| Registro completo |
| Provedor de dados: |
Anais da ABC (AABC)
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| País: |
Brazil
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| Título: |
Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
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| Autores: |
CECíLIO,ROBERTO A.
MOREIRA,MICHEL C.
PEZZOPANE,JOSé EDUARDO M.
PRUSKI,FERNANDO F.
FUKUNAGA,DANILO C.
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| Data: |
2013-01-01
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| Ano: |
2013
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| Palavras-chave: |
Interpolation
Rainfall generator
Soil conservation
Universal soil loss equation
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| 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>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>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE>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.
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| Tipo: |
Info:eu-repo/semantics/article
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| Idioma: |
Inglês
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| Identificador: |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523
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| Editor: |
Academia Brasileira de Ciências
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| Relação: |
10.1590/0001-3765201398012
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| Formato: |
text/html
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| Fonte: |
Anais da Academia Brasileira de Ciências v.85 n.4 2013
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| Direitos: |
info:eu-repo/semantics/openAccess
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