Sponsor
NOAA grant no. NA11NWS4680002
Document Type
Post-Print
Publication Date
11-13-2014
Subjects
Bayesian statistical decision theory, Streamflow -- Forecasting, Hydrologic models -- Data processing
Abstract
Bayesian Model Averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models, and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g. Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To overcome these limitations, we develop an approach to integrate a group of multivariate functions, the so-called copula functions, into BMA. Here, we introduce a copula-embedded BMA (Cop-BMA) method that relaxes any assumption on the shape of conditional PDFs. Copula functions have a flexible structure and do not restrict the shape of posterior distributions. Furthermore, copulas are effective tools in removing bias from hydrologic forecasts. To compare the performance of BMA with Cop-BMA, they are applied to hydrologic forecasts from different rainfall-runoff and land-surface models. We consider the streamflow observation and simulations for ten river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are more accurate and reliable, less biased, and more confident with small uncertainty after Cop-BMA application. It is also shown that the post-processed forecasts have better correlation with observation after Cop-BMA application.
DOI
10.1002/2014WR015965
Persistent Identifier
http://archives.pdx.edu/ds/psu/12980
Citation Details
Madadgar, Shahrbanou and Moradkhani, Hamid, "Improved Bayesian Multi-Modeling: Integration of Copulas and Bayesian Model Averaging" (2014). Civil and Environmental Engineering Faculty Publications and Presentations. 174.
http://archives.pdx.edu/ds/psu/12980
Description
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record to be published by the American Geophysical Union, Copyright 2014.