Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

TLDR

Hydrologic analyses rely on soil water infiltration, conductivity, storage, and plant‑water relationships, but field or laboratory measurements are difficult, costly, and often impractical, so existing equations such as those by Saxton et al. serve as useful references. This study aims to develop new soil‑water characteristic equations based on the USDA soil database that use only readily available soil texture and organic matter to define hydrologic soil‑water effects. The authors derived statistical correlations from the USDA database linking texture and organic matter to soil‑water potential and hydraulic conductivity, expanded the equations with additional variables and ranges, integrated them with existing tension and conductivity relationships and factors such as density, gravel, and salinity, verified the model against independent datasets, and implemented the system in a user‑friendly graphical computer model.

Abstract

Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant‐water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda.gov/soilwater/Index.htm