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Incorporating Climate Signals for Improved Ensemble Streamflow Prediction Public Deposited

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https://ir.library.oregonstate.edu/concern/conference_proceedings_or_journals/zg64tm60d

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  • 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.
  • Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR.
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  • description.provenance : Submitted by Andrea Wirth (andrea.wirth@oregonstate.edu) on 2011-10-17T15:06:14Z No. of bitstreams: 1 OWS2011_ESP-Weight_Najafi.pdf: 428857 bytes, checksum: 47b2a5227fca09e2895adad1c7dcd30e (MD5)
  • description.provenance : Made available in DSpace on 2011-10-17T15:06:14Z (GMT). No. of bitstreams: 1 OWS2011_ESP-Weight_Najafi.pdf: 428857 bytes, checksum: 47b2a5227fca09e2895adad1c7dcd30e (MD5) Previous issue date: 2011-05-24

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