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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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-185', Anonymous Referee #1, 04 Aug 2025
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AC1: 'Reply on RC1', Heye Bogena, 26 Aug 2025
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2025-185/essd-2025-185-AC1-supplement.pdf
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AC1: 'Reply on RC1', Heye Bogena, 26 Aug 2025
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RC2: 'Comment on essd-2025-185', Anonymous Referee #2, 20 Oct 2025
General comment
This manuscript describes a dataset on long-term high-frequency data from an experimental catchment in Germany undergoing a partly clear cut. This is a good fit for ESSD and a quite unique dataset totally deserving publication. The paper sufficiently describes how data was retrieved and especially how the hourly concentration data was interpolated using state-of-the-art methods. Results are compared to in-situ observation of nitrate. While I acknowledge the work put into the dataset I have two main points to raise: First, I totally miss information on the forest management. When was clearcutting done? Is there information on the vegetation before and after the clearcut (if not part of this manuscript, can it be found elsewhere?)? This is really needed for using the data with the aim of quantifying and characterizing effects of the management. Second, I found the evaluation of DOC flux estimation less stringent as for nitrate. For nitrate a comparison to rather independently measured concentration was done (though information on the data procedure is missing). Can this be done for DOC as well, even if only for a smaller portion of time? If not, I would like to see that more clearly stated in the manuscript.
For a couple of smaller points see my specific comments below.
Specific comments
Abstract:
L23: Consider to mention „observation“ or „monitoring“ or „data“ in this sentence as quantification alone could also refer to a model quantification only.
L33ff: I suggest to cut down the list of prediction methods and rather shortly mention the variables needed for the composite model.
Introduction:
L57ff: Check format of the references. Here, authors (year) seems to be the better format.
L70-73: Consider to move this sentence to the lack of long-term data statement above.
L91-93: Consider to remove this sentence and use the references for the sentence before.
Site description:
Consider to put key variables on landuse, topography, climate… into a table comparing both subcatchments. Consider to change the chapter name to acknowledge the bigger section on previous work. Will the exact timing of clearcutting activity be reported at a later point in the manuscript? If not, this could be a place.
Data and methods:
Fig. 2: Consider to indicate the time of clearcutting in the figure.
L213 and 214: Redox potential and pH are not a physical parameter from my point of view. Consider to change to “in situ parameters of water quality”. Change this also later on (e.g. Fig. A1, A2)
Fig. 3: This gives a good overview. However, time reference and number of samples are missing. This is data across a major management intervention. Does it make sense to report values before and after the clearcutting? That would, however, come with the uncertainty around the question “when does the after-clearcutting period ends?".
L260f: Make a link to the EC in the chapter before.
L315f: So far there is no description on how the data quality of the TriOS concentrations was ensured. If that is not described later, I suggest to ensure that this procedure is adequately described to referenced here. This also plays a role when you compare TriOS concentrations that are corrected to the lab-derived concentrations with the LOADFLEX estimated concentrations also relying on the same lab-derived concentrations. Something you should comment on.
L316: What is the reason not to do the same exercise for DOC concentrations? The lines 265-269 indicate that you test your models for two substances nitrate and DOC. Doesn’t TriOS also provide continuous DOC concentrations measurements? So, for DOC there is no “independent” verification at the moment, right?
L329f: Indicate if model parameters are constant over time (egret/ WRTDS uses a quite similar equation but estimates parameter for each time step). The same is true when describing the composite model line 345ff.
L359ff: In this chapter it is not clear if these metrics are applied to lab-measured concentrations vs. LOADFLEX-concentrations or/ and to TriOS vs. LOADFLEX.
Results:
Table 1: Indicate unit for RMSE.
Table 2: Mention that this is for nitrate.
L477ff: Consider to put this example-analysis into a separate sub-chapter of the results.
Fig. 9: It would be helpful to also report annual discharge as the first or final plot here.
Data access and availability:
Does it make sense to indicate where the measured concentrations from the lab-analysis can be found? This seem to be only given for Nitrate but not for all other substances.
Citation: https://doi.org/10.5194/essd-2025-185-RC2
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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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.