American Society of Civil Engineers


Forecasting Monthly Streamflow of Spring-Summer Runoff Season in Rio Grande Headwaters Basin Using Stochastic Hybrid Modeling Approach


by Shalamu Abudu, (Postdoctoral Researcher, Dept. of Civil Engineering, New Mexico State Univ., Box 30001, MSC 3CE, Las Cruces, NM 88003-0001; and Professor, Xinjiang Water Resources Research Institute, Urumqi, Xinjiang, China. E-mail: shalamu@nmsu.edu), J. Phillip King, M.ASCE, (Associate Professor, PE, Dept. of Civil Engineering, New Mexico State Univ., Box 30001, MSC 3CE, Las Cruces, NM 88003-0001. E-mail: jpking@nmsu.edu), and A. Salim Bawazir, M.ASCE, (Associate Professor, Dept. of Civil Engineering, New Mexico State Univ., Box 30001, MSC 3CE, Las Cruces, NM 88003-0001. E-mail: abawazir@nmsu.edu)

Journal of Hydrologic Engineering, Vol. 16, No. 4, April 2011, pp. 384-390, (doi:  http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000322)

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Document type: Technical Note
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 TFN+ANN. The results indicated that the hybrid TFN+ANN 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 TFN+ANN 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.


ASCE Subject Headings:
Time series analysis
Reservoirs
River flow
Forecasting
Neural networks
Rio Grande
Hybrid methods

Author Keywords:
Time series analysis
Reservoirs
River flow
Forecasting
Neural networks
Rio Grande
Hybrid methods