Articles | Volume 20, issue 7
Hydrol. Earth Syst. Sci., 20, 2611–2628, 2016
Hydrol. Earth Syst. Sci., 20, 2611–2628, 2016

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 et al.

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Cited articles

Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579,, 2007.
Achenef, H., Tilahun, A., and Molla, B.: Tana Sub Basin Initial Scenarios and Indicators Development Report, Tana Sub Basin Organization, Bahir Dar, Ethiopia, 8–9, 2013.
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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.