Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117517
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Type: Journal article
Title: Signature-domain calibration of hydrological models using approximate bayesian computation: empirical analysis of fundamental properties
Author: Fenicia, F.
Kavetski, D.
Reichert, P.
Albert, C.
Citation: Water Resources Research, 2018; 54(6):3958-3987
Publisher: American Geophysical Union
Issue Date: 2018
ISSN: 0043-1397
1944-7973
Statement of
Responsibility: 
Fabrizio Fenicia, Dmitri Kavetski, Peter Reichert, Carlo Albert
Abstract: This study investigates Bayesian signature‐domain inference of hydrological models using Approximate Bayesian Computation (ABC) algorithms, and compares it to “traditional” time‐domain inference. Our focus is on the quantification of predictive uncertainty in the streamflow time series and on understanding the information content of particular combinations of signatures. A combination of synthetic and real data experiments using conceptual rainfall‐runoff models is employed. Synthetic experiments demonstrate: (i) the general consistency of signature and time‐domain inferences, (ii) the ability to estimate streamflow error model parameters (reliably quantify streamflow uncertainty) even when calibrating in the signature domain, and (iii) the potential robustness of signature‐domain inference when the (probabilistic) hydrological model is misspecified (e.g., by unaccounted timing errors). The experiments also suggest limitations of the signature‐domain approach in terms of information loss when general (nonsufficient) statistics are used, and increased computational costs incurred by the ABC implementation. Real data experiments confirm the viability of Bayesian signature‐domain inference and its general consistency with time‐domain inference in terms of predictive uncertainty quantification. In addition, we demonstrate the utility of the flashiness index for the estimation of streamflow error parameters, and show that signatures based on the Flow Duration Curve alone are insufficient to calibrate parameters controlling streamflow dynamics. Overall, the study further establishes signature‐domain inference (implemented using ABC) as a promising method for comparing the information content of hydrological signatures, for prediction under data‐scarce conditions, and, under certain circumstances, for mitigating the impact of deficiencies in the formulation of the predictive model.
Keywords: Data signature; Bayesian model calibration; uncertainty; approximate Bayesian computation (ABC); flow duration curve; flashiness index
Rights: © 2018. American Geophysical Union. All Rights Reserved.
RMID: 0030095622
DOI: 10.1002/2017WR021616
Grant ID: http://purl.org/au-research/grants/arc/LP140100978
Appears in Collections:Civil and Environmental Engineering publications

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