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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 10 Be inventories and in situ 10 Be 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. [ABSTRACT FROM AUTHOR]

Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time‐consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi‐automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data. We selected a test area in the Dutch push‐moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques. [ABSTRACT FROM AUTHOR]

Purpose: Spatial and temporal patterns of past erosional events are a useful and needed information to explain observed soil patterns in different landscapes. Soil thickness reflects the overall expression of pedogenesis and erosion. Forested soils of Northern Germany exhibit varying soil thicknesses with thin soils on crest positions and buried soils at the footslope. The aim of this study is to reconstruct the complex soil mass redistribution and soil patterns of this forested area due to different periods of erosion and stability. Methods: We explored the explanatory power of both 10Be (in situ and meteoric) on a hillslope and we 14C-dated buried horizons at different depths. Results: The 10Be depth profiles did not show an exponential decrease with depth. They had a 'bulge' shape indicating clay translocation and interaction with oxyhydroxydes (meteoric 10Be), bioturbation and soil mass redistribution (in situ 10Be). The combined application of both 10Be and 14C dating revealed progressive and regressive phases of soil evolution. Although Melzower Forest is protected (same vegetation) since the past 250 years, both 10Be clearly indicated major soil mass redistribution along the investigated catena. Conclusion: A strong erosion impulse must have occurred between 4.5 and 6.8 kyr BP indicating an earlier human impact on soil erosion than previously postulated (~ 3 kyr earlier). Our findings correlate in fact with the first settlements reported for this region (~ 6.8 kyr BP) and show their immediate effect on soils. The overall soil redistribution rates in this forest are surprisingly similar to those obtained from a nearby agricultural area. [ABSTRACT FROM AUTHOR]