Scale-dependence of persistence in precipitation records

Y. Markonis, and D. Koutsoyiannis, Scale-dependence of persistence in precipitation records, Nature Climate Change, 6, 399–401, doi:10.1038/nclimate2894, 2016.



Long-term persistence or Hurst–Kolmogorov behaviour has been identified in many hydroclimatic records. Such time series are intriguing because they are the hallmark of multi-scale dynamical processes that govern the system from which they arise. They are also highly relevant for water resource managers because these systems exhibit persistent, for example, multi-decadal, mean shifts or extremes clustering that must be included into any long-term drought management strategy. During recent years the growing number of palaeoclimatic reconstructions has allowed further investigation of the long-term statistical properties of climate and an understanding of their implications for the observed change. Recently, the consistency of the proxy data for precipitation was strongly doubted, when their persistence property was compared to the corresponding estimates of instrumental records and model results. The latter suggest that droughts or extremely wet periods occur less frequently than depicted in the palaeoclimatic reconstructions. Here, we show how this could be the outcome of a varying scaling law and present some evidence supporting that proxy records can be reliable descriptors of the long-term precipitation variability.

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Our works referenced by this work:

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Our works that reference this work:

1. Y. Markonis, S. C. Batelis, Y. Dimakos, E. C. Moschou, and D. Koutsoyiannis, Temporal and spatial variability of rainfall over Greece, Theoretical and Applied Climatology, doi:10.1007/s00704-016-1878-7, 2016.
2. H. Tyralis, and D. Koutsoyiannis, On the prediction of persistent processes using the output of deterministic models, Hydrological Sciences Journal, 62 (13), 2083–2102, doi:10.1080/02626667.2017.1361535, 2017.
3. C. Pappas, M.D. Mahecha, D.C. Frank, F. Babst, and D. Koutsoyiannis, Ecosystem functioning is enveloped by hydrometeorological variability, Nature Ecology & Evolution, 1, 1263–1270, doi:10.1038/s41559-017-0277-5, 2017.
4. T. Iliopoulou, S.M. Papalexiou, Y. Markonis, and D. Koutsoyiannis, Revisiting long-range dependence in annual precipitation, Journal of Hydrology, 556, 891–900, doi:10.1016/j.jhydrol.2016.04.015, 2018.
5. D. Koutsoyiannis, P. Dimitriadis, F. Lombardo, and S. Stevens, From fractals to stochastics: Seeking theoretical consistency in analysis of geophysical data, Advances in Nonlinear Geosciences, edited by A.A. Tsonis, 237–278, doi:10.1007/978-3-319-58895-7_14, Springer, 2018.
6. H. Tyralis, P. Dimitriadis, D. Koutsoyiannis, P.E. O’Connell, K. Tzouka, and T. Iliopoulou, On the long-range dependence properties of annual precipitation using a global network of instrumental measurements, Advances in Water Resources, 111, 301–318, doi:10.1016/j.advwatres.2017.11.010, 2018.
7. Y. Markonis, Y. Moustakis, C. Nasika, P. Sychova, P. Dimitriadis, M. Hanel, P. Máca, and S.M. Papalexiou, Global estimation of long-term persistence in annual river runoff, Advances in Water Resources, 113, 1–12, doi:10.1016/j.advwatres.2018.01.003, 2018.
8. D. Koutsoyiannis, Knowable moments for high-order stochastic characterization and modelling of hydrological processes, Hydrological Sciences Journal, doi:10.1080/02626667.2018.1556794, 2019.

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Tagged under: Hurst-Kolmogorov dynamics, Climate stochastics, Rainfall models, Most recent works, Scaling