Generic and parsimonious stochastic modelling for hydrology and beyond

Hydrological Sciences Journal

Volumn 61, Issue 2, 2016, Pages 225-244

  Koutsoyiannis, Demetris ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

entropy; model fitting; model identification; parsimony; simulation; stochastics

Indexed keywords

CONTINUOUS TIME SYSTEMS; ENTROPY; MAXIMUM ENTROPY METHODS; RANDOM PROCESSES; RISK ASSESSMENT; STOCHASTIC MODELS; UNCERTAINTY ANALYSIS;

MODEL FITTING; MODEL IDENTIFICATION; PARSIMONY; SIMULATION; STOCHASTICS;

STOCHASTIC SYSTEMS;

ENTROPY; HYDROLOGICAL MODELING; MAXIMUM ENTROPY ANALYSIS; PARSIMONY ANALYSIS; STOCHASTICITY;

EID: 84958875098     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2015.1016950     Document Type: Article

References (38)

1. P.Dimitriadis, and D.Koutsoyiannis,, 2015. Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes. Stochastic Environmental Research and Risk Assessment, 29 (6), 1649–1669. doi:10.1007/s00477-015-1023-7.
2. A.Efstratiadis,, et al., 2014. A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence. Environmental Modelling and Software, 62, 139–152. doi:10.1016/j.envsoft.2014.08.017.
3. J.H.G.GauchJr., H.G., 2003. Scientific method in practice. Cambridge: Cambridge University Press.
4. T.Gneiting, and M.Schlather,, 2004. Stochastic models that separate fractal dimension and the Hurst effect. Society for Industrial and Applied Mathematics Review, 46 (2), 269–282.
5. T.Gneiting,, H.Ševčíková,, and D.B.Percival,, 2012. Estimators of fractal dimension: assessing the roughness of time series and spatial data. Statistical Science, 27 (2), 247–277. doi:10.1214/11-STS370
6. J.Hemelrijk,, 1966. Underlining random variables. Statistica Neerlandica, 20 (1), 1–7. doi:10.1111/j.1467-9574.1966.tb00488.x
7. H.E.Hurst,, 1951. Long term storage capacities of reservoirs. Transactions of the American Society of Civil Engineers, 116, 776–808.
8. E.T.Jaynes,, 1957. Information theory and statistical mechanics. Physical Review, 106 (4), 620–630. doi:10.1103/PhysRev.106.620
9. A.N.Kolmogorov,, 1940. Wienersche Spiralen und einige andere interessante Kurven im Hilberschen Raum. Dokl. Akad. Nauk SSSR, 26, 115–118, (English translation Wiener spirals and some other interesting curves in a Hilbert space. In: V.M. Tikhomirov, ed., 1991. Selected works of A.N. Kolmogorov, Volume I: mathematics and mechanics. Berlin: Springer, 324–326).
10. D.Koutsoyiannis,, 1999. Optimal decomposition of covariance matrices for multivariate stochastic models in hydrology. Water Resources Research, 35 (4), 1219–1229. doi:10.1029/1998WR900093
11. D.Koutsoyiannis,, 2000. A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series. Water Resources Research, 36 (6), 1519–1533. doi:10.1029/2000WR900044
12. D.Koutsoyiannis,, 2001. Coupling stochastic models of different time scales. Water Resources Research, 37 (2), 379–391. doi:10.1029/2000WR900200
13. D.Koutsoyiannis,, 2002. The Hurst phenomenon and fractional Gaussian noise made easy. Hydrological Sciences Journal, 47 (4), 573–595. doi:10.1080/02626660209492961
14. D.Koutsoyiannis,, 2003. Climate change, the Hurst phenomenon, and hydrological statistics. Hydrological Sciences Journal, 48 (1), 3–24. doi:10.1623/hysj.48.1.3.43481
15. D.Koutsoyiannis,, 2005a. Uncertainty, entropy, scaling and hydrological stochastics. 1. Marginal distributional properties of hydrological processes and state scaling. Hydrological Sciences Journal, 50 (3), 381–404. doi:10.1623/hysj.50.3.381.65031
16. D.Koutsoyiannis,, 2005b. Uncertainty, entropy, scaling and hydrological stochastics. 2. Time dependence of hydrological processes and time scaling. Hydrological Sciences Journal, 50 (3), 405–426. doi:10.1623/hysj.50.3.405.65028
17. D.Koutsoyiannis,, et al., 2009. HESS Opinions: “climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability”. Hydrology and Earth System Sciences, 13, 247–257. doi:10.5194/hess-13-247-2009
18. D.Koutsoyiannis,, 2010. HESS Opinions: “a random walk on water”. Hydrology and Earth System Sciences, 14, 585–601. doi:10.5194/hess-14-585-2010
19. D.Koutsoyiannis,, 2011. Hurst-Kolmogorov dynamics as a result of extremal entropy production. Physica A: Statistical Mechanics and its Applications, 390 (8), 1424–1432. doi:10.1016/j.physa.2010.12.035
20. D.Koutsoyiannis,, 2013a. Hydrology and Change. Hydrological Sciences Journal, 58 (6), 1177–1197. doi:10.1080/02626667.2013.804626
21. D.Koutsoyiannis,, 2013b. Encolpion of stochastics: fundamentals of stochastic processes. Athens: Department of Water Resources and Environmental Engineering – National Technical University of Athens. Available from: http://www.itia.ntua.gr/1317/ [Accessed 19 September 2015].
22. D.Koutsoyiannis,, 2014. Entropy: from thermodynamics to hydrology. Entropy, 16 (3), 1287–1314. doi:10.3390/e16031287
23. D.Koutsoyiannis,, A.Efstratiadis,, and K.Georgakakos,, 2007. Uncertainty assessment of future hydroclimatic predictions: a comparison of probabilistic and scenario-based approaches. Journal of Hydrometeorology, 8 (3), 261–281. doi:10.1175/JHM576.1
24. D.Koutsoyiannis, and A.Montanari,, 2007. Statistical analysis of hydroclimatic time series: uncertainty and insights. Water Resources Research, 43 (5), W05429. doi:10.1029/2006WR005592.
25. D.Koutsoyiannis,, C.Onof,, and H.S.Wheater,, 2003a. Multivariate rainfall disaggregation at a fine timescale. Water Resources Research, 39 (7), 1173. doi:10.1029/2002WR001600
26. D.Koutsoyiannis,, H.Yao,, and A.Georgakakos,, 2008. Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods. Hydrological Sciences Journal, 53 (1), 142–164. doi:10.1623/hysj.53.1.142
27. D.Koutsoyiannis,, et al., 2003b. A decision support system for the management of the water resource system of Athens. Physics and Chemistry of the Earth, Parts A/B/C, 28 (14–15), 599–609. doi:10.1016/S1474-7065(03)00106-2
28. A.Langousis, and D.Koutsoyiannis,, 2006. A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour. Journal of Hydrology, 322, 138–154. doi:10.1016/j.jhydrol.2005.02.037
29. F.Lombardo,, E.Volpi,, and D.Koutsoyiannis,, 2012. Rainfall downscaling in time: theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades. Hydrological Sciences Journal, 57 (6), 1052–1066. doi:10.1080/02626667.2012.695872
30. F.Lombardo,, et al., 2014. Just two moments! a cautionary note against use of high-order moments in multifractal models in hydrology. Hydrology and Earth System Sciences, 18, 243–255. doi:10.5194/hess-18-243-2014
31. B.B.Mandelbrot, and J.W.van Ness,, 1968. Fractional Brownian motions, fractional noises and applications. SIAM Review, 10 (4), 422–437. doi:10.1137/1010093
32. Y.Markonis, and D.Koutsoyiannis,, 2013. Climatic variability over time scales spanning nine orders of magnitude: connecting Milankovitch cycles with Hurst–Kolmogorov dynamics. Surveys in Geophysics, 34 (2), 181–207. doi:10.1007/s10712-012-9208-9
33. A.Montanari, and D.Koutsoyiannis,, 2012. A blueprint for process-based modeling of uncertain hydrological systems. Water Resources Research, 48, W09555. doi:10.1029/2011WR011412.
34. A.Montanari,, et al., 2013. “Panta Rhei—Everything Flows”: change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal, 58 (6), 1256–1275. doi:10.1080/02626667.2013.809088
35. S.-M.Papalexiou,, D.Koutsoyiannis,, and A.Montanari,, 2011. Can a simple stochastic model generate rich patterns of rainfall events? Journal of Hydrology, 411 (3–4), 279–289. doi:10.1016/j.jhydrol.2011.10.008
36. A.Papoulis,, 1991. Probability, random variables and stochastic processes. 3rd ed. New York: McGraw-Hill.
37. H.Tyralis, and D.Koutsoyiannis,, 2011. Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process. Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33. doi:10.1007/s00477-010-0408-x
38. H.Tyralis, and D.Koutsoyiannis,, 2014. A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables. Climate Dynamics, 42 (11–12), 2867–2883. doi:10.1007/s00382-013-1804-y.