Spatial distribution of annual and monthly rainfall erosivity in the Jaguarí River Basin

• Autor(a) principal: Pontes, Lucas Machado
• Data de Publicação: 2017
• Outros Autores: Silva, Marx Leandro Naves, Bispo, Diêgo Faustolo Alves, Avalos, Fabio Arnaldo Pomar, Oliveira, Marcelo Silva de, Rocha, Humberto Ribeiro da
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/32777
• topic: Geostatistics; Soil and water conservation; Erosion; Geoestatística; Conservação do solo e da água; Erosão

The Jaguarí River Basin forms the main water supply sources for the São Paulo Metropolitan Region and other cities in the state. Since the kinetic energy of rainfall is the driving force of water erosion, the main cause of land and water degradation, we tested the hypothesis of correlation between the erosive potential of rainfall (erosivity) and geographical coordinates and altitude for the purpose of predicting the spatial and temporal distribution of the rainfall erosivity index (EI30) in the basin. An equation was used to estimate the (EI30) in accordance with the average monthly and total annual rainfall at rainfall stations with data available for the study area. In the regression kriging technique, the deterministic part was modeled using multiple linear regression between the dependent variable (EI30) and environmental predictor variables: latitude, longitude, and altitude. From the result of equations and the maps generated, a direct correlation between erosivity and altitude could be observed. Erosivity has a markedly seasonal behavior in accordance with the rainy season from October to March. This season concentrates 86 % of the estimated EI30 values, with monthly maximum values of up to 2,342 MJ mm ha-1 h-1 month-1 between December and January, and minimum of 34 MJ mm ha-1 h-1 month-1 in August. The highest values were found in the Mantiqueira Range region (annual average of up to 12,000 MJ mm ha-1 h-1), a region that should be prioritized in soil and water conservation efforts. From this validation, good precision and accuracy of the model was observed for the long period of the annual average, which is the main factor used in soil loss prediction models.