Spatiotemporal analysis of transition probabilities of wet and dry days under SSPs scenarios in the semi-arid Susurluk Basin, Türkiye.

Precipitation, especially in regions dominated by the Mediterranean climate, is one of the most critical parameters of the hydrological cycle and the environment affected by climate change. One the one hand, the transition probabilities of wet and dry days in precipitation occurrence are a relatively new topic, on the other hand these are essential in defining the regional climate. For the first time, spatiotemporal variations of transition probabilities of wet and dry days in the Susurluk Basin, northwestern Türkiye, dominated by a semi-arid Mediterranean climate and also having a mountain climate, were analyzed based on the observation (1979-2014) and future terms (2030-2059 as short and 2070-2099 as long), under four Shared Socioeconomic Pathways (SSPs) scenarios. To do this, statistical downscaling was performed for 14 general circulation models (GCMs) from the CMIP6. By applying an ensemble of four high-performing GCMs, four indices for transition probabilities of wet and dry, i.e., a dry day following a dry day (FDD), a wet day following a dry day (FDW), a dry day following a wet day (FWD), and a wet day following a wet day (FWW), were calculated, and their changes were determined statistically. Monotonic and partial trends of the indices were also analyzed. According to the results, the FDD will increase in water year and wet period and autumn in the future, especially for the long term, in the basin dominated by the FDD (75 % in water year). The risks are higher in the western part of the basin, where human activities are intense, as the FDD is higher in this part than other parts especially in summer (90-100 %) in SSP3-7.0 and SSP5-8.5 scenarios for the long term. So, the length of consecutive dry days in the wet period and water year will increase in the basin, thus increasing the likelihood of droughts. As for the intra-term trends, the FDD increases and the FWW decreases in the water year and seasons in SSP3-7.0 and SSP5-8.5, contrary to the observation term.

Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates.

The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979-2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50° × 1.50° resolution showed similar performance to 0.25° × 0.25° resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02345-5.

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL.

Assessment of big floods in the Eastern Black Sea Basin of Turkey.

In this study, general knowledge and some details of the floods in Eastern Black Sea Basin of Turkey are presented. Brief hydro-meteorological analysis of selected nine floods and detailed analysis of the greatest flood are given. In the studied area, 51 big floods have taken place between 1955-2005 years, causing 258 deaths and nearly US $500,000,000 of damage. Most of the floods have occurred in June, July and August. It is concluded that especially for the rainstorms that have caused significantly damages, the return periods of the rainfall heights and resultant flood discharges have gone up to 250 and 500 years, respectively. A general agreement is observed between the return periods of rains and resultant floods. It is concluded that there has been no significant climate change to cause increases in flood harms. The most important human factors to increase the damage are determined as wrong and illegal land use, deforestation and wrong urbanization and settlement, psychological and technical factors. Some structural and non-structural measures to mitigate flood damages are also included in the paper. Structural measures include dykes and flood levees. Main non-structural measures include flood warning system, modification of land use, watershed management and improvement, flood insurance, organization of flood management studies, coordination between related institutions and education of the people and informing of the stakeholders.

Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks.

Suspended sediment concentration (SSC) is generally determined from the direct measurement of sediment concentration of river or from sediment transport equations. Direct measurement is very costly and cannot be conducted for all river gauge stations. Therefore, correct estimation of suspended sediment amount carried by a river is very important in terms of water pollution, channel navigability, reservoir filling, fish habitat, river aesthetics and scientific interests. This study investigates the feasibility of using turbidity as a surrogate for SSC as in situ turbidity meters are being increasingly used to generate continuous records of SSC in rivers. For this reason, regression analysis (RA) and artificial neural networks (ANNs) were employed to estimate SSC based on in situ turbidity measurements. The SSC was firstly experimentally determined for the surface water samples collected from the six monitoring stations along the main branch of the stream Harsit, Eastern Black Sea Basin, Turkey. There were 144 data for each variable obtained on a fortnightly basis during March 2009 and February 2010. In the ANN method, the used data for training, testing and validation sets are 108, 24 and 12 of total 144 data, respectively. As the results of analyses, the smallest mean absolute error (MAE) and root mean square error (RMSE) values for validation set were obtained from the ANN method with 11.40 and 17.87, respectively. However these were 19.12 and 25.09 for RA. It was concluded that turbidity could be a surrogate for SSC in the streams, and the ANNs method used for the estimation of SSC provided acceptable results.