Improving Hydrological Model Simulations with Combined Multi-Input and Multimodel Averaging Frameworks
Publication: Journal of Hydrologic Engineering
Volume 22, Issue 4
Abstract
It is well known that multimodel averaging can considerably improve hydrological model simulation skill. However, the need to set up and run different models can be a time-consuming task. This work expands on the classic multimodel averaging approach by feeding models with different climate datasets and treating each version as a unique model in the ensemble. Three hydrological models and four climate datasets were combined to produce multimodel/multi-input, multimodel/monoinput and monomodel/multi-input combined flows using a weighting scheme that minimizes the root mean square error (RMSE) between the combined and observed hydrographs. The results show that model averaging improves performance significantly and that the proposed multi-input averaging provides higher skill than multimodel averaging. A combination of all models run with all datasets (12 members in total) produced the best results with the averaged hydrograph being more accurate than any single member on 70% of the catchments. The median Nash-Sutcliffe Efficiency (NSE) metric value increase was 0.07 overall in validation under the multimodel/multi-input framework, all while providing improved simulation reliability.
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Acknowledgments
The authors would like to thank the four institutions who made their data available for this study. The datasets are available at ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/ (MOPEX), http://hydro.engr.scu.edu/files/gridded_obs/daily/ (Santa Clara), http://daymet.ornl.gov (Daymet), and http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.html/ (CPC). The authors would also like to thank three anonymous reviewers for their fruitful contributions to this paper.
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Published In
Journal of Hydrologic Engineering
Volume 22 • Issue 4 • April 2017
Copyright
©2016 American Society of Civil Engineers.
History
Received: May 29, 2015
Accepted: Sep 22, 2016
Published online: Nov 30, 2016
Published in print: Apr 1, 2017
Discussion open until: Apr 30, 2017
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