Translating deposition rates into erosion rates with landscape evolution modelling

Abstract

Abstract. Soil erosion is one of the main threats to agricultural food production due to the loss of fertile soil. Determination of erosion rates is essential to quantify the degree of land degradation, but it is inherently challenging to determine temporally dynamic erosion rates over agricultural time scales. Optically Stimulated Luminescence (OSL) dating can provide temporally-resolved deposition rates by determining the last moment of daylight exposure of buried colluvial deposits. However, these deposition rates may differ substantially from the actual hillslope erosion rates. In this study, OSL-based deposition rates were converted to hillslope erosion rates through inverse modelling with soil-landscape evolution model ChronoLorica. This model integrates geochronological tracers into the simulations of soil mixing and redistribution. The model was applied to a kettle hole catchment in north-eastern Germany, which has been affected by tillage erosion over the last 5000 years. The initial shape of the landscape and the land use history are well-constrained, enabling accurate simulations of the landscape evolution that incorporate uncertainties in the model inputs. The calibrated model reveals an increase in erosion rates of almost to orders of magnitude from pre-historic ard ploughing up to recent intensive land management. The simulated rates match well with independent age controls from the same catchment. Uncertainty in the reconstructed initial landscape and land use histories had a minor influence of 12–16 % on the simulated rates. The simulations showed that the deposition rates were on average 1.5 higher than the erosion rates due to the ratio of erosional and depositional area. Recent artificial drainage and land reclamation have increased deposition rates up to five times the erosion rates, emphasizing the need of cautious interpretation of deposition rates as erosion proxies. This study demonstrates the suitability of ChronoLorica for upscaling experimental geochronological data to better understand landscape evolution in agricultural settings.