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
T. Iliopoulou, P. Dimitriadis, A. Siganou, D. Markantonis, K. Moraiti, M. Nikolinakou, I. Meletopoulos, N. Mamassis, D. Koutsoyiannis, and G.-F. Sargentis, Modern use of traditional rainwater harvesting practices: An assessment of cisterns’ water supply potential in West Mani, Greece, Heritage, 5 (4), 2944–2954, doi:10.3390/heritage5040152, 2022.
Water has always been a driver of human civilization. The first human civilizations thrived in places with an abundance of water, typically nearby large rivers as the Tigris–Euphrates, Yang Che and Nile. The invention and construction of hydraulic infrastructure came only later, in prehistoric times, triggered by the expansion of humanity in water-scarce areas. The ancient Greeks invented impressive hydraulic works and small-scale structures, some of which, such as cisterns, were still fully operational until the 20th century. We present a model that explains the use of cisterns in the water-scarce area of West Mani, which allows us to assess the potential of this traditional rainfall harvesting practice to support the modern water supply needs. To assess the system’s reliability, we employ a long-term simulation of a typical cistern system, using synthetic rainfall series from a stochastic model, and assuming variable water demand on a monthly scale. We show that a proper restoration of the cisterns could be sustainable as a complementary water supply source, decreasing the area’s drinking water cost and increasing the locals’ resilience against water shortages. In addition, we highlight the links between the area’s hydroclimate and its history and discuss the cultural merits of reviving and preserving this ancient, long practice.
Full text: http://www.itia.ntua.gr/en/getfile/2243/1/documents/heritage-05-00152-v3.pdf (4196 KB)
A. Koskinas, E. Zacharopoulou, G. Pouliasis, I. Deligiannis, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Estimating the Statistical Significance of Cross–Correlations between Hydroclimatic Processes in the Presence of Long–Range Dependence, Earth, 3 (3), 1027-1041, doi:10.3390/earth3030059, 2022.
Hydroclimatic processes such as precipitation, temperature, wind speed and dew point are usually considered to be independent of each other. In this study, the cross–correlations between key hydrological-cycle processes are examined, initially by conducting statistical tests, then adding the impact of long-range dependence, which is shown to govern all these processes. Subsequently, an innovative stochastic test that can validate the significance of the cross–correlation among these processes is introduced based on Monte-Carlo simulations. The test works as follows: observations obtained from numerous global-scale timeseries were used for application to, and a comparison of, the traditional methods of validation of statistical significance, such as the t-test, after filtering the data based on length and quality, and then by estimating the cross–correlations on an annual-scale. The proposed method has two main benefits: it negates the need of the pre-whitening data series which could disrupt the stochastic properties of hydroclimatic processes, and indicates tighter limits for upper and lower boundaries of statistical significance when analyzing cross–correlations of processes that exhibit long-range dependence, compared to classical statistical tests. The results of this analysis highlight the need to acquire cross–correlations between processes, which may be significant in the case of long-range dependence behavior.
Full text: http://www.itia.ntua.gr/en/getfile/2234/1/documents/earth-03-00059-v3.pdf (5430 KB)
A. Pizarro, P. Dimitriadis, T. Iliopoulou, S. Manfreda, and D. Koutsoyiannis, Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions, Hydrology, 9 (7), 126, doi:10.3390/hydrology9070126, 2022.
The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magnitude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleoclimatic reconstructions. We aimed to determine the fractal behaviour and the long-range dependence behaviour of the stream- flow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as suggested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal distribution and a generalized Hurst–Kolmogorov (HK) dependence structure.
T. Iliopoulou, N. Malamos, and D. Koutsoyiannis, Regional ombrian curves: Design rainfall estimation for a spatially diverse rainfall regime, Hydrology, 9 (5), 67, doi:10.3390/hydrology9050067, 2022.
Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13 700 km² water district of Greece characterized by varying topography and hydrometeorological properties.
Full text: http://www.itia.ntua.gr/en/getfile/2188/1/documents/hydrology-09-00067-v3.pdf (9357 KB)
P. Dimitriadis, T. Iliopoulou, G.-F. Sargentis, and D. Koutsoyiannis, Spatial Hurst–Kolmogorov Clustering, Encyclopedia, 1 (4), 1010–1025, doi:10.3390/encyclopedia1040077, 2021.
The stochastic analysis in the scale domain (instead of the traditional lag or frequency domains) is introduced as a robust means to identify, model and simulate the Hurst–Kolmogorov (HK) dynamics, ranging from small (fractal) to large scales exhibiting the clustering behavior (else known as the Hurst phenomenon or long-range dependence). The HK clustering is an attribute of a multidimensional (1D, 2D, etc.) spatio-temporal stationary stochastic process with an arbitrary marginal distribution function, and a fractal behavior on small spatio-temporal scales of the dependence structure and a power-type on large scales, yielding a high probability of low- or high-magnitude events to group together in space and time. This behavior is preferably analyzed through the second-order statistics, and in the scale domain, by the stochastic metric of the climacogram, i.e., the variance of the averaged spatio-temporal process vs. spatio-temporal scale.
S. Vavoulogiannis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Multiscale temporal irreversibility of streamflow and its stochastic modelling, Hydrology, 8 (2), 63, doi:10.3390/hydrology8020063, 2021.
We investigate the impact of time's arrow on the hourly streamflow process. Although time asymmetry, i.e., temporal irreversibility, has been previously implemented in stochastics, it has only recently attracted attention in the hydrological literature. Relevant studies have shown that the time asymmetry of the streamflow process is manifested at scales up to several days and vanishes at larger scales. The latter highlights the need to reproduce it in flood simulations of fine-scale resolution. To this aim, we develop an enhancement of a recently proposed simulation algorithm for irreversible processes, based on an asymmetric moving average (AMA) scheme that allows for the explicit preservation of time asymmetry at two or more timescales. The method is successfully applied to a large hourly streamflow time series from the United States Geological Survey (USGS) database, with time asymmetry prominent at time scales up to four days.
Full text: http://www.itia.ntua.gr/en/getfile/2116/1/documents/hydrology-08-00063-v2.pdf (2541 KB)
P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.
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 secondorder 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 secondorder 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.
Full text: http://www.itia.ntua.gr/en/getfile/2114/1/documents/hydrology-08-00059-v5.pdf (7374 KB)
G.-F. Sargentis, T. Iliopoulou, P. Dimitriadis, N. Mamassis, and D. Koutsoyiannis, Stratification: An entropic view of society's structure, World, 2, 153–174, doi:10.3390/world2020011, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2107/1/documents/world-02-00011-v3.pdf (10384 KB)
G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, A stochastic view of varying styles in art paintings, Heritage, 4, 21, doi:10.3390/heritage4010021, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2086/1/documents/heritage-04-00021.pdf (3242 KB)
G.-F. Sargentis, R. Ioannidis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Landscape planning of infrastructure through focus points’ clustering analysis. Case study: Plastiras artificial lake (Greece), Infrastructures, 6 (1), 12, doi:10.3390/infrastructures6010012, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2083/1/documents/infrastructures-06-00012-v2.pdf (5634 KB)
K. Glynis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of daily air temperature extremes from a global ground station network, Stochastic Environmental Research & Risk Assessment, doi:10.1007/s00477-021-02002-3, 2021.
Near-surface air temperature is one of the most widely studied hydroclimatic variables, as both its regular and extremal behaviors are of paramount importance to human life. Following the global warming observed in the past decades and the advent of the anthropogenic climate change debate, interest in temperature’s variability and extremes has been rising. It has since become clear that it is imperative not only to identify the exact shape of the temperature’s distribution tails, but also to understand their temporal evolution. Here, we investigate the stochastic behavior of near-surface air temperature using the newly developed estimation tool of Knowable (K-)moments. K-moments, because of their property to substitute higher-order deviations from the mean with the distribution function, enable reliable estimation and an effective alternative to order statistics and, particularly for the outliers-prone distribution tails. We compile a large set of daily timeseries (30–200 years) of average, maximum and minimum air temperature, which we standardize with respect to the monthly variability of each record. Our focus is placed on the maximum and minimum temperatures, because they are more reliably measured than the average, yet very rarely analyzed in the literature. We examine segments of each timeseries using consecutive rolling 30-year periods, from which we extract extreme values corresponding to specific return period levels. Results suggest that the average and minimum temperature tend to increase, while overall the maximum temperature is slightly decreasing. Furthermore, we model the temperature timeseries as a filtered Hurst-Kolmogorov process and use Monte Carlo simulation to produce synthetic records with similar stochastic properties through the explicit Symmetric Moving Average scheme. We subsequently evaluate how the patterns observed in the longest records can be reproduced by the synthetic series.
G.-F. Sargentis, T. Iliopoulou, S. Sigourou, P. Dimitriadis, and D. Koutsoyiannis, Evolution of clustering quantified by a stochastic method — Case studies on natural and human social structures, Sustainability, 12 (19), 7972, doi:10.3390/su12197972, 2020.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2066/1/documents/sustainability-12-07972.pdf (8123 KB)
G.-F. Sargentis, P. Dimitriadis, R. Ioannidis, T. Iliopoulou, E. Frangedaki, and D. Koutsoyiannis, Optimal utilization of water resources for local communities in mainland Greece (case study of Karyes, Peloponnese), Procedia Manufacturing, 44, 253–260, doi:10.1016/j.promfg.2020.02.229, 2020.
Water is the basis of our civilization and the development of society is intertwined with the exploitation of water resources in various scales, from a well dug to irrigate a garden, to a large dam providing water and energy for a large area. However, for remote mountainous areas, intermittent natural water resources and high seasonal demand the above tasks become challenging. Here we discuss various alternative management options and appropriate solutions on how to exploit water resources meeting the above restrictions under limited infrastructure budgets. As a case study we examine the area of Karyes in Peloponnese that meets the above criteria, exploring various solutions to satisfy the water demand.
T. Iliopoulou, and D. Koutsoyiannis, Projecting the future of rainfall extremes: better classic than trendy, Journal of Hydrology, 588, doi:10.1016/j.jhydrol.2020.125005, 2020.
Non-stationarity approaches have been increasingly popular in hydrology, reflecting scientific concerns regarding intensification of the water cycle due to global warming. A considerable share of relevant studies is dominated by the practice of identifying linear trends in data through in-sample analysis. In this work, we reframe the problem of trend identification using the out-of-sample predictive performance of trends as a reference point. We devise a systematic methodological framework in which linear trends are compared to simpler mean models, based on their performance in predicting climatic-scale (30-year) annual rainfall indices, i.e. maxima, totals, wet-day average and probability dry, from long-term daily records. The models are calibrated in two different schemes: block-moving, i.e. fitted on the recent 30 years of data, obtaining the local trend and local mean, and global-moving, i.e. fitted on the whole period known to an observer moving in time, thus obtaining the global trend and global mean. The investigation of empirical records spanning over 150 years suggests that a great degree of variability has been ever present in the rainfall process, leaving small potential for long-term predictability. The local mean model ranks first in terms of average predictive performance, followed by the global mean and the global trend, in decreasing order of performance, while the local trend model ranks last among the models, showing the worst performance overall. Parallel experiments from synthetic timeseries characterized by persistence corroborated this finding, suggesting that future long-term variability of persistent processes is better captured using parsimonious features of the past. In line with the empirical findings, it is shown that, prediction-wise, simple is preferable to trendy.
Official site for free access (temporary): https://authors.elsevier.com/c/1b41M52cuR14A
R. Ioannidis, T. Iliopoulou, C. Iliopoulou, L. Katikas, A. Petsou, M.-E. Merakou, M.-E. Asimomiti, N. Pelekanos, G. Koudouris, P. Dimitriadis, C. Plati, E. Vlahogianni, K. Kepaptsoglou, N. Mamassis, and D. Koutsoyiannis, Solar-powered bus route: introducing renewable energy into a university campus transport system, Advances in Geosciences, 49, doi:10.5194/adgeo-49-215-2019, 2019.
We investigate the application of a solar-powered bus route to a small-scale transportation system, as such of a university campus. In particular, we explore the prospect of replacing conventional fossil fuel buses by electric buses powered by solar energy and electricity provided by the central grid. To this end, we employ GIS mapping technology to estimate the solar radiation at the university campus and, accordingly, we investigate three different scenarios for harnessing the available solar power: (1) solar panels installed on the roof of bus stop shelters, (2) solar panels installed at an unused open space in the university, and (3) solar roads, i.e. roads constructed by photovoltaic (PV) materials. For each of the three scenarios, we investigate the optimal technical configuration, the resulting energy generation, as well as the capital cost for application in the case of NTUA campus in Athens (Greece). The preliminary feasibility analysis showcases that all three scenarios contribute to satisfying transportation demand, proportionately to their size, with scenario (2) presenting the lowest capital cost in relation to energy generation. Therefore, we further explore this scenario by simulating its daily operation including the actions of buying and selling energy to the central grid, when there is energy deficit or surplus, respectively. A sensitivity analysis is carried out in order to ascertain the optimal size of the solar panel installation in relation to profit and reliability. Overall, results indicate that, albeit the high capital costs, solar-powered transportation schemes present a viable alternative for replacing conventional buses at the studied location, especially considering conventional PV panels. We note that present results heavily depend on the choice of capacity factors of PV materials, which differ among technologies. Yet, as capacity factors of PV panels are currently increasing, the studied schemes might be more promising in the future.
Full text: http://www.itia.ntua.gr/en/getfile/2016/1/documents/adgeo-49-215-2019.pdf (8167 KB)
T. Iliopoulou, and D. Koutsoyiannis, Revealing hidden persistence in maximum rainfall records, Hydrological Sciences Journal, 64 (14), 1673–1689, doi:10.1080/02626667.2019.1657578, 2019.
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.
G.-F. Sargentis, P. Dimitriadis, R. Ioannidis, T. Iliopoulou, and D. Koutsoyiannis, Stochastic evaluation of landscapes transformed by renewable energy installations and civil works, Energies, 12 (4), 2817, doi:10.3390/en12142817, 2019.
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.
Full text: http://www.itia.ntua.gr/en/getfile/1984/1/documents/energies-12-02817.pdf (2772 KB)
Other works that reference this work (this list might be obsolete):
|1.||Ding, L., Q. Li, J. Tang, J. Wang, and X. Chen, Linking land use metrics measured in aquatic-terrestrial interfaces to water quality of reservoir-based water sources in Eastern China, Sustainability, 11(18), 4860, doi:10.3390/su11184860, 2019.|
T. Iliopoulou, C. Aguilar , B. Arheimer, M. Bermúdez, N. Bezak, A. Ficchi, D. Koutsoyiannis, J. Parajka, M. J. Polo, G. Thirel, and A. Montanari, A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers, Hydrology and Earth System Sciences, 23, 73–91, doi:10.5194/hess-23-73-2019, 2019.
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.
Full text: http://www.itia.ntua.gr/en/getfile/1927/1/documents/hess-23-73-2019.pdf (6166 KB)
A. Koskinas, A. Tegos, P. Tsira, P. Dimitriadis, T. Iliopoulou, P. Papanicolaou, D. Koutsoyiannis, and Τ. Williamson, Insights into the Oroville Dam 2017 spillway incident, Geosciences, 9 (37), doi:10.3390/geosciences9010037, 2019.
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.
Full text: http://www.itia.ntua.gr/en/getfile/1926/1/documents/geosciences-09-00037-2.pdf (6834 KB)
T. Iliopoulou, D. Koutsoyiannis, and A. Montanari, Characterizing and modeling seasonality in extreme rainfall, Water Resources Research, 54 (9), 6242–6258, 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.
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)
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/
H. Tyralis, P. Dimitriadis, D. Koutsoyiannis, P.E. O’Connell, K. Tzouka, and T. Iliopoulou, On the long-range dependence properties of annual precipitation using a global network of instrumental measurements, Advances in Water Resources, 111, 301–318, doi:10.1016/j.advwatres.2017.11.010, 2018.
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.
Supplementary information files are hosted at: https://doi.org/10.6084/m9.figshare.4892447.v1
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).
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)
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.
Full text: http://www.itia.ntua.gr/en/getfile/1733/1/documents/electric_mix_energy_procedia.pdf (1118 KB)
Other works that reference this work (this list might be obsolete):
|1.||Bakanos, P. I., and K. L. Katsifarakis, Optimizing operation of a large-scale pumped storage hydropower system coordinated with wind farm by means of genetic algorithm, Global Nest Journal, 2019.|
|2.||Giudici, F., A. Castelletti, E. Garofalo, M. Giuliani, and H. R. Maier, Dynamic, multi-objective optimal design and operation of water-energy systems for small, off-grid islands, Applied Energy, 250, 605-616, doi:10.1016/j.apenergy.2019.05.084, 2019.|
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.
Full text: http://www.itia.ntua.gr/en/getfile/1732/1/documents/energy_proc_paper.pdf (2324 KB)
Other works that reference this work (this list might be obsolete):
|1.||Pouliasis, G., G. A. Torres-Alves, and O. Morales-Napoles, Stochastic modeling of hydroclimatic processes using vine copulas, Water, 13(16), 2156, doi:10.3390/w13162156, 2021.|
P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Assessing the spatial impact of the skewness-ratio originating from the time irreversibility and long-range dependence of streamflow in flood inundation mapping, Proceedings of 7th IAHR Europe Congress "Innovative Water Management in a Changing Climate”, Athens, International Association for Hydro-Environment Engineering and Research (IAHR), 2022.
T. Iliopoulou, and D. Koutsoyiannis, A parsimonious approach for regional design rainfall estimation: the case study of Athens, Proceedings of 7th IAHR Europe Congress "Innovative Water Management in a Changing Climate”, Athens, International Association for Hydro-Environment Engineering and Research (IAHR), 2022.
Design rainfall estimation at the regional scale is the cornerstone of hydrological design against flooding, particularly essential for ungauged areas. We devise a parsimonious and flexible methodology for regional estimation of rainfall extremes for time scales of minutes up to a few days and any return period, i.e. producing the ombrian curves. Estimation of the distribution parameters is performed by an advanced regional pooling approach employing knowable (K-) moments that allow reliable high-order moment estimation and handling of space dependence; which is non-negligible in homogenous regions. The regionalization approach is based on elevation, which is often sufficient to explain the rainfall variability within a generally homogenous climatic region. The methodology is effectively applied in the Attica region, comprising Athens and its surrounding basins.
D. Koutsoyiannis, and T. Iliopoulou, Ombrian curves advanced to stochastic modeling of rainfall intensity (Chapter 9), Rainfall Modeling, Measurement and Applications, 261–283, Elsevier, 2022.
Ombrian curves, i.e. mathematic relationships linking average rainfall intensity to time scale of averaging and return period, also known as IDF (intensity-duration-frequency) curves, are essential tools in hydrology and engineering. Their use is supported by long-term hydrological experience, yet related formulas remain mostly empirical and lack a theoretical basis. As such, they entail several theoretical inconsistencies, particularly over large scales, while they cannot be applied in simulation. This Chapter reviews the typical form of ombrian curves along with its merits and limitations, and presents a modelling framework to overcome the latter by advancing curves to stochastic models of rainfall intensity. This is achieved through stochastic modelling of the joint second-order and marginal higher-order properties of the parent process. Two variants of the ombrian model are presented; a full version valid over time scales spanning multiple orders of magnitude, and a simplified relationship applicable over fine scales of the order of common applications, i.e. sub-hourly to daily. Specific emphasis is given to the fitting procedure combining multiple data sources and addressing bias in the estimation induced by temporal dependence. A detailed application of the ombrian model is performed for the rainfall station in Bologna (Italy), highlighting the efficiency of the resulting curves over multiple scales.
M. Pantazidou, D. Koutsoyiannis, H. Saroglou, V. Marinos, and T. Iliopoulou, Infuse teaching with research practices: a pilot project – welcome presentation for first-year students on time scales in civil engineering projects, 1st Joint Conference of EUCEET and AECEF: The role of education for Civil Engineers in the implementation of the SDGs, Thessaloniki, 2021.
The seed motivation behind this paper is the realization that time is not given its due as a concept in Civil Engineering. The corresponding education need is expressed with the question “what educational material can stress the importance of time and how can it be produced?”. The approach chosen to answer the first part of the question was to juxtapose smaller and larger time scales and highlight their relevance to civil engineering projects in a video-presentation with the title “Earth, Water, Time and We, the civil engineers”. The project described in the paper consists of two products: the video-presentation and the methodology, which addresses the second part of the question motivating the work. The methodology infuses teaching with the research practices of teamwork and peer review, hence the production of the educational material can serve as a pilot for other endeavors to raise the standing of education relative to research.
Video of the conference presentation: https://youtu.be/OGX-Z-FsY_8?t=11720
N. Mamassis, A. Efstratiadis, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, and D. Koutsoyiannis, Water and Energy, Handbook of Water Resources Management: Discourses, Concepts and Examples, edited by J.J. Bogardi, T. Tingsanchali, K.D.W. Nandalal, J. Gupta, L. Salamé, R.R.P. van Nooijen, A.G. Kolechkina, N. Kumar, and A. Bhaduri, Chapter 20, 617–655, doi:10.1007/978-3-030-60147-8_20, Springer Nature, Switzerland, 2021.
The fundamental concepts in the field of water-energy systems and their historical evolution with emphasis on recent developments are reviewed. Initially, a brief history of the relation of water and energy is presented, and the concept of the water-energy nexus in the 21th century is introduced. The investigation of the relationship between water and energy shows that this relationship comprises both conflicting and synergistic elements. Hydropower is identified as the major industry of the sector and its role in addressing modern energy challenges by means of integrated water-energy management is highlighted. Thus, the modelling steps of designing and operating a hydropower system are reviewed, followed by an analysis of theory and physics behind energy hydraulics. The key concept of uncertainty, which characterises all types of renewable energy, is also presented in the context of the design and management of water-energy systems. Subsequently, environmental considerations and impacts of using water for energy generation are discussed, followed by a summary of the developments in the emerging field of maritime energy. Finally, present challenges and possible future directions are presented.
Other works that reference this work (this list might be obsolete):
|1.||Bertsiou, M. M., and E. Baltas, Management of energy and water resources by minimizing the rejected renewable energy, Sustainable Energy Technologies and Assessments, 52(A), 102002, doi:10.1016/j.seta.2022.102002, 2022.|
|2.||Spanoudaki, K., P. Dimitriadis, E. A. Varouchakis, and G. A. C. Perez, Estimation of hydropower potential using Bayesian and stochastic approaches for streamflow simulation and accounting for the intermediate storage retention, Energies, 15(4), 1413, doi:10.3390/en15041413, 2022.|
D. Dimitrakopoulou, R. Ioannidis, G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, E. Chardavellas, S. Vavoulogiannis, N. Mamassis, and D. Koutsoyiannis, Social uncertainty in flood risk: field research, citizens’ engagement, institutions' collaboration, IAHS 100th Anniversary – 11th IAHS-AISH Scientific Assembly 2022, Montpellier, France, IAHS2022-351, International Association of Hydrological Sciences, 2022.
The well-presented results and the high efficiency of new tools in the evaluation of flood risk leads us to forget the fundamental tool for analysis which is field research, citizens’ engagement and institutions collaboration.
Having in mind that field-research must be connected with modern tools, this paper shows that only engineers are appropriate for flood-study field-research. In addition, a training protocol is necessary. This protocol describes the method of the field-research, the organization of the team, legal distractions in field research, proper software needed for field research, characteristic points of interest, code name and proper depiction of the points. In addition, describes an efficient formula of the reports in order to be used in GIS and evaluated in DEM and risk analysis.
In addition, the cooperation of research and governmental institutions is crucial for the quantification of risks associated with natural hazards. Research institutions, local-government authorities and environmental agencies are all necessary, in order to combine both theoretical and practical knowledge for the generation of optimized risk-assessment results. Thus, a targeted methodology was formed including a process of successive cycles of communications relevant those agencies and institutions, aiming to utilize both their qualitative and quantitative knowledge and overall, to set a solid data-based foundation for the later stages of the flood-risk analysis.
Last but not least, in the process of investigating for locations with increased flood risk, citizens’ engagement should be sought. During the research field or through an online form, the citizens should be asked to fill in a relative questionnaire with brief, multiple choice questions, regarding their residence, their years of residence, the frequency of floods that they can recall and their location and other relates topics. The permanent residents' experience can lead to the location of areas prone to flood that cannot be located otherwise, in terms of designs. Consequently, it is argued that the residents must play an active role in the conception, design and implementation of flood protection projects and infrastructure projects, overall.
G.-F. Sargentis, I. Meletopoulos, T. Iliopoulou, P. Dimitriadis, E. Chardavellas, D. Dimitrakopoulou, A. Siganou, D. Markantonis, K. Moraiti, K. Kouros, M. Nikolinakou, and D. Koutsoyiannis, Modelling water needs; from past to present. Case study: The Municipality of Western Mani, IAHS 100th Anniversary – 11th IAHS-AISH Scientific Assembly 2022, Montpellier, France, IAHS2022-400, International Association of Hydrological Sciences, 2022.
In traditional and isolated societies human needs were limited and the resources were sufficient. For example, 70 years ago, water needs per capita in Greece were about 7,2 m3/year. But the basic perception of development is the abundance of water resources. For example, tourist development changes the culture of water consumption as modern way of living needs 150 m3/year per capita. In the same time one visitor would prefer accommodation with pools demanding even more fresh water.
Fortunately, there are many technological solutions to cover this gap of consumption. Unfortunately, some of them are not efficient or sustainable and other have big cost of energy.
This research examines the case study of the Municipality of Western Mani in South Greece, an area with high touristic development, detects the transformation of needs and potential technical solutions which are evaluated with criteria: needs coverage; sustainability; preservation of the landscape.
Stochastic models for the simulation of the function of water infrastructures in different scales (from traditional to modern) are applied.
Full text: http://www.itia.ntua.gr/en/getfile/2214/1/documents/IAHS_Sargentis.pdf (1662 KB)
S. Vavoulogiannis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Time Asymmetry and Stochastic Modelling of Streamflow, IAHS 100th Anniversary – 11th IAHS-AISH Scientific Assembly 2022, Montpellier, France, IAHS2022-270, International Association of Hydrological Sciences, 2022.
Time asymmetry, i.e., temporal irreversibility, has a very important role in many scientific fields and has been studied thoroughly. Its detection in time series indicates the need to preserve it in stochastic simulations. This also seems to be the case for the streamflow process in hydrological simulation. Relevant large-scale studies have shown that time asymmetry of the streamflow is absolutely evident at small scales (hours) and vanishes only at larger scales (several days). The latter highlights the need to reproduce it in flood simulations of fine-scale resolution. To this aim, an enhancement of a recently proposed simulation algorithm for irreversible processes was developed, based on an asymmetric moving average (AMA) scheme that allows for the explicit preservation of time asymmetry at two or more timescales simultaneously. The method is tested through some case studies from around the world to further explore the method’s strengths and limitations and to examine the stochastic characteristics of the simulated results.
P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Theoretical framework for the stochastic synthesis of the variability of global-scale key hydrological-cycle processes and estimation of their predictability limits under long-range dependence, IAHS 100th Anniversary – 11th IAHS-AISH Scientific Assembly 2022, Montpellier, France, IAHS2022-610, International Association of Hydrological Sciences, 2022.
Uncertainty and change in geophysical processes can be robustly quantified by analyzing the observed variability. A challenging task in engineering studies is to introduce a framework that can simulate this observed variability while preserving only important stochastic attributes. An innovative methodology for genuine simulation of stochastic processes is presented based on the recent work by Koutsoyiannis and Dimitriadis (2021). The proposed algorithm includes the demanding task of simulating any second-order dependence structure of a process (with a focus on long-range dependence behaviour) and any marginal distribution (with focus on heavy tails) through the explicit preservation of its autocovariance function and its cumulants. The long-range dependence behaviour (i.e., power-law drop of variance vs. scale) and heavy-tails are known to be highly associated with the variability magnitude of a process, through which the range of its predictability-window can be also quantified. To estimate this range, an extensive global-scale network of stations of key hydrological-cycle processes (i.e., near-surface hourly temperature, dew point, relative humidity, sea level pressure, atmospheric wind speed, streamflow, and precipitation; for details see Dimitriadis et al., 2021) is analyzed using ensemble techniques and the proposed stochastic simulation algorithm. The limitations of existing methodologies for the stochastic simulation and estimation of the predictability-window, and how can they be tackled through the proposed approach, are discussed over applications in flood risk management.
Koutsoyiannis, D., and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.
Dimitriadis, P., D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.
Acknowledgment: This research is in the context of the project “Development of Stochastic Methods for Extremes (ASMA): identification and simulation of dependence structures of extreme hydrological events” (MIS 5049175), which is co-financed by Greece and the European Union (European Social Fund; ESF).
T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Investigating the clustering mechanisms of hydroclimatic extremes: from identification to modelling strategies, IAHS 100th Anniversary – 11th IAHS-AISH Scientific Assembly 2022, Montpellier, France, IAHS2022-382, International Association of Hydrological Sciences, 2022.
The understanding of the temporal properties of hydroclimatic extremes is critical to the mitigation of related risk as well as to society’s perception of risk. While the marginal properties of extremes have been extensively studied in the literature, their temporal behaviours have been rather overlooked, or approached via deterministic reasoning. We focus on the temporal variability and clustering mechanisms of extremes as seen in long-term hydroclimatic records, highlighting their links to the inherent stochastic properties of the parent hydrological process. To this aim, we apply a new simulation algorithm (Koutsoyiannis and Dimitriadis, 2021) capable of simultaneously reproducing the time dependence structure of a stochastic process, from short-term dependence to persistence (i.e. Hurst-Kolmogorov dynamics), its time directionality as well as its marginal distribution, irrespective of its type. The performance of the methdology in reproducing the observed extremal patterns is evaluated and the practical implications of the findings are discussed.
Reference: D. Koutsoyiannis, and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.
Full text: http://www.itia.ntua.gr/en/getfile/2210/1/documents/Presentation_iahs_Iliopoulou.pdf (3549 KB)
M. Chiotinis, P. Dimitriadis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, To act or not to act. Predictability of intervention and non-intervention in health and environment, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-11747, doi:10.5194/egusphere-egu22-11747, European Geosciences Union, 2022.
The COVID-19 pandemic has brought forth the question of the need for draconian interventions before concrete evidence for their need and efficacy is presented. Such interventions could be critical if necessary for avoiding threats, or a threat in themselves if harms caused by the intervention are significant.
The interdisciplinary nature of such issues as well as the unpredictability of various local responses considering their potential for global impact further complicate the question.
The study aims to review the available evidence and discuss the problem of weighting the predictability of interventions vis-à-vis their intended results against the limits of knowability regarding complex non-linear systems and thus the predictability in non-interventionist approaches.
P. Dimitriadis, T. Iliopoulou, G.-F. Sargentis, and D. Koutsoyiannis, Spatial and temporal long-range dependence in the scale domain, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-13051, doi:10.5194/egusphere-egu22-13051, European Geosciences Union, 2022.
Long-range dependence (LRD) estimators are traditionally applied in the lag domain (e.g., through the autocovariance or variogram) or in the frequency domain (e.g., through the power-spectrum), but not as often in the scale domain (e.g., through variance vs. scale). It has been contended that the latter case introduces large estimation bias and thus, corresponds to "bad estimators" of the LRD. However, this reflects a misrepresentation or misuse of the concept of variance vs. scale. Specifically, it is shown that if the LRD estimator of variance vs. scale is properly defined and assessed (see literature studies for the so-called climacogram estimator), then the stochastic analysis of variance in the scale domain can be proven to be a robust means to identify and model any LRD process ranging from small scales (fractal behavior) to large scales (LRD, else known as the Hurst-Kolmogorov dynamics) for any marginal distribution. Here, we show how the above definitions can be applied both in spatial and temporal scales, with various applications in geophysical processes, key hydrological-cycle processes, and related natural hazards.
D. Markantonis, A. Siganou, K. Moraiti, M. Nikolinakou, G.-F. Sargentis, P. Dimitriadis, M. Chiotinis, T. Iliopoulou, N. Mamassis, and D. Koutsoyiannis, Determining optimal scale of water infrastructure considering economical aspects with stochastic evaluation – Case study at the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-3039, doi:10.5194/egusphere-egu22-3039, European Geosciences Union, 2022.
Infrastructures for the supply of water are one of the most necessary facilities in modern life. The optimal design of such infrastructures (for example, dams or even small-size tanks) is often a great challenge in civil engineering, given the large number of factors required for their design (e.g., feasibility, reliability, cost effectiveness, resilience). One of the most critical decisions that may have a great impact on the optimization procedure is the determination of the scale of the proposed system.
During a study of such a design of a water supply infrastructure in the Municipality of Western Mani, it became clear that several solutions of different scales coexisted. Ultimately, the cost-benefit factors were the most heavily considered ones, provided that the required reliability was met. Stochastic methods have been proven to be appropriate tools for studying such highly complex and uncertain puzzles. The current study intends to approach this problem by considering solutions of different scales, and to establish the long-term cost effectiveness as the main criterion to evaluate the different solutions.
K. Moraiti, D. Markantonis, M. Nikolinakou, A. Siganou, G.-F. Sargentis, T. Iliopoulou, P. Dimitriadis, I. Meletopoulos, N. Mamassis, and D. Koutsoyiannis, Optimizing water infrastructure solutions for small-scale distributed settlements – Case study at the Municipality of Western Mani., EGU General Assembly 2022, Vienna, Austria & Online, EGU22-3055, doi:10.5194/egusphere-egu22-3055, European Geosciences Union, 2022.
Water infrastructure is an indicator of human civilization and its evolution. The sustainable water management and distribution to local communities remains a critical engineering priority so that the most efficient usage is achieved. In this analysis the design of water-infrastructure establishments is studied for the community of the Municipality of Western Mani (western Peloponnese, Greece).
One of the main issues that arise is the presence of karstic-limestone geological structure at the study area with no permanent watercourses. Furthermore, the lack of data about the current quantity of surface water makes it difficult to formulate trustworthy conclusions on the availability of water resources. Additionally, the notable growth of the tourist sector during the summer months in the past few years exacerbates this issue. Due to the above reasons, the available water is not enough to cover the needs of the Municipality, especially during the summer.
After examining all the possible options that have been proposed to increase the water availability (e.g., through dams, wells, desalination, water ponds etc.), we investigate an optimal solution that aims to achieve a more efficient water management and distribution to the communities of Western Mani. To this aim, we apply a multi-criteria decision-making approach by also considering local traditional water harvesting systems to increase water resilience.
M. Nikolinakou, K. Moraiti, A. Siganou, D. Markantonis, G.-F. Sargentis, T. Iliopoulou, P. Dimitriadis, I. Meletopoulos, N. Mamassis, and D. Koutsoyiannis, Investigating the water supply potential of traditional rainwater harvesting techniques used – A case study for the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria & Online, European Geosciences Union, 2022.
Water availability is a critical issue for growing local communities. For example, in the Municipality of Western Mani (western Peloponnese, Greece) tourist development has caused scarcity of water intensifying during the summer period. In this context, multiple solutions are being studied in order to assist the local communities of Western Mani to deal with this situation.
This study focuses on traditional water harvesting structures and more specifically cisterns. In the past, a cistern was present nearby or almost at every house, collecting rain water so as to cover the various needs of the inhabitants, including human consumption and irrigation. However, although cisterns today have fallen into disuse due to the developments of modern water supply systems, they remain an important part of cultural heritage and an architectural element of great interest.
In this work, we evaluate the potential of traditional water infrastructures to cover domestic needs employing the method of stochastic simulation based on hydrological data and by also taking into account traditional architecture.
A. Siganou, M. Nikolinakou, D. Markantonis, K. Moraiti, G.-F. Sargentis, T. Iliopoulou, P. Dimitriadis, M. Chiotinis, N. Mamassis, and D. Koutsoyiannis, Stochastic simulation of hydrological timeseries for data scarce regions - Case study at the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-3086, doi:10.5194/egusphere-egu22-3086, European Geosciences Union, 2022.
West Mani, an attractive place in western Peloponnese, Greece, faces water shortage. The problem lies not only in the quantity but also in the quality of the available water. Investigating the options for the sustainable management of water resources, utilizing surface water seems to be the optimal solution. However, the complex geomorphology and geology of the study area, and its particular its karstic structure, when combined with the scarcity of hydrological data, makes the estimation of surface water availability challenging. As a result, it is considered necessary to take hydrological uncertainty into account using stochastic analysis. To this aim, we generate synthetic rainfall and streamflow timeseries based on available meteorological data from basins near the area of interest. We then appropriately adjust them so that they represent the magnitude and the variability of the rainfall and streamflow of the study area. For the timeseries generation algorithm, we employ a stochastic model following the Hurst-Kolmogorov dynamics by reproducing marginal distribution, seasonality and persistence.
I. Arvanitidis, Μ. Diamanta, G.-F. Sargentis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Identifying links between hydroclimatic variability and economical components using stochastic methods, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-5944, doi:10.5194/egusphere-egu22-5944, European Geosciences Union, 2022.
Since ancient times water has been a substantial factor for driving economic growth, as abundance in water resources can be linked to the development of prosperous communities. This study examines the effect of water resources availability on different sectors of the economy, by identifying components of Gross Domestic Product which are most affected by key water cycle processes and water infrastructures. In this analysis, we investigate the correlation among the above processes, on both temporal and spatial scale with the implementation of stochastic methods, in order to assess the sensitivity of the economy to hydroclimatic variability. We also take into consideration the effect of hydroclimatic extremes such as droughts and the limitations they may impose on growth. Differences between climate zones are taken into consideration by the Köppen climate index.
S. Vrettou, A. Trompouki, T. Iliopoulou, G.-F. Sargentis, P. Dimitriadis, and D. Koutsoyiannis, Investigation of stochastic similarities between wind and waves and their impact on offshore structures, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-3082, doi:10.5194/egusphere-egu22-3082, European Geosciences Union, 2022.
Offshore wind farms are increasingly gaining acceptance in the field of energy production. From an engineering point of view, such offshore structures are affected by various sources of uncertainty. The most severe one, is the impact that wave (height and period) and wind processes have, either at the fatigue, and in some cases failure of such structures, or at the efficiency of their energy production. In this work, we are focusing on the stochastic properties of the above processes and on their impacts on offshore structures. By extracting data from gauging stations at the Aegean Sea, we specifically examine the stochastic similarities among the marginal moments and the correlation function with focus on the extremes of the wind velocity and the wave height and period, and we discuss their impacts on open sea structures.
Full text: http://www.itia.ntua.gr/en/getfile/2201/1/documents/Presentation_O64qHGK.pdf (2641 KB)
A. Trompouki, S. Vrettou, G.-F. Sargentis, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation of the spatial correlation structure of 2-D wave fields at the Aegean Sea, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-3083, doi:10.5194/egusphere-egu22-3083, European Geosciences Union, 2022.
The great potential of oceanic energy resources adds a new challenge in the field of off-shore engineering, that of the efficient energy extraction from sophisticated structures in the open sea. An additional challenge that the engineers have to face is the intrinsic uncertainty of the oceanic processes. In this work, we investigate the uncertainty of the wave process through the estimation of the variability in two-dimensional wave height and direction data. These are retrieved from satellite images over the Aegean Sea for a 5-year period with a 3-hour resolution. Particularly, we estimate first-order moments, considering the double seasonality of the wave events, and also the correlation structure in terms of the climacogram (i.e., variance of the averaged process vs. spatial scale). Finally, we discuss on how the spatial dependence of the wave field is affected by various weather events.
T. Iliopoulou, and D. Koutsoyiannis, Preliminary flood hazard assessment for monuments in urbanized areas, 4th International Conference on Protection of Historical Constructions (PROHITECH 2020), Athens, 2021.
Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazard in the complex urban environment is subject to large spatial and temporal variability, and thus requires location-specific risk assessment and mitigation. We devise a methodological scheme for preliminary assessing flood hazard in urbanized regions ―at the monument’s scale, by coupling rainfall data from a local raingauge with a 2D hydraulic model of the monument’s sub-basin. Return periods of flood depths based on rainfall extremes are estimated using a novel statistical methodology (k-moments). As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme will be incorporated in a real-time monitoring platform for risk assessment in monuments (ARCHYTAS).
A. Lagos, S. Sigourou, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Land Cover Change: Does it affect temperature variability?, EGU General Assembly 2021, online, EGU21-9000, doi:10.5194/egusphere-egu21-9000, European Geosciences Union, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2112/2/documents/EGU21-9000_presentation.pdf (3350 KB)
T. Iliopoulou, and D. Koutsoyiannis, PythOm: A python toolbox implementing recent advances in rainfall intensity (ombrian) curves, EGU General Assembly 2021, online, EGU21-389, doi:10.5194/egusphere-egu21-389, European Geosciences Union, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2111/2/documents/EGU21-389_presentation.pdf (1647 KB)
G. Vagenas, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic analysis of time-series related to ocean acidification, EGU General Assembly 2021, online, EGU21-2637, doi:10.5194/egusphere-egu21-2637, European Geosciences Union, 2021.
Since the pre-industrial era at the end of the 18th century, the atmospheric carbon dioxide concentration (CO2) has increased by 47.46% from the level of 280 ppmv (parts per million volume) to 412.89 ppmv (Mauna Loa – NOAA Station, November 2020). These increased concentrations caused by natural & anthropogenic activities, interact with the aquatic environment which acts as a safety valve. Nevertheless, the absorbed CO2 amounts undergo chemical transformations, resulting in increasing ionized concentrations that can significantly reduce the water’s pH, a process described as ocean acidification. Here, we use the HOT (Hawaii-Ocean-Time series) to perform time series analysis for temperature, carbon dioxide partial pressure and pH. More specifically, we analyze their temporal changes in month and annual time lag. Then, we proceed in comparisons with relevant studies on atmospheric data to evaluate the produced results. Finally, we make an effort to disentangle the results with simplified assumptions connected with the observed impact of ocean acidification on the aquatic ecosystems.
Full text: http://www.itia.ntua.gr/en/getfile/2110/2/documents/EGU21-2637_presentation.pdf (5539 KB)
Ο. Akoumianaki, T. Iliopoulou, P. Dimitriadis, E. Varouchakis, and D. Koutsoyiannis, Stochastic analysis of the spatial stochastic structure of precipitation in the island of Crete, Greece, EGU General Assembly 2021, online, EGU21-4640, doi:10.5194/egusphere-egu21-4640, European Geosciences Union, 2021.
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.
Full text: http://www.itia.ntua.gr/en/getfile/2109/2/documents/EGU21-4640_presentation.pdf (1504 KB)
K. Papoulakos, T. Iliopoulou, P. Dimitriadis, D. Tsaknias, and D. Koutsoyiannis, Investigating the impacts of clustering of floods on insurance practices; a spatiotemporal analysis in the USA, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8667, doi:10.5194/egusphere-egu2020-8667, 2020.
Recent research has revealed the significance of Hurst-Kolmogorov dynamics and inherent uncertainties in flood inundation and flood mapping. However, classic risk estimation for flood insurance practices is formulated under the assumption of independence between the frequency and the severity of extreme flood events, which is unlikely to be tenable in real-world hydrometeorological processes exhibiting long range dependence. Furthermore, insurable flood losses are considered as ideally independent and non-catastrophic due to the widely spread perception of limited risk regarding catastrophically large flood losses. As the accurate risk assessment is a fundamental process on flood insurance and reinsurance practices, this study investigates the effects of lack of fulfillment of these assumptions, paving the way for a deeper understanding of the underlying clustering mechanisms of stream flow extremes. For this purpose, we present a spatiotemporal analysis of the daily stream flow series from the US-CAMELS dataset, comprising the impacts of clustering mechanisms on return intervals, duration and severity of the over-threshold events which are treated as proxies for collective risk. Moreover, an exploratory analysis is introduced regarding the stochastic aspects of the correlation between the properties of the extreme events and the actual claim records of the FEMA National Flood Insurance Program which are recently published.
Full text: http://www.itia.ntua.gr/en/getfile/2133/1/documents/papoulakos_2020.pdf (3143 KB)
G.T. Manolis, K. Papoulakos, T. Iliopoulou, P. Dimitriadis, D. Tsaknias, and D. Koutsoyiannis, Clustering mechanisms of flood occurrence; modelling and relevance to insurance practices, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-9357, doi:10.5194/egusphere-egu2020-9357, 2020.
Population growth, economic development and risk-blind urbanization often increase exposure to risk, including that due to floods. While rural flooding may affect much larger areas of land, urban floods are more challenging to manage, since the higher population and asset density in the urban environment increase the environmental and social impacts of floods and make the potential flood damages more costly. Therefore, the need for integrated flood insurance policy and products on extended parts of the world is pronounced in order to reduce the financial consequences of extreme flood events, which endanger in many cases the environmental, social and economic stability. As the assessment of the so-called collective risk is a typical issue faced in insurance and reinsurance practices, in this study we investigate the stochastic dynamics of daily stream flow series with particular interest to the existence of clustering mechanisms in floods, which is known to increase the potential risk. We analyse collective risk on the US-CAMELS dataset, treating the streamflow exceedances over given thresholds as proxies for insurance claim amounts. Moreover, we develop modelling and simulation approaches of extreme flows as a step towards the deeper understanding of the relationship between the stochastic patterns of flood occurrence and proxies of insurance claims, paving the way for a more efficient use of the available streamflow records.
Full text: http://www.itia.ntua.gr/en/getfile/2132/1/documents/manolis_egu20.pdf (1660 KB)
S. Vavoulogiannis, N. Ioannidis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of rainfall and runoff series from a large hydrometeorological dataset, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, European Geosciences Union, 2019.
We investigate the recently published CAMELS dataset, which is one of the most comprehensive large-scale datasets in terms of river flow timeseries and attributes of catchments minimally impacted by human activities. We examine the stochastic properties of daily river flow and rainfall series and investigate the links between the two at various lags, through climacogram-based stochastics tools (i.e. the climacogram and cross-climacogram) examining the variance versus spatio-temporal scale. We also explore the impact of various climatic and geophysical catchment attributes such as seasonality and timing of precipitation, aridity, mean catchment slope and soil conductivity, on the identified rainfall-runoff stochastic relationships.
Full text: http://www.itia.ntua.gr/en/getfile/1966/1/documents/egu_teliko_powerpoint.pdf (1006 KB)
T. Iliopoulou, and D. Koutsoyiannis, Comparing estimators for inferring dependence from records of hydrological extremes, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-9621, European Geosciences Union, 2019.
Hydrological extremes are regularly assumed independent in most practical and theoretical applications. The latter is indeed a convenient assumption as temporal independence is usually a prerequisite for the application of the widely used classical statistics. Motivated by the existence of dependence mechanisms in hydrological processes,i.e. Hurst-Kolmogorov dynamics or long-term persistence, we investigate the propagation of persistence from the parent processes into the series of extremes by focusing especially on the opportunity of inferring the former(persistence) from the latter (records of extremes). To this aim, we examine relevant stochastic indices such as the Hurst parameter and the Dispersion Index, and discuss their strengths and limitations. Additionally, we explore a new probabilistic characterization of clustering for extremes which is found to provide new insights into the identification and modeling of extremal dependence.
T. Goulianou, K. Papoulakos, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic characteristics of flood impacts for agricultural insurance practices, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-5891, European Geosciences Union, 2019.
During the last decades, the rising demand for crops for human consumption and industrial processes has led to a growth of investments and search for innovative solutions across the field of agriculture. However, one major risk that both investors and low-income farmers encounter worldwide is the impact of extreme weather events on their crop yield. The risk caused by extreme weather is an inhibitor of growth of agriculture and, apparently, agricultural insurance is strategically important for dealing with that risk. In particular, crop-yield insurance is purchased by agricultural producers, and in many cases is subsidized by governments, to protect them against the loss of their crops due to natural disasters, such as extreme flood events. In this context, the main subject of this research is to apply a stochastic approach of extremes for evaluating the impact of flood risk on agricultural insurance practices.We investigate stochastic aspects of extreme flows such as the right tail of the distribution of extremes and the existence of clustering mechanisms. For this purpose, we analyze daily flow series from the CAMELS dataset.Furthermore, we review current insurance practices in the agriculture domain in Greece and inspect the underlying stochastic assumptions, while evaluating changes in the estimated flood risk in the case that these assumptions are not valid.
D. Galanis, T. Andrikopoulou, P. Dimitriadis, T. Iliopoulou, and D. Koutsoyiannis, Investigation and stochastic simulation of the music of wind and precipitation, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-13332, European Geosciences Union, 2019.
Sound can be used as a means to detect and measure hydrometeorological variables that can generate sound.Thereby rain and wind over the sea surface can be estimated by the sound they produce if the ocean ambient noise is removed. A loud and distinctive sound is produced when the raindrops hit the ocean surface but waves also generate sound when they break. While rain and wind are difficult to measure over the ocean as gauges have to be mounted on surface buoys or ships, acoustic gauges placed beneath the ocean surface have been used as an alternative of measurement. The data that are collected from these gauges are then analysed using empirical models.In order for the sound data to be converted to wind speed and rainfall intensity, climacogram-based stochastic tools are used instead of the more traditional power spectrum ones. Furthermore, an application of this stochastic method is presented on the first ever recorded sound of wind on planet Mars, a mission executed by NASA’s In Sight lander.The study concludes with a discussion on possible similarities between the sound produced by the above variables and music (e.g. digital music for entertainment).
Full text: http://www.itia.ntua.gr/en/getfile/1960/1/documents/EGU_Poster_.pdf (3194 KB)
M. Karataraki, A. Thanasko, K. Printziou, G. Koudouris, R. Ioannidis, T. Iliopoulou, P. Dimitriadis, C. Plati, and D. Koutsoyiannis, Campus solar roads: a feasibility analysis, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-15648-2, European Geosciences Union, 2019.
We study the possibility of replacing conventional roads and buses with solar powered panel roads and electric buses fueled by solar energy within a closed system at a university campus. We also examine an alternative option of using solar buses equipped with panels on the rooftop. We review the recent advances in the technology of solar roads and buses and examine the modeling challenges and uncertainties of a transportation system powered by solar energy. We evaluate the economic aspects as well as the advantages and limitations of the proposed systems.The feasibility of this project is examined in terms of its application in the NTUA campus and possible directions for further research are identified.
Full text: http://www.itia.ntua.gr/en/getfile/1959/1/documents/Teliko_poster_egu_1_selida.pdf (1985 KB)
M.-E. Asimomiti, N. Pelekanos, P. Dimitriadis, T. Iliopoulou, E. Vlahogianni, and D. Koutsoyiannis, Campus solar roads: Stochastic modeling of passenger demand, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-10585, European Geosciences Union, 2019.
In the era of rapid technological advancements, innovations have started to reshape the field of transportation and energy management. University Campuses are considered as the ideal venue for implementing and testing innovative transportation services, as they usually encompass a closed form small-scale transportation infrastructure, and mainly involve users highly receptive to emerging technologies, due to their academic background. Nevertheless,the assessment of such services is a complex task, which should take into consideration issues related to energy sufficiency, passengers’ demand estimation and routing specifications. The present paper addresses the problem of stochastic passenger demand estimation under the uncertainties introduced by the implementation of a novel university bus service operated by hybrid vehicles under the concept of “opportunity charging” and solar powered buses. Aspects such as the relationship between the passengers’ need to move around the campus and parameters,such as time schedules, waiting time and alternative means of transportation are addressed. The passenger demand series generated by the models are linked to bus dwell times, which in turn determine the available charging time at each bus stop.
Full text: http://www.itia.ntua.gr/en/getfile/1958/1/documents/solar_roads.pdf (2434 KB)
A. Petsou, M.-E. Merakou, T. Iliopoulou, C. Iliopoulou, P. Dimitriadis, R. Ioannidis, K. Kepaptsoglou, and D. Koutsoyiannis, Campus solar roads: Optimization of solar panel and electric charging station location for university bus route, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-10832, European Geosciences Union, 2019.
We explore the prospect of replacing conventional university campus buses powered by fossil fuels with ones using solar energy. The proposed research investigates the emerging technology of solar powered road panels within a stochastic framework in order to optimally determine the corresponding infrastructure requirements for a university circulator line. More specifically, an optimization model is developed in order to determine the optimal locations for solar-powered roadway segments and electric charging stations for the existing university campus bus route. Since the availability of solar energy is linked to sunshine levels, we explore the possibility of using hybrid buses, powered by electricity and storing the energy to batteries in order to allow operation in days with no sunshine. As an alternative we study the use of solar buses equipped with panels on the rooftop. In order to account for the uncertainty associated with the system inputs, the transportation demand for the campus route and the availability of solar energy over the campus area are simulated using stochastic methods. The capital cost and energy consumption of the selected buses, charging stations and solar panels are also investigated in a case study for the NTUA campus.
Full text: http://www.itia.ntua.gr/en/getfile/1957/1/documents/EGU-Solar-Roads-FINAL.pdf (1082 KB)
R. Ioannidis, P. Dimitriadis, G.-F. Sargentis, E. Frangedaki, T. Iliopoulou, and D. Koutsoyiannis, Stochastic similarities between hydrometeorogical and art processes for optimizing architecture and landscape aesthetic parameters, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-11403, European Geosciences Union, 2019.
Stochastics help develop a unified perception for natural phenomena and expel dichotomies like random vs. deterministic, as both randomness and predictability coexist and are intrinsic to natural systems which can be deterministic and random at the same time, depending on the prediction horizon and the time scale. The high complexity and uncertainty of natural processes has been long identified through observations as well as extended analyses of hydrometeorological processes such as temperature, humidity, surface wind, precipitation, atmospheric pressure, river discharges etc. All these processes seem to exhibit high unpredictability due to the clustering of events. Art is a mix of determinism (e.g., certain rules have to be followed) and stochasticity (e.g., creativity and inspiration). However, in this analysis we analyse each artistic work in a stochastic approach, and attempt to identify their degree of intrinsic uncertainty. The stochastic analysis includes the investigation of possible Hurst-Kolmogorov behaviour in the art of different periods (visual arts, music, poetry) and of relationships with natural processes. Based on the stochastic analysis of different artworks, we make an image analysis of architectural elements in the landscape in order to formulate an indicator that can be used in engineering.
This research has been supported by the OptArch project: "Optimization Driven Architectural Design of Structures" (No: 689983) belonging to the Marie Skłodowska-Curie Actions (MSCA) Research and Innovation Staff Exchange (RISE) H2020-MSCA-RISE-2015.
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, doi:10.13140/RG.2.2.13555.78886, 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/1824/2/documents/2018EGU_DrySpells.pdf (2215 KB)
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)
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)
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)
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)
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)
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.
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)
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)
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)
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)
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)
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)
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)
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.
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)
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.
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)
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.
Other works that reference this work (this list might be obsolete):
|1.||Kanellopoulos, T. D., A. P. Karageorgis, A. Kikaki, S. Chourdaki, I. Hatzianestis, I. Vakalas, and G.-A. Hatiris, The impact of flash-floods on the adjacent marine environment: the case of Mandra and Nea Peramos (November 2017), Greece, Journal of Coastal Conservation, 24, 56, doi:10.1007/s11852-020-00772-6, 2020.|
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)
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)
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)
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)
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)
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)
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.
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)
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)
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.
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.
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.
Full text: http://www.itia.ntua.gr/en/getfile/1682/2/documents/2017_EGU_RRproject_final.pdf (2019 KB)
P. Dimitriadis, Y. Markonis, T. Iliopoulou, E. Feloni, N. Gournari, I. Deligiannis, P. Kastis, C. Nasika, E. Lerias, Y. Moustakis, A. Petsiou, A. Sotiriadou, A. Markopoulos, V. Tyrogiannis, and D. Koutsoyiannis, Stochastic similarities between hydroclimatic processes for variability characterization, European Geosciences Union General Assembly 2016, Geophysical Research Abstracts, Vol. 18, Vienna, European Geosciences Union, 2016.
The most important hydroclimatic processes such as temperature, dew point, wind, precipitation and river discharges are investigated for their stochastic behaviour on annual scale through several historical records. We investigate the stochastic similarities between them in terms of long-term persistence and we comment on their statistical variability giving emphasis on the last period. 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/1954/1/documents/StochSimilHydroClim2016.pdf (2569 KB)
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)
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.
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)
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.
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.
S. Sigourou, V. Pagana, P. Dimitriadis, A. Tsouni, T. Iliopoulou, G.-F. Sargentis, R. Ioannidis, E. Chardavellas, D. Dimitrakopoulou, N. Mamassis, C. Contoes, and D. Koutsoyiannis, Flood risk assessment in the region of Attica, 9th International Conference on Civil Protection & New Technologies - Safe Thessaloniki 2022, Thessaloniki, Greece, September 2022.
S. Sigourou, V. Pagana, P. Dimitriadis, A. Tsouni, T. Iliopoulou, G.-F. Sargentis, R. Ioannidis, E. Chardavellas, D. Dimitrakopoulou, N. Mamassis, C. Contoes, and D. Koutsoyiannis, Proposed methodology for urban flood-risk assessment at river-basin level: the case study of the Pikrodafni river basin in Athens, Greece, Global Flood Partnership 2022 Annual Meeting, Leeds, UK, September 2022.
The need for and the complexity of flood protection works require the development of advanced methodologies for flood risk assessment, especially considering that land cover changes, climate change and human interventions in the riverbed may severely affect the river flow. In the present study, a new methodology for urban flood risk assessment is introduced and implemented at the Pikrodafni river basin (Athens, Greece), by analyzing the vulnerability and the exposure of the river basin of Pikrodafni’s river to flood risk, in conjunction with the actual physical and socioeconomic parameters in order to propose mitigation measures. In March 2021, a Programming Agreement was signed between the Prefecture of Attica and the NOA – Part A – to conduct the study entitled ARIA «Earthquake, Fire and Flood risk assessment in the region of Attica» funded by the Prefecture of Attica. It’s the first time that such a holistic approach for flood risk assessment is implemented on building-block scale in Greece. The prototype knowledge created through the project supports the Prefecture of Attica in the optimum implementation of the National Civil Protection Plan. This serves the operational needs during crisis, as well as the preparedness and the strategic decision making towards disaster resilience. All the above-mentioned factors were also confirmed and positively evaluated according to the stakeholders’ feedback.
A. Tsouni, S. Sigourou, V. Pagana, D. Koutsoyiannis, N. Mamassis, A. Koukouvinos, P. Dimitriadis, T. Iliopoulou, G.-F. Sargentis, R. Ioannidis, D. Dimitrakopoulou, E. Chardavellas, S. Vavoulogiannis, and V. Kyriakouli, Flood risk assessment in the Pikrodafni basin, Presentation of results for the 1st Phase of the Program Agreement between Attica Regional Authority and NOA, Athens, National Observatory of Athens, 2022.
Full text: http://www.itia.ntua.gr/en/getfile/2190/1/documents/20220516.pdf (13374 KB)
A. Efstratiadis, N. Mamassis, A. Koukouvinos, T. Iliopoulou, S. Antoniadi, and D. Koutsoyiannis, Strategic plan for developing a National Hydrometric Network, Hellenic Integrated Marine and Inland water Observing, Forecasting and offshore Technology System (HIMIOFoTS) - Second meeting of project partners, Department of Water Resources and Environmental Engineering – National Technical University of Athens, 2019.
Full text: http://www.itia.ntua.gr/en/getfile/1973/1/documents/NTUA_pres_June2019_PartB.pdf (2262 KB)
T. Iliopoulou, Stochastic investigation of hydrological extremes: influence of temporal variability and dependence, PhD thesis, 237 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, October 2020.
The understanding and modelling of hydrological extremes is a classic endeavor in hydrology and engineering, one which has received renewed interest during the past decades under climate change theory. Long before concerns regarding intensification of extremes became prominent, their inherent variability and uncertainty sufficed to make their understanding and modelling challenging. Stochastics, integrating probability, statistics, and the theory of stochastic processes, offer a uniquely appropriate and consistent framework to deal with the uncertain nature of extremes. While the marginal properties of extremes have been extensively studied in the literature, the same does not hold for their temporal properties, since extremes are traditionally treated as temporally independent. As a consequence, their temporal behaviours have been either largely overlooked, or approached via deterministic reasoning. Yet, there are both empirical and theoretical grounds that question the independence assumption, namely the fact that hydrological extremes originate from natural processes characterized by marked dependence at various scales. This Thesis aims to stochastically investigate and model the temporal variability and dependence of hydrological extremes from seasonal to climatic scales. The key innovation of the analysis is the identification of the temporal behaviours of the extremes and their stochastic linkage to the inherent properties of the parent hydrological process. Such an approach creates new perspectives on understanding the temporal dynamics of hydrological extremes that can significantly improve the perception of related risk over time and inform advanced mitigation practices. Two complementary objectives are pursued in this respect: (a) the characterization of their temporal properties, including the multi-scale dependence dynamics, from long-term hydrological records, and (b) the development of hydrologically relevant modelling frameworks that reproduce the observed extremal patterns. These objectives unfold at the following three scales: (i) the seasonal scale, pertaining to extreme rainfall seasonality and dependence dynamics of seasonal streamflow extremes, (ii) the annual scale, with respect to the propagation of long-term persistence, i.e. Hurst-Kolmogorov (HK) dynamics, from the parent process to the extremes and properties thereof, and last, (iii) the climatic-scale, regarding the theoretical and empirical basis of climatic projections of future rainfall.
Full text: http://www.itia.ntua.gr/en/getfile/2106/1/documents/thesis_Iliopoulou.pdf (15995 KB)
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)