Quantitative analysis of soil erosion causative factors for susceptibility assessment in a complex watershed
Susceptibility analysis and mapping are prerequisites to sustainable land-use management and erosion prevention. Selection of appropriate erosion causative factors (CFs) is crucial in developing valid and accurate susceptibility models. However, existing literature lacks specific guidelines for its...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/78e5c53161914018b747fa2b2c8771b3 |
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| Sumario: | Susceptibility analysis and mapping are prerequisites to sustainable land-use management and erosion prevention. Selection of appropriate erosion causative factors (CFs) is crucial in developing valid and accurate susceptibility models. However, existing literature lacks specific guidelines for its selection. As such, some important dynamic CFs are often not considered in several previous studies. Thus, this study quantitatively evaluates the impacts of the addition of dynamic CFs to frequently used non-redundant static CFs in erosion susceptibility mapping using remote sensing, geographic information system (GIS) and statistical technique. Revised universal soil loss equation (RUSLE) was used to quantify soil loss and CFs’ maps for Cameron Highlands were developed in the GIS environment. The watershed was delineated, and the corresponding CFs were evaluated for each sub-watershed. The frequently used non-redundant CFs considered were lineament density, drainage density, soil erodibility, length-slope and normalized difference vegetation index. Hierarchical regression technique was adopted to evaluate the impacts of the addition of land surface temperature (LST), rainfall erosivity and soil moisture index (SMI). The results revealed that frequently used CFs accounted for 17.9% variation in soil loss. However, the successive inclusion of dynamic CFs such as LST, rainfall erosivity and SMI to the model further increased by 28.9%, 6.0% and 16.4%, respectively. This suggests that dynamic CFs, which often neglected in erosion susceptibility assessment could further increase modelling accuracy. |
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