Disaggregating soil erosion processes within an evolving experimental landscape

Soil‐mantled landscapes subjected to rainfall, runoff events, and downstream base level adjustments will erode and evolve in time and space. Yet the precise mechanisms for soil erosion also will vary, and such variations may not be adequately captured by soil erosion prediction technology. This study sought to monitor erosion processes within an experimental landscape filled with packed homogenous soil, which was exogenically forced by rainfall and base level adjustments, and to define the temporal and spatial variation of the erosion regimes. Close‐range photogrammetry and terrain analysis were employed as the primary methods to discriminate these erosion regimes. Results show that (1) four distinct erosion regimes can be identified (raindrop impact, sheet flow, rill, and gully), and these regimes conformed to an expected trajectory of landscape evolution; (2) as the landscape evolved, the erosion regimes varied in areal coverage and in relative contribution to total sediment efflux measured at the outlet of the catchment; and (3) the sheet flow and rill erosion regimes dominated the contributions to total soil loss. Disaggregating the soil erosion processes greatly facilitated identifying and mapping each regime in time and space. Such information has important implications for improving soil erosion prediction technology, for assessing landscape degradation by soil erosion, for mapping regions vulnerable to future erosion, and for mitigating soil losses and managing soil resources. Copyright © 2017 John Wiley & Sons, Ltd. An experimental landscape packed with soil was subjected to simulated rainfall and base level adjustments. The landscape was monitored using photogrammetry to identify four geomorphic regions of interest. Results show that the contribution of total soil loss from each regime could be determined in space and time, and that the rill and sheet flow regimes contributed the most sediment to the total load exiting the flume. Such information has important implications for improving soil erosion prediction technology.