Stochastic simulation of hydrosystems

D. Koutsoyiannis, Stochastic simulation of hydrosystems, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 421–430, doi:10.1002/047147844X.sw913, Wiley, New York, 2005.

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[English]

Due to their complexity, hydrosystems, including water resource systems, flood management systems, and hydropower systems are frequently studied using stochastic simulation. A generalized solution procedure for hydrosystems problems, including systems identification, modeling and forecasting, hydrologic design, water resources management, and flood management, is discussed. Emphasis is given on the stochastic representation of hydrologic processes, which have a dominant role in hydrosystems. Peculiarities of hydrologic and other geophysical processes (seasonality, long-term persistence, intermittency, skewness, spatial variability) gave rise to substantial research that resulted in numerous stochastic tools appropriate for applications in hydrosystems. Four examples of such tools are discussed: (1) the multivariate periodic autoregressive model of order 1 [PAR(1)], which reproduces seasonality and skewness but not long-term persistence;(2) a generalized multivariate stationary model that reproduces all kinds of persistence and simultaneously skewness but not seasonality; (3) a combination of the previous two cases in a multivariate disaggregation framework that can respect almost all peculiarities except intermittency; and (4) the Bartlett-Lewis process that is appropriate for modeling rainfall and emphasizes its intermittent character on a fine time scale.

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See also: http://dx.doi.org/10.1002/047147844X.sw913

Our works that reference this work:

1. I. Nalbantis, A. Efstratiadis, E. Rozos, M. Kopsiafti, and D. Koutsoyiannis, Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems, Hydrology and Earth System Sciences, 15, 743–758, doi:10.5194/hess-15-743-2011, 2011.
2. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.
3. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.
4. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.

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Tagged under: Course bibliography: Stochastic methods, Hydrosystems, Stochastics