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- W2999295510 abstract "Soil water retention curve (SWRC) and hydraulic conductivity curve (SHCC) are functions that contribute to the understanding and modeling of hydraulic processes in the vadose zone of the soil. However, their measurement is often difficult and expensive becoming impractical the large-scale monitoring. Pedotransfer functions (PTFs) are, therefore, an alternative to estimate SWRC and SHCC data. Most PTFs are usually calibrated with data from local soils and may be uncertain when applied to soils with different morphological properties. On the other hand, PTFs with a physico-empirical basis have as advantage their wide application. Splintex 1.0 is a physico-empirical model developed in BASIC language that is based on the particle size distribution and other basic soil information. This model estimates the parameters of the van Genuchten-Mualem equation that compose the SWRC without requiring prior calibration and the saturated hydraulic conductivity (Ks) using a texture-PTF. A second version with a user-friendly computational interface is presented to improve the estimates of the SWRC parameters and to introduce the estimation of the SHCC parameters with different PTFs, which are based on two physically-based models that can be applied universally. Computational procedures and equations of Splintex 2.0 were written in C++ language and the performance of both model versions was tested for different soil texture classes. The performance analysis was carried out using the Pearson correlation coefficient and the mean absolute and root mean square errors. Splintex 2.0 yielded good performance in the quantification of water retention data, showing its application to any soil class. As an advantage, the conductivity data can be estimated without the need of Ks and SWRC parameters." @default.
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- W2999295510 date "2020-02-01" @default.
- W2999295510 modified "2023-10-11" @default.
- W2999295510 title "Splintex 2.0: A physically-based model to estimate water retention and hydraulic conductivity parameters from soil physical data" @default.
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- W2999295510 doi "https://doi.org/10.1016/j.compag.2019.105157" @default.
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