I thank the reviewer for the very positive and constructive feedback. Please find my responses below.

1.    The title suggests that a part 2 exists. If so, please refers to it in the manuscript. What does it deal with?

Thanks, now mentioned in the abstract and in the introduction (L 60):

In a two-part series we aim to fill this research gap. In this first part we propose a mixed distribution approach for low-flows to perform frequency analysis in catchments with a mixed summer/winter regime. The method is based on an independence assumption which is explored in the second part of the series \citep{Laaha_copula_2022}, where we address possible seasonal dependency using a copula-based estimator.

2.    I suggest to include a figure that synthesizes the seasonal characteristics of the analyzed dataset, e.g. empirical cumulative distribution of the observed low-flow values, mean monthly series, so to appreciate the effectiveness of the mixed proposed approach.

The seasonal characteristics are characterized in Figure 5 and a detailed assessment is given in Laaha and Blöschl 2006ab. I am therefore reluctant to add “more of the same” information to this paper. However, I see your point and will add an additional Figure (attached) with regime plots (monthly mean and monthly low flow regime) of the four example gauges of Fig. 1. 

3.    Sometimes variables are not explicitly defined. Please, make sure of that. For example, at line 129, define clearly the utilized circular seasonality index (r) and the seasonality ratio (SR). I see the reference to Laaha and Blooschl (2006b), but I suggest to make the manuscript self-structured.

Added to the beginning of the section, which is now formulated in the following way:

Using archetypal examples, we demonstrate how the model behaves for a range of low-flow regimes: from weakly seasonal ones where summer and winter occurrences are equally likely, to strongly seasonal ones where low-flows in one season predominate. Seasonality is characterized by two indices: the seasonality ratio (SR), where SR > 1 indicates a winter and SR < 1 a summer low flow regime, and the circular seasonality index (r), where r=0 indicates the weakest and r=1 the strongest possible seasonality for the definition of indices see {laaha_seasonality_2006}). 

Seasonality is characterized by two indices: the seasonality ratio (SR), where SR > 1 indicates a winter and SR < 1 a summer low flow regime, and the circular seasonality index (r), where r=0 indicates the weakest and r=1 the strongest possible seasonality (for the definition of indices see{laaha_seasonality_2006}).

4.    The values of the relative frequency in Figure 3 are very low; the discussion cannot be appreciated very much. Maybe I would add the cumulative relative frequency or the CDF

Will be added.

5.    Comments to table 2 at lines L241-245 are a bit confusing. What does it mean 32 to 34 cases? I don’t have the total number of gauges to verify the 10%. Please, refer also to the syntax used in the header of the table, i.e. 1st and 3rd

The number of catchments is mentioned in L. 178. This number is also now added to L. 241, and to the caption of Fig. 2, and reference to the syntax in the header is given.

6.    Please, make clear in the text among which variables the correlations are computed (section 4.2.1)

Between the accuracy gain and three seasonality indices (see first sentence of this paragraph). 

7.    Please, note that Figure 4 is neither recalled nor discussed in the text. Add comments. Define the Relative error. Which return period does it refer to?

Thanks, will be included in the first sentence of the paragraph.

8.    Figure 6: be consistent with the shown variables (relative error not defined, performance gain not defined)

The term error was defined as a synonym for the deviation when we place emphasis on the inferior model (L. 194).  Analogously, the term gain was defined as the change in performance of the superior model compared to the inferior model (L. 192). All have been used throughout the text.

Technical corrections: 

Many thanks, will be amended in the revised version of the MS.

We thank the reviewer for the positive and constructive feedback. Please find my responses below.

1. Line 85 states that summer and winter events are independent of one another. This is a relatively strong assumption that underlies the method…

Response: I fully agree with this statement that the methods proposed in the paper rely on the independence assumption and there will be certainly catchments where the assumption is not strictly fulfilled. This sis similar to the iid  assumption of other estimators that is often not strictly fulfilled in practice, yet useful for model development.

I have therefore structured the research in a two-part paper series where the first part develops methods assuming there that dependency will have no significant effect. The second part extends the scope will explore seasonal dependency in detail. It examines the value of an extended estimator that accounts for the seasonal correlation of low-flow events and assesses its value in a hydrological context. This companion paper (hess-2022-358 A mixed distribution approach for low-flow frequency analysis – Part 2: Modeling dependency using a copula-based estimator) was submitted to HESS on 13 Oct, and is currently in the state of editor assignment.

In the MS of this paper, the effect of possible seasonal correlation has been discussed at the end of the paper and I see from your comment that it should come earlier, where the reader starts to question the assumptions of this paper. We will therefore add at Line 90:

It should be noted that the assumption of strict seasonal independence will only be met in part of the catchments, while there may be cases where some dependency of seasonal minima exists. This is explored in detail in the second part of this two-paper series \citep{Laaha_copula_2022}, where we examine the value of an extended estimator that accounts for the seasonal correlation of low-flow events.

I need to note here that the results of the second part show that there is indeed some correlation in a part of the catchments, but this has only impact on the estimates of mild (e.g. 2-year) low flow events, which are usually of little interest. For severe events the difference is negligible what supports the validity of the estimates of this study (and supports the interpretation of “deviation” being a “gain” based on being a valid generalization of the common estimator).

2. Overall issue - use of the word "gain" and, for example line 12 of the abstract "the error is reduced by ...". I am not convinced, if the underlying assumptions are not checked (above), that the new mixed distribution estimates are exactly correct…

I see your point, but argue from the findings of the companion paper (where not only the assumptions, but also their impact on the estimates have been checked) that for the return periods we are usually interested in, the differences between the mixed and the extended mixed copula estimator are negligible so that the mixed distribution approach is indeed a valid and accurate estimator of the low-flow event. Given that the terminology is also set out in Section 3.2.1, I would like to keep this wording, but suggest to add the following sentence (Line 212):

It should be noted again that the interpretation of the deviation as a gain depends on the (reasonable) assumption that the model is superior to the alternative model, and the terminology of the study should be understood as such.

3. Line 36 - You state that many studies have suggested defining summer from about April or May to November, and winter as remaining. But, you never state how you are defining the two seasons for the analysis in Sections 2.4 and  3. Is this the division? Is it April or May? Please provide the exact day.

Thanks – The period in the Austrian study starts in April and this will be added to the MS.

4. Please provide how many years of data are used for each river (n = ?).

Thanks – will be added.

5. Line 242 - Please explain how the deviation can be exactly zero. I find this highly unlikely given the fitting of two distributions and then comparisons of the extreme tails of each distribution. If this occurs because the Mixed Model defaults to the single season, explain this. Although, I doubt it would be exactly the same to 1 or 2 decimal places.

Yes, it occurs as you said because the Mixed Model defaults to the single season and this will be added.

Minor issues:

Many THANKS, they will be amended.