Predicting the temporal transferability of model parameters through a hydrological signature analysis

Dilhani Ishanka JAYATHILAKE, Tyler SMITH

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Front. Earth Sci. ›› 2020, Vol. 14 ›› Issue (1) : 110-123. DOI: 10.1007/s11707-019-0755-y
RESEARCH ARTICLE
RESEARCH ARTICLE

Predicting the temporal transferability of model parameters through a hydrological signature analysis

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Abstract

Attention has recently increased on the use of hydrological signatures as a potential tool for assessing the fidelity of model structures and providing insights into the transfer of model parameters. The utility of hydrological signatures as model performance/reliability indicators in a calibration-validation testing scenario (i.e., the temporal transfer of model parameters) is the focus of this study. The Probability Distributed Model, a flexible conceptual hydrological model, is used to test the approach across a number of catchments included in the MOPEX data set. We explore the change in model performance across calibration and validation time periods and contrast it to the corresponding change in several hydrological signatures to assess signature worth. Results are explored in finer detail by utilizing a moving window approach to calibration and validation time periods. The results of this study indicated that the most informative signature can vary, both spatially and temporally, based on physical and climatic characteristics and their interaction to the model parameterization. Thus, one signature could not adequately illustrate complex watershed behaviors nor predict model performance in new analysis periods.

Keywords

streamflow / hydrological signature / validation testing / model calibration

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Dilhani Ishanka JAYATHILAKE, Tyler SMITH. Predicting the temporal transferability of model parameters through a hydrological signature analysis. Front. Earth Sci., 2020, 14(1): 110‒123 https://doi.org/10.1007/s11707-019-0755-y

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Acknowledgments

We acknowledge the National Oceanic and Atmospheric Administration for making available the Model Parameter Estimation Experiment (MOPEX) data that was used for this study and S. Razavi for providing free access to the VARS toolbox. We thank the two anonymous reviewers for their contributions toward the improvement of this manuscript. Funding for this research was provided through a graduate scholarship from Clarkson University awarded to D. Jayathilake.

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2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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