A comparison of three approaches to spatial generalization of rainfall–runoff models

✍ Scribed by A. L. Kay; D. A. Jones; S. M. Crooks; A. Calver; N. S. Reynard

• Publisher: John Wiley and Sons
• Year: 2006
• Tongue: English
• Weight: 576 KB
• Volume: 20
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/hyp.6550

✦ Synopsis

The generalization of the parameters of rainfall-runoff models, to enable application at ungauged sites, is an important and ongoing area of research. This paper compares the performance of three alternative methods of generalization, for two parameter-sparse conceptual models (PDM and TATE), specifically for use in flood frequency estimation using continuous simulation. Two of the methods are based on fitting regression relationships between catchment properties and calibrated parameter values, using weighted or sequential regression (with weights based on estimates of calibration uncertainty), and the third is based on the use of pooling groups, defined through measures of site-similarity based on catchment properties. The study uses a relatively large sample of catchments in Britain.

For the PDM, the site-similarity method performs best, but not greatly better than either regression method, so there may be cases where the use of regression would be preferable. For the TATE model, weighted regression performs best (with a very similar level of performance to that of the PDM with site-similarity), whereas site-similarity performs worst (due to poor performance for catchments with higher baseflow), indicating that the choice of model and generalization method should not be separated. The use of sequential regression, which was developed to try to allow for parameter interdependence, shows no clear advantage for either model. Other than the poor performance of the TATE model with site-similarity for catchments with a higher baseflow index, there are no clear relationships between performance of any model/method and catchment type.