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Introduction. Adaptation and mitigation of climate change and its consequences through effective water resources management, among others, is essential for achieving sustainable development goals. The construction of water infrastructure facilities and the maintenance of existing ones which can be done with up-to-date hydrological information are required. Therefore, this study aims at obtaining unique information and choosing effective tools for stream flow simulations in Mereb-Gash river basin. Materials and methods. To this end, physically-based semi-distributed and conceptual models are investigated using climate reanalysis datasets and geomorphologic catchment characteristics. Prior to feeding model forcing variables, their statistical and spatial patterns, methods of potential evapotranspiration estimations, and basin drought conditions are studied. Two or more approaches have been employed for each of these tasks. Results. GIUH-Nash model has the potential to produce acceptable values in most cases irrespective of sources and resolutions of digital elevation models, but it is found to be sensitive to the type of algorithms selected for stream network generations. The continuous simulation models reveal considerable overestimation of most of the precipitation in the reanalysis datasets, which in turn has a significant effect on other variables, such as potential evapotranspiration and thereby leading to a substantial discrepancy between simulated and observed. Conclusions. Despite the fact that reanalysis dataset has a great advantage over ground-based observations in terms of their simplicity and accessibility, the research results have shown the need for a critical attitude to both sources of initial data. To minimize the uncertainties of mathematical models and thereby to improve modeling capabilities, there is no qualitative alternative to restoring existing as well as new ground-based observation stations.

Introduction. Surfacе runoff is of great importance for water resources formation processes in a river basin. The purpose of this study is to develop a user interface for a numerical solution to the problem of evaluating the runoff in the El-Asi (Orontes) River basin in Syria using artificial intelligence models. Materials and methods. The method of artificial neural networks was used to design the user interface. The task was solved in three stages: training, verification and testing. Several types of model algorithms were tested. Efficiency values were compared for different models using correlation coefficients and the mean root square error. Results. The authors have found that feed-forward artificial neural networks and back propagation artificial neural networks show the best result if used to make hydrological forecasts and simulate nonlinear functions; that’s why they were applied to the user interface. The developed software tool saves the user’s time and effort, because it generates a very large group of models pursuant to various parameters and functions. It selects the best model according to the effectiveness criteria (such as correlation coefficient R and mean root square error MRSE), and allows the performance of other operations, such as the graphical representation of output data, the structure of the model used or the onset of evaluation of runoff values. The software is developed in the operational environment of MATLAB. Conclusions. The software tool is simple and user friendly; it complies with the user-focused methodology, which is easily implemented from the moment the software, is launched through the sequence of activated interface commands. It is recommended to expand the use of artificial intelligence models for forecasting and evaluating elements of the hydrological cycle, especially in absence of source data.

This study demonstrates the ability to control the properties of TiO2–CuOx composite layers for photocatalytic applications by using a simple electrophoretic deposition method from isopropanol-based suspension. To obtain uniform layers with a controlled composition, the surfactant sodium lauryl sulfate was used, which influenced the electrophoretic mobility of the particles and the morphology of the deposited layers. The TiO2–CuOx composite layers with different CuOx contents (1.5, 5.5, and 11 wt.%) were obtained. It is shown that the optical band gap measured by UV–VIS–NIR diffuse reflectance spectra. When CuOx is added to TiO2, two absorption edges corresponding to TiO2 and CuOx are observed, indicating a broadening of the photosensitivity range of the material relative to pure TiO2. An open-circuit potential study shows that by changing the amount of CuOx in the composite material, one can control the ratio of free charge carriers (n and p) and, therefore, the catalytic properties of the material. As a result, the TiO2–CuOx composite layers have enhanced photocatalytic activity compared to the pure TiO2 layer: methanol yield grows with increasing CuOx content during CO2 photoreduction.