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Open AccessArticle

Hazard Assessment under Multivariate Distributional Change-Points: Guidelines and a Flood Case Study

Dipartimento di Matematica e Fisica, Università del Salento, 73100 Lecce, Italy
Dipartimento di Scienze dell’Economia, Università del Salento, 73100 Lecce, Italy
Department Civil and Environmental Eng., Politecnico di Milano, 20133 Milano, Italy
Department. of Statistical Sciences, University of Padua, 35121 Padua, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2018, 10(6), 751;
Received: 20 April 2018 / Revised: 25 May 2018 / Accepted: 30 May 2018 / Published: 8 June 2018
One of the ultimate goals of hydrological studies is to assess whether or not the dynamics of the variables of interest are changing. For this purpose, specific statistics are usually adopted: e.g., overall indices, averages, variances, correlations, root-mean-square differences, monthly/annual averages, seasonal patterns, maximum and minimum values, quantiles, trends, etc. In this work, a distributional multivariate approach to the problem is outlined, also accounting for the fact that the variables of interest are often dependent. Here, the Copula Theory, the Failure Probabilities, and suitable non-parametric statistical Change-Point tests are used in order to provide an assessment of the hazard. A hydrological case study is utilized to illustrate the issue and the methodology (viz., assessment of a dam spillway), considering the bivariate dynamics of annual maximum flood peak and volume observed at the Ceppo Morelli dam (located in the Piedmont region, Northern Italy) over a 50-year period. In particular, several problems—often present in hydrological analyses—are debated: namely, (i) the uncertainties due to the presence of heavy tailed random variables, and (ii) the hydrological meaning/interpretation of the results of statistical tests. Furthermore, the suitability of the procedures proposed to fulfill the goals of the study (viz., detecting and interpreting non-stationarity) is discussed. Overall, the main recommendation is that statistical (multivariate) investigations may represent a necessary step, though they may not be sufficient to assess hydrological (environmental) hazards. View Full-Text
Keywords: copula; hazard assessment; distributional change-point; climate change copula; hazard assessment; distributional change-point; climate change
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Salvadori, G.; Durante, F.; De Michele, C.; Bernardi, M. Hazard Assessment under Multivariate Distributional Change-Points: Guidelines and a Flood Case Study. Water 2018, 10, 751.

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