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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 3
Hydrol. Earth Syst. Sci., 14, 585–601, 2010
https://doi.org/10.5194/hess-14-585-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: HESS Opinions 2010

Hydrol. Earth Syst. Sci., 14, 585–601, 2010
https://doi.org/10.5194/hess-14-585-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Opinion article 25 Mar 2010

Opinion article | 25 Mar 2010

HESS Opinions "A random walk on water"

D. Koutsoyiannis D. Koutsoyiannis
  • Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Greece
  • Invited contribution by D. Koutsoyiannis, recipient of the EGU Henry Darcy Medal 2009.

Abstract. According to the traditional notion of randomness and uncertainty, natural phenomena are separated into two mutually exclusive components, random (or stochastic) and deterministic. Within this dichotomous logic, the deterministic part supposedly represents cause-effect relationships and, thus, is physics and science (the "good"), whereas randomness has little relationship with science and no relationship with understanding (the "evil"). Here I argue that such views should be reconsidered by admitting that uncertainty is an intrinsic property of nature, that causality implies dependence of natural processes in time, thus suggesting predictability, but even the tiniest uncertainty (e.g. in initial conditions) may result in unpredictability after a certain time horizon. On these premises it is possible to shape a consistent stochastic representation of natural processes, in which predictability (suggested by deterministic laws) and unpredictability (randomness) coexist and are not separable or additive components. Deciding which of the two dominates is simply a matter of specifying the time horizon and scale of the prediction. Long horizons of prediction are inevitably associated with high uncertainty, whose quantification relies on the long-term stochastic properties of the processes.

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