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Title: Bayesian inference of nonstationary precipitation intensity-duration-frequency curves for infrastructure design
Authors: Skahill, Brian E. (Brian Edward)
Cheng, Linyin.
Byrd, Aaron R.
Kanney, Joseph.
AghaKouchak, Amir
Keywords: Bayesian statistical decision theory
Markov processes
Monte Carlo method
Rain and rainfall
Publisher: Coastal and Hydraulics Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: ERDC/CHL;CHETN-X-2
Abstract: Purpose: The purpose of this document is to demonstrate the application of Bayesian Markov Chain Monte Carlo (MCMC) simulation as a formal probabilistic-based means by which to develop local precipitation Intensity-Duration-Frequency (IDF) curves using historical rainfall time series data collected for a given surface network station, including the treatment of a nonstationary climate condition. This objective will be accomplished by independently revisiting parts of an example originally profiled by Cheng and AghaKouchak (2014). This Technical Note will conclude with a brief discussion of some potential opportunities for future U.S. Army Corps of Engineers (USACE) research and development directed at extreme rainfall frequency analysis.
Appears in Collections:Technical Note

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