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Nonstationarity versus scaling in hydrology

Koutsoyiannis, D., Nonstationarity versus scaling in hydrology, Journal of Hydrology, 324, 239–254, 2006.

[doc_id=673]

[English]

The perception of a changing climate, which impacts also hydrological processes, is now generally admitted. However, the way of handling the changing nature of climate in hydrologic practice and especially in hydrological statistics has not become clear so far. The most common modelling approach is to assume that long-term trends, which have been found to be omnipresent in long hydrological time series, are "deterministic" components of the time series and the processes represented by the time series are nonstationary. In this paper, it is maintained that this approach is contradictory in its rationale and even in the terminology it uses. As a result, it may imply misleading perception of phenomena and estimate of uncertainty. Besides, it is maintained that a stochastic approach hypothesizing stationarity and simultaneously admitting a scaling behaviour reproduces climatic trends (considering them as large-scale fluctuations) in a manner that is logically consistent, easy to apply and free of paradoxical results about uncertainty.

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See also: http://dx.doi.org/10.1016/j.jhydrol.2005.09.022

Our works referenced by this work:

1. Nalbantis, I., N. Mamassis, et D. Koutsoyiannis, Le phénomène recent de sécheresse persistante et l' alimentation en eau de la cité d' Athènes, Publications de l'Association Internationale de Climatologie, 6eme Colloque International de Climatologie, edité par P. Maheras, Thessaloniki, 6, 123–132, Association Internationale de Climatologie, Aix-en-Provence Cedex, France, 1993.
2. Koutsoyiannis, D., A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, 2000.
3. Koutsoyiannis, D., The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, 2002.
4. Koutsoyiannis, D., Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, 2003.
5. Koutsoyiannis, D., G. Karavokiros, A. Efstratiadis, N. Mamassis, A. Koukouvinos, and A. Christofides, A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, 28 (14-15), 599–609, 2003.
6. Koutsoyiannis, D., and A. Efstratiadis, Climate change certainty versus climate uncertainty and inferences in hydrological studies and water resources management (solicited), European Geosciences Union General Assembly 2004, Geophysical Research Abstracts, Vol. 6, Nice, European Geosciences Union, 2004.
7. Rozos, E., A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49 (5), 819–842, 2004.
8. Koutsoyiannis, D., The water resource management of Athens in the perspective of the Olympic Games, The Olympic Games Athens 2004 and the National Technical University of Athens, edited by K. Moutzouris, Athens, 17–27, National Technical University of Athens, 2004.
9. Koutsoyiannis, D., Uncertainty, entropy, scaling and hydrological stochastics, 2, Time dependence of hydrological processes and time scaling, Hydrological Sciences Journal, 50 (3), 405–426, 2005.
10. Koutsoyiannis, D., A toy model of climatic variability with scaling behaviour, Journal of Hydrology, 322, 25–48, 2006.

Our works that reference this work:

1. Langousis, A., and D. Koutsoyiannis, A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour, Journal of Hydrology, 322, 138–154, 2006.
2. Koutsoyiannis, D., A. Efstratiadis, and K. Georgakakos, Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches, Journal of Hydrometeorology, 8 (3), 261–281, 2007.
3. Cudennec, C., C. Leduc, and D. Koutsoyiannis, Dryland hydrology in Mediterranean regions -- a review, Hydrological Sciences Journal, 52 (6), 1077–1087, 2007.
4. Koutsoyiannis, D., A. Efstratiadis, N. Mamassis, and A. Christofides, On the credibility of climate predictions, Hydrological Sciences Journal, 53 (4), 671–684, 2008.
5. Koutsoyiannis, D., C. Makropoulos, A. Langousis, S. Baki, A. Efstratiadis, A. Christofides, G. Karavokiros, and N. Mamassis, Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability, Hydrology and Earth System Sciences, 13, 247–257, 2009.
6. Koutsoyiannis, D., Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 2010, (in press).

Other works that reference this work:

1. Ellis, C., and C. Hudson, Scale-adjusted volatility and the Dow Jones index, Physica A: Statistical Mechanics and its Applications, 378(2), 374-386, 2007.
2. #Stockwell, D., Niche Modeling: Predictions from Statistical Distributions, Chapman & Hall, Boka Raton, USA, 2007.
3. #Iimi, A., Estimating global climate change impacts on hydropower projects: Applications in India, Sri Lanka and Vietnam, World Bank Policy Research Working Paper No. 4344, 2007.
4. Hamed, K.H., Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis, Journal of Hydrology, 349(3-4), 350-363, 2008.
5. Rybski, D., A. Bunde and H. von Storch, Long-term memory in 1000-year simulated temperature records, Journal of Geophysical Research-Atmospheres, 113(D2), D02106, 2008.
6. Rutten, M.M., N. van de Giesen, M. Baptist, J. Icke and W. Uijttewaal, Seasonal forecast of cooling water problems in the River Rhine, Hydrological Processes, 22(7), 1037-1045, 2008.
7. Khaliq, M.N., T.B.M.J. Ouarda, P. Gachon and L. Sushama, Temporal evolution of low-flow regimes in Canadian rivers, Water Resources Research, 44 (8), W08436, 2008.
8. Daniell, T.M., The Tao of hydrology and water resources: Some philosophical thoughts, Australian Journal of Water Resources, 12 (3), 189-204, 2008.
9. Rybski, D., and A. Bunde, On the detection of trends in long-term correlated records, Physica A, 388 (8), 1687-1695, 2009.
10. Hamed, K.H., Exact distribution of the Mann-Kendall trend test statistic for persistent data, Journal of Hydrology, 365(1-2), 86-94, 2009.
11. Hamed, K.H., Enhancing the effectiveness of prewhitening in trend analysis of hydrologic data, Journal of Hydrology, 368(1-4), 143-155, 2009.
12. Khaliq, M., T. Ouarda, and P. Gachon, Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short- and long-term persistence, Journal of Hydrology, 369(1-2), 183-197, 2009.
13. Déry, S. J., K. Stahl, R. D. Moore, P. H. Whitfield, B. Menounos, and J. E. Burford, Detection of runoff timing changes in pluvial, nival, and glacial rivers of western Canada, Water Resour. Res., 45, W04426, doi:10.1029/2008WR006975, 2009.
14. Hamed, K. H., Effect of persistence on the significance of Kendall’s tau as a measure of correlation between natural time series, The European Physical Journal, 174 (1), 65-79, 2009.
15. Bordi, I., K. Fraedrich, and A. Sutera, Observed drought and wetness trends in Europe: an update, Hydrol. Earth Syst. Sci., 13, 1519-1530, 2009.
16. Villarini, G., F. Serinaldi, J. A. Smith, and W. F. Krajewski, On the stationarity of annual flood peaks in the continental United States during the 20th century, Water Resour. Res., 45, W08417, doi:10.1029/2008WR007645, 2009.
17. #Zhang, H., N. Wang and K. Dong, Return Intervals Analysis of the Hong Kong Stock Market, 2009 International Conference on Computational Intelligence and Natural Computing, Art. no. 5231145, 262-265, 2009.
18. Fatichi, S., S. M. Barbosa, E. Caporali and M. E. Silva, Deterministic versus stochastic trends: Detection and challenges, Journal Of Geophysical Research-Atmospheres, 114, D18121, doi:10.1029/2009JD011960, 2009.
19. Caloiero, T., R. Coscarelli, E. Ferrari and M. Mancini, Trend detection of annual and seasonal rainfall in Calabria (Southern Italy), International Journal of Climatology, DOI: 10.1002/joc.2055, 2009.
20. #Baroang, K. M., M, Hellmuth and P. Block, Identifying uncertainty and defining risk in the context of the WWDR-4, Prepared for the World Water Assessment Programme, International Research Institute for Climate and Society, Earth Institute, Columbia University, 2009.
21. Ehsanzadeh, E., and K. Adamowski, Trends in timing of low stream flows in Canada: impact of autocorrelation and long-term persistence, Hydrological Processes, 24, 970–980, 2010.
22. Hossain, F., I. Jeyachandran and R. Pielke Sr., Dam safety effects due to human alteration of extreme precipitation, Water Resources Research, 46, W03301, doi:10.1029/2009WR007704, 2010.
23. Villarini, G., J. A. Smith and F. Napolitano, Nonstationary modeling of a long record of rainfall and temperature over Rome, Advances in Water Resources, DOI: 10.1016/j.advwatres.2010.03.013, 2010.
24. Gedikli, A., H. Aksoy, N. E. Unal and A. Kehagias, Modified dynamic programming approach for offline segmentation of long hydrometeorological time series, Stochastic Environmental Research and Risk Assessment, 24 (5), 547-557, 2010.
25. Rybski, D., H. D. Rozenfeld and J. P. Kropp, Quantifying long-range correlations in complex networks beyond nearest neighbors, EPL, 90, 28002, 2010.
26. Villarini, G., and J. A. Smith, Flood peak distributions for the eastern United States, Water Resour. Res., 46, W06504, doi:10.1029/2009WR008395, 2010.
27. Gautam, M. R., K. Acharya and M. K. Tuladhar, Upward trend of streamflow and precipitation in a small, non-snow-fed, mountainous watershed in Nepal, Journal of Hydrology, 387 (3-4), 304-311, 2010.
28. Dong, K. Q., P. J. Shang and H. Zhang, The multi-dependent Hurst exponent in traffic time series, Applied Mechanics and Materials, 20-23, 346-351, 2010.
29. Liu, Z. M., H. Zhang and K. Q. Dong, A new method to determine the periodicity of time series, Applied Mechanics and Materials, 26-28, 535-538, 2010.
30. Stahl, K., H. Hisdal, J. Hannaford, L. M. Tallaksen, H. A. J. van Lanen, E. Sauquet, S. Demuth, M. Fendekova, and J. Jódar, Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrol. Earth Syst. Sci. Discuss., 7, 5769-5804, doi: 10.5194/hessd-7-5769-2010, 2010.
31. Fan, J., W. Q. Li, H. Zhang and K.Q. Dong, Return intervals analysis of the sunspot time series, Applied Mechanics and Materials, 29, 1144-1149, 2010.
32. #Mudelsee, M., Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, 473 pp., Springer, Dordrecht, 2010.
33. Khaliq M. N., and P. Gachon, Pacific decadal oscillation climate variability and temporal pattern of winter flows in Northwestern North America, Journal of Hydrometeorology, 11 (4), 917-933, 2010.

Tagged under: Course bibliography: Stochastic methods, Climate stochastics, Determinism vs. stochasticity, Hurst-Kolmogorov dynamics, Scaling, Uncertainty