A Robust Algorithm for Estimating Soil Erodibility in Different Climates. Borselli, L.; Torri, D.; Poesen, J.; and Iaquinta, P. 97:85–94.
A Robust Algorithm for Estimating Soil Erodibility in Different Climates [link]Paper  doi  abstract   bibtex   
The analysis of global soil erodibility data by Salvador Sanchis et al. (2008) showed that there is a significant climate effect on soil erodibility which allows for a split of the data into two subsets, one for prevailing cool conditions and another for prevailing warm conditions (defined using the Köppen climate classification). Despite the recognition of this new dichotomous variable, prediction of soil erodibility values remained very poor. This paper presents a new technique for dealing with such a variability by calculating probability density functions of soil erodibility K values when the user knows a set of textural parameters and the climatic classification of the site. Finally the user has the possibility to decide, on the basis of local knowledge, which K value to use. The procedure has been implemented in a freeware software named KUERY available for the scientific community. Finally, as an illustration, the methodology is applied to a catchment in south Italy. ⺠New technique predicting soil erodibility knowing texture and the climatic classification. ⺠Guessing probability distribution of K of similar soils in global soil erodibility database. ⺠Procedure implemented in a freeware software (KUERY), available for scientific community. ⺠Finally, an example of the methodology is applied to a catchment in south Italy.
@article{borselliRobustAlgorithmEstimating2012,
  title = {A Robust Algorithm for Estimating Soil Erodibility in Different Climates},
  author = {Borselli, L. and Torri, D. and Poesen, J. and Iaquinta, P.},
  date = {2012-10},
  journaltitle = {CATENA},
  volume = {97},
  pages = {85--94},
  issn = {0341-8162},
  doi = {10.1016/j.catena.2012.05.012},
  url = {https://doi.org/10.1016/j.catena.2012.05.012},
  abstract = {The analysis of global soil erodibility data by Salvador Sanchis et al. (2008) showed that there is a significant climate effect on soil erodibility which allows for a split of the data into two subsets, one for prevailing cool conditions and another for prevailing warm conditions (defined using the Köppen climate classification). Despite the recognition of this new dichotomous variable, prediction of soil erodibility values remained very poor. This paper presents a new technique for dealing with such a variability by calculating probability density functions of soil erodibility K values when the user knows a set of textural parameters and the climatic classification of the site. Finally the user has the possibility to decide, on the basis of local knowledge, which K value to use. The procedure has been implemented in a freeware software named KUERY available for the scientific community. Finally, as an illustration, the methodology is applied to a catchment in south Italy. ⺠New technique predicting soil erodibility knowing texture and the climatic classification. ⺠Guessing probability distribution of K of similar soils in global soil erodibility database. ⺠Procedure implemented in a freeware software (KUERY), available for scientific community. ⺠Finally, an example of the methodology is applied to a catchment in south Italy.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-10818217,climate,environmental-predictors,erodibility,soil-erosion,soil-resources}
}
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