This dataset contains element concentrations of six different hydrological compartments sampled on a daily basis over the course of one year in two neighboured first order headwater catchments located in the Conventwald (Black Forest, Germany). Critical Zone water compartments include above-canopy precipitation (bulk precipitation including rainwater, snow and fog water), below-canopy precipitation (throughfall), subsurface flow from three distinct soil layers (organic layer, upper mineral soil, deep mineral soil), groundwater, creek water and spring water. Element concentrations include major elements (Ca, K, Mg, Na, Si, S), trace elements (Al, Ba, Cr, Cu, Fe, Li, Mn, P, Sr, Zn), anion (Cl), and dissolved organic elements (DOC, DON).
The data were used to explore concentration (C) - discharge (Q) relationships and to calculate short-term element-specific chemical weathering fluxes, which were compared with previously published long-term element-specific chemical weathering fluxes. The ratio of both weathering fluxes, described by the so-called “Dissolved Export Efficiency” (DEE) metric revealed deficits in the stream dissolved load. These deficits were attributed to colloid-bound export and either storage in re-growing forest biomass or export in biogenic particulate form.
Tables supplementary to the article, including data quality control, are provided in .pdf and .xlsx formats. In addition, data measured in the course of the study are also provided as machine readable ASCII files.
The main component of this data publication is a dataset of predicted daily nutrient concentrations for NO3-N and TP for 150 monitoring stations along 60 German rivers (main rivers). The aim of this dataset is to fill the data gap of daily nutrient concentrations for a better understanding of nutrient transport from the rivers to the seas. So far, nutrient concentrations are sampled on a fortnightly basis, which can be insufficient for nutrient retention models working on a daily basis. With this method and available datasets, river basin managers have the opportunity to look at nutrient concentrations or load patterns on a finer resolution to adapt their management to improve water quality.
The dataset was obtained by a random forest model (RF) based on measured NO3-N and TP concentrations between the years 2000 and 2019. The data was requested or where available downloaded from official websites of the Federal States or River Basins. Different variables for NO3-N and TP were finally considered in the models to produce the RF, like discharge, land use, day of the year.