Rainfall erosivity and erosivity density through rainfall synthetic series for Sao Paulo State, Brazil: Assessment, regionalization and modeling

Rainfall is the main cause of erosion of Brazilian soils, which makes assessing the rainfall erosivity factor (RE) and the erosivity density (ED) fundamental for soil and water conservation. Therefore, the objectives of this study were: i) to estimate the RE and ED for Sao Paulo State, Brazil, using synthetic series of pluviographic data; ii) to define homogeneous regions regarding rainfall erosivity; and iii) to generate regression models for rainfall erosivity estimates in each of the homogeneous regions. Synthetic series of pluviographic data were initially obtained on a sub-daily scale from the daily rainfall records of 696 rainfall gauges. The RE values were then estimated from the synthetic rainfall data, and ED was calcu-lated from the relationship between erosivity and rainfall amounts. Monthly and annual maps for RE and ED were obtained. Hierarchical clustering analysis was used to define homogeneous regions in terms of rainfall erosivity, and regionalized regression models for estimating RE were generated. The results demonstrate high spatial variability of RE in Sao Paulo, where the highest annual values were observed in the coastal region. December to March concentrate approximately 60% of the intra-annual erosivity. The highest values of annual ED were observed in regions with intense agricultural activity. The definition of five homogeneous regions concerning the rainfall erosive potential evidenced distinct seasonal patterns of the spatial distribution of erosivity. Finally, the high predictive accuracy of the regionalized models obtained characterizes them as essential tools for reliable estimates of rainfall erosivity, and contribute to better soil conservation planning. (c) 2021 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).