Understanding the effects of the asynchronous variability of biomass pools on erosion using a modelling approach

Abstract:

Erosion processes are responsible not only for the redistribution of sediments in the landscape but also soil carbon and nutrients, which affects agriculture and could lead to a decrease in crop productivity. Though the effect of vegetation on controlling erosion/deposition processes is well known, the dynamic nature of the various biomass pools is seldom included in erosion or Landform Evolution Models (LEMs). Here we use a new model that simulates the evolution of biomass pools over time and their effect on erosion. We compare the relative impact of biomass pools dynamics on various processes that drive erosion, including the effect of (1) root biomass on soil erodibility, (2) leaf cover pool on soil diffusivity (e.g. rain splash or creep), (3) litter pool on flow resistance, and (4) soil carbon pool on soil infiltration. The modelling framework consists of a coupled Landform Evolution Model (LEM) with dynamic biochemical vegetation, and includes modules to investigate the impact of biomass pools dynamics on erosion. Modelling results are presented for a watershed in Howard Springs (Northern Territory, Australia), that is a tropical open-forest savannah dominated by Eucalyptus with a grass understory. Our results show that the asynchronic dynamics of the various biomass pools (leaves, roots, litter and soil carbon) in this study site has important implications for soil protection and therefore erosion. We found that rainfall events at the beginning of the rainy season contribute heavily to erosion. Our results suggest that leaves and roots are critical factors for erosion control in Howard Springs, but not at the beginning of the wet season. At this time the effect of the litter, and to a lesser extent soil carbon (which increases infiltration) are more important for erosion control. Overall, this study highlights the importance of including biomass pools in order to understand and better predict erosion.