Forecasting Monthly Streamflow of Spring-Summer Runoff Season in Rio Grande Headwaters Basin Using Stochastic Hybrid Modeling Approach
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 4
Abstract
Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the spring-summer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid . The results indicated that the hybrid approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous option to improve one-month-ahead forecast accuracy of monthly streamflow in spring-summer runoff season in the Rio Grande Headwaters Basin.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 16 • Issue 4 • April 2011
Pages: 384 - 390
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jan 20, 2010
Accepted: Sep 1, 2010
Published online: Sep 7, 2010
Published in print: Apr 1, 2011
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