Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 2
Abstract
Hydrological forecasting is key for water resources allocation and flood risk management. Although a number of advanced hydrological forecasting methods have been developed in the past, daily (or subdaily) forecasting remains a major challenge in engineering hydrology. The uncertainties associated with input data, model parameters, and model structure necessitate developing more robust modeling techniques. In this study, a hybrid machine-learning approach based on hydrological and data-driven modeling is developed for daily streamflow forecasting. The proposed hybrid hydrological data-driven model (HHDD) approach succeeds in improving daily prediction compared to that predicted by the standard conceptual hydrological model (HYMOD). In addition, the developed HHDD model is more robust in terms of providing direct uncertainty analysis results. The results indicate that a better resemblance of streamflow pattern is achieved by integrating physically based and data-driven approaches into the developed HHDD model.
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Data Availability Statement
The rainfall data were obtained from the National Oceanic and Atmospheric Administration website at https://www.ncdc.noaa.gov/ (accessed August 1, 2017). The flow gauge data were obtained from the United States Geological Survey website at https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00012912/detail (accessed August 1, 2017). HYMOD is an open-source model and can be obtained from GitHub based on the model preferences. The developed HHDD model is available from the corresponding author by request.
Acknowledgments
This research was supported by the Natural Science and Engineering Research Council of Canada. The authors would like to thank the editor and the anonymous reviewers for their constructive comments that greatly contributed to improving the paper.
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Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 2 • February 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Dec 13, 2018
Accepted: Aug 23, 2019
Published online: Nov 23, 2019
Published in print: Feb 1, 2020
Discussion open until: Apr 23, 2020
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