Surface water resources management relies on the river flow in the region which, in turn, depends on numerous factors, resulting in the complexity of predicting the runoff. In this study, data-driven methods have been used to identify the relation between the river flow and regional climatic parameters and the teleconnection indexes. To achieve this, three nonlinear models of artificial neural networks, namely, generalized feedforward neural networks (GFNNs), Jordan–Elman network (JEN), and k-nearest neighbor (KNN), have been used to model monthly flow in a period of 30 years. The sensitivity analysis of input data was done using gamma test, and upon determination of the effective input parameters, modeling was done in four scenarios. The results reveal that among data-driven models, Jordan–Elman neural networks, compared with the other two models, show higher capabilities. On average, the JEN model, in comparison with the KNN and GFNN models, shows 23.4 and 23.04% less errors, respectively. Applying climatic parameters with remote sources, for instance, North Atlantic Oscillation and East Pacific/North Pacific, can enhance the efficiency of GFNN and JEN models.

Physical and hydraulic properties of soil are variable at different spatial scales. This indicates the necessity of understanding spatial patterns of soil properties. Scaling analysis, such as multifractal analysis, has been used to determine the spatial variability of soil properties. There are however limited numbers of studies concerning the applications of multifractal techniques applied to characterise spatial variability of soil properties in arid lands. The objective of this study was to quantify the scaling patterns of soil properties measured across a transect and to apply multifractal analysis in arid land areas. A transect with a length of 4.80 km was selected, and soil properties were measured at 0–20 cm depth every 145 m along the transect. The soil properties analysed were: texture (sand, silt, clay), pH, electrical conductivity (EC), bulk density (BD), soil hydraulic properties (saturated hydraulic conductivity Ks and the van Genuchten soil water-retention equation’s parameters nv and av), saturated water content (?s), and the slope of the soil water-retention curve at its inflection point (S). Results showed that the variability of pH and BD was characterised by quasi-monofractal behaviour. Results showed that soil hydraulic properties such as Ks, an, nv, S, and ?s were characterised by higher multifractal indices in the transects. EC showed the highest tendency to a multifractal type of scaling or the higher degree of multifractality.

Investigating the behavior of water surface evaporation is one of the basic issues in design, operation, and studies related to water engineering. Therefore, the application of new methods such as chaos theory in hydrology and water resources has recently been considered due to its innovation and capabilities. Since the fluctuations ...