TY  - JOUR
AU  - Stojković, Milan
AU  - Kostić, Srđan
AU  - Plavšić, Jasna
AU  - Prohaska, Stevan
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/855
AB  - The authors present a detailed procedure for modelling of mean monthly flow time-series using records of the Great Morava River (Serbia). The proposed procedure overcomes a major challenge of other available methods by disaggregating the time series in order to capture the main properties of the hydrologic process in both long-run and short-run, The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components, the former of which can be further decomposed into a composite trend and two periodic components (short-term or seasonal periodicity and long-term or multi-annual periodicity). In the present paper, the deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). For the calibration period, the results of the analysis infers a lower value of Kling-Gupta Efficiency in the case of transfer functions (0.736), whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values, 0.841 and 0.891, respectively. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output. The proposed methodology that uses a combination of artificial neural networks, spectral analysis and polynomial regression for deterministic and stochastic components can be applied to forecast monthly or seasonal flow rates.
PB  - Elsevier B.V.
T2  - Journal of Hydrology
T1  - A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates
EP  - 566
SP  - 555
VL  - 544
DO  - 10.1016/j.jhydrol.2016.11.025
ER  - 

@article{
author = "Stojković, Milan and Kostić, Srđan and Plavšić, Jasna and Prohaska, Stevan",
year = "2017",
abstract = "The authors present a detailed procedure for modelling of mean monthly flow time-series using records of the Great Morava River (Serbia). The proposed procedure overcomes a major challenge of other available methods by disaggregating the time series in order to capture the main properties of the hydrologic process in both long-run and short-run, The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components, the former of which can be further decomposed into a composite trend and two periodic components (short-term or seasonal periodicity and long-term or multi-annual periodicity). In the present paper, the deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). For the calibration period, the results of the analysis infers a lower value of Kling-Gupta Efficiency in the case of transfer functions (0.736), whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values, 0.841 and 0.891, respectively. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output. The proposed methodology that uses a combination of artificial neural networks, spectral analysis and polynomial regression for deterministic and stochastic components can be applied to forecast monthly or seasonal flow rates.",
publisher = "Elsevier B.V.",
journal = "Journal of Hydrology",
title = "A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates",
pages = "566-555",
volume = "544",
doi = "10.1016/j.jhydrol.2016.11.025"
}

Stojković, M., Kostić, S., Plavšić, J.,& Prohaska, S.. (2017). A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates. in Journal of Hydrology
Elsevier B.V.., 544, 555-566.
https://doi.org/10.1016/j.jhydrol.2016.11.025

Stojković M, Kostić S, Plavšić J, Prohaska S. A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates. in Journal of Hydrology. 2017;544:555-566.
doi:10.1016/j.jhydrol.2016.11.025 .

Stojković, Milan, Kostić, Srđan, Plavšić, Jasna, Prohaska, Stevan, "A joint stochastic-deterministic approach for long-term and short-term modelling of monthly flow rates" in Journal of Hydrology, 544 (2017):555-566,
https://doi.org/10.1016/j.jhydrol.2016.11.025 . .

TY  - JOUR
AU  - Stojković, Milan
AU  - Kostić, Srđan
AU  - Prohaska, Stevan
AU  - Plavšić, Jasna
AU  - Tripković, Vesna
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/835
AB  - The authors propose a new approach for trend assessment that takes into account long-term periodicity of annual flows. In particular, analysis is performed of annual flows recorded at the locations of 30 operating and designed hydropower plants (HPPs) in Serbia, in order to assess the current and future water availability for hydropower generation. The composite annual trend is determined by sliding a fixed time window of 30 years along the observed time series with a one-year time step. Such a linear moving window (LMW) approach enables the identification of the flow trend as a median of all values for each time step. Significant trend harmonics are determined using discrete spectral analysis. The results show an alternation of upward and downward trend phases of different durations, namely: 6787, 33-43 and 21-29 years. On the other hand, the results of the Mann-Kendall test indicate a monotonic downward trend at the studied sites in the Drina River Basin, while statistically insignificant trends are noted at other river basins. The Mann-Kendall test with the Theil-Sen estimator also implies a downward and statistically insignificant flow trend after the observed period, whereas the LMW approach indicates a probable trend increase at all the examined sites. The proposed approach can be used to predict annual flows in order to establish long-term water management plans at hydropower plants.
PB  - Springer Netherlands
T2  - Water Resources Management
T1  - A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia
EP  - 1103
IS  - 4
SP  - 1089
VL  - 31
DO  - 10.1007/s11269-017-1583-z
ER  - 

@article{
author = "Stojković, Milan and Kostić, Srđan and Prohaska, Stevan and Plavšić, Jasna and Tripković, Vesna",
year = "2017",
abstract = "The authors propose a new approach for trend assessment that takes into account long-term periodicity of annual flows. In particular, analysis is performed of annual flows recorded at the locations of 30 operating and designed hydropower plants (HPPs) in Serbia, in order to assess the current and future water availability for hydropower generation. The composite annual trend is determined by sliding a fixed time window of 30 years along the observed time series with a one-year time step. Such a linear moving window (LMW) approach enables the identification of the flow trend as a median of all values for each time step. Significant trend harmonics are determined using discrete spectral analysis. The results show an alternation of upward and downward trend phases of different durations, namely: 6787, 33-43 and 21-29 years. On the other hand, the results of the Mann-Kendall test indicate a monotonic downward trend at the studied sites in the Drina River Basin, while statistically insignificant trends are noted at other river basins. The Mann-Kendall test with the Theil-Sen estimator also implies a downward and statistically insignificant flow trend after the observed period, whereas the LMW approach indicates a probable trend increase at all the examined sites. The proposed approach can be used to predict annual flows in order to establish long-term water management plans at hydropower plants.",
publisher = "Springer Netherlands",
journal = "Water Resources Management",
title = "A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia",
pages = "1103-1089",
number = "4",
volume = "31",
doi = "10.1007/s11269-017-1583-z"
}

Stojković, M., Kostić, S., Prohaska, S., Plavšić, J.,& Tripković, V.. (2017). A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia. in Water Resources Management
Springer Netherlands., 31(4), 1089-1103.
https://doi.org/10.1007/s11269-017-1583-z

Stojković M, Kostić S, Prohaska S, Plavšić J, Tripković V. A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia. in Water Resources Management. 2017;31(4):1089-1103.
doi:10.1007/s11269-017-1583-z .

Stojković, Milan, Kostić, Srđan, Prohaska, Stevan, Plavšić, Jasna, Tripković, Vesna, "A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia" in Water Resources Management, 31, no. 4 (2017):1089-1103,
https://doi.org/10.1007/s11269-017-1583-z . .