Uncertainty, entropy, scaling and hydrological stochastics. 1. Marginal distributional properties of hydrological processes and state scaling / Incertitude, entropie, effet d'échelle et propriétés…

By Demetris Koutsoyiannis. 2005

143 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b27393a8551809911556c460cf5e2f7456288819

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Hilary K. McMillan
2. B. Jackson
3. Ilias Pechlivanidis
4. Mehmet Özger
5. Simon Michael Papalexiou
6. E. Volpi
7. F. Lombardo
8. Hoshin V. Gupta

In Global and Planetary Change

1. Demetris Koutsoyiannis
2. Simon Michael Papalexiou
3. G. Schoups
4. Alberto Montanari
5. Steven V. Weijs
6. E. Volpi
7. N. V. D. Giesen
8. Jasper A. Vrugt

In Statistical and Nonlinear Physics

1. Simon Michael Papalexiou
2. Steven V. Weijs
3. G. Schoups
4. N. V. D. Giesen
5. Demetris Koutsoyiannis
6. Mehmet Özger
7. Alberto Montanari
8. Hoshin V. Gupta

Citations in the corpus

• Why hydrological predictions should be evaluated using information theory (2010, background): “Such approaches can lie in studying the time-evolution of chaotic systems (Koutsoyiannis , 2010 ) or in applying the principle of maximum entropy in combination with macroscopic constraints, as for example suggested by Weijs (2009) and Koutsoyiannis (2005).”
• Rainfall downscaling in time: theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades (2012, background): “…behaviours: the scaling in state, which is related to the marginal distributional properties of the process (Koutsoyiannis 2005a), and the scaling in time, which is a property of the joint distribution function characterizing the time dependence structure of the process (Koutsoyiannis 2005b).”
• Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “This and further related measures have found scientific application in a range of problems in hydrology and water resources [Koutsoyiannis, 2005a, 2005b; Maruyama et al., 2005; Hejazi et al., 2008; Ruddell and Kumar, 2009; Singh, 2010]; see Singh [2011] for an extensive review.”
• Generic and parsimonious stochastic modelling for hydrology and beyond (2016, methodology): “An alternative numerical method to evaluate an upper bound of ΓC(t) and consequently Γ0C tð Þ can be based on the methodology presented in Koutsoyiannis (2005b) and Koutsoyiannis et al. (2007) for discretized processes.”
• Uncertainty, entropy, scaling and hydrological stochastics. 2. Time dependence of hydrological processes and time scaling / Incertitude, entropie, effet d'échelle et propriétés stochastiques hydrologiques. 2. Dépendance temporelle des processus hydrologiques et échelle temporelle (2005, background): “A detailed presentation of the entropy concept including definitions and generalizations are given in the first part of the study (Koutsoyiannis, 2005b).”
• An entropic‐stochastic representation of rainfall intermittency: The origin of clustering and persistence (2006, background): “A recent study [Koutsoyiannis, 2005a, 2005b] indicated the dominance of the same principle in other hydrological and meteorological processes, and provided explanations for marginal distributional properties (from Gaussian to power type) of variables and for the longrange dependence structure of…”
• Comparison of fuzzy inference systems for streamflow prediction (2009, background): “…common stochastic models have some drawbacks when applied to hydrological processes for several reasons, which were explained in detail by Koutsoyiannis et al. (2008), the most important ones being short time dependence (Koutsoyiannis, 2005b) and the normality assumption (Koutsoyiannis, 2005a).”
• Statistical analysis of hydroclimatic time series: Uncertainty and insights (2007, background): “For example, the maximum entropy principle results in Markovian dependence if the maximization of entropy is done on a particular timescale and in LTP if the maximization is done on a range of timescales [Koutsoyiannis, 2005b].”
• Entropy based derivation of probability distributions: A case study to daily rainfall (2012, methodology): “processes, mean and variance (as measures of central tendency and dispersion) provide useful information, which can at least explain general behaviours and shapes of probability density functions [11].”
• Robust informational entropy-based descriptors of flow in catchment hydrology (2016, background): “Koutsoyiannis (2005a) shows that (1) the entropy of a discrete variable is always positive”