Articles | Volume 20, issue 7
Hydrol. Earth Syst. Sci., 20, 2611–2628, 2016
https://doi.org/10.5194/hess-20-2611-2016
Hydrol. Earth Syst. Sci., 20, 2611–2628, 2016
https://doi.org/10.5194/hess-20-2611-2016

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. Shortridge1, Seth D. Guikema2, and Benjamin F. Zaitchik3 Julie E. Shortridge et al.
  • 1Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA
  • 2Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
  • 3Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA

Abstract. In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based models are limited. However, the majority of research has focused on a small number of methods, such as artificial neural networks, despite the development of multiple other approaches for non-parametric regression in recent years. Furthermore, this work has often evaluated model performance based on predictive accuracy alone, while not considering broader objectives, such as model interpretability and uncertainty, that are important if such methods are to be used for planning and management decisions. In this paper, we use multiple regression and machine learning approaches (including generalized additive models, multivariate adaptive regression splines, artificial neural networks, random forests, and M5 cubist models) to simulate monthly streamflow in five highly seasonal rivers in the highlands of Ethiopia and compare their performance in terms of predictive accuracy, error structure and bias, model interpretability, and uncertainty when faced with extreme climate conditions. While the relative predictive performance of models differed across basins, data-driven approaches were able to achieve reduced errors when compared to physical models developed for the region. Methods such as random forests and generalized additive models may have advantages in terms of visualization and interpretation of model structure, which can be useful in providing insights into physical watershed function. However, the uncertainty associated with model predictions under extreme climate conditions should be carefully evaluated, since certain models (especially generalized additive models and multivariate adaptive regression splines) become highly variable when faced with high temperatures.

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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.