Sponsor
Funding for this research was provided by a Science and Technology grant from Bureau of Reclamation, which is gratefully acknowledged. Thanks are due to three anonymous reviewers for their insightful comments which improved the manuscript. Preprocessing and postprocessing analysis was conducted using the R language (R Core Team, 2014). Data used can be found at http://civil.colorado.edu/bracken/multivariate_extremes/data/.
Published In
Water Resources Management
Document Type
Article
Publication Date
1-2018
Subjects
Hydrology, Data analysis, Hydrodynamics
Abstract
We present a general Bayesian hierarchical framework for conducting nonstationary frequency analysis of multiple hydrologic variables. In this, annual maxima from each variable are assumed to follow a generalized extreme value (GEV) distribution in which the location parameter is allowed to vary in time. A Gaussian elliptical copula is used to model the joint distribution of all variables. We demonstrate the utility of this framework with a joint frequency analysis model of annual peak snow water equivalent (SWE), annual peak flow, and annual peak reservoir elevation at Taylor Park dam in Colorado, USA. Indices of largescale climate drivers—El Ni~no Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are used as covariates to model temporal nonstationarity. The Bayesian framework provides the posterior distribution of the model parameters and consequently the return levels. Results show that performing a multivariate joint frequency analysis reduces the uncertainty in return level estimates and better captures multivariate dependence compared to an independent model.
DOI
https://doi.org/10.1002/2017WR020403
Persistent Identifier
http://archives.pdx.edu/ds/psu/25090
Citation Details
Bracken, C., Holman, K. D., Rajagopalan, B., & Moradkhani, H. (2018). A bayesian hierarchical approach to multivariate nonstationary hydrologic frequency analysis. Water Resources Research, 54(1), 243-255.
Description
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.