Uncertainty, entropy, scaling and hydrological stochastics. 2. Time dependence of hydrological processes and time scaling / Incertitude, entropie, effet d'échelle et propriétés stochastiques…

By Demetris Koutsoyiannis. 2005

100 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e9603d1e2ea360527de0e207796181cfd0409f43

How researchers see this paper

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

In Economics and Econometrics

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

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.”
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].”
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.”
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).”
Uncertainty, entropy, scaling and hydrological stochastics. 1. Marginal distributional properties of hydrological processes and state scaling / Incertitude, entropie, effet d'échelle et propriétés stochastiques hydrologiques. 1. Propriétés distributionnelles marginales des processus hydrologiques et (2005, methodology): “Interestingly, however, as discussed in the second part of the study (Koutsoyiannis, 2005b), the normal distribution combined with positive autocorrelation of a process results in time scaling behaviour due to the ME principle.”
Robust informational entropy-based descriptors of flow in catchment hydrology (2016, background): “Entropy (in Greek εντροπία, etymologised from τροπή, i.e. change, turn, drift) is variously described; examples include a measure “of the uncertainty associated with a random variable” or “of the lack of information about the system” (Koutsoyiannis 2005a).”
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…”