Thank you for the carefull reading of the manuscript, feedback and recommendations for improvement. Below, we only respond to the recommendations suggesting changes in the article, to propose how we are going to address them.

 Throughfall, also called net rainfall, is a part of gross rainfall after vegetation modification. Simply, it sources from gross rainfall. However, in your linear mixed effect models to evaluate the influence of potential drivers explaining soil water content or soil water content increase, gross rainfall was removed due to strong correlation with mean throughfall. I think you neglect the most important factor of soil water content. Please the authors consider it with alternative method. Such as in your equation 1, you used median of throughfall to calculate the normalized value of the spatially distributed measurements of throughfall. I recommend you can use gross rainfall instead of median of throughfall to calculate it, you can refer to Zhang et al. (2016) and Zhao et al. (2019).

Yes, we omitted rainfall from the analysis and only work with throughfall, which is the dominant part of the net-precipitation. We also agree that from a statistical perspective it would be possible to work with gross precipitation instead of the spatial median throughfall in the mixed effects model. However, after consideration, we believe the physically more consistent way forward is to stick with the spatial median throughfall itself. This way the same variable is partitioned to yield information about the temporal (median TF) and spatial variation (δP_TF). Also, the dependence of throughfall on rainfall is already very well established through previous research, while here we focus on the heritage of its spatial pattern.

I think the manuscript is long, some contents can be deleted or moved to supplementary, such as L268-274, only retain direct relevant contents. Also, the Abstract is long, need to be condensed.

Many long sentences, such as L85-87, L97-100. The language needs to be polished.

Thank you. We will shorten the abstract and also revise section of the main text.

Soil water content or soil moisture has a unified call in the full text. Also, the unit of soil water content should be used m3 m-3 instead of %.

Yes, we sometimes use the term soil moisture when we are referring more general to water in soil, not to the measurement. This was originally meant to improve the readability of the text. We will check for consistency once more. We also use Vol-% as unit for soil water content in the Tables, and will make sure it is used consistently thoughout.

References

We thank the reviewer for the very insightful feedback on the manuscript. Here we respond to the more critical feedback, suggestions for changes of analysis, figures and tables to state how we will address it in the revision. We will give a comprehensive point-by-point-response with the revision.

The statistical treatment is relatively heavy, in that there are multiple normalizations, rank transformations, and per-event terms. Interpreting results is difficult as a result. Some additional discussion on this point would be helpful, as would more precision in the presentation of results. In general, the statistical treatment makes it impossible to determine how much difference throughfall variation makes for soil moisture. De-transforming after analysis or repeating the analysis with raw variables and generalized linear models that do not require pre-analysis transformations are two options that would make the results more useful.

We agree. The statistical treatment renders it difficult to follow and we will improve the communication in the next version. Regarding the specific concern on data transformation. The mixed effects models, used to test how spatial fields of throughfall affected soil moisture response, did not strictly require transformations. Instead, we transformed data for the following reasons:

1. We separated mean and spatial deviation from the mean in order to be able to distinguish between, e.g. precipitation event size and the spatial distribution of the throughfall. This allowed us to pool the data across events. The same is true for the soil water content.
2. We z-transformed the data in order to be able to compare the effect sizes. The advantage will be seen more in the next version of the manuscript, where we present the effect sizes.

In the new version we will more clearly state how data were transformed and explain why.

With a few mild assumptions, this manuscript appears to have enough data to do a full water balance of the soil, including deep percolation during events and transpiration between events. Those are probably for the future, but maybe they can be added easily here for a richer description of the importance of spatial variability of throughfall.

Yes, there are several manuscripts in preparation dealing with the overall water balance of the forest plot, which turned out to be more complex than we expected. Therefore, the focus is really on different challenges. For example, determining transpiration from the soil water balance is not trivial in general (Hupet et al., 2003, Jackisch et al., 2020). In our case, we also observed species interacting belowground. We sincerely think that separating those narratives helps focusing the message, and we therefore prefer to focus on the effect of throughfall on soil water response.

How many precipitation events were there trends, and were those trends interpretable?

Somewhat less than half of the events had spatial trends (Table S2). We found those trends difficult to interpret. They changed direction with time, were of varying strength and occurred in small as well as in large events. Canopy cover had no spatial trend (Table S2). Spatial trends may have been related to the interaction of slope aspect and wind direction. We will add the reference to the Table in the supplement.

Why not use co-kriging, given the finding of correlation with local canopy density and distance to tree?

The kriging was performed for throughfall. This was done, because throughfall and soil moisture cannot be assessed at the same location. There was no relation between throughfall and distance to the next tree (Table S5). The latter was only the case for soil water content. There was a relation between canopy cover and throughfall. Thus, it is true that co-kriging with canopy cover would theoretically be possible. However, unfortunately, the canopy cover was only assessed at the throughfall locations, and not at the soil sensor locations. We therefore cannot apply co-kriging to support kriging to sensor locations.

L263 normalizing predictor variables is a very important decision that affects model selection and interpretation. Unfortunately, it is difficult to interpret here because “normalize” does not have a consistent meaning and the exact transformation is not specified (Fig 5 axis labels suggest z transform). Was the transformation parametric, and, if so, how well is that choice justified given the rank transformation used on many other variables, including most response variables? Why were all transformations necessary? Axis labels fig 5 and 6 suggest the response variables were also transformed, but that is not mentioned in the methods. Finally, the results and discussion should acknowledge the transformed nature of the analyses when making interpretations.

Yes, thank you for catching this. We did indeed apply a parametric z-transformation to all variables, including the response variable. We will add this information. As mentioned above, this was done to allow better interpretation of the effect sizes of the mixed effects models. We also agree that this motivation should be better stated.

Note that rank transformation was only done to differentiate between effect of the spatial mean like event size or general soil moisture state, and the spatial pattern, as stated above. We will make an effort to point the reader to this.

Tables 2, 3, and S5: except for the sign, it is redundant to present both t and p. It would be better to see effect size to help judge importance of these relationships, not just consistency of them. I do not understand what is literally meant by “variables are scaled such that the t-value indicates the effect strength” (Table 3 caption), because I do not know what “strength” means in this context.

This is true and also relates to the main comment stating that the interpretation of the results is difficult and agrees with Reviewer 2. We therefore present results differently in the revision, showing the effect size of each factor for the selected model. Figure R1.1 shows an example. With all variables being z-transformed, the slope estimate indicates the influence of the specific predictor on the response variable.

Figure R2.1: Factors influencing local soil water content response after rainfall (Δθ_i, i.e. difference between soil water content after and before each precipitation event). Shown are the slope estimates (dots) and their standard errors (lines) for the best linear mixed effects model including all events. Stars indicate the p-values (*** p < 0.001; ** p < 0.01; * p < 0.05). Variables are z-scaled such that the slope estimate reflects how strongly the predictor affects the soil moisture response.

Abbreviations: TF – throughfall; SWC – soil moisture

Fig 2 grouping by storm size is not necessary; please consider whether a scatterplot—with storm size as the independent variable—would give richer information.

Yes, good point. We will do this.

L420 it would be good to show at least some data vs. kriged surfaces; readers have no way to know how to judge this now except this sentence in the discussion.

We can add a figure showing kriged surfaces of a small and a large event to the appendix, to show the variation, and also demonstrating how the number of sensor locations decreases when in large compared to small events.

L426 the technique to estimate the variogram is irrelevant; the question is whether the assumptions of Kriging are useful in this application. Neither Voss nor Lark established that it is, so I do not agree with citing them here. Just because the data can be modeled by variograms does not mean Kriging assumptions hold, and there are good reasons to believe that throughfall does not vary smoothly in space. As the first attempt to use Kriging quantitatively for interpolation, the burden is on the current manuscript to establish the applicability of the technique. A more detailed discussion of the Kriging in light of what the literature says about spatial variability would be an important addition. For example, one issue that is not addressed here is the role of sampler size in spatial variability estimates and interpolation.

What we meant to say is that both applied method and the number of sensor locations adhere to the state of the art for derivation of the throughfall variogram that is next used for kriging. We will reformulate. We also can mention here the cross-correlation which yielded satisfactory results, plus refer to sampler size. We will however try to keep this section compact, partly to accommodate the other reviewer. However, we agree that the uncertainties need to be discussed.

References 

Hupet, F., Lambot, S., Feddes, R. A., van Dam, J. C., and Vanclooster, M.: Estimation of root water uptake parameters by inverse modeling with soil water content data, Water Resources Research, 39, 2003.

Jackisch, C., Knoblauch, S., Blume, T., Zehe, E., and Hassler, S. K.: Estimates of tree root water uptake from soil moisture profile dynamics, Biogeosciences, 17, 5787–5808, https://doi.org/10.5194/bg-17-5787-2020, 2020.

We thank the reviewer for the positive feedback to the manuscript and constructive recommendations. Below we respond to the main concerns to show how we are going to address them in the revision.

The abstract is a little bit long, some of the results are duplicated as those regarding the effect of soil water distribution on soil water dynamic (lines 29-33 and lines 35-37so the authors can consider to shorten it..

Yes, we agree. We can formulate the results part in a more compact manner.

There exist some confusion on using the terms δPTF ; δθpre  and  δθpost  do they refer to normalized variables; spatial distribution , spatial pattern?

Thank you for pointing this out. Indeed, both reviewers found that the normalization of variables was not easy to follow and we will make an effort to better motivate this in the revision.

Data on event topsoil seems in figure S3 seem not to follow a clear concave decreasing relationship with mean soil water content as said in line 298-299. Indeed, the variation around SWC equal to 20 is positive. I hesitate these results support the statements of lines 298-299.. As it is no longer discussed in the paper, it might be deleted. What does it mean “enhance” soil water content variation?

It is true that concave is probably not the best way to describe this form. We want to demonstrate how soil water content spatial variation (CQV, on the y-axis, the typo will be corrected in the revision) changes with the spatial median (x-axis). At the same soil water content, post event soil water content has higher spatial variation than the pre-event, especially in drier soils. This shows that precipitation events do imprint on the soil water content pattern. But surprisingly at the same time, the soil water contents does not correlate with the input, which suggests that other factors are active. We will reformulate and make sure to connect to this in the discussion.

Discussion of GLM model on the soil water response is rather complex. Particularly interpretation of the interactions of fixed effects in lines 376-378 and figure 6. A clearer a more detailed explanation (also in the legend of figure 6) would make easier to the reader understand the foundations of the final statement. “Locally drier soil increased soil water storage in wet, but decreased it in dry times”

Agreed, we admittedly struggled when writing this section. We will work on it once more, trying to make the interpretation more accessible. We will also change the name of the groups in Fig 6a and 6b. Furthermore, in response to this comment and comments by Reviewer 1, we will change the presentation of the mixed effects model results. Instead of giving the results in a Tables 3 and 4, we will visualize the effect sizes as shown in Figure R2.1.

Figure R2.1: Factors influencing local soil water content response after rainfall (Δθ_i, i.e. difference between soil water content after and before each precipitation event). Shown are the slope estimates (dots) and their standard errors (lines) for the best linear mixed effects model including all events. Stars indicate the p-values (*** p < 0.001; ** p < 0.01; * p < 0.05). Variables are z-scaled such that the slope estimate reflects how strongly the predictor affects the soil moisture response.

Abbreviations: TF – throughfall; SWC – soil water content