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Estimating Generalized Soil‐water Characteristics from Texture
Author(s) -
Saxton K. E.,
Rawls W. J.,
Romberger J. S.,
Papendick R. I.
Publication year - 1986
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1986.03615995005000040039x
Subject(s) - soil texture , pedotransfer function , hydraulic conductivity , texture (cosmology) , soil science , soil water , range (aeronautics) , environmental science , multivariate statistics , calibration , hydrology (agriculture) , mathematics , geotechnical engineering , geology , statistics , computer science , engineering , artificial intelligence , image (mathematics) , aerospace engineering
Soil‐water potential and hydraulic conductivity relationships with soil‐water content are needed for many plant and soil‐water studies. Measurement of these relationships is costly, difficult, and often impractical. For many purposes, general estimates based on more readily available information such as soil texture are sufficient. Recent studies have developed statistical correlations between soil texture and selected soil potentials using a large data base, and also between selected soil textures and hydraulic conductivity. The objective of this study was to extend these results by providing mathematical equations for continuous estimates over broad ranges of soil texture, water potentials, and hydraulic conductivities. Results from the recent statistical analyses were used to calculate water potentials for a wide range of soil textures, then these were fit by multivariate analyses to provide continuous potential estimates for all inclusive textures. Similarly, equations were developed for unsaturated hydraulic conductivities for all inclusive textures. While the developed equations only represent a statistical estimate and only the textural influence, they provide quite useful estimates for many usual soil‐water cases. The equations provide excellent computational efficiency for model applications and the textures can be used as calibration parameters where field or laboratory soil water characteristic data are available. Predicted values were successfully compared with several independent measurements of soil‐water potential.