Abstract
Flood forecasting in urban and semiurban catchments is often limited by the capability of the combined hydrological models and forecast inputs to predict floods accurately. The objective of this research is to develop an approach (1) to identify the best model forecast from multiple integrations of various hydrological models and numerical weather predictions (NWP), and (2) to find the best forecast combination method for an improved short-range flood forecasting. Seven selected hydrological models were coupled, each with two high-resolution NWP forecasts to provide several alternatives of deterministic hydrological forecasts at a catchment outlet. As such, the different model-input combinations were used to generate 14 hydrological forecasts. Hydrological forecast verification was then carried out over a one-year hindcast period. A comparison between six forecast combination methods, including a benchmark Bayesian model averaging (BMA) method, was also performed for the multiple available short-term streamflow forecasts. Results indicate that the coupling of the Sacramento soil moisture accounting (SACSMA) model with both High-Resolution Deterministic Precipitation System and High-Resolution Rapid Refresh inputs outperformed other model-input integrations. Maximum forecast errors in all model-input integration outputs occurred at forecast lead times of , corresponding to the time of concentration of the catchment. Providing constraints on the estimation of model weights was found to be a significant factor for obtaining an improved combined streamflow forecast. In general, the regression-based forecast combination method of the constrained ordinary least squares (CLS) has emerged as a possible alternative to the widely used BMA method for hydrology application.
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Data Availability Statement
The following data used during the study were provided by a third party: hourly historical precipitation and temperature data (TRCA). Direct requests for these materials may be made to the provider, as indicated in the Acknowledgments section.
The following models and code generated or used during the study are available from the second author by request: MACHBV with SNOW 17 (MATLAB code).
Acknowledgments
This work was supported by the Natural Science and Engineering Research Council (NSERC) Canadian FloodNet (Grant No. NETGP 451456). We owe a big thanks to Dr. James M. Leach for his help in proofreading the manuscript. The authors would like to thank the TRCA, NOAA, ECCC, Water Survey of Canada (WSC), Canadian Surface Prediction Archive (CaSPAr), and Computational Hydraulics International (CHI) for providing models, data, and technical support for this research.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 26 • Issue 1 • January 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Feb 11, 2020
Accepted: Jul 13, 2020
Published online: Nov 6, 2020
Published in print: Jan 1, 2021
Discussion open until: Apr 6, 2021
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