TY  - JOUR
AU  - Marković, Đurica
AU  - Ilić, Siniša
AU  - Pavlović, Dragutin
AU  - Plavšić, Jasna
AU  - Ilich, Nesa
PY  - 2019
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1824
AB  - A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively.
T2  - Journal of Hydroinformatics
T1  - Multivariate and multi-scale generator based on non-parametric stochastic algorithms
EP  - 1117
IS  - 6
SP  - 1102
VL  - 21
DO  - 10.2166/hydro.2019.071
ER  - 

@article{
author = "Marković, Đurica and Ilić, Siniša and Pavlović, Dragutin and Plavšić, Jasna and Ilich, Nesa",
year = "2019",
abstract = "A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively.",
journal = "Journal of Hydroinformatics",
title = "Multivariate and multi-scale generator based on non-parametric stochastic algorithms",
pages = "1117-1102",
number = "6",
volume = "21",
doi = "10.2166/hydro.2019.071"
}

Marković, Đ., Ilić, S., Pavlović, D., Plavšić, J.,& Ilich, N.. (2019). Multivariate and multi-scale generator based on non-parametric stochastic algorithms. in Journal of Hydroinformatics, 21(6), 1102-1117.
https://doi.org/10.2166/hydro.2019.071

Marković Đ, Ilić S, Pavlović D, Plavšić J, Ilich N. Multivariate and multi-scale generator based on non-parametric stochastic algorithms. in Journal of Hydroinformatics. 2019;21(6):1102-1117.
doi:10.2166/hydro.2019.071 .

Marković, Đurica, Ilić, Siniša, Pavlović, Dragutin, Plavšić, Jasna, Ilich, Nesa, "Multivariate and multi-scale generator based on non-parametric stochastic algorithms" in Journal of Hydroinformatics, 21, no. 6 (2019):1102-1117,
https://doi.org/10.2166/hydro.2019.071 . .

TY  - JOUR
AU  - Marković, Durica
AU  - Plavšić, Jasna
AU  - Ilich, Nesa
AU  - Ilić, Siniša
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/662
AB  - A non-parametric method for generating stationary weekly hydrologic time series at multiple locations is presented. The procedure has three distinct steps: first, the Monte Carlo method is used to obtain 1000 years of simulated weekly flows having statistical properties as close as possible to the observed series; second, rearranging the order of simulated data in the series to achieve target spatial and temporal correlations within each simulated year; and third, the permutation of annual partial series to adjust the correlation of weekly streamflows at the beginning of a year with that at the end of a previous year while also adjusting the auto-correlation of annual flows. In this paper the method is applied for the first time on log-transformed data, and contributes to this methodology by introducing an additional criterion related to the possibility to obtain a desired frequency of occurrence of extremely dry years in the simulated series. The method is tested in two case studies, which use data from three hydrologic stations on the Studenica River in Serbia, and from seven stations in the Oldman River basin in Southern Alberta, Canada. The results show that the simulated data correspond to the observed data in all their stochastic properties and that they can be consequently used in the studies related to planning and design of reservoirs and other water management systems.
PB  - Kluwer Academic Publishers
T2  - Water Resources Management
T1  - Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations
EP  - 4801
IS  - 13
SP  - 4787
VL  - 29
DO  - 10.1007/s11269-015-1090-z
ER  - 

@article{
author = "Marković, Durica and Plavšić, Jasna and Ilich, Nesa and Ilić, Siniša",
year = "2015",
abstract = "A non-parametric method for generating stationary weekly hydrologic time series at multiple locations is presented. The procedure has three distinct steps: first, the Monte Carlo method is used to obtain 1000 years of simulated weekly flows having statistical properties as close as possible to the observed series; second, rearranging the order of simulated data in the series to achieve target spatial and temporal correlations within each simulated year; and third, the permutation of annual partial series to adjust the correlation of weekly streamflows at the beginning of a year with that at the end of a previous year while also adjusting the auto-correlation of annual flows. In this paper the method is applied for the first time on log-transformed data, and contributes to this methodology by introducing an additional criterion related to the possibility to obtain a desired frequency of occurrence of extremely dry years in the simulated series. The method is tested in two case studies, which use data from three hydrologic stations on the Studenica River in Serbia, and from seven stations in the Oldman River basin in Southern Alberta, Canada. The results show that the simulated data correspond to the observed data in all their stochastic properties and that they can be consequently used in the studies related to planning and design of reservoirs and other water management systems.",
publisher = "Kluwer Academic Publishers",
journal = "Water Resources Management",
title = "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations",
pages = "4801-4787",
number = "13",
volume = "29",
doi = "10.1007/s11269-015-1090-z"
}

Marković, D., Plavšić, J., Ilich, N.,& Ilić, S.. (2015). Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations. in Water Resources Management
Kluwer Academic Publishers., 29(13), 4787-4801.
https://doi.org/10.1007/s11269-015-1090-z

Marković D, Plavšić J, Ilich N, Ilić S. Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations. in Water Resources Management. 2015;29(13):4787-4801.
doi:10.1007/s11269-015-1090-z .

Marković, Durica, Plavšić, Jasna, Ilich, Nesa, Ilić, Siniša, "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations" in Water Resources Management, 29, no. 13 (2015):4787-4801,
https://doi.org/10.1007/s11269-015-1090-z . .