1. The paper is excellent, it only needs minor corrections/clarifications.

2. Abstract "Our results show that this approach is generally better than the best randomly selected wells". This sentence must be changed because it seems to insinuate than one could change n monitoring wells randomly and they could performed better than the optimized approach (because of the world "generally').

3.  Introduction: "there is a dualism between monitoring costs and monitoring quality (i.e., the information gained by monitoring)" This sentence is not logical. Perhaps you mean that the higher the cost of monitoring, the better the quality of the data, provided that the monitoring is well designed. In other words, one could spend a lot of money and yet do not improve the quality of information unless the money is well spent. A deeper issue not addressed in the paper is how the expenditure in monitoring may improve the quality of groundwater, which means that groundwater monitoring is tied up to groundwater management.

4. Introduction: "How does the reconstruction/interpolation error develop when a given number of monitoring wells are reduced? How

does the error of reducing wells according to information content compare to a random reduction?" These sentences are confusing. Perhaps you mean: "How does the reconstruction/interpolation error varies with changes in the number of monitoring wells?  How does a random reduction of monitoring wells affect the information content gained by groundwater monitoring?" I urge the authors to improve the logical meaning of their paper's text.

5. Section 2.1.4:  you propose that an mxn matrix can be decomposed into matrices Q and R, such that A = QR; then you propose that there is matrix C such that A C^T = Q R; unclear why not : A C^T not equal to QR C^T

6. Section 2.4.2 "Outlier values that exceeded a moving average  (window size 11) of ±3 σ were removed during preprocessing"

Perhaps you mean: "Data values that deviate by more than ±3 σ from the moving average (with a window size of 11 values) are considered outliers and were removed from further processsing"

7. Section 2.4.3 " omnidirectional Gaussian semivariogram model" I believe you mean "an isotropic Gausian semivariogram model"

8. Figure 2b: the Pareto front of number of wells vs RMSE: what about Pareto fronts for the other goodness-of-fit criteria? such the NSE or the KGE?

9. Figure 7: add GMW (groundwater monitoring well) to the list of acronyms

General comments:

The submitted manuscript presents a versatile optimization strategy for monitoring well networks that can be used on temporal and/or spatial hydraulic head data. The methodology and its demonstration using the example data set are presented nicely with sophisticated plots and graphs. The analysis and interpretation of results are considerably elaborate, which increases the quality of the manuscript but on the other hand, makes the discussion hard to follow in some of the paragraphs for the non-expert reader. 

The manuscript is in general very well written. There are only some minor issues that need to be addressed for improved readability.

Specific comments:

• The terms “sensor” and “well” are used interchangeably throughout the manuscript. For the sake of clarity, I’d suggest sticking with only one term. The sensor could be viewed as a part of the monitoring well, therefore in my opinion it makes more sense to use “well”. The goal is to optimally select wells.
• 7 and fig.6 on p.17: The performance metric nRMSE is used in Figure 6. Is it the RMSE relative to the standard deviation or the range of observations? Please explain this detail on page 7, where the RMSE equation is given.
• Lines 248-253: Although it is clear, why a single set of kriging parameters is used in the production of the map series, on what basis this particular parameter set is selected. Perhaps the values for the parameters can be provided in one additional sentence.
• Line 346: It seems that reduction stages (or steps as it is used in the caption of Fig.5) refer actually to the fraction of the wells used in the analysis. My understanding from a 10% reduction is that 90% of the wells that is 432 wells are used. Please consider rewording or clarifying this issue where necessary.
• Line 346-347: “Consequently, well 154-304-1, the highest-ranked well shown with rank 59 (bottom),…” – This is confusing because one would expect the well with rank number 1 the “most important” well.
• I’d suggest adding a little discussion in the conclusions section about the relative value of 1-D (hydrograph) data versus 2-D data. Which should be preferred if both are available? For which type of monitoring data does the presented approach work better?

Technical corrections:

• The website link for the reference on lines 559-560 needs to be corrected as it does not seem to be active.