Ioannis Tsoukalas

Civil Engineer, MSc, Dr. Engineer
jtsoukalas@hotmail.com

Participation in research projects

Participation as Researcher

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

Published work

Publications in scientific journals

  1. G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, doi:10.1080/02626667.2021.2000992, 2021, (in press).
  2. G. Papaioannou, L. Vasiliades, A. Loukas, A. Alamanos, A. Efstratiadis, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, A flood inundation modelling approach for urban and rural areas in lake and large-scale river basins, Water, 13 (9), 1264, doi:10.3390/w13091264, 2021.
  3. I. Tsoukalas, The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Studying the k-length block-maxima distribution, 2021, (in review).
  4. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Generalized storage-reliability-yield framework for hydroelectric reservoirs, Hydrological Sciences Journal, 66 (4), 580–599, doi:10.1080/02626667.2021.1886299, 2021.
  5. H. Elsayed, S. Djordjević, D. Savic, I. Tsoukalas, and C. Makropoulos, The Nile water-food-energy nexus under uncertainty: Impacts of the Grand Ethiopian Renaissance Dam, Journal of Water Resources Planning and Management - ASCE, 146 (11), 04020085, doi:10.1061/(ASCE)WR.1943-5452.0001285, 2020.
  6. I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020.
  7. P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019.
  8. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.
  9. Ε. Psarrou, I. Tsoukalas, and C. Makropoulos, A Monte-Carlo-based method for the optimal placement and operation scheduling of sewer mining units in urban wastewater networks, Water, 10 (2), 200, doi:10.3390/w10020200, 2018.
  10. I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018.
  11. I. Tsoukalas, S.M. Papalexiou, A. Efstratiadis, and C. Makropoulos, A cautionary note on the reproduction of dependencies through linear stochastic models with non-Gaussian white noise, Water, 10 (6), 771, doi:10.3390/w10060771, 2018.
  12. G. Papaioannou, A. Efstratiadis, L. Vasiliades, A. Loukas, S.M. Papalexiou, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, An operational method for Floods Directive implementation in ungauged urban areas, Hydrology, 5 (2), 24, doi:10.3390/hydrology5020024, 2018.
  13. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.
  14. C. Makropoulos, E. Rozos, I. Tsoukalas, A. Plevri, G. Karakatsanis, L. Karagiannidis, E. Makri, C. Lioumis, K. Noutsopoulos, D. Mamais, K. Ripis, and T. Lytras, Sewer-mining: A water reuse option supporting circular economy, public service provision and entrepreneurship, Journal of Environmental Management, 216, 285–298, doi:10.1016/j.jenvman.2017.07.026, 2018.
  15. I. Tsoukalas, C. Makropoulos, and S. Mihas, Identification of potential sewer mining locations: A Monte-Carlo based approach, Water Science and Technology, 76 (12), 3351–3357, doi:10.2166/wst.2017.487, 2017.
  16. A. Tegos, N. Malamos, A. Efstratiadis, I. Tsoukalas, A. Karanasios, and D. Koutsoyiannis, Parametric modelling of potential evapotranspiration: a global survey, Water, 9 (10), 795, doi:10.3390/w9100795, 2017.
  17. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: Ecosystem services from treated wastewater, Desalination and Water Treatment, 91 (2017), 2017.
  18. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.
  19. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.
  20. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.

Book chapters and fully evaluated conference publications

  1. D. Nikolopoulos, C. Makropoulos, D. Kalogeras, K. Monokrousou, and I. Tsoukalas, Developing a stress-testing platform for cyber-physical water infrastructure, 2018 International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), New Jersey, 9–11, doi:10.1109/CySWater.2018.00009, 2018.
  2. C. Rebolho, V. Andréassian, I. Tsoukalas, et A. Efstratiadis, La crue du Loing de Juin 2016 était-elle exceptionnelle?, De la prévision des crues à la gestion de crise, Avignon, Société Hydrotechnique de France, 2018.
  3. I. Tsoukalas, C. Makropoulos, and A. Efstratiadis, Stochastic simulation of periodic processes with arbitrary marginal distributions, 15th International Conference on Environmental Science and Technology (CEST2017), Rhodes, Global Network on Environmental Science and Technology, 2017.
  4. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: ecosystem services from treated wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, National Technical University of Athens, 2016, (in press).
  5. I. Tsoukalas, P. Dimas, and C. Makropoulos, Hydrosystem optimization on a budget: Investigating the potential of surrogate based optimization techniques, 14th International Conference on Environmental Science and Technology (CEST2015), Global Network on Environmental Science and Technology, University of the Aegean, 2015.

Conference publications and presentations with evaluation of abstract

  1. G. Moraitis, D. Nikolopoulos, I. Koutiva, I. Tsoukalas, G. Karavokiros, and C. Makropoulos, The PROCRUSTES testbed: tackling cyber-physical risk for water systems, EGU General Assembly 2021, online, EGU21-14903, doi:10.5194/egusphere-egu21-14903, European Geosciences Union, 2021.
  2. K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Setting the problem of energy production forecasting for small hydropower plants in the Target Model era, EGU General Assembly 2021, online, EGU21-3168, doi:10.5194/egusphere-egu21-3168, European Geosciences Union, 2021.
  3. V. Kourakos, A. Efstratiadis, and I. Tsoukalas, Can hydrological model identifiability be improved? Stress-testing the concept of stochastic calibration, EGU General Assembly 2021, online, EGU21-11704, doi:10.5194/egusphere-egu21-11704, European Geosciences Union, 2021.
  4. G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, A dilemma of small hydropower plants: Design with uncertainty or uncertainty within design?, EGU General Assembly 2021, online, EGU21-2398, doi:10.5194/egusphere-egu21-2398, European Geosciences Union, 2021.
  5. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Revisiting the storage-reliability-yield concept in hydroelectricity, EGU General Assembly 2021, online, EGU21-10528, doi:10.5194/egusphere-egu21-10528, European Geosciences Union, 2021.
  6. M. Nezi, C. Ntigkakis, I. Tsoukalas, and A. Efstratiadis, Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-19674, doi:egusphere-egu2020-19674, 2020.
  7. A. G. Pettas, P. Mavritsakis, I. Tsoukalas, N. Mamassis, and A. Efstratiadis, Empirical metric for uncertainty assessment of wind forecasting models in terms of power production and economic efficiency, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8018, doi:10.5194/egusphere-egu2020-8018, 2020.
  8. G.-K. Sakki, V. Papalamprou, I. Tsoukalas, N. Mamassis, and A. Efstratiadis, Stochastic modelling of hydropower generation from small hydropower plants under limited data availability: from post-assessment to forecasting, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8129, doi:10.5194/egusphere-egu2020-8129, 2020.
  9. L. M. Tsiami, E. Zacharopoulou, D. Nikolopoulos, I. Tsoukalas, N. Mamassis, A. Kallioras, and A. Efstratiadis, The use of Artificial Neural Networks with different sources of spatiotemporal information for flash flood predictions, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-7315, European Geosciences Union, 2019.
  10. P. Mavritsakis, A. G. Pettas, I. Tsoukalas, G. Karakatsanis, N. Mamassis, and A. Efstratiadis, A stochastic simulation framework for representing water, energy and financial fluxes across a non-connected island, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-8758, European Geosciences Union, 2019.
  11. E. Zacharopoulou, I. Tsoukalas, A. Efstratiadis, and D. Koutsoyiannis, Impact of sample uncertainty of inflows to stochastic simulation of reservoirs, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-17233, European Geosciences Union, 2019.
  12. Y. Moustakis, P. Kossieris, I. Tsoukalas, and A. Efstratiadis, Quasi-continuous stochastic simulation framework for flood modelling, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-534, European Geosciences Union, 2017.
  13. P. Kossieris, A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Assessing the performance of Bartlett-Lewis model on the simulation of Athens rainfall, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-8983, doi:10.13140/RG.2.2.14371.25120, European Geosciences Union, 2015.
  14. A. Efstratiadis, I. Tsoukalas, P. Kossieris, G. Karavokiros, A. Christofides, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Computational issues in complex water-energy optimization problems: Time scales, parameterizations, objectives and algorithms, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5121, doi:10.13140/RG.2.2.11015.80802, European Geosciences Union, 2015.
  15. A. Drosou, P. Dimitriadis, A. Lykou, P. Kossieris, I. Tsoukalas, A. Efstratiadis, and N. Mamassis, Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece), European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-9148, European Geosciences Union, 2015.
  16. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Handling time-expensive global optimization problems through the surrogate-enhanced evolutionary annealing-simplex algorithm, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5923, European Geosciences Union, 2015.

Academic works

  1. I. Tsoukalas, Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty, PhD thesis, 339 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, December 2018.

Research reports

  1. A. Efstratiadis, N. Mamassis, I. Tsoukalas, and S. Manouri, [No English title available], Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 35 pages, May 2021.
  2. A. Efstratiadis, I. Tsoukalas, and S. Manouri, Investigation of the water supply system's management for period March-September 2021, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 38 pages, March 2021.
  3. A. Efstratiadis, I. Papakonstantis, P. Papanicolaou, N. Mamassis, D. Nikolopoulos, I. Tsoukalas, and P. Kossieris, First year synopsis, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 55 pages, December 2020.
  4. A. Efstratiadis, S. Manouri, D. Nikolopoulos, and I. Tsoukalas, Investigation of the water supply system's management for period March-September 2020, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 31 pages, March 2020.
  5. A. Efstratiadis, and I. Tsoukalas, Update of water balance of Hylike and Paralimni and assesment of their risk of spilling during the current hydrological year, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 56 pages, November 2019.
  6. A. Efstratiadis, N. Mamassis, and I. Tsoukalas, Synoptic report on the evaluation of the flood risk for areas affected by the ongoing spilling of the Hylike-Paralimni system, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 25 pages, March 2019.

Details on research projects

Participation as Researcher

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

    Duration: October 2008–November 2011

    Budget: €72 000

    Project director: N. Mamassis

    Principal investigator: D. Koutsoyiannis

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

Published work in detail

Publications in scientific journals

  1. G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, doi:10.1080/02626667.2021.2000992, 2021, (in press).

    The limited availability of hydrometric data makes the design, management, and real-time operation of water systems a difficult task. Here, we propose a generic stochastic framework for the so-called inverse problem of hydroelectricity, using energy production data from small hydropower plants (SHPPs) to retrieve the upstream inflows. In this context, we investigate the alternative configurations of water-energy transformations across SHPPs of negligible storage capacity, which are subject to multiple uncertainties. We focus on two key sources, i.e. observational errors in energy production and uncertain efficiency curves of turbines. In order to extract the full hydrograph, we also extrapolate the high and low flows outside of the range of operation of turbines, by employing empirical rules for representing the rising and falling limbs of the simulated hydrographs. This framework is demonstrated to a real-world system at Evinos river basin, Greece. By taking advantage of the proposed methodology, SHPPs may act as potential hydrometric stations and improve the existing information in poorly gauged areas.

  1. G. Papaioannou, L. Vasiliades, A. Loukas, A. Alamanos, A. Efstratiadis, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, A flood inundation modelling approach for urban and rural areas in lake and large-scale river basins, Water, 13 (9), 1264, doi:10.3390/w13091264, 2021.

    Fluvial floods are one of the primary natural hazards to our society, and the associated flood risk should always be evaluated for present and future conditions. The European Union’s Floods Di-rective highlights the importance of flood mapping as a key-stage for detecting vulnerable areas, assessing floods’ impacts, and identifying damages and compensation plans. The implementation of the E.U. Flood Directive in Greece is challenging, because of its geophysical and climatic var-iability and diverse hydrologic and hydraulic conditions. This study addresses this challenge by modelling of design rainfall at sub-watershed level and subsequent estimation of flood design hydrographs using the NRCS Unit Hydrograph Procedure. HEC-RAS 2D model is used for flood routing, estimation of flood attributes (i.e., water depths and flow velocities) and mapping of inundated areas. The modelling approach has been applied at two complex and ungauged rep-resentative basins: Lake Pamvotida basin located in the Epirus Region of the wet western Greece and Pinios River basin located in Thessaly Region of the drier central Greece, a basin with a complex dendritic hydrographic system, expanding to more than 1188 river-km. The proposed modelling approach aims to better estimation and mapping of flood inundation areas including relative uncertainties and providing guidance to professionals and academics.

    Full text: http://www.itia.ntua.gr/en/getfile/2121/1/documents/water-13-01264-v2.pdf (45029 KB)

    See also: https://www.mdpi.com/2073-4441/13/9/1264

    Other works that reference this work (this list might be obsolete):

    1. Varlas, G., A. Papadopoulos, G. Papaioannou, and E. Dimitriou, Evaluating the forecast skill of a hydrometeorological modelling system in Greece, Atmosphere, 12(7), 902, doi:10.3390/atmos12070902, 2021.
    2. Karamvasis, K., and V. Karathanassi, FLOMPY: An open-source toolbox for floodwater mapping using Sentinel-1 intensity time series, Water, 13(21), 2943, doi:10.3390/w13212943, 2021.

  1. I. Tsoukalas, The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Studying the k-length block-maxima distribution, 2021, (in review).

    Focal point of this work is the estimation of the distribution of maxima without the use of classic extreme value theory and asymptotic properties, which may not be ideal for hydrological processes. The problem is revisited from the perspective of non-asymptotic conditions, and regards the so-called exact distribution of block-maxima of finite-sized k-length blocks. First, we review existing non-asymptotic approaches/models, and also introduce an alternative and faster model. Next, through simulations and comparisons (using asymptotic and non-asymptotic models), involving intermittent processes (e.g., rainfall), we highlight the capability of non-asymptotic approaches to model the distribution of maxima with reduced uncertainty and variability. Finally, we discuss an alternative use of such models that concerns the theoretical estimation of the multi-scale probability of obtaining a zero value. A useful finding when the scope is the multi-scale modeling of intermittent hydrological processes (e.g., intensity-duration-frequency models). The work also entails step-by-step recipes and an R-package.

    Full text: http://www.itia.ntua.gr/en/getfile/2094/1/documents/Correlated_Extremes_iTsoukal_v1.1.pdf (6864 KB)

  1. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Generalized storage-reliability-yield framework for hydroelectric reservoirs, Hydrological Sciences Journal, 66 (4), 580–599, doi:10.1080/02626667.2021.1886299, 2021.

    Although storage-reliability-yield (SRY) relationships have been widely used in the design and planning of water supply reservoirs, their application in hydroelectricity is practically nil. Here, we revisit the SRY analysis and seek its generic configuration for hydroelectric reservoirs, following a stochastic simulation approach. After defining key concepts and tools of conventional SRY studies, we adapt them for hydropower systems, which are subject to several peculiarities. We illustrate that under some reasonable assumptions, the problem can be substantially simplified. Major innovations are the storage-head-energy conversion via the use of a sole parameter, representing the reservoir geometry, and the development of an empirical statistical metric expressing the reservoir performance on the basis of the simulated energy-probability curve. The proposed framework is applied to numerous hypothetical reservoirs at three river sites in Greece, using monthly synthetic inflow data, to provide empirical expressions of reliable energy as a function of reservoir storage and geometry.

    Additional material:

  1. H. Elsayed, S. Djordjević, D. Savic, I. Tsoukalas, and C. Makropoulos, The Nile water-food-energy nexus under uncertainty: Impacts of the Grand Ethiopian Renaissance Dam, Journal of Water Resources Planning and Management - ASCE, 146 (11), 04020085, doi:10.1061/(ASCE)WR.1943-5452.0001285, 2020.

    Achieving a water, food, and energy (WFE) nexus balance through policy interventions is challenging in a transboundary river basin because of the dynamic nature and intersectoral complexity that may cross borders. The Nile basin is shared by a number of riparian countries and is currently experiencing rapid population and economic growth. This has sparked new developments to meet the growing water, food, and energy demands, alleviate poverty, and improve the livelihood in the basin. Such developments could result in basinwide cooperation or trigger conflicts among the riparian countries. A system dynamics model was developed for the entire Nile basin and integrated with the food and energy sectors in Egypt to investigate the future of the WFE nexus with and without the Grand Ethiopian Renaissance Dam (GERD) during filling and subsequent operation using basinwide stochastically generated flows. Different filling rates from 10% to 100% of the average monthly flow are considered during the filling process. Results suggest that the GERD filling and operation would affect the WFE nexus in Egypt, with the impact likely to be significant if the filling process occurred during a dry period. Food production from irrigated agriculture would be reduced by 9%–19% during filling and by about 4% during GERD operation compared with the case without it. The irrigation water supply and hydropower generation in Sudan will be reduced during the filling phase of the GERD, but this is expected to be improved during the dam operation phase as a result of the regulation afforded by the GERD. Ethiopian hydropower generation is expected to be boosted by the GERD during the filling and operation of the dam, adding an average of 15,000  GWh/year once GERD comes online. Lastly, the results reveal the urgency of cooperation and coordination among the riparian countries to minimize the regional risks and maximize the regional rewards associated with the GERD.

    See also: https://ascelibrary.org/doi/10.1061/%28ASCE%29WR.1943-5452.0001285

  1. I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020.

    Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically used to generate synthetic weather data with the desired properties, often resembling those of hydrometeorological observations, which are then used to drive deterministic models of the understudy system. However, generating synthetic weather data with the desired properties is not an easy task. This is due to the peculiarities of such processes, i.e., non-Gaussianity, intermittency, dependence, and periodicity, and the limited availability of open-source software for such purposes. This work aims to simplify the synthetic data generation procedure by providing an R-package called anySim, specifically designed for the simulation of non-Gaussian correlated random variables, stochastic processes at single and multiple temporal scales, and random fields. The functionality of the package is demonstrated through seven simulation studies, accompanied by code snippets, which resemble real-world cases of stochastic simulation (i.e., generation of synthetic weather data) of hydrometeorological processes and fields (e.g., rainfall, streamflow, temperature, etc.), across several spatial and temporal scales (ranging from annual down to 10-min simulations).

    Full text: http://www.itia.ntua.gr/en/getfile/2049/1/documents/water-12-01645.pdf (4754 KB)

    See also: https://www.mdpi.com/2073-4441/12/6/1645

  1. P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019.

    Uncertainty-aware design and management of urban water systems lies on the generation of synthetic series that should precisely reproduce the distributional and dependence properties of residential water demand process (i.e., significant deviation from Gaussianity, intermittent behaviour, high spatial and temporal variability and a variety of dependence structures) at various temporal and spatial scales of operational interest. This is of high importance since these properties govern the dynamics of the overall system, while prominent simulation methods, such as pulse-based schemes, address partially this issue by preserving part of the marginal behaviour of the process (e.g., low-order statistics) or neglecting the significant aspect of temporal dependence. In this work, we present a single stochastic modelling strategy, applicable at any fine time scale to explicitly preserve both the distributional and dependence properties of the process. The strategy builds upon the Nataf’s joint distribution model and particularly on the quantile mapping of an auxiliary Gaussian process, generated by a suitable linear stochastic model, to establish processes with the target marginal distribution and correlation structure. The three real-world case studies examined, reveal the efficiency (suitability) of the simulation strategy in terms of reproducing the variety of marginal and dependence properties encountered in water demand records from 1-min up to 1-h.

    Full text: http://www.itia.ntua.gr/en/getfile/1950/1/documents/water-11-00885.pdf (6862 KB)

    See also: https://www.mdpi.com/2073-4441/11/5/885

  1. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.

    The generation of hydrometeorological time series that exhibit a given probabilistic and stochastic behavior across multiple temporal levels, traditionally expressed in terms of specific statistical characteristics of the observed data, is a crucial task for risk-based water resources studies, and simultaneously a puzzle for the community of stochastics. The main challenge stems from the fact that the reproduction of a specific behavior at a certain temporal level does not imply the reproduction of the desirable behavior at any other level of aggregation. In this respect, we first introduce a pairwise coupling of Nataf-based stochastic models within a disaggregation scheme, and next we propose their puzzle-type configuration to provide a generic stochastic simulation framework for multivariate processes exhibiting any distribution and any correlation structure. Within case studies we demonstrate two characteristic configurations, i.e., a three-level one, operating at daily, monthly and annual basis, and a two-level one to disaggregate daily to hourly data. The first configuration is applied to generate correlated daily rainfall and runoff data at the river basin of Achelous, Western Greece, which preserves the stochastic behavior of the two processes at the three temporal levels. The second configuration disaggregates daily rainfall, obtained from a meteorological station at Germany, to hourly. The two studies reveal the ability of the proposed framework to represent the peculiar behavior of hydrometeorological processes at multiple temporal resolutions, as well as its flexibility on formulating generic simulation schemes.

    Other works that reference this work (this list might be obsolete):

    1. Macian-Sorribes, H., J.-L. Molina, S. Zazo, and M. Pulido-Velázquez, Analysis of spatio-temporal dependence of inflow time series through Bayesian causal modelling, Journal of Hydrology, 597, 125722, doi:10.1016/j.jhydrol.2020.125722, 2021.
    2. Wang, Q., J. Zhou, K. Huang, L. Dai, B. Jia, L. Chen, and H. Qin, A procedure for combining improved correlated sampling methods and a resampling strategy to generate a multi-site conditioned streamflow process, Water Resources Management, 35, 1011-1027, doi:10.1007/s11269-021-02769-8, 2021.
    3. Brereton, R. G., P values and multivariate distributions: Non-orthogonal terms in regression models, Chemometrics and Intelligent Laboratory Systems, 210, 104264, doi:10.1016/j.chemolab.2021.104264, 2021.
    4. 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.
    5. Biondi, D., E. Todini, and A. Corina, A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: An application to quantitative precipitation forecasts for civil protection purposes, Hydrology Research, nh20210452021, doi:10.2166/nh.2021.045, 2021.

  1. Ε. Psarrou, I. Tsoukalas, and C. Makropoulos, A Monte-Carlo-based method for the optimal placement and operation scheduling of sewer mining units in urban wastewater networks, Water, 10 (2), 200, doi:10.3390/w10020200, 2018.

    Pressures on water resources, which have increased significantly nowadays mainly due to rapid urbanization, population growth and climate change impacts, necessitate the development of innovative wastewater treatment and reuse technologies. In this context, a mid-scale decentralized technology concerning wastewater reuse is that of sewer mining. It is based on extracting wastewater from a wastewater system, treating it on-site and producing recycled water applicable for non-potable uses. Despite the technology’s considerable benefits, several challenges hinder its implementation. Sewer mining disturbs biochemical processes inside sewers and affects hydrogen sulfide build-up, resulting in odor, corrosion and health-related problems. In this study, a tool for optimal sewer mining unit placement aiming to minimize hydrogen sulfide production is presented. The Monte-Carlo method coupled with the Environmental Protection Agency’s Storm Water Management Model (SWMM) is used to conduct multiple simulations of the network. The network’s response when sewage is extracted from it is also examined. Additionally, the study deals with optimal pumping scheduling. The overall methodology is applied in a sewer network in Greece providing useful results. It can therefore assist in selecting appropriate locations for sewer mining implementation, with the focus on eliminating hydrogen sulfide-associated problems while simultaneously ensuring that higher water needs are satisfied.

    Full text: http://www.itia.ntua.gr/en/getfile/1903/1/documents/water-10-00200.pdf (8639 KB)

    See also: https://www.mdpi.com/2073-4441/10/2/200

  1. I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018.

    Hydrometeorological processes are typically characterized by temporal dependence, short‐ or long‐range (e.g., Hurst behavior), as well as by non‐Gaussian distributions (especially at fine time scales). The generation of long synthetic time series that resemble the marginal and joint properties of the observed ones is a prerequisite in many uncertainty‐related hydrological studies, since they can be used as inputs and hence allow the propagation of natural variability and uncertainty to the typically deterministic water‐system models. For this reason, it has been for years one of the main research topics in the field of stochastic hydrology. This work presents a novel model for synthetic time series generation, termed Symmetric Moving Average (neaRly) To Anything (SMARTA), that holds out the promise of simulating stationary univariate and multivariate processes with any‐range dependence and arbitrary marginal distributions, provided that the former is feasible and the latter have finite variance. This is accomplished by utilizing a mapping procedure in combination with the relationship that exists between the correlation coefficients of an auxiliary Gaussian process and a non‐Gaussian one, formalized through the Nataf's joint distribution model. The generality of SMARTA is stressed through two hypothetical simulation studies (univariate and multivariate), characterized by different dependencies and distributions. Furthermore, we demonstrate the practical aspects of the proposed model through two real‐world cases, one that concerns the generation of annual non‐Gaussian streamflow time series at four stations, and another that involves the synthesis of intermittent, non‐Gaussian, daily rainfall series at a single location.

    Additional material:

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

    Other works that reference this work (this list might be obsolete):

    1. Brunner, M. I., A. Bárdossy, and R. Furrer, Technical note: Stochastic simulation of streamflow time series using phase randomization, Hydrology and Earth System Sciences, 23, 3175-3187, doi:10.5194/hess-23-3175-2019, 2019.
    2. Cheng, Y., P. Feng, J. Li, Y. Guo, and P. Ren, Water supply risk analysis based on runoff sequence simulation with change point under changing environment, Advances in Meteorology, 9619254, doi:10.1155/2019/9619254, 2019.
    3. #Elsayed, H., S. Djordjević, and D. Savić, The Nile water, food and energy nexus – A system dynamics model, 7th International Computing & Control for the Water Industry Conference, Exeter, United Kingdom, 2019.

  1. I. Tsoukalas, S.M. Papalexiou, A. Efstratiadis, and C. Makropoulos, A cautionary note on the reproduction of dependencies through linear stochastic models with non-Gaussian white noise, Water, 10 (6), 771, doi:10.3390/w10060771, 2018.

    Since the prime days of stochastic hydrology back in 1960s, autoregressive (AR) and moving average (MA) models (as well as their extensions) have been widely used to simulate hydrometeorological processes. Initially, AR(1) or Markovian models with Gaussian noise prevailed due to their conceptual and mathematical simplicity. However, the ubiquitous skewed behavior of most hydrometeorological processes, particularly at fine time scales, necessitated the generation of synthetic time series to also reproduce higher-order moments. In this respect, the former schemes were enhanced to preserve skewness through the use of non-Gaussian white noise— a modification attributed to Thomas and Fiering (TF). Although preserving higher-order moments to approximate a distribution is a limited and potentially risky solution, the TF approach has become a common choice in operational practice. In this study, almost half a century after its introduction, we reveal an important flaw that spans over all popular linear stochastic models that employ non-Gaussian white noise. Focusing on the Markovian case, we prove mathematically that this generating scheme provides bounded dependence patterns, which are both unrealistic and inconsistent with the observed data. This so-called “envelope behavior” is amplified as the skewness and correlation increases, as demonstrated on the basis of real-world and hypothetical simulation examples.

    Full text: http://www.itia.ntua.gr/en/getfile/1848/1/documents/water-10-00771.pdf (14101 KB)

    See also: http://www.mdpi.com/2073-4441/10/6/771

    Other works that reference this work (this list might be obsolete):

    1. Papalexiou, S. M., Y. Markonis, F. Lombardo, A. AghaKouchak, and E. Foufoula‐Georgiou, Precise temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for stationary and non‐stationary processes, Water Resources Research, 54(10), 7435-7458, doi:10.1029/2018WR022726, 2018.
    2. Cheng, Y., P. Feng, J. Li, Y. Guo, and P. Ren, Water supply risk analysis based on runoff sequence simulation with change point under changing environment, Advances in Meteorology, 9619254, doi:10.1155/2019/9619254, 2019.
    3. Marković, D., S. Ilić, D. Pavlović, J. Plavšić, and N. Ilich, Multivariate and multi-scale generator based on non-parametric stochastic algorithms, Journal of Hydroinformatics, 21(6), 1102–1117, doi:10.2166/hydro.2019.071, 2019.
    4. Nazemi, A., M. Zaerpour, and E. Hassanzadeh, Uncertainty in bottom-up vulnerability assessments of water supply systems due to regional streamflow generation under changing conditions, Journal of Water Resources Planning and Management, 146(2), doi:10.1061/(ASCE)WR.1943-5452.0001149, 2020.
    5. Wang, Q., J. Zhou, K. Huang, L. Dai, B. Jia, L. Chen, and H. Qin, A procedure for combining improved correlated sampling methods and a resampling strategy to generate a multi-site conditioned streamflow process, Water Resources Management, 35, 1011-1027, doi:10.1007/s11269-021-02769-8, 2021.
    6. Zounemat-Kermani, M., A. Mahdavi-Meymand, and A. Hinkelmann, A comprehensive survey on conventional and modern neural networks: application to river flow forecasting, Earth Science Informatics, doi:10.1007/s12145-021-00599-1, 2021.
    7. 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.

  1. G. Papaioannou, A. Efstratiadis, L. Vasiliades, A. Loukas, S.M. Papalexiou, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, An operational method for Floods Directive implementation in ungauged urban areas, Hydrology, 5 (2), 24, doi:10.3390/hydrology5020024, 2018.

    An operational framework for flood risk assessment in ungauged urban areas is developed within the implementation of the EU Floods Directive in Greece, and demonstrated for Volos metropolitan area, central Greece, which is frequently affected by intense storms causing fluvial flash floods. A scenario-based approach is applied, accounting for uncertainties of key modeling aspects. This comprises extreme rainfall analysis, resulting to spatially-distributed Intensity-Duration-Frequency (IDF) relationships and their confidence intervals, and flood simulations, through the SCS-CN method and the unit hydrograph theory, producing design hydrographs at the sub-watershed scale, for several soil moisture conditions. The propagation of flood hydrographs and the mapping of inundated areas are employed by the HEC-RAS 2D model, with flexible mesh size, by representing the resistance caused by buildings through the local elevation rise method. For all hydrographs, upper and lower estimates on water depths, flow velocities and inundation areas are estimated, for varying roughness coefficient values. The methodology is validated against the flood event of the 9th October 2006, using observed flood inundation data. Our analyses indicate that although typical engineering practices for ungauged basins are subject to major uncertainties, the hydrological experience may counterbalance the missing information, thus ensuring quite realistic outcomes.

    Remarks:

    This article won the Hydrology Best Paper Award for 2020 (https://www.mdpi.com/journal/hydrology/awards/850)

    Full text: http://www.itia.ntua.gr/en/getfile/1829/1/documents/hydrology-05-00024_Idnk8fW.pdf (5243 KB)

    Additional material:

    Other works that reference this work (this list might be obsolete):

    1. Petroselli, A., M. Vojtek, and J. Vojteková, Flood mapping in small ungauged basins: A comparison of different approaches for two case studies in Slovakia, Hydrology Research, 50(1), 379-392, doi:10.2166/nh.2018.040, 2018.
    2. Manfreda, S., C. Samela, A. Refice, V. Tramutoli, and F. Nardi, Advances in large-scale flood monitoring and detection, Hydrology, 5(3), 49, doi:10.3390/hydrology5030049, 2018.
    3. Doroszkiewicz, J., R. J. Romanowicz, and A. Kiczko, The influence of flow projection errors on flood hazard estimates in future climate conditions, Water, 11(1), 49, doi:10.3390/w11010049, 2019.
    4. Enigl, K., C. Matulla, M. Schlögla, and F. Schmid, Derivation of canonical total-sequences triggering landslides and floodings in complex terrain, Advances in Water Resources, 129, 178-188, doi:10.1016/j.advwatres.2019.04.018, 2019.
    5. Chen, N., S. Yao, C. Wang, and W. Du, A method for urban flood risk assessment and zoning considering road environments and terrain, Sustainability, 11(10), 2734, doi:10.3390/su11102734, 2019.
    6. Jiang, X., L., Yang, and H. Tatano, Assessing spatial flood risk from multiple flood sources in a small river basin: A method based on multivariate design rainfall, Water, 11(5), 1031, doi:10.3390/w11051031, 2019.
    7. Vojtek, M., A. Petroselli, J. Vojteková, and S. Asgharinia, Flood inundation mapping in small and ungauged basins: sensitivity analysis using the EBA4SUB and HEC-RAS modeling approach, Hydrology Research, 50(4), 1002-1019, doi:10.2166/nh.2019.163, 2019.
    8. Lorenzo-Lacruz, J., C. Garcia, E. Morán-Tejeda, A. Amengual, V. Homar, A. Maimó-Far, A. Hermoso, C. Ramis, and R. Romero, Hydro-meteorological reconstruction and geomorphological impact assessment of the October, 2018 catastrophic flash flood at Sant Llorenç, Mallorca (Spain), Natural Hazards and Earth System Sciences, 19(11), 2597-2617, doi:10.5194/nhess-19-2597-2019, 2019.
    9. Hamdan, A. N. A., A. A. Abbas, and A. T. Najm, Flood hazard analysis of proposed regulator on Shatt Al-Arab river, Hydrology, 6(3), 80, doi:0.3390/hydrology6030080, 2019.
    10. Deby, R., V. Dermawan, and D. Sisinggih, Analysis of Wanggu river flood inundation Kendari City Southeast Sulawesi province using HEC RAS 5.0.6, International Research Journal of Advanced Engineering and Science, 4(2), 270-275, 2019.
    11. Rauter, M., T. Thaler, M.-S. Attems, and S. Fuchs, Obligation or innovation: Can the EU Floods Directive Be seen as a tipping point towards more resilient flood risk management? A case study from Vorarlberg, Austria, Sustainability, 11, 5505, doi:10.3390/su11195505, 2019.
    12. Papaioannou, G., G. Varlas, G. Terti, A. Papadopoulos, A. Loukas, Y. Panagopoulos, and E. Dimitriou, Flood inundation mapping at ungauged basins using coupled hydrometeorological-hydraulic modelling: The catastrophic case of the 2006 flash flood in Volos City, Greece, Water, 11, 2328, doi:10.3390/w11112328, 2019.
    13. Rahmati, O., H. Darabi, A. T. Haghighi, S. Stefanidis, A. Kornejady, O. A. Nalivan, and D. T. Bui, Urban flood hazard modeling using self-organizing map neural network, Water, 11(11), 2370, doi:10.3390/w11112370, 2019.
    14. Dano, U. L., A.-L. Balogun, A.-N. Matori,K. Wan Yusouf, I. R. Abubakar, M. A. Said Mohamed, , Y.A. Aina, and B. Pradhan, Flood susceptibility mapping using an improved analytic network process with statistical models, Water, 11(3), 615, doi:10.3390/w11030615, 2019.
    15. Petroselli, A., S. Grimaldi, R. Piscopia, and F. Tauro, Design hydrograph estimation in small and ungauged basins: a comparative assessment of event based (EBA4SUB) and continuous (COSMO4SUB) modeling approaches, Acta Scientiarum Polonorum Formatio Circumiectus, 18(4), 113-124, doi:10.15576/ASP.FC/2019.18.4.113, 2019.
    16. Nguyen, V.-N., P. Yariyan, M. Amiri, A. Dang Tran, T.D. Pham, M.P. Do, P. T. Thi Ngo, V.-H. Nhu, N. Quoc Long, and D. Tien Bui, A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data, Remote Sensing, 12(9), 1373, doi:10.3390/rs12091373, 2020.
    17. Kastridis, A., and D. Stathis, Evaluation of hydrological and hydraulic models applied in typical Mediterranean ungauged watersheds using post-flash-flood measurements, Hydrology, 7(1), 12, doi:10.3390/hydrology7010012, 2020.
    18. Stavropoulos, S., G. N. Zaimes, E. Filippidis, D. C. Diaconu, and D. Emmanouloudis, Mitigating flash floods with the use of new technologies: A multi-criteria decision analysis to map flood susceptibility for Zakynthos island, Greece, Journal of Urban & Regional Analysis, 12(2), 233-248, 2020.
    19. Kastridis, A., C. Kirkenidis, and M. Sapountzis, An integrated approach of flash flood analysis in ungauged Mediterranean watersheds using post‐flood surveys and Unmanned Aerial Vehicles (UAVs), Hydrological Processes, 34(25), 4920-4939, doi:10.1002/hyp.13913, 2020.
    20. Abdrabo, K. I., S. A. Kantoush, M. Saber, T. Sumi, O. M. Habiba, D. Elleithy, and B. Elboshy, Integrated methodology for urban flood risk mapping at the microscale in ungauged regions: A case study of Hurghada, Egypt, Remote Sensing, 12(21), 3548, doi:10.3390/rs12213548, 2020.
    21. Yariyan, P., M. Avand, R. A. Abbaspour, A. T. Haghighi, R. Costache, O. Ghorbanzadeh, S. Janizadeh, and T. Blaschke, Flood susceptibility mapping using an improved analytic network process with statistical models, Geomatics, Natural Hazards and Risk, 11(1), 2282-2314, doi:10.1080/19475705.2020.1836036, 2020.
    22. Papaioannou, G., C. Papadaki, and E. Dimitriou, Sensitivity of habitat hydraulic model outputs to DTM and computational mesh resolution, Ecohydrology, 13(2), e2182, doi:10.1002/eco.2182, 2020.
    23. Papaioannou, G., G. Varlas, A. Papadopoulos, A. Loukas, P. Katsafados, and E. Dimitriou, Investigating sea‐state effects on flash flood hydrograph and inundation forecasting, Hydrological Processes, 35(4), e14151, doi:10.1002/hyp.14151, 2021.
    24. Mahamat Nour, A., C. Vallet‐Coulomb, J. Gonçalves, F. Sylvestre, and P. Deschamps, Rainfall-discharge relationship and water balance over the past 60 years within the Chari-Logone sub-basins, Lake Chad basin, Journal of Hydrology: Regional Studies, 35, 100824, doi:10.1016/j.ejrh.2021.100824, 2021.
    25. Varlas, G., A. Papadopoulos, G. Papaioannou, and E. Dimitriou, Evaluating the forecast skill of a hydrometeorological modelling system in Greece, Atmosphere, 12(7), 902, doi:10.3390/atmos12070902, 2021.
    26. Khalaj, M. R., H. Noor, and A. Dastranj, Investigation and simulation of flood inundation hazard in urban areas in Iran, Geoenvironmental Disasters, 8, 18, doi:10.1186/s40677-021-00191-1, 2021.
    27. Hooke, J., J. Souza, and M. Marchamalo, Evaluation of connectivity indices applied to a Mediterranean agricultural catchment, Catena, 207, 105713, doi:10.1016/j.catena.2021.105713, 2021.
    28. Seleem, O., M. Heistermann, and A. Bronstert, Efficient hazard assessment for pluvial floods in urban environments: A benchmarking case study for the city of Berlin, Germany, Water, 13(18), 2476, doi:10.3390/w13182476, 2021.
    29. Cotugno, A., V. Smith, T. Baker, and R. Srinivasan, A framework for calculating peak discharge and flood inundation in ungauged urban watersheds using remotely sensed precipitation data: A case study in Freetown, Sierra Leone, Remote Sensing, 13(19), 3806, doi:10.3390/rs13193806, 2021.
    30. Berteni, F., A. Dada, and G. Grossi, Application of the MUSLE model and potential effects of climate change in a small Alpine catchment in Northern Italy, Water, 13(19), 2679, doi:10.3390/w13192679, 2021.

  1. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.

    Stochastic models in hydrology traditionally aim at reproducing the empirically derived statistical characteristics of the observed data rather than any specific distribution model that attempts to describe the usually non-Gaussian statistical behavior of the associated processes. SPARTA (Stochastic Periodic AutoRegressive To Anything) offers an alternative and novel approach which allows the explicit representation of each process of interest with any distribution model, while simultaneously establishes dependence patterns that cannot be fully captured by the typical linear stochastic schemes. Cornerstone of the proposed approach is the Nataf joint-distribution model, which is related with the Gaussian copula, combined with Gaussian periodic autoregressive processes. In order to obtain the target stochastic structure, we have also developed a computationally simple and efficient algorithm, based on a hybrid Monte-Carlo procedure that is used to approximate the required equivalent correlation coefficients. Theoretical and practical benefits of the proposed method, contrasted to outcomes from widely used stochastic models, are demonstrated by means of real-world as well as hypothetical monthly simulation examples involving both univariate and multivariate time series.

    Additional material:

    Other works that reference this work (this list might be obsolete):

    1. Papalexiou, S. M., Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency, Advances in Water Resources, 115, 234-252, doi:10.1016/j.advwatres.2018.02.013, 2018.
    2. Brunner, M. I., A. Bárdossy, and R. Furrer, Technical note: Stochastic simulation of streamflow time series using phase randomization, Hydrology and Earth System Sciences, 23, 3175-3187, doi:10.5194/hess-23-3175-2019, 2019.
    3. Marković, D., S. Ilić, D. Pavlović, J. Plavšić, and N. Ilich, Multivariate and multi-scale generator based on non-parametric stochastic algorithms, Journal of Hydroinformatics, 21(6), 1102-1117, doi:10.2166/hydro.2019.071, 2019.
    4. #Elsayed, H., S. Djordjević, and D. Savić, The Nile water, food and energy nexus – A system dynamics model, 7th International Computing & Control for the Water Industry Conference, Exeter, United Kingdom, 2019.
    5. Nazemi, A., M. Zaerpour, and E. Hassanzadeh, Uncertainty in bottom-up vulnerability assessments of water supply systems due to regional streamflow generation under changing conditions, Journal of Water Resources Planning and Management, 146(2), doi:10.1061/(ASCE)WR.1943-5452.0001149, 2020.
    6. Barber, C., J. R. Lamontagne, and R. M. Vogel, Improved estimators of correlation and R2 for skewed hydrologic data, Hydrological Sciences Journal, 65(1), 87-101, doi:10.1080/02626667.2019.1686639, 2020.
    7. Dutta, R., and R. Maity, Temporal networks based approach for non‐stationary hydroclimatic modelling and its demonstration with streamflow prediction, Water Resources Research, 56(8), e2020WR027086, doi:10.1029/2020WR027086, 2020.
    8. Demetriou, E., G. Mallouppas, and C.Hadjistassou, Embracing carbon neutral electricity and transportation sectors in Cyprus, Energy, doi:10.1016/j.energy.2021.120625, 2021.
    9. 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.

  1. C. Makropoulos, E. Rozos, I. Tsoukalas, A. Plevri, G. Karakatsanis, L. Karagiannidis, E. Makri, C. Lioumis, K. Noutsopoulos, D. Mamais, K. Ripis, and T. Lytras, Sewer-mining: A water reuse option supporting circular economy, public service provision and entrepreneurship, Journal of Environmental Management, 216, 285–298, doi:10.1016/j.jenvman.2017.07.026, 2018.

    Water scarcity, either due to increased urbanisation or climatic variability, has motivated societies to reduce pressure on water resources mainly by reducing water demand. However, this practice alone is not sufficient to both protect resources and guarantee the quality of life water services underpin especially within a context of increased urbanisation. As such, the idea of water reuse has been gaining momentum for some time in the water sector and has recently found a more general context within the emerging concept of the Circular Economy. As a result of this growing trend, water recycling schemes at various scales have been applied worldwide. The most common scale of water reuse is reusing the effluent of a wastewater treatment plant for irrigation or industrial uses (e.g. cooling towers, or rinsing). This is favoured by economies of scale, but to be economically viable it requires that the recycled-water user is close enough to the treatment plant (and at a more or less similar or lower elevation), otherwise capital and operational costs for transmission getratherhigh. Another downside with this scale of (centralised) reuse is that this scheme does not break the monopoly of water supply, since it is again the water company that runs the treatment unit and provides the effluent for reuse and as such offers reduced benefits in terms of job creation, innovation drive and entrepreneurship. On the other side of the scale spectrum, at the level of the household, reuse options include mostly the reuse of grey water for non-potable uses (such as toilet flushing and garden irrigation). Although promising and with significant potential for demand reduction, this scale of reuse is not necessarily cost effective, with all costs borne by the end user, and usually relies on additional motivation, such as drought conditions or environmental attitudes to be implemented. This study argues for an intermediate scale of water reuse, termed sewer-mining, which is a water recycling scheme at the neighbourhood scale. We suggest it provides a feasible alternative reuse option when the geography of the wastewater treatment plant is problematic, it relies on mature treatment technologies and presents an excellent opportunity for Small Medium Enterprises (SME) to be involved in the water supply market, thus securing both environmental, social and economic benefits (including but not restricted to water for ecosystem services). To support this argument, we report on a pilot sewer mining application. The pilot, integrates to important subsystems: a packaged treatment unit and an Information and Communications Technology (ICT) infrastructure that would allow an operator to manage remotely several sewer mining units thus rendering the provided service economically viable even for SMEs. The paper reports on the pilot’s overall performance and critically evaluates the potential of the sewer mining idea to become a significant piece of the circular economy puzzle for water.

  1. I. Tsoukalas, C. Makropoulos, and S. Mihas, Identification of potential sewer mining locations: A Monte-Carlo based approach, Water Science and Technology, 76 (12), 3351–3357, doi:10.2166/wst.2017.487, 2017.

    Rapid urbanization affecting demand patterns, coupled with potential water shortages due to supply side impacts of climatic changes, has led to the emergence of new technologies for water and wastewater reuse. Sewer mining (SM) is a novel decentralized option that could potentially provide non-potable water for urban uses, including for example the irrigation of urban green spaces, providing a mid-scale solution to effective wastewater reuse. SM is based on extracting wastewater from local sewers and treatment at the point of demand and entails in some cases the return of treatment residuals back to the sewer system. Several challenges are currently in the way of such applications in Europe, including public perception, inadequate regulatory frameworks and engineering issues. In this paper we consider some of these engineering challenges, looking at the sewer network as a system where multiple physical, biological and chemical processes take place. We argue that prior to implementing SM, the dynamics of the sewer system should be investigated in order to identify optimum ways of deploying SM without endangering the reliability of the system. Specifically, both wastewater extraction and sludge return could result in altering the biochemical process of the network, thus unintentionally leading to degradation of the sewer infrastructure. We propose a novel Monte-Carlo based method that takes into account both spatial properties and water demand characteristics of a given area of SM deployment while simultaneously accounting for the variability of sewer network dynamics in order to identify potential locations for SM implementation. The outcomes of this study suggest that the method can provide rational results and useful guidelines for upscale SM technologies at a city level.

    Full text: http://www.itia.ntua.gr/en/getfile/1909/1/documents/wst076123351.pdf (459 KB)

    See also: https://iwaponline.com/wst/article/76/12/3351-3357/38389

  1. A. Tegos, N. Malamos, A. Efstratiadis, I. Tsoukalas, A. Karanasios, and D. Koutsoyiannis, Parametric modelling of potential evapotranspiration: a global survey, Water, 9 (10), 795, doi:10.3390/w9100795, 2017.

    We present and validate a global parametric model of potential evapotranspiration (PET) with two parameters which are estimated through calibration, using as explanatory variables temperature and extraterrestrial radiation. The model and the parameters estimation approach were tested over the globe, using the FAO CLIMWAT database that provides monthly averaged values of meteorological inputs at 4300 locations worldwide. A preliminary analysis of these data allowed explaining the major drivers of PET over the globe and across seasons. Next, we developed an automatic optimization software tool to calibrate the model and provide point PET estimations against the given Penman-Monteith values. We also employed extended analysis of model inputs and outputs, including the production of global maps of optimized model parameters and associated performance metrics. Also, we employed interpolated values of the optimized parameters to validate the predictive capacity of our model against monthly meteorological time series, at several stations worldwide. The results were very encouraging, since even with the use of abstract climatic information for model calibration and the use of interpolated parameters as local predictors, the model generally ensures reliable PET estimations. In few cases the model performs poorly in estimating the reference PET, due to irregular interactions between temperature and extraterrestrial radiation, as well as because the associated processes are influenced by additional drivers, e.g. relative humidity and wind speed. However, the analysis of the residuals showed that the model is consistent in terms of parameters estimation and model validation. The provided parameters maps allow the direct use of the parametric model wherever in the world, providing PET estimates in case of missing data, that can be further improved even with a short term acquisition of meteorological data.

    Full text: http://www.itia.ntua.gr/en/getfile/1738/2/documents/water-09-00795.pdf (6428 KB)

    Additional material:

    See also: http://www.mdpi.com/2073-4441/9/10/795

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

    Other works that reference this work (this list might be obsolete):

    1. Elferchichi, A., G. A. Giorgio, N. Lamaddalena, M. Ragosta, and V. Telesca, Variability of temperature and its impact on reference evapotranspiration: the test case of the Apulia region (Southern Italy), Sustainability, 9(12), 2337, doi:10.3390/su9122337, 2017.
    2. Li, M., R. Chu, S. Shen, and A. R. T. Islam, Quantifying climatic impact on reference evapotranspiration trends in the Huai River Basin of Eastern China, Water, 10(2), 144, doi:10.3390/w10020144, 2018.
    3. Yan, N., F. Tian, B. Wu, W. Zhu, and M. Yu, Spatiotemporal analysis of actual evapotranspiration and its causes in the Hai basin, Remote Sensing, 10(2), 332; doi:10.3390/rs10020332, 2018.
    4. Li, M., R. Chu, A.R.M.T. Islam, and S. Shen, Reference evapotranspiration variation analysis and its approaches evaluation of 13 empirical models in sub-humid and humid regions: A case study of the Huai River Basin, Eastern China, Water, 10(4), 493, doi:10.3390/w10040493, 2018.
    5. Hao, X., S. Zhang, W. Li, W. Duan, G. Fang, Y. Zhang , and B. Guo, The uncertainty of Penman-Monteith method and the energy balance closure problem, Journal of Geophysical Research – Atmospheres, 123(14), 7433-7443, doi:10.1029/2018JD028371, 2018.
    6. Giménez, P. O., and S. G. García-Galiano, Assessing Regional Climate Models (RCMs) ensemble-driven reference evapotranspiration over Spain, Water, 10(9), 1181, doi:10.3390/w10091181, 2018.
    7. Storm, M. E., R. Gouws, and L. J. Grobler, Novel measurement and verification of irrigation pumping energy conservation under incentive-based programmes, Journal of Energy in Southern Africa, 29(3), 10–21, doi:10.17159/2413-3051/2018/v29i3a3058, 2018.
    8. Tam, B. Y., K. Szeto, B. Bonsal, G. Flato, A. J. Cannon, and R. Rong, CMIP5 drought projections in Canada based on the Standardized Precipitation Evapotranspiration Index, Canadian Water Resources Journal, 44(1), 90-107, doi:10.1080/07011784.2018.1537812, 2019.
    9. Dalezios, N. R., N. Dercas, A. Blanta, and I. N. Faraslis, Remote sensing in water balance modelling for evapotranspiration at a rural watershed in Central Greece, International Journal of Sustainable Agricultural Management and Informatics, 4(3-4), 306-337, doi:10.1504/IJSAMI.2018.099219, 2019.
    10. Gan, G., Y. Liu, X. Pan, X. Zhao, M. Li, and S. Wang, Testing the symmetric assumption of complementary relationship: A comparison between the linear and nonlinear advection-aridity models in a large ephemeral lake, Water, 11(8), 1574, doi:10.3390/w11081574, 2019.
    11. Zhang, T., Y. Chen, and K. Tha Paw U, Quantifying the impact of climate variables on reference evapotranspiration in Pearl River Basin, China, Hydrological Sciences Journal, 64(16), 1944-1956, doi:10.1080/02626667.2019.1662021, 2019.
    12. Hua, D., X. Hao, Y. Zhang, and J. Qin, Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method, Journal of Arid Land, 12, 166–180, doi:10.1007/s40333-020-0093-7, 2020.
    13. Shirmohammadi-Aliakbarkhani, Z., and S. F. Saberali, Evaluating of eight evapotranspiration estimation methods in arid regions of Iran, Agricultural Water Management, 239, 106243, doi:10.1016/j.agwat.2020.106243, 2020.
    14. Kim, C.-G., J. Lee, J. E. Lee, and H. Kim, Evaluation of improvement effect on the spatial-temporal correction of several reference evapotranspiration methods, Journal of Korea Water Resources Association, 53(9), 701-715, doi:10.3741/JKWRA.2020.53.9.701, 2020.
    15. Gui, Y., Q. Wang, Y. Zhao, Y. Dong, H. Li, S. Jiang, X. He, and K. Liu, Attribution analyses of reference evapotranspiration changes in China incorporating surface resistance change response to elevated CO2, Journal of Hydrology, 599, 126387, doi:10.1016/j.jhydrol.2021.126387, 2021.
    16. Mohanasundaram, S., M. M. Mekonnen, E. Haacker, C. Ray, S. Lim, and S. Shrestha, An application of GRACE mission datasets for streamflow and baseflow estimation in the Conterminous United States basins, Journal of Hydrology, 601, 126622, doi:10.1016/j.jhydrol.2021.126622, 2021.
    17. Gentilucci, M., M. Bufalini, M. Materazzi, M. Barbieri, D. Aringoli, P. Farabollini, and G. Pambianchi, Calculation of potential evapotranspiration and calibration of the Hargreaves equation using geostatistical methods over the last 10 years in Central Italy, Geosciences, 11(8), 348, doi:10.3390/geosciences11080348, 2021.
    18. Hajek, O. L., and A. K. Knapp, Shifting seasonal patterns of water availability: ecosystem responses to an unappreciated dimension of climate change, New Phytologist, doi:10.1111/nph.17728, 2021.
    19. Dos Santos, A. A., J. L. M. de Souza, and S. L. K. Rosa, Evapotranspiration with the Moretti-Jerszurki-Silva model for the Brazilian subtropical climate, Hydrological Sciences Journal, doi:10.1080/02626667.2021.1988610, 2021.
    20. Stefanidis, S., and V. Alexandridis, Precipitation and potential evapotranspiration temporal variability and their relationship in two forest ecosystems in Greece, Hydrology, 8(4), 160, doi:10.3390/hydrology8040160, 2021.

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: Ecosystem services from treated wastewater, Desalination and Water Treatment, 91 (2017), 2017.

    To reduce the impact of urban effluents on the environment, strict regulatory requirements have been set up for the disposal of wastewater, in most parts of the western world, requiring treatment before disposal. At the same time, the urban environment requires water inflows to satisfy a range of urban water demands, and the corresponding water abstractions put pressure on (often scarce) water resources. A suggested synergistic solution is to use the effluents from treatment plants as an alternative resource for irrigation or for industrial uses. Despite the existence of numerous successful applications, this practice is not very common mainly because of increased capital and operational costs, usually exceeding the cost of fresh water. A possible response of the market to this drawback could be to introduce in-situ small scale treatment units to cover local water needs. In this study, we assess the benefits of such a compact wastewater treatment unit that is used to provide water for irrigating an urban green area. Apart from the aesthetic improvement, the evaporative cooling (latent heat), which reduces the air temperature, is expected to have a positive impact on thermal comfort. A pilot scheme was deployed in KEREFYT, the research centre of the Athens Water Supply and Sewerage Company (EYDAP). This scheme was simulated with the UWOT model to estimate heat fluxes and the results were fed into Energy2D (a model that simulates heat transfer) to estimate the expected temperature drop. The results are promising and suggest that these technologies could play an important role in a more sustainable, circular water economy.

    Full text: http://www.itia.ntua.gr/en/getfile/1715/1/documents/Manuscript_subm2_CM.pdf (636 KB)

  1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.

    In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget.

    Related works:

    • [41] Early presentation if EGU conference

    Full text: http://www.itia.ntua.gr/en/getfile/1587/2/documents/SEEAS_paper.pdf (4310 KB)

    Additional material:

    Other works that reference this work (this list might be obsolete):

    1. Dariane , A. B., and M. M. Javadianzadeh, Towards an efficient rainfall–runoff model through partitioning scheme, Water, 8, 63, doi:10.3390/w8020063, 2016.
    2. Yaseen, Z. M., O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Quilty, and A. El-Shafie, Boost stream-flow forecasting model with extreme learning machine data-driven: A case study in a semi-arid region in Iraq, Journal of Hydrology, 542, 603-614, doi:10.1016/j.jhydrol.2016.09.035, 2016.
    3. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
    4. #Christelis, V., V. Bellos, and G. Tsakiris, Employing surrogate modelling for the calibration of a 2D flood simulation model, Sustainable Hydraulics in the Era of Global Change: Proceedings of the 4th IAHR Europe Congress (Liege, Belgium, 27-29 July 2016), A. S. Erpicum, M. Pirotton, B. Dewals, P. Archambeau (editors), CRC Press, 2016.
    5. Salazar, J. Z., P. M. Reed, J. D. Quinn, M. Giuliani, and A. Castelletti, Balancing exploration, uncertainty and computational demands in many objective reservoir optimization, Advances in Water Resources, 109, 196-210, doi:10.1016/j.advwatres.2017.09.014, 2017.
    6. Christelis, V., and A. Mantoglou, Physics-based and data-driven surrogate models for pumping optimization of coastal aquifers, European Water, 57, 481–488, 2017.
    7. #Thandayutham, K., E. Avital, N. Venkatesan, and A. Samad, Design and analysis of a marine current turbine, Proceedings of ASME 2017 Gas Turbine India Conference and Exhibition, GTINDIA2017-4912, V001T02A014, Bangalore, India, doi:10.1115/GTINDIA2017-4912, 2017.
    8. Christelis, V., R. G. Regis, and A. Mantoglou, Surrogate-based pumping optimization of coastal aquifers under limited computational budgets, Journal of Hydroinformatics, 20(1), 164-176, doi:10.2166/hydro.2017.063, 2018.
    9. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, 107, 298-306, doi:10.1016/j.envsoft.2018.05.004, 2018.
    10. Zischg, A. P., G. Felder, M. Mosimann, V. Röthlisberger, and R. Weingartner, Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses, Environmental Modelling and Software, 108, 174-185, doi:10.1016/j.envsoft.2018.08.009, 2018.
    11. Christelis, V., and A. Mantoglou, Pumping optimization of coastal aquifers using seawater intrusion models of variable-fidelity and evolutionary algorithms, Water Resources Management, 33(2), 555-558, doi:10.1007/s11269-018-2116-0, 2019.
    12. Thandayutham, K., L. K. Mishra, and A. Samad, Optimal design of a marine current turbine using CFD and FEA, Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018), K. Murali, V. Sriram, A. Samad, N. Saha (editors), Lecture Notes in Civil Engineering, 23, 675-690, doi:10.1007/978-981-13-3134-3, 2019.
    13. Christelis, V., G. Kopsiaftis, and A. Mantoglou, Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers, Hydrological Sciences Journal, 64(3), 336-349, doi:10.1080/02626667.2019.1584400, 2019.
    14. Cai, X., L. Gao, X. Li, and H-. Qiu, Surrogate-guided differential evolution algorithm for high dimensional expensive problems, Swarm and Evolutionary Computation, 48, 288-311, doi:10.1016/j.swevo.2019.04.009, 2019.
    15. Huot, P.-L., A. Poulin, C. Audet, and S. Alarie, A hybrid optimization approach for efficient calibration of computationally intensive hydrological models, Hydrological Sciences Journal, 64(9), 1204-1222, doi:10.1080/02626667.2019.1624922, 2019.
    16. Jahandideh-Tehrani, M., O. Bozorg-Haddad, and H. A. Loáiciga, Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview, Environmental Monitoring and Assessment, 191:439, doi:10.1007/s10661-019-7581-2, 2019.
    17. Sandoval, S., and J.-L. Bertrand-Krajewski, From marginal to conditional probability functions of parameters in a conceptual rainfall-runoff model: an event-based approach, Hydrological Sciences Journal, 64(11), 1340-1350, doi:10.1080/02626667.2019.1635696, 2019.
    18. Zhao, C. S., T. L. Pan, J. Xi, S. T. Yang, J. Zhao, X. J. Gan, L. P. Hou, and S. Y. Ding, Streamflow calculation for medium-to-small rivers in data scarce inland areas, Science of The Total Environment, 693, 133571, doi:10.1016/j.scitotenv.2019.07.377, 2019.
    19. Monteil, C., F. Zaoui, N. Le Moine, and F. Hendrickx, Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm, Hydrology and Earth System Sciences, 24, 3189-3209, 10.5194/hess-24-3189-2020, 2020.
    20. Muhammed, K. A., and R. Farmani, Energy optimization using a pump scheduling tool in water distribution systems, ARO – The Scientific Journal of Koya University, 8(1), 112-123, doi:10.14500/aro.10635, 2020.
    21. #Castro-Gama M., C. Agudelo-Vera, and D. Bouziotas, A bird’s-eye view of data validation in the drinking water industry of the Netherlands, The Handbook of Environmental Chemistry, Springer, Berlin, Heidelberg, doi:10.1007/698_2020_609, 2020.
    22. Xai, W., C. Shoemaker, T. Akhtar, and M.-T. Nguyen, Efficient parallel surrogate optimization algorithm and framework with application to parameter calibration of computationally expensive three-dimensional hydrodynamic lake PDE models, Environmental Modelling and Software, 135, 104910, doi:10.1016/j.envsoft.2020.104910, 2021.
    23. Saadatpour, M., S. Javaheri, A. Afshar, and S. S. Solis, Optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects, Expert Systems with Applications, 184, 115474, doi:10.1016/j.eswa.2021.115474, 2021.
    24. Zhao, T., and B. Minsker, Efficient metamodel approach to handling constraints in nonlinear optimization for drought management, Journal of Water Resources Planning and Management, 147(12), doi:10.1061/(ASCE)WR.1943-5452.0001476, 2021.
    25. Anahideh, H., J. Rosenberger, and V. Chen, High-dimensional black-box optimization under uncertainty, Computers & Operations Research, 137, 105444, doi:10.1016/j.cor.2021.105444, 2022.

  1. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.

    Operation of large-scale hydropower reservoirs is a complex problem that involves conflicting objectives, such as hydropower generation and water supply. Deriving optimal operational rules is a challenging task due to the non-linearity of the system dynamics and the uncertainty of future inflows and water demands. A common approach to derive optimal control policies is to couple simulation models with optimization algorithms. This paper in order to investigate the performance of a future reservoir and safely infer about its significance employs stochastic simulation, thus long synthetically generated time-series and a multi-objective version of the Parameterization-Simulation-Optimization (PSO) framework to develop uncertainty-aware operational rules. Furthermore, in order to handle the high computational effort that ensues from that coupling we investigate the potential of a surrogate-based multi-objective optimization algorithm, ParEGO. The PSO framework is deployed with WEAP21 water resources management model as simulation engine and MATLAB for the implementation of optimization algorithms. A comparison between NSGAII and ParEGO optimization algorithms is performed to assess the effectiveness of the proposed algorithm. The aforementioned comparison showed that ParEGO provides efficient approximations of the Pareto front while reducing the computational effort required. Finally, the potential benefit and the significance of the future reservoir is underlined.

    Full text: http://www.itia.ntua.gr/en/getfile/1569/1/documents/tsoukalas_WRM.pdf (2008 KB)

    Other works that reference this work (this list might be obsolete):

    1. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
    2. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, doi:10.1016/j.envsoft.2018.05.004, 2018.

  1. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.

    Developing long term operation rules for multi-reservoir systems is complicated due to the number of decision variables, the non-linearity of system dynamics and the hydrological uncertainty. This uncertainty can be addressed by coupling simulation models with multi-objective optimisation algorithms driven by stochastically generated hydrological timeseries but the computational effort required imposes barriers to the exploration of the solution space. The paper addresses this by (a) employing a parsimonious multi-objective parameterization-simulation-optimization (PSO) framework, which incorporates hydrological uncertainty through stochastic simulation and allows the use of probabilistic objective functions and (b) by investigating the potential of multi-objective surrogate based optimisation (MOSBO) to significantly reduce the resulting computational effort. Three MOSBO algorithms are compared against two multi-objective evolutionary algorithms. Results suggest that MOSBOs are indeed able to provide robust, uncertainty-aware operation rules much faster, without significant loss of neither the generality of evolutionary algorithms nor of the knowledge embedded in domain-specific models.

    Other works that reference this work (this list might be obsolete):

    1. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
    2. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, doi:10.1016/j.envsoft.2018.05.004, 2018.
    3. Christelis, V., G. Kopsiaftis, and A. Mantoglou, Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers, Hydrological Sciences Journal, doi:10.1080/02626667.2019.1584400, 2019.

Book chapters and fully evaluated conference publications

  1. D. Nikolopoulos, C. Makropoulos, D. Kalogeras, K. Monokrousou, and I. Tsoukalas, Developing a stress-testing platform for cyber-physical water infrastructure, 2018 International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), New Jersey, 9–11, doi:10.1109/CySWater.2018.00009, 2018.

    Water supply and sanitation infrastructures are essential for our welfare, but vulnerable to several attacks, typically of physical and cyber types. Cyber-physical attacks on critical infrastructures include chemical and/or biological contamination, physical or communications disruption between the network elements and the supervisory SCADA. Due to the ever-changing landscape of the digital world and the rising concerns about security, there is an emerging need for conceptualizing critical infrastructure as cyber-physical systems and develop a holistic risk management framework for its physical and cyber protection. The framework aims to strengthen the capacities of water utilities to systematically protect their systems, determine gaps in security technologies and improve risk management approaches. Our work envisions the development of a stress testing modelling platform, able to simulate the water system as a complete cyber-physical infrastructure and investigate attack scenarios and possible mitigation measures.

    Full text: http://www.itia.ntua.gr/en/getfile/1965/1/documents/08434711.pdf (332 KB)

  1. C. Rebolho, V. Andréassian, I. Tsoukalas, et A. Efstratiadis, La crue du Loing de Juin 2016 était-elle exceptionnelle?, De la prévision des crues à la gestion de crise, Avignon, Société Hydrotechnique de France, 2018.

    A heavy rainfall event affected the northern center part of France from May 30 to June 6, 2016, leading to a general overflowing of rivers in the Seine and Loire catchments. The resulting inundations exceeded the previous records on some catchments, such as the River Loing where the water height of January 1910 was outreached for the first time. This event results from the combination of an extremely wet month of May and a rainfall accumulation of 130 mm in one week which led to a daily peak flow of 450 m3/s on this catchment. The main goal of this study is to show the limitations of standard methods for the estimation of return periods of extreme events. Usually, statistic laws such as Gumbel of GEV are used to calculate such return periods. However, various fitting methods exist and can be used to assess the parameters of the theoretical laws. In this study, we found that depending on the methodology, the return period varies from 260 to 2 400 years when using the observed discharges. To address this issue we simulated a long series of streamflows by coupling a rainfall generator and the conceptual hydrological model GR4J. The empirical return period given by the models is 1 000 years. But in this case, we also have the uncertainties of the two models, particularly the hydrological model which struggles reproducing the non-linearities of the catchment behaviour especially when modelling extreme events. This is why it is difficult to assign a single value to the return period of extreme events when only a range is available.

    Full text: http://www.itia.ntua.gr/en/getfile/1918/1/documents/REBOLHO_ARTICLE_SHF_PREVISION.pdf (467 KB)

    See also: http://www.shf-hydro.org/223-1-events-16.html

  1. I. Tsoukalas, C. Makropoulos, and A. Efstratiadis, Stochastic simulation of periodic processes with arbitrary marginal distributions, 15th International Conference on Environmental Science and Technology (CEST2017), Rhodes, Global Network on Environmental Science and Technology, 2017.

    Stochastic simulation of hydrological processes has a key role in water resources planning and management due to its ability to incorporate hydrological uncertainty within decision-making. Due to seasonality, the statistical characteristics of such processes are considered periodic functions, thus implying the use of cyclostationary stochastic models, typically using a common statistical distribution. Yet, this may not be representative of the statistical structure of such processes across all seasons. In this context, we introduce a novel model suitable for the simulation of periodic processes with arbitrary marginal distributions, called Stochastic Periodic AutoRegressive To Anything (SPARTA). Apart from capturing the periodic correlation structure of the underlying processes, its major advantages are a) the accurate preservation of seasonally-varying marginal distributions; b) the explicit generation of non-negative values; and c) the parsimonious model structure. Finally, the performance of the model is demonstrated through a theoretical (artificial) case study.

    Full text: http://www.itia.ntua.gr/en/getfile/1731/1/documents/cest2017_00797_oral_paper_V2.pdf (655 KB)

    Additional material:

    See also: http://cest.gnest.org/sites/default/files/presentation_file_list/cest2017_00797_oral_paper.pdf

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: ecosystem services from treated wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, National Technical University of Athens, 2016, (in press).

    In order to reduce the impact of the urban effluents on the environment, modern societies have imposed restrictions regarding the quality of the disposals. For this reason, in the majority of the western world cities, the wastewater is treated before disposal. However, on the other side of the urban water cycle, water abstractions keep putting an increasing pressure on the water resources. As a countermeasure, treated wastewater is used occasionally as an alternative resource by employing large scale infrastructure to treat and supply water for either irrigation or industrial uses. Despite the existence of numerous successful applications, this practice is not very common mainly because of the increased capital and operational costs, usually exceeding the cost of fresh water. The response of the market to this drawback was to introduce in-situ small scale treatment units to cover local water needs. In this study, we assess the benefits of a compact wastewater treatment unit that is used to provide water for irrigating a green area. Apart from the aesthetic improvement, benefits are expected because of the evaporative cooling (latent heat), which reduce the air temperature. A pilot scheme was set up in KEREFYT, the research centre of Athens water supply company. This scheme was simulated with UWOT model to estimate the heat fluxes and the results were fed into Energy2D (a model that simulates heat transfer) to estimate the expected temperature drop.

    Full text: http://www.itia.ntua.gr/en/getfile/1600/1/documents/Manuscript_QiNArbH.pdf (509 KB)

  1. I. Tsoukalas, P. Dimas, and C. Makropoulos, Hydrosystem optimization on a budget: Investigating the potential of surrogate based optimization techniques, 14th International Conference on Environmental Science and Technology (CEST2015), Global Network on Environmental Science and Technology, University of the Aegean, 2015.

    Development of uncertainty-aware operational rules for multi-reservoir systems is a demanding and challenging task due to the complexity of the system dynamics, the number of decision variables and the hydrological uncertainty. In order to overcome this issue the parsimonious parameterization-simulation-optimization (PSO) framework is employed coupled with stochastically generated hydrological time-series. However, when the simulation model requires long computational time this coupling imposes a computational barrier to the framework. The purpose of this paper is threefold: a) Investigate the potential of Efficient Global Optimization (EGO) algorithm (and its variants) which is capable of reaching global optima within a few simulation model evaluations (~500 or less). b) Extend the capabilities of WEAP21 water resources management model by using it within PSO framework (named WEAP21-PSO) and c) Validate and compare the results of WEAP21-PSO using the well-known hydrosystem management model Hydronomeas coupled with Evolutionary Annealing Simplex (EAS) optimization algorithm. Results confirm that EGO has the potential and the capabilities to handle computationally demanding problems and furthermore is capable of locating the optimal solution within few simulation model evaluations and that the WEAP21-PSO framework performs well at the task at hand.

    Full text: http://www.itia.ntua.gr/en/getfile/1574/1/documents/cest2015_00162_oral_paper.pdf (475 KB)

    See also: http://cest.gnest.org/cest15proceedings/public_html/papers/cest2015_00162_oral_paper.pdf

Conference publications and presentations with evaluation of abstract

  1. G. Moraitis, D. Nikolopoulos, I. Koutiva, I. Tsoukalas, G. Karavokiros, and C. Makropoulos, The PROCRUSTES testbed: tackling cyber-physical risk for water systems, EGU General Assembly 2021, online, EGU21-14903, doi:10.5194/egusphere-egu21-14903, European Geosciences Union, 2021.

    Our modern urban environment relies on critical infrastructures that serve vital societal functions, such as water supply and sanitation, which are exposed to various threats of both physical and cyber nature. Despite the progress in protection and increased vigilance, long-established practices within the water utilities may rely on precarious methods for the characterization and assessment of threats, with uncertainty pertaining to risk-relevant data and information. Sources for uncertainty can be attributed to e.g. limited capabilities of deterministic approaches, siloed analysis of water systems, use of ambiguous measures to describe and prioritise risks or common security misconceptions. To tackle those challenges, this work brings together an ensemble of solutions, to form a novel, unified process of resilience assessment for the water sector against an emerging cyber-physical threat landscape e.g., cyber-attacks on the command and control sub-system. Specifically, the proposed framework sets out an operational workflow that combines, inter alia, a) an Agent-Based Modelling (ABM) approach to derive alternative routes to quantify risks considering the dynamics of socio-technical systems, b) an adaptable optimisation platform which integrates advanced multi-objective algorithms for system calibration, uncertainty propagation analysis and asset criticality prioritization and c) a dynamic risk reduction knowledge-base (RRKB) designed to facilitate the identification and selection of suitable risk reduction measures (RRM). This scheme is overarched by a cyber-physical testbed, able to realistically model the interactions between the information layer (sensors, PLCs, SCADA) and the water distribution network. The testbed is designed to assess the water system beyond normal operational capacity. It facilitates the exploration of emergent and unidentified threats and vulnerabilities leading to Low Probability, High Consequence (LPHC) events that systems are not originally designed to handle. It also evaluates alternative risk treatment options against case-appropriate indicators. The final product is the accretion of actionable information to integrate risk into decision-making in a practical and standardized form. Our work envisions to bring forth state-of-art technologies and approaches for the cyber-wise water sector. We aspire to enhance existing capabilities for large utilities and enable small and medium water utilities with typically less resources, to reinforce their systems’ resilience and be better prepared against cyber-physical and other threats.

    Full text: http://www.itia.ntua.gr/en/getfile/2122/1/documents/EGU21-14903_presentation.pdf (1404 KB)

  1. K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Setting the problem of energy production forecasting for small hydropower plants in the Target Model era, EGU General Assembly 2021, online, EGU21-3168, doi:10.5194/egusphere-egu21-3168, European Geosciences Union, 2021.

    The highly-competitive electricity market over EU and the challenges induced by the so-called “Target Model”, introduce significant uncertainties to day-ahead trades involving renewable energy, since most of these sources are driven by non-controllable weather processes (wind, solar, hydro). Here, we explore the case of small hydropower plants that have negligible storage capacity, and thus their production is just a nonlinear transformation of inflows. We discuss different forecasting approaches, which take advantage of alternative sources of information, depending on data availability. Among others, we investigate whether is it preferable to employ day-ahead predictions based on past energy production data per se, or use these data in order to retrieve past inflows, which allows for introducing hydrological knowledge within predictions. Overall objective is to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of hydropower production, thus quantifying the overall uncertainty of each forecasting method. Power forecasts are evaluated in terms of economic efficiency, accounting for the impacts of over- and under-estimations in the real-world electricity market.

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  1. V. Kourakos, A. Efstratiadis, and I. Tsoukalas, Can hydrological model identifiability be improved? Stress-testing the concept of stochastic calibration, EGU General Assembly 2021, online, EGU21-11704, doi:10.5194/egusphere-egu21-11704, European Geosciences Union, 2021.

    Hydrological calibrations with historical data are often deemed insufficient for deducing safe estimations about a model structure that imitates, as closely as possible, the anticipated catchment behaviour. Ιn order to address this issue, we investigate a promising strategy, using as drivers synthetic time series, which preserve the probabilistic properties and dependence structure of the observed data. The key idea is calibrating a model on the basis of synthetic rainfall-runoff data, and validating against the full observed data sample. To this aim, we employed a proof of concept on few representative catchments, by testing several lumped conceptual hydrological models with alternative parameterizations and across two time-scales, monthly and daily. Next, we attempted to reinforce the validity of the recommended methodology by employing monthly stochastic calibrations in 100 MOPEX catchments. As before, a number of different hydrological models were used, for the purpose of proving that calibration with stochastic inputs is independent of the chosen model. The results highlight that in most cases the new approach leads to stronger parameter identifiability and stable predictive capacity across different temporal windows, since the model is trained over much extended hydroclimatic conditions.

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  1. G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, A dilemma of small hydropower plants: Design with uncertainty or uncertainty within design?, EGU General Assembly 2021, online, EGU21-2398, doi:10.5194/egusphere-egu21-2398, European Geosciences Union, 2021.

    Small hydropower plants (SHPPs) are subject to multiple uncertainties and complexities, despite their limited scale. These uncertainties are often ignored in the typical engineering practice, which results in risky design. As this type of renewable energy rapidly penetrates the electricity mix, the impacts of their uncertainties, exogenous and endogenous, become critical. In this vein, we develop a stochastic simulation-optimization framework tailored for small hydropower plants. First, we investigate the underlying multicriteria design problem and its peculiarities, in order to determine a best-compromise performance metric that ensures efficient and effective optimizations. Next, we adjust to the optimal design problem a modular uncertainty assessment procedure. This combines statistical and stochastic approaches to quantify the uncertainty of the inflow process per se, the associated input data, the initial selection of efficiency curves for the turbine mixing in the design phase, as well as the drop of efficiency due to aging effects. Overall, we propose a holistic framework for the optimal design of SHPPs, highlighting the added value of considering the stochasticity of input processes and parameters. The novelty of this approach is the transition from the conventional to the uncertainty-aware design; from the unique value to Pareto-optimality, and finally to the reliability of the expected performance, in terms of investment costs, hydropower production, and associated revenues.

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  1. A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Revisiting the storage-reliability-yield concept in hydroelectricity, EGU General Assembly 2021, online, EGU21-10528, doi:10.5194/egusphere-egu21-10528, European Geosciences Union, 2021.

    The storage-reliability-yield (SRY) relationship is a well-established tool for preliminary design of reservoirs fulfilling consumptive water uses, yet rarely employed within hydropower planning studies. Here, we discuss the theoretical basis for representing the trade-offs between reservoir size and expected revenues from hydropower production, under uncertain inflows, by taking advantage of the stochastic simulation-optimization approach. We also demonstrate that under some assumptions, the complex and site-specific problem, mainly induced by the nonlinearity of storage-head-energy conversion, can be significantly simplified and generalized as well. The methodology is tested across varying runoff regimes and under a wide range of potential reservoir geometries, expressed in terms of a generic shape parameter of the head-storage relationship. Based on the outcomes of these analyses we derive empirical expressions that link reliable energy with summary inflow statistics, reservoir capacity and geometry.

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  1. M. Nezi, C. Ntigkakis, I. Tsoukalas, and A. Efstratiadis, Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-19674, doi:egusphere-egu2020-19674, 2020.

    Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.

    Full text: http://www.itia.ntua.gr/en/getfile/2029/1/documents/EGU2020-19674-print.pdf (291 KB)

    See also: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-19674.html

  1. A. G. Pettas, P. Mavritsakis, I. Tsoukalas, N. Mamassis, and A. Efstratiadis, Empirical metric for uncertainty assessment of wind forecasting models in terms of power production and economic efficiency, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8018, doi:10.5194/egusphere-egu2020-8018, 2020.

    As made for most of renewable energy sources, wind energy is driven by highly uncertain and thus unpredictable meteorological processes. In the context of wind power scheduling and control, reliable wind predictions across scales is a challenging problem. However, since the generation of wind energy is, in fact, a nonlinear transformation of wind velocity through the power curve of each specific turbine, the errors in meteorological predictions have different impacts on wind power forecasts. It is well-known that for quite a large range of wind velocity values, the wind power production is either zero or constant, thus independent of the individual wind velocity value. This interesting feature allows for ensuring better predictions of the output, i.e. the energy production, with respect to input, i.e. wind velocity. Taking advantage of this, we present a hybrid stochastic framework for multi-step ahead wind velocity predictions and their evaluation by means of power production and economic efficiency. The methodology is tested for different wind regimes and different layouts of wind turbine systems, emphasizing to mixing of different turbine types, which allows for minimizing uncertainties. Finally, we investigate the use of this index in the technical and operational optimization of wind energy systems.

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    See also: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-8018.html

  1. G.-K. Sakki, V. Papalamprou, I. Tsoukalas, N. Mamassis, and A. Efstratiadis, Stochastic modelling of hydropower generation from small hydropower plants under limited data availability: from post-assessment to forecasting, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8129, doi:10.5194/egusphere-egu2020-8129, 2020.

    Due to their negligible storage capacity, small hydroelectric plants cannot offer regulation of flows, thus making the prediction of energy production a very difficult task, even for small time horizons. Further uncertainties arise due to the limited hydrological information, in terms of upstream inflow data, since usually the sole available measurements refer to the power production, which is a nonlinear transformation of the river discharge. In this context, we develop a stochastic modelling framework comprising two steps. Initially, we extract past inflows on the basis of energy data, which may be referred to as the inverse problem of hydropower. Key issue of this approach is that the model error is expressed in stochastic terms, which allows for embedding uncertainties within calculations. Next, we generate stochastic forecasting ensembles of future inflows and associated hydropower production, spanning from small (daily to weekly) to meso-scale (monthly to seasonal) time horizons. The methodology is tested in the oldest (est. 1926) small hydroelectric plant of Greece, located at Glafkos river, in Northern Peloponnese. Among other complexities, this comprises a mixing of Pelton and Francis turbines, which makes the overall modelling procedure even more challenging.

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    See also: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-8129.html

  1. L. M. Tsiami, E. Zacharopoulou, D. Nikolopoulos, I. Tsoukalas, N. Mamassis, A. Kallioras, and A. Efstratiadis, The use of Artificial Neural Networks with different sources of spatiotemporal information for flash flood predictions, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-7315, European Geosciences Union, 2019.

    For more than two decades, the use of artificial neural networks (ANNs) in hydrology has become an effective and efficient alternative against traditional modeling approaches, i.e. physically-based or conceptual. These can take advantage of any type of available information to predict the hydrological response of complex systems, with missing data and limited knowledge about the transformation mechanisms. A promising area of application is the real-time prediction of flood propagation, which is essential element of early warning and early notification systems. In this work we focus to flash floods, considering as areas of application two medium-scale catchments in Greece with substantially different characteristics. The first one is the highly urbanized river basin of Kephissos (380 km2), which is the main drainage channel of the Athens Metropolitan area, while the second is the rural catchment of Nedontas, SW Greece (120 km2). Both areas have been recently equipped with automatic hydrometric stations, while online rainfall data are also available at a representative number of meteorological stations. For the two case studies we investigate several setups of ANNs, in order to predict the river stage at the catchment outlet for several lead times, using different combinations of input sets, by means of upstream stage and point rainfall data.

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  1. P. Mavritsakis, A. G. Pettas, I. Tsoukalas, G. Karakatsanis, N. Mamassis, and A. Efstratiadis, A stochastic simulation framework for representing water, energy and financial fluxes across a non-connected island, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-8758, European Geosciences Union, 2019.

    Integrated modeling of hybrid water-energy systems, comprising conventional and renewable energy sources, pumped-storage facilities and other hydraulic infrastructures, which aim to serve combined water and energy uses, is a highly challenging problem. On the one hand, such systems are subject to significant uncertainties that span over all associated input processes, physical and anthropogenic (i.e. hydrometeorological drivers and water-energy demands, respectively). On the other hand, the everyday operation of such systems is subject to multiple complexities, due to the conflicting uses, constraints and economic interests. Taking as example a future configuration of the electric system of Ikaria Island, Greece, we demonstrate a stochastic simulation framework, comprising: (a) a synthetic time series generator that reproduces the statistical and stochastic properties (i.e. marginal distributions, auto- and cross-dependencies) of all input processes, at multiple temporal scales; and (b) a simulation module employing the hourly operation of the system, to estimate the associated water, energy and financial fluxes. This scheme is used within two case studies, i.e. the optimal design of key system components, and the real-time operation of a hypothetical energy market, involving different energy providers and associated electricity sources, conventional and renewable.

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  1. E. Zacharopoulou, I. Tsoukalas, A. Efstratiadis, and D. Koutsoyiannis, Impact of sample uncertainty of inflows to stochastic simulation of reservoirs, European Geosciences Union General Assembly 2019, Geophysical Research Abstracts, Vol. 21, Vienna, EGU2019-17233, European Geosciences Union, 2019.

    Design and management of water resource systems are arguably challenging tasks, as they are mainly driven by hydrological processes that are dominated by “structured” randomness. In this vein, the stochastic simulation of the input processes is regarded an essential component for such studies. Typically, the objective of stochastic models is the generation of long synthetic time series that reproduce the statistical and dependence properties of the historical data, ideally at multiple time scales (including long-term changes, such as those induced by the Hurst-Kolmogorov behavior). However, the sample statistical characteristics that are forced to be reproduced entail an inherent uncertainty, due to the generally short length of historical data. This key shortcoming is not typically accounted for within the current practices. This work is an attempt to investigate and quantify the input uncertainty within stochastic models, and eventually assess its impact on reservoir systems. Towards this, we establish a methodology for the quantification of the sample uncertainty, involving the essential statistical characteristics of historical inflows in a multiscale context, by using as background stochastic simulator the CastaliaR model. Initially, this model is employed for the generation of a large set of synthetic time series with the same length with the historical sample, and thus provide multiple “pseudo-historic” realizations. Subsequently, the statistical properties of the ensemble of pseudo-historic data are extracted and employed to generate long synthetic time series, which are finally used as inputs to a reservoir simulation model. In this context, the above procedure is demonstrated for the derivation of ensembles of storage-yield-reliability relationships. Furthermore, multiple analyses for different sample sizes and Hurst coefficients are performed, aiming to investigate the uncertainty imposed by the sample size and the long-term persistence of the inflow processes.

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    Other works that reference this work (this list might be obsolete):

    1. Koskinas, A., Stochastics and ecohydrology: A study in optimal reservoir design, Dams and Reservoirs, 30(2), 53-59, doi:10.1680/jdare.20.00009, 2020.
    2. Saengsuwan, T., Prediction model for solar PV rooftop production, Journal of Renewable Energy and Smart Grid Technology, 15(2), 16-25, 2020.

  1. Y. Moustakis, P. Kossieris, I. Tsoukalas, and A. Efstratiadis, Quasi-continuous stochastic simulation framework for flood modelling, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-534, European Geosciences Union, 2017.

    Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event. In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS), while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall. This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

    Full text: http://www.itia.ntua.gr/en/getfile/1680/2/documents/FINAL_Moustakis_EGU2017.pdf (1492 KB)

    Additional material:

  1. P. Kossieris, A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Assessing the performance of Bartlett-Lewis model on the simulation of Athens rainfall, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-8983, doi:10.13140/RG.2.2.14371.25120, European Geosciences Union, 2015.

    Many hydrological applications require the use of long rainfall data across a wide range of fine time scales. To meet this necessity, stochastic approaches are usually employed for the generation of large number of rainfall events, following a Monte Carlo approach. In this framework, Bartlett-Lewis model (BL) is a key representative from the family of Poisson-cluster stochastic processes. Here, we examine the performance of three different versions of BL model, with number of parameters varying from 5 up to 7, in representing the characteristics of convective and frontal rainfall of Athens (Greece). Apart from the typical statistical characteristics that are explicitly preserved by the stochastic model (mean, variance, lag-1 autocorrelation, probability dry), we also attempt to preserve the statistical distribution of annual rainfall maxima, as well as two important temporal properties of the observed storm events, i.e. the duration of storms and the time distance between subsequent events. This task is not straightforward, given that these characteristics are not described in the theoretical equations of the model, but they should be empirically evaluated on the basis of synthetic data. The analysis is conducted on monthly basis and for multiple time scales, i.e. from hourly to daily. Further to that, we focus on the formulation of the calibration problem, by assessing the performance of the BL model against issues such as choice of statistics to preserve, time scales, distance metrics, etc.

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    See also: http://dx.doi.org/10.13140/RG.2.2.14371.25120

    Other works that reference this work (this list might be obsolete):

    1. Li, X., A. Meshgi, X. Wang, J. Zhang, S. H. X. Tay, G. Pijcke, N. Manocha, M. Ong, M. T. Nguyen, and V. Babovic, Three resampling approaches based on method of fragments for daily-to-subdaily precipitation disaggregation, International Journal of Climatology, 38(Suppl.1), e1119-e1138, doi:10.1002/joc.5438, 2018.
    2. Park, J., C. Onof, and D. Kim, A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales, Hydrology and Earth System Sciences, 23, 989-1014, doi:10.5194/hess-23-989-2019, 2019.
    3. Kim, D., and C. Onof, A stochastic rainfall model that can reproduce important rainfall properties across the timescales from several minutes to a decade, Journal of Hydrology, 589(2), 125150, doi:10.1016/j.jhydrol.2020.125150, 2020.

  1. A. Efstratiadis, I. Tsoukalas, P. Kossieris, G. Karavokiros, A. Christofides, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Computational issues in complex water-energy optimization problems: Time scales, parameterizations, objectives and algorithms, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5121, doi:10.13140/RG.2.2.11015.80802, European Geosciences Union, 2015.

    Modelling of large-scale hybrid renewable energy systems (HRES) is a challenging task, for which several open computational issues exist. HRES comprise typical components of hydrosystems (reservoirs, boreholes, conveyance networks, hydropower stations, pumps, water demand nodes, etc.), which are dynamically linked with renewables (e.g., wind turbines, solar parks) and energy demand nodes. In such systems, apart from the well-known shortcomings of water resources modelling (nonlinear dynamics, unknown future inflows, large number of variables and constraints, conflicting criteria, etc.), additional complexities and uncertainties arise due to the introduction of energy components and associated fluxes. A major difficulty is the need for coupling two different temporal scales, given that in hydrosystem modeling, monthly simulation steps are typically adopted, yet for a faithful representation of the energy balance (i.e. energy production vs. demand) a much finer resolution (e.g. hourly) is required. Another drawback is the increase of control variables, constraints and objectives, due to the simultaneous modelling of the two parallel fluxes (i.e. water and energy) and their interactions. Finally, since the driving hydrometeorological processes of the integrated system are inherently uncertain, it is often essential to use synthetically generated input time series of large length, in order to assess the system performance in terms of reliability and risk, with satisfactory accuracy. To address these issues, we propose an effective and efficient modeling framework, key objectives of which are: (a) the substantial reduction of control variables, through parsimonious yet consistent parameterizations; (b) the substantial decrease of computational burden of simulation, by linearizing the combined water and energy allocation problem of each individual time step, and solve each local sub-problem through very fast linear network programming algorithms, and (c) the substantial decrease of the required number of function evaluations for detecting the optimal management policy, using an innovative, surrogate-assisted global optimization approach.

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    See also: http://dx.doi.org/10.13140/RG.2.2.11015.80802

  1. A. Drosou, P. Dimitriadis, A. Lykou, P. Kossieris, I. Tsoukalas, A. Efstratiadis, and N. Mamassis, Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece), European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-9148, European Geosciences Union, 2015.

    We present a multi-criteria simulation-optimization framework for the optimal design and setting of flood protection structures along river banks. The methodology is tested in the lower course of the Arachthos River (Epirus, Greece), downstream of the hydroelectric dam of Pournari. The entire study area is very sensitive, particularly because the river crosses the urban area of Arta, which is located just after the dam. Moreover, extended agricultural areas that are crucial for the local economy are prone to floods. In the proposed methodology we investigate two conflicting criteria, i.e. the minimization of flood hazards (due to damages to urban infrastructures, crops, etc.) and the minimization of construction costs of the essential hydraulic structures (e.g. dikes). For the hydraulic simulation we examine two flood routing models, named 1D HEC-RAS and quasi-2D LISFLOOD, whereas the optimization is carried out through the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the EAS method.

    Full text:

    Other works that reference this work (this list might be obsolete):

    1. Xu, Z., P. Plink-Björklund, S. Wu, Z. Liu, W. Feng, K. Zhang, Z. Yang, and Y. Zhong, Sinuous bar fingers of digitate shallow‐water deltas: Insights into their formative processes and deposits from integrating morphological and sedimentological studies with mathematical modelling, Sedimentology, doi:10.1111/sed.12923, 2021.

  1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Handling time-expensive global optimization problems through the surrogate-enhanced evolutionary annealing-simplex algorithm, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5923, European Geosciences Union, 2015.

    In water resources optimization problems, the calculation of the objective function usually presumes to first run a simulation model and then evaluate its outputs. In several cases, however, long simulation times may pose significant barriers to the optimization procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required by the problem’s complexity. A promising novel strategy to address these shortcomings is the use of surrogate modelling techniques within global optimization algorithms. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modelling with the effectiveness and efficiency of the EAS method. The algorithm combines three different optimization approaches (evolutionary search, simulated annealing and the downhill simplex search scheme), in which key decisions are partially guided by numerical approximations of the objective function. The performance of the proposed algorithm is benchmarked against other surrogate-assisted algorithms, in both theoretical and practical applications (i.e. test functions and hydrological calibration problems, respectively), within a limited budget of trials (from 100 to 1000). Results reveal the significant potential of using SE-EAS in challenging optimization problems, involving time-consuming simulations.

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Academic works

  1. I. Tsoukalas, Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty, PhD thesis, 339 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, December 2018.

    Hydrometeorological inputs are a key ingredient and simultaneously one of the main sources of uncertainty of every hydrological study. This type of uncertainty is referred to as hydrometeorological uncertainty and is of utmost importance in risk-based engineering works, due the high variability and randomness that is naturally embedded in physical processes. Considering hydrometeorological time series as realizations of stochastic processes allow their analysis, modeling, simulation and forecasting. Embracing the existence of randomness and unpredictability in such processes is a first step towards their understanding and the development of uncertainty-aware methodologies for water-systems optimization.

    In this vein, due to the typical size of historical data, which is not (neither will ever be) sufficient to extract safe conclusions about the long-term performance of a system, the common procedure entails driving the typically deterministic water-system models (conceptual or physical-based) using stochastic inputs (that in a statistical sense resemble the parent information; typically, but not exclusively derived from the historical time series). This essentially enables the establishment of Monte Carlo experiments where the intrinsic uncertainty of the inputs (i.e., hydrometeorological processes) is propagated through a deterministic filter (i.e., a water-system simulation model) in order to derive, or assess, the probabilistic behavior of the output of interest (e.g., water supply coverage). Further to this, when the objective is the optimization of the deterministic model’s control variables (i.e., model’s parameters) with respect to some quantity or metric (i.e., objective), this procedure can (and should) be embed within an iterative scheme driven by an optimization algorithm (i.e., establishing uncertainty-aware simulation-optimization frameworks). An important step of this procedure is the realistic simulation of hydrometeorological processes, since they are the main drivers of the whole procedure, and eventually determine its accuracy, as well as the probabilistic behavior of the output of interest. This in turn, poses an intriguing challenge that arises from a series of unique peculiarities that characterize such processes, namely, non-Gaussianity, intermittency, auto-dependence (short- or long-range), cross-dependence and periodicity. Despite the significant amount of research during last decades, these challenges remain partially unresolved. To a large extent, this is due to the standard hypothesis of most simulation schemes that does not lie in the reproduction of a specific distribution, but on the reproduction of low-order statistics (e.g., mean, variance, skewness) and correlations in time and space. This is a problem because, a) for a given set of low-order statistics multiple distributions may be represented, thus making the simulation problem only partially defined, and b) as shown herein, this practice may lead to bounded, and thus unrealistic dependence forms among consecutive time steps and/or processes.

    Further to this, driving water-system simulation models with long stochastically generated sequences, thus accounting for input (hydrometeorological) uncertainty, inevitably increases the required computational effort, especially within the context of simulation-optimization frameworks. This in turn, poses the challenge of addressing and ensuring the practical implementation of water-system optimization problems under uncertainty.

    Thereby, the main research objectives and contributions of this Thesis are related to:

    1. The development of novel non-Gaussian stochastic simulation models, able to account also for the other peculiarities typically encountered in hydrometeorological processes, such as, intermittency, auto- and cross- dependence, periodicity, as well as their scale-varying probabilistic and stochastic behavior.
    2. The development of surrogate-based optimization methodologies and algorithms that can efficiently and effectively confront water-system simulation-optimization problems under uncertainty, i.e., when using stochastic inputs to drive the simulation-optimization procedure.

    Specifically, herein a by building upon copula concepts, probability laws and the theory of stochastic processes, a theoretically justified family of univariate and multivariate non-Gaussian stationary and cyclostationary models is defined and thoroughly investigated. This type of models have been unknown to the hydrological community, and this Thesis is the first attempt to align them with hydrological stochastics. The developed models are shown to be able to account for all the typical characteristics of hydrometeorological processes and simultaneously exhibit a simple and parsimonious character. Furthermore, these models are then coupled, using a disaggregation approach, thus eventually enabling the development of a modular stochastic simulation framework that allows the simultaneous reproduction of the probabilistic and stochastic behavior (including non-Gaussian distributions) of hydrometeorological processes at multiple time scales (from annual to daily; as well as finer time scales). The advantages of this class of stochastic processes and models, as well as of the modular stochastic simulation framework for multi-scale simulations, are demonstrated and verified through numerous hypothetical and real-world simulation studies.

    Finally, in order to ensure the effective exploitation and practical implementation of these new developments in the stochastic simulation of hydrometeorological processes within the uncertainty-aware, engineering design and management of water-systems (i.e., driven by stochastic inputs), this Thesis develops appropriate surrogate-based computationally-efficient methodologies and algorithms, that effectively handle water-system simulation-optimization problems under hydrometeorological uncertainty, thus alleviating the associated computational barrier.

    Full text: http://www.itia.ntua.gr/en/getfile/1933/1/documents/Tsoukalas_Phd_LQ2.pdf (41012 KB)

Research reports

  1. A. Efstratiadis, N. Mamassis, I. Tsoukalas, and S. Manouri, [No English title available], Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 35 pages, May 2021.

    Related project: Modernization of the management of the water supply system of Athens - Update

  1. A. Efstratiadis, I. Tsoukalas, and S. Manouri, Investigation of the water supply system's management for period March-September 2021, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 38 pages, March 2021.

    Related project: Modernization of the management of the water supply system of Athens - Update

  1. A. Efstratiadis, I. Papakonstantis, P. Papanicolaou, N. Mamassis, D. Nikolopoulos, I. Tsoukalas, and P. Kossieris, First year synopsis, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 55 pages, December 2020.

    Related project: Modernization of the management of the water supply system of Athens - Update

  1. A. Efstratiadis, S. Manouri, D. Nikolopoulos, and I. Tsoukalas, Investigation of the water supply system's management for period March-September 2020, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 31 pages, March 2020.

    Related project: Modernization of the management of the water supply system of Athens - Update

  1. A. Efstratiadis, and I. Tsoukalas, Update of water balance of Hylike and Paralimni and assesment of their risk of spilling during the current hydrological year, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 56 pages, November 2019.

    Related project: Modernization of the management of the water supply system of Athens - Update

    Full text: http://www.itia.ntua.gr/en/getfile/2014/1/documents/NTUA_Paradoteo3_YlikiUpdate_20191126_DFmfzrX.pdf (1776 KB)

    Other works that reference this work (this list might be obsolete):

    1. #Gourgouletis, N., G. Bariamis, and E. Baltas, Estimation of characteristics of surface water bodies based on Sentinel-2 images: The case study of Yliki reservoir, Proceedings of the Eighth International Conference on Environmental Management, Engineering, Planning & Economics, 551-558, Thessaloniki, Greece, 2021.

  1. A. Efstratiadis, N. Mamassis, and I. Tsoukalas, Synoptic report on the evaluation of the flood risk for areas affected by the ongoing spilling of the Hylike-Paralimni system, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 25 pages, March 2019.

    Related works:

    • [47] Updated study (November 2019)

    Related project: Modernization of the management of the water supply system of Athens - Update

    Full text: http://www.itia.ntua.gr/en/getfile/1988/1/documents/NTUA_Paradoteo1_YlikiPreliminary_20190321.pdf (1015 KB)