainfall is essential for climate studies, flood prediction, and various hydrological applications such as scheduling season‐specific engineering works, intra‐annual management of reservoirs, seasonal flood risk mitigation, and stormwater management. To identify seasonality in extreme rainfall and quantify its impact in a theoretically consistent yet practically appealing manner, we investigate a data set of 27 daily rainfall records spanning at least 150 years. We aim to objectively identify periods within the year with distinct seasonal properties of extreme rainfall by employing the Akaike information criterion. Optimal partitioning of seasons is identified by minimizing the within‐season variability of extremes. The statistics of annual and seasonal extremes are evaluated by fitting a generalized extreme value distribution to the annual and seasonal block maxima series. The results indicate that seasonal properties of rainfall extremes mainly affect the average values of seasonal maxima and their variability, while the shape of their probability distribution and its tail do not substantially vary from season to season. Uncertainty in the estimation of the generalized extreme value parameters is quantified by employing three different estimation methods (maximum likelihood, method of moments, and least squares), and the opportunity for joint parameter estimation of seasonal and annual probability distributions of extremes is discussed. The effectiveness of the proposed scheme for seasonal characterization and modeling is highlighted when contrasted to results obtained from the conventional approach of using fixed climatological seasons.

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Section: Discussionsupporting

confidence: 86%

Characterizing and Modeling Seasonality in Extreme Rainfall

Iliopoulou

Koutsoyiannis

Montanari

2018

Water Resources Research

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“…However, their methods are based on adapting the skewness coefficient of seasonal distributions to ensure a satisfactory fit of the annual peak flows, thereby putting more emphasis on the annual distribution. Allamano et al (2011) analyse the magnitude of under-(or over-) estimation of design events in the presence of seasonality by using the POT or AM approach. Bowers et al (2012) presents a statistical procedure to partition river flow data into three seasons and focuses on two particular distributions to describe the constructed seasonal river flows: power law and lognormal.…”

Section: Introductionmentioning

confidence: 99%

Estimating the flood frequency distribution at seasonal and annual time scales

Baratti

Montanari

Castellarin

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract.We propose an original approach to infer the flood frequency distribution at seasonal and annual time scale. Our purpose is to estimate the peak flow that is expected for an assigned return period T , independently of the season in which it occurs (i.e. annual flood frequency regime), as well as in different selected sub-yearly periods (i.e. seasonal flood frequency regime). While a huge literature exists on annual flood frequency analysis, few studies have focused on the estimation of seasonal flood frequencies despite the relevance of the issue, for instance when scheduling along the months of the year the construction phases of river engineering works directly interacting with the active river bed, like for instance dams. An approximate method for joint frequency analysis is presented here that guarantees consistency between fitted annual and seasonal distributions, i.e. the annual cumulative distribution is the product of the seasonal cumulative distribution functions, under the assumption of independence among floods in different seasons. In our method the parameters of the seasonal frequency distributions are fitted by maximising an objective function that accounts for the likelihoods of both seasonal and annual peaks. In contrast to previous studies, our procedure is conceived to allow the users to introduce subjective weights to the components of the objective function in order to emphasize the fitting of specific seasons or of the annual peak flow distribution. An application to the time series of the Blue Nile daily flows at the Sudan-Ethiopia border is presented.

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“…Indeed, natural phenomena can have very different physical genesis, they can exhibit strong seasonality of the observed phenomena or they can depend on other covariates. Among others, Adamowski (2000), Garavaglia et al (2010) and Allamano et al (2011) show that mixing heterogeneous samples can lead to biased estimation of extreme value probability of occurrence. Garavaglia et al (2010) introduced a compound distribution for extreme rainfalls taking into account seasonality and different physical genesis.…”

Section: P Bernardara Et Al: a Two-step Framework For Over-thresholmentioning

confidence: 99%

A two-step framework for over-threshold modelling of environmental extremes

Bernardara

Mazas

Kergadallan

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. The evaluation of the probability of occurrence of extreme natural events is important for the protection of urban areas, industrial facilities and others. Traditionally, the extreme value theory (EVT) offers a valid theoretical framework on this topic. In an over-threshold modelling (OTM) approach, Pickands' theorem, (Pickands, 1975) states that, for a sample composed by independent and identically distributed (i.i.d.) values, the distribution of the data exceeding a given threshold converges through a generalized Pareto distribution (GPD). Following this theoretical result, the analysis of realizations of environmental variables exceeding a threshold spread widely in the literature. However, applying this theorem to an auto-correlated time series logically involves two successive and complementary steps: the first one is required to build a sample of i.i.d. values from the available information, as required by the EVT; the second to set the threshold for the optimal convergence toward the GPD. In the past, the same threshold was often employed both for sampling observations and for meeting the hypothesis of extreme value convergence. This confusion can lead to an erroneous understanding of methodologies and tools available in the literature. This paper aims at clarifying the conceptual framework involved in threshold selection, reviewing the available methods for the application of both steps and illustrating it with a double threshold approach.

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Effects of disregarding seasonality on the distribution of hydrological extremes

Cited by 35 publications

References 28 publications

Characterizing and Modeling Seasonality in Extreme Rainfall

Characterizing and Modeling Seasonality in Extreme Rainfall

Estimating the flood frequency distribution at seasonal and annual time scales

A two-step framework for over-threshold modelling of environmental extremes

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