Watershed

Oral

397851 - Practical Use of Markov Chain Monte Carlo Simulation in the Hydrologic Modeling System

Monday, June 4
10:30 AM - 12:00 PM
Location: Greenway IJ
Co-Authors: Brian Skahill, Portland, OR – U.S. Army Corps of Engineers; Angela Duren, Portland, OR – U.S. Army Corps of Engineers

Markov Chain Monte Carlo (MCMC) simulation offers a statistically robust method for inferring hydrologic model parameters. It can serve as the core of an uncertainty framework that incorporates knowledge uncertainty and natural variability. Because it generates parameter estimates with uncertainty, MCMC is a natural fit for applications taking a risk-based approach to analyzing water resource problems. The MCMC technique is relatively new to hydrologic simulation and has already been added to the Hydrologic Modeling System (HEC-HMS). Within HEC-HMS, the MCMC technique can be used on its own for probabilistic parameter estimation, or as an adjunct to classical Monte Carlo simulation for predicting uncertainty in output variables.
The practical use of MCMC in analyzing water resource problems extends rather than replaces the typical approach to building a good hydrologic simulation model. Using MCMC cannot be considered a replacement for careful data analysis or thoughtful model configuration and calibration. Determining convergence requires a weight-of-evidence approach. Using multiple calibration events takes on new meaning. These and other factors to consider in the practical use of MCMC will be discussed through an example application using HEC-HMS for the Willamette River watershed in Oregon, United States.

William A. Scharffenberg, PhD

HEC-HMS Lead Developer
US Army Corps of Engineers (USACE)

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