A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series

By Demetris Koutsoyiannis. 2000

174 citations recorded by the source corpus.

Subfields: Environmental Engineering, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6ef41d9b1498749799cced61919483dd83a88ae8

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

In Finance

In Global and Planetary Change

Citations in the corpus

Coupling stochastic models of different timescales (2001, methodology): “…implied about the involved models, the methodology can be combined with linear and nonlinear stochastic models also including generalized autocovariance models [Koutsoyiannis, 2000], nonparametric models [Lall and Sharma, 1996; Sharma et al.,1997] and hybrid models [Srinivas and Srinivasan, 2000].”
Wavelet Transform Method for Synthetic Generation of Daily Streamflow (2011, background): “Currently, great progress has been made in applying stochastic hydrological models to the field of hydrology and water resources extensively (Yevjevich 1972; Kottegota 1980; Lall and Sharma 1996; Sharma et al. 1997; Aksoy and Bayazit 2000; Koutsoyiannis 2000; Srinivas and Srinivasan 2005 ;W ang et”
Nature's style: Naturally trendy (2005, methodology): “1 where c is a constant and jcj 1 [Koutsoyiannis, 2000], which implies short-term persistence in the sense that the covariance structure involves exponential decay.”
The Hurst phenomenon and fractional Gaussian noise made easy (2002, methodology): “…integrated moving-average models (Hosking, 1981, 1984), and symmetric moving average models based on a generalized autocovariance structure (Koutsoyiannis, 2000), have been proposed to reproduce the Hurst phenomenon when generating synthetic time series (see also Bras & Rodriguez-Iturbe,…”
Generic and parsimonious stochastic modelling for hydrology and beyond (2016, methodology): “(the procedure is the same for the averaged process), it has been shown (Koutsoyiannis 2000) that the Fourier transform sd ω ð Þ of the al series of coefficients is related to the power spectrum of the discrete-time process by”
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, methodology): “…is assumed, i.e.: γj = γ0(1 + κ β |j|α)–1/β (25) This generalized Cauchy-type expression with parameters κ, α and β that are positive numbers was studied by Koutsoyiannis (2000) in a simpler Pareto-type form, i.e. with α = 1, and later, in some modified forms, by Gneiting & Schlather (2004).”