2022 drought consequences on nutrient dynamics in forest soil solutions of a declining spruces plot in the Strengbach catchment (Vosges Mountains, France)

Saphy, Adrien; Beaulieu, Emilie; Pons, Marie-Noëlle; Belfort, Benjamin; Pierret, Marie-Claire

doi:10.5194/egusphere-2025-4622

Preprints

https://doi.org/10.5194/egusphere-2025-4622

Preprints

15 Oct 2025

| 15 Oct 2025

2022 drought consequences on nutrient dynamics in forest soil solutions of a declining spruces plot in the Strengbach catchment (Vosges Mountains, France)

Adrien Saphy, Emilie Beaulieu, Marie-Noëlle Pons, Benjamin Belfort, and Marie-Claire Pierret

Abstract. 2022 was the hottest and driest year ever recorded in France, including within the Strengbach catchment, a Critical Zone Observatory (http://ohge.unistra.fr) located in a forested watershed of the Vosges Mountains and characterized by declining Norway spruce (Picea abies) stands. During and following the summer drought of 2022, an unusual chemical signature was detected in soil solutions, marked by elevated concentrations of K⁺, Ca²⁺, Mg²⁺, NO₃^-, NH₄⁺, Al³⁺, and Dissolved Organic Carbon (DOC) with significant variations of fluorescence indices (HIX, BIX and FI). Thanks to interdisciplinary monitoring of soil solution chemistry, the impacts of drought on biogeochemical processes – and more broadly, on forest soil fertility – are now better understood. The 2022 drought induced (1) lower mineral dissolution, (2) reduced plant nutrient uptake, (3) increased concentrations in throughfall (4) biological stress on soil microfauna, leading to organic matter accumulation during the dry period and subsequent release upon rewetting, (5) disruption of the nitrogen cycle, with ammonium accumulation during drought followed by intense nitrification after rainfall resumed, and (6) acidification of the soil solution, enhancing the desorption of both nutrient cations and toxic Al³⁺. Drought affects forest soil reactivity and fertility through physical (water deficit), chemical (nutrient leaching and acidification), and biological (vegetation and microbiota stress) mechanisms. The decline in soil fertility during and after drought is especially concerning for forest ecosystems already subject to nutrient deficiency, such as those in the Strengbach catchment. Understanding these drought-induced biogeochemical disturbances is essential for predicting ecosystem responses to extreme climatic events, whose intensity and frequency are expected to increase in the Vosges Mountains under ongoing climate change.

Received: 19 Sep 2025 – Discussion started: 15 Oct 2025

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Adrien Saphy, Emilie Beaulieu, Marie-Noëlle Pons, Benjamin Belfort, and Marie-Claire Pierret

Status: final response (author comments only)

RC1: 'Comment on egusphere-2025-4622', Anonymous Referee #1, 23 Oct 2025

I have reviewed the manuscript “2022 drought consequences on nutrient dynamics in forest soil solutions of a declining spruces plot in the Strengbach catchment (Vosges Mountains, France)” from Saphy et al.. The manuscript is based on a comprehensive dataset covering major ion concentrations, dissolved organic carbon (DOC) concentrations, and fluorescence indices, which shed light on the composition of DOC and biological stress. Drought and post-drought anomalies in soil solution quality are discussed in great detail, supported by appropriate literature. The results are nicely summarized in the discussion (Figure 6, which should be renamed to Figure 7) and the conclusion. That being said, I have some doubts about the methodological approach, which needs revision to ensure results are robust, and I recommend checking and improving the grammar and writing style (both aspects are explained in more detail below). If addressed, I believe that the manuscript would be a valuable contribution to the readers of Biogeochemistry.

Major comments:

Methodological approach

For the identification of drought-induced anomalies, the authors used a Grubbs test for outlier detection and characterized any outliers during the drought period as a significant drought effect. However, the Grubbs test relies on roughly normally distributed data, while the normality of the data was not tested, and I would doubt that the concentrations are normally distributed. In stream water concentration, assuming a log-normal distribution often gives reasonable results, meaning concentrations need to be log-transformed before applying the Grubbs test. Second, no outliers in the rest of the time series have been shown in the study. Therefore, it remains unclear whether a similar number of outliers occurred during normal conditions, less intense droughts, or wet periods. Especially in combination with the unjustified assumption of normality, it might be that too many outliers have been identified that are not that exceptional after all. It also makes it hard to say for sure if post-drought conditions differ from any other rain event.

Writing

The writing is generally acceptable, but before publication in BG, additional improvement in style and grammar is needed. This also includes checking the tenses, which sometimes switch from past to present and future for no apparent reason. Additionally, the separation between the Chapters is not always adequate: Some parts of the results already include discussion points, but mostly, there are many new methods and results at the start of the discussion that do not belong there.

Minor line-by-line comments:

L1: The title could be misinterpreted as a number of 2022 drought consequences. To clarify, I would rephrase it to something like “Consequences of the 2022 drought …”

L13: The same here and elsewhere. I would avoid numbers at the start of a sentence. One could start with “The year 2022 …”, for example.

L18-19: When first reading this sentence, I was not sure what this was supposed to tell me. Is that work done by others, or in this study? What is the interdisciplinary part here? It becomes a little clearer later, but I recommend sticking more to the active form to make clear that this was done as part of this manuscript.

L53-54: That is not restricted to the US, but occurs massively across Europe as well. Hartmann et al. (2022) documented elevated tree mortality globally.

L57: I suggest differentiating that this is especially true when spruce is growing out of its natural distribution range

L58: Species names need to be in italics

L65: In which way? Does high nutrient availability make them more vulnerable (as is the case for plants that are over-fertilized), or does a lack of sufficient nutrients weaken the trees?

L68-69: I suggest adding Winter et al. (2025) here.

L79-81: If I understood it right, diverse forest types are not covered in this study. Hence, it does not go well with the introduction to this work, but would rather fit into the discussion.

L115: This needs to be specified. How did the rainfall distribution change?

L163: Which drought events? I understood it was only one event? Or should this refer to drought events in general? Then “the” needs to be deleted. The same applies to “the changes”

L165: This needs more explanation of the model. What type of model is that? It also requires some discussion on the model's uncertainty in the discussion section, which is currently missing.

L170: Is that implemented in the model or proven elsewhere?

L174-176: See my major comment above.

L176-177: NMDS needs further explanation.

L183: 1 September – 31 August is not a typical hydrological year!

L187-188: A figure illustration how anomalous the drought was would be a great benefit here or in the SI. For example, one could show precipitation vs. temperature anomalies. That would also better characterize the drought, rather than being restricted to information about lower precipitation.

L198: I suggest not using an abbreviation for water content; it is not an especially complicated word.

L213: due to stomatal closure or downregulation

Fig 2: The background does not look orange to me, but rather light red.

L273: Isn’t that discussion already?

L295-L340: A lot of this is what I would classify as methods and results, not discussion.

Figure 6: This is a result as well. Furthermore, more information is needed to understand what is shown on the axes.

Table 3: Results as well.

L344: How is significance defined here?

L365-367: At what time scale would weathering be expected to play a role?

L390-435: Grundmann et al. (2024) would fit well in here.

L485: Was there even enough soil water to sample during the drought?

L525: I assume this is a fast recovery? How does this result align with the discussion on accumulated material flushed with rewetting?

L580: This should be Figure 7; Figure 6 already exists.

L620: Some context on the impact of drought on water quality as an ecosystem service provided by healthy forests would broaden the scope of this conclusion, which is, so far, a little narrow on forest management.

L625: Then the data should be made available elsewhere. “Not yet available” is not an argument to bypass open science.

References

Grundmann, M. H., Molnar, P., and Floriancic, M. G.: Quantification of enrichment processes in throughfall and stemflow in a mixed temperate forest, Hydrological Processes, 38, e15224, https://doi.org/10.1002/hyp.15224, 2024.

Hartmann, H., Bastos, A., Das, A. J., Esquivel-Muelbert, A., Hammond, W. M., Martínez-Vilalta, J., McDowell, N. G., Powers, J. S., Pugh, T. A. M., Ruthrof, K. X., and Allen, C. D.: Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide, Annual Review of Plant Biology, 73, 673–702, https://doi.org/10.1146/annurev-arplant-102820-012804, 2022.

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/egusphere-2025-4622-RC1

AC1: 'Reply on RC1', Adrien Saphy, 06 Nov 2025

We are grateful to anonymous referee #1 for this comprehensive and relevant feedback on our preprint. We address all the comments and propose a point-by-point response, starting with the major comments and then the minor ones. We would like to submit a revised version of the paper after receiving feedback from all referees.

Major comments

Outlier statistic test

Referee comment:

“For the identification of drought-induced anomalies, the authors used a Grubbs test for outlier detection and characterized any outliers during the drought period as a significant drought effect. However, the Grubbs test relies on roughly normally distributed data, while the normality of the data was not tested, and I would doubt that the concentrations are normally distributed. In stream water concentration, assuming a log-normal distribution often gives reasonable results, meaning concentrations need to be log-transformed before applying the Grubbs test. Second, no outliers in the rest of the time series have been shown in the study. Therefore, it remains unclear whether a similar number of outliers occurred during normal conditions, less intense droughts, or wet periods. Especially in combination with the unjustified assumption of normality, it might be that too many outliers have been identified that are not that exceptional after all. It also makes it hard to say for sure if post-drought conditions differ from any other rain event.”

Thank you, you are perfectly right. We propose to change section 2.5 (lines 174-175) by the following method description, to adjust some sentences accordingly to the new statistical analysis, and to add the figure S2 to supplementary material.

After performing a Shapiro-Wilk test (Shapiro test function in the rstatix library) to verify whether or not the data are normally distributed, it appears that the time series of concentrations in soil solutions do not follow a normal distribution (except for Na+, Si, and pH), even after log-transformation. So, Grubbs test cannot be applied. To find the outlier points using the same method for all types of distribution, we propose a simple and robust method based on interquartile range (IQR) with a visualisation by boxplots.

L174-175:

Previous text:

To highlight outlier concentrations, a Grubbs statistical test is performed on the entire 2015-2023 period for each parameter, using the ‘outliers’ package on R (Komsta, 2022).

Replaced by:

To find outlier points, we propose a robust method based on the calculation of the first and the third quartiles (Q1 and Q3) and the interquartile range (IQR = Q3 - Q1). It consists of determining an interval with the lower bound (Q1 – 3 x IQR) and the upper bound (Q3 + 3 x IQR), whereby points outside this interval are considered outliers. The value of 3 is chosen to identify only extreme outliers (Schwertman et al., 2004; Grunsky, 2010). A box plot representation allows outliers to be visualized as points outside this interval (Figure S2). This representation, with the points corresponding to those for summer 2022 (June to September 2022) shown with red crosses, allows to visualize the anomaly at 30 cm during and after the 2022 drought.

We also propose to correct Table 1 as follows to show the new statistical method for determining outliers and to mention other points that may be considered as outliers.

	pH	Cond.	Na⁺	K⁺	Mg²⁺	Ca²⁺	Al	Si	NH₄⁺	NO3^-	DOC
Mean	4.600	26.700	0.070	0.049	0.005	0.006	0.024	0.066	0.006	0.014	18.130
Median	4.543	23.85	0.066	0.025	0.004	0.005	0.023	0.061	<d.l.	0.005	14.945
IQR	0.324	11.422	0.025	0.032	0.003	0.004	0.010	0.046	0.003	0.010	9.233
Q₃ + 3xIQR	5.757	63.177	0.158	0.146	0.014	0.019	0.057	0.225	0.011	0.044	47.885
30cm outliers	X X	94.100 X	X X	0.301 0.198	0.018 X	0.026 0.023	0.072 X	X X	0.096 0.055	0.117 0.078	66.810 X
Dates of outliers	X X	08/30/22 X	X X	08/30/22 06/21/22	08/30/22 X	08/30/22 06/21/22	08/30/22 X	X X	08/28/18 06/21/22	08/30/22 10/23/18	08/30/22 X

Table 1: Mean and median of pH, Conductivity (Cond. in μS/cm), Dissolved Organic Carbon (DOC in ppm-C), and concentrations (mmol/L) in ions (Na⁺, K⁺, Mg²⁺, Ca²⁺, Al, Si, NH₄⁺ and NO₃^-) for the 2015-2023 period in the soil solutions at 30 cm depth. Q₃ is 75th percentile, IQR the interquartile range and (Q₃ + 3 x IQR) the upper bond above which points are considered outliers. Values of outliers at 30cm depth are given in the fifth line with the corresponding date in the sixth line. ‘X’ means that no outlier is found at 30cm depth during the 2015-2023 period. “<d.l.” means below detection limit.

This new methodology implies different modifications in the Result & Discussion part. All references to the Grubs test have been modified to correspond to this new test.

L225-227:

Previous text:

The time series exhibits annual seasonality in the chemical signal, as well as significant anomalies (Grubbs test p-value < 10-2; Table 1) during the exceptionally severe drought of summer 2022 (Fig. 3; Table 1).

Replaced by:

The time series exhibits annual seasonality in the chemical signal, as well as significant anomalies during the exceptionally severe drought of summer 2022 with numerous outlier concentrations (Fig. 3; Table 1; Fig S2).

L245-247

Previous text:

These values are well above the inter-annual variability and are the highest ever registered at this depth, considered as outliers by Grubbs tests with p-value about 10-7 and 10-4 respectively (Table 1).

Replaced by:

These values are well above the inter-annual variability and are the highest ever registered at this depth, considered as outliers because they are above Q3+3*IQR (Table 1; Fig S2).

L251-252

Previous text:

The peaks are confirmed by the Grubb tests, conducted at 30cm depth over the period 2015-2023, meaning that these maximum concentrations are outliers (Table 1). Focusing on major cations at 30cm depth, the K+, Ca2+, Mg2+ and Al concentrations increase twice successively between 21 June 2022 and 30 August 2022. The highest concentrations ever registered during 2015-2023 period in 30 cm depth soil solution were reached for the second peak (Table 1).

Replaced by:

Those peaks are confirmed as outlier concentrations for K+, Ca2+, Mg2+, Al, and NO3- on the 30 August 2022 (Table 1). Focusing on major cations at 30cm depth, the K+, Ca2+, Mg2+ and Al concentrations increase twice successively between 21 June 2022 and 30 August 2022. K+, Ca2+, NH4+ also admit outlier concentrations on the 21 June 2022 (Table 1; Fig S2). The highest concentrations ever registered during 2015-2023 period in 30 cm depth soil solution were reached for the second peak on the 30 August 2022 (Fig. 3, Table 1).

The table 2 must also be changed with the new method of outlier determination.

	HIX	FI	BIX
Mean 2020-2023	8.01	1.74	0.73
Median 2020-2023	8.00	1.76	0.78
Q₁ - 3xIQR	< 0	1.42	0.40
Value of outlier	X	1.32	X
Date of outlier	X	06/21/2022	X

Table 2: Mean, median of HIX, FI and BIX. Q1 is the 25th percentile, IQR the interquartile range, (Q1 – 3*IQR) the lower bond bellow which points are considered outliers. Values of outliers at 30cm depth are given in the fourth line with the corresponding date in the fifth line. ‘X’ means that no outlier is found at 30cm depth during the 2015-2023 period.

L286-287

Previous text:

… with an FI of 1.32, considered as an outlier by the Grubbs test (p-value = 4.8e-3), compared with a mean value of 1.74 ± 0.14 at this depth (Fig. 5; Table 2).

Replaced by:

with an FI of 1.32 on the 21 June 2022, considered as an outlier because the value is bellow (Q1 – 3*IQR = 1.42; Table 2).

L347-348

Previous text:

In addition, the Grubbs test also confirms that Na and Si are the only elements that do not show abnormal values (outliers) during the drought of 2022 (Table 1).

Replaced by:

X (Deletion of the sentence)

Writing

Referee comment:

“The writing is generally acceptable, but before publication in BG, additional improvement in style and grammar is needed. This also includes checking the tenses, which sometimes switch from past to present and future for no apparent reason. Additionally, the separation between the Chapters is not always adequate: Some parts of the results already include discussion points, but mostly, there are many new methods and results at the start of the discussion that do not belong there.”

Once the revision process is complete, we will proofread the new manuscript before submitting the revised version. We will improve the writing of the paper by checking the grammar and phrasing and paying particular attention to the use of tenses. The corrected version will fully meet the journal’s language requirements.

Minor comments

L1:

Referee comment:

“The title could be misinterpreted as a number of 2022 drought consequences. To clarify, I would rephrase it to something like “Consequences of the 2022 drought …”

Previous text:

2022 drought consequences on nutrient dynamics in forest soil solutions of a declining spruces plot in the Strengbach catchment (Vosges Mountains, France)

Replaced by:

Consequences of the 2022 drought on nutrient dynamics in forest soil solutions of a declining spruces plot in the Strengbach catchment (Vosges Mountains, France).

L13:

Referee comment:

“The same here and elsewhere. I would avoid numbers at the start of a sentence. One could start with “The year 2022 …”, for example.”

Previous text:

2022 was the hottest and driest year ever recorded in France,…

Replaced by:

The year 2022 was the hottest and driest year ever recorded in France, …

L18-19:

Referee comment:

“When first reading this sentence, I was not sure what this was supposed to tell me. Is that work done by others, or in this study? What is the interdisciplinary part here? It becomes a little clearer later, but I recommend sticking more to the active form to make clear that this was done as part of this manuscript.”

Previous text:

Thanks to interdisciplinary monitoring of soil solution chemistry (in particular, the combination of analytical tools for inorganic and organic chemistry), the impacts of drought on biogeochemical processes—and more broadly, on forest soil fertility—are now better understood.

Replaced by:

Through interdisciplinary monitoring of soil solution chemistry, we gain a better understanding of the impacts of drought on biogeochemical processes—and, more broadly, on forest soil fertility.

L53-57:

Referee comment:

“That is not restricted to the US, but occurs massively across Europe as well. Hartmann et al. (2022) documented elevated tree mortality globally.”

Previous text:

In addition, global warming and frequent droughts have accelerated the spread and intensity of insect attacks, as documented in US forests (Weed et al., 2013; Vose et al., 2016; Frank, 2021).

Replaced by:

In addition, global warming and frequent droughts have accelerated the spread and intensity of insect attacks, as documented in US forests (Weed et al., 2013; Vose et al., 2016; Frank, 2021) and in Europe (Hartmann et al. 2022).

L57-59:

Referee comment:

“I suggest differentiating that this is especially true when spruce is growing out of its natural distribution range.”; “Species names need to be in italics”

Previous text:

Spruces are boreal that are particularly vulnerable to drought, leading to an increase in the tree's vulnerability to parasites, such as the bark beetle (Ips typographus) which devastates Vosges spruce forests (Saintonge, 2022; Gomez et al., 2023; Knutzen et al., 2025).

Replaced by:

Spruces are trees from boreal region. Out of its natural localisation, spruces can be more affected by droughts, leading to an increase in the tree's vulnerability to parasites, such as the bark beetle (Ips typographus), which devastates Vosges spruce forests (Saintonge, 2022; Gomez et al., 2023; Knutzen et al., 2025).

L65:

Referee comment:

“In which way? Does high nutrient availability make them more vulnerable (as is the case for plants that are over-fertilized), or does a lack of sufficient nutrients weaken the trees?”

Previous text:

Another factor of tree vulnerability to drought is nutrient availability

Replaced by:

Another factor of tree vulnerability to drought may be a reduce of nutrient availability

L68-69:

Referee comment:

“I suggest adding Winter et al. (2025) here.”

Previous text:

Nitrogen is an essential plant nutrient, and the consequences of drought on the nitrogen cycle are documented (Lamersdorf et al., 1998; Muhr et al., 2008; Deng et al., 2021; Krüger et al., 2021; Winter et al., 2023).

Replaced by:

Nitrogen is an essential plant nutrient, and the consequences of drought on the nitrogen cycle are documented (Lamersdorf et al., 1998; Muhr et al., 2008; Deng et al., 2021; Krüger et al., 2021; Winter et al., 2023, 2025).

L79-82:

Referee comment:

“If I understood it right, diverse forest types are not covered in this study. Hence, it does not go well with the introduction to this work, but would rather fit into the discussion.”

Previous text:

However, studies addressing the impact of natural drought on mineral nutrient dynamics in soil solution remain scarce and require further extension to diverse forest types.

Replaced by:

However, studies addressing the impact of natural drought on mineral nutrient dynamics in soil solution remain scarce and require multiple observations to be better understood.

L115:

Referee comment:

“This needs to be specified. How did the rainfall distribution change?”

Previous text:

But we can already observe some climate change consequences at the local scale in the OHGE, so that the OHGE databank is a key tool to understand environmental change consequences (Pierret et al., 2018; Strohmenger et al., 2022).

Replaced by:

But we can already observe some climate change consequences at the local scale in the OHGE, with wetter summers and winters, and dryer autumns comparing before and after 2006 (Strohmenger et al., 2022). The OHGE databank is a key tool to understand environmental change consequences (Pierret et al., 2018; Strohmenger et al., 2022).

L163-171:

Referee comment:

“This needs more explanation of the model. What type of model is that? It also requires some discussion on the model's uncertainty in the discussion section, which is currently missing.”

To address the reviewer’s comment, we provide additional details about the model; these should be sufficient to understand the approach. However, since the model does not allow for a quantitative estimation of water flow and transport of associated nutrients, a discussion of model uncertainties or sensitivity would be beyond the objectives of our study.

New section 2.4:

2.4 Hydrological soil modelling

Drought events can create variations in soil water content. To evaluate changes in water availability in the soil profile induced by rainfall decrease, an in-house daily water balance calculation code is applied to a soil profile under spruces. The BILHYDAY code, whose structure is inspired by the model BILJOU developed by Granier et al. (1999), takes meteorological data as inputs with stand parameters (leaf area index, soil porosity and residual water content). Based on a conceptual approach, the model operates on a daily time step to describe the hydrological processes of the soil–tree–atmosphere continuum. Potential evapotranspiration is estimated following the Penman formulation. Precipitation feeds a canopy reservoir that simulates interception up to a threshold capacity, while transpiration is represented as a function of potential evapotranspiration, soil water storage, and leaf area index. Transpiration is distributed among soil layers according to the root density profile. Soil evaporation is estimated from the incident energy (induced from direct radiation) and an extinction coefficient. Five soil layers are represented as cascading reservoirs that drain sequentially from top to bottom according to their field capacity. The uppermost layer is supplied by non-intercepted precipitation and is also subject to evaporation losses. Root water uptake associated with transpiration is distributed among the different soil layers according to the root density profile. The BILHYDAY model is used qualitatively as complementary information to support the interpretation of geochemical measurements of soil solutions. Model outputs are corroborated by soil moisture measurements, which are not shown here since they are not directly co-located. The relative extractable soil water is calculated similarly to Granier et al. (1999) and can be used to assess water stress. Although the model does not simulate photosynthesis by integrating a dynamic stomatal conductance, transpiration is nevertheless adjusted according to soil water availability, with regulation occurring below a threshold of 0.4, as proposed by Bréda et al. (2006).

L175-178 :

Referee comment:

“NMDS needs further explanation.”

Previous text:

Multivariate statistical analysis helps to highlight atypical observations in long-term monitoring and can reveal the impact of extreme events on soil when many parameters are measured (Knight et al., 2024). A non-metric multidimensional scaling (NMDS) statistical analysis was conducted using the metaMDS function in the ‘vegan’ library in R (Oksanen et al., 2001).

Replaced by:

Non-metric multidimensional scaling (NMDS) analysis helps to highlight atypical observations in long-term monitoring and can reveal the impact of extreme events on soil when many parameters are measured (Knight et al., 2024) or the consequences of extreme meteorological events on hydrochemistry (Setia et al., 2021). We ran NMDS statistical analysis using the metaMDS function in the ‘vegan’ library in R (Oksanen et al., 2001). NMDS were carried out on the dataset of chemical measurements (pH, conductivity, Na+, K+, Mg2+, Ca2+, Al, Si, NH4+, NO3-, DOC) and during the period 2015-2023, separating points by depth of sampling (5, 10, 30 and 60 cm) to visualise anomalies at each depth. Plotting the coordinates of different soil solution samples highlights atypical points. The scores assigned to the chemical parameters provide information on which parameters distinguish the samples from one another and which ones explain the anomalies observed.

L184:

Referee comment:

“1 September – 31 August is not a typical hydrological year!”; A figure illustration how anomalous the drought was would be a great benefit here or in the SI. For example, one could show precipitation vs. temperature anomalies. That would also better characterize the drought, rather than being restricted to information about lower precipitation.”

We Update values with hydrological year from 1 October to 30 September and we propose a new first paragraph for the section 3.1. as follow:

On the OHGE site, considering the hydrological years over the recorded period from 1987 to 2023 (year running Y from 1 October to 30 September of year Y+1), 2021/2022 (including drought 2022) was the third driest year on record (after 1990 and 1995) with a cumulated precipitation amount of 970 mm (-20% compared to the average value over the period), the fourth warmest year with an average annual temperature of 7.47°C (+20% compared to the average for the period – just after 2019, 2023 and 2022), and with the 2nd highest number of days with an average daily temperature above 18°C, i.e. 39 days compared to an average of 17 days. Among the years with the least precipitation, 2021/2022 was the warmest year, which in combination makes 2021/2022 the most drought-affected year.

The longest period of summer meteorological drought observed between 1987 and 2024 was 19 days in 2018, and then 17 days in 2022, 2013 and 1990. Nevertheless, the summer of 2022 was exceptional because this first drought period from 2 July to 19 July was very quickly followed by a second period of 24 days during which daily rainfall did not exceed 0.7 mm (between 21 July and 13 August 2022; Fig 2-A). It is important to notice that a rainfall of 8.9 mm, on 20 July 2022, separates those two meteorological drought events (Fig. 2.A). The summer months of July and August, with only 61 mm of rainfall, were the driest ever recorded at the site (Fig. S1), representing a decrease of nearly 70% relative to normal conditions. The particularly dry July month (10.2 mm cumulated; Fig. S1; Fig 2A) was amplified by the previous dry March and May months with respectively 33 mm and 36 mm cumulated, compared with the 102 mm/month usually observed on the Strengbach watershed.

We provide several graphs of precipitation, temperature and precipitation versus temperature anomalies in SI (Fig. S1). You’re right that these can help illustrate the significance of the 2022 drought.

L198:

Referee comment:

“I suggest not using an abbreviation for water content; it is not an especially complicated word.”

Previous text:

…the average water content (WC) in each layer is simulated. The WC of the three intermediate layers…

Replaced by:

…the average water content in each layer is simulated. The water content of the three intermediate layers…

L213:

Referee comment:

“due to stomatal closure or downregulation”

Previous text:

…tree transpiration reaches a very low value due to stomatal regulation, and this continues until rain returns.

Replaced by:

…tree transpiration reaches a very low value due to stomatal regulation or downregulation, and this continues until rain returns.

L222:

Referee comment:

“The background does not look orange to me, but rather light red.”

Previous text:

The orange background indicates the meteorological drought period,

Replaced by:

The red background indicates the meteorological drought period,

L273:

Referee comment:

“Isn’t that discussion already?”

Previous text:

Throughfall of 16 August 2022 (red cross on Fig. 4) corresponds to the lixiviation of needle depositions after the dry period (Fig. 2-A).

Replaced by:

Throughfall of 16 August 2022 (red cross on Fig. 4) corresponds to the end of the dry period with the return of rainfall on the 14 August 2022 (Fig. 2-A).

L295-340:

Referee comment:

“A lot of this is what I would classify as methods and results, not discussion.”

Previous text:

In order to gain an overall view of the data, and visualise anomalies at each depth, multivariate NMDS statistical analyses were carried out on the dataset of chemical measurements (pH, conductivity, Na+, K+, Mg2+, Ca2+, Al, Si, NH4+, NO3-, DOC) and during the time period 2015-2023, separating points by depth of sampling (5, 10, 30 and 60 cm). Soil solution samples from the 2022 drought (from June to November) have a statistically distinct signature only at 30 cm depth (Fig. 6).

Replaced by:

NMDS plots at each depth (Fig. 6) show that soil solution samples from the 2022 drought (from June to November) have a statistically distinct signature only at 30 cm depth (Fig. 6).

We have developed the section on materials and methods for NMDS analysis in greater detail and have therefore removed this information from section 4.1. However, NMDS analyses are data processing and their description is already part of the interpretation of the data. Thus, we prefer to leave this part to the discussion and not to the results.

L344:

Referee comment:

“ How is significance defined here?”

Previous text:

… ,which is within the range of inter-annual variability with a mean concentration during 2015-2023 period of 0.070 ± 0.019 mmol/L and 0.066 ± 0.032 mmol/L for Na and Si respectively (Table 1).

Replaced by:

… ,which is within the range of inter-annual variability with a mean concentration during 2015-2023 period of 0.066 ± 0.025 (median ± IQR) mmol/L and 0.061 ± 0.046 mmol/L for Na and Si respectively (Table 1).

L365-367 :

Referee question:

“At what time scale would weathering be expected to play a role?”

Belyazid et al. (2022), with numerical modelling, describe that weathering rate varies considerably within the year. Weathering is expected to play a role at the seasonal scale. But depending on the element studied, the mineral weathering may be (or not) a dominant flux that will influence significantly the composition of soil solution. For instance, in the soil of the Strengbach catchment, the silicium is almost completely controlled by the mineral weathering, as it is in negligeable concentration in throughfall and litter (Pierret et al., 2018; Oursin et al., 2023). A decrease of weathering would be expected to play an immediate role in silicium concentration in soil solution. In another hand, an element such as calcium, is mainly provided by litter degradation and throughfall (Beaulieu et al. 2020). A decrease in mineral alteration should not have a significant impact on the calcium concentration of the soil solution.

L485:

Referee question:

“Was there even enough soil water to sample during the drought?”

Yes, even though some samples had low volumes, particularly on 20 July 2022 and 30 August 2022. This is possible thanks to the highly draining soil of the Strengbach watershed.

L525:

Referee question:

“ I assume this is a fast recovery? How does this result align with the discussion on accumulated material flushed with rewetting?”

The flushing of accumulated material results in highly concentrated soil solution (nutrients and OM), with relatively high temperature and sufficient water content. That is an ideal medium for microbial (or fungal) recovery. For instance, the high ammonium concentration, higher pH, relatively high temperature and sufficient water content promotes growth of nitrifiers bacteria after rewetting as observed by Krüger et al. (2021).

L580 :

Previous text:

Figure 6

Replaced by:

Figure 7

CLL :

Referee comment:

“Some context on the impact of drought on water quality as an ecosystem service provided by healthy forests would broaden the scope of this conclusion, which is, so far, a little narrow on forest management.”

Previous text:

Forest management must take this into account to achieve more sustainable forests.

Replaced by:

Forest management must take this into account to achieve more sustainable forests and preserving the ecosystem services provided by forests, such as carbon sinks, biodiversity preservation, island of coolness that help mitigate heatwaves and protection of water quality in mountain areas.

L625

Referee comment:

“Then the data should be made available elsewhere. “Not yet available” is not an argument to bypass open science.”

Previous text:

Data concerning soil solutions are not yet available online. However, weather data and data on the chemistry of throughfall are available on the BDOH platform: https://bd-ohge.unistra.fr/OHGE/

Replaced by:

Weather data and data on the chemistry of throughfall are available on the BDOH platform: https://bd-ohge.unistra.fr/OHGE/. Data concerning soil solution are provided in the supplementary information (Table S1).

We supply a data table showing time series of concentrations in soil solutions (Table S1) .

References to add:

Grunsky, E. C.: The interpretation of geochemical survey data, GEEA, 10, 27–74, https://doi.org/10.1144/1467-7873/09-210, 2010.

Schwertman, N. C., Owens, M. A., and Adnan, R.: A simple more general boxplot method for identifying outliers, Computational Statistics & Data Analysis, 47, 165–174, https://doi.org/10.1016/j.csda.2003.10.012, 2004.

Setia, R., Lamba, S., Chander, S., Kumar, V., Singh, R., Litoria, P. K., Singh, R. P., and Pateriya, B.: Spatio-temporal variations in water quality, hydrochemistry and its controlling factors in a perennial river in India, Appl Water Sci, 11, 169, https://doi.org/10.1007/s13201-021-01504-3, 2021.

Citation: https://doi.org/10.5194/egusphere-2025-4622-AC1

RC2:
'Comment on egusphere-2025-4622', Anonymous Referee #2, 05 Jan 2026
The manuscript submitted by Saphy et al. addresses how the 2022 summer drought that occurred in central Europe affected the soil nutrient dynamics of forest soil solutions of a declining spruce plot (Strengbach catchment, NE France). Drought is recognised as a cause of forest health decline in multiple European regions, and its disturbance on “soil fertility” (as described by the authors) is not fully understood. Therefore, the issue merits further investigation. The soil solution dataset explored in the study (sampled every 6 weeks at 4 soil depths) is impressive for its temporal coverage, spanning 9 years (2015-2023). And the site is located within the Strengbach Critical Zone Observatory, where research has been conducted for more than 30 years. In my opinion, the manuscript will make a significant contribution to science. Figures are very relevant and informative. However, I outline below several points that should be clarified/improved prior to acceptance.

MAJOR COMMENTS:
- Structure of the paper:
I agree with Referee #1 in that the structure should be revised. There should not be citations in the Results section (line 209), and I think the statistical analysis of the soil solution chemistry would fit better in the results section 3.2 than in the Discussion (i.e. “4.1. Statistical overview of drought consequences on soil solutions chemistry”).

- Hypothesis/Research questions:
The authors stated a clear overarching objective (“to evaluate the potential impacts of drought 2022 on biogeochemcical cycles of majors elements, on organic matter and on the fertility of forest soils”). I find the objective too general and ambitious. There is no right or wrong in this respect, however, I think the paper would improve if the authors stated a working hypothesis or research questions(s) that help the reader better identify which key knowledge gaps are being addressed.
In the current version of the manuscript the authors opted for a general introduction (increase of droughts and their impact on forest ecosystems, two general paragraphs on drought-related biogeochemical disturbances – including nutrients and K+). The reader only learns about previous work related to soil solution biogeochemistry and throughfall signatures in the catchment in the discussion. An alternative would be to introduce earlier what was already known in the catchment and previous relevant work, and formulate hypothesis/research questions related to what you were expecting to observe during a major drought. The discussion would then address the coherence of the findings. But again, this is just a suggestion.
In this respect, I found it somewhat strange to read at the end of Section 4.1: “The following discussion provides an overview of the biogeochemical processes involved in the dynamics of nutrients in soil solution to understand how some of these are affected by drought” (referring to Discussion Sections 4.2–4.8). I nevertheless enjoyed reading this part of the Discussion. The authors clearly demonstrate a very strong understanding of the biogeochemical cycles of major elements and organic matter, built through extensive past work. However, I was somewhat puzzled by this sentence. Perhaps it would be helpful to clarify that the objective of these sections is to interpret the results rather than to provide general background.

- Soil solution sampling:
It is difficult to sample soil water during dry periods. It would be helpful to know a bit more about the zero-tension lysimeter plates. I am not sure if the information can be found in Pierret et al. 2018. I would also be relevant to know the volume of water collected at each depth every 6 weeks. Which was the minimum amount of water collected and needed for the analyses? How big are the plates?
The authors could also consider adding a more detailed explanation about their functioning. I understand they collect water that flows by gravity. The paper strongly focus on nutrient availability for plants and some papers show that gravitational water is not used by vegetation. Do you think that gravitational water has the same signature as the water that will be used by vegetation? I think this must be better explained and supported by literature.
Some literature show that zero-tension lysimeter plates mainly collect water from preferential flow paths. Do you think this is the case for your site? How is this relevant? Does this modify to some extent your conclusions?
The authors associate the fact that only the 30cm depth soil water signal is modified during the 2022 drought to root depth (lines 316-322). Whereas at 10 and 20-cm the concentrations are less altered. For those elements that are highly influence by throughfall (as K+) do you also observe anomalies during the 2022 drought at 5 and 10 cm depth? (not clear in lines 418-419).

- Throughfall sampling:
More information about the size of the gutters would also be appreciated. Did you consider using volume-weighted means (Figure 4)? Despite the concentration are higher during summer droughts, the amount of water that enters the soil ant reaches 30-cm depths should be very small.
If I am not mistaken only K+ and NH4+ concentrations are shown. However, data for other elements are discussed (“The concentrations of Na+ in throughfall do not seem to be affected by drought”, line 354). It would be nice to somehow be able to see this data.

- Reproducible methods:
All presented methods should be reproducible. I quickly rode the answer of the authors to the Referee #1 regarding BILHYDAY. Despite the authors provide more information, I do not think that the results are reproducible. The authors mention that “BILHYDAY model is used qualitatively”. However, section 3.1 (including Figure 2) describes the results (e.g. trees transpiration and soil moisture at different depths) of the model and the reader does not have the feeling that the authors are describing “qualitative” data. The title of the section is “3.1. Meteorological drought and soil water deficit modelling”… and the end of the intro it is stated that “the combined analysis of mineral nutrients and dissolved organic matter, coupled with hydrological modelling, provides a global view of the biogeochemical consequences of drought on the chemistry of soil solutions”
I think this is a weak point. I also disagree with the fact that the authors do not aim to show or describe any calibration/validation exercise: “Model outputs are corroborated by soil moisture measurements, which are not shown here since they are not directly co-located.” How do the modelled results compared to the soil moisture data collected nearby in the catchment?
In my opinion if the authors decide to use the model, then it should be fully described and the results reproducible. The calibration/validation should also be described somewhere. Language such as “similar to…” should be avoided.
Why did you decide to define 5 soil layers?
Are meteorological droughts defined as the number of days with zero rain?

- Conclusions supported by data:
I think the authors should be more careful in reporting results and conclusions. I think this is mainly an issue associated with language. I found sometimes difficult to differentiate facts that were already known, from hypothesis and conclusions of the study – all this became clearer in the discussion, but abstract and conclusions should be revised. For instance, in the abstract the authors mention: “the 2022 drought induced: (1) lower mineral dissolution, (2) reduced plant nutrient uptake, (3) increased concentrations in throughfall (4) biological stress on soil microfauna, leading to organic matter accumulation during the dry period and subsequent release upon rewetting, (5) disruption of the nitrogen cycle, with ammonium accumulation during drought followed by intense nitrification after rainfall resumed, and (6) acidification of the soil solution, enhancing the desorption of both nutrient cations and toxic Al". Which of these facts are direct conclusions derived from the study? For instance, we do not see microfauna data, point 4 cannot be a conclusion of the study. I suggest revising the text and use a more appropriate language (e.g. The results of this study suggest that…”).
The authors also conclude that droughts induce a reduction in “soil reactivity and soil fertility through physical, chemical and biological mechanisms”. I think this is not directly shown by the results of this study, so careful language should also be used.

- Limitations of the study:
I also missed some text discussing the limitations of the study. For instance, the authors decided to focus on the 2022 drought, and only one sample (at different depths) was collected before, during and after the meteorological drought at 6 weeks intervals.

- References:
Some references are not in the Reference list: Lamersdorf et al, 1998.

MINOR COMMENTS:
Line 18: interdisciplinary monitoring of soil solutions?

Line 20-24: not clear if these are conclusions of the study.

Line 36: Wouldn’t be more relevant to compare 2022 with the overall average/normal (1900-to date) instead of the period 1900-1930?

Line 43: “could be” reads vague.

Löine 183: this paragraph is a bit difficult to read if one aims to compare warmest years, driest, warmest summers and meteorological droughts.

Line 197: Some of the information here should be in the methods. Correct the thickness of the second soil layers: 5-5.

Line 198: was simulated.

Caption Fig 2: corresponding to before, during and after the drought period.

Line 304: suggest.

Line 390: Throughfall has been mentioned many times before line 390, maybe there is no need to add a definition here.

Lines 489-495: This should be explained in the methods.

Section 4.6. BIX shows a high variability (Figure 5). Is the explanation given (i.e. decrease when drying out and increase when rain returns) also applicable during the rest of the data series?

Figure 7 (wrongly labelled as Fig. 6) is very informative and summarises very well the findings. However, I do not think it is cited in the text.
Citation: https://doi.org/10.5194/egusphere-2025-4622-RC2
- AC2:
  'Reply on RC2', Adrien Saphy, 22 Jan 2026
  We thank anonymous referee #2 for this relevant review. We propose responding to all comments one by one and then suggesting corresponding changes to the paper.
  
  MAJOR COMMENTS:
  
  Referee comment : “ I agree with Referee #1 in that the structure should be revised. There should not be citations in the Results section (line 209), and I think the statistical analysis of the soil solution chemistry would fit better in the results section 3.2 than in the Discussion (i.e. “4.1. Statistical overview of drought consequences on soil solutions chemistry”).”
  
  We propose to present the statistical analysis and comment the figure 6 and the table 3 in the results section 3.2 at the line 366. The new accompanying text is as follows:
  
  At 30cm depth, the three points between June and August 2022, which were particularly affected by drought events, were significantly shifted to the right of the plot along the MDS1 axis (Fig 4). The samples from the summer 2018, which was also a drought episode (cf. 3.1), also show statistically different coordinates at this depth. Conversely, the values for soil solutions in summer 2022 at the three other depths (5, 10, and 60 cm) did not show any atypical values, except for those in August but only at 5 and 10 cm (Fig. 4). However, these values were less atypical than those observed for soil solutions at a depth of 30 cm (Fig. 4). These observations confirm that the impact of droughts on soil solution chemistry was the most important at 30 cm depth. The NMDS scores for 30 cm soil solutions showed that MDS1 exhibits the highest positive value for NH₄⁺, followed by NO₃⁻, meaning a particular anomaly concerning nitrogen species (Table 3). K⁺ and Ca²⁺ concentrations also had high scores along the MDS1 axis, indicating that the dynamics of cationic mineral nutrients were also atypical during 2022 drought.
  
  We propose to delete the section 4.1. “Statistical overview of drought consequences on soil solutions chemistry” and to replace it with a brief introduction to the discussion. The purpose of this paragraph is to justify the fact that our discussion focuses on interpreting and understanding the change in the geochemical signature of soil solutions at a depth of 30 cm in response to drought. The text would be:
  
  Soil solutions appeared to be most affected by drought at a depth of 30 cm, with significantly different chemical signatures in June, July and August as shown by NMDS analysis (Fig. 4). The highest spruce root density is found in the upper soil, from the base of organic layer to 20 cm depth and sharply decreases with depth (Schmid and Kazda, 2002; Borja et al., 2008). At the Strengbach watershed, the maximum root density ranges from 10 to 30 cm depth (Oursin et al., 2023). Since soil solutions collected at a depth of 30 cm using a zero-tension lysimeter plate incorporate the top 30 centimeters of the profile, this depth is essential for better understanding the processes of tree uptake, and their modifications in response to drought. Moreover, the availability of nutrients for trees is a major concern in soils as poor in alkaline earth elements (Ca and Mg) as those in the Strengbach watershed (Probst et al. 1992; Dambrine et al., 1998; Oursin et al. 2023). We will therefore focus the rest of the discussion on interpreting the data obtained at 30 cm, depth of maximum water/soil/plant interactions, using data from other depths where necessary.
  
  Chemical concentrations in soil solutions are the result of a balance between inputs (throughfall, mineral dissolution, cationic exchange desorption and organic matter degradation) and outputs (plant uptake, mineral precipitation, cationic exchange adsorption and drainage). The following discussion aims to better understand how drought affected the biogeochemical processes responsible for soil solution composition, as weathering, atmospheric inputs, vegetation uptake, organic matter reactivity, N cycle dynamics and acidification.
  
  To avoid the use of citations in the results section, we suggest modifying the following sentence.
  
  L209
  
  Previous text:
  
  Following the definition given by Granier et al. (1999), during the 2022 summer period, a total of 25 days of soil water deficit occurred, meaning that the relative extractable soil water dropped below the threshold of 0.4 (Fig. 2.C).
  
  Modified text:
  
  During the summer 2022, a total of 25 days of water deficit occurred with the relative extractable soil water below the threshold of 0.4 (Fig. 2.C).
  
  Referee comment: “ The authors stated a clear overarching objective (“to evaluate the potential impacts of drought 2022 on biogeochemical cycles of majors elements, on organic matter and on the fertility of forest soils”). I find the objective too general and ambitious. There is no right or wrong in this respect, however, I think the paper would improve if the authors stated a working hypothesis or research questions(s) that help the reader better identify which key knowledge gaps are being addressed.
  
  In the current version of the manuscript the authors opted for a general introduction (increase of droughts and their impact on forest ecosystems, two general paragraphs on drought-related biogeochemical disturbances – including nutrients and K+). The reader only learns about previous work related to soil solution biogeochemistry and throughfall signatures in the catchment in the discussion. An alternative would be to introduce earlier what was already known in the catchment and previous relevant work, and formulate hypothesis/research questions related to what you were expecting to observe during a major drought. The discussion would then address the coherence of the findings. But again, this is just a suggestion.
  
  In this respect, I found it somewhat strange to read at the end of Section 4.1: “The following discussion provides an overview of the biogeochemical processes involved in the dynamics of nutrients in soil solution to understand how some of these are affected by drought” (referring to Discussion Sections 4.2–4.8). I nevertheless enjoyed reading this part of the Discussion. The authors clearly demonstrate a very strong understanding of the biogeochemical cycles of major elements and organic matter, built through extensive past work. However, I was somewhat puzzled by this sentence. Perhaps it would be helpful to clarify that the objective of these sections is to interpret the results rather than to provide general background.”
  
  We thank the reviewer for pointing out that the formulation of the objectives is too general and ambitious. In this study, we attempted to develop integrative scenarios of the impact of drought on different compartments (water, soil, vegetation), with perhaps too many assumptions. It would indeed be clearer and more accurate to break down the discussion into more specific scientific questions which could be introduce in the beginning of the discussion as suggest earlier. We clarify the objective of the study with two modifications:
  
  – Modify the introduction to better introduce what we already know in the catchment and what we expect to observe during drought.
  
  – Modify the beginning of the discussion as we have proposed above.
  
  L87(Introduction)
  
  New text:
  
  An experiment of water shortage was conducted on a spruce plot in the Strengbach catchment in 1990 (Dambrine et al. 1993 ; Lu et al. 1995). These studies reported reduced sap flow as well as lower concentration of nutrients Ca, Mg and K in the sap during periods of water deficit. This indicates that tree nutrient uptake is reduced during a water deficit field experiment. However, this reduction may also result from disturbances in various biogeochemical processes that control nutrient availability in the soil, such as mineral weathering, cation exchange, or organic matter degradation. In addition, a severe drought such as that experienced in 2022 is far more complex than an experimental water deficit, as high temperatures influence all the biogeochemical processes. In 2022, long-term monitoring of soil solution chemistry captured an unprecedented severe drought event, providing a unique opportunity to elucidate its impact on the biogeochemical cycling of major elements and organic matter. Such climatic extremes may be a high cause of concern for nutrient-deficient soils of the Strengbach catchment.
  
  Referee comments: “It is difficult to sample soil water during dry periods. It would be helpful to know a bit more about the zero-tension lysimeter plates. I am not sure if the information can be found in Pierret et al. 2018. I would also be relevant to know the volume of water collected at each depth every 6 weeks. Which was the minimum amount of water collected and needed for the analyses? How big are the plates?”
  
  “More information about the size of the gutters would also be appreciated.”
  
  We propose to add more details to the method section and to provide collected volumes in the SI (table S1)
  
  L124-126
  
  Previous text:
  
  Soil solutions are sampled with zero-tension lysimetric plates at four depths (5, 10, 30, 60 cm) every 6 weeks and throughfall water samples are collected with two gutters every two weeks (Pierret et al., 2018).
  
  Modified text:
  
  The soil solutions were sampled with 40 x 40 cm zero tension lysimeter plates installed at four depths (5, 10, 30, 60 cm) and sampled every 6 weeks during the period 2015-2023. Throughfall water samples were collected with two gutters (2.0 x 0.2m) every two weeks (Pierret et al., 2018). The minimum volume of water needed for analyses is 25 ml, volumes of soil solution samples collected are given in table S1.
  
  Referee comment: "The authors could also consider adding a more detailed explanation about their functioning. I understand they collect water that flows by gravity. The paper strongly focus on nutrient availability for plants and some papers show that gravitational water is not used by vegetation. Do you think that gravitational water has the same signature as the water that will be used by vegetation? I think this must be better explained and supported by literature.ˮ
  
  Indeed, in soils, gravitational water and bound water are often distinguished and have different chemical concentrations. Gravitational water is free water that drains quickly through large pores under gravity and bound water is retained by capillary and adhesive forces around soil particles. However, water collected using lysimeter plates is a mixture of these two types of water. The distinction between free (gravity) water and bound water depends directly on soil porosity and tends to be less pronounced in sandy soils. This is because the presence of a significant proportion of sand results in high total porosity with a higher proportion of macropores and fewer micropores and adsorption surfaces. The coarser the soil texture (sand), the lower the proportion of water retained by capillarity/adhesion compared to water that will drain quickly by gravity.
  
  The soils of the Strengbach watershed originating from the plot studied in this article have sand fractions ranging from 50% on the surface (0-5 cm depth) to over 70% (Oursin et al., 2023).
  
  In addition, we tried to install devices for collecting bound soil solutions using porous ceramic tube. Unfortunately, as the soils in the Strengbach watershed are sandy, porous, and well-drained, we are unable to collect water easily. The ceramic tube only fill up after sufficient rainfall, which means that not all of the soil water flux can be collected. We therefore decided to set up long-term, integrated monitoring of soil solutions using only zero-tension lysimeter plates.
  
  Watmough et al (2013) have compared soil water chemistry from tension and zero-tension lysimeters in sandy soils. They show that pH and NO3 concentrations were similar whereas Sulphate, Ca and Mg concentrations were higher in tension lysimeters. For K and Na the differences are not clear. Interestingly, the Ca/Mg ratios are comparable. These various observations suggest that the processes responsible for the concentrations of Ca, Mg, NO3, Na, and K in different soil water types are similar and that the study of gravity waters remains relevant in our case.
  
  We suggest mentioning in the text this limitation in the discussion section 4.4. Vegetation uptake
  
  L462
  
  New text:
  
  However, soil solution collected by zero-tension lysimeters is the fraction of soil water participating in gravitational flow, circulating mainly in the macropores (Singh et al., 2018). It cannot be considered similar to water collected by vegetation, since roots also collect water from microporosity with a certain suction pressure (Rüdinger et al., 1994). Furthermore, gravitational solutions have a different chemical composition from other fractions of soil water collected under a certain pressure (Haines et al., 1982; Watmough et al., 2013), also under the spruce forests of the Strengbach watershed (Ranger et al., 1993). But the soil on this plot is very sandy, with a sand content ranging from 50% on the surface to 70% at depth, and are relatively homogeneous (Oursin et al., 2023). Thus, the studied soil is very porous (more macro- than micro-porosity) and well-drained (more gravity flow than capillarity), therefore we assume that gravitational soil solution is representative of the whole soil water. In addition, gravity soil solutions enable input-output balances to be calculated, given that the soil solution at a depth of 30 cm can be considered as water leaving the root zone of spruce trees (Ranger et al., 1993; Marques et al. 1996). The chemical composition of the solution thus collected depends on all the biogeochemical processes taking place in the soil and can indirectly indicate a change in root uptake.
  
  Referee comment: "Some literature show that zero-tension lysimeter plates mainly collect water from preferential flow paths. Do you think this is the case for your site? How is this relevant? Does this modify to some extent your conclusions?"
  
  The occurrence of preferential flow path in soil and their influence on soil water collection device depend on the type of soil (their hydraulic parameters) as well as on the heterogeneity of soil (Peters and Durner, 2009). In well-drained sandy soils, as it is the case for the Strengbach watershed (with 50% of sand at the surface to 70% deeper), lateral water flows and preferential flow path can be neglected as a first approximation, because water mainly moves vertically under gravity due to high permeability and low water retention. There is no less permeable layers in the soil profile. Maybe lateral flows may become significant during very intense rainfall events, which was not the case in the studied period. In addition, below 5 cm depth, the sand proportion (60-70%) and more generally the granulometry in the Strengbach watershed soil remains homogenous until 80 cm depth (Dambrine et al., 1991; Fichter et al., 1998a,b, Jussy Et al., 2004; Oursin et al., 2023, Belfort 2025), which reduces the occurrence of preferential flow path (Peters and Durner, 2009).
  
  Referee comment: "The authors associate the fact that only the 30cm depth soil water signal is modified during the 2022 drought to root depth (lines 316-322). Whereas at 10 and 20-cm the concentrations are less altered. For those elements that are highly influence by throughfall (as K+) do you also observe anomalies during the 2022 drought at 5 and 10 cm depth? (not clear in lines 418-419). ˮ
  
  L418-149
  
  Previous text :
  
  No other cation (Na+, Ca2+, Mg2+) exhibits a maximum peak in both throughfalls and surface soil solutions (5 and 10 cm deep).
  
  Modified text :
  
  K+ was the only cation that exhibited a maximum peak in both throughfalls and surface soil solutions (5 and 10 cm deep).
  
  Referee comment: "Did you consider using volume-weighted means (Figure 4)? Despite the concentration are higher during summer droughts, the amount of water that enters the soil ant reaches 30-cm depths should be very small."
  
  You are right, the corresponding quantities were low because the volumes of water were low during summer 2022 (for throughfall and soil solutions). In this article, we discuss the concentrations of soil solutions, thus we compare them with the concentrations of throughfall. If we had worked in terms of soil solution fluxes, we would have used volume-weighted means instead of concentration for throughfall.
  
  Referee comment: "If I am not mistaken only K+ and NH4+ concentrations are shown. However, data for other elements are discussed (“The concentrations of Na+ in throughfall do not seem to be affected by drought”, line 354). It would be nice to somehow be able to see this data."
  
  Throughfall chemical data are provided in Supplementary information table S2 and are also available on https://bd-ohge.unistra.fr/OHGE/ (section Data availability).
  
  Referee comment: “All presented methods should be reproducible. I quickly rode the answer of the authors to the Referee #1 regarding BILHYDAY. Despite the authors provide more information, I do not think that the results are reproducible. The authors mention that “BILHYDAY model is used qualitatively”. However, section 3.1 (including Figure 2) describes the results (e.g. trees transpiration and soil moisture at different depths) of the model and the reader does not have the feeling that the authors are describing “qualitative” data.”
  
  You are right that the use of the term “qualitative” is misleading, as numerical values are presented in the manuscript. The work carried out in this study does not constitute a modelling exercise aimed at simulating water flow, solute transport, or water-soil interactions for direct comparison with soil solution measurements. Rather, the objective is not to validate coupled hydrological-biogeochemical models by confronting model outputs with the soil solution data collected in this study.
  
  Referee comment: “The title of the section is “3.1. Meteorological drought and soil water deficit modelling” … and the end of the intro it is stated that “the combined analysis of mineral nutrients and dissolved organic matter, coupled with hydrological modelling, provides a global view of the biogeochemical consequences of drought on the chemistry of soil solutions”
  
  You are right, this wording is misleading with respect to both the scope of the work and our intentions. We have therefore reformulated this sentence to clarify that the simulated hydrological variables are used to provide contextual information for interpreting the observed changes in nutrient concentrations during the drought period, rather than to imply a fully coupled hydrological-biogeochemical modelling approach.
  
  L93-95
  
  Previous text:
  
  The combined analysis of mineral nutrients and dissolved organic matter, coupled with hydrological modeling, provides a global view…
  
  Modified text:
  
  The combined analysis of mineral nutrients and dissolved organic matter, supported by hydrological modeling, provides a global view …
  
  L181
  
  Previous text:
  
  3.1 Meteorological drought and soil water deficit modelling
  
  Modified text:
  
  3.1 Meteorological drought and soil water deficit: insights from hydrological modelling
  
  Referee comment: “I think this is a weak point. I also disagree with the fact that the authors do not aim to show or describe any calibration/validation exercise: “Model outputs are corroborated by soil moisture measurements, which are not shown here since they are not directly co-located.” How do the modelled results compared to the soil moisture data collected nearby in the catchment?”
  
  We partly share your view. In general, a model requires calibration and validation, particularly when the objective is to interpret the processes it simulates through its output variables. The BILHYDAY model has undergone previous developments, including calibration exercises on several experimental plots, during which the different components of the water balance were assessed. Unfortunately, these calibration efforts were not formally documented in a peer-reviewed publication (with the exception of a PhD thesis written in French that is not available online), and the datasets used at that time are no longer accessible. We are currently working to address this limitation.
  
  In the present study, we therefore relied on the existing model structure and parameterisation for soil properties and updated mainly the leaf area index (LAI) parameter to account for changes in forest stand development. At this stage, we are not able to provide a full, site-specific calibration and validation following standard modelling practices (as you suggest).
  
  Nevertheless, to avoid any ambiguity regarding the realism of the simulated soil moisture values, we propose to include, in the Supplementary Information, soil water content measurements collected at a location close to the soil solution sampling site. These data show consistent temporal dynamics with the simulated soil moisture. Accordingly, the corresponding sentence in the manuscript has been revised and the Supplementary Information has been expanded (Fig. S2).
  
  L163
  
  New text:
  
  The BILHYDAY model is used as a diagnostic tool to provide complementary hydrological information supporting the interpretation of geochemical measurements of soil solutions. The model structure and parameterisation are based on previous developments and applications, notably those described by Biron (1994), during which the different components of the soil water balance were assessed. In the present study, this existing parameterisation was retained for soil properties, and the leaf area index (LAI) was updated to account for changes in forest stand development. Such an approach has recently been used for the soil hydrodynamic characterization of two other plots in the Strengbach catchment; BILHYDAY was run in combination with a physically based hydrological model coupled with an inverse parameter estimation module (Belfort et al., 2026). To address potential concerns regarding the realism of the simulated soil moisture dynamics, additional soil water content measurements collected at locations close to the soil solution sampling site are provided in the Supplementary Information. Although these measurements are not strictly co-located with the simulated soil profile, they exhibit temporal dynamics consistent with the model outputs, thereby supporting the coherence of the simulated soil moisture values used in this study.
  
  Referee comment: "In my opinion if the authors decide to use the model, then it should be fully described and the results reproducible. The calibration/validation should also be described somewhere. Language such as “similar to…” should be avoided."
  
  We partly agree with this point, as our intention is not to develop or present a fully parameterised model aimed at calculating element concentrations that could be readily applied to other sites. The issue could be framed differently for the community: given the availability of soil solution data, meteorological forcing, and complementary soil water content measurements, one could implement alternative modelling approaches and examine whether process-based interpretations of drought effects would lead to similar conclusions or not. This, however, represents a different type of exercise (and further research also for us). The objective of this study is therefore to propose an interpretation of the observed soil solution measurements, supported by complementary hydrological variables, rather than to provide a modelling framework intended to guide or prescribe how exchanges within the soil-vegetation-atmosphere continuum should be modelled.
  
  Referee comment: "Why did you decide to define 5 soil layers?"
  
  The discretization of the soil is based specifically on the instrumentation that was set up as early as the 1990s. The top layer (5 cm) is particularly sensitive to exchanges with the atmosphere. The three intermediate layers (5 cm, 20 cm, and 30 cm of thickness) correspond to the depths at which soil solution samples were collected. The model also includes a final layer (40 cm) that drains towards the deeper soil zone. This can be understood by analogy with physically based modelling, where it is common to avoid interpreting state variable results near boundary zones, as boundary conditions can have a strong influence.
  
  L167 (section 2.4. Hydrological soil modelling)
  
  New text:
  
  This discretization of the vertical soil profile is specifically based on the installed instrumentation. It includes a top layer (0 to 5 cm), which is particularly sensitive to exchanges with the atmosphere, and a bottom layer (60 to 100 cm) that drains toward the deeper soil zone, with three intermediate layers (5 to 10 cm, 10 to 30 cm, and 30 to 60 cm) corresponding to the layers at which samples were collected.
  
  Referee comment: "Are meteorological droughts defined as the number of days with zero rain?"
  
  Apologies and thank you for your question, you are right, we need to define this term. Meteorological drought is generally defined as a period during which precipitation is significantly below the normal or expected level for a given location and season, leading to a temporary deficit of soil moisture and water availability. In our study, between 3 July 2022 and 13 August 2022, we observed 42 consecutive days with a cumulative rainfall of 10 mm, occurring in five events, one of which amounted to 8.9 mm. Notably, there were 17, 9, and 6 consecutive days without any precipitation. If days with less than 0.5 mm are considered as dry, this corresponds to two drought periods of 17 and 24 days, respectively.
  
  We propose to add a definition of the meteorological drought and to explicit below which threshold we consider day as dry.
  
  L188
  
  New text:
  
  Meteorological drought is generally defined as a prolonged period during which precipitation is significantly below normal or expected levels for a given location and season, resulting in a deficit of moisture relative to climatic norms (Wilhite and Glantz, 1985). The longest period of summer meteorological drought (defined here as consecutive days without rainfall) observed between 1987 and 2024 was 19 days in 2018, followed by 17 days in 2022, 2013 and 1990. Nevertheless, the summer of 2022 was exceptional because this first drought period from 2 July to 19 July was very quickly followed by a second period of 24 days during which daily rainfall did not exceed 0.7 mm (between 21 July and 13 August 2022; see the red band in Fig 2-A).
  
  Referee comment:
  
  “I think the authors should be more careful in reporting results and conclusions. I think this is mainly an issue associated with language. I found sometimes difficult to differentiate facts that were already known, from hypothesis and conclusions of the study – all this became clearer in the discussion, but abstract and conclusions should be revised. For instance, in the abstract the authors mention: “the 2022 drought induced: (1) lower mineral dissolution, (2) reduced plant nutrient uptake, (3) increased concentrations in throughfall (4) biological stress on soil microfauna, leading to organic matter accumulation during the dry period and subsequent release upon rewetting, (5) disruption of the nitrogen cycle, with ammonium accumulation during drought followed by intense nitrification after rainfall resumed, and (6) acidification of the soil solution, enhancing the desorption of both nutrient cations and toxic Al". Which of these facts are direct conclusions derived from the study? For instance, we do not see microfauna data, point 4 cannot be a conclusion of the study. I suggest revising the text and use a more appropriate language (e.g. The results of this study suggest that…”).
  
  The authors also conclude that droughts induce a reduction in “soil reactivity and soil fertility through physical, chemical and biological mechanisms”. I think this is not directly shown by the results of this study, so careful language should also be used.”
  
  You are right, we must be more careful in writing the conclusions of the study. We propose modify the abstract and the conclusion
  
  L18-26 (abstract)
  
  Previous text:
  
  Thanks to interdisciplinary monitoring of soil solution chemistry, the impacts of drought on biogeochemical processes—and more broadly, on forest soil fertility—are now better understood. The 2022 drought induced (1) lower mineral dissolution, (2) reduced plant nutrient uptake, (3) increased concentrations in throughfall (4) biological stress on soil microfauna, leading to organic matter accumulation during the dry period and subsequent release upon rewetting, (5) disruption of the nitrogen cycle, with ammonium accumulation during drought followed by intense nitrification after rainfall resumed, and (6) acidification of the soil solution, enhancing the desorption of both nutrient cations and toxic Al³⁺. Drought affects forest soil reactivity and fertility through physical (water deficit), chemical (nutrient leaching and acidification), and biological (vegetation and microbiota stress) mechanisms.
  
  Modified text:
  
  The long-term monitoring of soil solution chemistry exhibited that drought affects soil nutrient dynamics at the soil-water-plant interface through different mechanisms: (1) lower mineral dissolution, (2) reduced plant nutrient uptake, (3) increased concentrations in throughfall (4) organic matter accumulation during the dry period and subsequent release upon rewetting (5) changes in dissolved organic matter quality suggesting a soil biological stress (6) disruption of the nitrogen cycle, with ammonium accumulation during drought followed by intense nitrification after rainfall resumed, and (7) acidification of the soil solution, enhancing the desorption of both nutrient cations and toxic Al³⁺.
  
  L597-599 (Conclusion)
  
  Previous text:
  
  The exceptionally dry belowground conditions cause an important biological stress on soil microfauna and an important decrease in microbial activity and mortality of bacteria, fungi and roots.
  
  Modified text:
  
  A modification of the DOM quality in soil solution was also observed suggesting a biological stress on soil biosphere due to the exceptionally dry belowground conditions.
  
  Referee comment: "I also missed some text discussing the limitations of the study. For instance, the authors decided to focus on the 2022 drought, and only one sample (at different depths) was collected before, during and after the meteorological drought at 6 weeks intervals."
  
  The time-step sampling of soil solution should be mentioned as a limitation of the study, we propose to talk about that in the discussion 4.3.
  
  L428
  
  New text:
  
  More frequent sampling of soil solutions would have provided a better match between the chemical composition of throughfalls (sampled every two weeks) and that of soil solutions (sampled every six weeks), particularly for better observation of the behaviour of highly reactive species such as nitrates and ammonium.
  
  Referee comment: "Some references are not in the Reference list: Lamersdorf et al, 1998."
  
  Reference to add :
  
  L798
  
  Lamersdorf, N. P., Beier, C., Blanck, K., Bredemeier, M., Cummins, T., Farrell, E. P., Kreutzer, K., Rasmussen, L., Ryan, M., Weis, W., and Xu, Y.-J.: Effect of drought experiments using roof installations on acidification/nitrification of soils, Forest Ecology and Management, 101, 95–109, https://doi.org/10.1016/S0378-1127(97)00128-X, 1998
  
  MINOR COMMENTS:
  
  Line 18: interdisciplinary monitoring of soil solutions?
  
  Modified in the new abstract
  
  Line 20-24: not clear if these are conclusions of the study.
  
  Modified in the new abstract
  
  Line 36: Wouldn’t be more relevant to compare 2022 with the overall average/normal (1900-to date) instead of the period 1900-1930?
  
  We wanted to compare with the pre-industrial temperature
  
  Line 43: “could be” reads vague.
  
  Previous text:
  
  … predict for France: […] ii) an increase in the frequency of heat waves in summer could be +10 to +15 days for the RCP4.5 scenario and approximately double for RCP8.5.
  
  Modified text:
  
  … predict for France: […] ii) an increase in the frequency of heat waves in summer about +10 to +15 days for the RCP4.5 scenario and approximately double for RCP8.5.
  
  Line 183: this paragraph is a bit difficult to read if one aims to compare warmest years, driest, warmest summers and meteorological droughts.
  
  We have proposed a new version of the section 3.1 in response to the first review by referee#1 accompanied by new figures that will be in SI. We hope this section will be easier to read this way.
  
  Line 197: Some of the information here should be in the methods. Correct the thickness of the second soil layers: 5-5.
  
  Vertical discretisation corresponds to lysimetric plate depth. That is why we choose layer thickness of 5-5-20-30-40 to distinguish five layer : 1) 0 to 5cm 2) 5 to 10cm 3) 10cm to 30cm 4) 30 to 60cm 5) 60cm to 1m.
  
  Line 198: was simulated.
  
  Caption Fig 2: corresponding to before, during and after the drought period.
  
  Line 304: suggest.
  
  Those 3 remarks will be modified in the text.
  
  Line 390: Throughfall has been mentioned many times before line 390, maybe there is no need to add a definition here.
  
  We propose to precise the meaning of throughfall line 125
  
  L125:
  
  New text:
  
  Throughfall (fraction of precipitation that reaches the ground after passing through the canopy)
  
  Lines 489-495: This should be explained in the methods.
  
  Those information will be added in the method section 2.3.
  
  L150
  
  Previous text :
  
  Fluorescence spectra were obtained on a Hitachi F-2500 spectrofluorometer equipped with a Xenon lamp, using FL Solution 2.0 software and a 1 cm x 1 cm (3.5 mL) quartz cuvette. Emission spectra were collected for three excitation wavelengths (λex = 254 nm, 310 nm and 370 nm) with a step of 1 nm and slits of 2.5 nm. The humification index HIX was calculated on emission spectra for λex = 254 nm as the ratio of the sum of the fluorescence intensities (F) between 435 and 480nm over the sum of the fluorescence intensities between 300 and 345nm (Zsolnay et al., 1999), (eq.1). The index BIX was calculated as the ratio of fluorescence intensity emitted at 380nm over 430nm for λex = 310 nm (Huguet et al., 2009) (Eq 2). The index FI was calculated as the ratio of fluorescence intensities at 450 and 500nm for λex = 370 nm (eq. 3) (McKnight et al., 2001).
  
  Modified text:
  
  To characterise the dissolved organic matter UV- fluorescence of soil solution was measured. Fluorescence spectra were obtained on a Hitachi F-2500 spectrofluorometer equipped with a Xenon lamp, using FL Solution 2.0 software and a 1 cm x 1 cm (3.5 mL) quartz cuvette. Emission spectra were collected for three excitation wavelengths (λex = 254 nm, 310 nm and 370 nm) with a step of 1 nm and slits of 2.5 nm. The humification index HIX was calculated on emission spectra for λex = 254 nm as the ratio of the sum of the fluorescence intensities (F) between 435 and 480nm over the sum of the fluorescence intensities between 300 and 345nm (Zsolnay et al., 1999), (eq.1). HIX quantify aromatic molecules in solutions and is also qualitative of the complexity of aromatics (Zsolnay et al. 1999; Serène et al. 2025). The index BIX was calculated as the ratio of fluorescence intensity emitted at 380nm over 430nm for λex = 310 nm (Huguet et al., 2009) (Eq 2). BIX is a proxy of biological activity in aquatic environment (Parlanti et al. 2000; Huguet et al. 2009). The index FI was calculated as the ratio of fluorescence intensities at 450 and 500nm for λex = 370 nm (eq. 3) (McKnight et al., 2001). FI indicates the origin of DOM and distinguishes two endmembers: terrestrially and microbially derived DOM.
  
  Section 4.6. BIX shows a high variability (Figure 5). Is the explanation given (i.e. decrease when drying out and increase when rain returns) also applicable during the rest of the data series?
  
  In the figure 5, at first sight, an annual seasonality can be observed at 10 and 30cm depth with higher BIX during wet period and lower BIX during dry period. However, no clear correlation between cumulated rainfall and BIX exists due the numerous environmental parameters that influence the biological activity (temperature, water content, pH, nutrient availability).
  
  Figure 7 (wrongly labelled as Fig. 6) is very informative and summarises very well the findings. However, I do not think it is cited in the text.
  
  We must cite this figure in the conclusion.
  
  L593-594
  
  Previous text:
  
  The consequences of drought on biogeochemical processes can be separated into two time periods: during drought and after drought.
  
  Modified text:
  
  The consequences of drought on biogeochemical processes can be separated into two time periods: during drought and after drought (summarised in the figure 7).
  
  References to add in the paper:
  
  Belfort, B., Alzein, A., Cotel, S., Julien, A., and Weill, S.: Hydrodynamic Parameter Estimation for Simulating Soil-Vegetation-Atmosphere Hydrology Across Forest Stands in the Strengbach Catchment, Hydrology, 13, 11, https://doi.org/10.3390/hydrology13010011, 2025.
  
  Biron, P. : Le Cycle de l’eau En Forêt de Moyenne Montagne: Flux de Sêve et Bilans Hydriques Stationnels: Bassin Versant Du Strengbach à Aubure, Hautes Vosges. Ph.D. Thesis, Université Louis Pasteur (1971–2008), Strasbourg, France, 1994.
  
  Haines, B. L., Waide, J. B., and Todd, R. L.: Soil Solution Nutrient Concentrations Sampled with Tension and Zero‐Tension Lysimeters: Report of Discrepancies, Soil Science Soc of Amer J, 46, 658–661, https://doi.org/10.2136/sssaj1982.03615995004600030042x, 1982.
  
  Marques, R., Ranger, J., Gelhaye, D., Pollier, B., Ponette, Q., and Gœdert, O.: Comparison of chemical composition of soil solutions collected by zero‐tension plate lysimeters with those from ceramic‐cup lysimeters in a forest soil, European J Soil Science, 47, 407–417, https://doi.org/10.1111/j.1365-2389.1996.tb01414.x, 1996.
  
  Rüdinger, M., Hallgren, S. W., Steudle, E., and Schulze, E.-D.: Hydraulic and osmotic properties of spruce roots, J Exp Bot, 45, 1413–1425, https://doi.org/10.1093/jxb/45.10.1413, 1994.
  
  Singh, G., Kaur, G., Williard, K., Schoonover, J., and Kang, J.: Monitoring of Water and Solute Transport in the Vadose Zone: A Review, Vadose Zone Journal, 17, 1–23, https://doi.org/10.2136/vzj2016.07.0058, 2018
  
  Wilhite, D. A., Glantz, M. H.: Understanding: the Drought Phenomenon: The Role of Definitions. Water International, 10(3), 111–120. https://doi.org/10.1080/02508068508686328, 1985.
  
  References to answer online review:
  
  Dambrine E., Le Goaster S. and Ranger J. 1991. Croissance et nutrition minérale d’un peuplement d’épicéa sur sol pauvre. II. Prélèvement racinaire et translocation d’éléments minéraux au cours de la croissance. Acta Œcol. 12: 791–808.
  
  Fichter, J., Turpault, M.P., Dambrine, E. and Ranger, J., 1998a. Mineral evolution of acid forest soils in the Strengbach catchment (Vosges mountains, NE France). Geoderma, 82(4), pp.315-340.
  
  Jussy, J.H., Colin-Belgrand, M., Dambrine, E., Ranger, J., Zeller, B. and Bienaime, S., 2004. N deposition, N transformation and N leaching in acid forest soils. Biogeochemistry, 69(2), pp.241-262.
  
  Peters, A. and Durner, W., 2009. Large zero-tension plate lysimeters for soil water and solute collection in undisturbed soils. Hydrology and Earth System Sciences, 13(9), pp.1671-1683.
  
  Watmough, S. A., Koseva, I., and Landre, A.: A Comparison of Tension and Zero-Tension Lysimeter and PRSTM Probes for Measuring Soil Water Chemistry in Sandy Boreal Soils in the Athabasca Oil Sands Region, Canada, Water Air Soil Pollut, 224, 1663, https://doi.org/10.1007/s11270-013-1663-5, 2013.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4622-AC2
AC3: 'Comment on egusphere-2025-4622', Adrien Saphy, 22 Jan 2026

First, we thank anonymous referee #1 and anonymous referee #2 for their detailed and relevant reviews. We appreciate the efforts the reviewers have invested in our manuscript, which resulted in a set of revisions. We are grateful to them for providing stimulating comments and advice to improve the initial version of the manuscript. A new version of the article that incorporates all the changes is ready if you invite us to submit a revised manuscript.
In our online responses to each review, we proposed changes to the article to best address the comments. The major changes that have been made concern the following elements.

- A new statistical method to demonstrate that a point is an outlier when the distribution is not normal. We replaced a statistical test, which was only applicable to a normal distribution, with a more robust method known as the Inter-Quartile Range method.

- The multivariate analysis by NMDS was moved to the results section instead of being in the discussion section.

- The BILHYDAY hydrological model is now described in greater detail in the Methods section. In addition, measured water content data have been added as Supplementary Material in order to corroborate the simulation results obtained with BILHYDAY, through comparison with soil water content measurements with sensors installed nearby on the same experimental plot as the soil solutions.

- We have restructured part of the discussion and exercised greater caution and nuance in some of our conclusions or interpretations, particularly to avoid confusion between soil solution and nutrient solution.

- Tables of hydrogeochemical data concerning soil solutions and throughfall have been added as supplementary material.

- Linguistic corrections have been made, paying particular attention to tense consistency and grammar.
Other more specific additional corrections suggested by the referees have been taken into account and are detailed in the discussion with the reviewers.
To conclude, we have carefully addressed all the comments and suggestions raised by both reviewers, which has led to a substantial improvement of the manuscript. We therefore hope that this revised version now fully meets the requirements of Biogeosciences. We would be pleased to submit the new revised version of the manuscript.

Citation: https://doi.org/10.5194/egusphere-2025-4622-AC3

Adrien Saphy, Emilie Beaulieu, Marie-Noëlle Pons, Benjamin Belfort, and Marie-Claire Pierret

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Short summary

In 2022, France faced its driest year on record, severely affecting the forests of the Strengbach catchment. We observed unusual chemical changes in the soil, linked to drought stress. These included nutrient loss, soil acidification, and stress of soil organisms. Our findings show how drought harms soil health, especially in forests already lacking nutrients, and help predict how ecosystems may respond to future climate extremes.


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