The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff volume for a given rainfall event. The applicability of the SCS-CN method and the direct runoff generation mechanism were thoroughly analysed in a Mediterranean experimental watershed in Greece. The region is characterized by a Mediterranean semi-arid climate. A detailed land cover and soil survey using remote sensing and GIS techniques, showed that the watershed is dominated by coarse soils with high hydraulic conductivities, whereas a smaller part is covered with medium textured soils and impervious surfaces. The analysis indicated that the SCS-CN method fails to predict runoff for the storm events studied, and that there is a strong correlation between the CN values obtained from measured runoff and the rainfall depth. The hypothesis that this correlation could be attributed to the existence of an impermeable part in a very permeable watershed was examined in depth, by developing a numerical simulation water flow model for predicting surface runoff generated from each of the three soil types of the watershed. Numerical runs were performed using the HYDRUS-1D code. The results support the validity of this hypothesis for most of the events examined where the linear runoff formula provides better results than the SCS-CN method. The runoff coefficient of this formula can be taken equal to the percentage of the impervious area. However, the linear formula should be applied with caution in case of extreme events with very high rainfall intensities. In this case, the medium textured soils may significantly contribute to the total runoff and the linear formula may significantly underestimate the runoff produced.

The present study investigates the effect of three different methods of obtaining 1:1 and 1:5 soil-over-water mass ratios (soil:water) extracts for soil electrical conductivity (EC) measurements (EC_1:1, EC_1:5). On the same soil samples, also the electrical conductivity of the saturated paste extract (EC_e) was determined and the relationships between EC_e and each of the three of EC_1:1 and EC_1:5 values were examined. The soil samples used were collected from three areas over Greece (Laconia, Argolida and Kos) and had EC_e values ranging from 0.611 to 25.9 dS m⁻¹. From the results, it was shown that for soils with EC_e < 3 dS m⁻¹ the higher EC values were obtained by the method where the suspension remained at rest for 23 hours and then shaken mechanically for 1 h. On the contrary, no differences were observed among the three methods for soils with EC_e > 3 dS m⁻¹. Also, in the case of EC_1:5, the optimal times for equilibration were much longer when EC_e < 3 dS m⁻¹. Across all soils, the relationships between EC_e and each of three methods of obtaining EC_1:1 and EC_1:5 were strongly linear (0.953 < R² < 0.991 and 0.63 < RMSE < 1.27 dS m⁻¹). Taking into account the threshold of EC_e = 3 dS m⁻¹, different EC_e = f(EC_1:5) linear relationships were obtained. Although the linear model gave high values of R² and RMSE for EC_e < 3 dS m⁻¹, the quadratic model resulted in better R² and RMSE values for all methods examined. Correspondingly, in the 1:1 method, two of the three methods used exhibited similar slope values of the linear relationships independent of EC_e value (EC_e < 3 or EC_e > 3 dS m⁻¹), while one method (23 h rest and then shaken mechanically for 1 hour) showed significant differences in the slopes of the linear relationships between the two ranges of EC_e.

The electrical conductivity of soil saturated paste extract (EC_e) is used as an indicator for estimating soil salinity. This method is time consuming and laborious and therefore, easier and faster methods are usually used with different soil/water ratios, such as 1:5 (EC_1:5), for estimating the EC_e. Usually, the relationship between EC_e and EC_1:5 is described by a simple linear empirical equation. The value of the conversion factor (CF) of EC_1:5 to EC_e is affected by the particular characteristics of the soil, such as its texture. The objective of this study is to investigate models that allow the inclusion of soil texture in the calculation of the CF, in order to improve the prediction of the EC_e. A total of 148 soil samples with different soil texture and salinity levels were selected from three regions of Greece, and EC_e, EC_1:5, as well as clay and sand contents were determined. The results show that the CF can be estimated from an equation which incorporates the clay and sand contents through the soil saturation percentage (SP) and can give a fairly good prediction of EC_e from EC_1:5 (R² = 0.9887 and RMSE = 1.39 dSm⁻¹).