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.
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.
the Creative Commons Attribution 3.0 License.
Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA
Seth D. Guikema
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
Benjamin F. Zaitchik
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
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Latest update: 04 Oct 2024
Short summary
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.
This paper compares six methods for data-driven rainfall–runoff simulation in terms of...