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Abstract ▼ AI-Helper

Civil engineers are often required to predict streamflow for catchments with limited or no flow measurements for various water resources projects. The objective of this paper is to develop an appropriate method for predicting steam flow at un gauged catchment, through a regionalisation of rainfall-r...

Civil engineers are often required to predict streamflow for catchments with limited or no flow measurements for various water resources projects. The objective of this paper is to develop an appropriate method for predicting steam flow at un gauged catchment, through a regionalisation of rainfall-runoff models. This paper presents the establishment of regression relationships between 17 physical catchment characteristics and 5 parameters of a Probability distributed model (Moore, 1985) by using a database of 126 well-gauged UK catchments. These devoped relationships allow predictive models to be identified without calibration proceures. The catchment clustering is attempted to improve the relevance of the relationships: more and less permeable catchment types (i.e. BFIHOST = 0.5 is used as a criteria). Stepeeise regression equations are developed for each group individuelly. Nash Sutcliffe Effciency is used as an objective function. Furthermore, the uncertainty is indicated as confidence intervals on the estimated streamflow. The results show that there are no physically meaningful relationships between model parameters and catchment characteristics. The applications results indicate that the best regression model improves our capability to predict the higher flow range in the less permeable catchments. However, predicting low flows in these catchments, and the full range of flows in more permeable catchments are still remained as unsolved problems.

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