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Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils

^a George E. Brown, Jr. Salinity Laboratory, USDA, ARS, 450 W. Big Springs Road, Riverside, CA 92507
^b Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences, Herman Ottó u. 15, H-1525 Budapest, Hungary, currently at USDA-ARS Hydrology and Remote Sensing Laboratory, USDA, ARS, 10300 Baltimore Ave., Bldg. 007, BARC-West, Beltsville, MD 20705

View larger version (43K): [in a new window]   Fig. 1. Textural distribution of the 2134 samples used to calibrate Models H1 through H5. The textural classes are given in Fig. 2.

View larger version (72K): [in a new window]   Fig. 2. Textural distribution of 47435 samples used to evaluate the pedotransfer functions selected for this study. Displayed is the number of samples at intervals of 1, 5, 10, 15, 20 samples per square percentage. S, sand; lS, loamy sand; sL, sandy loam; scL, sandy clay loam; sC, sandy clay; L, loam; siL, silty loam; Si, silt; sicL, silty clay loam; siC, silty clay; cL, clay loam; C, clay.

View larger version (39K): [in a new window]   Fig. 3. Mean errors for the H1 through H5 models in Rosetta (a) before and (b) after parameter transformation. The mean errors were computed for nine pressure head classes with boundaries at 0, 3, 10, 100, 300, 1000, 3000, 10000, and 30000 cm. The results were plotted at the midpoint of each pressure class (e.g., 1.5, 6.5, 20, 65 cm). For reference, the direct fit to the original retention data is also shown (FIT).