TITLE:
Long Lead-Time Streamflow Forecasting Using Oceanic-Atmospheric Oscillation Indices
AUTHORS:
Niroj Kumar Shrestha
KEYWORDS:
Oscillation Indices, Streamflow, Lead-Time, Prediction
JOURNAL NAME:
Journal of Water Resource and Protection,
Vol.6 No.6,
April
29,
2014
ABSTRACT:
Climatic variability
influences the hydrological cycle that subsequently affects the discharge in
the stream. The variability in the climate can be represented by the ocean-atmospheric
oscillations which provide the forecast opportunity for the streamflow. Prediction
of future water availability accurately and reliably is a key step for
successful water resource management in the arid regions. Four popular
ocean-atmospheric indices were used in this study for annual streamflow volume
prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern
Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic
Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven
model based on Bayesian learning approach was used as a prediction model. The
model was applied to four unimpaired stream gages in Utah that spatially covers
the state from north to south. Different models were developed based on the combinations
of oscillation indices in the input. A total of 60 years (1950-2009) of data
were used for the analysis. The model was trained on 50 years of data
(1950-1999) and tested on 10 years of data (2000-2009). The best combination of
oscillation indices and the lead-time were identified for each gage which was
used to develop the prediction model. The predicted flow had reasonable
agreement with the actual annual flow volume. The sensitivity analysis shows that
the PDO and ENSO have relatively stronger effect compared to other oscillation
indices in Utah. The prediction results from the MVRVM were compared with the Support
Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM
performed relatively better.