Unraveling debris-flow erosion: experimentally assessing the effects of debris-flow composition on erosion

Understanding erosion and entrainment of material by debris flows is essential for modelling debris-flow volume growth and prediction of hazard potential. Recent advances have highlighted two driving forces behind debris flow erosion; impact and shear forces. How erosion and these forces depend on debris-flow composition and interact remains unclear. We experimentally investigated the effects of debris-flow composition and volume on erosion processes in a small-scale flume with a loosely packed bed. We quantified the effects of gravel, clay and solid fraction in the debris flow on bed erosion. Erosion increased linearly with gravel fraction and volume, and decreased with increasing solid fraction. Erosion was maximal around a volumetric clay fraction of 0.075 (fraction of the total solid volume). Under varying gravel fractions and flow volumes erosion was positively related to both impact and shear forces, while these forces themselves correlate. Results further show that the internal dynamics driving the debris flows, quantified by Bagnold and Savage numbers, correlate to erosional processes and quantity. Impact forces became increasingly important for bed erosion with increasing grain size. The experiments with varying clay and solid fractions showed that the abundance and viscosity of the interstitial fluid affect debris-flow dynamics, erosional mechanisms and erosion magnitude. High viscosity of the interstitial fluid inhibits the mobility of the debris flow, the movement of the individual grains, the transfer of momentum to the bed by impacts, and therefore inhibits erosion. High solid content possibly decreases the pore pressures in the debris flow and the transport capacity, inhibiting erosion, despite high shear stresses and impact forces. Our results show that bed erosion quantities and mechanisms may vary between debris flows with contrasting composition, and stress that entrainment models and volume-growth predictions may be substantially improved by including compositional effects.