HESS Opinions "A random walk on water"

By Demetris Koutsoyiannis. 2009

190 citations recorded by the source corpus.

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.

Subfields: Global and Planetary Change, History and Philosophy of Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9d98a49fc1b9ef133a6b988df1b797a0d1cab94a

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In Global and Planetary Change

In History and Philosophy of Science

In Statistical and Nonlinear Physics

Citations in the corpus

A philosophical basis for hydrological uncertainty (2016, background): “There is an ongoing discussion in hydrology about the role (e.g. Pappenberger and Beven 2006, Sivapalan 2009), nature (e.g. Montanari 2007, Koutsoyiannis 2010), and appropriate handling (e.g. Mantovan and Todini 2006, Beven et al. 2008, 2012, Stedinger et al. 2008, Vrugt et al. 2009, Clark et al.…”
Negligent killing of scientific concepts: the stationarity case (2015, background): “For a simple account as to why evolution of probability densities provide a better description of a system than trajectories of points, and for more details about transformation operators, the reader is referred to Koutsoyiannis (2010).”
Complexity–entropy analysis of daily stream flow time series in the continental United States (2014, background): “By studying a simple (non-linear deterministic) toy model describing a caricature hydrologic system, Koutsoyiannis (2010) showed that determinism and randomness coexist, and deciding which of them dominates depends on the horizon and scale of prediction.”
Robust informational entropy-based descriptors of flow in catchment hydrology (2016, background): “Despite these difficulties, a major aim of the hydrological community is to describe causal relationships between hydrological quantities (Beven 2001, Koutsoyiannis 2010, Pechlivanidis et al. 2011).”
Generic and parsimonious stochastic modelling for hydrology and beyond (2016, methodology): “● The framework puts special emphasis on the climacogram, which is defined to be the variance (or the standard deviation) of the time-averaged process as a function of time scale of averaging (Koutsoyiannis 2010).”
Why hydrological predictions should be evaluated using information theory (2010, methodology): “In Weijs et al. (2010), a decomposition of the divergence score was presented that was inspired by a decomposition of the Brier score (Brier, 1950) into uncertainty, reliability and resolution (see Table 1) due to Murphy (1973).”
Factors influencing long range dependence in streamflow of European rivers (2014, methodology): “Koutsoyiannis and Montanari (2007) point out that ‘the statistical uncertainty is dramatically increased in the presence of dependence, especially if this dependence is long-term persistence’ and highlight the need of examining this phenomenon with respect to ‘other climatic hypothesis’. Alternative”
Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology (2013, background): “Let us now investigate the climacogram of the process x j , which is defined to be the variance (or the standard deviation) of the time-averaged process x j as a function of the timescale of averaging 1 (Koutsoyiannis, 2010).”
A quick gap filling of missing hydrometeorological data (2014, background): “Koutsoyiannis (2014), A quick gap filling of missing hydrometeorological data, J.”
Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, methodology): “In Class 1 we place the classical definition of non-stationarity discussed by Koutsoyiannis and Montanari (2015) in”