Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-1015-2019
https://doi.org/10.5194/hess-23-1015-2019
Research article
 | 
19 Feb 2019
Research article |  | 19 Feb 2019

Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory

Stephanie Thiesen, Paul Darscheid, and Uwe Ehret

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Cited articles

Bellman, R.: Dynamic Programming, Princeton University Press, Princeton, USA, 1957. 
Blower, G. and Kelsall, J. E.: Nonlinear Kernel Density Estimation for Binned Data: Convergence in Entropy, Bernoulli, 8, 423–449, 2002. 
Blume, T., Zehe, E., and Bronstert, A.: Rainfall-runoff response, event-based runoff coefficients and hydrograph separation, Hydrolog. Sci. J., 52, 843–862, https://doi.org/10.1623/hysj.52.5.843, 2007. 
Brunsell, N. A.: A multiscale information theory approach to assess spatial-temporal variability of daily precipitation, J. Hydrol., 385, 165–172, https://doi.org/10.1016/j.jhydrol.2010.02.016, 2010. 
Chapman, T. G.: Entropy as a measure of hydrologic data uncertainty and model performance, J. Hydrol., 85, 111–126, https://doi.org/10.1016/0022-1694(86)90079-X, 1986. 
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Short summary
We present a data-driven approach created to explore the full information of data sets, avoiding parametric assumptions. The evaluations are based on Information Theory concepts, introducing an objective measure of information and uncertainty. The approach was applied to automatically identify rainfall-runoff events in discharge time series, however it is generic enough to be adapted to other practical applications.
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