P.E. O’Connell, G. O’Donnell, and D. Koutsoyiannis, On the spatial scale dependence of long-term persistence in global annual precipitation data and the Hurst Phenomenon, Water Resources Research, 59 (4), e2022WR033133, doi:10.1029/2022WR033133, 2023.
[doc_id=2283]
[English]
Precipitation deficits are the main physical drivers of droughts across the globe, and their level of persistence can be characterised by the Hurst coefficient H (0.5<H<1), with high H indicating strong long-term persistence (LTP). Previous analyses of point and gridded annual global precipitation datasets have concluded that LTP in precipitation is weak (H∼0.6) which is inconsistent with higher values of H for large river basins e.g. the Nile. Based on an analysis of gridded annual precipitation data for eight selected regions distributed across the globe, an important new finding is that H increases with the spatial scale of averaging, with mean H values at the grid and regional scale of 0.66 and 0.83, respectively. The discovery of enhanced LTP at the regional scale of averaging of precipitation has important implications for characterising the severity of regional droughts, as well as LTP in the annual flows of large rivers and recharge to major aquifers. Teleconnections with known modes of low frequency variability in the global climate system are demonstrated using correlation analysis and stepwise regression. Despite having several constituent regions exhibiting LTP, the Northern Hemisphere surprisingly has no LTP; this is shown to result from different modes of low frequency climatic variability cancelling each other out. LTP for the Southern Hemisphere is moderate, and weak for Global average precipitation. LTP in Blue Nile basin scale precipitation is shown to explain the Hurst Phenomenon in naturalised annual flows for the River Nile, more than seventy years after its discovery by Hurst.
Our works referenced by this work:
1. | D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002. |
2. | D. Koutsoyiannis, A. Efstratiadis, N. Mamassis, and A. Christofides, On the credibility of climate predictions, Hydrological Sciences Journal, 53 (4), 671–684, doi:10.1623/hysj.53.4.671, 2008. |
3. | D. Koutsoyiannis, A random walk on water, Hydrology and Earth System Sciences, 14, 585–601, doi:10.5194/hess-14-585-2010, 2010. |
4. | G. G. Anagnostopoulos, D. Koutsoyiannis, A. Christofides, A. Efstratiadis, and N. Mamassis, A comparison of local and aggregated climate model outputs with observed data, Hydrological Sciences Journal, 55 (7), 1094–1110, doi:10.1080/02626667.2010.513518, 2010. |
5. | H. Tyralis, and D. Koutsoyiannis, Simultaneous estimation of the parameters of the Hurst-Kolmogorov stochastic process, Stochastic Environmental Research & Risk Assessment, 25 (1), 21–33, 2011. |
6. | D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011. |
7. | D. Koutsoyiannis, 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, 2011. |
8. | Y. Markonis, and D. Koutsoyiannis, 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, 2013. |
9. | Y. Markonis, and D. Koutsoyiannis, Scale-dependence of persistence in precipitation records, Nature Climate Change, 6, 399–401, doi:10.1038/nclimate2894, 2016. |
10. | D. Koutsoyiannis, Entropy production in stochastics, Entropy, 19 (11), 581, doi:10.3390/e19110581, 2017. |
11. | 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. |
12. | D. Koutsoyiannis, Revisiting the global hydrological cycle: is it intensifying?, Hydrology and Earth System Sciences, 24, 3899–3932, doi:10.5194/hess-24-3899-2020, 2020. |
13. | 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. |
14. | P. Dimitriadis, T. Iliopoulou, G.-F. Sargentis, and D. Koutsoyiannis, Spatial Hurst–Kolmogorov Clustering, Encyclopedia, 1 (4), 1010–1025, doi:10.3390/encyclopedia1040077, 2021. |
Our works that reference this work:
1. | D. Koutsoyiannis, and C. Vournas, Revisiting the greenhouse effect—a hydrological perspective, Hydrological Sciences Journal, 69 (2), 151–164, doi:10.1080/02626667.2023.2287047, 2024. |
2. | G.-K. Sakki, A. Castelletti, C. Makropoulos, and A. Efstratiadis, Unwrapping the triptych of climatic, social and energy-market uncertainties in the operation of multipurpose hydropower reservoirs, Journal of Hydrology, 628, 132416, doi:10.1016/j.jhydrol.2024.132416, 2025. |
Tagged under: Hurst-Kolmogorov dynamics