Civil Engineer, MSc., Dr. Engineer
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
G. Moraitis, G.-K. Sakki, G. Karavokiros, D. Nikolopoulos, P. Kossieris, I. Tsoukalas, and C. Makropoulos, Exploring the cyber-physical threat landscape of water systems: A socio-technical modelling approach, Water, 15 (9), 1687, doi:10.3390/w15091687, 2023.
The identification and assessment of the cyber-physical-threat landscape that surrounds water systems in the digital era is governed by complex socio-technical dynamics and uncertainties that exceed the boundaries of traditional risk assessment. This work provides a remedy for those challenges by incorporating socio-technical modelling to account for the adaptive balance between goal-driven behaviours and available skills of adversaries, exploitable vulnerabilities of assets and utility’s security posture, as well as an uncertainty-aware multi-scenario analysis to assess the risk level of any utility against cyber-physical threats. The proposed risk assessment framework, underpinned by a dedicated modelling chain, deploys a modular sequence of processes for (a) the estimation of vulnerability-induced probabilities and attack characteristics of the threat landscape under a spectrum of adversaries, (b) its formulation to a representative set of stochastically generated threat scenarios, (c) the combined cyber-physical stress-testing of the system against the generated scenarios and (d) the inference of the system’s risk level at system and asset level. The proposed framework is demonstrated by exploring different configurations of a synthetic utility case study that investigate the effects and efficiency that different cyber-security practices and design traits can have over the modification of the risk level of the utility at various dimensions.
Full text: http://www.itia.ntua.gr/en/getfile/2289/1/documents/water-15-01687.pdf (2852 KB)
See also: https://www.mdpi.com/2073-4441/15/9/1687
S. Tsattalios, I. Tsoukalas, P. Dimas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search, Environmental Modelling and Software, 162, 105639, doi:10.1016/j.envsoft.2023.105639, 2023.
Complex environmental optimization problems often require computationally expensive simulation models to assess candidate solutions. However, the complexity of response surfaces necessitates multiple such assessments and thus renders the search procedure too laborious. Surrogate-based optimization is a powerful approach for accelerating convergence towards promising solutions. Here we introduce the Adaptive Multi-Surrogate Enhanced Evolutionary Annealing Simplex (AMSEEAS) algorithm, as an extension of SEEAS, which is another well-established surrogate-based global optimization method. AMSEEAS exploits the strengths of multiple surrogate models that are combined via a roulette-type mechanism, for selecting a specific metamodel to be activated in every iteration. AMSEEAS proves its robustness and efficiency via extensive benchmarking against SEEAS and other state-of-the-art surrogate-based global optimization methods in both theoretical mathematical problems and in a computationally demanding real-world hydraulic design application. The latter seeks for cost-effective sizing of levees along a drainage channel to minimize flood inundation, calculated by the time-expensive hydrodynamic model HEC-RAS.
Full text: http://www.itia.ntua.gr/en/getfile/2266/1/documents/AMSEEAS_paper.pdf (14432 KB)
G. Moraitis, I. Tsoukalas, P. Kossieris, D. Nikolopoulos, G. Karavokiros, D. Kalogeras, and C. Makropoulos, Assessing cyber-physical threats under water demand uncertainty, Environmental Sciences Proceedings, 21 (1), 18, doi:10.3390/environsciproc2022021018, October 2022.
This study presents an approach for the assessment of cyber-physical threats to water distribution networks under the prism of the uncertainty which stems from the variability and stochastic nature of nodal water demands. The proposed framework investigates a single threat scenario under a spectrum of synthetic, yet realistic, system states which are driven by an ensemble of stochastically generated nodal demands. This Monte Carlo-type experiment enables the probabilistic inference about model outputs, and hence the derivation of probabilistic estimates over consequences. The approach is showcased for a cyber-physical attack scenario against the monitoring and control system of a benchmark network.
Full text: http://www.itia.ntua.gr/en/getfile/2250/1/documents/environsciproc-21-00018.pdf (933 KB)
See also: https://www.mdpi.com/2673-4931/21/1/18
G.-K. Sakki, I. Tsoukalas, P. Kossieris, C. Makropoulos, and A. Efstratiadis, Stochastic simulation-optimisation framework for the design and assessment of renewable energy systems under uncertainty, Renewable and Sustainable Energy Reviews, 168, 112886, doi:10.1016/j.rser.2022.112886, 2022.
As the share of renewable energy resources rapidly increases in the electricity mix, the recognition, representation, quantification, and eventually interpretation of their uncertainties become important. In this vein, we propose a generic stochastic simulation-optimization framework tailored to renewable energy systems (RES), able to address multiple facets of uncertainty, external and internal. These involve the system’s drivers (hydrometeorological inputs) and states (by means of fuel-to-energy conversion model parameters and energy market price), both expressed in probabilistic terms through a novel coupling of the triptych statistics, stochastics and copulas. Since the most widespread sources (wind, solar, hydro) exhibit several common characteristics, we first introduce the formulation of the overall modelling context under uncertainty, and then offer uncertainty quantification tools to put in practice the plethora of simulated outcomes and resulting performance metrics (investment costs, energy production, revenues). The proposed framework is applied to two indicative case studies, namely the design of a small hydropower plant (particularly, the optimal mixing of its hydro-turbines), and the long-term assessment of a planned wind power plant. Both cases reveal that the ignorance or underestimation of uncertainty may hide a significant perception about the actual operation and performance of RES. In contrast, the stochastic simulation-optimization context allows for assessing their technoeconomic effectiveness against a wide range of uncertainties, and as such provides a critical tool for decision making, towards the deployment of sustainable and financially viable RES.
Full text: http://www.itia.ntua.gr/en/getfile/2229/1/documents/stochasticRES.pdf (6011 KB)
Other works that reference this work (this list might be obsolete):
|1.||Woon, K. S., Z. X. Phuang, J. Taler, P. S. Varbanov, C. T. Chong, J. J. Klemeš, and C. T. Lee, Recent advances in urban green energy development towards carbon neutrality, Energy, 126502, doi:10.1016/j.energy.2022.126502, 2022.|
|2.||Angelakis, A., Reframing the high-technology landscape in Greece: Empirical evidence and policy aspects, International Journal of Business & Economic Sciences Applied Research, 15(2), 58-70, doi:10.25103/ijbesar.152.06, 2022.|
|3.||Kim, J., M. Qi, J. Park, and I. Moon, Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach, Applied Energy, 339, 121015, doi:10.1016/j.apenergy.2023.121015, 2023.|
|4.||Yin, S., L. Chen, and H. Qin, Reduced space optimization-based evidence theory method for response analysis of space-coiled acoustic metamaterials with epistemic uncertainty, Mathematical Problems in Engineering, 2023, 9937158, doi:10.1155/2023/9937158, 2023.|
|5.||Qu, K., H. Zhang, X. Zhou, F. Causone, X. Huang, X. Shen, and X. Zhu, Optimal design of building integrated energy systems by combining two-phase optimization and a data-driven model, Energy and Buildings, 295, 113304, doi:10.1016/j.enbuild.2023.113304, 2023.|
|6.||Wang, Z., W. Zhang, H. Fan, C. Zhang, Y. Zhao, and Z. Huang, An uncertainty-tolerant robust distributed control strategy for building cooling water systems considering measurement uncertainties, Journal of Building Engineering, 76, 107162, doi:10.1016/j.jobe.2023.107162, 2023.|
|7.||Caputo, A. C., A. Federici, P. M. Pelagagge, and P. Salini, Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty, Applied Energy, 350, 121585, doi:10.1016/j.apenergy.2023.121585, 2023.|
|8.||Liu, J., Y. Li, Y. Ma, R. Qin, X. Meng, and J. Wu, Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy, Energy, 285, 128673, doi:10.1016/j.energy.2023.128673, 2023.|
A. Efstratiadis, P. Dimas, G. Pouliasis, I. Tsoukalas, P. Kossieris, V. Bellos, G.-K. Sakki, C. Makropoulos, and S. Michas, Revisiting flood hazard assessment practices under a hybrid stochastic simulation framework, Water, 14 (3), 457, doi:10.3390/w14030457, 2022.
We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modelling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms to antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modelling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically-derived quantiles for given return periods. Finally, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.
Full text: http://www.itia.ntua.gr/en/getfile/2170/1/documents/water-14-00457.pdf (6083 KB)
See also: https://www.mdpi.com/2073-4441/14/3/457
Other works that reference this work (this list might be obsolete):
|1.||Tegos, A., A. Ziogas, V. Bellos, and A. Tzimas, Forensic hydrology: a complete reconstruction of an extreme flood event in data-scarce area, Hydrology, 9(5), 93, doi:10.3390/hydrology9050093, 2022.|
|2.||Afzal, M. A., S. Ali, A. Nazeer, M. I. Khan, M. M. Waqas, R. A. Aslam, M. J. M. Cheema, M. Nadeem, N. Saddique, M. Muzammil, and A. N. Shah, Flood inundation modeling by integrating HEC–RAS and satellite imagery: A case study of the Indus river basin, Water, 14(19), 2984, doi:10.3390/w14192984, 2022.|
|3.||Vangelis, H., I. Zotou, I. M. Kourtis, V. Bellos, and V. A. Tsihrintzis, Relationship of rainfall and flood return periods through hydrologic and hydraulic modeling, Water, 14(22), 3618, doi:10.3390/w14223618, 2022.|
|4.||Maranzoni, A., M. D’Oria, and C. Rizzo, Quantitative flood hazard assessment methods: A review, Journal of Flood Risk Management, 16(1), e12855, doi:10.1111/jfr3.12855, 2022.|
|5.||Szeląg, B., P. Kowal, A. Kiczko, A. Białek, D. Majerek, P. Siwicki, F. Fatone, and G. Boczkaj, Integrated model for the fast assessment of flood volume: Modelling – management, uncertainty and sensitivity analysis, Journal of Hydrology, 625(A), 129967, doi:10.1016/j.jhydrol.2023.129967, 2023.|
K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data, Advances in Geosciences, 56, 155–162, doi:10.5194/adgeo-56-155-2022, 2022.
Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert’s knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.
The simulation and forecasting models have been developed in the R environment and they are available at: https://github.com/corinadrakaki/Day-ahead-energy-production-in-small-hydropower-plants
Full text: http://www.itia.ntua.gr/en/getfile/2165/1/documents/adgeo-56-155-2022.pdf (217 KB)
Other works that reference this work (this list might be obsolete):
|1.||Krechowicz, A., M. Krechowicz, and K. Poczeta, Machine learning approaches to predict electricity production from renewable energy sources, Energies, 15(23), 9146, doi:10.3390/en15239146, 2022.|
|2.||Ghobadi, F., and D. Kang, Application of machine learning in water resources management: A systematic literature review, Water, 15(4), 620, doi:10.3390/w15040620, 2023.|
|3.||Chen, B., Y. Long, H. Wei, B. Li, Y. Zhang, W. Deng, and C. Li, A weak-coupling flow-power forecasting method for small hydropower station group, International Journal of Energy Research, 2023, 1214269, doi:10.1155/2023/1214269, 2023.|
|4.||Karakuş, M. O., Impact of climatic factors on the prediction of hydroelectric power generation: A deep CNN-SVR approach, Geocarto International, doi:10.1080/10106049.2023.2253203, 2023.|
P. Kossieris, I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Generic framework for downscaling statistical quantities at fine time-scales and its perspectives towards cost-effective enrichment of water demand records, Water, 13 (23), 3429, doi:10.3390/w13233429, 2021.
The challenging task of generating synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities, based solely on available information at coarser time scales. The methodology is based on three key elements: a) the analysis of statistics’ behaviour across multiple temporal scales; b) the use of parametric functions to model this behaviour; and c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records, and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.
Full text: http://www.itia.ntua.gr/en/getfile/2164/1/documents/water-13-03429.pdf (2042 KB)
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.|
|3.||Alamanos, A., P. Koundouri, L. Papadaki, and T. Pliakou, A system innovation approach for science-stakeholder interface: theory and application to water-land-food-energy nexus, Frontiers in Water, 3, 744773, doi:10.3389/frwa.2021.744773, 2022.|
|4.||Papaioannou, G., V. Markogianni, A. Loukas, and E. Dimitriou, Remote sensing methodology for roughness estimation in ungauged streams for different hydraulic/hydrodynamic modeling approaches, Water, 14(7), 1076, doi:10.3390/w14071076, 2022.|
|5.||Borowska-Stefańska, M., L. Balážovičová, K. Goniewicz, M. Kowalski, P. Kurzyk, M. Masný, S. Wiśniewski, M. Žoncová, and A. Khorram-Manesh, Emergency management of self-evacuation from flood hazard areas in Poland, Transportation Research Part D: Transport and Environment, 107, 103307, doi:10.1016/j.trd.2022.103307, 2022.|
|6.||#Alamanos, A., and P. Koundouri, Emerging challenges and the future of water resources management, DEOS Working Papers, 2221, Athens University of Economics and Business, 2022.|
|7.||Ciurte, D. L., A. Mihu-Pintilie, A. Urzică, and A. Grozavu, Integrating LIDAR data, 2d HEC-RAS modeling and remote sensing to develop flood hazard maps downstream of a large reservoir in the inner Eastern Carpathians, Carpathian Journal of Earth and Environmental Sciences, 18(1), 149-169, doi:10.26471/cjees/2023/018/248, 2023.|
|8.||Vasiliades, L., G. Papaioannou, and A. Loukas, A unified hydrologic framework for flood design estimation in ungauged basins, Environmental Sciences Proceedings, 25(1), 40, doi:10.3390/ECWS-7-14194, 2023.|
|9.||Iliadis, C., P. Galiatsatou, V. Glenis, P. Prinos, and C. Kilsby, Urban flood modelling under extreme rainfall conditions for building-level flood exposure analysis, Hydrology, 10(8), 172, doi:10.3390/hydrology10080172, 2023.|
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
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
P. Kossieris, and C. Makropoulos, Exploring the statistical and distributional properties of residential water demand at fine time scales, Water, 10 (10), 1481, doi:10.3390/w10101481, 2018.
Residential water demand consists one of the most uncertain factors posing extra difficulties in the efficient planning and management of urban water systems. Currently, high resolution data from smart meters provide the means for a better understanding and modelling of this variable at a household level and fine temporal scales. Having this in mind, this paper examines the statistical and distributional properties of residential water demand at a 15-minute and hourly scale, which are the temporal scales of interest for the majority of urban water modeling applications. Towards this, we investigate large residential water demand records of different characteristics. The analysis indicates that the studied characteristics of the marginal distribution of water demand vary among households as well as on the basis of different time intervals. Both month-to-month and hour-to-hour analysis reveal that the mean value and the probability of no demand exhibit high variability while the changes in the shape characteristics of the marginal distributions of the nonzero values are significantly less. The investigation of performance of 10 probabilistic models reveals that Gamma and Weibull distributions can be used to adequately describe the nonzero water demand records of different characteristics at both time scales.
Full text: http://www.itia.ntua.gr/en/getfile/1904/1/documents/water-10-01481.pdf (23829 KB)
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.
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)
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.|
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P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018.
Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 minute time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-minute time scale. The applicability of the methodology was assessed on a 5-minute rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.
Temporary free access: https://authors.elsevier.com/c/1WHlB52cuBmT2
Other works that reference this work (this list might be obsolete):
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|5.||Onof, C., and L.-P. Wang, Modelling rainfall with a Bartlett–Lewis process: New developments, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2019-406, 2019.|
E. Dodangeh, K. Shahedi, K. Solaimani, and P. Kossieris, Usability of the BLRP model for hydrological applications in arid and semi-arid regions with limited precipitation data, Modeling Earth Systems and Environment, 2017.
In this study, Hydrological Simulation Program-FORTRAN (HSPF) is used to investigate rainfall-runoff process in Taleghan watershed, northern Iran. Despite the high accuracy of the model, the lack of rainfall data at short time scales (hour and less than hour) restricted implementation of the model especially for long time simulations. Some studies use simple division for daily rainfall disaggregation into the hourly values to provide data requirements of HSPF model. In simple division, each rainfall event is divided into 24 pulse stochastically and the peak flows may not properly being simulated due to the lower rainfall intensities. In this study, random parameter Bartlett–Lewis rectangular pulse (BLRP) model was implemented to disaggregate daily rainfall time series into the hourly values and the results compared with that of simple division. In BLRP model, parameters of the model calibrated against the 1, 24 and 48 h mean, variance, lag1 auto covariance and proportion dry of observed rainfall. The calibrated model was then implemented to disaggregate daily rainfall data into the hourly values. To compare two disaggregation approaches, daily stream flow simulation by HSPF model is initialized in 2 scenarios by applying the hourly rainfall data resulted from two disaggregation methods. The results indicated that while using the simple division method leads to the underestimation of peak flows, using the BLRP model improved peak flow simulations. This study indicated usability of the BLRP model for rainfall disaggregation in arid and semi-arid regions with limited fine scale precipitation data availability.
P. Kossieris, C. Makropoulos, E. Creaco, L. Vamvakeridou-Lyroudia, and D. Savic, Assessing the applicability of the Bartlett-Lewis model in simulating residential water demands, Procedia Engineering, 154, 123–131, 2016.
This paper presents the set-up and application of the Bartlett-Lewis clustering mechanism to simulate residential water demand at fine, i.e. sub-hourly, time scales. Two different variants of the model, i.e., the original and the random-parameter model, are examined. The models are assessed in terms of preserving the main statistical characteristics and temporal properties of demand series at a range of fine time scales, i.e., from 1-min up to 15-min. The comparison against the typical Poisson rectangular pulse model showed that clustering mechanism enables a better reproduction of demand characteristics at levels of aggregation other than those used in the fitting procedure.
Other works that reference this work (this list might be obsolete):
|1.||Onof, C., and L.-P. Wang, Modelling rainfall with a Bartlett–Lewis process: New developments, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2019-406, 2019.|
E. Creaco, P. Kossieris, L. Vamvakeridou-Lyroudia, C. Makropoulos, Z. Kapelan, and D. Savic, Parameterizing residential water demand pulse models through smart meter readings, Environmental Modelling and Software, 80, 33–40, 2016.
This paper proposes a method for parameterizing the Poisson models for residential water demand pulse generation, which consider the dependence of pulse duration and intensity. The method can be applied to consumption data collected in households through smart metering technologies. It is based on numerically searching for the model parameter values associated with pulse frequencies, durations and intensities, which lead to preservation of the mean demand volume and of the cumulative trend of demand volumes, at various time aggregation scales at the same time. The method is applied to various case studies, by using two time aggregation scales for demand volumes, i.e. fine aggregation scale (1 min or 15 min) and coarse aggregation scale (1 day). The fine scale coincides with the time resolution for reading acquisition through smart metering whereas the coarse scale is obtained by aggregating the consumption values recorded at the fine scale. Results show that the parameterization method presented makes the Poisson model effective at reproducing the measured demand volumes aggregated at both time scales. Consistency of the pulses improves as the fine scale resolution increases.
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.
Full text: http://www.itia.ntua.gr/en/getfile/1587/2/documents/SEEAS_paper.pdf (4310 KB)
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.|
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|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.|
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|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.|
|26.||Pang, M., E. Du, C. A. Shoemaker, and C. Zheng, Efficient, parallelized global optimization of groundwater pumping in a regional aquifer with land subsidence constraints, Journal of Environmental Management, 310, 114753, doi:10.1016/j.jenvman.2022.114753, 2022.|
|27.||Lu, W., W. Xia, and C. A. Shoemaker, Surrogate global optimization for identifying cost-effective green infrastructure for urban flood control with a computationally expensive inundation model, Water Resources Research, 58(4), e2021WR030928, doi:10.1029/2021WR030928, 2022.|
|28.||Kopsiaftis, G., M. Kaselimi, E. Protopapadakis, A. Voulodimos, A. Doulamis, N. Doulamis, and A. Mantoglou, Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems, Frontiers in Water, 5, 1195029, doi:10.3389/frwa.2023.1195029, 2023.|
|29.||Christelis, V., G. Kopsiaftis. R. G. Regis, and A. Mantoglou, An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management, Advances in Water Resources, 180, 104537, doi:10.1016/j.advwatres.2023.104537, 2023.|
P. Kossieris, S. Kozanis, A. Hashmi, E. Katsiri, L. Vamvakeridou-Lyroudia, R. Farmani, C. Makropoulos, and D. Savic, A web-based platform for water efficient households, Procedia Engineering, 89, 1128–1135, 2014.
The advent of ICT services on water sector offers new perspective towards sustainable water management. This paper presents an innovative web-based platform, targeting primarily the household end-users. The platform enables consumers to monitor and control, on real-time basis, the water and energy consumption of their household providing valuable information and feedback. At the same time, the platform further supports end-users to modify and improve their consumption profile via an interactive educational process that comprises a variety of online tools and applications. This paper discusses the rationale, structure and technologies upon which the platform has been developed and presents an early prototype of the various tools, applications and facilities.
Full text: http://www.itia.ntua.gr/en/getfile/1590/1/documents/kossieris_procedia2014.pdf (1131 KB)
P. Kossieris, Panayiotakis, K. Tzouka, E. Rozos, and C. Makropoulos, An e-Learning approach for improving household water efficiency, Procedia Engineering, WDSA 2014, Bari, Italy, Water Distribution Systems Analysis, 2014.
This paper, presents the development of an e-learning platform, associated with smart metering infrastructure, developed in Moodle. The platform aims to support further householders to improve the water efficiency of their household by understanding their current consumption and identifying practices, technologies that can save water. The platform is built around an interactive, multi-stage, educational process, which begins with a preparatory ("Exposing") stage in which the users receive useful information and feedback about their "water identity", continuous through a self-assessment ("Understanding") stage and finally provides (customized) smart and cost-effective tips and suggestions ("Acting" stage). This paper presents the components of the platform, including, inter alia, FAQ's, quizzes, advanced water calculators and customized tips.
A. Efstratiadis, I. Tsoukalas, and P. Kossieris, Improving hydrological model identifiability by driving calibration with stochastic inputs, Advances in Hydroinformatics: Machine Learning and Optimization for Water Resources, edited by G. A. Corzo Perez and D. P. Solomatine, doi:10.1002/9781119639268.ch2, American Geophysical Union, 2024.
For a long time, the classical problem of identifying the optimal modeling structure and/or parameters has been based on the calibration-validation norm, originating from the iconic split-sample scheme by Vit Klemeš and subsequently evolved in several ways. A common feature of such approaches is their dependence on the length and representativeness of the available historical data. This introduces several questions since the derived parameters are selected on the basis of a specific subset (or more generally subsets) of data, while the rest of data is used to evaluate the predictive capacity of the calibrated model. To address this shortcoming, we propose a novel and conceptually simple approach driven by the well-known stochastic simulation paradigm. The method builds upon the idea of calibrating hydrological models using alternative, yet probabilistically consistent, stochastically generated data. Decoupling this way, the available historical data now become the basis to generate synthetic input data, as well as for model validation and parameter uncertainty assessment. One main advantage is embedding the stochasticity of real-world drivers (rainfall, evapotranspiration) and responses (runoff) and thus their hydrological uncertainty. Another advantage is identification of stable and robust models since the calibration procedure is performed using long enough time series that reproduce important stochastic and probabilistic properties that are associated with the changing climate (e.g., long-term persistence) that are generally hidden in the short historical samples. Identifying this way, the derived parameters are optimal not only for the historical data set, but for any alternative plausible realization of the modeled processes.
P. Dimas, G.-K. Sakki, P. Kossieris, I. Tsoukalas, A. Efstratiadis, C. Makropoulos, N. Mamassis, and K. Pipili, Outlining a master plan framework for the design and assessment of flood mitigation infrastructures across large-scale watersheds, 12th World Congress on Water Resources and Environment (EWRA 2023) “Managing Water-Energy-Land-Food under Climatic, Environmental and Social Instability”, 75–76, European Water Resources Association, Thessaloniki, 2023.
Full text: http://www.itia.ntua.gr/en/getfile/2306/1/documents/EWRA2023-dimas.pdf (232 KB)
V. Bellos, P. Kossieris, A. Efstratiadis, I. Papakonstantis, P. Papanicolaou, P. Dimas, and C. Makropoulos, Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system, Proceedings of 7th IAHR Europe Congress "Innovative Water Management in a Changing Climate”, Athens, International Association for Hydro-Environment Engineering and Research (IAHR), 2022.
In this work, we investigate whether the parameters of physics-based hydraulic models, omnipresent in every relevant engineering handbook, can be used in blind trust in a real-world complex system. Here, we focus on the discharge coefficient for flows through a sluice gate and the Manning’s coefficient for steady flows, and we compare their typical literature values (experimentally derived) against the ones obtained via a “grey-box” calibration approach using real flow data from the complex raw-water conveyance system of Athens, Greece.
V. Bellos, P. Kossieris, A. Efstratiadis, I. Papakonstantis, P. Papanicolaou, P. Dimas, and C. Makropoulos, Fiware-enabled tool for real-time control of the raw-water conveyance system of Athens, Proceedings of the 39th IAHR World Congress, Granada, 2859–2865, doi:10.3850/IAHR-39WC2521716X20221468, International Association for Hydro-Environment Engineering and Research (IAHR), 2022.
The raw water network system of Athens (Greece) is a complex infrastructure comprising around 500 km of aqueducts, conveying water from four reservoirs to four water treatment plants, while serving several other local users. In this work, we focus on the most important part of this system, namely the open-channel aqueduct of Mornos. This extends over 200 km and has a dual operation, namely water conveyance and flow regulation through temporary storage along the channel. This is achieved by a series of Λ-type structures, each one comprising sluice gates for flow control and a lateral ogee spillway. Currently, the regulation across the channel is performed through empirical rules, and according to target volumes requested by the operators of the downstream water treatment plants, on a daily basis. However, this management policy, which is strongly based on expert’s knowledge, is neither sustainable nor safe, from a resilience perspective. Furthermore, the system is subject to occasional failures, due to undesirable overflows resulting to non-negligible water losses. In order to establish an optimal control policy, we developed an operational tool for the real- time scheduling of the sluice gate settings. Core of the tool is a conceptual model that incorporates the following assumptions: a) the operation of a Λ-type structure does not affect the operation of the other relevant structures; b) the Λ-type structure has two flow components, namely through the sluice gate and over the lateral spillway, which can be described by theoretical and semi-empirical hydraulic formulas, considering as unknown parameters the discharge coefficients of all sluice gates. On the other hand, the known model inputs are the geometrical characteristics of Λ-type structures and the real-time data for discharge, water level and gate opening, which are obtained from the telemetric monitoring system of the channel. In this respect, the key challenge is the determination of the discharge coefficients. This is employed through a grey-box approach, in which the model parameters are calibrated in continuous mode, using real-time data. To check the plausibility of the discharge coefficients, as derived by the real-time calibration phase, a comparison is made with the corresponding coefficients derived by historical data (off-line calibration). The tool, along with other analytics and algorithms developed, has been seamlessly integrated with the existing legacy system (e.g., SCADA, databases) of the system’s operator (Athens Water Supply and Sewerage Company - EYDAP), using the FIWARE standardization protocol.
Full text: http://www.itia.ntua.gr/en/getfile/2226/1/documents/04-07-014-1468.pdf (10700 KB)
D. Nikolopoulos, P. Kossieris, and C. Makropoulos, Stochastic stress-testing approach for assessing resilience of urban water systems from source to tap, EGU General Assembly 2021, online, EGU21-13284, doi:10.5194/egusphere-egu21-13284, European Geosciences Union, 2021.
Urban water systems are designed with the goal of delivering their service for several decades. The infrastructure will inevitably face long-term uncertainty in a multitude of parameters from the hydroclimatic and socioeconomic realms (e.g., climate change, limited supply of water in terms quantity and acceptable quality, population growth, shifting demand patterns, industrialization), as well as from the conceptual realm of the decision maker (e.g., changes in policy, system maintenance incentives, investment rate, expansion plans). Because urban water systems are overly complex, a holistic analysis involves the use of various models that individually pertain to a smaller sub-system and a variety of metrics to assess performance, whereas the analysis is accomplished at different temporal and spatial scales for each sub-system. In this work, we integrate a water resources management model with a water distribution model and a water demand generation model at smaller (household and district) scale, allowing us to simulate urban water systems “from source to tap”, covering the entire water cycle. We also couple a stochastic simulation module that supports the representation of uncertainty throughout the water cycle. The performance of the integrated system under long term uncertainty is assessed with the novel measure of system’s resilience i.e. the degree to which a water system continues to perform under progressively increasing disturbance. This evaluation is essentially a framework of systematic stress-testing, where the disturbance is described via stochastically changing parameters in an ensemble of scenarios that represent future world views. The framework is showcased through a synthesized case study of a medium-sized urban water system.
This research is carried out / funded in the context of the project “A resilience assessment framework for water supply infrastructure under long-term uncertainty: A Source-to-Tap methodology integrating state of the art computational tools” (MIS 5049174) under the call for proposals “Researchers' support with an emphasis on young researchers- 2nd Cycle”. The project is co-financed by Greece and the European Union (European Social Fund- ESF) by the Operational Programme Human Resources Development, Education and Lifelong Learning 2014-2020.”
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.
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.
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)
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.
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.|
|4.||Bulti, D. T., B. G. Abebe, and Z. Biru, Climate change-induced variations in future extreme precipitation intensity-duration-frequency in flood-prone city of Adama, central Ethiopia, Environmental Monitoring and Assessment, 193, 784, 10.1007/s10661-021-09574-1, 2021.|
A. Koukouvinos, D. Nikolopoulos, A. Efstratiadis, A. Tegos, E. Rozos, S.M. Papalexiou, P. Dimitriadis, Y. Markonis, P. Kossieris, H. Tyralis, G. Karakatsanis, K. Tzouka, A. Christofides, G. Karavokiros, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Integrated water and renewable energy management: the Acheloos-Peneios region case study, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-4912, doi:10.13140/RG.2.2.17726.69440, European Geosciences Union, 2015.
Within the ongoing research project “Combined Renewable Systems for Sustainable Energy Development” (CRESSENDO), we have developed a novel stochastic simulation framework for optimal planning and management of large-scale hybrid renewable energy systems, in which hydropower plays the dominant role. The methodology and associated computer tools are tested in two major adjacent river basins in Greece (Acheloos, Peneios) extending over 15 500 km2 (12% of Greek territory). River Acheloos is characterized by very high runoff and holds ~40% of the installed hydropower capacity of Greece. On the other hand, the Thessaly plain drained by Peneios – a key agricultural region for the national economy – usually suffers from water scarcity and systematic environmental degradation. The two basins are interconnected through diversion projects, existing and planned, thus formulating a unique large-scale hydrosystem whose future has been the subject of a great controversy. The study area is viewed as a hypothetically closed, energy-autonomous, system, in order to evaluate the perspectives for sustainable development of its water and energy resources. In this context we seek an efficient configuration of the necessary hydraulic and renewable energy projects through integrated modelling of the water and energy balance. We investigate several scenarios of energy demand for domestic, industrial and agricultural use, assuming that part of the demand is fulfilled via wind and solar energy, while the excess or deficit of energy is regulated through large hydroelectric works that are equipped with pumping storage facilities. The overall goal is to examine under which conditions a fully renewable energy system can be technically and economically viable for such large spatial scale.
Other works that reference this work (this list might be obsolete):
|1.||Stamou, A. T., and P. Rutschmann, Pareto optimization of water resources using the nexus approach, Water Resources Management, 32, 5053-5065, doi:10.1007/s11269-018-2127-x, 2018.|
|2.||Stamou, A.-T., and P. Rutschmann, Optimization of water use based on the water-energy-food nexus concept: Application to the long-term development scenario of the Upper Blue Nile River, Water Utility Journal, 25, 1-13, 2020.|
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.
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.
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.|
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.
I. Pappa, Y. Dimakos, P. Dimas, P. Kossieris, P. Dimitriadis, and D. Koutsoyiannis, Spatial and temporal variability of wind speed and energy over Greece, European Geosciences Union General Assembly 2014, Geophysical Research Abstracts, Vol. 16, Vienna, EGU2014-13591, doi:10.13140/RG.2.2.11238.63048, European Geosciences Union, 2014.
To appraise the wind potential over Greece we analyse the main statistical properties of wind speed through time. To this end, we use 66 time series from 1932 to 2013 on daily and monthly time scale and examine the spatial variability of wind speed over Greece. To depict the main statistical behavior and potential of the wind over Greece, maps have been created illustrating the basic statistical characteristics of wind speed on monthly to annual time scale. We also examine time series of energy production from the currently developed system of key wind parks and we compare the theoretical potential with the actually produced energy. Finally, we explore a methodology to simulate wind energy production in a stochastic framework. In that context we generate hourly wind speed synthetic data using a modified Bartlett-Lewis model implemented in Hyetos. The results of our analysis offer an improved overall picture of wind speed variability over Greece and help us clarify to which extent Hyetos is applicable in the stochastic generation of wind speed time series.
P. Kossieris, A. Efstratiadis, and D. Koutsoyiannis, Coupling the strengths of optimization and simulation for calibrating Poisson cluster models, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.2.15223.21929, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.
Many hydrological applications require use of rainfall data across a wide range of time scales. To simulate rainfall at fine time scales, stochastic approaches are usually enrolled. A leading representative is the Bartlett-Lewis model, which belongs to the family of Poisson-cluster processes that represent rainfall events. The usual approach of model calibration comprises the incorporation of the theoretical model equations in an objective function and the optimization of that function. However, it is obvious that this procedure is limited to the case that analytical equations exist for the modelled stochastic properties of the process. Yet such analytical equations cannot be derived for key characteristics such as skewness and parameters determining the distribution of extreme values. Here we present an innovative approach that remedies those weaknesses through the combined use of simulation and optimization. During model calibration, the model statistics are derived by Monte Carlo simulation, instead of theoretical equations. Various calibration criteria as well as statistical parameters are introduced aiming at more faithful representation of the rainfall process at different time scales. The efficiency of the proposed method is demonstrated using a long data series from a rain gauge in Athens.
Full text: http://www.itia.ntua.gr/en/getfile/1388/1/documents/Kos_BartlettLewis_poster.pdf (1605 KB)
Other works that reference this work (this list might be obsolete):
|1.||De Luca, D. L., and L. Galasso, Calibration of NSRP models from extreme value distributions, Hydrology, 6(4), 89, doi:10.3390/hydrology6040089, 2019.|
|2.||Park, J., D. Cross, C. Onof, Y. Chen, and D. Kim, A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales, Journal of Hydrology, 598, 126296, doi:10.1016/j.jhydrol.2021.126296, 2021.|
|3.||De Luca, D. L., and A. Petroselli, STORAGE (STOchastic RAinfall GEnerator): A user-friendly software for generating long and high-resolution rainfall time series, Hydrology, 8(2), 76, doi:10.3390/hydrology8020076, 2021.|
P. Kossieris, A. Efstratiadis, and D. Koutsoyiannis, The use of stochastic objective functions in water resource optimization problems, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.2.18578.66249, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.
The hydrological and water resource problems are characterized by the presence of multiple sources of uncertainty. The implementation of Monte Carlo simulation techniques within powerful optimization methods are required, in order to handle such uncertainties. Here we examine the combined performance of those two powerful tools to a wide range of global optimization applications, which extend from mathematical problems to hydrological calibration problems. In all cases, uncertainty is explicitly considered in terms of stochastic objective functions. In particular, we test a number of benchmark functions to assess the effectiveness and efficiency of alternative optimization techniques. Moreover, we examine two real-world calibration problems, involving a lumped rainfall-runoff models and a stochastic disaggregation model. We investigate them with different calibration criteria and under different sources of uncertainty, in order to assess not only the robustness of the derived parameters but also the predictive capacity of the models.
Y. Markonis, P. Kossieris, A. Lykou, and D. Koutsoyiannis, Effects of Medieval Warm Period and Little Ice Age on the hydrology of Mediterranean region, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 12181, doi:10.13140/RG.2.2.30565.19683, European Geosciences Union, 2012.
Medieval Warm Period (950 – 1250) and Little Ice Age (1450 – 1850) are the most recent periods that reflect the magnitude of natural climate variability. As their names suggest, the first one was characterized by higher temperatures and a generally moister climate, while the opposite happened during the second period. Although their existence is well documented for Northern Europe and North America, recent findings suggest strong evidence in lower latitudes as well. Here we analyze qualitatively the influence of these climatic fluctuations on the hydro-logical cycle all over the Mediterranean basin, highlighting the spatial characteristics of precipitation and runoff. We use both qualitative estimates from literature review in the field of paleoclimatology and statistical analysis of proxy data series. We investigate possible regional patterns and possible tele-connections with large scale atmospheric circulation phenomena such as North Atlantic Oscillation, Siberian High, African Sahel Rainfall and Indian Monsoon.
P. Kossieris, D. Koutsoyiannis, C. Onof, H. Tyralis, and A. Efstratiadis, HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union, 2012.
A complete software package for the temporal stochastic simulation of rainfall process at fine time scales is developed in the R programming environment. This includes several functions for sequential simulation or disaggregation. Specifically, it uses the Bartlett-Lewis rectangular pulses rainfall model for rainfall generation and proven disaggregation techniques which adjust the finer scale (hourly) values in order to obtain the required coarser scale (daily) value, without affecting the stochastic structure implied by the model. Additionally, a repetition scheme is incorporated in order to improve the Bartlett-Lewis model performance without significant increase of computational time. Finally, the package includes an enhanced version of the evolutionary annealing-simplex optimization method for the estimation of Bartlett-Lewis parameters. Multiple calibration criteria are introduced, in order to reproduce the statistical characteristics of rainfall at various time scales. This upgraded version of the original HYETOS program (Koutsoyiannis, D., and Onof C., A computer program for temporal stochastic disaggregation using adjusting procedures, European Geophysical Society, 2000) operates on several modes and combinations thereof (depending on data availability), with many options and graphical capabilities. The package, under the name HyetosR, is available free in the CRAN package repository.
Software page: http://itia.ntua.gr/en/softinfo/3/
Other works that reference this work (this list might be obsolete):
|1.||#Montesarchio, V., F. Napolitano, E. Ridolfi and L. Ubertini, A comparison of two rainfall disaggregation models, In Numerical Analysis and Applied Mathematics ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics, AIP Conference Proceedings, Vol. 1479, 1796-1799, 2012.|
|2.||#Villani, V., L. Cattaneo, A. L. Zollo, and P. Mercogliano, Climate data processing with GIS support: Description of bias correction and temporal downscaling tools implemented in Clime software, Euro-Mediterranean Center on Climate Change (RMCC) Research Papers, RP0262, 2015.|
|3.||Förster, K., F. Hanzer, B. Winter, T. Marke, and U. Strasser, An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1), Geoscientific Model Development, 9, 2315-2333, doi:10.5194/gmd-9-2315-2016, 2016.|
|4.||Devkota, S., N. M. Shakya, K. Sudmeier-Rieux, M. Jaboyedoff, C. J. Van Westen, B. G. Mcadoo, and A. Adhikari, Development of monsoonal rainfall intensity-duration-frequency (IDF) relationship and empirical model for data-scarce situations: The case of the Central-Western Hills (Panchase Region) of Nepal, Hydrology, 5(2), 27, doi:10.3390/hydrology5020027, 2018.|
|5.||Cordeiro, M. R. C., J. A. Vanrobaeys, and H. F. Wilson, Long-term weather, streamflow, and water chemistry datasets for hydrological modelling applications at the upper La Salle River watershed in Manitoba, Canada, 6(1), 41-57, Geoscience Data Journal, doi:10.1002/gdj3.67, 2019.|
|6.||#Thomson, H., and L. Chandler, Tailings storage facility landform evolution modelling, Proceedings of the 13th International Conference on Mine Closure, A. B. Fourie & M. Tibbett (eds.), Australian Centre for Geomechanics, Perth, 385-396, 2019.|
|7.||Sun, Y., D. Wendi, D. E., Kim, and S.-Y. Liong, Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data, Geoscience Letters, 6(17), doi:10.1186/s40562-019-0147-x, 2019.|
|8.||Oruc, S., I. Yücel, and A. Yılmaz, Investigation of the effect of climate change on extreme precipitation: Capital Ankara case, Teknik Dergi, 33(2), doi:10.18400/tekderg.714980, 2021.|
|9.||Hayder, A. M., and M. Al-Mukhtar, Modelling the IDF curves using the temporal stochastic disaggregation BLRP model for precipitation data in Najaf City, Arabian Journal of Geosciences, 14, 1957, doi:10.1007/s12517-021-08314-6, 2021.|
|10.||Diez-Sierra, J., S. Navas, and M. del Jesus, Neoprene: An open-source Python library for spatial rainfall generation based on the Neyman-Scott process, doi:10.2139/ssrn.4092195, 2022.|
|11.||Cordeiro, M. R. C., K. Liang, H. F. Wilson, J. Vanrobaeys, D. A. Lobb, X. Fang, and J. W. Pomeroy, Simulating the hydrological impacts of land use conversion from annual crop to perennial forage in the Canadian Prairies using the Cold Regions Hydrological Modelling platform, Hydrology and Earth System Sciences, 26, 5917-5931, doi:10.5194/hess-26-5917-2022, 2022.|
Y. Dialynas, P. Kossieris, K. Kyriakidis, A. Lykou, Y. Markonis, C. Pappas, S.M. Papalexiou, and D. Koutsoyiannis, Optimal infilling of missing values in hydrometeorological time series, European Geosciences Union General Assembly 2010, Geophysical Research Abstracts, Vol. 12, Vienna, EGU2010-9702, doi:10.13140/RG.2.2.23762.56005, European Geosciences Union, 2010.
Being a group of undergraduate students in the National Technical University of Athens, attending the course of Stochastic Methods in Water Resources, we study, in cooperation with our tutors, the infilling of missing values of hydrometeorological time series from measurements at neighbouring times. The literature provides a plethora of methods, most of which are reduced to a linear statistical interpolating relationship. Assuming that the underlying hydrometeorological process behaves like either a Markovian or a Hurst-Kolmogorov process we estimate the missing values using two techniques, i.e., (a) a local average (with equal weights) based on the optimal number of measurements referring to a number of forward and backward time steps, and (b) a weighted average using all available data. In each of the cases we determine the unknown quantities (the required number of neighbouring values or the sequence of weights) so as to minimize the estimation mean square error. The results of this investigation are easily applicable for infilling time series in real-world applications.
Other works that reference this work (this list might be obsolete):
|1.||#Rianna, M., E. Ridolfi, L. Lorino, L. Alfonso, V. Montesarchio, G. Di Baldassarre, F. Russo and F. Napolitano, Definition of homogeneous regions through entropy theory, 3rd STAHY International Workshop on Statistical Methods for Hydrology and Water Resources Management, Tunis, Tunisia, 2012.|
P. Kossieris, Elements of Applied Hydraulics for Urban Hydraulic Works, University of Western Attica, May 2021.
Full text: http://www.itia.ntua.gr/en/getfile/2241/1/documents/pressured_flow.pdf (1038 KB)
C. Makropoulos, A. Efstratiadis, and P. Kossieris, Lecture notes on Hydraulics and Hydraulic Works: Water Supply, 80 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, December 2019.
P. Kossieris, Develpment of a computer system for one-dimensional stochastic disaggragation of daily rainfall to hourly, 39 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, January 2012.
Presentation within undergraduate course "Stochastic Methods in Water Resources".
Full text: http://www.itia.ntua.gr/en/getfile/1194/1/documents/StochMethodsKossieris2012.pdf (1391 KB)
P. Kossieris, Multi-scale stochastic analysis and modelling of residential water demand processes, PhD thesis, 350 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, February 2020.
Residential water demand is a key element of urban water systems and, at the same time, one of the most influential sources of uncertainty due its high spatio-temporal variability and random nature. Embracing and incorporating uncertainty in the modelling of urban water systems is of high-importance for their uncertainty-aware planning, management and performance evaluation. This is feasible by treating water demand as a stochastic process that is analysed on the basis of probabilistic and stochastic concepts. Such considerations allow, among others, the development of stochastic modelling and simulation methodologies that generate synthetic time series that can be employed as non-deterministic inputs to assess system responses under different load scenarios.
At the same time, technological innovations in Information and Communication Technologies provide new opportunities for the design, operation and management of urban water systems. A case in point is smart metering systems which provide the means to advance risk-based modelling of the urban water systems by delivering new streams of data that in turn pave the ground for a thorough analysis, modelling and simulation of water demand processes. With these new richer datasets at hand, much effort has been invested in the development of stochastic methodologies to generate synthetic water demand series at very fine temporal (i.e., down to 1 s) and spatial (i.e., at household or even water appliance level) scales. These synthetic series can be then aggregated temporally and/or spatially, following a bottom-up procedure, to construct the coarser-resolution synthetic demand series which are to be used as inputs in system models. This approach receives more and more attention due to the more realistic representation of the varying and uncertain character of the process at fine scales. It also poses a series of intriguing challenges which have been only partially addressed to date, thus opening up a promising domain for further research.
The peculiarities of water demand processes at fine scales, such as their non-Gaussian behaviour, the intermittent nature, the variety of temporal and spatial dependence structures and the various types of seasonality make its modelling and simulation a rather hard task. The problem is getting even more demanding considering that these peculiarities depend on the temporal and spatial scale of study, while applications require statistically and stochastically consistent synthetic series across a wide range of scales.
Further to the modelling challenges, another issue is the deployment of such methodologies, and hence the implementation of uncertainty-aware study of the systems, on a broader scale that is currently hampered by the limited availability of fine-resolution demand observations. In this respect, the existing demand datasets have a key role to play as a valuable source of information through which we can extract concrete and possible transferable insights on the peculiarities of the processes. Despite its practical significance, the systematic and extensive analysis of such datasets, has currently received little attention. Although very high-resolution demand data is generally unavailable, longer series at coarser resolution (e.g., 5-min or 15-min) do exist and are becoming increasingly more available, while the metering devices with such sampling capabilities have potential for a wider deployment in the near future. Having said this, a major question that naturally arises is whether and how we can take advantage of these coarser measurements to enrich the information at finer scales (both in terms of data and characteristics of the process) addressing the issue of data unavailability in a cost-effective way.
Following from the above, the main objectives of this thesis are: a) The systematic analysis and modelling of the marginal and stochastic behaviour of water demand process on a multi-scale basis; b) The study of existing approaches as well as the development of novel methodologies for stochastic modelling and simulation of water demand process in single and multi-scale context; c) The development of methodologies to enhance the availability of information on water demand process (in terms of data and statistics) at fine time scales.
Specifically, in this thesis, we analyse, model, and simulate residential water demand as a discrete-time stochastic process and we treat the observed data as realisations of it. As a first step, the marginal and stochastic properties of the process are investigated, across a wide range of fine time scales, i.e., from 1 s up to 1 h. Various statistical and probabilistic concepts and tools (novel and classical) are introduced for the first time in the field of water demand. The analyses reveal the most suitable probability distribution models to describe the non-zero demand values, the scaling behaviour of the intermittent nature of the process (i.e., probability of no demand over temporal scales) and the scaling behaviour of its temporal auto-dependence structure (i.e., second-order properties across scales). In order to obtain concrete insights on these characteristics, we take advantage of two large datasets of demand measurements.
We examine the well-established pulse-based schemes, by comparing two non-cluster Poisson models along with two Bartlett-Lewis models, highlighting the flexibility of the clustering mechanism in terms of better reproducing the above described peculiarities of the discrete-time process, across different temporal scales. Additionally, we introduce in water demand modelling a novel modelling strategy that combines the widely-used class of linear stochastic models with the Nataf’s joint distribution model; thereby allowing the preservation of the entire marginal distribution and correlation structure of the processes. The problem of multi-scale modelling is examined and addressed via a scale- and model-free disaggregation framework where the generated synthetic series are fully consistent with (i.e., sum up exactly to) the coarser-level given data. The applicability and flexibility of this framework is demonstrated by employing both pulse- and Nataf-based schemes.
With these modular components at hand we then develop an integrated framework to enhance the availability of information at fine time scales. In this context, we also develop lower-scale extrapolation methodologies to provide estimations of the essential statistics (i.e., probability of no demand and second-order properties) for model’s setup at a finer scale, based on available coarser level information and scaling behaviour. The framework is demonstrated under different scenarios in terms of data availability, revealing the tradeoff between estimation accuracy and metering resolution.
P. Kossieris, Adaptation of evolutionary annealing-simplex algorithm for optimization of stochastic objective functions in water resource problems, Postgraduate Thesis, 209 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, December 2013.
Water resource problems are characterized by the presence of multiple sources of uncertainty. The implementation of Monte Carlo simulation techniques within powerful optimization methods is required, in order to handle these uncertainties. In the framework of the present thesis we investigate how the various sources of uncertainty affect the optimization procedure as well as the various models. Furthermore, we investigate a modified version of the evolutionary annealing-simplex method in global optimization applications, where uncertainty is explicitly considered in terms of stochastic objective functions. We evaluate the algorithm against several benchmark functions, as well as in the stochastic calibration of a lumped rainfall-runoff model (Zygos). In this context, we examine different calibration criteria and different sources of uncertainty, in order to assess not only the robustness of the derived parameters but also the predictive capacity of the models. As one other problem that requires the combined use of optimization and simulation, we examine the applicability of a widely used rainfall model for the case of Athens. Taking advantage of the simulation and optimization functionalities of HyetosR package, we evaluate the performance of two versions of Bartlett-Lewis model in representing the convective and frontal rainfall of Athens. We demonstrate that although these models reproduce the essential statistical characteristics of rainfall at the hourly as well as daily time scales (mean, variance, autocorrelation structure), they fail to preserve important temporal properties, such as the duration and time distance of rainfall events.
Full text: http://www.itia.ntua.gr/en/getfile/1419/1/documents/Master_thesis_PK_9GCRvkx.pdf (7185 KB)
P. Kossieris, A computer program for temporal stochastic disaggregation of a fine-scale rainfall, on R environment, Diploma thesis, 224 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, October 2011.
The study and analysis of hydrological variables require the development and use of special types of stochastic simulation models. These models are powerful tools for the stochastic simulation and forecast of hydro-meteorological processes. The intermittent character of rainfall time series on fine time scales justify the use of special simulation models. Among the successful model types are the point process models. This type of model has the important feature of representing rainfall in continuous time. According to these models, the rainfall events are simulated through the generation of clustered point or rectangular pulses. The Bartlett - Lewis model has the ability to reproduce important features of the rainfall field from hourly to daily scale and above. A combination of the Bartlett - Lewis rainfall model with proven disaggregation methodology, has proposed by Koutsoyiannis and Onof. This combination improves the ability of the Bartlett - Lewis model to simulate rainfall on fine time scales. In the framework of the thesis, the model is implemented in a computer program under the name HYETOS-R, on the R environment. The package HYETOS-R provides a complete tool for the simulation of rainfall process on fine time scales. The main purpose of the package is the disaggregation of daily to hourly rainfall depths. The package can work in several modes appropriate for operational use and model testing. Additionally, the user can produce synthetic time series by the Bartlett - Lewis model.
Full text: http://www.itia.ntua.gr/en/getfile/1185/1/documents/kossieris_diplom.pdf (5519 KB)
V. Bellos, P. Kossieris, I. Papakonstantis, P. Papanicolaou, C. Ntemiroglou, and A. Efstratiadis, [No English title available], Modernization of the management of the water supply system of Athens - Update, 46 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, November 2022.
A. Efstratiadis, N. Mamassis, G.-K. Sakki, I. Tsoukalas, P. Kossieris, P. Dimas, and N. Pelekanos, [No English title available], Modernization of the management of the water supply system of Athens - Update, 141 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, June 2022.
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.
A. Efstratiadis, N. Mamassis, Y. Markonis, P. Kossieris, and H. Tyralis, Methodological framework for optimal planning and management of water and renewable energy resources, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 154 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, April 2015.
We describe a stochastic simulation and optimization framework for hybrid renewable energy systems, based on effective coupling of different models. Initially, we explain the problem of combined management of water and energy resources, we introduce the main concepts and highlight the peculiarities of the problem, by means of methodology and computational implementation. Next is presented the general context, which is based on the combined use of an hourly simulation model for the renewables of a specific study area (wind and solar units), and a daily simulation model for the water resource system and the associated energy components. The models are fed by synthetic time series of hydrological inflows, wind velocity, solar radiation and electricity demand over the study area, for the generation of which are used appropriate stochastic schemes. The theoretical background of all models and related software systems is based on original methodologies or existing approaches that have been improved or generalized in the context of the research project.
Full text: http://www.itia.ntua.gr/en/getfile/1599/1/documents/Report_EE2.pdf (3766 KB)
D. Koutsoyiannis, S.M. Papalexiou, Y. Markonis, P. Dimitriadis, and P. Kossieris, Stochastic framework for uncertainty assessment of hydrometeorological procesess, Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO), 231 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, January 2015.
Full text: http://www.itia.ntua.gr/en/getfile/1589/1/documents/Report_EE1.pdf (14753 KB)
S.M. Papalexiou, and P. Kossieris, Theoretical documentation of model for synthetic hyetograph generation, DEUCALION – Assessment of flood flows in Greece under conditions of hydroclimatic variability: Development of physically-established conceptual-probabilistic framework and computational tools, Contractors: ETME: Peppas & Collaborators, Grafeio Mahera, Department of Water Resources and Environmental Engineering – National Technical University of Athens, National Observatory of Athens, 97 pages, May 2014.
The simulation of flood events necessitates the simulation of the rainfall over small times scales (e.g., smaller than the monthly scale). Nevertheless, rainfall modelling at small time scales is not simple as rainfall at these scales is an intermittent process and exhibits large variability in its statistical-stochastic characteristics. In this context, a flexible multivariate framework of stochastic simulation of rainfall was developed that can be applied to a large range of times scales. The proposed methodology is based on the cyclostationary multivariate autoregressive model of order 1 (PAR1), while the intermittency characteristics were reproduced using a novel transformation structure. The methodology was verified in the basin of Boeotikos Kephisos and it was verified that the model preserves satisfactorily the basic statistical characteristics of daily rainfall, including the probability dry, as well as the autocorrelation and the cross correlation structures. As an alternative for the generation of synthetic hyetographs the stochastic model known as the rectangular pulse Bartlett-Lewis model is presented. This model is widely accepted for the single-variate simulation of rainfall at fine time scales and in continuous time. The implementation was done in R programming environment and is available through the computer package HyetosR.
Related project: DEUCALION – Assessment of flood flows in Greece under conditions of hydroclimatic variability: Development of physically-established conceptual-probabilistic framework and computational tools
Full text: http://www.itia.ntua.gr/en/getfile/1457/1/documents/Report_3_4.pdf (3599 KB)