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A robust algorithm for estimating soil erodibility in different climates
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DOI：10.1016/j.catena.2012.05.012.png)
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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 Kappen 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. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.
Keyword:
Soil erosion
Soil erodibility
Quantile regression
Probability density function
KUERY software
期刊
IF:
5.7
论文数:
9.5K
被引数:
3.8W
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引用论文
Recent advances in quantile regression models - A practical guideline for empirical research分位数回归模型的最新进展-实证研究的实用指南
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