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To seek stochastic analogies in key processes related to the hydrological cycle, an extended collection of several billions of data values from hundred thousands of worldwide stations is used in this work. The examined processes are the near-surface hourly temperature, dew point, relative humidity, sea level pressure, and atmospheric wind speed, as well as the hourly/daily streamflow and precipitation. Through the use of robust stochastic metrics such as the K-moments and a second-order climacogram (i.e., variance of the averaged process vs. scale), it is found that several stochastic similarities exist in both the marginal structure, in terms of the first four moments, and in the second-order dependence structure. Stochastic similarities are also detected among the examined processes, forming a specific hierarchy among their marginal and dependence structures, similar to the one in the hydrological cycle. Finally, similarities are also traced to the isotropic and nearly Gaussian turbulence, as analyzed through extensive lab recordings of grid turbulence and of turbulent buoyant jet along the axis, which resembles the turbulent shear and buoyant regime that dominates and drives the hydrological-cycle processes in the boundary layer. The results are found to be consistent with other studies in literature such as solar radiation, ocean waves, and evaporation, and they can be also justified by the principle of maximum entropy. Therefore, they allow for the development of a universal stochastic view of the hydrological-cycle under the Hurst–Kolmogorov dynamics, with marginal structures extending from nearly Gaussian to Pareto-type tail behavior, and with dependence structures exhibiting roughness (fractal) behavior at small scales, long-term persistence at large scales, and a transient behavior at intermediate scales.
Panayiotis Dimitriadis; Demetris Koutsoyiannis; Theano Iliopoulou; Panos Papanicolaou. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes. Hydrology 2021, 8, 59 .
AMA StylePanayiotis Dimitriadis, Demetris Koutsoyiannis, Theano Iliopoulou, Panos Papanicolaou. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes. Hydrology. 2021; 8 (2):59.
Chicago/Turabian StylePanayiotis Dimitriadis; Demetris Koutsoyiannis; Theano Iliopoulou; Panos Papanicolaou. 2021. "A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes." Hydrology 8, no. 2: 59.
In human societies, we observe a wide range of types of stratification, i.e., in terms of financial class, political power, level of education, sanctity, and military force. In financial, political, and social sciences, stratification is one of the most important issues and tools as the Lorenz Curve and the Gini Coefficient have been developed to describe some of its aspects. Stratification is greatly dependent on the access of people to wealth. By “wealth”, we mean the quantified prosperity which increases the life expectancy of people. Prosperity is also connected to the water-food-energy nexus which is necessary for human survival. Analyzing proxies of the water-food-energy nexus, we suggest that the best proxy for prosperity is energy, which is closely related to Gross Domestic Product (GDP) per capita and life expectancy. In order to describe the dynamics of social stratification, we formulate an entropic view of wealth in human societies. An entropic approach to income distribution, approximated as available energy in prehistoric societies, till present-day economies, shows that stratification can be viewed as a stochastic process subject to the principle of maximum entropy and occurring when limits to the wealth of society are set, either by the political and economic system and/or by the limits of available technology.
G.-Fivos Sargentis; Theano Iliopoulou; Panayiotis Dimitriadis; Nikolaos Mamassis; Demetris Koutsoyiannis. Stratification: An Entropic View of Society’s Structure. World 2021, 2, 153 -174.
AMA StyleG.-Fivos Sargentis, Theano Iliopoulou, Panayiotis Dimitriadis, Nikolaos Mamassis, Demetris Koutsoyiannis. Stratification: An Entropic View of Society’s Structure. World. 2021; 2 (2):153-174.
Chicago/Turabian StyleG.-Fivos Sargentis; Theano Iliopoulou; Panayiotis Dimitriadis; Nikolaos Mamassis; Demetris Koutsoyiannis. 2021. "Stratification: An Entropic View of Society’s Structure." World 2, no. 2: 153-174.
During the last decades, scientific research in the field of flood risk management has provided new insights and strong computational tools towards the deeper understanding of the fundamental probabilistic and stochastic behaviour that characterizes such natural hazards. Flood hazards are controlled by hydrometeorological processes and their inherent uncertainties. Historically, a high percentage of flood disasters worldwide are inaccurately or ineffectively reported regarding the aggregated number of the affected people, economic losses and generated flood insurance claims. In this respect, the recently published National Flood Insurance Program (NFIP) data by the Federal Emergency Management Agency (FEMA), including more than two million claims records dating back to 1978 and more than 47 million policy records for transactions, may provide new insights into flood impacts. The aim of this research is to process the actual insurance data derived from this database, in order to detect the underlying patterns and investigate its stochastic structure, paving the way for the development of more accurate flood risk assessment and modelling strategies.
Konstantinos Papoulakos; Theano Iliopoulou; Panayiotis Dimitriadis; Dimosthenis Tsaknias; Demetris Koutsoyiannis. Investigating the stochastic structure of the recently published Redacted Claims data set by the FEMA National Flood Insurance Program. 2021, 1 .
AMA StyleKonstantinos Papoulakos, Theano Iliopoulou, Panayiotis Dimitriadis, Dimosthenis Tsaknias, Demetris Koutsoyiannis. Investigating the stochastic structure of the recently published Redacted Claims data set by the FEMA National Flood Insurance Program. . 2021; ():1.
Chicago/Turabian StyleKonstantinos Papoulakos; Theano Iliopoulou; Panayiotis Dimitriadis; Dimosthenis Tsaknias; Demetris Koutsoyiannis. 2021. "Investigating the stochastic structure of the recently published Redacted Claims data set by the FEMA National Flood Insurance Program." , no. : 1.
Changes in the land cover occur all the time at the surface of the Earth both naturally and anthropogenically. In the last decades, certain types of land cover change, including urbanization, have been correlated to local temperature increase, but the general dynamics of this relationship are still not well understood. This work examines whether land cover is a parameter affecting temperature increase by employing global datasets of land cover change, i.e. the Historical Land-Cover Change Global Dataset, and daily temperature from the NOAA database. We thoroughly investigate the temperature variability and its possible correlation to the different types of land-cover changes. A comparison is specifically made between the rate of temperature increase measured in urban areas, and the same rate measured in nearby non-urban areas.
Aristoklis Lagos; Stavroula Sigourou; Panayiotis Dimitriadis; Theano Iliopoulou; Demetris Koutsoyiannis. Land Cover Change: Does it affect temperature variability? 2021, 1 .
AMA StyleAristoklis Lagos, Stavroula Sigourou, Panayiotis Dimitriadis, Theano Iliopoulou, Demetris Koutsoyiannis. Land Cover Change: Does it affect temperature variability? . 2021; ():1.
Chicago/Turabian StyleAristoklis Lagos; Stavroula Sigourou; Panayiotis Dimitriadis; Theano Iliopoulou; Demetris Koutsoyiannis. 2021. "Land Cover Change: Does it affect temperature variability?" , no. : 1.
Curves of rainfall intensity at various scales and for various return periods, else known as ombrian (or IDF) curves, are central design tools in hydrology and engineering. Construction of such curves often relies heavily on empirical or semi-empirical approaches, which hinder their applicability over large scales, and preclude simulation. Recent work by Koutsoyiannis (2020) has advanced these curves to theoretically-consistent stochastic models of rainfall intensity (ombrian models) extending their applicability to the full range of available scales, e.g. from minutes to decades. We present an open-source python toolbox implementing these advances in a straightforward and user-friendly manner and prove its applicability. The toolbox also employs advanced statistical fitting methods for extremes (K-moments), accounts for bias induced by temporal dependence, and allows optional blending of daily-scale data to reduce uncertainty of sub-daily records. The end result is the parameterization of the ombrian model and the graphical representation of rainfall intensity for any range of scales (supported by the data) and return periods.
Reference: Koutsoyiannis, D. 2020. ‘Rainfall extremes and Ombrian modelling’ in Stochastics of Hydroclimatic Extremes - A Cool Look at Risk (ed 0), National Technical University of Athens, Athens, pp 243-273, http://www.itia.ntua.gr/en/docinfo/2000/.
Theano Iliopoulou; Demetris Koutsoyiannis. PythOm: A python toolbox implementing recent advances in rainfall intensity (ombrian) curves. 2021, 1 .
AMA StyleTheano Iliopoulou, Demetris Koutsoyiannis. PythOm: A python toolbox implementing recent advances in rainfall intensity (ombrian) curves. . 2021; ():1.
Chicago/Turabian StyleTheano Iliopoulou; Demetris Koutsoyiannis. 2021. "PythOm: A python toolbox implementing recent advances in rainfall intensity (ombrian) curves." , no. : 1.
In the last few years, the island of Crete (Greece - Eastern Mediterranean) has been affected by extreme events. In recent decades, hydrometeorological processes in the island of Crete are monitored by an extensive network of meteorological stations. Here we stochastically analyze the spatial stochastic structure of precipitation in the island by employing sophisticated statistical tools, as well as by analyzing a large database of daily precipitation records. We investigate fifty-eight rainfall stations scattered in the four prefectures of Crete, for the years 1974-2020. Descriptive statistical analysis of precipitation examines several temporal properties in the data, while correlation analysis of precipitation variability provides relations between stations and regions for spatial patterns identification. This work also investigates the precipitation variability by employing statistical tools such as the autocorrelation, autoregressive (seasonal) analysis, probability distribution function fitting, and climacogram calculation, i.e. variance of the averaged process vs. spatial and temporal scales, to identify statistical properties, temporal dependencies, potential similarities in the dependence structure and marginal probability distribution.
Olianna Akoumianaki; Theano Iliopoulou; Panayiotis Dimitriadis; Emmanouil Varouchakis; Demetris Koutsoyiannis. Stochastic analysis of the spatial stochastic structure of precipitation in the island of Crete, Greece. 2021, 1 .
AMA StyleOlianna Akoumianaki, Theano Iliopoulou, Panayiotis Dimitriadis, Emmanouil Varouchakis, Demetris Koutsoyiannis. Stochastic analysis of the spatial stochastic structure of precipitation in the island of Crete, Greece. . 2021; ():1.
Chicago/Turabian StyleOlianna Akoumianaki; Theano Iliopoulou; Panayiotis Dimitriadis; Emmanouil Varouchakis; Demetris Koutsoyiannis. 2021. "Stochastic analysis of the spatial stochastic structure of precipitation in the island of Crete, Greece." , no. : 1.
A physical process is characterized as complex when it is difficult to analyze and explain in a simple way, and even more difficult to predict. The complexity within an art painting is expected to be high, possibly comparable to that of nature. Herein, we apply a 2D stochastic methodology to images of both portrait photography and artistic portraits, the latter belonging to different genres of art, with the aim to better understand their variability in quantitative terms. To quantify the dependence structure and variability, we estimate the Hurst parameter, which is a common dependence metric for hydrometeorological processes. We also seek connections between the identified stochastic patterns and the desideratum that each art movement aimed to express. Results show remarkable stochastic similarities between portrait paintings, linked to philosophical, cultural and theological characteristics of each period.
G.-Fivos Sargentis; Panayiotis Dimitriadis; Theano Iliopoulou; Demetris Koutsoyiannis. A Stochastic View of Varying Styles in Art Paintings. Heritage 2021, 4, 333 -348.
AMA StyleG.-Fivos Sargentis, Panayiotis Dimitriadis, Theano Iliopoulou, Demetris Koutsoyiannis. A Stochastic View of Varying Styles in Art Paintings. Heritage. 2021; 4 (1):333-348.
Chicago/Turabian StyleG.-Fivos Sargentis; Panayiotis Dimitriadis; Theano Iliopoulou; Demetris Koutsoyiannis. 2021. "A Stochastic View of Varying Styles in Art Paintings." Heritage 4, no. 1: 333-348.
Even though landscape quality is largely a subjective issue, the integration of infrastructure into landscapes has been identified as a key element of sustainability. In a spatial planning context, the landscape impacts that are generated by infrastructures are commonly quantified through visibility analysis. In this study, we develop a new method of visibility analysis and apply it in a case study of a reservoir (Plastiras dam in Greece). The methodology combines common visibility analysis with a stochastic tool for visual-impacts evaluation; points that generate high visual contrasts in landscapes are considered Focus Points (FPs) and their clustering in landscapes is analyzed trying to answer two questions: (1) How does the clustering of Focus Points (FPs) impact the aesthetic value of the landscape? (2) How can the visual impacts of these FPs be evaluated? Visual clustering is calculated utilizing a stochastic analysis of generated Zones of Theoretical Visibility. Based on the results, we argue that if the visual effect of groups of FPs is positive, then the optimal sitting of FPs should be in the direction of faint clustering, whereas if the effect is negative, the optimal sitting of FPs should be directed to intense clustering. In order to optimize the landscape integration of infrastructure, this method could be a useful analytical tool for environmental impact assessment or a monitoring tool for a project’s managing authorities. This is demonstrated through the case study of Plastiras’ reservoir, where the clustering of positively perceived FPs is found to be an overlooked attribute of its perception as a highly sustainable infrastructure project.
G.-Fivos Sargentis; Romanos Ioannidis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsoyiannis. Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece). Infrastructures 2021, 6, 12 .
AMA StyleG.-Fivos Sargentis, Romanos Ioannidis, Theano Iliopoulou, Panayiotis Dimitriadis, Demetris Koutsoyiannis. Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece). Infrastructures. 2021; 6 (1):12.
Chicago/Turabian StyleG.-Fivos Sargentis; Romanos Ioannidis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsoyiannis. 2021. "Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)." Infrastructures 6, no. 1: 12.
Clustering structures appearing from small to large scales are ubiquitous in the physical world. Interestingly, clustering structures are omnipresent in human history too, ranging from the mere organization of life in societies (e.g., urbanization) to the development of large-scale infrastructure and policies for meeting organizational needs. Indeed, in its struggle for survival and progress, mankind has perpetually sought the benefits of unions. At the same time, it is acknowledged that as the scale of the projects grows, the cost of the delivered products is reduced while their quantities are maximized. Thus, large-scale infrastructures and policies are considered advantageous and are constantly being pursued at even great scales. This work develops a general method to quantify the temporal evolution of clustering, using a stochastic computational tool called 2D-C, which is applicable for the study of both natural and human social spatial structures. As case studies, the evolution of the structure of the universe, of ecosystems and of human clustering structures such as urbanization, are investigated using novel sources of spatial information. Results suggest the clear existence both of periods of clustering and declustering in the natural world and in the human social structures; yet clustering is the general trend. In view of the ongoing COVID-19 pandemic, societal challenges arising from large-scale clustering structures are discussed.
G.-Fivos Sargentis; Theano Iliopoulou; Stavroula Sigourou; Panayiotis Dimitriadis; Demetris Koutsoyiannis. Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures. Sustainability 2020, 12, 7972 .
AMA StyleG.-Fivos Sargentis, Theano Iliopoulou, Stavroula Sigourou, Panayiotis Dimitriadis, Demetris Koutsoyiannis. Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures. Sustainability. 2020; 12 (19):7972.
Chicago/Turabian StyleG.-Fivos Sargentis; Theano Iliopoulou; Stavroula Sigourou; Panayiotis Dimitriadis; Demetris Koutsoyiannis. 2020. "Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures." Sustainability 12, no. 19: 7972.
The integration of renewable energy sources in modern society has been given priority as these sources are regarded environmentally friendly. However, the variability of natural energy sources, combined with that of energy consumption, demands a different management of the energy system. In this work, we investigate the uncertainty of all variables combined, in order to take this variability into account in energy management.
Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods" 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.
Ioannis Vatsikouridis; Konstantinos Karkanis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Nikolaos Mamassis. Investigating the variability of renewable sources for energy management. 2020, 1 .
AMA StyleIoannis Vatsikouridis, Konstantinos Karkanis, Theano Iliopoulou, Panayiotis Dimitriadis, Demetris Koutsoyiannis, Nikolaos Mamassis. Investigating the variability of renewable sources for energy management. . 2020; ():1.
Chicago/Turabian StyleIoannis Vatsikouridis; Konstantinos Karkanis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Nikolaos Mamassis. 2020. "Investigating the variability of renewable sources for energy management." , no. : 1.
We simulate the electrical energy production in the remote island of Astypalaia, Greece. Solar, wind, hydropower, biomass and marine energy are used for the energy mix. The hypothetical energy system has also the ability to store energy through a pumped-storage unit. We use available data at various time scales. The aim of this work is to optimize the energy management of the hypothetical system studied.
Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods" 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.
Konstantinos Karkanis; Ioannis Vatsikouridis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsogiannis; Nikolaos Mamassis. Simulation of electricity production in a remote island for optimal management of a hybrid renewable energy system. 2020, 1 .
AMA StyleKonstantinos Karkanis, Ioannis Vatsikouridis, Theano Iliopoulou, Panayiotis Dimitriadis, Demetris Koutsogiannis, Nikolaos Mamassis. Simulation of electricity production in a remote island for optimal management of a hybrid renewable energy system. . 2020; ():1.
Chicago/Turabian StyleKonstantinos Karkanis; Ioannis Vatsikouridis; Theano Iliopoulou; Panayiotis Dimitriadis; Demetris Koutsogiannis; Nikolaos Mamassis. 2020. "Simulation of electricity production in a remote island for optimal management of a hybrid renewable energy system." , no. : 1.
Clustering of extremes is critical for hydrological design and risk management and challenges the popular assumption of independence of extremes. We investigate the links between clustering of extremes and long-term persistence, else Hurst-Kolmogorov (HK) dynamics, in the parent process exploring the possibility of inferring the latter from the former. We find that (a) identifiability of persistence from maxima depends foremost on the choice of the threshold for extremes, the skewness and kurtosis of the parent process, and less on sample size; and (b) existing indices for inferring dependence from series of extremes are downward biased when applied to non-Gaussian processes. We devise a probabilistic index based on the probability of occurrence of peak-over-threshold events across multiple scales, which can reveal clustering, linking it to the persistence of the parent process. Its application shows that rainfall extremes may exhibit noteworthy departures from independence and consistency with an HK model.
Theano Iliopoulou; Demetris Koutsoyiannis. Revealing hidden persistence in maximum rainfall records. Hydrological Sciences Journal 2019, 64, 1673 -1689.
AMA StyleTheano Iliopoulou, Demetris Koutsoyiannis. Revealing hidden persistence in maximum rainfall records. Hydrological Sciences Journal. 2019; 64 (14):1673-1689.
Chicago/Turabian StyleTheano Iliopoulou; Demetris Koutsoyiannis. 2019. "Revealing hidden persistence in maximum rainfall records." Hydrological Sciences Journal 64, no. 14: 1673-1689.
Renewable energy (RE) installations and civil works are beneficial in terms of sustainability, but a considerable amount of space in the landscape is required in order to harness this energy. In contemporary environmental theory the landscape is considered an environmental parameter and the transformation of the landscape by RE works has received increasing attention by the scientific community and affected societies. This research develops a novel computational stochastic tool the 2D Climacogram (2D-C) that allows the analysis and comparison of images of landscapes, both original and transformed by RE works. This is achieved by a variability characterization of the grayscale intensity of 2D images. A benchmark analysis is performed for art paintings in order to evaluate the properties of the 2D-C for image analysis, and the change in variability among images. Extensive applications are performed for landscapes transformed by RE works. Results show that the 2D-C is able to quantify the changes in variability of the image features, which may prove useful in the landscape impact assessment of large-scale engineering works.
G.-Fivos Sargentis; Panayiotis Dimitriadis; Romanos Ioannidis; Theano Iliopoulou; Demetris Koutsoyiannis. Stochastic Evaluation of Landscapes Transformed by Renewable Energy Installations and Civil Works. Energies 2019, 12, 2817 .
AMA StyleG.-Fivos Sargentis, Panayiotis Dimitriadis, Romanos Ioannidis, Theano Iliopoulou, Demetris Koutsoyiannis. Stochastic Evaluation of Landscapes Transformed by Renewable Energy Installations and Civil Works. Energies. 2019; 12 (14):2817.
Chicago/Turabian StyleG.-Fivos Sargentis; Panayiotis Dimitriadis; Romanos Ioannidis; Theano Iliopoulou; Demetris Koutsoyiannis. 2019. "Stochastic Evaluation of Landscapes Transformed by Renewable Energy Installations and Civil Works." Energies 12, no. 14: 2817.
In February 2017, a failure occurring in Oroville Dam’s main spillway risked causing severe damages downstream. A unique aspect of this incident was the fact that it happened during a flood scenario well within its design and operational procedures, prompting research into its causes and determining methods to prevent similar events from reoccurring. In this study, a hydroclimatic analysis of Oroville Dam’s catchment is conducted, along with a review of related design and operational manuals. The data available allows for the comparison of older flood-frequency analyses to new alternative methods proposed in this paper and relevant literature. Based on summary characteristics of the 2017 floods, possible causes of the incident are outlined, in order to understand which factors contributed more significantly. It turns out that the event was most likely the result of a structural problem in the dam’s main spillway and detrimental geological conditions, but analysis of surface level data also reveals operational issues that were not present during previous larger floods, promoting a discussion about flood control design methods, specifications, and dam inspection procedures, and how these can be improved to prevent a similar event from occurring in the future.
Aristotelis Koskinas; Aristoteles Tegos; Penelope Tsira; Panayiotis Dimitriadis; Theano Iliopoulou; Panos Papanicolaou; Demetris Koutsoyiannis; Tracey Williamson. Insights into the Oroville Dam 2017 Spillway Incident. Geosciences 2019, 9, 37 .
AMA StyleAristotelis Koskinas, Aristoteles Tegos, Penelope Tsira, Panayiotis Dimitriadis, Theano Iliopoulou, Panos Papanicolaou, Demetris Koutsoyiannis, Tracey Williamson. Insights into the Oroville Dam 2017 Spillway Incident. Geosciences. 2019; 9 (1):37.
Chicago/Turabian StyleAristotelis Koskinas; Aristoteles Tegos; Penelope Tsira; Panayiotis Dimitriadis; Theano Iliopoulou; Panos Papanicolaou; Demetris Koutsoyiannis; Tracey Williamson. 2019. "Insights into the Oroville Dam 2017 Spillway Incident." Geosciences 9, no. 1: 37.
The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.
Theano Iliopoulou; Cristina Aguilar; Berit Arheimer; María Bermúdez; Nejc Bezak; Andrea Ficchì; Demetris Koutsoyiannis; Juraj Parajka; María José Polo; Guillaume Thirel; Alberto Montanari. A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers. Hydrology and Earth System Sciences 2019, 23, 73 -91.
AMA StyleTheano Iliopoulou, Cristina Aguilar, Berit Arheimer, María Bermúdez, Nejc Bezak, Andrea Ficchì, Demetris Koutsoyiannis, Juraj Parajka, María José Polo, Guillaume Thirel, Alberto Montanari. A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers. Hydrology and Earth System Sciences. 2019; 23 (1):73-91.
Chicago/Turabian StyleTheano Iliopoulou; Cristina Aguilar; Berit Arheimer; María Bermúdez; Nejc Bezak; Andrea Ficchì; Demetris Koutsoyiannis; Juraj Parajka; María José Polo; Guillaume Thirel; Alberto Montanari. 2019. "A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers." Hydrology and Earth System Sciences 23, no. 1: 73-91.
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.
Theano Iliopoulou; Demetris Koutsoyiannis; Alberto Montanari. Characterizing and Modeling Seasonality in Extreme Rainfall. Water Resources Research 2018, 54, 6242 -6258.
AMA StyleTheano Iliopoulou, Demetris Koutsoyiannis, Alberto Montanari. Characterizing and Modeling Seasonality in Extreme Rainfall. Water Resources Research. 2018; 54 (9):6242-6258.
Chicago/Turabian StyleTheano Iliopoulou; Demetris Koutsoyiannis; Alberto Montanari. 2018. "Characterizing and Modeling Seasonality in Extreme Rainfall." Water Resources Research 54, no. 9: 6242-6258.
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.
Elli Klousakou; Maria Chalakatevaki; Panayiotis Dimitriadis; Theano Iliopoulou; Romanos Ioannidis; Georgios Karakatsanis; Andreas Efstratiadis; Nikos Mamasis; Romina Tomani; Efthimis Chardavellas; Demetris Koutsoyiannis. A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources. Advances in Geosciences 2018, 45, 193 -199.
AMA StyleElli Klousakou, Maria Chalakatevaki, Panayiotis Dimitriadis, Theano Iliopoulou, Romanos Ioannidis, Georgios Karakatsanis, Andreas Efstratiadis, Nikos Mamasis, Romina Tomani, Efthimis Chardavellas, Demetris Koutsoyiannis. A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources. Advances in Geosciences. 2018; 45 ():193-199.
Chicago/Turabian StyleElli Klousakou; Maria Chalakatevaki; Panayiotis Dimitriadis; Theano Iliopoulou; Romanos Ioannidis; Georgios Karakatsanis; Andreas Efstratiadis; Nikos Mamasis; Romina Tomani; Efthimis Chardavellas; Demetris Koutsoyiannis. 2018. "A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources." Advances in Geosciences 45, no. : 193-199.
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 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.
Giannis Koudouris; Panayiotis Dimitriadis; Theano Iliopoulou; Nikos Mamassis; Demetris Koutsoyiannis. A stochastic model for the hourly solar radiation process for application in renewable resources management. Advances in Geosciences 2018, 45, 139 -145.
AMA StyleGiannis Koudouris, Panayiotis Dimitriadis, Theano Iliopoulou, Nikos Mamassis, Demetris Koutsoyiannis. A stochastic model for the hourly solar radiation process for application in renewable resources management. Advances in Geosciences. 2018; 45 ():139-145.
Chicago/Turabian StyleGiannis Koudouris; Panayiotis Dimitriadis; Theano Iliopoulou; Nikos Mamassis; Demetris Koutsoyiannis. 2018. "A stochastic model for the hourly solar radiation process for application in renewable resources management." Advances in Geosciences 45, no. : 139-145.
Hristos Tyralis; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Patrick Enda O'Connell; Katerina Tzouka; Theano Iliopoulou. On the long-range dependence properties of annual precipitation using a global network of instrumental measurements. Advances in Water Resources 2018, 111, 301 -318.
AMA StyleHristos Tyralis, Panayiotis Dimitriadis, Demetris Koutsoyiannis, Patrick Enda O'Connell, Katerina Tzouka, Theano Iliopoulou. On the long-range dependence properties of annual precipitation using a global network of instrumental measurements. Advances in Water Resources. 2018; 111 ():301-318.
Chicago/Turabian StyleHristos Tyralis; Panayiotis Dimitriadis; Demetris Koutsoyiannis; Patrick Enda O'Connell; Katerina Tzouka; Theano Iliopoulou. 2018. "On the long-range dependence properties of annual precipitation using a global network of instrumental measurements." Advances in Water Resources 111, no. : 301-318.
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).
Theano Iliopoulou; Simon Michael Papalexiou; Yannis Markonis; Demetris Koutsoyiannis. Revisiting long-range dependence in annual precipitation. Journal of Hydrology 2018, 556, 891 -900.
AMA StyleTheano Iliopoulou, Simon Michael Papalexiou, Yannis Markonis, Demetris Koutsoyiannis. Revisiting long-range dependence in annual precipitation. Journal of Hydrology. 2018; 556 ():891-900.
Chicago/Turabian StyleTheano Iliopoulou; Simon Michael Papalexiou; Yannis Markonis; Demetris Koutsoyiannis. 2018. "Revisiting long-range dependence in annual precipitation." Journal of Hydrology 556, no. : 891-900.