Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m³/s to 1888.3 m³/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km² to 5767 km²), and the impacted areas were classified as extremely risky by the DEFRA method.

In recent years, the potential impacts of climate change on water resources and the hydrologic cycle have gained importance especially for snow-dominated mountainous basins. Within this scope, the Euphrates-Tigris Basin, a snow-fed transboundary river with several large dams, was selected to investigate the effects of changing climate on seasonal snow and runoff. In this study, two headwater basins of the Euphrates River, ranging in elevation between 1500–3500 m, were assigned and SWAT was employed as a hydrological modeling tool. Model calibration and validation were conducted in a stepwise manner for snow and runoff consecutively. For the snow routine, model parameters were adjusted using MODIS daily snow-covered area, achieving hit rates of more than 95% between MODIS and SWAT. Other model parameters were calibrated successively and later validated according to daily runoff, reaching a Nash-Sutcliffe efficiency of 0.64–0.82 in both basins. After the modeling stage, the focus was drawn to the impacts of climate change under two different climate scenarios (RCP4.5 and RCP8.5) in two 30-year projection periods (2041–2070 and 2071–2099). From the results, it is estimated that on average snow water equivalent decreases in the order of 30–39% and snow-covered days shorten by 37–43 days for the two basins until 2099. In terms of runoff, a slight reduction of at most 5% on average volume is projected but more notably, runoff center-time is expected to shift 1–2 weeks earlier by the end of the century.

The aim of this study is to analyze the effects of a possible dam failure under various scenarios and to generate a flood hazard map for two consecutive dams located in a study area with a dense-residential region and a heavy-traffic highway. Two consecutive dams consist of Elmalı 2, a concrete-buttress dam and Elmalı 1, an earth-fill gravity dam in the upstream and downstream, respectively. Hydrologic Engineering Center-River Analysis System (HEC-RAS) was used to develop a dam failure model. Dam failure scenarios were examined regarding three main criteria: the Breach Formation Time (BFT), the Number of Failed Buttresses (NFB) of Elmalı 2, and the Reservoir Volume Ratio (RVR) of Elmalı 1. Accordingly, flood peak depth (Hp), peak flow rate (Qp), peak velocity (vp), and time to reach the peak (tp) are discussed. The results showed that BFT and NFB of Elmalı 2 were highly effective on these values, whereas RVR of Elmalı 1 had no significant effect. Moreover, the total area affected by potential floods was calculated with a comparative areal change analysis using flood inundation and flood hazard maps obtained. Estimated damage costs indicate that in the worst-case scenario, more than 500 buildings will be affected in the region.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

Water quality modeling is very important for the management of water resources. In this study, the upper part of the Porsuk Basin in Türkiye is analyzed using SWAT. In the analysis, in addition to the data provided by the General Directorate of State Hydraulic Works (DSI), the freely available flow and water quality data from the GEMStat data portal were used. This study presents a discussion of the practicality of GEMStat data for a water quality model. For this purpose, firstly, the SWAT model was constructed with freely available global data sources on elevation, land use/land cover, and soil type. Then, the model flow outputs were calibrated and validated for both DSI and GEMStat data in three different time periods. As a result, the flow calibration and validation success in the daily time step is 0.64 and 0.44 according to the Nash–Sutcliffe efficiency (NSE). The model was also validated using GEMStat flow data and calibrated using GEMStat water quality data such as nitrate (NO3), total suspended solids (TSS), and dissolved oxygen (DO) with a reasonable value. Hence, the results showed that GEMStat flow and water quality data can be used as auxiliary open-source data in the modeling process.