Deforestation effects on the spatial distribution of C and N in the soils of a forested headwater catchment in the Eifel, Germany

Riverine systems are distinct components of the natural environment which have significant roles in storing and processing terrestrial carbon. While processing organic matter, rivers release large amount of greenhouse gasses to the atmosphere. In this light, headwater streams are particularly interesting. Due to their high connectivity with the surrounding landscapes, these small streams are strongly influenced by terrestrial inputs of carbon and nitrogen and groundwater inflow. Therefore, despite smaller surface area, their role in C and N cycling is crucial. The amount and character of C and N inputs to headwater streams is highly dependent on soil and vegetation types of the catchment. Soils, connecting the atmosphere, hydrosphere and lithosphere and supporting various ecosystem processes, are believed to exhibit strong responses to ecosystem disturbances. Among these disturbances are climate change impacts and modified landcover.

The aim of this study is to analyze three-dimensional variability of biogeochemical processes in the soils of a forested headwater catchment, following partial clearcut of a spruce forest.

The high resolved data on soil properties were collected in the Wüstebach catchment (Eifel/Lower Rhine Valley), a long-term environmental observation site of the TERENO (Terrestrial Environmental Observatories) project. The Wüstebach is a forested catchment in which nine hectares of Norway spruce was cleared in 2013 and has been replanted with original beech.

Three extensive soil sampling campaigns were conducted in the catchment: the first just before a partial clear-cut in 2013, the second after the clearcut in 2014 and the third, five years after the clearcut in 2018. The sampling produced high-resolution data on physical and biogeochemical soil parameters per horizon.

The plan of this study is to perform geostatistical analysis of the data and produce three-dimensional surface prediction models of the spatial distribution of the two nutrients: C and N in 2013, 2014 and 2018 over the entire catchment.