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Published online 20 November 2007
Published in Vadose Zone J 6:868-878 (2007)
DOI: 10.2136/vzj2007.0055
© 2007 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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Multiscale Pedotransfer Functions for Soil Water Retention

Raghavendra B. Janaa, Binayak P. Mohantya,* and Everett P. Springerb

a Dep. of Biological and Agricultural Engineering, Texas A&M Univ., College Station, TX 77843
b Earth & Environmental Sciences Division, Los Alamos National Lab., Los Alamos, NM 87845


Figure 1
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FIG. 1. Study area showing the Rio Grande Basin in New Mexico and location of the city of Las Cruces (trench site).

 

Figure 2
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FIG. 2. Validation study area in Oklahoma and Little Washita (LW) watershed data locations.

 

Figure 3
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FIG. 3. Artificial neural network model: w represents the layer weights, b the bias, f is the transfer function, and {theta} is the output (water content). After Demuth et al. (2005).

 

Figure 4
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FIG. 4. Illustration of the effect of bias correction. Application of bias correction by linear regression brings the mean of predicted values closer to the mean of target values.

 

Figure 5
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FIG. 5. Sample soil water retention curves: {psi} is the pressure head and {theta} the water content; NN-Pred are the neural network predicted values. Values at each point are averaged across 10 clustering levels. Error bars show deviation in {theta}({psi}) values across the clustering levels.

 

Figure 6
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FIG. 6. Average soil water retention curve for the Rio Grande region: {psi} is the pressure head and {theta} the water content; NN-Pred are the neural network predicted values. Values are averaged across 10 clustering levels and across the 50 ground points. Error bars show deviation in {theta}({psi}) values across the clustering levels.

 

Figure 7
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FIG. 7. Variation across clustering levels in estimated van Genuchten {alpha} values averaged across 50 ground points.

 

Figure 8
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FIG. 8. Variation across clustering levels in estimated van Genuchten n values averaged across 50 ground points.

 

Figure 9
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FIG. 9. Variation across clustering levels in estimated saturated volumetric water content ({theta}s) values averaged across 50 ground points.

 

Figure 10
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FIG. 10. Variation across clustering levels in estimated residual volumetric water content ({theta}r) values averaged across 50 ground points.

 

Figure 11
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FIG. 11. Variation across clustering levels in estimated volumetric water content at field capacity ({theta}f) values averaged across 50 ground points.

 

Figure 12
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FIG. 12. Average soil water retention curve for the Little Washita (LW) watershed region: {psi} is the pressure head; {theta} is the soil water content; ANN-Pred are the artificial neural network predicted values before bias correction; BC are the bias-corrected values. Values are averaged across the 70 ground points. Error bars show deviation in {theta}({psi}) values across the 70 ground points.

 

Figure 13
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FIG. 13. Average soil water retention curve for the Rio Grande region: {psi} is the pressure head; {theta} is the soil water content; ANN-Pred are the artificial neural network predicted values before bias correction; BC are the bias-corrected values. Values are averaged across the 50 ground points. Error bars show deviation in {theta}({psi}) values across the 50 ground points.

 





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