SPATIAL AND TEMPORAL PATTERNS OF ANTHROPOGENIC-DRIVEN EROSION CONTROLLED BY ROCK TYPE

Erosion is a frequent result of human activity in natural environments. Heterogeneities within a landscape, (e.g. varying lithology) influence the spatial patterns of erosion. Commonly used cosmogenic radionuclides (CRN) measure erosion over timescales too long (10⁵yr) for constraining newly triggered erosion. Spatially averaged CRN rates also obscure local differences in erosion caused by changes in rock weathering characteristics and the associated feedbacks between soil and vegetation. This study uses high-resolution topographic data to constrain lithologic-driven patterns of soil loss caused by recent anthropogenic disturbance.

Our project takes place in Pozo Catchment, located on the SW side of Santa Cruz Island (SCI), CA. Much of SCI, including Pozo, experienced high stocking levels of ungulates from the mid 1800’s to early 2000’s; resulting in landslides, gullying, and sheet soil loss during periods of intense rain. Qualitatively, the erosion severity across the island is correlated with lithology. In Pozo, the three main rock types – mudstone, conglomerate, and volcanic tuff/breccia – display varying degrees of soil coverage and incision unique to each lithology. Dates from 0.5 m thick fill deposits in Pozo valley shows grazing-induced erosion starting by 1890 and indicate erosion rates faster than the long-term rates derived from CRN catchment averages from SCI.

We quantify soil coverage and loss rates at a <1m spatial scale to uncover the role lithology plays in the landscape response. High density lidar point clouds are used to reconstruct a pre-grazing landscape for Pozo. Subtracting the modeled point cloud from the current, we estimate volumetric soil loss for sub-catchments underlain by the three main rock types. Sediment fluxes are calculated by dividing sediment loss volumes over the disturbance period. Deeply incised sub-catchments underlain by mudstone have sediment fluxes more than one order of magnitude higher than CRN-based erosion rates for SCI. These results are combined with a vegetation map derived from lidar return intensities to identify regions vulnerable to continued erosion; i.e. vegetated slopes proximal to bare, eroding areas. This work indicates the dramatic spatial and temporal patterns of recent erosion in Pozo are controlled by variation in underlying lithology.