Detalhes bibliográficos
| Ano de defesa: |
2021 |
| Autor(a) principal: |
RAQUEL DE FARIA GODOI |
| Orientador(a): |
Dulce Buchala Bicca Rodrigues |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Fundação Universidade Federal de Mato Grosso do Sul
|
| Programa de Pós-Graduação: |
Não Informado pela instituição
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Link de acesso: |
https://repositorio.ufms.br/handle/123456789/3847
|
Resumo: |
Large-scale soil erosion modeling has a crucial role in the understanding and planning of soil and water conservation strategies. The lack of spatial data on soil characteristics required to compute the soil erodibility (K-factor) has been one of the greatest obstacles in Brazil. The K-factor is a complex property that expresses the susceptibility of soil to erode according to its inherent characteristics. This factor is a key input parameter for the most widely applied soil erosion models: the Universal Soil Loss Equation (USLE) and the Revised USLE (RUSLE). Here, we computed a high-resolution (250 m cell size) spatially explicit soil erodibility map across Brazil. To compute the K-factor, we applied the equations originally proposed in the USLE nomograph (USDA-Agriculture Handbook, 537, 1978) and EPIC (Journal of Soil and Water Conservation, 38, 381–383, 1983), using the following soil properties, organic matter content, soil texture, soil structure, and permeability. To qualitatively evaluate our new K-factor map, its values were compared against standard K-factor values obtained from experimental plots across Brazil. We find that the USLE nomograph leads to a more precise estimation of the K-factor in Brazil than EPIC. The K-factor estimates by the USLE nomograph ranges from 0.0002 to 0.0636 t ha h ha-1 MJ-1 mm-1, with a mean value of 0.0181 t ha h ha-1 MJ-1 mm-1. Our findings pave the way for a better understanding of soil erosion across multiple scales and thereby contributing to better land-use planning and management in Brazil. The dataset is freely available at https://doi.org/10.5281/zenodo.4279869 |
