the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Jun 2025
Long-term hourly stream-water flux data to study the effects of forest management on solute transport processes at the catchment scale
Abstract. A substantial body of knowledge concerning the functioning of catchments has been derived from the quantification of solute and suspended matter fluxes in rivers. The Wüstebach catchment is a hydrological observatory that is part of the German TERENO (Terrestrial Environmental Observatories) network. In 2013, the Eifel National Park undertook a partial deforestation of the spruce forest with the objective of facilitating the regrowth of a natural forest. This data paper presents 16 years of estimated hourly stream-water flux data of nine continuously monitored macro- and micronutrients, as well as dissolved ionic aluminum and dissolved organic carbon (DOC), along with the measured solute concentrations and discharge rates observed in the Wüstebach catchment (from 2010 to 2024).
To estimate hourly stream-water fluxes from weekly manual grab samples and event autosampler data, we employed the R software package LOADFLEX, which implements a number of solute prediction methods, including regressions, interpolations, the period-weighted approach, and the more recently developed composite method. A comparison of the predicted nitrate concentrations with hourly nitrate reference data was conducted to assess the optimal prediction approach for the Wüstebach catchment. The analysis showed that the composite model is best suited to calculate the nitrate fluxes. Accordingly, this model was selected to calculate the fluxes of all considered macro- and micronutrients, dissolved aluminum and DOC. Flux data were compiled in the same way for a neighboring reference catchment with similar characteristics but without clear-cutting, in order to identify the effects of deforestation and afforestation on the cycling and transport of nutrients. We anticipate that this comprehensive data set will facilitate new insights into the influence of deforestation and afforestation on solute fluxes at the catchment scale. The dataset, entitled “Wüstebach data paper: Long-term hourly solute flux data 2010–2024”, is shared via Forschungszentrum Jülich: https://doi.org/10.26165/JUELICH-DATA/AKAMNQ (Bogena and Herrmann, 2025).
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RC1: 'Comment on essd-2025-185', Anonymous Referee #1, 04 Aug 2025
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Review on „Long-term hourly stream-water flux data to study the effects of forest management on solute transport processes at the catchment scale“
The data description paper from Bogena et al. (2025) presents a comprehensive dataset of water quantity and quality measurements in the Wüstenbach catchment, part of the TERENO network, and a neighboring reference catchment. Data from these catchments is especially interesting, as they are designed as a paired-catchment study with a >3 year calibration time before forest interventions (i.e., clear-cut) in the Wüstenbach catchment. Therefore, the data have great potential for further investigation on the impact of forest loss on catchment-scale water and solute fluxes, which is a highly relevant topic. To support such analysis, the authors present ways to estimate solute fluxes from grab sample concentration data. The manuscript is well written, and I have no doubt that it will be a valuable contribution to the readers of ESSD. Particularly, I appreciate that all original and processed data are made publicly available. The only major point I have is that there is no detailed data on the quality of the high-frequency concentration measurements via the optical TriOS sensor. Spectral absorbance data, obtained from optical sensors, need calibration and additional quality checks (such as calibration with grab sample data), which should be provided if the data is to be published. Please see my one major and some minor comments below:
Major:
Chapter 3.3 (starting in line 231): As mentioned above, I appreciate the additional provision of high-frequency concentration measurements, which clearly strengthens the manuscript. However, information on the quality control procedure and its results (calibration, outlier detection and comparison to grab sample data) should be provided so that readers of ESSD can trust and better understand the data.
Minor:
L60-63: with serious consequences for the water quality of rivers (Musolff et al., 2024), reservoirs (Kong et al., 2022), and groundwater (Winter et al., 2025). I recommend adding these two citations.
L194-195: Why linear? Why not other methods like cubic spline interpolation or similar? Is linear reliable enough? I suggest to indicate periods of interpolated Q in Figure 2 or to put an additional figure with those periods into the SI, so readers can see that this does not severely affect the Q time series.
Figure 4: Is there a reason the x-axes are log10-transformed and the y-axes are not? I suggest log10-transforming the y-axes as well, in line with commonly applied power law C-Q relationships.
Figure 5: I suggest adding the R², NSE, etc. directly in the Figure. This would make a first visual assessment easier.
L397: As Figure A5 appears to be quite relevant for the presented results, I suggest adding it to the main manuscript.
L462-476: I think the analysis is sufficient for the purpose presented. However, I would add a sentence to inform readers that nitrate export patterns at the long-term (analyzed via low-frequency data) and the event scale (analyzed via high-frequency data) can considerably diverge, because of different mechanisms that dominate at different time scales (Winter et al., 2024).
L484: How much is this mean value influenced by the drought in 2018-2020? I could imagine that this lowers the mean substantially, while other years might have been just as wet as 2024…? In this light, how plausible is an explanation purely based on the wetness state of the riparian zone? Couldn’t it be that the decrease in nitrate concentrations might have contributed to this pattern as well, similar to what was argued in Musolff et al. (2017)?
L491-492: I suggest citing Škerlep et al. (2023) here, who also found simultaneous increases of Mn and Fe(II) and related them to changes in catchment wetness and related redox conditions.
L505: Cl- not CL-
Figure 9: It would make it even clearer if the reference catchment were directly indicated as such in the legend, but I leave this to the discretion of the authors
References:
Musolff, A., Selle, B., Büttner, O., Opitz, M., and Tittel, J.: Unexpected release of phosphate and organic carbon to streams linked to declining nitrogen depositions, Global change biology, 23, 1891–1901, 2017.
Musolff, A., Tarasova, L., Rinke, K., and Ledesma, J. L. J.: Forest Dieback Alters Nutrient Pathways in a Temperate Headwater Catchment, Hydrological Processes, 38, e15308, https://doi.org/10.1002/hyp.15308, 2024.
Škerlep, M., Nehzati, S., Sponseller, R. A., Persson, P., Laudon, H., and Kritzberg, E. S.: Differential Trends in Iron Concentrations of Boreal Streams Linked to Catchment Characteristics, Global Biogeochemical Cycles, 37, e2022GB007484, https://doi.org/10.1029/2022GB007484, 2023.
Winter, C., Jawitz, J. W., Ebeling, P., Cohen, M. J., and Musolff, A.: Divergence between long‐term and event‐scale nitrate export patterns, Geophysical Research Letters, 51, e2024GL108437, 2024.
Winter, C., Müller, S., Kattenborn, T., Stahl, K., Szillat, K., Weiler, M., and Schnabel, F.: Forest Dieback in Drinking Water Protection Areas—A Hidden Threat to Water Quality, Earth’s Future, 13, e2025EF006078, https://doi.org/10.1029/2025EF006078, 2025.
Citation: https://doi.org/10.5194/essd-2025-185-RC1
Data sets
Wüstebach data paper: Long-term hourly solute flux data 2010-2024 Heye Bogena and Frank Herrmann https://doi.org/10.26165/JUELICH-DATA/AKAMNQ
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