Regional soil organic carbon stocks: Enhancing the usability of soil databases

Storage of soil organic carbon (SOC) is an essential function of ecosystems underpinning the delivery of multiple services to society: e.g. the production of food, wood, bio-energy and water and the regulation of the global climate, nutrient availability and the hydrological cycle. Whereas SOC content (%) is part of the routine measurements in general-purpose soil survey projects and in monitoring schemes of agricultural and forest soils, the resulting datasets are not readily fit for regional SOC stock (kg m-2) assessments. This dissertation focused on two bottlenecks: (i) the spatial density of available soil profile collections is mostly too limited and too heterogeneously distributed across the territory to assign at least one observation to each combination of soil type and land cover type, and (ii) the sampling depth is often limited to the topsoil even though a considerable fraction of the total stock is known to be stored in the subsoil. To overcome the lack-of-data-problem related to the limited spatial density of existing soil profile collections, a multi-level generalisation approach was applied. This approach takes full advantage of all available data, by working with detailed land units in regions where sufficient data is available and with generalised units elsewhere. Applied to land units defined by the intersection of the 1990 Corine Land Cover dataset and the soil map of Flanders, Belgium (18,809 non-built-up land units for 929,546 ha), a legacy database (1947 - 1974) of 7,020 profile descriptions with 42,529 horizons and a 3- (coastal region) and 5-level (non-coastal region) generalisation, the approach succeeded in assigning at least one profile to 18,731 land units or 98.71% of the territory's area. This resulted in an estimated legacy stock of 87.49 Mt OC in the upper 100 cm of soil. This stock is smaller than those revealed by previous assessments even though they are based on the same soil profile collection. Since the proposed method preserves the highest possible spatial and semantic detail, its result can be considered as closer to reality. The applicability of the multi-level spatial generalisation in more data-scarce conditions was confirmed by its application to the soil map of forests in Flanders (153,544 ha) and a collection of 276 forest soil profiles. Approximately 90.45% of the forest area could be characterised and a total stock of 25.31 Mt OC was obtained with a firm contribution of Histosols. However, for the particular