A Dataset for Three‐Dimensional Distribution of 39 Elements Including Plant Nutrients and Other Metals and Metalloids in the Soils of a Forested Headwater Catchment

Abstract

Quantification and evaluation of elemental distribution in forested ecosystems are key requirements to understand element fluxes and their relationship with hydrological and biogeochemical processes in the system. However, datasets supporting such a study on the catchment scale are still limited. Here we provide a dataset comprising spatially highly resolved distributions of 39 elements in soil profiles of a small forested headwater catchment in western Germany () to gain a holistic picture of the state and fluxes of elements in the catchment. The elements include both plant nutrients and other metals and metalloids that were predominately derived from lithospheric or anthropogenic inputs, thereby allowing us to not only capture the nutrient status of the catchment but to also estimate the functional development of the ecosystem. Soil samples were collected at high lateral resolution (≤60 m), and element concentrations were determined vertically for four soil horizons (L/Of, Oh, A, B). From this, a three-dimensional view of the distribution of these elements could be established with high spatial resolution on the catchment scale in a temperate natural forested ecosystem. The dataset can be combined with other datasets and studies of the TERENO (Terrestrial Environmental Observatories) Data Discovery Portal () to reveal elemental fluxes, establish relations between elements and other soil properties, and/or as input for modeling elemental cycling in temperate forested ecosystems.

Short-term impacts of forest clear-cut on soil structure and consequences for organic matter composition and nutrient speciation: A case study

“…Samples were taken as described in Siebers et al [10]. The L/Of- and Oh-horizon was removed and the mineral horizons below were sampled at depth of 0 to 30 cm and 30 to 60 cm incorporating the two upper mineral horizons (A- and B-horizons, [34] subsequently referred to as top- (A-horizon) and subsoil (B-horizon) using a mineral soil corer as described in Wu et al [35]. The cores were separated into the two horizons with a thickness of the A horizon of about 10 cm [36].…”

“…Samples taken in 2013 on the clear-felling area were taken shortly before the clear-cut. As the sampling was destructive, the sampling place was slightly shifted from the initial sampling for every year within an area of 40 x 40 cm [35].…”

The TERENO‐Rur Hydrological Observatory: A Multiscale Multi‐Compartment Research Platform for the Advancement of Hydrological Science

“… How do patterns of soil moisture impact hydrological processes such as runoff, evapotranspiration, and water storage (e.g., Graf et al, 2014; Wiekenkamp et al, 2016b)? What are the key drivers for biogeochemical processes, e.g., related to nutrient transport in stream and groundwater (e.g., Wu et al, 2017; Weigand et al, 2017) and greenhouse gas emission (e.g., Gottselig et al, 2017; Liu et al, 2016; Klosterhalfen et al, 2017; Wang et al, 2018)? What are the key controls on travel time distributions (e.g., Stockinger et al, 2014, 2017)? What is the information content of spatially and temporally highly resolved soil moisture data (e.g., Korres et al, 2015; Baatz et al, 2017)? How do patterns in crop development and yield relate to soil water dynamics under drought conditions (e.g., Rudolph et al, 2015; Jakobi, 2017)? How can data assimilation and cloud computing support the real‐time management of water resources (e.g., Kurtz et al, 2017)? …”

Uranium Vertical and Lateral Distribution in a German Forested Catchment

“…The second dataset comprised spatial distributions of an additional set of 39 elements including U concentration at the same sampling locations [29] (http://teodoor.icg.kfa-juelich.de/ibg3searchportal2/ dispatch?searchparams=freetext-Wuestebach&metadata.detail.view.id=7d37ae00-20f6-408e-8660-33bfba07c869). The concentration data of U, as well as other elements (Mg, Fe, Cu, Mn, Mo, Zn, Cr, Co, As, Pb, Ni, Ga, Hg, Cd) and rare earth elements (REEs) (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu), were obtained from this dataset.…”