FORECASTS OF ANNUAL RUNOFF OF THE ZHAIYK RIVER (URAL) TAKING INTO ACCOUNT AUTOCORRELATION MODELS OF ITS MULTI-YEAR FLUCTUATIONS FOR INDIVIDUAL MONTHS

Abstract

The study is devoted to the development and application of autocorrelation and general regression models for long-term forecasting of the Ural (Zhaiyk) River flow based on the analysis of multi-year fluctuations. The Ural River is an important water resource of the Russian Federation and the Republic of Kazakhstan, demonstrating significant variability in annual runoff, which affects various sectors of economic activity. In the course of the study, annual and monthly series of the river flow for the period from 1943 to 2010 were estimated using the autocorrelation method of Y.M. Alekhin. Based on these data, forecasts were made for the period from 2011 to 2015. The results show that autocorrelation models provide more accurate forecasts compared to models based on average values of series. The general regression model integrating monthly and annual data showed the best results, confirming the effectiveness of the combined approach in predicting hydrological characteristics. The scientific significance of the work is to improve the accuracy and reliability of the Ural River flow forecasts, which contributes to more effective water resources management in this region.