While 1D, 2D, and coupled 1D/2D models are widely used in flood extent mapping, a significant research gap remains in comparative analyses of 2D and coupled 1D/2D approaches. Study of the Dinsiz Stream Basin is of critical importance due to its proximity to industrial zones and residential areas, as well as its susceptibility to flood risk. Due to the lack and insufficiency of flow data in the basin, only long-term rainfall data were used in the analysis. Rainfall return periods of 50, 100, 200, and 500 years were estimated using statistical methods, and these values were utilized to generate flood hydrographs for this study. These values were then transferred to HEC-HMS, and the resulting hydrographs were input into HEC-RAS to establish coupled 1D/2D and 2D models for comparison. Flood mapping was performed for different return periods to evaluate the flood impact. This study revealed that maximum water levels in the 1D/2D models were higher than in the 2D models. The results showed that Dinsiz Stream could cause major losses for the second organized industrial zone located nearby when it overflows. The accuracy of the model was ensured with photographs of the flood event that occurred in 2021, ensuring the reliability of the findings.

As in the rest of the world, the impact of climate change is clearly felt in Turkey, while the effects of global warming, drought and flood-induced disasters continue to increase day by day. At the same time, water consumption is crucial for the survival of people. Efficient water use and conservation, facilitated by water resources management, should be recognized as a pressing contemporary concern. Recently, floods that cause a great deal of damage have started to occur frequently in Turkey. A possible flood that may occur in the demolition of Gökçe Dam has been a matter of curiosity. This dam is the most important water source for Yalova province and has been in service for a long time. Therefore, the aim of this study is to examine the flood risk situations of settlements in case of a possible Gökçe Dam failure or basin flooding. Additionally, the study aims to estimate the damages that may occur in houses of flooded areas. Piping and overtopping methods were used in HEC-RAS program to create flood maps, and the Van Eck and Kok damage curves were used to assess the damages. While water level varies between 0-1 meters in the most affected buildings in the overtopping method, this level is 2-3 meters in the piping method. The damage to buildings with a water level of 4 meters and above is quite high in the piping method, and it has been observed that greater damage may occur for lower water levels in the overtopping method. The total damage and number of affected buildings are higher in the piping method. Accordingly, a possible Gökçe dam failure would cause significant losses in both methods, especially in the piping method.

This study examines the historical and anticipated effects of climate change on essential hydroclimatic variables (temperature, precipitation, evapotranspiration, and soil moisture) in the Southeastern Anatolia Project (GAP) region of Türkiye, a semi-arid and agriculturally significant basin experiencing heightened water stress. The analysis employs a collection of CMIP6 Global Climate Models (GCM) and integrates three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5), utilizing statistical bias correction methods such as Delta Change, Quantile Mapping (QM), and Empirical Quantile Mapping (EQM) to improve the regional accuracy of the projections. The ACCESS-CM2 model, validated with data from Türkiye’s Meteorological General Directorate (MGM), was chosen for comprehensive spatial mapping, utilizing Inverse Distance Weighting (IDW) interpolation across seven temporal intervals encompassing past, present, and future periods. The findings indicate a steady increase in temperature and evapotranspiration, especially under high-emission scenarios, with temperature rises above +4 °C and considerable water losses anticipated by century’s end. Soil moisture exhibits a declining tendency, particularly in the southern and eastern regions, signifying increasing drought susceptibility. Precipitation patterns demonstrate significant spatial variability and rising uncertainty, with relative error (RE%) values increasing under SSP5-8.5. Historical data from 1963 to 2022 corroborate these conclusions, indicating a progressive shift towards a warmer and drier regional climate. These observations highlight the importance of climate adaptation strategies and water management in the GAP region. The research provides decision-makers a high-resolution, bias-corrected hydroclimatic dataset.

Drought is a natural disaster with serious impacts on agriculture, water resources management and ecosystem balance. Ongoing global warming and climate change exacerbate drought cases. Therefore, proper tools are essential for long-term drought monitoring and efficient drought forecasts. The Palmer Drought Severity Index (PDSI) is a reliable indicator developed to monitor long-term drought trends and is widely used to define agricultural and hydrological droughts. In this study, drought predictions were performed with the use of PDSI data for the period January 1958–December 2023 for Kayseri meteorological station of Türkiye. Four different machine learning models Least squares boosting, Regression Quantile Neural Network (RQNN), Convolutional Neural Network—Recurrent Neural Network (CNN-RNN) and Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) were combined with Empirical mode decomposition (EMD), Maximal overlap discrete wavelet transform (MODWT), Tunable Q-factor wavelet transform (TQWT), Empirical wavelet transform (EWT) and Variational Mode Decomposition (VMD) data decomposition techniques. Then, PDSI prediction performance of independent and hybrid models was assessed in detail. Data from January 1958 to March 2007 was used in the training phase of the models, while data from April 2007 to December 2023 was used in the testing phase. The t-1, t-2, and t-3 lagged values of the PDSI were set as input variables and the t value was set as the output variable. Among the evaluated models, EWT-RQNN yielded relatively low error values and high values for the considered performance criteria. The TQWT-RQNN and EWT-CNN-LSTM models also attracted attention with their superior performance. Present findings revealed that machine learning models hybridized with appropriate data decomposition techniques significantly improved PDSI prediction performance. Kruskal–Wallis H-test results revealed no significant difference between predicted and observed PDSI values and confirmed that the developed models generally produced successful predictions. It was concluded based on present findings that not only robust model architectures, but also effective pre-processing methods play a critical role in predicting drought-like complex and non-linear processes.