A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence

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.

[doc_id=1488]

[English]

A time series generator is presented, employing a robust three-level multivariate scheme for stochastic simulation of correlated processes. It preserves the essential statistical characteristics of historical data at three time scales (annual, monthly, daily), using a disaggregation approach. It also reproduces key properties of hydrometeorological and geophysical processes, namely the long-term persistence (Hurst-Kolmogorov behaviour), the periodicity and intermittency. Its efficiency is illustrated through two case studies in Greece. The first aims to generate monthly runoff and rainfall data at three reservoirs of the hydrosystem of Athens. The second involves the generation of daily rainfall for flood simulation at five rain gauges. In the first emphasis is given to long-term persistence – a dominant characteristic in the management of large-scale hydrosystems, comprising reservoirs with carry-over storage capacity. In the second we highlight to the consistent representation of intermittency and asymmetry of daily rainfall, and the distribution of annual daily maxima.

Full text is only available to the NTUA network due to copyright restrictions

PDF Additional material:

See also: http://dx.doi.org/10.1016/j.envsoft.2014.08.017

Our works referenced by this work:

1. D. Koutsoyiannis, A disaggregation model of point rainfall, PhD thesis, 310 pages, doi:10.12681/eadd/0910, National Technical University of Athens, Athens, 1988.
2. D. Koutsoyiannis, A stochastic disaggregation method for design storm and flood synthesis, Journal of Hydrology, 156, 193–225, doi:10.1016/0022-1694(94)90078-7, 1994.
3. D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.
4. 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.
5. 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.
6. D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
7. A. Efstratiadis, Investigation of global optimum seeking methods in water resources problems, MSc thesis, 139 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, May 2001.
8. D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002.
9. D. Koutsoyiannis, Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, doi:10.1623/hysj.48.1.3.43481, 2003.
10. 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.
11. D. Koutsoyiannis, G. Karavokiros, A. Efstratiadis, N. Mamassis, A. Koukouvinos, and A. Christofides, A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, 28 (14-15), 599–609, doi:10.1016/S1474-7065(03)00106-2, 2003.
12. A. Efstratiadis, D. Koutsoyiannis, and D. Xenos, Minimizing water cost in the water resource management of Athens, Urban Water Journal, 1 (1), 3–15, doi:10.1080/15730620410001732099, 2004.
13. A. Efstratiadis, and D. Koutsoyiannis, Castalia (version 2.0) - A system for stochastic simulation of hydrological variables, Modernisation of the supervision and management of the water resource system of Athens, Report 23, 103 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, January 2004.
14. 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.
15. 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.
16. D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008.
17. 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.
18. I. Nalbantis, A. Efstratiadis, E. Rozos, M. Kopsiafti, and D. Koutsoyiannis, Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems, Hydrology and Earth System Sciences, 15, 743–758, doi:10.5194/hess-15-743-2011, 2011.
19. Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A computer system for the stochastic disaggregation of monthly into daily hydrological time series as part of a three–level multivariate scheme, European Geosciences Union General Assembly 2011, Geophysical Research Abstracts, Vol. 13, Vienna, EGU2011-290, doi:10.13140/RG.2.2.23814.98885, European Geosciences Union, 2011.
20. Y. Dialynas, A computer system for the multivariate stochastic disaggregation of monthly into daily hydrological time series, Diploma thesis, 337 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, March 2011.
21. S.M. Papalexiou, and D. Koutsoyiannis, Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resources Research, 49 (1), 187–201, doi:10.1029/2012WR012557, 2013.
22. A. Venediki, S. Giannoulis, C. Ioannou, L. Malatesta, G. Theodoropoulos, G. Tsekouras, Y. Dialynas, S.M. Papalexiou, A. Efstratiadis, and D. Koutsoyiannis, The Castalia stochastic generator and its applications to multivariate disaggregation of hydro-meteorological processes, European Geosciences Union General Assembly 2013, Geophysical Research Abstracts, Vol. 15, Vienna, EGU2013-11542, doi:10.13140/RG.2.2.15675.41764, European Geosciences Union, 2013.
23. G. Tsekouras, and D. Koutsoyiannis, Stochastic analysis and simulation of hydrometeorological processes associated with wind and solar energy, Renewable Energy, 63, 624–633, doi:10.1016/j.renene.2013.10.018, 2014.
24. A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015.

Our works that reference this work:

1. A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015.
2. 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.
3. I. Tsoukalas, P. Dimas, and C. Makropoulos, Hydrosystem optimization on a budget: Investigating the potential of surrogate based optimization techniques, 14th International Conference on Environmental Science and Technology (CEST2015), Global Network on Environmental Science and Technology, University of the Aegean, 2015.
4. 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.
5. 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.
6. S.M. Papalexiou, Y. Dialynas, and S. Grimaldi, Hershfield factor revisited: Correcting annual maximum precipitation, Journal of Hydrology, 542, 884–895, doi:10.1016/j.jhydrol.2016.09.058, 2016.
7. F. Lombardo, E. Volpi, D. Koutsoyiannis, and F. Serinaldi, A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall, Water Resources Research, 53 (6), 4586–4605, doi:10.1002/2017WR020529, 2017.
8. 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.
9. K. Papoulakos, G. Pollakis, Y. Moustakis, A. Markopoulos, T. Iliopoulou, P. Dimitriadis, D. Koutsoyiannis, and A. Efstratiadis, Simulation of water-energy fluxes through small-scale reservoir systems under limited data availability, Energy Procedia, 125, 405–414, doi:10.1016/j.egypro.2017.08.078, 2017.
10. M. Chalakatevaki, P. Stamou, S. Karali, V. Daniil, P. Dimitriadis, K. Tzouka, T. Iliopoulou, D. Koutsoyiannis, P. Papanicolaou, and N. Mamassis, Creating the electric energy mix in a non-connected island, Energy Procedia, 125, 425–434, doi:10.1016/j.egypro.2017.08.089, 2017.
11. 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.
12. P. Dimitriadis, and D. Koutsoyiannis, Stochastic synthesis approximating any process dependence and distribution, Stochastic Environmental Research & Risk Assessment, 32 (6), 1493–1515, doi:10.1007/s00477-018-1540-2, 2018.
13. I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.
14. 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.
15. 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.
16. 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.
17. G.-F. Sargentis, R. Ioannidis, G. Karakatsanis, S. Sigourou, N. D. Lagaros, and D. Koutsoyiannis, The development of the Athens water supply system and inferences for optimizing the scale of water infrastructures, Sustainability, 11 (9), 2657, doi:10.3390/su11092657, 2019.
18. I. Koutiva, and C. Makropoulos, Exploring the effects of alternative water demand management strategies using an agent-based model, Water, 11 (11), 2216, doi:10.3390/w11112216, 2019.
19. A. Koskinas, and A. Tegos, StEMORS: A stochastic eco-hydrological model for optimal reservoir sizing, Open Water Journal, 6 (1), 1, 2020.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.

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

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

1. Huo, S.-C., S.-L. Lo, C.-H. Chiu, P.-T. Chiueh, and C.-S. Yang, Assessing a fuzzy model and HSPF to supplement rainfall data for nonpoint source water quality in the Feitsui reservoir watershed, Environmental Modelling and Software, 72, 110-116, doi:10.1016/j.envsoft.2015.07.002, 2015.
2. Read, L., and R. M. Vogel, Reliability, return periods, and risk under nonstationarity, Water Resources Research, 51(8), 6381–6398, doi:10.1002/2015WR017089, 2015.
3. Steidl, J., J. Schuler, U. Schubert, O. Dietrich, and P. Zander, Expansion of an existing water management model for the analysis of opportunities and impacts of agricultural irrigation under climate change conditions, Water, 7, 6351-6377, doi:10.3390/w7116351, 2015.
4. Hao, Z., and V. P. Singh, Review of dependence modeling in hydrology and water resources, Progress in Physical Geography, 40(4), 549-578, doi:10.1177/0309133316632460, 2016.
5. Srivastav, R., K. Srinivasan, and S. P. Sudheer, Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling, Journal of Hydrology, 542, 506-531, doi:10.1016/j.jhydrol.2016.09.025, 2016.
6. Dialynas, Y. G., S. Bastola, R. L. Bras, E. Marin-Spiotta, W. L. Silver, E. Arnone, and L. V. Noto, Impact of hydrologically driven hillslope erosion and landslide occurrence on soil organic carbon dynamics in tropical watersheds, Water Resources Research, 52(11), 8895–8919, doi:10.1002/2016WR018925, 2016.
7. Stojković, M., S. Kostić, J. Plavšić, and S. Prohaska, A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates, Journal of Hydrology, 544, 555–566, doi:10.1016/j.jhydrol.2016.11.025, 2017.
8. Bardsley, E., A finite mixture approach to univariate data simulation with moment matching, Environmental Modelling & Software, 90, 27-33, doi:10.1016/j.envsoft.2016.11.019, 2017.
9. Dialynas, Y. D., R. L. Bras, and D. deB. Richter, Hydro-geomorphic perturbations on the soil-atmosphere CO2 exchange: How (un)certain are our balances?, Water Resources Research, 53(2), 1664–1682, doi:10.1002/2016WR019411, 2017.
10. Feng , M., P. Liu, S. Guo, Z. Gui, X. Zhang, W. Zhang, and L. Xiong, Identifying changing patterns of reservoir operating rules under various inflow alteration scenarios, Advances in Water Resources, 104, 23-26, doi:10.1016/j.advwatres.2017.03.003, 2017.
11. Stojković, M., J. Plavšić, and S. Prohaska, Annual and seasonal discharge prediction in the middle Danube River basin based on a modified TIPS (Tendency, Intermittency, Periodicity, Stochasticity) methodology, Journal of Hydrology and Hydromechanics, 65(2), doi:10.1515/johh-2017-0012, 2017.
12. Hanel, M., R. Kožín, M. Heřmanovský, and R. Roub, An R package for assessment of statistical downscaling methods for hydrological climate change impact studies, Environmental Modelling & Software, 95, 22–28, doi:10.1016/j.envsoft.2017.03.036, 2017.
13. Vogel, M., Stochastic watershed models for hydrologic risk management, Water Security, 1, 28-35, doi:10.1016/j.wasec.2017.06.001, 2017.
14. #McLachlan, S., K. Dube, T. Gallagher, B. Daley, and J. Walonoski, The ATEN Framework for creating the realistic synthetic electronic health record, 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), Madeira, Portugal, 2018.
15. Salas, J. D., J. Obeysekera, and R. M. Vogel, Techniques for assessing water infrastructure for nonstationary extreme events: a review, Hydrological Sciences Journal, 63(3), 325-352, doi:10.1080/02626667.2018.1426858, 2018.
16. #Hnilica, J., M. Hanel, and V. Puš, Technical note: Changes of cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2018-7, 2018.
17. Hua, Y., and B. Cui, Environmental flows and its satisfaction degree forecasting in the Yellow River, Ecological Indicators, 92, 207-220, doi:10.1016/j.ecolind.2017.02.017, 2018.
18. Ilich, N., A. Gharib, and E. G. R. Davies, Kernel distributed residual function in a revised multiple order autoregressive model and its applications in hydrology, Hydrological Sciences Journal, 63(12), 1745-1758, doi:10.1080/02626667.2018.1541090, 2018.
19. Henao, F., Y. Rodriguez, J. P. Viteri, and I. Dyner, Optimising the insertion of renewables in the Colombian power sector, Renewable Energy, 132, 81-92, doi:10.1016/j.renene.2018.07.099, 2019.
20. 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.
21. Ferreira, D. M., C. V. S. Fernandes, E. Kaviski, and D. Fontane, Water quality modelling under unsteady state analysis: Strategies for planning and management, Journal of Environmental Management, 239, 150-158, doi:10.1016/j.jenvman.2019.03.047, 2019.
22. Seo, S. B., Y.-O. Kim, and S.-U. Kang, Time-varying discrete hedging rules for drought contingency plan considering long-range dependency in streamflow, Water Resources Management, 33(8), 2791-2807, doi:10.1007/s11269-019-02244-5, 2019.
23. #McLachlan, S., K. Dube, T. Gallagher, J. A. Simmonds, and N. E. Fenton, The ATEN Framework for creating the realistic synthetic electronic health record, Biomedical Engineering Systems and Technologies, BIOSTEC 2018, Communications in Computer and Information Science, Vol. 1024, Springer, Cham, doi:10.1007/978-3-030-29196-9_25, 2019.
24. Yu, Z., S. Miller, F. Montalto, and U. Lall, Development of a non-parametric stationary synthetic rainfall generator for use in hourly water resource simulations, Water, 11, 1728, doi:10.3390/w11081728, 2019.
25. Bermúdez, M., L. Cea, and J. Sopelana, Quantifying the role of individual flood drivers and their correlations in flooding of coastal river reaches, Stochastic Environmental Research and Risk Assessment, 33(10), 1851-1861, doi:10.1007/s00477-019-01733-8, 2019.
26. Henao, F., and I. Dyner, Renewables in the optimal expansion of Colombian power considering the Hidroituango crisis, Renewable Energy, 158, 612-627, doi:10.1016/j.renene.2020.05.055, 2020.
27. Peng, Y., X. Yu, H. Yan, and J. Zhang, Stochastic simulation of daily suspended sediment concentration using multivariate copulas, Water Resources Management, 34, 3913-3932, doi:10.1007/s11269-020-02652-y, 2020.
28. Sobhaniyeh, Z., M. H. Niksokhan, and B. Omidvar, Investigation of uncertainties in a rainfall-runoff conceptual model for simulation of basin using Bayesian method, Iranian Journal of Ecohydrology, 7(1), 223-236, doi:10.22059/ije.2020.294740.1264, 2020.
29. 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.
30. Brunner, M. I., L. Slater, L. M. Tallaksen, and M. Clark, Challenges in modeling and predicting floods and droughts: A review, Wiley Interdisciplinary Reviews: Water, 8(3), e1520, doi:10.1002/wat2.1520, 2021.
31. Wang, Q., J. Zhou, L. Dai, K. Huang, and G. Zha, Risk assessment of multireservoir joint flood control system under multiple uncertainties, Journal of Flood Risk Management, e12740, doi:10.1111/jfr3.12740, 2021.
32. Bahrpeyma, F., M. Roantree, P. Cappellari, M. Scriney, and A. McCarren, A methodology for validating diversity in synthetic time series generation, MethodsX, 101459, doi:10.1016/j.mex.2021.101459, 2021.
33. 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.
34. Santana, R. F., and A. B. Celeste, Stochastic reservoir operation with data-driven modeling and inflow forecasting, Journal of Applied Water Engineering and Research, doi:10.1080/23249676.2021.1964389, 2021.
35. Araújo, J. E. S., J. Í. P. Siqueira, and A. B. Celeste, Incorporação de estocasticidade em ferramenta computacional para dimensionamento de reservatórios, Scientia cum Industria, 9(2), 36-40, doi:10.18226/23185279.v9iss2p36, 2021.
36. Bekri, E. S., P. Economou, P. C. Yannopoulos, and A. C. Demetracopoulos, Reassessing existing reservoir supply capacity and management resilience under climate change and sediment deposition, Water, 13(13), 1819, doi:10.3390/w13131819, 2021.
37. Salcedo-Sanz, S., D. Casillas-Pérez, J. Del Ser, C. Casanova-Mateo, L. Cuadra, M. Piles, G. Camps-Valls, Persistence in complex systems, Physics Reports, 957, 1-73, doi:10.1016/j.physrep.2022.02.002, 2022.
38. Bărbulescu, A., Monthly precipitation field generation at Sulina (Romania), IOP Conference Series: Materials Science and Engineering, 1242, 012004, doi:10.1088/1757-899X/1242/1/01200, 2022.
39. Haktanir, T., M. Bircan Kara, and N. Acanal, A multiseries stochastic model for synthetic monthly flows, Hydrological Sciences Journal, 67(5), 741-758, doi:10.1080/02626667.2022.2039662, 2022.
40. Kämper, A., R. Delorme, L. Leenders, and A. Bardow, Boosting operational optimization of multi-energy systems by artificial neural nets, Computers & Chemical Engineering, 173, 108208, doi:10.1016/j.compchemeng.2023.108208, 2023.
41. Ang, C. Y. S., Y. S. Chiew, X. Wang, E. H. Ooi, M. B. M. Nor, M. E. Cove, and J. G. Chase, Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach, Computer Methods and Programs in Biomedicine, 240, 107728, doi:10.1016/j.cmpb.2023.107728, 2023.
42. Hu, C., Z. Sun, C. Li, Y. Zhang, and C. Xing, Survey of time series data generation in IoT, Sensors, 23(15), 6976, doi:10.3390/s23156976, 2023.
43. Zaniolo, M., S. Fletcher, and M. Mauter, FIND: A synthetic weather generator to control drought frequency, intensity, and duration, Environmental Modelling & Software, 172, 105927, doi:10.1016/j.envsoft.2023.105927, 2024.
44. Swagatika, S., J. C. Paul, B. B. Sahoo, S. K. Gupta, and P. K. Singh, Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model, Journal of Water and Climate Change, 15(1), 139-156, doi:10.2166/wcc.2023.487, 2024.

Tagged under: Stochastic disaggregation, Hurst-Kolmogorov dynamics, Software, Stochastics, Students' works