Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 9
Abstract
A key challenge in enhancing flood forecast relies in the difficulty of reducing predictive uncertainty. The precipitation-dependent hydrologic uncertainty processor (HUP) is a flexible model independent Bayesian processor that can be used with any hydrologic model to provide probabilistic forecast. This study investigates the use of HUP with different hydrologic models for hydrologic uncertainty quantification in a flood forecasting scheme for a semiurban watershed of southern Ontario, Canada. The purpose is to better understand predictive uncertainty and enhance flood forecasting system reliability in semiurban conditions. HUP is based on Bayes’ theorem, and it updates the prior distribution given available information at the forecast time to obtain the posterior distribution that is close to future unknown actual value. In this study, the hydrological model (HYMOD) and the modèle du Génie Rural à 4 paramètres Horaire (GR4H) were selected to work with HUP, and the Bayesian processor was calibrated using a number of selected flood events from 2005 to 2014. The performance of the processor was assessed by graphical tools and performance metrics, like reliability plots, Nash-Sutcliffe efficiency (NSE), and continuous ranked probability score (CRPS). Results show that HUP provides a robust framework and a reliable analytic-numerical method for the quantification of hydrologic uncertainty, the actual values are well captured by the uncertainty bounds, the CRPS values are relatively small, and reliability curves lie close to the bisector. The comparison between the NSE calculated from the output of the sole deterministic hydrologic model (HYMOD/GR4H) and from the median of the predictive distribution produced by HUP-HYMOD/HUP-GR4H, demonstrates that HUP has the ability to improve the deterministic forecast. For low peak flow events, HUP combining with different hydrologic models presents similar predictive performance, while for high peak flow events, a well performed deterministic model is required in HUP to produce an accurate probabilistic forecast.
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Acknowledgments
This study was funded by the Natural Science and Engineering Research Council (NSERC) Canadian FloodNet (Grant No. NETGP 451456), and by the China Scholarship Council (CSC). The authors would like to thank the Toronto and Region Conservation Authority (TRCA) and Water Survey of Canada for providing the study data. The authors acknowledge four anonymous reviewers for their comments that helped to improve the manuscript.
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Journal of Hydrologic Engineering
Volume 24 • Issue 9 • September 2019
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©2019 American Society of Civil Engineers.
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Received: Jun 18, 2018
Accepted: Apr 18, 2019
Published online: Jul 10, 2019
Published in print: Sep 1, 2019
Discussion open until: Dec 10, 2019
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