Application of GLUE method to estimate uncertainty of alpha and n parameters in soil moisture characteristic curve

Document Type : Complete scientific research article

Authors

1 Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman

2 Faculty member of Birjand University

3 Department of Water Engineering, Faculty of Agriculture, University of Birjand.

Abstract

Background and Objectives: The soil moisture characteristic curve (SMCC) is a key concept in the modeling process of physical and hydrological studies of soil that plays a critical role in soil and water management. At the same time, the accuracy of the models used to describe the SMCC is affected by the trend of its parameters changes. The uncertainty analysis of hydraulic parameters of SMCC plays an important role in the modeling process, determining the model input parameters and evaluating the performance of the models. Therefore, the objective of this study was to evaluate the application of the GLUE simulation method, which is based on Monte Carlo simulation method, to estimate the uncertainty of alpha and n variables with constant assumption of other SMCC parameters, in three models of vanGenuchten, vanGenuchten-Mualem and vanGenuchten-Burden.
Materials and Methods: Initially, two soil samples were taken from Shahid Bahonar University of Kerman field and the SMCC for both soil samples (sandy loam and silty clay loam textures) was plotted on the basis of all three models using the Pressure Plate data and RETC Software and their moisture curve parameters were derived. Then, using the GLUE method, the uncertainty of alpha and n parameters in all three models were investigated. In addition, based on GLUE performance, the intrinsic uncertainty of each of the three models was evaluated for each of soil textures.
Results: The posterior distribution for each of the studied hydraulic parameters were obtained for three models in each textural class. The 95% confidence interval of SMCC simulations was obtained for all three models in two texture classes as the main output of this study. To quantify the uncertainty of the models, four uncertainty assessment indices were calculated and evaluated. Based on the evaluation indices, the best models for silty clay loam and loamy sand were the vanGenuchten-Mualem model (PCI = 85.71, d-factor 0.2013, S = 0.079, T = 0.4642) and the vanGenuchten model, (PCI = 28.75, d-factor = 0.0766, S = 0.6453, T = 1.1034) respectively.
Conclusion: The results of the posterior distributions diagrams showed that the alpha and n hydraulic variables were less identifiable in the calibration process and could not determine the optimal range for them, therefore, these two variables play a major role in the uncertainty of soil moisture curve. Also the uncertainty analysis of all three models showed that GLUE method was able to estimate soil moisture curve points so that the moisture curve obtained by RETC software for all three models was within 95% confidence level. The presence of about 85% of the soil moisture curve points for silty clay loam texture within the 95% confidence interval indicates the high capability of the GLUE method.

Keywords

References

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Journal of Water and Soil Conservation
Volume 27, Issue 1
March and April 2020
Pages 197-211

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