ChronoLorica: introduction of a soil–landscape evolution model combined with geochronometers

Abstract

Abstract. Understanding long-term soil and landscape evolution can help us understand the threats to current-day soils, landscapes and their functions. The temporal evolution of soils and landscapes can be studied using geochronometers, such as optically stimulated luminescence (OSL) particle ages or radionuclide inventories. Also, soil–landscape evolution models (SLEMs) can be used to study the spatial and temporal evolution of soils and landscapes through numerical modelling of the processes responsible for the evolution. SLEMs and geochronometers have been combined in the past, but often these couplings focus on a single geochronometer, are designed for specific idealized landscape positions, or do not consider multiple transport processes or post-depositional mixing processes that can disturb the geochronometers in sedimentary archives. We present ChronoLorica, a coupling of the soil–landscape evolution model Lorica with a geochronological module. The module traces spatiotemporal patterns of particle ages, analogous to OSL ages, and radionuclide inventories during the simulations of soil and landscape evolution. The geochronological module opens rich possibilities for data-based calibration of simulated model processes, which include natural processes, such as bioturbation and soil creep, as well as anthropogenic processes, such as tillage. Moreover, ChronoLorica can be applied to transient landscapes that are subject to complex, non-linear boundary conditions, such as land use intensification, and processes of post-depositional disturbance which often result in complex geo-archives. In this contribution, we illustrate the model functionality and applicability by simulating soil and landscape evolution along a two-dimensional hillslope. We show how the model simulates the development of the following three geochronometers: OSL particle ages, meteoric 10Be inventories and in situ 10Be inventories. The results are compared with field observations from comparable landscapes. We also discuss the limitations of the model and highlight its potential applications in pedogenical, geomorphological or geological studies.