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Multiscale Pedotransfer Functions for Soil Water Retention

^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

View larger version (28K): [in this window] [in a new window]   FIG. 1. Study area showing the Rio Grande Basin in New Mexico and location of the city of Las Cruces (trench site).

View larger version (25K): [in this window] [in a new window]   FIG. 2. Validation study area in Oklahoma and Little Washita (LW) watershed data locations.

View larger version (19K): [in this window] [in a new window]   FIG. 3. Artificial neural network model: w represents the layer weights, b the bias, f is the transfer function, and is the output (water content). After Demuth et al. (2005).

View larger version (20K): [in this window] [in a new window]   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.

View larger version (15K): [in this window] [in a new window]   FIG. 5. Sample soil water retention curves: is the pressure head and 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 () values across the clustering levels.

View larger version (10K): [in this window] [in a new window]   FIG. 6. Average soil water retention curve for the Rio Grande region: is the pressure head and 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 () values across the clustering levels.

View larger version (6K): [in this window] [in a new window]   FIG. 7. Variation across clustering levels in estimated van Genuchten values averaged across 50 ground points.

View larger version (8K): [in this window] [in a new window]   FIG. 8. Variation across clustering levels in estimated van Genuchten n values averaged across 50 ground points.

View larger version (8K): [in this window] [in a new window]   FIG. 9. Variation across clustering levels in estimated saturated volumetric water content (s) values averaged across 50 ground points.

View larger version (9K): [in this window] [in a new window]   FIG. 10. Variation across clustering levels in estimated residual volumetric water content (r) values averaged across 50 ground points.

View larger version (7K): [in this window] [in a new window]   FIG. 11. Variation across clustering levels in estimated volumetric water content at field capacity (f) values averaged across 50 ground points.

View larger version (10K): [in this window] [in a new window]   FIG. 12. Average soil water retention curve for the Little Washita (LW) watershed region: is the pressure head; 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 () values across the 70 ground points.

View larger version (10K): [in this window] [in a new window]   FIG. 13. Average soil water retention curve for the Rio Grande region: is the pressure head; 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 () values across the 50 ground points.