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Performance of a Set of Soil Water Retention Models for Fitting Soil Water Retention Data Covering All Textural Classes

Author

Listed:
  • Ali Rasoulzadeh

    (Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Javad Bezaatpour

    (Environmental Engineering Research Center, Chemical Engineering Department, Sahand University of Technology, Sahand New Town, Tabriz 51335-1996, Iran)

  • Javanshir Azizi Mobaser

    (Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Jesús Fernández-Gálvez

    (Department of Regional Geographic Analysis and Physical Geography, University of Granada, 18016 Granada, Spain)

Abstract

A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a fundamental role in irrigation and drainage, soil conservation, as well as water and contaminant transport in the vadose zone. This study evaluates the accuracy, performance, and prediction capabilities of 15 SWR models. A total of 140 soil samples were collected from different sites, covering all textural classes. Standard suction tests, using both hanging column and ceramic pressure plate extractors, were conducted to compile the SWR databank. 15 SWR models were selected and fitted to the SWR data points. Soil texture, bulk density, and organic matter were used to determine their effect on the performance of the SWR models. The results indicate that the Tani and Russo models exhibit the lowest levels of accuracy and performance among the selected models. Based on the Akaike and Bayesian information criteria analysis, the van Genuchten model exhibits the lowest values among the selected models, with poor prediction capabilities in estimating the SWR curve. The significance test at the 0.05 level (95% confidence interval) shows that according to the calculated p -values for the Pearson correlation coefficient between RMSE and texture, the Brooks-Corey and van Genuchten models are poorly influenced by soil properties. The performance of the models is not significantly affected by the soil organic matter. Similarly, bulk density does not significantly affect model performance except for the Brooks–Corey, van Genuchten, Tani, and Russo models. Among the SWR models considered, the double exponential, Groenevelt and Grant, and Khlosi et al. models demonstrate superior accuracy and performance in predicting the SWR curve. This is supported by lower values for RMSE , Akaike, and Bayesian information criteria.

Suggested Citation

  • Ali Rasoulzadeh & Javad Bezaatpour & Javanshir Azizi Mobaser & Jesús Fernández-Gálvez, 2024. "Performance of a Set of Soil Water Retention Models for Fitting Soil Water Retention Data Covering All Textural Classes," Land, MDPI, vol. 13(4), pages 1-22, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:4:p:487-:d:1372635
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    References listed on IDEAS

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    1. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
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