A new method for disentangling the coupling effect of slaking and mechanical breakdown on aggregate stability: Validation on splash erosion

Aggregate breakdown strongly influences soil erosion process, during which slaking and mechanical breakdown are two dominant breakdown mechanisms. Despite of many methods on aggregate stability, there is a paucity of satisfactory one to disentangle how these two mechanisms interact in aggregate breakdown. Herein, on the basis of Le Bissonnais method (1996) including three treatments (i.e., fast wetting (FW), stirring after pre-wetting (WS), and slow wetting (SW)), a new treatment of water-stirring after fast wetting (SF) was proposed in order to evaluate the coupling effect of slaking and mechanical breakdown on aggregate stability. The coupling effect of slaking and mechanical breakdown was larger to a varying extent than their individual effects, and its difference with slaking showed a unimodal variation with increased aggregate stability. This new method can partition the unique and shared effects of these two breakdown mechanisms; the shared effect (0-77%) decreased logarithmically with increased aggregate stability; the unique effect of slaking (16-52%) showed a unimodal variation and much larger than that of mechanical breakdown (0-17%). As validated by previously published splash erosion data over a wide range of soils (including various soil types, land uses, textures and parent materials), the proposed aggregate stability indices in this new method showed better performance in predicting splash erosion compared to existing ones (e.g., RSIxRMI and RSI/RMI). Collectively, this new method facilitates a systematic understanding on aggregate breakdown mechanisms and exhibits great superiority in aggregate stability measurement and erosion prediction.

Automated summary

The coupling effect of slaking and mechanical breakdown on aggregate stability was evaluated using a new method. This method can partition the unique and shared effects of these two breakdown mechanisms and showed better performance in predicting soil erosion compared to existing methods.