One-Day-Ahead Streamflow Forecasting Using Artificial Neural Networks and a Meteorological Mesoscale Model
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Volume 20, Issue 9
Abstract
An approach to modeling daily flows using artificial neural networks (ANNs) is presented. In addition to previous streamflow values and mean areal rainfall sequences, a new runoff index was used and tested as ANN input. This runoff index was generated as a combination of two output variables of the weather research and forecasting (WRF) mesoscale model, which contains an integrated land surface model. Inclusion of the new index improved ANN model performance and increased simulation skill. A case study was conducted for the northeast Guadalquivir catchment in southeastern Spain. Accurate one-day-ahead streamflow forecasts were achieved in terms of overall fit and timing of peaks. Model performance was satisfactory, with a persistence index (PI) equal to 0.81 and a Nash–Sutcliffe efficiency equal to 0.95 for an independent data set. These favorable results prove that WRF outputs contain useful information on the hydrologic state of a basin and can therefore be used as valuable ANN inputs.
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Acknowledgments
This work was supported by the Ministry of the Interior and Justice of Andalusia (Project EXP-2407). The data were kindly provided by the Regional Office of Agriculture and Fishing of Andalusia and the Automatic Hydrological Information System of the Guadalquivir River basin.
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Journal of Hydrologic Engineering
Volume 20 • Issue 9 • September 2015
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© 2015 American Society of Civil Engineers.
History
Received: Mar 24, 2014
Accepted: Dec 2, 2014
Published online: Jan 12, 2015
Discussion open until: Jun 12, 2015
Published in print: Sep 1, 2015
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