Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R 2 ) = 0.787 and Nash-Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R 2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models. explicit knowledge of the physical behavior of the system [8]. In addition, adequate data should be provided for the training process in data-driven models. SWAT, a physically-based model frequently used by different disciplines, evaluates the watershed from a wider perspective [9][10][11][12][13][14]. The SWAT model is widely used in the simulation of the quality and quantity of surface and groundwater, in estimating the environmental impacts of different land use/land management practices and climate change, in calculating loads from pollutants, in evaluating best management practices, and in the simulation of various hydrological processes (runoff, infiltration, evapotranspiration, lateral flow, tile drainage, return flow, sediment etc) [15]. SWAT employs two different methods, the soil conservation services-curve number (SCS-CN) and the Green Ampt-MeinLarsen, for streamflow estimation [16][17][18][19]. Concurrent use of a digital elevation model (DEM), land use/land cover (LULC), and soil map alongside meteorological inputs also enables spatial analysis of the outputs produced by the model. As it includes physical inputs, the SWAT model yields successful results also in ungauged catchments [20,21].AI models such as support vector machines (SVM), artificial neural networks (ANN) and adaptive network-based fuzzy inference system (ANFIS) are widely used in estimating hydrological and meteorological phenomena. Tongal [22] used a chaotic approach (k-nearest neighbor-kNN) and neural networks (feed-forward neural networks, FFNN) the non-linear estimation of the streamflow of Yamula station in Kızılırmak Basin and found that the kNN model was more successful than the FFNN model for streamflow estimation. Buyukyildiz et al. [23] used five different methods, including support vector regression (SVR), artificial neur...

Climate change is expressed as major changes in the average climate which exist for many years. Although climate change occurs on a global scale, the impact of climate change varies from region to region. Therefore, the analysis of the variations in meteorological variables is a very vital issue in monitoring of climate change. In this study, assignment of the change points and trends of annual temperature (Tmean, Tmin and Tmax) data of the Konya Closed Basin in Turkey is examined. For this aim, air temperature data of 11 meteorology station were used. The change point of temperature data was examined using the Pettitt test, Standard Normal Homogeneity test and Buishand Range test. The Mann-Kendall, Spearman Rho and Innovative Şen Trend methods were used to determine trends of air temperature. Most of the change points in annual temperature data were determined as 1993-1994. The trend results show that annual temperature at most stations have increased, more than 80% of which are statistically significant.

Spatial and temporal variability of precipitation increases with the effect of climate change. In this study, the Seyhan Basin has been determined as the study area. It is aimed to examine the spatiotemporal variability of precipitation and extreme precipitation indices in the Seyhan Basin. For this purpose, the period 1970–2019 was divided into three periods with the change point detection methods (Pettitt, Buishand rank and standard normal homogeneity test). Trends were examined by applying modified Mann–Kendall and Spearman's rho tests to precipitation and extreme indices for all periods and sub-periods. Then, temporal and spatial analyses of extreme indices were performed. According to the results obtained, there is no precipitation homogeneity throughout the basin. While the threat of drought comes to the fore with the decrease in rainy days and precipitation in the north, the risk of flooding is effective with the increase in precipitation intensity in the south.

No abstract

ÖzThe changes in the intensity and frequency of extreme climatic events bring about many important problems in terms of social, economic and environmental. Especially, it is very important to inquire the influences of climate change on extreme precipitation. Spatial and temporal trends of extreme precipitation have been investigated by many researchers at global, regional and local scales in recent years. This study is about the temporal variability of extreme precipitation of 4 meteorology station (Konya, Karaman, Aksaray, Nigde) on Konya Closed Basin in Turkey. Trend analysis of extreme precipitations was conducted by means of the Spearman's Rho (SR), Mann-Kendall (MK) and Innovative Şen Trend test. According to the results of trend methods, non-significant decreasing trends are observed in extreme precipitation data of Aksaray, Karaman and Nigde meteorology stations while non-significant increasing trend is shown only in Konya meteorology station. Aşırı iklim olaylarının sıklığı ve yoğunluğundaki değişiklikler önemli sosyal, ekonomik ve çevresel sorunlara neden olmaktadır. Özellikle, iklim değişikliğinin ekstrem yağışlar üzerindeki etkilerini araştırmak çok önemlidir. Son yıllarda birçok araştırmacı tarafından küresel, bölgesel ve yerel ölçekte ekstrem yağışların mekansal ve zamansal değişimi araştırılmıştır. Bu çalışmada, Türkiye'de Konya Kapalı Havzası'nda bulunan 4 meteoroloji istasyonunun (Konya, Karaman, Aksaray, Niğde)ekstrem yağışlarının zamansal değişimi araştırılmıştır. Bu amaçla, Mann-Kendall (MK), Spearman's Rho ve Innovative Şen Trend testleri kullanılmıştır. Trend yöntemlerinin sonuçlarına göre Aksaray, Karaman ve Niğde meteoroloji istasyonlarında ekstrem yağış verileri istatistiksel olarak anlamlı olmayan azalma, Konya meteoroloji istasyonunda ise anlamlı olmayan artış eğilimleri elde edilmiştir.