The University of Massachusetts Amherst
University of Massachusetts Amherst

Search Google Appliance

Links

Combining regression and spatial proximity for catchment model regionalization: a comparative study

TitleCombining regression and spatial proximity for catchment model regionalization: a comparative study
Publication TypeJournal Article
Year of Publication2014
AuthorsSteinschneider S, Yang Y-CEthan, Brown C
JournalHydrological Sciences Journal
Volume60
Issue6
Start Page1026
Pagination1026-1043
Date Published03/2014
Keywordsbassins versants non jaugés, hydrological models, modèles hydrologiques, predictions, simulations, ungauged catchments
Abstract

Spatial error regression is employed to regionalize the parameters of a rainfall–runoff model. The approach combines regression on physiographic watershed characteristics with a spatial proximity technique that describes the spatial dependence of model parameters. The methodology is tested for the monthly abcd model at a network of gauges in southeast United States and compared against simpler regression and spatial proximity approaches. Unlike other comparative regionalization studies that only evaluate the skill of regionalized streamflow predictions in ungauged catchments, this study also examines the fit between regionalized parameters and their optimal (i.e. calibrated) values. Interestingly, the spatial error model produces parameter estimates that better resemble the optimal parameters than either of the simpler methods, but the spatial proximity method still yields better hydrologic simulations. The analysis suggests that the superior streamflow predictions of spatial proximity result from its ability to better preserve correlations between compensatory hydrological parameters.

URLhttp://dx.doi.org/10.1080/02626667.2014.899701
DOI10.1080/02626667.2014.899701