A nonlinear disaggregation method with a reduced parameter set for simulation of hydrologic series

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.



A multivariate dynamic disaggregation model is developed as a stepwise approach to stochastic disaggregation problems, oriented towards hydrologic applications. The general idea of the approach is the conversion of a sequential stochastic simulation model, such as a seasonal AR(1), into a disaggregation model. Its structure includes two separate parts, a linear step-by-step moments determination procedure, based on the associated sequential model, and an independent nonlinear bivariate generation procedure (partition procedure). The model assures the preservation of the additive property of the actual (not transformed) variables. Its modular structure allows for various model configurations. Two different configurations (PAR(1) and PARX(1)) both associated with the sequential Markov model are studied. Like the sequential Markov model, both configurations utilize the minimum set of second order statistics and the marginal means and third moments of the lower level variables. All these statistics are approximated by the model with the use of explicit relations. Both configurations perform well with regard to the correlation of consecutive lower-level variables each located in consecutive higher-level time steps. The PARX(1) configuration exhibits better behaviour with regard to the correlation properties of lower level variables with lagged higher level variables.

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See also: http://dx.doi.org/10.1029/92WR01299


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, N. Mamassis, and I. Nalbantis, Stochastic simulation of hydrological variables, Appraisal of existing potential for improving the water supply of greater Athens - Phase 2, Report 13, 313 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, March 1990.

Our works that reference this work:

1. 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.
2. D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.
3. I. Nalbantis, and D. Koutsoyiannis, A parametric rule for planning and management of multiple reservoir systems, Water Resources Research, 33 (9), 2165–2177, doi:10.1029/97WR01034, 1997.
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, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
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, 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.
8. 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.

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

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1. Foufoula-Georgiou, E., and W. Krajewski, Recent advances in rainfall modeling, estimation, and forecasting, Reviews of Geophysics, 33(Pt2 SS), 1125-1137, 1995.
2. Robinson, J.S., and M. Sivapalan, Temporal scales and hydrological regimes: Implications for flood frequency scaling, Water Resources Research, 33(12), 2981-2999, 1997.
3. Deo, M.C., M. Sherief, and A. Sarkar, Wave height estimation using disaggregation models, Journal of Waterway Port Coastal and Ocean Engineering-ASCE, 123(2), 63-67, 1997.
4. Chaleeraktrakoon, C., Stochastic procedure for generating seasonal flows, Journal of Hydrologic Engineering, 4(4), 337-343, 1999.
5. Kumar, D.N., U. Lall, and M.R. Petersen, Multisite Disaggregation of Monthly to Daily Streamflow, Water Resources Research, 36(7), 1823-1833, 2000.
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. Srinivas, V.V., and K. Srinivasan, Hybrid matched-block bootstrap for stochastic simulation of multiseason streamflows, Journal of Hydrology, 329(1-2), 2006.
8. 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.
9. 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.
10. 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.
11. Chaleeraktrakoon, C., Parsimonious SVD/MAR(1) procedure for generating multisite multiseason flows, Journal of Hydrologic Engineering, 14(5), 516-527, 2009.
12. Kalra, A., and S. Ahmad, Evaluating changes and estimating seasonal precipitation for the Colorado River Basin using a stochastic nonparametric disaggregation technique, Water Resources Research, 47, W05555, doi: 10.1029/2010WR009118, 2011.
13. 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.
14. Anis, M. R., and M. Rode, A new magnitude-category disaggregation approach for temporal high-resolution rainfall intensities, Hydrological Processes, 10.1002/hyp.10227, 2014.
15. Edwin, A. and O. Martins, O., Stochastic characteristics and modelling of monthly rainfall time series of Ilorin, Nigeria, Open Journal of Modern Hydrology, 4, 67-79, 2014.
16. Srivastav, R., K. Srinivasan, and S. P. Sudheer, Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling, Journal of Hydrology, doi:10.1016/j.jhydrol.2016.09.025, 2016.

Tagged under: Stochastic disaggregation, Stochastics