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Soils and landscapes can show complex, non-linear evolution, especially under changing climate or land use. Soil-landscape evolution models (SLEMs) are increasingly equipped to simulate the development of soils and landscapes over long timescales under these changing drivers, but provide large data output that can be difficult to interpret and communicate. New tools are required to analyse and communicate large model output. In this work, I show how spatial and temporal trends in previously published model results can be summarized and conceptualized with evolutionary pathways, which are possible trajectories of the development of soil patterns. Simulated differences in rainfall and land use control progressive or regressive soil development and convergence or divergence of the soil pattern. These changes are illustrated with real-world examples of soil development and soil complexity. The use of evolutionary pathways for analysing the results of SLEMs is not limited to the examples in this paper, but they can be used on a wide variety of soil properties, soil pattern statistics and models. With that, evolutionary pathways provide a promising tool to analyse and communicate soil model output, not only for studying past changes in soils, but also for evaluating future spatial and temporal effects of soil management practices in the context of sustainability.

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.