Theano-Any Iliopoulou

Civil Engineer, MSc., PhD candidate

Participation in research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

Published work

Publications in scientific journals

  1. T. Iliopoulou, D. Koutsoyiannis, and A. Montanari, Characterizing and modeling seasonality in extreme rainfall, Water Resources Research, doi:10.1029/2018WR023360, 2018.
  2. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, A stochastic model for the hourly solar radiation process for application in renewable resources management, Advances in Geosciences, 45, 139–145, doi:10.5194/adgeo-45-139-2018, 2018.
  3. E. Klousakou, M. Chalakatevaki, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, G. Karakatsanis, A. Efstratiadis, N. Mamassis, R. Tomani, E. Chardavellas, and D. Koutsoyiannis, A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources, Advances in Geosciences, 45, 193–199, doi:10.5194/adgeo-45-193-2018, 2018.
  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. 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, doi:10.1016/j.advwatres.2017.11.010, 2017.
  6. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Investigation on the stochastic nature of the solar radiation process, Energy Procedia, 125, 398–404, 2017.
  7. M. Chalakatevaki, P. Stamou, S. Karali, V. Daniil, P. Dimitriadis, K. Tzouka, T. Iliopoulou, D. Koutsoyiannis, P. Papanicolaou, and N. Mamassis, Creating the electric energy mix in a non-connected island, Energy Procedia, 125, 425–434, doi:10.1016/j.egypro.2017.08.089, 2017.
  8. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, Energy Procedia, 125, 405–414, doi:10.1016/j.egypro.2017.08.078, 2017.

Conference publications and presentations with evaluation of abstract

  1. A. Zoukos, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Global investigation of the multi-scale probabilistic behaviour of dry spells from rainfall records, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17966-1, European Geosciences Union, 2018.
  2. V. Skoura, P. Dimitriadis, T. Iliopoulou, M. Crok, and D. Koutsoyiannis, A trendy analysis for the identification of extremal changes and trends in hydroclimatic processes; application to global precipitation, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17889-1, European Geosciences Union, 2018.
  3. P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Simulating precipitation at a fine time scale using a single continuous-state distribution, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18614, European Geosciences Union, 2018.
  4. M. Chalakatevaki, E. Klousakou, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of the uncertainty of hydrometeorological processes by means of the climacogram, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17714-1, European Geosciences Union, 2018.
  5. G. Karakatsanis, E. Kontarakis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Hydroclimate and agricultural output in developing countries, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-13059-1, European Geosciences Union, 2018.
  6. T. Iliopoulou, and D. Koutsoyiannis, A probabilistic index based on a two-state process to quantify clustering of rainfall extremes, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-9777, European Geosciences Union, 2018.
  7. G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, and D. Koutsoyiannis, Stochastic investigation of the Hurst-Kolmogorov behaviour in arts, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17740-1, European Geosciences Union, 2018.
  8. S. Sigourou, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, A. Skopeliti, K. Sakellari, G. Karakatsanis, L. Tsoulos, and D. Koutsoyiannis, Comparison of climate change vs. urbanization, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18598-2, European Geosciences Union, 2018.
  9. S. Sigourou, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, A. Skopeliti, K. Sakellari, G. Karakatsanis, L. Tsoulos, and D. Koutsoyiannis, Statistical and stochastic comparison of climate change vs. urbanization, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18608-2, European Geosciences Union, 2018.
  10. P. Dimitriadis, H. Tyralis, T. Iliopoulou, K. Tzouka, Y. Markonis, N. Mamassis, and D. Koutsoyiannis, A climacogram estimator adjusted for timeseries length; application to key hydrometeorological processes by the Köppen-Geiger classification, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17832, European Geosciences Union, 2018.
  11. T. Iliopoulou, A. Montanari, and D. Koutsoyiannis, Estimating seasonality in river flows, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-12772, European Geosciences Union, 2018.
  12. A. Gkolemis, P. Dimitriadis, G. Karakatsanis, T. Iliopoulou, and D. Koutsoyiannis, A stochastic investigation of the intermittent behaviour of wind; application to renewable energy resources management, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-15979-3, European Geosciences Union, 2018.
  13. Y. Kalogeris, P. Dimitriadis, T. Iliopoulou, V. Papadopoulos, and D. Koutsoyiannis, Investigation of the correlation structure behaviour through intermediate storage retention, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17247-1, European Geosciences Union, 2018.
  14. P. Dimitriadis, E. Varouchakis, T. Iliopoulou, G. Karatzas, and D. Koutsoyiannis, Stochastic investigation of the spatial variability of precipitation over Crete, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17155-1, European Geosciences Union, 2018.
  15. M. Nezi, P. Dimitriadis, A. Pizarro, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of the streamflow process adjusted for human impact, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17473-1, European Geosciences Union, 2018.
  16. G. Koudouris, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, and D. Koutsoyiannis, A stochastic model for hourly solar radiation process applied in renewable resources management, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-16275-2, European Geosciences Union, 2018.
  17. E. Klousakou, M. Chalakatevaki, R. Tomani, P. Dimitriadis, A. Efstratiadis, T. Iliopoulou, R. Ioannidis, N. Mamassis, and D. Koutsoyiannis, Stochastic investigation of the uncertainty of atmospheric processes related to renewable energy resources, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-16982-2, European Geosciences Union, 2018.
  18. P. Dimitriadis, T. Iliopoulou, A. Efstratiadis, P. Papanicolaou, and D. Koutsoyiannis, Stochastic investigation of the uncertainty in common rating-curve relationships, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18947-2, European Geosciences Union, 2018.
  19. C. Ntigkakis, G. Markopoulos-Sarikas, P. Dimitriadis, T. Iliopoulou, A. Efstratiadis, A. Koukouvinos, A. D. Koussis, K. Mazi, D. Katsanos, and D. Koutsoyiannis, Hydrological investigation of the catastrophic flood event in Mandra, Western Attica, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17591-1, European Geosciences Union, 2018.
  20. T. Iliopoulou, C. Aguilar , B. Arheimer, M. Bermúdez, N. Bezak, A. Ficchi, D. Koutsoyiannis, J. Parajka, M. J. Polo, G. Thirel, and A. Montanari, Investigating the physical basis of river memory and application to flood frequency prediction, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, European Geosciences Union, 2017.
  21. P. Dimitriadis, T. Iliopoulou, H. Tyralis, and D. Koutsoyiannis, Identifying the dependence structure of a process through pooled timeseries analysis, IAHS Scientific Assembly 2017, Port Elizabeth, South Africa, IAHS Press, Wallingford – International Association of Hydrological Sciences, 2017.
  22. T. Iliopoulou, and D. Koutsoyiannis, Investigating links between Long-Range Dependence in mean rainfall and clustering of extreme rainfall, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-9890-1, doi:10.13140/RG.2.2.25992.21763, European Geosciences Union, 2017.
  23. H. Tyralis, P. Dimitriadis, T. Iliopoulou, K. Tzouka, and D. Koutsoyiannis, Dependence of long-term persistence properties of precipitation on spatial and regional characteristics, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-3711, doi:10.13140/RG.2.2.13252.83840/1, European Geosciences Union, 2017.
  24. V. Daniil, G. Pouliasis, E. Zacharopoulou, E. Demetriou, G. Manou, M. Chalakatevaki, I. Parara, C. Georganta, P. Stamou, S. Karali, E. Hadjimitsis, G. Koudouris, E. Moschos, D. Roussis, K. Papoulakos, A. Koskinas, G. Pollakis, N. Gournari, K. Sakellari, Y. Moustakis, N. Mamassis, A. Efstratiadis, H. Tyralis, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, K. Tzouka, I. Deligiannis, V. Tsoukala, P. Papanicolaou, and D. Koutsoyiannis, The uncertainty of atmospheric processes in planning a hybrid renewable energy system for a non-connected island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-16781-4, doi:10.13140/RG.2.2.29610.62406, European Geosciences Union, 2017.
  25. P. Stamou, S. Karali, M. Chalakatevaki, V. Daniil, K. Tzouka, P. Dimitriadis, T. Iliopoulou, P. Papanicolaou, D. Koutsoyiannis, and N. Mamassis, Creating the electric energy mix of a non-connected Aegean island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10130-10, doi:10.13140/RG.2.2.36537.77927, European Geosciences Union, 2017.
  26. E. Hadjimitsis, E. Demetriou, K. Sakellari, H. Tyralis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10164-5, European Geosciences Union, 2017.
  27. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Investigation of the stochastic nature of solar radiation for renewable resources management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10189-4, doi:10.13140/RG.2.2.16215.06564, European Geosciences Union, 2017.
  28. E. Moschos, G. Manou, C. Georganta, P. Dimitriadis, T. Iliopoulou, H. Tyralis, D. Koutsoyiannis, and V. Tsoukala, Investigation of the stochastic nature of wave processes for renewable resources management: a pilot application in a remote island in the Aegean sea, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10225-3, doi:10.13140/RG.2.2.30226.66245, European Geosciences Union, 2017.
  29. A. Koskinas, E. Zacharopoulou, G. Pouliasis, I. Engonopoulos, K. Mavroyeoryos, I. Deligiannis, G. Karakatsanis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and H. Tyralis, Simulation of electricity demand in a remote island for optimal planning of a hybrid renewable energy system, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10495-4, doi:10.13140/RG.2.2.10529.81767, European Geosciences Union, 2017.
  30. D. Roussis, I. Parara, N. Gournari, Y. Moustakis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and G. Karakatsanis, Energy, variability and weather finance engineering, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-16919, European Geosciences Union, 2017.
  31. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10334-4, European Geosciences Union, 2017.
  32. E. Lerias, A. Kalamioti, P. Dimitriadis, Y. Markonis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of temperature process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-14828-3, European Geosciences Union, 2016.
  33. I. Deligiannis, V. Tyrogiannis, Ο. Daskalou, P. Dimitriadis, Y. Markonis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of wind process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-14946-6, doi:10.13140/RG.2.2.26681.36969, European Geosciences Union, 2016.
  34. A. Sotiriadou, A. Petsiou, E. Feloni, P. Kastis, T. Iliopoulou, Y. Markonis, H. Tyralis, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of precipitation process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-15137-5, doi:10.13140/RG.2.2.28955.46881, European Geosciences Union, 2016.
  35. T. Iliopoulou, S.M. Papalexiou, and D. Koutsoyiannis, Assessment of the dependence structure of the annual rainfall using a large data set, European Geosciences Union General Assembly 2013, Geophysical Research Abstracts, Vol. 15, Vienna, EGU2013-5276, doi:10.13140/RG.2.2.13080.19202, European Geosciences Union, 2013.
  36. E. Houdalaki, M. Basta, N. Boboti, N. Bountas, E. Dodoula, T. Iliopoulou, S. Ioannidou, K. Kassas, S. Nerantzaki, E. Papatriantafyllou, K. Tettas, D. Tsirantonaki, S.M. Papalexiou, and D. Koutsoyiannis, On statistical biases and their common neglect, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 4388, doi:10.13140/RG.2.2.25951.46248, European Geosciences Union, 2012.

Academic works

  1. T. Iliopoulou, Investigation of long range dependence in the annual rainfall structure from a global database , Diploma thesis, 146 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, March 2013.

Details on research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

    Duration: October 2008–November 2011

    Budget: €72 000

    Project director: N. Mamassis

    Principal investigator: D. Koutsoyiannis

    This research project includes the maintenance, upgrading and extension of the Decision Support System that developed by NTUA for EYDAP in the framework of the research project “Updating of the supervision and management of the water resources’ system for the water supply of the Athens’ metropolitan area”. The project is consisted of the following parts: (a) Upgrading of the Data Base, (b)Upgrading and extension of hydrometeorological network, (c) upgrading of the hydrometeorological data process software, (d) upgrading and extension of the Hydronomeas software, (e) hydrological data analysis and (f) support to the preparation of the annual master plans

Published work in detail

Publications in scientific journals

  1. T. Iliopoulou, D. Koutsoyiannis, and A. Montanari, Characterizing and modeling seasonality in extreme rainfall, Water Resources Research, doi:10.1029/2018WR023360, 2018.

    A comprehensive understanding of seasonality in extreme rainfall is essential for climate studies, flood prediction and various hydrological applications such as scheduling season‐specific engineering works, intra‐annual management of reservoirs, seasonal flood risk mitigation and stormwater management. To identify seasonality in extreme rainfall and quantify its impact in a theoretically consistent yet practically appealing manner, we investigate a dataset of 27 daily rainfall records spanning at least 150 years. We aim to objectively identify periods within the year with distinct seasonal properties of extreme rainfall by employing the Akaike Information Criterion (AIC). Optimal partitioning of seasons is identified by minimizing the within‐season variability of extremes. The statistics of annual and seasonal extremes are evaluated by fitting a generalized extreme value (GEV) distribution to the annual and seasonal block maxima series. The results indicate that seasonal properties of rainfall extremes mainly affect the average values of seasonal maxima and their variability, while the shape of their probability distribution and its tail do not substantially vary from season to season. Uncertainty in the estimation of the GEV parameters is quantified by employing three different estimation methods (Maximum Likelihood, Method of Moments and Least Squares) and the opportunity for joint parameter estimation of seasonal and annual probability distributions of extremes is discussed. The effectiveness of the proposed scheme for seasonal characterization and modeling is highlighted when contrasted to results obtained from the conventional approach of using fixed climatological seasons.

    Additional material:

    Works that cite this document: View on ResearchGate

  1. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, A stochastic model for the hourly solar radiation process for application in renewable resources management, Advances in Geosciences, 45, 139–145, doi:10.5194/adgeo-45-139-2018, 2018.

    Since the beginning of the 21st century, the scientific community has made huge leaps to exploit renewable energy sources, with solar radiation being one of the most important. However, the variability of solar radiation has a significant impact on solar energy conversion systems, such as in photovoltaic systems, characterized by a fast and nonlinear response to incident solar radiation. The performance prediction of these systems is typically based on hourly or daily data because those are usually available at these time scales. The aim of this work is to investigate the stochastic nature and time evolution of the solar radiation process for daily and hourly scale, with the ultimate goal of creating a new cyclostationary stochastic model capable of reproducing the dependence structure and the marginal distribution of hourly solar radiation via the clearness index KT .

    Full text: http://www.itia.ntua.gr/en/getfile/1867/1/documents/adgeo-45-139-2018.pdf (4911 KB)

    Works that cite this document: View on Google Scholar or ResearchGate

  1. E. Klousakou, M. Chalakatevaki, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, G. Karakatsanis, A. Efstratiadis, N. Mamassis, R. Tomani, E. Chardavellas, and D. Koutsoyiannis, A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources, Advances in Geosciences, 45, 193–199, doi:10.5194/adgeo-45-193-2018, 2018.

    The ever-increasing energy demand has led to overexploitation of fossil fuels deposits, while renewables offer a viable alternative. Since renewable energy resources derive from phenomena related to either atmospheric or geophysical processes, unpredictability is inherent to renewable energy systems. An innovative and simple stochastic tool, the climacogram, was chosen to explore the degree of unpredictability. By applying the climacogram across the related timeseries and spatial-series it was feasible to identify the degree of unpredictability in each process through the Hurst parameter, an index that quantifies the level of uncertainty. All examined processes display a Hurst parameter larger than 0.5, indicating increased uncertainty on the long term. This implies that only through stochastic analysis may renewable energy resources be reliably manageable and cost efficient. In this context, a pilot application of a hybrid renewable energy system in the Greek island of Astypalaia is discussed, for which we show how the uncertainty (in terms of variability) of the input hydrometeorological processes alters the uncertainty of the output energy values.

    Full text: http://www.itia.ntua.gr/en/getfile/1864/1/documents/adgeo-45-193-2018.pdf (559 KB)

    See also: https://www.adv-geosci.net/45/193/2018/

    Works that cite this document: View on Google Scholar or ResearchGate

  1. 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.

    Long-range dependence (LRD), the so-called Hurst-Kolmogorov behaviour, is considered to be an intrinsic characteristic of most natural processes. This behaviour manifests itself by the prevalence of slowly decaying autocorrelation function and questions the Markov assumption, often habitually employed in time series analysis. Herein, we investigate the dependence structure of annual rainfall using a large set, comprising more than a thousand stations worldwide of length 100 years or more, as well as a smaller number of paleoclimatic reconstructions covering the last 12,000 years. Our findings suggest weak long-term persistence for instrumental data (average H = 0.59), which becomes stronger with scale, i.e. in the paleoclimatic reconstructions (average H = 0.75).

    Additional material:

    See also: http://dx.doi.org/10.1016/j.jhydrol.2016.04.015

    Works that cite this document: View on Google Scholar or ResearchGate

  1. 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, doi:10.1016/j.advwatres.2017.11.010, 2017.

    The long-range dependence (LRD) is considered an inherent property of geophysical processes, whose presence increases uncertainty. Here we examine the spatial behaviour of LRD in precipitation by regressing the Hurst parameter estimate of mean annual precipitation instrumental data which span from 1916-2015 and cover a big area of the earth’s surface on location characteristics of the instrumental data stations. Furthermore, we apply the Mann-Kendall test under the LRD assumption (MKt-LRD) to reassess the significance of observed trends. To summarize the results, the LRD is spatially clustered, it seems to depend mostly on the location of the stations, while the predictive value of the regression model is good. Thus when investigating for LRD properties we recommend that the local characteristics should be considered. The application of the MKt-LRD suggests that no significant monotonic trend appears in global precipitation, excluding the climate type D (snow) regions in which positive significant trends appear.

    Remarks:

    Supplementary information files are hosted at: https://doi.org/10.6084/m9.figshare.4892447.v1

    Additional material:

    Works that cite this document: View on Google Scholar or ResearchGate

  1. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Investigation on the stochastic nature of the solar radiation process, Energy Procedia, 125, 398–404, 2017.

    A detailed investigation of the variability of solar radiation can be proven useful towards more efficient and sustainable design of renewable resources systems. In this context, we analyze observations from Athens, Greece and we investigate the marginal distribution of the solar radiation process at a daily and hourly step, the long-term behavior based on the annual scale of the process, as well as the double periodicity (diurnal-seasonal) of the process. Finally, we apply a parsimonious double-cyclostationary stochastic model to generate hourly synthetic time series preserving the marginal statistical characteristics, the double periodicity and the dependence structure of the process.

    Full text: http://www.itia.ntua.gr/en/getfile/1736/1/documents/solar_procedia.pdf (804 KB)

    Works that cite this document: View on Google Scholar or ResearchGate

  1. M. Chalakatevaki, P. Stamou, S. Karali, V. Daniil, P. Dimitriadis, K. Tzouka, T. Iliopoulou, D. Koutsoyiannis, P. Papanicolaou, and N. Mamassis, Creating the electric energy mix in a non-connected island, Energy Procedia, 125, 425–434, doi:10.1016/j.egypro.2017.08.089, 2017.

    As the electric energy in the non-connected islands is mainly produced by oil-fueled power plants, the unit cost is extremely high due to import cost. The integration of renewable resources in the energy mix is essential for reducing the financial and environmental cost. In this work, various energy resources (renewable and fossil fuels) are evaluated using technical, environmental and economic criteria with an emphasis to biomass, pumped hydro storage and replacement of oil power plants. Finally, a synthesis is presented as a toy-model in an Aegean island that satisfies the electric energy demand including base and peak electric loads.

    Related works:

    • [33] Initial presentation in EGU conference

    Full text: http://www.itia.ntua.gr/en/getfile/1733/1/documents/electric_mix_energy_procedia.pdf (1118 KB)

    Works that cite this document: View on Google Scholar or ResearchGate

  1. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, Energy Procedia, 125, 405–414, doi:10.1016/j.egypro.2017.08.078, 2017.

    We present a stochastic approach accounting for input uncertainties within water-energy simulations. The stochastic paradigm, which allows for quantifying the inherent uncertainty of hydrometeorological processes, becomes even more crucial in case of missing or inadequate information. Our scheme uses simplified conceptual models which are subject to significant uncertainties, to generate the inputs of the overall simulation problem. The methodology is tested in a hypothetical hybrid renewable energy system across the small Aegean island of Astypalaia, comprising a pumped-storage reservoir serving multiple water uses, where both inflows and demands are regarded as random variables as result of stochastic inputs and parameters.

    Related works:

    • [39] Initial presentation in EGU conference

    Full text: http://www.itia.ntua.gr/en/getfile/1732/1/documents/energy_proc_paper.pdf (2324 KB)

    Works that cite this document: View on Google Scholar or ResearchGate

Conference publications and presentations with evaluation of abstract

  1. A. Zoukos, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Global investigation of the multi-scale probabilistic behaviour of dry spells from rainfall records, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17966-1, European Geosciences Union, 2018.

    Understanding and modelling the rainfall process at fine timescales has been a classic endeavor of hydrology, particularly because of its importance in everyday life, hydrological design and water resources management. At fine timescales, the rainfall process alternates between wet and dry states exhibiting pronounced clustering behavior. Herein, we employ a probabilistic characterization of rainfall intermittency as a two-state process and estimate the probability-dry across a range of timescales from minutes to months. To model the resulting multi-scale behavior, we employ a stochastic model derived from an entropy maximization framework at a multi-scale setting, which was previously found to successfully describe sub-daily rainfall in single case studies. We investigate whether the proposed model is able to capture the wide range of rainfall regimes observed worldwide and discuss its potential generality. Furthermore, we show how such a modelling approach of rainfall intermittency can prove valuable for practical purposes, such as the derivation of ombrian (intensity-duration-frequency) curves.

    Full text: http://www.itia.ntua.gr/en/getfile/1824/1/documents/EGU2018-17966-1.pdf (32 KB)

  1. V. Skoura, P. Dimitriadis, T. Iliopoulou, M. Crok, and D. Koutsoyiannis, A trendy analysis for the identification of extremal changes and trends in hydroclimatic processes; application to global precipitation, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17889-1, European Geosciences Union, 2018.

    During the last decades it has been trendy to identify trends in extreme phenomena and attribute them to anthropogenic climate change. Although the majority of analyses tend to identify increasing (and sometimes decreasing) trends in hydrometeorological extremes, there are a few works that show no significant change in the distribution tail of the processes. A few analyses have shown that changes in the extremes can be adequately explained by the Hurst-Kolmogorov (HK) behaviour. In this work, we test the tail behaviour of several well-known distributions when combined to an HK model. Finally, we provide illustrative examples on whether or not the observed variability in precipitation extremes could be explained by the HK behaviour.

    Full text: http://www.itia.ntua.gr/en/getfile/1823/1/documents/EGU2018-17889-1.pdf (32 KB)

  1. P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Simulating precipitation at a fine time scale using a single continuous-state distribution, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18614, European Geosciences Union, 2018.

    Some hydrometeorological processes such as precipitation are usually modelled as two-state processes distinguishing the wet and dry state and simulating each of the two in different ways. It can be noted that the assignment of either of the two states in observation records involves some difficulties, as the accuracy of measurements in the area of low values is problematic. This is even more perplexed by the fact that the low values are the most frequent as in most rainfall records, measured at a fine temporal scale, the mode of the continuous part of the distribution is zero. However, the separation in two states may not be necessary. Here we apply a modelling framework of geophysical processes, such as precipitation, without treating them as two-state processes but with a single continuous-type distribution, which has very high densities at values close to zero. This requires the simulation of arbitrary marginal distributions, with very high skewness and kurtosis, as well as ability to preserve any dependence structure. These requirements can be satisfied in a rather simple manner using a recent simulation framework (Dimitriadis and Koutsoyiannis, 2017), which is here tested with fine time scale precipitation.

    Full text: http://www.itia.ntua.gr/en/getfile/1821/1/documents/EGU2018-18614.pdf (32 KB)

  1. M. Chalakatevaki, E. Klousakou, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of the uncertainty of hydrometeorological processes by means of the climacogram, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17714-1, European Geosciences Union, 2018.

    An important characteristic of the atmospheric processes is their inherent uncertainty. As randomness and predictability coexist and are intrinsic to natural systems, these systems can be treated as deterministic and random at the same time, depending on the prediction horizon and the time scale. Specifically, the more complex a process is, the larger the Hurst parameter, which quantifies a natural behaviour (called Hurst-Kolmogorov HK), identified in numerous geophysical processes. Although several methods can be used to estimate the Hurst parameter, the climacogram (i.e. variance of the averaged process vs. scale; Koutsoyiannis, 2003) is one of the most powerful ones, with a lower statistical estimation uncertainty compared to the autocovariance and power spectrum. We apply the climacogram to real-world timeseries from various atmospheric processes in order to infer their dependence structure, characterize them and compare their degree of uncertainty across different timescales.

    Full text: http://www.itia.ntua.gr/en/getfile/1818/1/documents/EGU2018-17714-1.pdf (31 KB)

  1. G. Karakatsanis, E. Kontarakis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Hydroclimate and agricultural output in developing countries, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-13059-1, European Geosciences Union, 2018.

    According to international data on developing countries we observe a strong correlation of their Gross Domestic Product (GDP) to their agricultural output, suggesting that a large fraction of total income in the developing world derives from domestic agricultural value added. In addition, the significant lack of irrigation infrastructure (e.g. reservoirs and irrigation networks) forces these countries’ income into strong dependence from local hydroclimatological conditions; as the majority of crop output is -in turn- based on rain-fed agriculture. In our work we examine -via annual time-series analysis- the temporal dynamics between hydroclimate data (mainly precipitation), GDP, agricultural value added and the international prices of agricultural commodities, for developing countries, in order to study how these variables are mutually entwined in time. Furthermore, we perform various econometric tests on their correlation validity. An important aspect of our work concerns the detection of change in the composition of the economies of developing countries. Specifically, as developing countries acquire infrastructure it is highly probable to expect a gradual decoupling of the climate-agricultural output-GDP relationship.

    Full text: http://www.itia.ntua.gr/en/getfile/1817/1/documents/EGU2018-13059-1.pdf (33 KB)

  1. T. Iliopoulou, and D. Koutsoyiannis, A probabilistic index based on a two-state process to quantify clustering of rainfall extremes, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-9777, European Geosciences Union, 2018.

    Long term persistence, also known as Hurst-Kolmogorov (HK) behavior, is an intrinsic property of various geophysical processes, resulting among others in the temporal clustering of extremes. In the rainfall process, the latter signifies a pronounced clustering of wet and/or dry periods. While several indexes of clustering exist, attempts to quantitatively relate clustering behavior to HK dynamics have been in general limited. We devise a simple metric based on a two-state process (inspired by the probability-dry concept of the rainfall process) across different temporal scales which fully describes the multi-scale clustering behavior of extremes and links it to the persistence magnitude of the parent process. We test the index on real-world long rainfall series and provide analytical equations for various combinations of persistence magnitude and distribution type of the extremes generating process.

    Full text: http://www.itia.ntua.gr/en/getfile/1816/1/documents/EGU2018-9777.pdf (31 KB)

  1. G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, and D. Koutsoyiannis, Stochastic investigation of the Hurst-Kolmogorov behaviour in arts, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17740-1, European Geosciences Union, 2018.

    The Hurst-Kolmogorov (HK) behaviour (i.e. power-law decrease of the process variance vs. scale of averaging) has been already identified in numerous geophysical processes highlighting the large uncertainty of Nature in all time scales. In this study, we investigate through the climacogram whether or not some art works (such as paintings, music pieces and poems) also exhibit this behaviour and try to interpret the results in terms of (un)predictability in works of art.

    Full text:

  1. S. Sigourou, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, A. Skopeliti, K. Sakellari, G. Karakatsanis, L. Tsoulos, and D. Koutsoyiannis, Comparison of climate change vs. urbanization, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18598-2, European Geosciences Union, 2018.

    Urbanization has long been identified as one of the major human impacts on the micro-climate of urban areas and has been linked to large (and often disastrous) changes into several hydroclimatic processes such as temperature, humidity and precipitation. However, climate change studies have rarely separated the urban local-scale influence from the global one. In this study, we thoroughly investigate and compare the changes in the variability of the above hydroclimatic processes in urban regions and in the ones with small or negligible human impact. The analysis includes global historical databases of the above processes as well as of the urbanization impact through land-use change.

    Full text: http://www.itia.ntua.gr/en/getfile/1810/1/documents/EGU2018-18598-2.pdf (31 KB)

  1. S. Sigourou, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, A. Skopeliti, K. Sakellari, G. Karakatsanis, L. Tsoulos, and D. Koutsoyiannis, Statistical and stochastic comparison of climate change vs. urbanization, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18608-2, European Geosciences Union, 2018.

    Urbanization has long been identified as one of the major human impacts on the micro-climate of urban areas and has been linked to large (and often disastrous) changes into several hydroclimatic processes such as temperature, humidity and precipitation. However, climate change studies have rarely separated the urban local-scale influence from the global one. In this study, we thoroughly investigate and compare the changes in the variability of the above hydroclimatic processes in urban regions and in the ones with small or negligible human impact. The analysis includes Monte-Carlo experiments to assess how the aforementioned variability can be simulated through a stochastic model.

    Full text: http://www.itia.ntua.gr/en/getfile/1809/1/documents/EGU2018-18608-2.pdf (31 KB)

  1. P. Dimitriadis, H. Tyralis, T. Iliopoulou, K. Tzouka, Y. Markonis, N. Mamassis, and D. Koutsoyiannis, A climacogram estimator adjusted for timeseries length; application to key hydrometeorological processes by the Köppen-Geiger classification, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17832, European Geosciences Union, 2018.

    We present a climacogram estimator (variance of the scaled process vs. scale) that employs all the available information through a pooled time series estimation approach. This method does not discard time-series of short length or of high percentage of missing values; a common practice in hydrometeorology. Furthermore, we estimate and compare the second-order dependence structure (overall and classified by the Köppen-Geiger system) over the last two climatic periods (60 years) for several processes (temperature, dew-point, wind, precipitation, river discharge and atmospheric pressure) using worldwide surface stations. This analysis is performed based on the standardized climacogram, which shows numerous benefits compared to the autocorrelation and standardized power-spectrum.

    Full text: http://www.itia.ntua.gr/en/getfile/1800/1/documents/EGU2018-17832.pdf (34 KB)

  1. T. Iliopoulou, A. Montanari, and D. Koutsoyiannis, Estimating seasonality in river flows, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-12772, European Geosciences Union, 2018.

    Seasonal flow prediction is crucial for various aspects of intra-annual water resources management, including flood protection and drought management. To exploit the degree of predictability encompassed in the seasonal flow formation processes, one needs first and foremost a characterization of the seasonality of the process of interest both in terms of timing and magnitude. Yet there exists no single approach but several methodologies varying in scope and characteristics. In this study, we compare two approaches of different rationale in their performance as part of a seasonal flow prediction scheme. The first one identifies high and low flow periods within a year through a fixed time-window method centered around the months receiving the majority of maximum and minimum flows respectively, while the second is based on the identification of an optimal number of seasons allowed to have varying lengths. We characterize the seasonal regime within the year by means of the two methods and we employ a meta-Gaussian bivariate model to condition selected flow signatures in the seasons of interest, i.e. peak or mean flows, on the mean flows observed in the previous season. The model is used to update the flow distribution one season in advance upon observance of a mean flow of certain magnitude in the previous season. In this framework, we compare the two seasonality approaches in terms of robustness, objectivity, efficiency and in their overall relevance for the purpose of seasonal flood and drought prediction.

    Full text: http://www.itia.ntua.gr/en/getfile/1799/1/documents/EGU2018-12772.pdf (31 KB)

  1. A. Gkolemis, P. Dimitriadis, G. Karakatsanis, T. Iliopoulou, and D. Koutsoyiannis, A stochastic investigation of the intermittent behaviour of wind; application to renewable energy resources management, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-15979-3, European Geosciences Union, 2018.

    A challenging characteristic of renewable energy systems is intermittence of the related natural processes (such as wind), whose management imposes an additional cost. This also implies the need to immediate back up the extra supply (introduced by the resource’s physical bursts) to other units (e.g. in a hybrid pumped storage hydropower system). The complexity of this issue does not just rely on the need for optimizing the hybrid system but rather on the requirement for simulating these bursts. In this study, we introduce and test an innovative model for the wind process by simultaneously preserving not only the marginal distribution (including extreme events), correlation structure (from small to large scales) and internal double (diurnal and seasonal) periodicities but also its intermittent behaviour. Furthermore, we present a pilot application including a pumped storage hydropower system and we show how the additional cost imposed by the intermittent behaviour of wind can be estimated.

    Full text: http://www.itia.ntua.gr/en/getfile/1795/1/documents/EGU2018-15979-3.pdf (33 KB)

  1. Y. Kalogeris, P. Dimitriadis, T. Iliopoulou, V. Papadopoulos, and D. Koutsoyiannis, Investigation of the correlation structure behaviour through intermediate storage retention, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17247-1, European Geosciences Union, 2018.

    A typical problem in stochastic dynamics is the change of variability of a process through intermediate storage retention. In this study, we perform exhaustive Monte-Carlo experiments as to quantify this change through the estimation of the autovariance function, power-spectrum and climacogram (i.e. variance of scaled process vs. scale) and with focus in short-term (e.g. Markov or powered-exponential) and long-term (such as Hurst-Kolmogorov) processes. Also, we show how the simulation methods and results from this analysis can be used to perform a sensitivity analysis to real case applications of seismic activity through geological formations as well as of rainfall-runoff cross-correlations through soil.

    Full text: http://www.itia.ntua.gr/en/getfile/1794/1/documents/EGU2018-17247-1.pdf (29 KB)

  1. P. Dimitriadis, E. Varouchakis, T. Iliopoulou, G. Karatzas, and D. Koutsoyiannis, Stochastic investigation of the spatial variability of precipitation over Crete, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17155-1, European Geosciences Union, 2018.

    The island of Crete is located at the Eastern Mediterranean and is expected to be significantly affected by future climatic variations. The island is monitored from 82 rainfall stations that cover the whole area of the island. Information is available at monthly and annual basis since 1981. This work examines potential spatial and temporal rainfall variability by employing statistical tools (such as the climacogram, i.e. variance of the scaled process vs. scale) to identify potential similarities in the dependence structure and marginal probability distribution. Finally, the spatial analysis involves the application of novel spatial dependence functions as well as a common expression for the correlation structure and marginal density distribution.

    Full text: http://www.itia.ntua.gr/en/getfile/1792/1/documents/EGU2018-17155-1.pdf (30 KB)

  1. M. Nezi, P. Dimitriadis, A. Pizarro, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of the streamflow process adjusted for human impact, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17473-1, European Geosciences Union, 2018.

    The streamflow process is important in water resources management and although it has been thoroughly examined in a stochastic framework, still an integrated model that takes into account the human impact has not yet been thoroughly studied. Here we examine several datasets, in numerous locations under different climatic regimes, with long time series comprising streamflow measurements from small and large catchments in order to identify patterns induced by human impact and in particular streamflow regulation by upstream reservoirs. Based on the above results and on the concepts of ergodicity, stationarity and homogeneity, we try to identify stochastic similarities in regulated flow regimes in different catchments.

    Full text:

  1. G. Koudouris, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, and D. Koutsoyiannis, A stochastic model for hourly solar radiation process applied in renewable resources management, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-16275-2, European Geosciences Union, 2018.

    Since the beginning of the 21st century, the scientific community has made huge leaps to exploit renewable energy sources, with solar radiation being one of the most important. However, the variability of solar radiation has a significant impact on solar energy conversion systems, such as in photovoltaic systems, characterized by a fast and non-linear response to incident solar radiation. The performance prediction of these systems is typically based on hourly or daily data because those are usually available at these time scales. The aim of this work is to investigate the stochastic nature and time evolution of the solar radiation process in a daily and hourly step on a monthly basis scale, with the ultimate goal of creating a stochastic model capable of generating hourly solar radiation. For this purpose, an analysis was initially made at stations in Greece and then on a global scale. We propose a distribution that can adequately describe daily solar radiation and a new distribution consisting of the sum of two known distribution functions that is capable of capturing all aspects of the hourly solar radiation. Also, we exploit the clear sky index coefficient (T) for the double periodicity of the process, so as to achieve an integrated framework for the description of the solar radiation at all scales. Also, we use statistical tests and selection criteria, in order to verify the goodness of fit of the selected distribution. Then, we propose a cyclostationary model that can handle long-term persistence and reproduce the clear sky index coefficient (KT). The model can preserve the probability density function and also the dependence structure. Finally, we apply the proposed stochastic model to a theoretical case of renewable energy management, with an ultimate goal to maximize the financial profit of the production system.

    Full text: http://www.itia.ntua.gr/en/getfile/1790/1/documents/EGU2018-16275-2.pdf (32 KB)

  1. E. Klousakou, M. Chalakatevaki, R. Tomani, P. Dimitriadis, A. Efstratiadis, T. Iliopoulou, R. Ioannidis, N. Mamassis, and D. Koutsoyiannis, Stochastic investigation of the uncertainty of atmospheric processes related to renewable energy resources, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-16982-2, European Geosciences Union, 2018.

    Renewable energy resources, e.g., wind and solar energy, are characterized by great degree of uncertainty and in general, limited predictability, because of the irregular variability of the related geophysical processes. A simple and robust measure of the inherent uncertainty of a process is the Hurst parameter. Specifically, the more complex a process is, the larger the introduced uncertainty (unpredictability) and the larger the Hurst parameter. This behaviour (called Hurst-Kolmogorov, HK) has been identified in numerous geophysical processes. Although there are several methods for estimating the Hurst parameter, the climacogram (i.e. variance of the averaged process vs. scale of averaging) is one of the most powerful ones, with a lower statistical estimation uncertainty compared to the autocovariance and power spectrum. We apply the climacogram method to timeseries from processes related to renewable energy systems (wind, solar, ocean etc.) with the aim to characterize their degree of uncertainty and predictability across different timescales. We compare results among the different processes and we provide real-world examples of renewable energy systems management to illustrate the technical relevance of our findings.

    Full text:

  1. P. Dimitriadis, T. Iliopoulou, A. Efstratiadis, P. Papanicolaou, and D. Koutsoyiannis, Stochastic investigation of the uncertainty in common rating-curve relationships, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-18947-2, European Geosciences Union, 2018.

    A common issue in the river analysis is that most discharges measurements are taken from stage measurements and then an empirical expression is applied often called rating curves. There are several empirical relationships to determine the rating curves in order to estimate the river discharge when the water-surface is known and vice versa. Here, we investigate the stochastic uncertainty induced in empirical expressions of common rating curves. For this, we perform exhaustive Monte-Carlo experiments by assuming a theoretical stochastic structure (with or without fixed trends) for the river stage and we estimate the change in the dependence structure and marginal distribution of the river discharge. We further perform a sensitivity analysis on the input parameters of the common stage-discharge expressions in order to identify and estimate the overall induced uncertainty. Finally, we discuss on the results and we derive some preliminary conclusions on whether a stochastic structure (including trends) empirically estimated in terms of stage can be arbitrarily translated into discharge.

    Full text: http://www.itia.ntua.gr/en/getfile/1787/1/documents/EGU2018-18947-2.pdf (31 KB)

  1. C. Ntigkakis, G. Markopoulos-Sarikas, P. Dimitriadis, T. Iliopoulou, A. Efstratiadis, A. Koukouvinos, A. D. Koussis, K. Mazi, D. Katsanos, and D. Koutsoyiannis, Hydrological investigation of the catastrophic flood event in Mandra, Western Attica, European Geosciences Union General Assembly 2018, Geophysical Research Abstracts, Vol. 20, Vienna, EGU2018-17591-1, European Geosciences Union, 2018.

    A recent storm event, of substantial yet unknown local intensity, in Western Attica (west of Athens, Greece) has caused a flash flood with many fatalities in the city of Mandra as well as material damages. After this incident a debate started on whether the devastating results were due to the extreme nature of the rainfall event or to the poor flood protection works. In this study, we present information gathered from several sources (including hydrometric data from a neighboring catchment, point rainfall data from the broader area of interest, satellite observations and audiovisual material) in an attempt to represent the rainfall-runoff event. We further analyze the available data to approximately estimate the return period of the storm event. Finally, we discuss on the feasibility of the prediction of this storm.

    Full text:

  1. T. Iliopoulou, C. Aguilar , B. Arheimer, M. Bermúdez, N. Bezak, A. Ficchi, D. Koutsoyiannis, J. Parajka, M. J. Polo, G. Thirel, and A. Montanari, Investigating the physical basis of river memory and application to flood frequency prediction, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, European Geosciences Union, 2017.

    We investigate the long memory properties of 224 european rivers spanning more than 50 years of daily flow data. For this purpose, we identify two periods of interest; High Flow Seasons (HFS) as 3-month periods receiving the maximum occurrences of peaks-over-threshold flows and Dry Months (DM) as 1-month periods with the minimum average flow. We compute the lagged seasonal correlation for the peak flows in the HFS and the average flows in the DM both against the average flows in the antecedent months. The HFS and DM correlations are compared in terms of magnitude and variability and both are linked to geophysical river characteristics, e.g. basin size and baseflow index along with various site-specific catchment controls (e.g. lakes, glaciers etc.). Through a Meta-Gaussian data assimilation approach, we explore the benefit from conditioning the peak flow distribution in the HFS upon observance of a higher-than-usual (e.g. 95th quantile) flow in the pre-HFS month. To this end, the estimated correlation between the peak flows in HFS and average flows in the pre-HFS month is employed in fitting a bivariate Meta-Gaussian probability distribution model. The benefit of the suggested approach is showcased by updating the flood frequency distribution in real-world applications. Our findings suggest that river memory has a prominent physical basis and a high technical relevance in the case of seasonal flood frequency prediction.

    Full text: http://www.itia.ntua.gr/en/getfile/1771/1/documents/EGU2017-14533.pdf (36 KB)

  1. P. Dimitriadis, T. Iliopoulou, H. Tyralis, and D. Koutsoyiannis, Identifying the dependence structure of a process through pooled timeseries analysis, IAHS Scientific Assembly 2017, Port Elizabeth, South Africa, IAHS Press, Wallingford – International Association of Hydrological Sciences, 2017.

    Geophysical processes are known to exhibit significant departures from time-independence, ranging from short-range Markovian structure to Hurst-Kolmogorov behavior with large Hurst parameters. However, the identification of the dependence structure of a process is subject to many uncertainties, namely model uncertainty and estimation uncertainty particularly arising from the short length of available timeseries. Here we apply the climacogram (i.e. plot of the variance of the averaged process vs. scale) estimation method which has been shown to be the more robust and less uncertain among various stochastic metrics for the characterization of time-dependence. We further investigate the possibility of eliminating the sampling uncertainty by adequately employing all the available information through a pooled timeseries estimation approach, instead of discarding time-series of short length or of high percentage of missing values as typically performed in such tasks. We compare the merits and demerits of each approach as related to the strength of the dependence structure, the number and sample size of the available timeseries.

    Full text: http://www.itia.ntua.gr/en/getfile/1770/1/documents/IAHS2017-182-1.pdf (196 KB)

  1. T. Iliopoulou, and D. Koutsoyiannis, Investigating links between Long-Range Dependence in mean rainfall and clustering of extreme rainfall, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-9890-1, doi:10.13140/RG.2.2.25992.21763, European Geosciences Union, 2017.

    Clustering of extremes is a statistical behavior often observed in geophysical timeseries. However, it is usually studied independently of the theoretical framework of Long-Range Dependence, or the Hurst-Kolmogorov behavior, which provides consistent theoretical and practical tools for identifying it and understanding it. Herein, a dataset of daily rainfall records spanning more than 150 years is studied in order to investigate the dependence properties of extreme rainfall at the annual and seasonal timescale. The same investigation is carried out for mean rainfall at the annual scale. The research question is focused on investigating the link between the Hurst behavior in the mean rainfall, which is already acknowledged in literature, and the Hurst behavior in extreme rainfall timeseries, which is also to be testified.

    Full text: http://www.itia.ntua.gr/en/getfile/1709/1/documents/2017_egu_poster_LRD_extremes.pdf (1571 KB)

    Additional material:

  1. H. Tyralis, P. Dimitriadis, T. Iliopoulou, K. Tzouka, and D. Koutsoyiannis, Dependence of long-term persistence properties of precipitation on spatial and regional characteristics, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-3711, doi:10.13140/RG.2.2.13252.83840/1, European Geosciences Union, 2017.

    The long-term persistence (LTP), else known in hydrological science as the Hurst phenomenon, is a behaviour observed in geophysical processes in which wet years or dry years are clustered to respective long time periods. A common practice for evaluating the presence of the LTP is to model the geophysical time series with the Hurst-Kolmogorov process (HKp) and estimate its Hurst parameter H where high values of H indicate strong LTP. We estimate H of the mean annual precipitation using instrumental data from approximately 1 500 stations which cover a big area of the earth’s surface and span from 1916 to 2015. We regress the H estimates of all stations on their spatial and regional characteristics (i.e. their location, elevation and Köppen-Geiger climate class) using a random forest algorithm. Furthermore, we apply the Mann-Kendall test under the LTP assumption (MKt-LTP) to all time series to assess the significance of observed trends of the mean annual precipitation. To summarize the results, the LTP seems to depend mostly on the location of the stations, while the predictive value of the fitted regression model is good. Thus when investigating for LTP properties we recommend that the local characteristics should be considered. Additionally, the application of the MKt-LTP suggests that no significant monotonic trend can characterize the global precipitation. Dominant positive significant trends are observed mostly in main climate type D (snow), while in the other climate types the percentage of stations with positive significant trends was approximately equal to that of negative significant trends. Furthermore, 50% of all stations do not exhibit significant trends at all.

    Full text: http://www.itia.ntua.gr/en/getfile/1695/1/documents/EGU2017-3711presentation_.pdf (1608 KB)

    Additional material:

  1. V. Daniil, G. Pouliasis, E. Zacharopoulou, E. Demetriou, G. Manou, M. Chalakatevaki, I. Parara, C. Georganta, P. Stamou, S. Karali, E. Hadjimitsis, G. Koudouris, E. Moschos, D. Roussis, K. Papoulakos, A. Koskinas, G. Pollakis, N. Gournari, K. Sakellari, Y. Moustakis, N. Mamassis, A. Efstratiadis, H. Tyralis, P. Dimitriadis, T. Iliopoulou, G. Karakatsanis, K. Tzouka, I. Deligiannis, V. Tsoukala, P. Papanicolaou, and D. Koutsoyiannis, The uncertainty of atmospheric processes in planning a hybrid renewable energy system for a non-connected island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-16781-4, doi:10.13140/RG.2.2.29610.62406, European Geosciences Union, 2017.

    Non-connected islands to the electric gird are often depending on oil-fueled power plants with high unit cost. A hybrid energy system with renewable resources such as wind and solar plants could reduce this cost and also offer more environmental friendly solutions. However, atmospheric processes are characterized by high uncertainty that does not permit harvesting and utilizing full of their potential. Therefore, a more sophisticated framework that somehow incorporates this uncertainty could improve the performance of the system. In this context, we describe several stochastic and financial aspects of this framework. Particularly, we investigate the cross-correlation between several atmospheric processes and the energy demand, the possibility of mixing renewable resources with the conventional ones and in what degree of reliability, and critical financial subsystems such as weather derivatives. A pilot application of the above framework is also presented for a remote island in the Aegean Sea.

    Full text: http://www.itia.ntua.gr/en/getfile/1689/1/documents/EGU2017oral_16781_final.pdf (3038 KB)

    Additional material:

  1. P. Stamou, S. Karali, M. Chalakatevaki, V. Daniil, K. Tzouka, P. Dimitriadis, T. Iliopoulou, P. Papanicolaou, D. Koutsoyiannis, and N. Mamassis, Creating the electric energy mix of a non-connected Aegean island, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10130-10, doi:10.13140/RG.2.2.36537.77927, European Geosciences Union, 2017.

    As the electric energy in the non-connected islands is mainly produced by oil-fueled power plants, the unit cost is extremely high. Here the various energy sources are examined in order to create the appropriate electric energy mix for a non-connected Aegean island. All energy sources (renewable and fossil fuels) are examined and each one is evaluated using technical, environmental and economic criteria. Finally the most appropriate energy sources are simulated considering the corresponding energy works. Special emphasis is given to the use of biomass and the possibility of replacing (even partially) the existing oil-fueled power plant. Finally, a synthesis of various energy sources is presented that satisfies the electric energy demand taking into account the base and peak electric loads of the island.

    Full text: http://www.itia.ntua.gr/en/getfile/1688/2/documents/posterEGU.pdf (2687 KB)

    Additional material:

  1. E. Hadjimitsis, E. Demetriou, K. Sakellari, H. Tyralis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10164-5, European Geosciences Union, 2017.

    Atmospheric temperature and dew point, in addition to their role in atmospheric processes, influence the management of energy systems since they highly affect the energy demand and production. Both temperature and humidity depend on the climate conditions and geographical location. In this context, we analyze numerous of observations around the globe and we investigate the long-term behaviour and periodicities of the temperature and humidity processes. Also, we present and apply a parsimonious stochastic double-cyclostationary model for these processes to an island in the Aegean Sea and investigate their link to energy management.

    Additional material:

  1. G. Koudouris, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Investigation of the stochastic nature of solar radiation for renewable resources management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10189-4, doi:10.13140/RG.2.2.16215.06564, European Geosciences Union, 2017.

    A detailed investigation of the variability of solar radiation can be proven useful towards more efficient and sustainable design of renewable resources systems. This variability is mainly caused from the regular seasonal and diurnal variation, as well as its stochastic nature of the atmospheric processes, i.e. sunshine duration. In this context, we analyze numerous observations in Greece (Hellenic National Meteorological Service; http://www.hnms.gr/) and around the globe (NASA SSE - Surface meteorology and Solar Energy; http://www.soda-pro.com/webservices/radiation/nasa-sse) and we investigate the long-term behaviour and double periodicity of the solar radiation process. Also, we apply a parsimonious double-cyclostationary stochastic model to a theoretical scenario of solar energy production for an island in the Aegean Sea.

    Full text: http://www.itia.ntua.gr/en/getfile/1686/1/documents/SGU2017_solar_pres.pdf (1812 KB)

    Additional material:

  1. E. Moschos, G. Manou, C. Georganta, P. Dimitriadis, T. Iliopoulou, H. Tyralis, D. Koutsoyiannis, and V. Tsoukala, Investigation of the stochastic nature of wave processes for renewable resources management: a pilot application in a remote island in the Aegean sea, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, EGU2017-10225-3, doi:10.13140/RG.2.2.30226.66245, European Geosciences Union, 2017.

    The large energy potential of ocean dynamics is not yet being efficiently harvested mostly due to several technological and financial drawbacks. Nevertheless, modern renewable energy systems include wave and tidal energy in cases of nearshore locations. Although the variability of tidal waves can be adequately predictable, wind-generated waves entail a much larger uncertainty due to their dependence to the wind process. Recent research has shown, through estimation of the wave energy potential in coastal areas of the Aegean Sea, that installation of wave energy converters in nearshore locations could be an applicable scenario, assisting the electrical network of Greek islands. In this context, we analyze numerous of observations and we investigate the long-term behaviour of wave height and wave period processes. Additionally, we examine the case of a remote island in the Aegean sea, by estimating the local wave climate through past analysis data and numerical methods, and subsequently applying a parsimonious stochastic model to a theoretical scenario of wave energy production.

    Full text: http://www.itia.ntua.gr/en/getfile/1685/1/documents/EGU2017-10225-3_poster.pdf (3588 KB)

    Additional material:

  1. A. Koskinas, E. Zacharopoulou, G. Pouliasis, I. Engonopoulos, K. Mavroyeoryos, I. Deligiannis, G. Karakatsanis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and H. Tyralis, Simulation of electricity demand in a remote island for optimal planning of a hybrid renewable energy system, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10495-4, doi:10.13140/RG.2.2.10529.81767, European Geosciences Union, 2017.

    We simulate the electrical energy demand in the remote island of Astypalaia. To this end we first obtain information regarding the local socioeconomic conditions and energy demand. Secondly, the available hourly demand data are analysed at various time scales (hourly, weekly, daily, seasonal). The cross-correlations between the electrical energy demand and the mean daily temperature as well as other climatic variables for the same time period are computed. Also, we investigate the cross-correlation between those climatic variables and other variables related to renewable energy resources from numerous observations around the globe in order to assess the impact of each one to a hybrid renewable energy system. An exploratory data analysis including all variables is performed with the purpose to find hidden relationships. Finally, the demand is simulated considering all the periodicities found in the analysis. The simulation time series will be used in the development of a framework for planning of a hybrid renewable energy system in Astypalaia.

    Full text: http://www.itia.ntua.gr/en/getfile/1684/2/documents/EGU2017_CrossCorr-EnergyDemand.pdf (2668 KB)

    Additional material:

  1. D. Roussis, I. Parara, N. Gournari, Y. Moustakis, P. Dimitriadis, T. Iliopoulou, D. Koutsoyiannis, and G. Karakatsanis, Energy, variability and weather finance engineering, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-16919, European Geosciences Union, 2017.

    Most types of renewable energies are characterized by intense intermittency, causing significant instabilities to the grid; further requiring additional infrastructure (e.g. pumped-storage) for buffering hydrometeorological uncertainties, as well as complex operational rules for load balancing. In addition, most intermittent renewable units are subsidized, creating significant market inefficiencies.Weather derivatives comprise mature financial tools for integrating successfully the intermittent-load and base-load components into a unified hybrid energy system and establish their operation within a generalized uncertainty management market. With a growing global market share and 46% utilization of this financial tool by the energy industry and 12% by agriculture (that partially concerns biofuel resources), weather derivatives are projected to constitute a critical subsystem of many grids for buffering frequent hydrometeorological risks of low and medium impacts –which are not covered by standard insurance contracts that aim exclusively at extreme events and high financial damages. In this context, we study the attributes of hydrometeorological time series in a remote and small island in Greece, powered by an autonomous hybrid energy system. Upon the results we choose the optimal underlying index and we further compose and engineer a weather derivative with features of a typical option contract –which we consider most flexible and appropriate for the case– to test our assumptions on its beneficiary effects for both the budget of private energy producers and the island’s public administration.

    Additional material:

  1. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10334-4, European Geosciences Union, 2017.

    Small islands are regarded as promising areas for developing hybrid water-energy systems that combine multiple sources of renewable energy with pumped-storage facilities. Essential element of such systems is the water storage component (reservoir), which implements both flow and energy regulations. Apparently, the representation of the overall water-energy management problem requires the simulation of the operation of the reservoir system, which in turn requires a faithful estimation of water inflows and demands of water and energy. Yet, in small-scale reservoir systems, this task in far from straightforward, since both the availability and accuracy of associated information is generally very poor. For, in contrast to large-scale reservoir systems, for which it is quite easy to find systematic and reliable hydrological data, in the case of small systems such data may be minor or even totally missing. The stochastic approach is the unique means to account for input data uncertainties within the combined water-energy management problem. Using as example the Livadi reservoir, which is the pumped storage component of the small Aegean island of Astypalaia, Greece, we provide a simulation framework, comprising: (a) a stochastic model for generating synthetic rainfall and temperature time series; (b) a stochastic rainfall-runoff model, whose parameters cannot be inferred through calibration and, thus, they are represented as correlated random variables; (c) a stochastic model for estimating water supply and irrigation demands, based on simulated temperature and soil moisture, and (d) a daily operation model of the reservoir system, providing stochastic forecasts of water and energy outflows.

    Related works:

    • [8] Associated paper in Energy Procedia

    Full text: http://www.itia.ntua.gr/en/getfile/1682/2/documents/2017_EGU_RRproject_final.pdf (2019 KB)

    Additional material:

  1. E. Lerias, A. Kalamioti, P. Dimitriadis, Y. Markonis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of temperature process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-14828-3, European Geosciences Union, 2016.

    The temperature process is considered as the most characteristic hydrometeorological process and has been thoroughly examined in the climate-change framework. We use a dataset comprising hourly temperature and dew point records to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e. mean process variance vs. scale) for various time periods.

    Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly

    Full text: http://www.itia.ntua.gr/en/getfile/1660/1/documents/TempDewP.pdf (2727 KB)

    Additional material:

  1. I. Deligiannis, V. Tyrogiannis, Ο. Daskalou, P. Dimitriadis, Y. Markonis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic investigation of wind process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-14946-6, doi:10.13140/RG.2.2.26681.36969, European Geosciences Union, 2016.

    The wind process is considered one of the hydrometeorological processes that generates and drives the climate dynamics. We use a dataset comprising hourly wind records to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e. mean process variance vs. scale) for various time periods.

    Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

    Full text:

    Additional material:

  1. A. Sotiriadou, A. Petsiou, E. Feloni, P. Kastis, T. Iliopoulou, Y. Markonis, H. Tyralis, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of precipitation process for climatic variability identification, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, EGU2016-15137-5, doi:10.13140/RG.2.2.28955.46881, European Geosciences Union, 2016.

    The precipitation process is important not only to hydrometeorology but also to renewable energy resources management. We use a dataset consisting of daily and hourly records around the globe to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e. mean process variance vs. scale).

    Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

    Full text: http://www.itia.ntua.gr/en/getfile/1658/1/documents/RainP.pdf (3820 KB)

    Additional material:

  1. T. Iliopoulou, S.M. Papalexiou, and D. Koutsoyiannis, Assessment of the dependence structure of the annual rainfall using a large data set, European Geosciences Union General Assembly 2013, Geophysical Research Abstracts, Vol. 15, Vienna, EGU2013-5276, doi:10.13140/RG.2.2.13080.19202, European Geosciences Union, 2013.

    Natural processes are considered to be influenced by long-term persistence, the so-called Hurst effect. A variety of studies have been conducted to identify the Hurst behaviour in different data sets and different scientific disciplines ranging from geophysics to economics and to social sciences. In this study we try to test the hypothesis of the existence of long-range dependence in annual rainfall by applying the aggregated variance method in a large set of annual rainfall time series from more than a thousand stations worldwide. In addition, we figure out a simple statistical test in order to assess the hypothesis that the dependence structure of annual rainfall is Markovian.

    Full text:

    Additional material:

    See also: http://dx.doi.org/10.13140/RG.2.2.13080.19202

  1. E. Houdalaki, M. Basta, N. Boboti, N. Bountas, E. Dodoula, T. Iliopoulou, S. Ioannidou, K. Kassas, S. Nerantzaki, E. Papatriantafyllou, K. Tettas, D. Tsirantonaki, S.M. Papalexiou, and D. Koutsoyiannis, On statistical biases and their common neglect, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 4388, doi:10.13140/RG.2.2.25951.46248, European Geosciences Union, 2012.

    The study of natural phenomena such as hydroclimatic processes demands the use of stochastic tools and the good understanding thereof. However, common statistical practices are often based on classical statistics, which assumes independent identically distributed variables with Gaussian distributions. However, in most cases geophysical processes exhibit temporal dependence and even long term persistence. Also, some statistical estimators for nonnegative random variables have distributions radically different from Gaussian. We demonstrate the impact of neglecting dependence and non-normality in parameter estimators and how this can result in misleading conclusions and futile predictions. To accomplish that, we use synthetic examples derived by Monte Carlo techniques and we also provide a number of examples of misuse.

    Full text:

    Additional material:

    See also: http://dx.doi.org/10.13140/RG.2.2.25951.46248

Academic works

  1. T. Iliopoulou, Investigation of long range dependence in the annual rainfall structure from a global database , Diploma thesis, 146 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, March 2013.

    High scientific interest is raised around the hypothesis testing of long range dependence structure in geophysical data since Hurst discovered the phenomenon in 1951. Herein, we examine the hypothesis of long range dependence in the annual rainfall structure. A global data base of daily rainfall data GHCN (Global Historical Climatology Network) is processed along with the infilling of missing values. Based on length and quality criteria, 1265 rainfall stations are chosen for the estimation. Statistical properties of rainfall are also derived from the stations. In order to estimate the Hurst coefficient, the aggregated variance method as well as the LSSD (Least Squares based on Standard Deviation) method are applied to the data. Hypothesis testing for the existence of a common Hurst coefficient is conducted. In addition, the study of the autocorrelation function and the comparison with an AR(1) structure are performed as they provide a deeper insight on the matter. The results are in favor of the existence of long range dependence for the 3/4 of rainfall stations, although there exist considerable differences among them in the intensity of the phenomenon. In the future, the longer existing data lengths will perhaps enable a more certain estimation.

    Full text: http://www.itia.ntua.gr/en/getfile/1368/1/documents/iliopoulout_hurst.pdf (5086 KB)