Oct 30, 2024

Data and Workflows Supporting Ensemble Methods for Parameter Estimation of WRF-Hydro


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Version: 1.0

The WRF-Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open-source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF-Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, where we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.

DOI
https://doi.org/10.5065/pvfd-tn60
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Support Contact
Arezoo RafieeiNasab
UCAR/NCAR - Research Applications Laboratory
arezoo@ucar.edu

Data Curator
GDEX Curator
UCAR/NCAR - GDEX
gdex@ucar.edu

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1.0
Authors
Arezoo RafieeiNasab
Michael N. Fienen
Nina Omani
Ishita Srivastava
Aubrey L. Dugger
Publisher
UCAR/NCAR - GDEX

Suggested Citation
Arezoo RafieeiNasab, Michael N. Fienen, Nina Omani, Ishita Srivastava, Aubrey L. Dugger. (2024). Data and Workflows Supporting Ensemble Methods for Parameter Estimation of WRF-Hydro. Version 1.0. UCAR/NCAR - GDEX. https://doi.org/10.5065/pvfd-tn60. Accessed 06 Nov 2024.
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