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HESS | Articles | Volume 23, issue 2
Hydrol. Earth Syst. Sci., 23, 1015–1034, 2019
https://doi.org/10.5194/hess-23-1015-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 1015–1034, 2019
https://doi.org/10.5194/hess-23-1015-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

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Revised manuscript under review for HESS
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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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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.
We present a data-driven approach created to explore the full information of data sets, avoiding...
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