Hydraulic conductivity is an essential base for applied research in soil and water management, landscape, and environmental disciplines. Saturated hydraulic conductivity (Ksat) is one of the most important soil physical properties, which is considered in the planning of irrigation and drainage and predicting other soil hydrological processes. However, it has been frequently reported that measurement of Ksat is laborious, time-consuming, and expensive due to its high spatial variability and this has motivated researchers to develop indirect methods such as pedotransfer functions (PTFs) for developing Ksat-database in regional and national scales. In this study, eight Ksat studies with the PTFs in Anatolian soils were reviewed. PTFs were evaluated regarding their type, predictors used, and their performance. The majority of studied PTFs were developed on alluvial, colluvial, and alkaline soils in semi-arid and semi-humid climates. Multiple linear regression (MLR) and artificial neural networks (ANNs) have been common PTFs, and soil texture, bulk density, organic matter content, and pH have been common predictors used with these PTFs. Root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were the commonly used criteria in the verification and validation of the PTFs. Studies on the use of Ksat and PTFs are inadequate, and researches are still needed to be able to use it nationwide and can develop an adequate database. According to the results of PTF studies, the highest R2 and correlation coefficient (r) values belong to the Rosetta and MLR types of the PTFs, respectively. The lowest RMSE value was obtained with the equations in which the physical and chemical soil properties were used together as input data for PTFs. In addition, it has been noted that the soil morphological properties should be used as input data in PTFs studies, especially in Ksat estimation.
Predicting saturated hydraulic conductivity using soil morphological properties