Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ2/r/munzur/1525
  An Investigation of the Variability of Short-Term Drought with SPEI-Based Severity, Duration and Magnitude Parameters in a Transboundary Basin

  Akbas, Ergun; Acar, Ramazan; Eşit, Musa; Koycegiz, Cihangir

  Climate change-induced variability in the water budget leads to significant drought events. In this study, severity, duration, and magnitude parameters obtained from 1-month and 3-month series of the Standardized Precipitation Evapotranspiration Index (SPEI) were used to investigate short-term drought from different perspectives. The lower part of the Euphrates-Tigris Basin, which is a transboundary basin, was preferred as the study area within the borders of Türkiye. In this study, monthly total precipitation and monthly mean temperature data for the period 1963–2021 were used. Both graphical (Şen Innovative Trend Analysis) and statistical (Mann–Kendall, Spearman’s Rho, Sen’s Slope Estimator) trend analysis methods were applied to detect, categorize, and analyze the variability of drought. The Combination of Wilcoxon Test and Scatter Diagram (CWTSD) method, which offers both graphical and statistical trend analysis capabilities, was preferred to enhance consistency and facilitate comparison of results. According to the findings, more severe droughts are observed in the eastern part of the basin compared to the western part. Across the basin, severity and magnitude are decreasing, while duration is increasing. The increase in duration indicates that droughts are shifting from short-term to long-term periods. This situation is of great importance in terms of planning agricultural strategies.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1494
  Local Scour Process in Conduit Aerated Water Jets

  Tuna, M. Cihat; Acar, Ramazan; Tunç, Mustafa

  High-velocity water jets emerging from hydraulic structures cause local scouring in downstream areas, creating significant risks to structural safety. In this study, the effects of natural aeration provided through pressurized pipes on jet-induced scouring were investigated through laboratory experiments. A total of 60 experiments were conducted for different gate openings, flow rates, and downstream water levels; the scour geometries created by aerated and non-aerated jets were compared. The aeration level was defined by the dimensionless air-to-water flow ratio (Qa/Qw); maximum scour depth, scour length, scour slope angle, and jet fall distance were considered as basic evaluation parameters. The experimental results showed that increased aeration significantly reduced the maximum scour depth and scour length while moving the point of contact of the jet further upstream. While the scour depth and slope angles reached their highest values in non-aerated jets, shallower and flatter scour profiles were formed in aerated conditions. An empirical relationship predicting the maximum scour depth using experimental data has been developed and found to be in high correlation with the measured values (R2 = 0.93). The findings reveal that channel aeration is an effective method for reducing scouring and demonstrate that this approach can be considered a safe and economical alternative in the design of spillways, wastewater treatment plants, and similar hydraulic systems.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1158
  Using Decision Tree and K-Means to Improve ANFIS for Predicting Missing Flow Data in Çoruh Basin

  Acar, Ramazan

  Complete and continuous flow data provides contributions to the water resources projects appropriate design. However, both financial and technical factors prevent the regular collection of data and such a situation leads to missing data problems. Input variables are from five stations in the Çoruh Basin in the northeast province of Türkiye, and output variables are from one station. In the study, 75% (171 months of data) of the 228 months flow data between the years 1993–2011 are used in the training phase and 25% (57 months of data) in the testing phase. In the ANFIS models, Decision Tree (DT) and K-means (KM) methods are used in selecting the input variables and determining the number of membership functions. In addition, artificial neural network (ANN), multiple linear regression (MLR), and ANFIS models with randomly determined cluster numbers are also used to compare the model performance of these methods. The results show that using DT and KM methods, the ANFIS models generate more reliable results than other models. During the study, the regression coefficient (R2), weighted mean square error (WMSE), and Wilcoxon (Z) values are taken into account for reliability measure. The DT–K-means–ANFIS model achieved the highest accuracy with R2 = 0.98 and WMSE = 5.89 during the testing phase, demonstrating superior performance to the ANN and MLR models. It is shown that the most successful models can be determined in a shorter time by using DT and KM methods before generating ANFIS models.

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