Combining regression and spatial proximity for catchment model regionalization: a comparative study |
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Authors: | Scott Steinschneider Yi-Chen E Yang Casey Brown |
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Institution: | 1. Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01002, USAscottsteinschneider@gmail.com;3. Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01002, USA |
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Abstract: | AbstractSpatial 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. Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei |
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Keywords: | ungauged catchments predictions hydrological models |
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