Improved Ensemble Streamflow Prediction Using a New ESP Weighting Scheme

Editors

R. E. Beighley II; M. W. Kilgore

Document Type

Conference Proceeding

Publication Date

5-22-2011

Publication Title

World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability

Publisher

American Society of Civil Engineers

First page number:

3733

Last page number:

3742

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.

Keywords

Forecasting; Hydrological forecasting; Predictions; Streamflow; Streamflow--Forecasting

Disciplines

Civil and Environmental Engineering | Engineering | Environmental Engineering | Environmental Sciences | Water Resource Management

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

UNLV article access

Search your library

Share

COinS