Bayesian Inference of Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2016
Accession Number
AD1005455

Entities

People

  • Aaron Byrd
  • Amir AghaKouchak
  • Brian E. Skahill
  • Joseph Kanney
  • Linyin Cheng

Organizations

  • Engineer Research and Development Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Climate Change
  • Data Analysis
  • Engineers
  • Equations
  • Flood Control
  • Flood Damage
  • Floods
  • Greenhouse Effect
  • Information Science
  • Markov Chains
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Rainfall Intensity
  • Random Variables
  • Stochastic Processes

Readers

  • Climatology
  • Computational Modeling and Simulation
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference