The manuscript ‘Towards reducing the high cost of parameter sensitivity analysis in hydrologic modelling: a regional parameter sensitivity analysis approach’ assesses the spatial pattern of parameter sensitivity and the performance of sensitive parameter transferability over 25 basins of the Pacific Northwest region of North America by using the VIC model. It is notable because a larger suite of 44 parameters parameter considered and the multiple output variables were assessed in the model. Overall, I think that the manuscript is well-written and the topic is attractive. My recommendation is that the paper be published in Hydrology and Earth System Sciences provided the following points are addressed by the authors. 

Major comments:

I notice the authors clustered 25 basins and then test the ability of sensitive parameter transferring. To be honest, the number of basins used for clustering and parameter transferring (i.e., regionalization) is insufficient, you see, the basins of cluster #2 are only three in Figure 7. Insufficient samples will cause the classification results to show great randomness, thus impacting the parameter transferring.

The result that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a targeted basin, is doubtful. Have you compared the result to the previous research for parameter regionalization? As far as I know, at least using more than 5 donor basins is credible for parameter regionalization (e.g., Oudin et al., WRR, 2008; Bao et al., JH, 2012).

Have you considered the cross-validation for the evaluation of the transferability of parameter sensitivity? I think cross-validation is a good way to check whether the sensitive parameter regionalization is reliable, i.e., using each of the basins in turn as if it were ungauged (Gou et al., BAMS, 2022).

This research does not involve parameter calibration, so what is the significance of sensitive parameter transferring? Why not transfer the calibrated parameters to ungauged basins directly?

Minor comments:

Line 93-Line 98: Check the data ranges again. ‘average annual precipitation over the 25 basins ranges from 448 mm/year to 1666 mm/year.’ But the average annual precipitation of basin ‘BruneauR’ is 337 mm. Meanwhile, you round to one decimal for temperature but keep two decimals for snow index and aridity index, need full text unified. Last, the definition of snow index should be merged with the caption of Table 2, i.e., here gives the calculation method and reference.

Line 172: ‘Arno’ to ‘ARNO’

Line 220: ‘x’ to ‘×’

Line 220: What is the definition of elementary effects (EEs)? Is this value calculated based on the model output? Is the mean of the target modeled output?

Line 239: 75 sets of noninformative/informative parameters are 75 sets of outputs or 75 experiments? Here 75 sets equal to the 25 basins multiply 3 model outputs. I think here cannot be represented as ‘75 sets of noninformative/informative parameters’ because you adopted 44 parameters for each experiment, right?

Line 241: ‘all 75 experiments’ is right.

Table1. The meaning of showing relief?

Table2. The definition and method of the ‘Snow Index’ and ‘Aridity index’ should show in the methods section. There is no point in repeating the emphasis here. And the temperature threshold (i.e., 2°C) for snow index calculation, why? has any references?

Table3. Again, the details of the table header, delete or move to the end of the table by ‘Note’.

Figure 1. The basin ID can be marked.

Figure 2. How to distinguish whether the parameter is invariant-informative? ‘Parameters are considered invariant-informative if the count of basins in which they are informative’, how many informative basins are eligible for invariant-informative? 10 or 15?

Review of the manuscript „Towards reducing the high cost of parameter sensitivity analysis in hydrologic modelling: a regional parameter sensitivity analysis approach” by Larabi et al.

In this manuscript, Larabi et al. present a sensitivity analysis for 25 basins in North America using the VIC model. In addition to discharge, they used evapotranspiration and snow water equivalent as target variable for the sensitivity analysis. For an efficient simulation, they have clustered the basins based on catchment attributes. Based on the results of the sensitivity analysis, they distinguished into parameters that are overall informative / non-informative and into those that are relevant in a part of the catchments (variant-informative).

This study is well-written. All steps are clearly described. The results are supported by good figures.

Thus, I have only a few comments and can recommend it for publication.

Comments:

L.38-50: I suggest to add that the parameter space could also be reduced by constraining parameter ranges to a smaller range.

247: Sawicz

258: Line below the table is missing.