Modeling of the Rainfall and R-Factor for Tocantins State, Brazil

Abstract

The state of Tocantins is inserted in the new Brazilian agricultural frontier and has shown enormous potential for expansion of the agricultural lands. However, there is a lack of more elaborate scientific information for better planning and guide agricultural activities, especially regarding the soil and water conservation. Tocantins has a relevant rainfall spatial variability and, consequently, rainfall erosivity. Thus, this work aimed to develop models to estimate the mean monthly and annual rainfall and means rainfall erosivity factor (R-factor) of the Universal Soil Loss Equation (USLE) for the state of Tocantins. For that, 97 historical series of daily rainfall were studied, considering a standard period of 25 years (1985-2009). The fitting of the models adjustment datasets consists of 86 rain-gauges, out of which, 11 were randomly selected and used solely for model validation. Afterward, maps of these variables were generated based on the regression-kriging procedure. The fitted models presented precision statistical that allow characterizing them as "good quality", with a coefficient of determination higher than 0.68 for rainfall models and 0.65 for the R-factor model, besides acceptable bias. Therefore, the application of the models can be successfully carried out, aiding the agricultural planning in the state of Tocantins.

Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil

“…This is in accordance with the results of Oğuz (2019) 2009) may be due to an embedded underestimation of the kinetic energy due to RUSLE methodology. Although there was still no test of the effectiveness of the modified Fournier index -until this study-, the MF method has been used for Brazilian conditions as a rainfall erosivity estimator in several parts of the country, as the states of Santa Catarina (Back et al, 2018), Espírito Santo (Moreira et al, 2020), Tocantins (Avanzi et al, 2019), Mato Grosso (Di Raimo et al, 2018), São Paulo (Lombardi Neto, 1977, and Amazonas (Silva et al, 2020), along with large areas for regional studies (Mello et al, 2013(Mello et al, , 2015Oliveira et al, 2013).…”