Willem Marijn van der Meij

Soils exhibit various development trends due to differences in initial conditions and external driving forces, leading to spatial and temporal variation in their properties. This study integrates digital soil mapping (DSM) with a modified evolutionary pathway approach (Δcomplexity) to quantify horizontal and vertical soil heterogeneity across an agro-pastoral transitional zone (APTZ) in northern China. We focused on particle size distribution (PSD) and magnetic susceptibility (MS), and quantified Δcomplexity between different soil depths to provide valuable insights into soil-landscape evolution. This study measured the PSD and MS of 1317 samples and determined Δcomplexity for 148 catchments in the study area. The R² for the prediction accuracy of DSM ranged between 0.481 and 0.729. The Δcomplexity of PSD spans from -0.089 to 0.042 % cm^-1 for both deep and shallow layers, whereas that of MS ranges from -0.6 to 0.96 10^-8 m³ kg^-1 cm^-1. Vertical heterogeneity, which is determined by the positive and negative shifts in Δcomplexity in every 5 cm soil, is primarily associated with soil forming factors such as parent material, topography and vegetation, and it is further regulated by tree throw and human activities. For horizontal heterogeneity, which derived from pedogenesis and also soil redistribution induced by both water and wind erosion, is determined by the positive and negative variations in both shallow and deep complexity. Our approach enables the identification of highly heterogeneous areas that may be particularly sensitive to soil change and degradation. It offers a transferable framework for assessing soil development and guiding sustainable land use. The soil Δcomplexity metric provides a promising tool for analyzing and visualizing the state and trends of soil-landscape evolution, and provides a scientific basis for informing land management decisions.

Soils exhibit various development trends due to differences in initial conditions and external driving forces, leading to spatial and temporal variation in their properties. This study integrates digital soil mapping (DSM) with a modified evolutionary pathway approach (Δcomplexity) to quantify horizontal and vertical soil heterogeneity across an agro-pastoral transitional zone (APTZ) in northern China. We focused on particle size distribution (PSD) and magnetic susceptibility (MS), and quantified Δcomplexity between different soil depths to provide valuable insights into soil-landscape evolution. This study measured the PSD and MS of 1317 samples and determined Δcomplexity for 148 catchments in the study area. The R² for the prediction accuracy of DSM ranged between 0.481 and 0.729. The Δcomplexity of PSD spans from -0.089 to 0.042 % cm^-1 for both deep and shallow layers, whereas that of MS ranges from -0.6 to 0.96 10^-8 m³ kg^-1 cm^-1. Vertical heterogeneity, which is determined by the positive and negative shifts in Δcomplexity in every 5 cm soil, is primarily associated with soil forming factors such as parent material, topography and vegetation, and it is further regulated by tree throw and human activities. For horizontal heterogeneity, which derived from pedogenesis and also soil redistribution induced by both water and wind erosion, is determined by the positive and negative variations in both shallow and deep complexity. Our approach enables the identification of highly heterogeneous areas that may be particularly sensitive to soil change and degradation. It offers a transferable framework for assessing soil development and guiding sustainable land use. The soil Δcomplexity metric provides a promising tool for analyzing and visualizing the state and trends of soil-landscape evolution, and provides a scientific basis for informing land management decisions.

Soils exhibit various development trends due to differences in initial conditions and external driving forces, leading to spatial and temporal variation in their properties. This study integrates digital soil mapping (DSM) with a modified evolutionary pathway approach (Δcomplexity) to quantify horizontal and vertical soil heterogeneity across an agro-pastoral transitional zone (APTZ) in northern China. We focused on particle size distribution (PSD) and magnetic susceptibility (MS), and quantified Δcomplexity between different soil depths to provide valuable insights into soil-landscape evolution. This study measured the PSD and MS of 1317 samples and determined Δcomplexity for 148 catchments in the study area. The R² for the prediction accuracy of DSM ranged between 0.481 and 0.729. The Δcomplexity of PSD spans from -0.089 to 0.042 % cm^-1 for both deep and shallow layers, whereas that of MS ranges from -0.6 to 0.96 10^-8 m³ kg^-1 cm^-1. Vertical heterogeneity, which is determined by the positive and negative shifts in Δcomplexity in every 5 cm soil, is primarily associated with soil forming factors such as parent material, topography and vegetation, and it is further regulated by tree throw and human activities. For horizontal heterogeneity, which derived from pedogenesis and also soil redistribution induced by both water and wind erosion, is determined by the positive and negative variations in both shallow and deep complexity. Our approach enables the identification of highly heterogeneous areas that may be particularly sensitive to soil change and degradation. It offers a transferable framework for assessing soil development and guiding sustainable land use. The soil Δcomplexity metric provides a promising tool for analyzing and visualizing the state and trends of soil-landscape evolution, and provides a scientific basis for informing land management decisions.

Soils exhibit various development trends due to differences in initial conditions and external driving forces, leading to spatial and temporal variation in their properties. This study integrates digital soil mapping (DSM) with a modified evolutionary pathway approach (Δcomplexity) to quantify horizontal and vertical soil heterogeneity across an agro-pastoral transitional zone (APTZ) in northern China. We focused on particle size distribution (PSD) and magnetic susceptibility (MS), and quantified Δcomplexity between different soil depths to provide valuable insights into soil-landscape evolution. This study measured the PSD and MS of 1317 samples and determined Δcomplexity for 148 catchments in the study area. The R² for the prediction accuracy of DSM ranged between 0.481 and 0.729. The Δcomplexity of PSD spans from -0.089 to 0.042 % cm^-1 for both deep and shallow layers, whereas that of MS ranges from -0.6 to 0.96 10^-8 m³ kg^-1 cm^-1. Vertical heterogeneity, which is determined by the positive and negative shifts in Δcomplexity in every 5 cm soil, is primarily associated with soil forming factors such as parent material, topography and vegetation, and it is further regulated by tree throw and human activities. For horizontal heterogeneity, which derived from pedogenesis and also soil redistribution induced by both water and wind erosion, is determined by the positive and negative variations in both shallow and deep complexity. Our approach enables the identification of highly heterogeneous areas that may be particularly sensitive to soil change and degradation. It offers a transferable framework for assessing soil development and guiding sustainable land use. The soil Δcomplexity metric provides a promising tool for analyzing and visualizing the state and trends of soil-landscape evolution, and provides a scientific basis for informing land management decisions.

This paper introduces a modular research data framework designed for geoscientific research across disciplinary boundaries. It is specifically designed to support small research projects, providing a bottom-up solution that empowers individual teams that need to adhere to strict data management requirements from funding bodies, but often lack the financial and human resources to do so. The framework supports the transformation of raw research data into scientific knowledge. It addresses critical challenges, such as the rapid increase in the volume, variety and complexity of geoscientific datasets, data heterogeneity, spatial complexity, and the need to comply with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The framework uses a dual-component architecture. First, an Online Transaction Processing (OLTP) system features a user interface and a persistent relational database, ensuring accurate and consistent data storage when capturing and managing diverse geoscientific research data. Complementing this, an orchestration layer manages automated data pipelines to process the stored data and generate dynamic in-memory Online Analytical Processing (OLAP) databases that allow flexible, high-performance analysis. It is adaptable to evolving research requirements and supports various data types and methodological approaches, such as machine learning and deep learning, that place high demands on the data and their formats. A case study in Western Romania demonstrates the application of the data framework in an interdisciplinary geoarchaeological research project by processing and storing heterogeneous datasets, thereby reducing data management efforts, improving findability, replicability, and reproducibility, and streamlining the integration of high-dimensional data for small, interdisciplinary teams.

Soils exhibit various development trends due to differences in initial conditions and external driving forces, leading to spatial and temporal variation in their properties. This study integrates digital soil mapping (DSM) with a modified evolutionary pathway approach (Δcomplexity) to quantify horizontal and vertical soil heterogeneity across an agro-pastoral transitional zone (APTZ) in northern China. We focused on particle size distribution (PSD) and magnetic susceptibility (MS), and quantified Δcomplexity between different soil depths to provide valuable insights into soil-landscape evolution. This study measured the PSD and MS of 1317 samples and determined Δcomplexity for 148 catchments in the study area. The R² for the prediction accuracy of DSM ranged between 0.481 and 0.729. The Δcomplexity of PSD spans from -0.089 to 0.042 % cm^-1 for both deep and shallow layers, whereas that of MS ranges from -0.6 to 0.96 10^-8 m³ kg^-1 cm^-1. Vertical heterogeneity, which is determined by the positive and negative shifts in Δcomplexity in every 5 cm soil, is primarily associated with soil forming factors such as parent material, topography and vegetation, and it is further regulated by tree throw and human activities. For horizontal heterogeneity, which derived from pedogenesis and also soil redistribution induced by both water and wind erosion, is determined by the positive and negative variations in both shallow and deep complexity. Our approach enables the identification of highly heterogeneous areas that may be particularly sensitive to soil change and degradation. It offers a transferable framework for assessing soil development and guiding sustainable land use. The soil Δcomplexity metric provides a promising tool for analyzing and visualizing the state and trends of soil-landscape evolution, and provides a scientific basis for informing land management decisions.

Carbon redistribution along with accelerated erosion in agricultural landscapes is an important component of the global carbon cycle. However, it remains largely untested whether soil particles and soil organic carbon (SOC) are always preserved synchronously across hillslopes, and what mechanisms govern potential decoupling. In hilly Northeastern China, carbon-rich black soils have experienced only recently severe agriculture-induced erosion over the past ~130 years, providing an ideal setting to examine sediment and carbon behaviour over the Holocene and to assess how accelerated anthropogenic erosion influences long-term carbon dynamics. In this study, we investigated single-grain post-infrared infrared stimulated luminescence (pIRIR) signals of feldspar along two black soil catenae with contrasting slopes. 31 luminescence samples collected from five soil profiles along a catena with a slope gradient of 0.37°, 19 luminescence samples from three soil profiles along a catena with a slope gradient of 1.91°. Geochronological constrains from luminescence and 137Cs were combined with soil properties to trace soil redistribution and reconstruct Holocene erosion phases along the catenae. We identified a clear topographic control on whether sediment and carbon are redistributed synchronously. On gentle slopes, colluvial deposits formed primarily at backslope positions, yet no buried carbon horizons were preserved. This decoupling results from prolonged soil residence times for sufficient bioturbation , as indicated by abundant krotovina and distinct equivalent dose (De) distributions between krotovina and non-krotovina samples from same depth (~80 cm) in the summit profile. In contrast, steeper slopes favour rapid deposition and carbon burial at toeslope position due to higher erosion relative to soil mixing. Overall, our findings demonstrate that topography and post-depositional bioturbation jointly determine whether sediment flux translates into long-term carbon preservation. Gentle slopes promote redistribution without carbon burial, while steeper slopes facilitate synchronous sediment and carbon accumulation. This contrast is further amplified by anthropogenic erosion, leading to situations in which sediment transported is enhanced while carbon burial lags behind. These results highlight the critical role of landscape configuration and biological processes in mediating carbon fate in cultivated soils and emphasize the need to account for such controls when quantify carbon dynamic under human-induced erosion.

Roads in Sub-Saharan Africa provide essential transportation functions; however, they often cause adverse environmental impacts, such as enhanced erosion, that are frequently underestimated or overlooked during planning and implementation. Global digital soil mapping (DSM) products such as OpenLandMap and SoilGrids provide open-source soil information for large-scale ecosystem service assessment and monitoring. However, the accuracy of these modelled datasets varies spatially because the input data used for model development are unevenly distributed. Thus, their reliability and implications for erosion modelling in data-scarce semi-arid regions remain insufficiently understood.In this study, we investigated the spatial variability of soil properties (soil texture fractions, soil organic carbon, bulk density, and pH) in Baringo County, Kenya and examined whether paved and unpaved roads influence this variability. We evaluated the accuracy of SoilGrids250m 2.0 and OpenLandMap (30 m) in representing field-measured soil properties and assessed how DSM-related uncertainties propagate into erosion estimates. Using pedotransfer functions based on soil texture fractions and soil organic carbon, we derived soil erodibility factors from both field data and OpenLandMap. The factors were subsequently used in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Sediment Delivery Ratio model to estimate spatial patterns of soil loss.Both DSM products underestimated topsoil silt content (RMSE = 35.6% and 36.1% for OpenLandMap and SoilGrids, respectively) and overestimated clay (RMSE = 17.8% and 22.3%) and sand contents (21.4% and 17.9%, respectively), with accuracy decreasing with depth. Field data revealed significantly lower silt content and higher sand content in topsoils near roads compared to further away with a moderate effect size. Sediment deposition and export computed using the parametrized OpenLandMap factor showed high correlation with results parametrized from field data across varying distances for both paved roads and unpaved roads (R2 > 0.87). Despite high correlations, modelling results parametrized with OpenLandMap underestimated sediment deposition and export by factors of approximately 2.2 and 2.7 for paved and unpaved roads, respectively. Unpaved roads showed greater sediment export near the road corridor compared to paved roads, while paved roads were associated with greater sediment deposition.Our results demonstrate that while global DSM products can reproduce relative spatial patterns of road-related erosion, systematic biases in soil property predictions an affect erosion estimate. This highlights the need to explicitly consider DSM uncertainty when using open soil data for erosion modelling and infrastructure, especially in data-scarce regions.

The sandy soil region of northwestern Europe often experiences excess water during winter and water scarcity during summer, causing a compromised hydrological balance. Lowering of groundwater levels by anthropogenic interventions such as intensive drainage and groundwater abstraction threatens land use and biodiversity that are dependent on phreatic groundwater. Current spatial planning hardly considers natural soil-water systems, rendering these landscapes unsustainable for the future. This calls for a paradigm shift in which land use is guided by the potential of natural systems to sustainably support human needs.Understanding how landscapes function under minimal human influence is crucial for understanding the natural processes, reversing declining trends and developing climate-robust land-use strategies. Therefore, we studied the Chaamse beek catchment (southern Netherlands) to develop a spatially explicit, quantitative understanding of the natural soil-water land use system and to assess the impact of human interventions over the late Holocene (2000 BCE till now) For this, we reconstructed palaeogroundwater levels using a regionally calibrated numerical model, which consists of MODFLOW (saturated zone) and MetaSWAP (unsaturated zone) for the time slices 2000 BCE, 0, 500, 1500 and 1850 CE. Topography, land use, hydrological features, and soils were reconstructed for each time slice and human interventions (e.g., ditches, artificial structures, abstraction wells) were chronologically removed (from present to past) to simulate increasingly natural conditions and evaluate their effects on groundwater dynamics. Model results were evaluated against historical maps.Results show that removing drainage ditches (simulating conditions prior to 1500 CE) substantially increases areas with shallow groundwater (0-50 cm). These areas become even larger when removing abstraction wells. Notably, the model successfully simulated the presence of swamps at locations where they historically existed, as verified by historical maps from 1850 CE. These findings provide quantitative insights into human-modified hydrological systems and support the development of nature-based solutions, water buffer zones, and adaptive land-use planning for climate-resilient management of sandy catchments. By constraining hydrological models with palaeogroundwater reconstructions, future forecasting and scenario-based water management strategies can be made more robust.

Soil organic carbon (SOC) plays a large role in sustainable soil management and climate change mitigation. To understand the potential of soils to sequester additional carbon requires detailed knowledge of the underlying processes and drivers. In this study, we use soil evolution model SoilGen3.8.2 to assess the effects of environmental drivers (bioclimate, erosion level and land use) and four protection mechanisms on long-term SOC dynamics. The protection mechanisms (aggregation, clay mineralogy, microporosity and metal oxyhydroxides, MOOHs) showed large differences with different temporal patterns, where aggregation and clay mineralogy dominated during 10 ka of pedogenesis and MOOHs had a negligible effect. Ranking internal and external controls on SOC stocks revealed a decreasing influence of bioclimate > protection mechanism > erosion rate > land use > time. Topsoil and subsoil SOC recovery after agricultural use revealed different dynamics, controlled by the history of environmental drivers and pedogenesis. Natural SOC recovery showed lowest rates for subsoils and highest rates for topsoils, with a strong control of erosion and pedogenetic history. The addition of ground rock of different mineralogies to enhance SOC sequestration had some effect, mainly for goethite, montmorillonite and a temporary effect of calcite. Our simulations demonstrate how SoilGen can improve understanding of soil processes, while also highlighting knowledge gaps, such as missing experimental insights in key SOC stabilization mechanisms. Our study shows that soil models such as SoilGen cannot act as digital twins of a soil that represent the entire soil, as not all processes and parameters of the complex soil system are represented. These models can, however, form the basis of topical digital twins that target specific processes or properties. We provide a roadmap for developing such topical digital twins and recommend to start from a complex model that accounts for pedogenetic history.