University of Oulu

Sadegh, M., Shakeri Majd, M., Hernandez, J. et al. The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models. Water Resour Manage 32, 1867–1881 (2018) doi:10.1007/s11269-018-1908-6

The quest for hydrological signatures : effects of data transformation on bayesian inference of watershed models

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Author: Sadegh, Mojtaba1; Majd, Morteza Shakeri2; Hernandez, Jairo1;
Organizations: 1Department of Civil Engineering, Boise State University, Boise, ID, USA
2Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA
3Water Resources and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Springer Nature, 2018
Publish Date: 2020-01-10


Hydrological models contain parameters, values of which cannot be directly measured in the field, and hence need to be meaningfully inferred through calibration against historical records. Although much progress has been made in the model inference literature, relatively little is known about the effects of transforming calibration data (or error residual) on the identifiability of model parameters and reliability of model predictions. Such effects are analyzed herein using two hydrological models and three watersheds. Our results depict that calibration data transformations significantly influence parameter and predictive uncertainty estimates. Those transformations that distort the temporal distribution of calibration data, such as flow duration curve, normal quantile transform, and Fourier transform, considerably deteriorate the identifiability of model parameters derived in a formal Bayesian framework with a residual-based likelihood function. Other transformations, such as wavelet, BoxCox and square root, while demonstrating some merits in identifying specific model parameters, would not consistently improve predictive capability of hydrological models in a single objective inverse problem. Multi-objective optimization schemes, however, may present a more rigorous basis to extract several independent pieces of information from different data transformations. Finally, data transformations might offer a greater potential to evaluate model performance and assess specific sections of model behavior, rather than to calibrate models in a single objective framework. Findings of this study shed light on the importance and impacts of data transformations in search of hydrological signatures.

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Series: Water resources management
ISSN: 0920-4741
ISSN-E: 1573-1650
ISSN-L: 0920-4741
Volume: 32
Issue: 5
Pages: 1867 - 1881
DOI: 10.1007/s11269-018-1908-6
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Copyright information: © Springer Science+Business Media B.V., part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Water resources management. The final authenticated version is available online at: