D. Koutsoyiannis, Hydrologic persistence and the Hurst phenomenon, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 210–221, doi:10.1002/047147844X.sw434, Wiley, New York, 2005.
Unlike common random series like those observed, for example, in games of chance, hydrologic (and other geophysical) time series have some structure, that is, consecutive values of hydrologic time series depend on each other. A special kind of dependence observed on large timescales was discovered by Hurst half a century ago and has been known by several names such as long-range dependence, long-term persistence, or simply the Hurst phenomenon. Since then, it has been verified that this behaviour is almost omnipresent in several processes in nature (e.g., hydrology), technology (e.g., computer networks), and society (e.g., economics). The consequences of this behavior are very significant, because it increases dramatically the uncertainty of the related processes. However, even today its importance and its consequences are not widely understood or are ignored, its nature is regarded as difficult to understand, and its reproduction in hydrologic simulation is considered a hard task or not necessary. This article shows that the Hurst phenomenon can have an easy explanation and easy stochastic representation and that simple algorithms can generate time series exhibiting long-term persistence.
Full text is only available to the NTUA network due to copyright restrictions
Alternative names for Hurst phenomenon are Hurst effect, Joseph effect, Long term persistence, Long range dependence, Scaling behaviour (in time), Multi-scale fluctuation, etc.
Our works that reference this work:
|1.||P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.|
Other works that reference this work (this list might be obsolete):
|1.||Bloschl, G., and E. Zehe, On hydrological predictability, Hydrological Processes, 19(19), 3923-3929, 2005.|
|2.||M. Mudelsee, Long memory of rivers from spatial aggregation, Water Resources Research, 43(1), W01202, 2007.|
|3.||#McKitrick, R., C. Essex, I. Clark, J. D'Aleo, O. Kärner, R. Willson, C. Idso, W. Kininmonth and M. Khandekar, Critical Topics in Global Warming, 124 pp., Fraser Institute, Calgary, Alberta, Canada, 2009.|
|4.||#Mudelsee, M., Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, 473 pp., Springer, Dordrecht, 2010.|
|5.||Szolgayova, E., G. Laaha, G. Blöschl and C. Bucher, Factors influencing long range dependence in streamflow of European rivers, Hydrological Processes, 28 (4), 1573-1586, 2014.|
|6.||Odongo, V.O., C. van der Tol, P.R. van Oel, F.M. Meins, R. Becht, J. Onyando and Z.B. Su, Characterisation of hydroclimatological trends and variability in the Lake Naivasha basin, Kenya, Hydrological Processes, 29 (15), 3276-3293, 10.1002/hyp.10443, 2015.|
Tagged under: Hurst-Kolmogorov dynamics