Data/hora: 12/06/2024 03:33:19

Provedor de dados: | 42
País: | Brazil
Título: | Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
Autores: | CECíLIO,ROBERTO A.; MOREIRA,MICHEL C.; PEZZOPANE,JOSé EDUARDO M.; PRUSKI,FERNANDO F.; FUKUNAGA,DANILO C.
Data: | 2013-01-01
Ano: | 2013
Palavras-chave: | Interpolation; Rainfall generator; Soil conservation; Universal soil loss equation.
Resumo: | The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE&gt;25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI30 and KE&gt;25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE&gt;25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523
Editor: | Academia Brasileira de Ciências
Relação: | 10.1590/0001-3765201398012
Formato: | text/html
Fonte: | Anais da Academia Brasileira de Ciências  v.85 n.4 2013
Fonte: | info:eu-repo/semantics/openAccess