Prediction of saturated hydraulic conductivity with pedotransfer functions for Sistan Plain soils

Document Type : Research Paper

Author

• parisa kahkhamoghadam

Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran

Abstract

Keywords

• Bulk density
• Organic matter
• Particle size distribution
• Porosity
• Sistan Plain

Main Subjects

• فیزیک خاک

Introduction

Accurate prediction of soil processes usually requires accurate measurements of soil hydraulic properties. Determination of the saturated hydraulic conductivity (K_sat) is required for many studies related to irrigation, drainage, water movement, and solute transport in soils. K_sat can be obtained through direct laboratory or field measurements, however, in many cases, its direct measurement is time-consuming and laborious. On the other hand, K_sat can be estimated based on readily available soil properties such as texture, particle size distribution, bulk density, porosity, and organic matter content. This alternative indirect method is called soil pedotransfer functions (PTF). PTFs are regression-based models that use available information to provide estimates of other relevant factors and have attracted the attention of soil researchers mainly due to their simplicity and relatively high efficiency. Therefore, the aim of this study is to investigate the performance of 23 PTFs using readily available soil parameters to predict K_sat in the Sistan Plain. In order to determine the best PTF that is appropriate for the conditions of the study area, 100 soil samples from the Sistan region were used.

Method

In order to conduct this research, first, the saturated hydraulic conductivity of the soil was measured using the inverted well method at 100 points in the Sistan Plain.

Twenty-three PTFs were classified into three groups based on their required inputs. Group 1: This group of PTFs is an empirical relationship between K_sat and effective porosity. Seven functions from this group were evaluated in this study. Group 2: This group of PTFs requires more inputs than Group 1. The functions in this group predict K_sat based on particle size distribution (percentage of sand, silt, and clay), bulk density, and total porosity. Eight functions from this group were evaluated in this study. Group 3: This group of PTFs requires input components including particle size distribution, bulk density, and organic matter content. Eight functions from this group were evaluated in this study. Finally, logarithmic transformations were used for statistical analysis.

Results

Among the group 1 functions, the F3 model, which is the function developed by Suleiman et al. (2001), has the closest predictions to the measured values (GMER=1.15), followed by the Ahuja et al. (1989) model, F7, with a GMER of 1.50. Among the functions in Group 2, the F11 function (Cosby et al., 1984) is the closest to the measured values, with a slight difference. The Ottoni et al. (2019) function (F15), which is one of the most recent functions presented by soil researchers in the world, ranked second. The Saxton et al. (1986) (F13) and Brakensiek et al. (1984) (F12) functions also ranked third and fourth, respectively. Among the functions in Group 3, the functions F23, F18 and F21 have the best performance in this group. The Zuo and He (2021) function (F23), which is one of the most recent functions presented by soil researchers in this group, provides the most accurate K_sat predictions for the soils under study. Finally, the performance of the twenty-three functions examined in this study was examined. The results indicated that the Cosby et al. (1984) function, although it uses only two components of sand and clay percentage, has the highest performance. With a slight difference, the functions of Zuo and He (2021), Ottoni et al. (2019), Wosten et al. (2001) and Saxton et al. (1986) and in the next rank, the functions of Brakensiek et al. (1984) and Suleiman et al. (2001) also provided the highest performance among the three groups.

Conclusions

In this study, twenty-three PTFs were evaluated for Ksat prediction in the Sistan region. The results of this study showed that some of the new functions presented in this field can also provide good performance compared to the basic functions in predicting Ksat. Given that factors such as soil structure have received less attention in the development of PTFs, the accuracy of PTFs varies for different regions and their performance is not affected by the number of their input components. The results presented in this study can be used to predict Ksat in the study area, using the soil transfer function technique as an input parameter in various modeling.

Author Contributions

Conceptualization, P.K.; methodology, P.K.; software, P.K.; validation, P.K.; formal analysis, P.K.; investigation, P.K.; resources, P.K.; data curation, P.K.; writing—original draft preparation, P.K.; writing review and editing, P.K; visualization, P.K; supervision, P.K; project administration, P.K; funding acquisition, P.K. Authors have read and agreed to the published version of the manuscript. 

Data Availability Statement

Data is available on reasonable request from the author.

Acknowledgements

The author wish to acknowledge the support from the University of Zabol, Iran for providing the financial support for this research project.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.