I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019.
[Κατασκευάζοντας ένα πάζλ ώστε να λυθεί ένα αίνιγμα: Μια προσέγγιση επιμερισμού πολλαπλών κλιμάκων για πολυμεταβλητές στοχαστικές διεργασίες από οποιαδήποτε περιθώρια κατανομή και δομή συσχέτισης]
[doc_id=1914]
[Αγγλικά]
Η γέννηση υδρομετεωρολογικών χρονοσειρών που ακολουθούν μια δεδομένη πιθανοτική και στοχαστική δίαιτα σε πολλαπλές χρονικές κλίμακες, παραδοσιακά εκφραζόμενη σε όρους συγκεκριμένων στατιστικών χαρακτηριστικών των παρατηρημένων δεδομένων, αποτελεί κρίσιμο ζητούμενο σε μελέτες ρίσκου στους υδατικούς πόρους και, ταυτόχρονα, ένα αίνιγμα για την κοινότητα της στοχαστικής. Η κύρια πρόκληση πηγάζει από το γεγονός ότι η αναπαραγωγή μιας συγκεκριμένης δίαιτας για μια συγκεκριμένη χρονική κλίμακα δεν επιβάλει την αναπαραγωγή της επιθυμητής δίαιτας σε καμία άλλη κλίμακα συνάθροισης. Στο πλαίσιο αυτό, εισάγουμε αρχικά μια σειριακή σύζευξη στοχαστικών μοντέλων τύπου Nataf, μέσω ενός σχήματος επιμερισμού, και στη συνέχεια προτείνουμε μια διαμόρφωση αρθρωτής μορφής (τύπου "παζλ"), που παρέχει ενα γενικό πλαίσιο στοχαστικής προσομοίωσης για πολυμεταβλητές διεργασίες που ακολουθούν οποιαδήποτε κατανομή και δομή συσχέτισης. Στις μελέτες περίπτωσης παρουσιάζουμε δύο χαρακτηριστικές διαμορφώσεις, ήτοι ένα σχήμα τριών επιπέδων, που λειτουργεί σε ημερήσια, μηνιαία και ετήσια βάση, και ένα δύο επιπέδων, για τον επιμερισμό ημερήσιων σε ωριαία δεδομένα. Η πρώτη διαμόρφωση εφαρμόζεται για τη γέννηση συσχετισμένων ημερήσιων δεδομένων βροχόπτωσης και απορροής στη λεκάνη του Αχελώου, στη Δυτική Ελλάδα, που διατηρεί τη στοχαστική δομή των δύο διεργασιών στις τρεις χρονικές κλίμακες. Η δεύτερη διατύπωση επιμερίζει ημερήσιες βροχοπτώσεις, που λαμβάνονται από έναν μετεωρολογικό σταθμό στη Γερμανία, σε ωριαίες. Οι δύο μελέτες αναδεικνύουν την ικανότητα του προτεινόμενου πλαισίου να αναπαραστήσει την ιδιάζουσα συμπεριφορά των υδρομετεωρολογικών διεργασιών σε πολλαπλές κλίμακες χρονικής ανάλυσης, καθώς και την ευελιξία του ως προς τη διατύπωση γενικών σχημάτων προσομοίωσης.
Πλήρες κείμενο (16518 KB)
Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:
1. | D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996. |
2. | D. Koutsoyiannis, Optimal decomposition of covariance matrices for multivariate stochastic models in hydrology, Water Resources Research, 35 (4), 1219–1229, doi:10.1029/1998WR900093, 1999. |
3. | D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, doi:10.1029/2000WR900044, 2000. |
4. | D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001. |
5. | D. Koutsoyiannis, and C. Onof, Rainfall disaggregation using adjusting procedures on a Poisson cluster model, Journal of Hydrology, 246, 109–122, 2001. |
6. | D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002. |
7. | D. Koutsoyiannis, C. Onof, and H. S. Wheater, Multivariate rainfall disaggregation at a fine timescale, Water Resources Research, 39 (7), 1173, doi:10.1029/2002WR001600, 2003. |
8. | D. Koutsoyiannis, and A. Economou, Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems, Water Resources Research, 39 (6), 1170, doi:10.1029/2003WR002148, 2003. |
9. | D. Koutsoyiannis, Stochastic simulation of hydrosystems, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 421–430, doi:10.1002/047147844X.sw913, Wiley, New York, 2005. |
10. | A. Langousis, and D. Koutsoyiannis, A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour, Journal of Hydrology, 322, 138–154, 2006. |
11. | D. Koutsoyiannis, and A. Montanari, Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resources Research, 43 (5), W05429, doi:10.1029/2006WR005592, 2007. |
12. | D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011. |
13. | F. Lombardo, E. Volpi, and D. Koutsoyiannis, Rainfall downscaling in time: Theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades, Hydrological Sciences Journal, 57 (6), 1052–1066, 2012. |
14. | F. Lombardo, E. Volpi, D. Koutsoyiannis, and S.M. Papalexiou, Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology, Hydrology and Earth System Sciences, 18, 243–255, doi:10.5194/hess-18-243-2014, 2014. |
15. | A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014. |
16. | P. Dimitriadis, and D. Koutsoyiannis, Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes, Stochastic Environmental Research & Risk Assessment, 29 (6), 1649–1669, doi:10.1007/s00477-015-1023-7, 2015. |
17. | I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015. |
18. | 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. |
19. | I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015. |
20. | D. Koutsoyiannis, Generic and parsimonious stochastic modelling for hydrology and beyond, Hydrological Sciences Journal, 61 (2), 225–244, doi:10.1080/02626667.2015.1016950, 2016. |
21. | 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. |
22. | P.E. O’Connell, D. Koutsoyiannis, H. F. Lins, Y. Markonis, A. Montanari, and T.A. Cohn, The scientific legacy of Harold Edwin Hurst (1880 – 1978), Hydrological Sciences Journal, 61 (9), 1571–1590, doi:10.1080/02626667.2015.1125998, 2016. |
23. | I. Tsoukalas, C. Makropoulos, and A. Efstratiadis, Stochastic simulation of periodic processes with arbitrary marginal distributions, 15th International Conference on Environmental Science and Technology (CEST2017), Rhodes, Global Network on Environmental Science and Technology, 2017. |
24. | 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. |
25. | I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018. |
26. | I. Tsoukalas, S.M. Papalexiou, A. Efstratiadis, and C. Makropoulos, A cautionary note on the reproduction of dependencies through linear stochastic models with non-Gaussian white noise, Water, 10 (6), 771, doi:10.3390/w10060771, 2018. |
27. | I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018. |
28. | I. Tsoukalas, Modelling and simulation of non-Gaussian stochastic processes for optimization of water-systems under uncertainty, PhD thesis, 339 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Δεκέμβριος 2018. |
Εργασίες μας που αναφέρονται σ' αυτή την εργασία:
1. | P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019. |
2. | D. Nikolopoulos, G. Moraitis, D. Bouziotas, A. Lykou, G. Karavokiros, and C. Makropoulos, Cyber-physical stress-testing platform for water distribution networks, Journal of Environmental Engineering, 146 (7), 04020061, doi:10.1061/(ASCE)EE.1943-7870.0001722, 2020. |
3. | 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. |
4. | H. Elsayed, S. Djordjević, D. Savic, I. Tsoukalas, and C. Makropoulos, The Nile water-food-energy nexus under uncertainty: Impacts of the Grand Ethiopian Renaissance Dam, Journal of Water Resources Planning and Management - ASCE, 146 (11), 04020085, doi:10.1061/(ASCE)WR.1943-5452.0001285, 2020. |
5. | N. Mamassis, A. Efstratiadis, P. Dimitriadis, T. Iliopoulou, R. Ioannidis, and D. Koutsoyiannis, Water and Energy, Handbook of Water Resources Management: Discourses, Concepts and Examples, edited by J.J. Bogardi, T. Tingsanchali, K.D.W. Nandalal, J. Gupta, L. Salamé, R.R.P. van Nooijen, A.G. Kolechkina, N. Kumar, and A. Bhaduri, Chapter 20, 617–655, doi:10.1007/978-3-030-60147-8_20, Springer Nature, Switzerland, 2021. |
6. | A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Generalized storage-reliability-yield framework for hydroelectric reservoirs, Hydrological Sciences Journal, 66 (4), 580–599, doi:10.1080/02626667.2021.1886299, 2021. |
7. | P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021. |
8. | 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. |
9. | 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. |
10. | 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, Οκτώβριος 2022. |
11. | 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. |
12. | A. Zisos, G.-K. Sakki, and A. Efstratiadis, Mixing renewable energy with pumped hydropower storage: Design optimization under uncertainty and other challenges, Sustainability, 15 (18), 13313, doi:10.3390/su151813313, 2023. |
13. | 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. |
14. | G.-K. Sakki, A. Castelletti, C. Makropoulos, and A. Efstratiadis, Unwrapping the triptych of climatic, social and energy-market uncertainties in the operation of multipurpose hydropower reservoirs, Journal of Hydrology, 2024, (υπό έκδοση). |
Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία (αυτός ο κατάλογος μπορεί να μην είναι ενημερωμένος):
1. | Macian-Sorribes, H., J.-L. Molina, S. Zazo, and M. Pulido-Velázquez, Analysis of spatio-temporal dependence of inflow time series through Bayesian causal modelling, Journal of Hydrology, 597, 125722, doi:10.1016/j.jhydrol.2020.125722, 2021. |
2. | Wang, Q., J. Zhou, K. Huang, L. Dai, B. Jia, L. Chen, and H. Qin, A procedure for combining improved correlated sampling methods and a resampling strategy to generate a multi-site conditioned streamflow process, Water Resources Management, 35, 1011-1027, doi:10.1007/s11269-021-02769-8, 2021. |
3. | Brereton, R. G., P values and multivariate distributions: Non-orthogonal terms in regression models, Chemometrics and Intelligent Laboratory Systems, 210, 104264, doi:10.1016/j.chemolab.2021.104264, 2021. |
4. | Pouliasis, G., G. A. Torres-Alves, and O. Morales-Napoles, Stochastic modeling of hydroclimatic processes using vine copulas, Water, 13(16), 2156, doi:10.3390/w13162156, 2021. |
5. | Biondi, D., E. Todini, and A. Corina, A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: An application to quantitative precipitation forecasts for civil protection purposes, Hydrology Research, 52(6), 1405-1422, doi:10.2166/nh.2021.045, 2021. |
6. | Jahangir, M. S., and J. Quilty, Temporal hierarchical reconciliation for consistent water resources forecasting across multiple timescales: An application to precipitation forecasting, Water Resources Research, 58(6), e2021WR031862, doi:10.1029/2021WR031862, 2022. |
7. | Wan Mazlan, W. A. S., and N. N. A. Tukimat, Comparative analyses on disaggregation methods for the rainfall projection, Water Resources Management, 37, 4195-4209, doi:10.1007/s11269-023-03546-5, 2023. |
8. | Zeng, J., B. Yu, X. Fu, and h. Hu, Multi-century flow reconstruction of the Lhasa River, China, Journal of Hydrology: Regional Studies, 53, 101795, doi:10.1016/j.ejrh.2024.101795, 2024. |
Κατηγορίες: Στοχαστικός επιμερισμός, Στοχαστική