###### Abstract  
To cope with the challenges in forest management that are contemporarily caused by climate change, data on current chemical and physical soil properties are more and more necessary. For this purpose, we present a further amalgam of depth functions and SCORPAN modelling to provide data at arbitrary depth layers. In this concept, regionalisation is split up into the modelling of plot totals and the estimation of vertical distributions. The intended benefits by splitting up are: consistency between estimates on plot level and depth layer level, avoidance of artificial depth gradients, straightforward interpretation of covariates in the sense of pedogenetic processes, and circumnavigation of the propagation of uncertainties associated with separation between horizons during field sampling. The methodology was tailored to the circumstances within the north-eastern lowlands and the utilisation of current inventory data of the National Forest Soil Inventory (NFSI) in Brandenburg (Germany). Using the regionalisation of soil organic carbon (SOC) as an example, the application is demonstrated and discussed in detail. The depth to groundwater table and terrain parameters related to the catchment area were the main factors in SOC storage. The use of kriging did not improve the model performance. The relative depth gradients of SOC were especially distinguished by tree species composition and stand age. We suppose that interesting fields of application may be found in scenario-based modelling of SOC and when SOC serves as a basis for hydrological modelling. depth functions; SCORPAN modelling; soil forming factors; forest soils; soil organic carbon; regionalisation; north-eastern lowlands; forest site mapping; Brandenburg +
Footnote †: journal: Medical  
Whereas in the glacial deposits of the north-eastern lowlands, soil texture is largely predetermined by parent material, other soil forming factors, like groundwater, vegetation/forest management, and climate, also have to be taken into consideration for soil organic carbon. In particular, due to the influence of groundwater and vegetation, the temporal variation of SOC is expected to be high (compared to soil texture). Since enrichment of soils with soil organic matter usually starts just after deposition of parent material, tighter correlations with current terrain can also be expected. On the other hand, to cope with the temporal variation, contemporary measurements of SOC are strictly needed for regionalisation. Over the last decades, several approaches for mapping SOC have been developed, which generally make intense use of terrain parameters derived from digital elevation models. Further covariates related to soil forming factors, like climate data, geological mapping units, or data on landcover, are also frequently used. The methods usually involved range from machine learning techniques, like support vector machines [16] or artificial neural networks [17], to all kinds of statistical models, such as stepwise linear regression and decision trees [18; 19; 20; 21]. Especially since the introduction of the SCORPAN framework for digital soil mapping by [PERSON] et al. [22], which extended the classic concept of soil forming factors [23] by soil and space, the additional use of geostatistics in regionalisation approaches for SOC became a widely used technique [24]. Newer approaches also use deep learning [25], data augmentation [26], and increment-averaged kriging [27] to predict multiple depths with a single model. The majority of studies conducted are on the prediction of SOC for predefined depths of the entire soil colum or discrete soil layers. Prior to development of prediction models, it is therefore necessary that the column depth or soil layer boundaries are well defined for the respective purposes. If the SOC of several soil layers should be mapped, fitting of the increasing number of prediction models becomes more and more time consuming. The SOC stock of every layer is then treated as unbounded random variables (see also [28]).