Scholarly record
ARMA MODELS APPLICATION FOR FORECASTING OF THE RIVER DISCHARGE AND THE HYDROLOGICAL DROUGHT INDEX SRI IN DAMS MANAGEMENT
Abstract
Reproducing the hydrological process of river runoff is a fundamental part of water resources planning and management. Finding a mathematical model to reproduce the runoff time series aims not only to extract maximum information from the limited available data, but also to extrapolate into the future by representatively generating the historical runoff process. Almost all management decisions are based on forecasts. Our present is characterized by the increasingly frequent occurrence of hydrometeorological processes that lead to flash floods and critical droughts. The river runoff, as the main element of water management systems, must be as well analyzed and substantiated as possible, so that its estimated values are of acceptable accuracy for practical application of water management. There are two forecasting approaches: real-time forecasting and observational time series forecasting. A stochastic method used in the second approach is presented here. The method uses the most applied class of models for forecasting time series - the ARMA models. Their application in forecasting one of the main indicators of the hydrological drought occurrence is shown. This is the SRI index, i.e. the standardized normal value from the probability distribution of the monthly river runoff observations. River runoff is a non-stationary process and ARIMAs are those ARMA models that transform, by differentiation, river runoff time series into stationary ones. The method is applied for a 43-year period of monthly river runoff observations on the tributary of the Ogosta river. With an ARIMA model is forecasted the runoff for the next few months and with it the SRI index monthly values are estimated. The accuracy of the results is investigated. The developed method makes it possible to predict hydrological drought and can be used in optimizing the regimes of dams with hydropower, irrigation water supply and environmental purposes.
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References9
Hurvich, C. M., and Tsai, C.L., Regression and time series model selection in small samples, Biometrika 76, 1989. DOI: 10.2307/2336663
Box,G., Jenkins, G.: Time series analysis-forecasting and control. Revised edition, Holden-Day, 1976.
Chang, Dayly Precipitation Modelling by Diiscrete Autoregressive Moving Avarege Processes, v.20,No.5, 1984. DOI: 10.1029/wr020i005p00565
Joseph, E.S., Some Applications of Statistic Hydrological Models. Proc. Int. Symp. on Modeling Techniques in Water Resources Systems, Canada,1972.
Salas, J.D.,D.C. Boes, ARMA Modeling of Seasonal Hydrologic Series. J. Hyd. Div., ASCE, 1980.
Storm, R., P. Muller, P. Neumann, Tables of Mathematical Statistics, M., "Finance and Statistics",1982.
Dimitrov Y. "Application of the hydrological indices in determining the types of drought in the river Botunya", Journal of Meteorology and Hydrology,19/1 �.88�96, 2015.
Monzonis, M., et al., A review of water scarcity and drought indexes in water resources planning and management, Journal of Hydrology, 2015.
Ortega-Gomes,T., et al., Improvement of the drought indicators system in the Jucar River Basin, Spain, Science of the Total Environment, 2018. DOI: 10.1016/j.scitotenv.2017.07.250
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