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Incorporating Climate Signals for Improved Ensemble Streamflow Prediction

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dc.creator Najafi, Mohammad Reza
dc.creator Moradkhani, Hamid
dc.date.accessioned 2011-10-17T15:06:14Z
dc.date.available 2011-10-17T15:06:14Z
dc.date.issued 2011-05-24
dc.identifier.uri http://hdl.handle.net/1957/24175
dc.description Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR. en_US
dc.description.abstract Ensemble Streamflow Prediction (ESP) provides the means for statistical post-processing of the forecasts and estimating the inherent uncertainties. On the other hand large scale climate variables provide valuable information for hydrologic predictions. In this study we propose a post-processing procedure that assigns weights to streamflow ensemble members using these large scale climate signals. Analysis is performed over the snow dominated East River basin in Colorado to improve the spring ensemble streamflow volume forecast. We employ Fuzzy C-Means clustering method for the weighting and it is found that Principle Component Analysis (PCA) improve the accuracy of the weighting scheme considerably. The presented objective method can be applied to enhance the final ESPs; nevertheless the user expertise may change any of the process steps. The current predictions based on simple average or the median of the ensemble members may come with the weighted ensemble forecasts to better provide possible ranges and uncertainty bounds. en_US
dc.language.iso en_US en_US
dc.publisher The Oregon Water Conference en_US
dc.subject Ensemble Streamflow Prediction (ESP) en_US
dc.subject Large-scale climate variables en_US
dc.subject East River Basin en_US
dc.title Incorporating Climate Signals for Improved Ensemble Streamflow Prediction en_US
dc.type Presentation en_US
dc.description.peerreview no en_US

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