Erosividade da chuva: sua distribuição e relação com as perdas de solo em Campinas (SP)

Neto, Francisco Lombardi; Moldenhauer, W. C.

doi:10.1590/s0006-87051992000200009

Cited by 106 publications

(68 citation statements)

References 3 publications

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“…The average temperature is 20.5°C (Setzer, 1966). The average annual erosivity is 6,738 mm ha -1 h -1 year -1 , with 62% of the annual erosion potential occurring from December to February (Lombardi & Moldenhauer, 1992). The history of the experimental area is described in Marques (1951), Marques et al (1961) and Tengberg et al (1997).…”

Section: Study Areamentioning

confidence: 99%

Perdas e custos associados à erosão hídrica em função de taxas de cobertura do solo

et al. 2015

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The aim of this study was to estimate water erosion costs associated with nutrient losses in four different rates of artificial cover. The experiment was carried out from 1987 to 1996 in runoff plots to measure soil losses in Campinas, State of Sao Paulo, in an Ferralsol under natural rainfall. The experimental design was completely randomized, with four treatments (0%, 24%, 40% and 90% artificial cover) and three replications. To estimate costs, P, K + , Ca 2+ and Mg 2+ concentrations in runoff water and soil removed by erosion were summed up, converted into triple superphosphate, potassium chloride and dolomitic limestone and multiplied by their respective market prices. The results indicate that, under the experimental conditions, the greater the percentage of soil cover, the lower the losses of water, soil, organic matter and nutrients. The soil with 90% cover presented reduction of 51.97% in average water losses, 54.44 in soil losses and 54.91% in organic matter losses than the treatment with 0% cover. In bare soil, losses were estimated at 16% P 2 O 5 and 8% KCl in terms of the amount of fertilizers recommended. Costs varied from US$ 107.76 ha -1 year -1 in bare soil (0% cover) to US$ 18.15 ha -1 year -1 in soil with 90% cover. With these values, losses were estimated at 616.5 million tons of soil per year for Brazil, as a result of soil erosion in annual crops, and cost of this process is around US$ 1.3 billion per year.

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Section: Study Areamentioning

confidence: 99%

Perdas e custos associados à erosão hídrica em função de taxas de cobertura do solo

et al. 2015

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“…Visando superar esta limitação, alguns autores propõem a utilização de equações empíricas, baseadas nas precipitações mensais e anuais, para estimar o valor de R (Val et al, 1986;Morais et al, 1991;Lombardi Neto & Moldenhauer, 1992;Rufino et al, 1993). Entretanto, mesmo com estas equações, a obtenção da erosividade da chuva fica restrita a localidades em que exista uma base de dados pluviométricos satisfatória.…”

Section: Introductionunclassified

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

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Cecílio

Pinto

et al. 2006

Rev. Bras. Ciênc. Solo

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RESUMOO conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

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“…However, due to the scarcity of adequate historical rainfall series, erosivity has been estimated by equations that relate the EI 30 to monthly rainfall values and annual rainfall (Oliveira et al, 2012). In this study, the equation 1, obtained for the region of Campinas, São Paulo (Lombardi Neto and Moldenhauer, 1992) Eq. 1 in which the EI 30 is the index that represents rainfall erosivity in MJ mm ha -1 h -1 ; p is the total monthly rainfall in mm; and P is the annual rainfall in mm.…”

Section: Estimation Of Erosivitymentioning

confidence: 99%

Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

Pontes

Silva

Bispo

et al. 2017

Rev. Bras. Ciênc. Solo

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ABSTRACT: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 (EI 30 ) in the basin. An equation was used to estimate the (EI 30 ) 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 (EI 30 ) 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. ), 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.

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Erosividade da chuva: sua distribuição e relação com as perdas de solo em Campinas (SP)

Cited by 106 publications

References 3 publications

Perdas e custos associados à erosão hídrica em função de taxas de cobertura do solo

Perdas e custos associados à erosão hídrica em função de taxas de cobertura do solo

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

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