Estimation of saturated hydraulic conductivity of soil using soil retrieval parameters and regression modeling

Subject Areas : Water and Environment

Abdolfattah Salarashayeri ¹ , Ali Saremi ² , Maaroof Siosemarde ³ , Hossein Sedghi ⁴ , Hossein Babazadeh ⁵

1 - Department of Water Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Water Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Water Engineering, Mahabad Branch, Islamic Azad University, Mahabad, Iran.*(Corresponding Author)
4 - Department of Water Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
5 - Department of Water Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2021-03-07 Accepted : 2022-12-07 Published : 2023-06-22

Keywords: Granulation parameters, Regression, Modeling,

Abstract :

Background and Objective: Direct measurement of saturated hydraulic conductivity of soil is time consuming and costly and today this parameter can be estimated using soil retrieval parameters. Therefore, this study aimed to use regression modeling to estimate the saturated hydraulic conductivity of soil based on grain size parameters i.e. d10, d50, and d60.   Material and Methodology: First, 25 soil samples with sandy texture were randomly collected in the spring of 2017 from the agricultural lands of Saqez city and the samples were collected in a container and taken to the laboratory for analysis and hydraulic guidance using the Darcy’s law was calculated. Using the available data, univariate and multivariate regression relationships were fitted on the data and based on the model evaluation statistics, the relationship that had the best estimate of saturated hydraulic conductivity of soil was determined. Findings: The results of this study showed that the linear equation with 3 inputs saturated hydraulic conductivity of soil more accurately than the equations with 1 or 2 inputs. The results showed that the parameter d10 had a more effective role for estimating saturated hydraulic conductivity of soil than the parameters d50 and d60 and the effective parameter for comparison of saturated hydraulic conductivity is called d10. Discussion and Conclusion: The main purpose of this study was to provide models that can estimate the saturated hydraulic conductivity of soil with cost reduction and time savings with acceptable precision, and in summary, regression modeling can be used to estimate the saturated hydraulic conductivity of soil.

References:

1. Farahnak, M., Mitsuyasu, K., Jeong, S., Otsuki, K., Chiwa, M., Sadeghi, S. M. M., & Kume, A. (2019). Soil hydraulic conductivity differences between upslope and downslope of two coniferous trees on a hillslope. Journal of Forest Research, 24(3), 143-152.


2. Farahnak, M., Mitsuyasu, K., Hishi, T., Katayama, A., Chiwa, M., Jeong, S., Otsuki, K., Sadeghi, S.M.M., and Kume, A. 2020. Relationship between very fine root distribution and soil water content in pre-and post-harvest areas of two coniferous tree species. Forests, 11(11): 1227.


3. Bagarello, V., Di Prima, S., & Iovino, M. (2017). Estimating saturated soil hydraulic conductivity by the near steady-state phase of a Beerkan infiltration test. Geoderma, 303, 70-77.


4. Radinja, M., Vidmar, I., Atanasova, N., Mikoš, M., & Šraj, M. (2019). Determination of spatial and temporal variability of soil hydraulic conductivity for urban runoff modelling. Water, 11(5), 941.


5. Ghanbarian, B., Hunt, A. G., Skaggs, T. H., & Jarvis, N. (2017). Upscaling soil saturated hydraulic conductivity from pore throat characteristics. Advances in Water Resources, 104, 105-113.


6. Vogeler, I., Carrick, S., Cichota, R., & Lilburne, L. (2019). Estimation of soil subsurface hydraulic conductivity based on inverse modelling and soil morphology. Journal of Hydrology, 574, 373-382.


7. Jabro, J. D. (1992). Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. Transactions of the ASAE, 35(2), 557-560.


8. Ahmadi, A., Palizvan Zand, P., & Palivan Zand, H. (2018). Estimation of saturated hydraulic conductivity by using gene expression programming and ridge regression (A case study in East Azerbaijan province). Iranian Journal of Soil and Water Research, 48(5), 1087-1095.


9. Amirabedi H., Asghari Sh., Mesri Gandoshmin T., Balandeh N., & Johari E. (2019). Estimating the soil saturated hydraulic conductivity in Ardabil Plain soils using artificial neural networks and regression models. Applied Soil Research. 7(4), 124-136.


10. Azamathulla, H. M., & Jarrett, R. D. (2013) Use of gene-expression programming to estimate Manning’s roughness coefficient for high gradient streams. Water Resources Management, 27, 715-729.


11. Kozeny, J. (1927). Uber Kapillare Leitung Des Wassers in Boden. Sitzungsber Akad. Wiss.Wien Math.Naturwiss.Kl., Abt.2a, 136,271-306. (In German)


12. Sadeghi, S.M.M., Attarod, P., Van Stan II, J.T., Pypker, T.G., & Dunkerley, D. (2015). Efficiency of the reformulated Gash's interception model in semiarid afforestations. Agricultural and Forest Meteorology, Vol. 201, pp. 76-85.


13. Sadeghi, S. M. M., Van Stan II, J. T., Pypker, T. G., & Friesen, J. (2017). Canopy hydrometeorological dynamics across a chronosequence of a globally invasive species, Ailanthus altissima (Mill., tree of heaven). Agricultural and forest meteorology, 240, 10-17.


14. Nazari, M., Sadeghi, S. M. M., Van Stan II, J. T., & Chaichi, M. R. (2020a). Rainfall interception and redistribution by maize farmland in central Iran. Journal of Hydrology: Regional Studies, 27, 100656.


15. Nazari M., Chaichi M R., Kamel H., Grismer M., Sadeghi S M M. 2020b. Evaluation of estimation methods for monthly reference evapotranspiration in arid climates. Arid Ecosystems, 10, 329-336.


16. Freeze, R.A., and Cherry, J. A. 1979. Ground water. Prentice Hall Inc., Englewood Cliffs, New Jersey.


17. Wagner, B., Tarnawski, V. R., Hennings, V., Müller, U., Wessolek, G., and Plagge, R., 2001. Evaluation of pedo-transfer functions for unsaturated soil hydraulic conductivity using an independent data set. Geoderma, 102(3-4), 275-297.


18. Tian, Z., Kool, D., Ren, T., Horton, R., and Heitman, J. L., 2019. Approaches for estimating unsaturated soil hydraulic conductivities at various bulk densities with the extended Mualem-van Genuchten model. Journal of Hydrology, 572, 719-731.


19. Kashani, M. H., Ghorbani, M. A., Shahabi, M., Naganna, S. R., and Diop, L., 2020. Multiple AI model integration strategy—application to saturated hydraulic conductivity prediction from easily available soil properties. Soil and Tillage Research, 196, 104449.


20. Hassan Shah A., Lone M., Stephen I., & Anderson H. (1997). Regression model to predict hydraulic conductivity from simple soil physical and chemical properties. 7th ICID international drainage workshop. Malaysia.


21. Jabro J. A. (1992). Estimation of saturated hydraulic conductivity of soils from particle size distribution and bulk density data. Transactions of the ASAE, 35, 557-560.