Forecasting Using Oceanic-Atmospheric Oscillation Indices
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 acc...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.636.3645 2023-05-15T17:33:40+02:00 Forecasting Using Oceanic-Atmospheric Oscillation Indices Long Lead-time Streamflow Niroj Kumar Shrestha The Pennsylvania State University CiteSeerX Archives http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.636.3645 http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.636.3645 http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 text ftciteseerx 2016-01-08T15:43:08Z 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-Niño Southern Oscillation (ENSO), At-lantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relev-ance 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 com-binations 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 da-ta (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 Text North Atlantic North Atlantic oscillation Unknown Pacific |
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Open Polar |
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ftciteseerx |
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English |
description |
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-Niño Southern Oscillation (ENSO), At-lantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relev-ance 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 com-binations 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 da-ta (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 |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Long Lead-time Streamflow Niroj Kumar Shrestha |
spellingShingle |
Long Lead-time Streamflow Niroj Kumar Shrestha Forecasting Using Oceanic-Atmospheric Oscillation Indices |
author_facet |
Long Lead-time Streamflow Niroj Kumar Shrestha |
author_sort |
Long Lead-time Streamflow |
title |
Forecasting Using Oceanic-Atmospheric Oscillation Indices |
title_short |
Forecasting Using Oceanic-Atmospheric Oscillation Indices |
title_full |
Forecasting Using Oceanic-Atmospheric Oscillation Indices |
title_fullStr |
Forecasting Using Oceanic-Atmospheric Oscillation Indices |
title_full_unstemmed |
Forecasting Using Oceanic-Atmospheric Oscillation Indices |
title_sort |
forecasting using oceanic-atmospheric oscillation indices |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.636.3645 http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.636.3645 http://www.scirp.org/journal/PaperDownload.aspx?paperID=45472 |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766132250157514752 |