Erosive rainfall in Rio do Peixe Valley in Santa Catarina, Brazil: Part I - Determination of the erosivity index

Back, Álvaro José; Pola, Augusto Carlos; Ladwig, Nilzo Ivo; Schwalm, Hugo

doi:10.1590/1807-1929/agriambi.v21n11p774-779

Cited by 2 publications

(1 citation statement)

References 14 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Determination of the EI30 using rainfall intensity data is laborious because extensive and consistent data series are necessary to determine the kinetic energy and the maximum intensity of the rainfall events. Since there is a lack of data from self-recording rain gauges, mainly at the national level, daily rainfall data from non-recording rain gauges have been used to estimate the EI30 (Santos and Montenegro;2012;Back et al, 2017). Thus, it is possible to correlate the EI30 with the Fournier's index, established as the rainfall coefficient (Rc), which represents the relationship between the square of monthly average rainfall and annual average rainfall (Equation 2).…”

Section: Erosivity Factor (R)mentioning

confidence: 99%

Soil loss estimated by means of the RUSLE model in a subtropical climate watershed

Zanchin¹,

Moura²,

Nunes³

et al. 2021

Revista Brasileira De Ciência Do Solo

View full text Add to dashboard Cite

Erosion process occurs naturally, shaping the Earth's surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.

show abstract

Section: Erosivity Factor (R)mentioning

confidence: 99%