Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

Extreme rainfall events and the clustering of extreme values provide fundamental information which can be used for the risk assessment of extreme floods. Event probability can be estimated using the extreme value index (γ) which describes the behavior of the upper tail and measures the degree of ext...

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Published in:Water resources management 2010-04, Vol.24 (6), p.1229-1249
Main Authors: AghaKouchak, Amir, Nasrollahi, Nasrin
Format: Article
Language:English
Subjects:
Atmospheric Sciences
Civil Engineering
Climate change
Earth and Environmental Science
Earth Sciences
Earth, ocean, space
Environment
Exact sciences and technology
Extreme rainfall
Extreme value index
Extreme weather
Generalized Pareto Distribution
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrologic data
Hydrology
Hydrology. Hydrogeology
Hydrology/Water Resources
Parameter estimation
Precipitation
Rainfall
Rainfall measurement
Risk assessment
Semi-parametric and parametric estimators
Studies
Time series
Water resources
Water resources management
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description Extreme rainfall events and the clustering of extreme values provide fundamental information which can be used for the risk assessment of extreme floods. Event probability can be estimated using the extreme value index (γ) which describes the behavior of the upper tail and measures the degree of extreme value clustering. In this paper, various semi-parametric and parametric extreme value index estimators are implemented in order to characterize the tail behavior of long-term daily rainfall time series. The results obtained from different estimators are then used to extrapolate the distribution function of extreme values. Extrapolation can be employed to estimate the occurrence probability of rainfall events above a given threshold. The results indicated that different estimators may result in considerable differences in extreme value index estimates. The uncertainty of the extreme value estimators is also investigated using the bootstrap technique. The analyses showed that the parametric methods are superior to the semi-parametric approaches. In particular, the likelihood and Two-Step estimators are preferred as they are found to be more robust and consistent for practical application.
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1573-1650
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source ABI/INFORM Global; Springer Nature
subjects Atmospheric Sciences
Civil Engineering
Climate change
Earth and Environmental Science
Earth Sciences
Earth, ocean, space
Environment
Exact sciences and technology
Extreme rainfall
Extreme value index
Extreme weather
Generalized Pareto Distribution
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrologic data
Hydrology
Hydrology. Hydrogeology
Hydrology/Water Resources
Parameter estimation
Precipitation
Rainfall
Rainfall measurement
Risk assessment
Semi-parametric and parametric estimators
Studies
Time series
Water resources
Water resources management
title Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data
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