Hydrological catchment classification using a data-based mechanistic strategy

Thorsten Wagener*, Neil McIntyre

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

3 Citations (Scopus)


Catchment classification remains a significant challenge for hydrologists, with available schemes not providing a sufficient basis for consistently distinguishing between different types of hydrological behavior. We analyze 278 catchments distributed across the Eastern USA using a data-based mechanistic (DBM) strategy. We attempt to understand the catchment similarity that can be found with respect to both model parameters (if the same model structure is applied) and with respect to model structures identified as most suitable. Finally, we relate the identified structures and parameters to available physical and climatic catchment-scale characteristics to see whether a further generalization of our result is possible. A significant regional pattern emerged, reflecting the influences of aridity, elevation (steepness) and temperature. In terms of parameter estimates, the most interesting variability between catchments is seen in the response nonlinearity. Significant regional patterns in the non-linearity parameters emerged, and reasonable physical explanations were proposed. Overall, the results of our preliminary study provided here give the impression that the DBM method could be fruitfully applied towards the objective of catchment classification.

Original languageEnglish
Title of host publicationSystem Identification, Environmental Modelling, and Control System Design
PublisherSpringer London
Number of pages18
ISBN (Print)9780857299741, 0857299735, 9780857299734
Publication statusPublished - 1 Jun 2014

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