Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112618
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Type: Journal article
Title: Bayesian spectral likelihood for hydrological parameter inference
Author: Schaefli, B.
Kavetski, D.
Citation: Water Resources Research, 2017; 53(8):6857-6884
Publisher: American Geophysical Union
Issue Date: 2017
ISSN: 0043-1397
1944-7973
Statement of
Responsibility: 
Bettina Schaefli and Dmitri Kavetski
Abstract: This paper proposes a spectral domain likelihood function for the Bayesian estimation of hydrological model parameters from a time series of model residuals. The spectral domain error model is based on the power-density spectrum (PDS) of the stochastic process assumed to describe residual errors. The Bayesian spectral domain likelihood (BSL) is mathematically equivalent to the corresponding Bayesian time domain likelihood (BTL) and yields the same inference when all residual error assumptions are satisfied (and all residual error parameters are inferred). However, the BSL likelihood function does not depend on the residual error distribution in the original time domain, which offers a theoretical advantage in terms of robustness for hydrological parameter inference. The theoretical properties of BSL are demonstrated and compared to BTL and a previously proposed spectral likelihood by Montanari and Toth (2007), using a set of synthetic case studies and a real case study based on the Leaf River catchment in the U.S. The empirical analyses confirm the theoretical properties of BSL when applied to heteroscedastic and autocorrelated error models (where heteroscedasticity is represented using the log-transformation and autocorrelation is represented using an AR(1) process). Unlike MTL, the use of BSL did not introduce additional parametric uncertainty compared to BTL. Future work will explore the application of BSL to challenging modeling scenarios in arid catchments and ‘‘indirect’’ calibration with nonconcomitant input/output time series.
Keywords: Hydrology; Bayesian inference; spectral domain inference; rainfall‐runoff modeling; frequency domain inference; model calibration
Rights: © 2017. American Geophysical Union. All Rights Reserved.
RMID: 0030075146
DOI: 10.1002/2016WR019465
Appears in Collections:Civil and Environmental Engineering publications

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