Drought incidents occur due to the fact that precipitation values are below average for many years. Drought causes serious effects in many sectors, such as agriculture, economy, health, and energy. Therefore, the determination of drought and water scarcity, monitoring, management, and planning of drought and taking early measures are important issues. In order to solve these issues, the advantages and disadvantages of five different meteorological drought indices were compared, and the most effective drought index was determined for monitoring drought. Accordingly, in the monthly, 3-month, and 12-month time period, covering the years between 1966 and 2017 (52 years), Standardized Precipitation Index (SPI), Statistical Z-Score Index (ZSI), Rainfall Anomaly Index (RAI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) were used. It was concluded that precipitation-based SPI and ZSI are similar patterns and precipitation, and temperature-based SPEI and RDI are similar patterns. Also, it has been determined that RAI is more effective than other indices in determining the periods of extreme drought or wet. Furthermore, SPEI and RDI have been found to be superior to other indices as they take into account the water consumption and climate effects caused by evapotranspiration.
Stochastic modelling of streamflows is vital for planning water resource systems. In this study, a stochastic model of the mean monthly streamflows at 2154 A¸sag ıkag dariç Gauging Station on Karasu River was constructed. Studies were carried out using data from the water yearbooks published by chk later onEIE. The modelling procedure for streamflows with constant coefficient autoregressive moving average (ARMA) models was given in detail and indicated models were constructed. Analysis with streamflows at 2154 A¸sagıkagdariç Gauging Station showed that the autoregressive (AR) (1) model is the most appropriate model among the competing models. While selecting the most efficient model the Akaike information criterion (AIC) was used. The Port-Manteau test showed that residuals are white noise series. Using the AR(1) model, 100 synthetic series were generated and the time series generated were found to have the same statistical parameters (monthly mean, monthly standard deviation and autocorrelation) as historic time series within 95% confidence intervals.
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