On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates

This paper discusses some aspects of flood frequency analysis using the peaks-over-threshold model with Poisson arrivals and generalized Pareto (GP) distributed peak magnitudes under nonstationarity, using climate covariates. The discussion topics were motivated by a case study on the influence of E...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2016-01, Vol.30 (1), p.207-224
Main Authors: Silva, Artur Tiago, Naghettini, Mauro, Portela, Maria Manuela
Format: Article
Language:English
Subjects:
Aquatic Pollution
Asymptotic properties
Chemistry and Earth Sciences
Climate
Climate science
Computational Intelligence
Computer Science
Design analysis
Earth and Environmental Science
Earth Sciences
El Nino
Environment
Estimates
Flood frequency
Flood hazards
Floods
Frequency analysis
Hazards
Hurricanes
Math. Appl. in Environmental Science
Mathematical models
Original Paper
Physics
Poisson distribution
Probability Theory and Stochastic Processes
River basins
Southern Oscillation
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
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Summary:This paper discusses some aspects of flood frequency analysis using the peaks-over-threshold model with Poisson arrivals and generalized Pareto (GP) distributed peak magnitudes under nonstationarity, using climate covariates. The discussion topics were motivated by a case study on the influence of El Niño–Southern Oscillation on the flood regime in the Itajaí river basin, in Southern Brazil. The Niño3.4 (DJF) index is used as a covariate in nonstationary estimates of the Poisson and GP distributions scale parameters. Prior to the positing of parametric dependence functions, a preliminary data-driven analysis was carried out using nonparametric regression models to estimate the dependence of the parameters on the covariate. Model fits were evaluated using asymptotic likelihood ratio tests, AIC, and Q–Q plots. Results show statistically significant and complex dependence relationships with the covariate on both nonstationary parameters. The nonstationary flood hazard measure design life level (DLL) was used to compare the relative performances of stationary and nonstationary models in quantifying flood hazard over the period of records. Uncertainty analyses were carried out in every step of the application using the delta method.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-015-1072-y