Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 20, issue 7
Articles | Volume 20, issue 7
https://doi.org/10.5194/hess-20-2611-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2611-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 20, issue 7
Research article
 | 
04 Jul 2016
Research article |  | 04 Jul 2016

Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds

Julie E. Shortridge, Seth D. Guikema, and Benjamin F. Zaitchik

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This paper compares six methods for data-driven rainfall–runoff simulation in terms of predictive accuracy, error structure, interpretability, and uncertainty. We demonstrate that autocorrelation in model errors can result in biased estimates of important values and show how certain model structures can be more easily interpreted to yield insights on physical watershed function. Finally, we explore how model structure can impact uncertainty in climate change sensitivity estimates.
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