Mejoras en la estimación de la textura del suelo y su aplicación al factor K de erosionabilidaduna aproximación cuantílica

  1. Eva Corral Pazos de Provens
Supervised by:
  1. Juan Manuel Domingo Santos Director
  2. Igor Rapp Arrarás Director

Defence university: Universidad de Huelva

Year of defence: 2021

  1. Jaume Porta Casanellas Chair
  2. Gloria López Pantoja Secretary
  3. José Anastasio Fernández Yuste Committee member

Type: Thesis


In this work we perform an analysis of the main problems suffered for the calculation of the K factor of the USLE and a set of improvements is presented, both for the automation of its calculation and to avoid existing errors. One of the main problems is that ordinary soil tests do not provide a basic input to calcu late the K factor, such as the percentage of very fine sand (VFS). There are several models for estimating the fraction of VFS, which are analyzed in this work against the largest soil database worldwide. The acceptability of these models is found to be very low, and we propose, as an alternative, the use of the aforementioned database transferred to texture triangles that offer quartile prediction intervals. The problem posed by the disparity of particle size intervals according to the different existing textural classifications is also addressed. The application of pedotransfer formu las, such as calculating the K factor, is impeded when the textural data appears in a system other than the original of the formula. The most frequent case occurs with the particle size of the silt fraction, which in the USDA System covers the range of 0.002 to 0.05 mm, while in the Simplified International System it ranges from 0.002 to 0.02 mm. Applying a methodology similar to that of the VFS, we analyse the current models of conversion be tween these textural limits; one of these models offers broadly acceptable results; we also indicate the regions of the texture triangle where each model performs better. An alter native for estimating the USDA silt fraction based on a local quantile regression is also proposed. The use of the nomograph of Wischmeier et al. (1971) for the calculation of the K factor was very useful when the access to calculation machines was very limited; However, the massive calculation of this factor, for its mapping or other applications, requires analytical calculation procedures. The nomograph and its underlying equation have been analyzed, as well as the quality of the fit to the nomogram of the analytical models that try to cover those regions where the original equation was not applicable. It has been found that all the models have areas of poor or even unacceptable fit and that, based on the evidence analyzed, the drawing of the curves that adjusted the K factor as a function of the organic matter content is erroneous in the nomograph. Finally, a calculation model for the K factor is proposed with a good fit to the different parts of the nomograph and without the afore mentioned error, which we did not found either in the analytical approach by Wischmeier and Meyer (1973). We also make clear the regions of the texture triangle containing the soils tested by the creators of the USLE in order to give a lower predictive consideration to those regions in which the K factor calculations would be extrapolated. To round off this research, we propose a tool that facilitates obtaining the K factor for different situations of data availability.