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The effect of scale in daily precipitation hazard assessment

Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24 h. Events are modelled as a Poisson process and the 24 h precipitation by a Generalised Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. Bayesian techniques are used to estimate the parameters. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimated GPD is mainly in the Fréchet DA, something incompatible with the common sense assumption of that precipitation is a bounded phenomenon. The bounded character of precipitation is then taken as a priori hypothesis. Consistency of this hypothesis with the data is checked in two cases: using the raw-data (in mm) and using log-transformed data. As expected, a Bayesian model checking clearly rejects the model in the raw-data case. However, log-transformed data seem to be consistent with the model. This fact may be due to the adequacy of the log-scale to represent positive measurements for which differences are better relative than absolute

Natural Hazards and Earth System Sciences, 2006, vol. 6, p. 459-470

European Geosciences Union (EGU)

Author: Egozcue, Juan José
Pawlowsky-Glahn, Vera
Ortego, M.I.
Tolosana Delgado, Raimon
Date: 2006
Abstract: Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24 h. Events are modelled as a Poisson process and the 24 h precipitation by a Generalised Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. Bayesian techniques are used to estimate the parameters. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimated GPD is mainly in the Fréchet DA, something incompatible with the common sense assumption of that precipitation is a bounded phenomenon. The bounded character of precipitation is then taken as a priori hypothesis. Consistency of this hypothesis with the data is checked in two cases: using the raw-data (in mm) and using log-transformed data. As expected, a Bayesian model checking clearly rejects the model in the raw-data case. However, log-transformed data seem to be consistent with the model. This fact may be due to the adequacy of the log-scale to represent positive measurements for which differences are better relative than absolute
Format: application/pdf
ISSN: 1561-8633 (versió paper)
1684-9981 (versió electrònica)
Document access: http://hdl.handle.net/10256/8997
Language: eng
Publisher: European Geosciences Union (EGU)
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.5194/nhess-6-459-2006
Articles publicats (D-IMA)
Is part of: Natural Hazards and Earth System Sciences, 2006, vol. 6, p. 459-470
Rights: Attribution-NonCommercial-ShareAlike 3.0 Spain
Rights URI: http://creativecommons.org/licenses/by-nc-sa/3.0/es/
Subject: Anàlisi multivariable
Multivariate analysis
Poisson, Processos de
Poisson processes
Title: The effect of scale in daily precipitation hazard assessment
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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