This study is intended to determine potential trends in annual rainfall series with the parametric Student’s t test and the nonparametric Mann–Kendall, Spearman’s rho, and Sneyers tests. The study includes a trend analysis of annual rainfall data from 47 rain gauges, mostly located at rural sites, in the Aegean region of Turkey. The chi-square and Kolmogorov–Smirnov tests showed that the null hypothesis of normality for the majority of the data (45 out of 47 stations) is acceptable. Moreover, the serial independence assumption, based on the lag-1 sample autocorrelations, is rejected in 14 datasets. The parametric Student’s t test detected significant downward trends at 15 rain gauge stations; 14 of them were also confirmed by the three nonparametric tests and by the trend-free version of the Mann–Kendall test. The results of the Sneyers test revealed that the approximate start years of significant downward trends were in the early 1970s and, sometimes, in the early 1980s. Moreover, the testing found that the normalized slopes of linear downward trends were significantly dependent on the station’s longitude, which meant that the farther the station was located from the coast, the smaller the decreasing trend in annual rainfall was. Additional studies carried out on the normalized regional data and on the 5-yr running means showed that a considerable portion of the detected downward trends were mainly due to interactions of the particular start and end times of a large number of stations with interdecadal fluctuations and the dry conditions over the Mediterranean region during the last 25–30 years.

This study investigates the existence of a single parent flood frequency distribution on a Turkish scale. In the design of hydraulic structures, estimating the project flood of a given period or probability is usually the first step. Determining an acceptable design criterion depends substantially on the probability distribution of floods. In this study, annual peak series from 268 Turkish rivers was collected and a database of L-Moment ratios was constructed. The best-fit probability distribution (PD) model was investigated among seven distribution models (generalized logistic, GLO, generalized extreme value, GEV, generalized Pareto, GPA, three-parameter lognormal, LN3, Pearson type 3, P3, Gumbel, GUM, and normal distribution, N) by using a framework of L-moment ratio diagrams. The L-Moment ratio framework used in this study consisted of two sequential procedures. The first graphical procedure evaluated the L-Moment diagrams visually while the second procedure is based on the hypothetical testing procedure of L-Moment diagrams. The results of a graphical inspection of the dataset show GEV distribution as a potential parent PD of the floods in Turkey. A more detailed hypothetical testing procedure comprises Monte Carlo simulations produced from a GEV model. In a hypothetical testing procedure the variability of L-skewness and L-kurtosis values of sample data are situated within the theoretical limits of GEV distribution. Consequently, the GEV distribution is accepted as a single parent PD for annual maximum flow series of Turkey.

In this study, the Acısu Basin—viz., the headwater of the Gediz Basin—in Turkey, was modelled using three types of hydrological models and three different calibration algorithms. A well-known lumped model (GR4J), a commonly used semi-distributed (SWAT+) model, and a skillful distributed (mHM) hydrological model were built and integrated with the Parameter Estimation Tool (PEST). PEST is a model-independent calibration tool including three algorithms—namely, Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES). The calibration period was 1991–2000, and the validation results were obtained for 2002–2005. The effect of the model structure and calibration algorithm selection on the discharge simulation was evaluated via comparison of nine different model-algorithm combinations. Results have shown that mHM and CMA-ES combination performed the best discharge simulation according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the models mostly missed the peak values in the hydrograph. This problem may be related to the interventions made in 2000–2001 and may be overcome by changing the calibration and validation periods, increasing the number of iterations, or using the naturalized gauge data.