Abstract

AbstractThe most applied pedotransfer functions (PTFs) often suffer from two main limitations: (a) the soil hydraulic models (SHMs) only account for capillary forces and/or the models show an unrealistic decrease near saturation for fine‐textured soils; (b) the observations of soil hydraulic properties (SHPs) used to generate the PTF generally do not cover very dry conditions. In this paper, we first present a simple method for predicting SHPs in the dry range from soil texture information. Together with measurements that cover only a relatively high matric potential range, the method yielded a good prediction of the complete SHPs from saturation to oven dryness. With this method, we extended a public dataset to cover dry conditions, and then applied it to develop a new PTF for a SHM that accounts for both capillary and adsorption forces and overcomes the unrealistic decrease near saturation for fine‐textured soils. A comparison with other PTF that was developed for the capillary‐based soil hydraulic model showed that the new PTF provided the most accurate predictions of SHPs. It reduced the root‐mean‐square‐error value from 0.055 to 0.045 cm3 cm−3 in predicting water content and from 0.84 to 0.66 log10 (cm d−1) in predicting hydraulic conductivity. We further applied this method to extend an existing capillary‐based PTF to dry conditions. The results showed an improved performance, with reported RMSE reduced from 0.058 (original) to 0.056 (extended) cm3 cm−3 and from 1.43 (original) to 1.20 (extended) log10 (cm d−1) for prediction of water content and hydraulic conductivity, respectively.

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