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.

Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied Hydrology,
McGraw-Hill, New York, USA, 1988.

Cover, T. M. and Thomas, J. A.: Elements of Information Theory, 2nd ed., John
Wiley & Sons, New Jersey, USA, 2006.

Darbellay, G. A. and Vajda, I.: Estimation of the information by an adaptive
partitioning of the observation space, IEEE T. Inform. Theory, 45,
1315–1321, 1999.

Darscheid, P.: Quantitative analysis of information flow in hydrological
modelling using Shannon information measures, Karlsruhe Institute of
Technology, Karlsruhe, Germany, 73 pp., 2017.

Darscheid, P., Guthke, A., and Ehret, U.: A Maximum-Entropy Method to
Estimate Discrete Distributions from Samples Ensuring Nonzero Probabilities,
Entropy, 20, 601, https://doi.org/10.3390/e20080601, 2018.

Eckhardt, K.: How to construct recursive digital filters for baseflow
separation, Hydrol. Process., 19, 507–515, https://doi.org/10.1002/hyp.5675, 2005.

Ehret, U. and Zehe, E.: Series distance – an intuitive metric to quantify
hydrograph similarity in terms of occurrence, amplitude and timing of hydrological
events, Hydrol. Earth Syst. Sci., 15, 877–896, https://doi.org/10.5194/hess-15-877-2011, 2011.

Fawcett, T.: An introduction to ROC analysis Tom, Irbm, 35, 299–309,
https://doi.org/10.1016/j.patrec.2005.10.010, 2005.

Gong, W., Yang, D., Gupta, H. V., and Nearing, G.: Estimating information
entropy for hydrological data: One dimensional case, Water Resour. Res., 1,
5003–5018, https://doi.org/10.1002/2014WR015874, 2014.

Habibzadeh, F., Habibzadeh, P., and Yadollahie, M.: On determining the most
appropriate test cut-off value: The case of tests with continuous results,
Biochem. Medica, 26, 297–307, https://doi.org/10.11613/BM.2016.034, 2016.

Hall, F. R.: Base-Flow Recessions – A Review, Water Resour. Res., 4,
973–983, 1968.

Horton, R. E.: The role of infiltration in the hydrologic cycle, Trans. Am.
Geophys. Union, 14, 446–460, 1933.

Hoyt, W. G. and others: Studies of relations of rainfall and run-off in the
United States, Geol. Surv. of US, US Govt. Print. Off., Washington, 301 pp.,
available at: https://pubs.usgs.gov/wsp/0772/report (last access:
12 February 2019), 1936.

Knuth, K. H.: Optimal Data-Based Binning for Histograms, 2, 30, arXiv 2013,
available at: https://arxiv.org/pdf/physics/0605197 (last access: 12 February 2019), 2013.

Koskelo, A. I., Fisher, T. R., Utz, R. M., and Jordan, T. E.: A new
precipitation-based method of baseflow separation and event identification
for small watersheds (<50 km^{2}), J. Hydrol., 450–451, 267–278,
https://doi.org/10.1016/j.jhydrol.2012.04.055, 2012.

Liu, D., Wang, D., Wang, Y., Wu, J., Singh, V. P., Zeng, X., Wang, L., Chen,
Y., Chen, X., Zhang, L., and Gu, S.: Entropy of hydrological systems under
small samples: Uncertainty and variability, J. Hydrol., 532, 163–176,
https://doi.org/10.1016/j.jhydrol.2015.11.019, 2016.

Mei, Y. and Anagnostou, E. N.: A hydrograph separation method based on
information from rainfall and runoff records, J. Hydrol., 523, 636–649,
https://doi.org/10.1016/j.jhydrol.2015.01.083, 2015.

Merz, R. and Blöschl, G.: A regional analysis of event runoff
coefficients with respect to climate and catchment characteristics in
Austria, Water Resour. Res., 45, 1–19, https://doi.org/10.1029/2008WR007163, 2009.

Merz, R., Blöschl, G., and Parajka, J.: Spatio-temporal variability of
event runoff coefficients, J. Hydrol., 331, 591–604,
https://doi.org/10.1016/j.jhydrol.2006.06.008, 2006.

Mishra, A. K., Özger, M., and Singh, V. P.: An entropy-based
investigation into the variability of precipitation, J. Hydrol., 370,
139–154, https://doi.org/10.1016/j.jhydrol.2009.03.006, 2009.

Nearing, G. S. and Gupta, H. V.: Information vs. Uncertainty as the
Foundation for a Science of Environmental Modeling, eprint arXiv:1704.07512,
1–23, available at: http://arxiv.org/abs/1704.07512 (last access:
12 February 2019), 2017.

Pechlivanidis, I. G., Jackson, B., Mcmillan, H., and Gupta, H. V.: Robust
informational entropy-based descriptors of flow in catchment hydrology,
Hydrol. Sci. J., 61, 1–18, https://doi.org/10.1080/02626667.2014.983516, 2016.

Ruddell, B. L. and Kumar, P.: Ecohydrologic process networks:
1. Identification, Water Resour. Res., 45, 1–23, https://doi.org/10.1029/2008WR007279,
2009.

Seibert, S. P., Ehret, U., and Zehe, E.: Disentangling timing and amplitude
errors in streamflow simulations, Hydrol. Earth Syst. Sci., 20, 3745–3763,
https://doi.org/10.5194/hess-20-3745-2016, 2016.

Sharma, A. and Mehrotra, R.: An information theoretic alternative to model a
natural system using observational information alone, Water Resour. Res., 50,
650–660, https://doi.org/10.1002/2013WR013845, 2014.

Simonoff, J. S.: Smoothing Methods in Statistics, Springer,
Berlin/Heidelberg, Germany, 1996.

Solomatine, D., See, L. M., and Abrahart, R. J.: Data-Driven Modelling: Concepts, Approaches and
Experiences, in: Practical hydroinformatics, Springer, Berlin, Heidelberg,
Germany, 17–31, 2009.

Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past
experiences and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008.

Thiesen, S., Darscheid, P., and Ehret, U.: Event Detection Method Based on
Information Theory, Zenodo, https://doi.org/10.5281/zenodo.1404638, 2018.

Weijs, S. V.: Information Theory for Risk-based Water System Operation,
Technische Universiteit Delft, Delft, the Netherlands, 210 pp., 2011.