Simple disaggregation by accurate adjusting procedures

D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.



A multivariate disaggregation method is developed for stochastic simulation of hydrologic series. The method is based on three simple ideas that have been proven effective. First, it starts using directly a typical PAR(1) model and keeps its formalism and parameter set, which is the most parsimonious among linear stochastic models. This model is run for the lower-level variables without any reference to the known higher-level variables. Second, it uses accurate adjusting procedures to allocate the error in the additive property, i.e., the departure of the sum of lower-level variables within a period from the corresponding higher-level variable. They are accurate in the sense that they preserve explicitly certain statistics or even the complete distribution of lower-level variables. Three such procedures have been developed and studied in this paper, both theoretically and empirically. Third, it uses repetitive sampling in order to improve the approximations of statistics that are not explicitly preserved by the adjusting procedures. The model, owing to the wide range of probability distributions it can handle (from bell-shaped to J-shaped) and to its multivariate framework, is useful for a plethora of hydrologic applications such as disaggregation of annual rainfall or runoff into monthly or weekly amounts, and disaggregation of event rainfall depths into partial amounts of hourly or even less duration. Such real world hydrologic applications have been explored in this study to test the model performance, which has proven very satisfactory.

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

See also:

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, and Th. Xanthopoulos, A dynamic model for short-scale rainfall disaggregation, Hydrological Sciences Journal, 35 (3), 303–322, doi:10.1080/02626669009492431, 1990.
3. D. Koutsoyiannis, A nonlinear disaggregation method with a reduced parameter set for simulation of hydrologic series, Water Resources Research, 28 (12), 3175–3191, doi:10.1029/92WR01299, 1992.
4. 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.

Our works that reference this work:

1. 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.
2. 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.
3. D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
4. D. Koutsoyiannis, and C. Onof, Rainfall disaggregation using adjusting procedures on a Poisson cluster model, Journal of Hydrology, 246, 109–122, 2001.
5. D. Koutsoyiannis, A. Efstratiadis, and G. Karavokiros, A decision support tool for the management of multi-reservoir systems, Journal of the American Water Resources Association, 38 (4), 945–958, doi:10.1111/j.1752-1688.2002.tb05536.x, 2002.
6. 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.
7. 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.
8. D. Koutsoyiannis, and A. Efstratiadis, Experience from the development of decision support systems for the management of large-scale hydrosystems of Greece, Proceedings of the Workshop "Water Resources Studies in Cyprus", edited by E. Sidiropoulos and I. Iakovidis, Nikosia, 159–180, Water Development Department of Cyprus, Aristotle University of Thessaloniki, Thessaloniki, 2003.
9. 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.
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. 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.
12. Y. Markonis, and D. Koutsoyiannis, Scale-dependence of persistence in precipitation records, Nature Climate Change, 6, 399–401, doi:10.1038/nclimate2894, 2016.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 3, ISBN: 978-618-85370-0-2, 391 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2023.

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

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

1. Kumar, D.N., U. Lall and M. R. Petersen, Multisite Disaggregation of Monthly to Daily Streamflow, Water Resources Research, 36(7), 1823-1833, 2000.
2. Gudmundsson, G., Estimation of continuous flows from observed aggregates, Journal of the Royal Statistical Society Series D-The Statistician, 50, 285-293, Part 3, 2001.
3. #Xenos, D., C. Karopoulos and E. Parlis, Modern confrontation of the management of Athens' water supply system, Proc. 7th Conference on Environmental Science and Technology, Syros, Greece, 952-958, 2001.
4. Sharma A., and R. O'Neill, A nonparametric approach for representing interannual dependence in monthly streamflow sequences, Water Resources Research, 38 (7), art. no. 1100, 2002.
5. Kandel,D.D., A.W. Western, R.B. Grayson and H.N. Turra, Process parameterization and temporal scaling in surface runoff and erosion modelling, Hydrological Processs, 18 (8), 1423-1446, 2004.
6. Srinivas, V.V., and K. Srinivasan, Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows, Journal of Hydrology, 302(1-4), 307-330, 2005.
7. #Loucks, D.P., E. van Beek, J.R. Stedinger, J.P.M. Dijkman and M.T. Villars, Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications, UNESCO, 2005.
8. Mohymont, B., and G.R. Demaree, Intensity-duration-frequency curves for precipitation at Yangambi, Congo, derived by means of various models of Montana type, Hydrological Sciences Journal, 51 (2), 239-253, 2006.
9. Srikanthan, R., A. Sharma and T.A. McMahon, Comparison of two nonparametric alternatives for stochastic generation of monthly rainfall, Journal of Hydrologic Engineering, 11(3), 2006.
10. Srinivas, V.V., and K. Srinivasan, Hybrid matched-block bootstrap for stochastic simulation of multiseason streamflows, Journal of Hydrology, 329(1-2), 2006.
11. Wu, S.-J., Y.-K. Tung and J.-C. Yang, Stochastic generation of hourly rainstorm events, Stochastic Environmental Research and Risk Assessment, 21(2), 195-212, 2006.
12. Debele, B., R. Srinivasan and J. Yves Parlange, Accuracy evaluation of weather data generation and disaggregation methods at finer timescales, Advances in Water Resources, 30(5), 1286-1300, 2007.
13. Prairie, J., B. Rajagopalan, U. Lall and T. Fulp, A stochastic nonparametric technique for space-time disaggregation of streamflows, Water Resources Research, 43(3), W03432, 2007.
14. Prairie, J.R., and B. Rajagopalan, A basin wide stochastic salinity model, Journal of Hydrology, 344(1-2), 43-54, 2007.
15. Wang, Q.J., and R.J. Nathan, A method for coupling daily and monthly time scales in stochastic generation of rainfall series, Journal of Hydrology, 346(3-4), 122-130, 2007.
16. Prairie, J., K. Nowak, B. Rajagopalan, U. Lall and T. Fulp, A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data, Water Resources Research, 44 (6), W06423, 2008.
17. Salas, J. D., and T. Lee, Nonparametric simulation of single-site seasonal streamflows, Journal of Hydrologic Engineering, 15 (4), 284-296, 2010.
18. #Ortiz, E., and E. Todini, Acople modelos numéricos de tiempo (NWP) a modelos hidrológicos distribuidos. Sistema de predicciones hidrometeorológicas en tiempo real en las cuencas de Galicia Costa. El sistema ARTEMIS, “Meteorología y Energías Renovables ”. XXXI Jornadas Científicas de la Asociación Meteorológica Española, 2010.
19. Lee, T., J. D. Salas, and J. Prairie, An enhanced nonparametric streamflow disaggregation model with genetic algorithm, Water Resour. Res., 46, W08545, doi:10.1029/2009WR007761, 2010.
20. Hao, Z., and V. P. Singh, Single-site monthly streamflow simulation using entropy theory, Water Resour. Res., 47, W09528, doi: 10.1029/2010WR010208, 2011.
21. Lu, B. H., H. H. Gu, Z. Y. Xie, J. F. Liu, L. J. Ma and W. X. Lu, Stochastic simulation for determining the design flood of cascade reservoir systems, Hydrology Research, 43 (1-2), 54-63, 2012.
22. Hidayah, E., Implementing of temporal rainfall disaggregation model using Bayesian PAR1 model combined with adjusting and filtering procedure in Sampean catchments area, Journal of Applied Sciences Research, 8 (1), 314-320, 2012.
23. Astutik, S., N. Iriawan, G. Nair and S. Suhartono, Bayesian state space modeling for spatio-temporal rainfall disaggregation, International Journal of Applied Mathematics and Statistics, 37 (7), 26-37, 2013.
24. Hao, Z., and V. P. Singh, Modeling multi-site streamflow dependence with maximum entropy copula, Water Resources Research, 10.1002/wrcr.20523, 2013.
25. You, G. J.-Y. B.-H. Thum and F.-H. Lin, The examination of reproducibility in hydro-ecological characteristics by daily synthetic flow models, Journal of Hydrology, 511, 904-919, 2014.
26. Villani, V., D. Di Serafino, G., Rianna, and P. Mercogliano, Stochastic models for the disaggregation of precipitation time series on sub-daily scale: identification of parameters by global optimization, CMCC Research Paper, RP0256, 2015.

Tagged under: Stochastic disaggregation, Stochastics