Why Brooks corey and Van genuchten have different values for Ks (saturated conductivity), Theta s and Theta r for same soil. How to compare Van Genuchten and Brooks Corey for same soil eg. Sand? Should we use different values of Ks, Thera s and theta r? But then we get very different results. Results only match if I use a common value of Ks for both models.
Haider,
Just a few other considerations, building on Seyed Hamid Ahmadi’s answers. 1) VG and BC are empirical model’s (Fetter, 1999) and, therefore, will give you an approximation of the retention curve. In this case, they don’t need to be necessarily the same. 2) When comparing Ks, especially estimated values, don’t focus too much on specific numbers. Sometimes, the order of magnitude is more important. For example, Ks for sand soils can vary from 10^-5 to 10^-3 m/s (Hillel, 1998) 3) It seems you are using the textural classification (i.e. sand) to arrive at the estimates. I’d suggest you using the textural fractions (i.e. %Sand, %Silt, %Clay) in the neural network of Hydrus/RetC/Rosetta to refine your estimates. If you have the extra info (e.g. BD and SMC at FC and PWP, OM content), use those as well. The parameters can vary a lot for several soils broadly classified as “sand”. 4) The variables you mention are usually manipulated during model calibration; so, take those estimates as approximate values. Even field measurements of Ks can vary quite a lot within short distances. Residual saturation (Qr) is usually a fitted parameter anyways (Corey, 1994) and it is used to find the effective or relative saturation. I’d suggest you to have a look at the literature below for more info:
Fetter, C. W.: Contaminant Hydrogeology, Prentice Hall, 1999. (pp. 176-180)
Corey, A. T.: Mechanics of Immiscible Fluids in Porous Media, Water Resources Publications, 1994. (p.47, section 2.3.6)
Hillel, D.: Environmental Soil Physics: Fundamentals, Applications, and Environmental Considerations, Elsevier Science, London, UK, 1998. (p. 195)
Schaap, M.G. and W. Bouten. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32:3033-3040.
Schaap, M.G., Leij F.J. and van Genuchten M.Th. 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 62:847-855.
Schaap, M.G., and F.J. Leij, 1998. Database Related Accuracy and Uncertainty of Pedotransfer Functions, Soil Science 163:765-779.
Schaap, M.G., F.J. Leij and M. Th. van Genuchten. 1999. A bootstrap-neural network
approach to predict soil hydraulic parameters. In: van Genuchten, M.Th., F.J. Leij, and L. Wu (eds), Proc. Int. Workshop, Characterization and Measurements of the Hydraulic Properties of Unsaturated Porous Media, pp 1237-1250, University of California, Riverside, CA.
Schaap, M.G., F.J. Leij, 1999, Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten, Submitted to Soil Sci. Soc. Am. J.
Haidar,
There are some points for your notice. 1) Necessarily, the VG and BC should not lead to the same results due to inherent differences in the models. All these are models to predict the Ks based on some input parameters. 2) Either using the HYDRUS or RETC, you need to first calibrate the parameters of the soil physical characteristics. You might have some differences when you parameterize or calibrate the models. 3) This depends on you to decide to use which model, VG or BC, depending on the measured Ks or the measured/simulated soil water dynamics.
Haider,
Just a few other considerations, building on Seyed Hamid Ahmadi’s answers. 1) VG and BC are empirical model’s (Fetter, 1999) and, therefore, will give you an approximation of the retention curve. In this case, they don’t need to be necessarily the same. 2) When comparing Ks, especially estimated values, don’t focus too much on specific numbers. Sometimes, the order of magnitude is more important. For example, Ks for sand soils can vary from 10^-5 to 10^-3 m/s (Hillel, 1998) 3) It seems you are using the textural classification (i.e. sand) to arrive at the estimates. I’d suggest you using the textural fractions (i.e. %Sand, %Silt, %Clay) in the neural network of Hydrus/RetC/Rosetta to refine your estimates. If you have the extra info (e.g. BD and SMC at FC and PWP, OM content), use those as well. The parameters can vary a lot for several soils broadly classified as “sand”. 4) The variables you mention are usually manipulated during model calibration; so, take those estimates as approximate values. Even field measurements of Ks can vary quite a lot within short distances. Residual saturation (Qr) is usually a fitted parameter anyways (Corey, 1994) and it is used to find the effective or relative saturation. I’d suggest you to have a look at the literature below for more info:
Fetter, C. W.: Contaminant Hydrogeology, Prentice Hall, 1999. (pp. 176-180)
Corey, A. T.: Mechanics of Immiscible Fluids in Porous Media, Water Resources Publications, 1994. (p.47, section 2.3.6)
Hillel, D.: Environmental Soil Physics: Fundamentals, Applications, and Environmental Considerations, Elsevier Science, London, UK, 1998. (p. 195)
Schaap, M.G. and W. Bouten. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32:3033-3040.
Schaap, M.G., Leij F.J. and van Genuchten M.Th. 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 62:847-855.
Schaap, M.G., and F.J. Leij, 1998. Database Related Accuracy and Uncertainty of Pedotransfer Functions, Soil Science 163:765-779.
Schaap, M.G., F.J. Leij and M. Th. van Genuchten. 1999. A bootstrap-neural network
approach to predict soil hydraulic parameters. In: van Genuchten, M.Th., F.J. Leij, and L. Wu (eds), Proc. Int. Workshop, Characterization and Measurements of the Hydraulic Properties of Unsaturated Porous Media, pp 1237-1250, University of California, Riverside, CA.
Schaap, M.G., F.J. Leij, 1999, Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten, Submitted to Soil Sci. Soc. Am. J.
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