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.

Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can differ significantly depending on the PDF. So, the success of the probability distribution function in representing the data of extreme value series of natural events such as hydrology and climatology is of great importance. Depending on whether the series consists of maximum or minimum values, the theoretical probability density function must be appropriately fit to the right or left tail of the extreme data, which contains the most critical information. This study includes a combined evaluation of the performance of four different tests for selecting the appropriate probability distribution of maximum rainfall in Türkiye: Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD) test, Probability Plot Correlation Coefficient (PPCC) test, and L-Moments Z^DIST test. Within the scope of the study, maximum rainfall series of seven rainfall durations from 15 to 1440 min, at rain gauge stations in 81 provinces of Türkiye, were examined. Goodness of fit was performed based on ranking using a combination of four different numerical tests (KS, AD, PPCC, Z^DIST). The probabilistic character of maximum rainfall was evaluated using a large dataset consisting of 567 time series with record lengths ranging from 45 to 80 years. The goodness of fit of distributions was examined from three different perspectives. The first is an examination considering rainfall durations, the second is a province-based examination, and the third is a general country-based assessment. In all three different perspectives, the Wakeby distribution was determined as the best fit candidate to represent the maximum rainfall in Türkiye.

The fact that 2023 and subsequently 2024 were the hottest years in history makes it even more important to monitor temperature changes over time. In this study, trends in the mean, maximum, and minimum temperature data of 81 provinces in Türkiye were examined using three traditional methods (Mann–Kendall, Linear Regression Analysis and Sen’s slope), one innovative method (ITA), and the QuarTrend (QT) method proposed in this study, which uses quartiles of the data series. The objectives of this research are (1) to determine and evaluate the long-term temperature trends in Türkiye (1960–2022) and (2) to comparatively evaluate the trend results of the proposed QT method, traditional trend detection methods, and ITA. In the study, a statistically significant (p < 0.05) increasing trend was found in the mean (0.027 °C/year), maximum (0.031 °C/year), and minimum (0.038 °C/year) annual temperatures of Türkiye. While traditional trend tests detected similar trends with ITA and QT for mean temperatures; ITA and QT detected more trends than traditional methods for maximum and minimum temperatures. The results have direct implications for the impacts of climate change in the study region. The results have the potential to support the development of climate-resilient and adaptive policies for effective water resource planning and management to sustain the environment and agricultural productivity in Türkiye.

In this study, Acısu Basin, which is upper side of one of the most important basins (Gediz) in Türkiye, was modelled using three types of hydrological models and three different calibration algorithms. Lumped (GR4J), semi-distributed (SWAT+), and fully distributed (mHM) hydrological models were built and integrated with Parameter Estimation Tool (PEST) which is a model independent calibration tool including various algorithms. Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES) were selected as calibration algorithms. Calibration period was 1991-2000, and validation results were obtained for 2002-2005. The effect of model structure and calibration algorithm selection on discharge simulation was evaluated via comparison of 9 different model-algorithm combinations. Results have shown that mHM and CMA-ES combination showed the best discharge simulation performance according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the pattern comparison of discharge value shows that all of the models miss the peak values in significant amounts. This problem may be related to the interventions made in 2000-2001 years interval and possible to overcome by changing the calibration and validation periods, increasing the number of iterations or using the naturalized gauge data.