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Peer-Review Record

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

Remote Sens. 2020, 12(19), 3241;
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Remote Sens. 2020, 12(19), 3241;
Received: 24 August 2020 / Revised: 24 September 2020 / Accepted: 3 October 2020 / Published: 6 October 2020
(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

Round 1

Reviewer 1 Report

The paper deals with the calibration of the Hillslope River Routing-VIC hydrological model thorough the use of synthetic SWOT data. The simulated data comes from ground observations extracted only during the (hypothetical) passage of the satellite and introducing the uncertainty linked to the mission requirements.

I read the article in one go. It is simple, complete and clear. In addition, the calibration exercise that is performed is well illustrated and immediately shows the results obtained, without too many unnecessary idiomatic expressions. Regarding the analysis, I have no comments, while I have small suggestions for the text:

1) MATERIALS AND METHODS: I agree with no repeat all the time the methods already published in previous papers, but I am strongly convinced that a paper should avoid that the reader is forced to search materials or information in other papers. For this reason, here I have my major suggestion and request that is to provide the uncertainty of the third dataset Qs. It is not sufficient to say “The distinction and process of deriving the Qgs and Qs timeseries’ as synthetic SWOT data are described fully in [19]”. Given the importance of the time series that should describe the synthetic SWOT data and, hence, should be tested for the calibration of the hydraulic model, I think is necessary to describe briefly the uncertainty used to change the time series.



- legend of Figure 3 is not correct. In green you should put Ds and in red Dsmax. Please, correct it.

- lines 247-249 refer to the changed colors of the usoilD plot of Figure 3. I think that you can add a reference on the marker colors in the plot to emphasize what you are commenting and to facilitate the reader in the understanding of Figure 3.

- About Table 2, I have some doubt about the range of parameters used in the calibration. For bi, Dsmax and Ds there are cases in which the optimal parameter coincides with the minimum or maximum limit of the input range. I refer to 0.04 of bi (parameter set B), to 40 for Dsmax (parameter set C) and to 1.0 and 0.1 for Ds (parameter set B and C). About the selection of the parameters, are the ranges of variability physically right? The selection on the boundary of the range suggest that the parameter can vary beyond the threshold and provide still good results. Similarly, if you fix this parameters, especially Dsmax and Ds, you do not decrease the performance deeply. I would like to read some comment on this aspect.

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript entitled “The applicability of SWOT’s Non-uniform space time sampling in hydrologic model calibration” by Nickles et al. submitted to Remote Sensing journal. The authors compared model calibration results from a SWOT-like dataset derived from 39 gauging stations in the Ohio River Basin in the US. The study sounds interesting, but I suggest the authors conduct a more serious literature review and present model setup clearly. General comments: In general, could the authors clearly state the novelty of this study; the application of SWOT in hydrological model calibration was studied in Huang et al. (2020) (for example). They used SWOT-like data to calibrate a CREST-RS hydrological model for ungauged basins. I think the authors should conduct a rigorous literature view and improve the current introduction and discussion part. I suggest they modify the data and methodology sections. In the current stage, it is difficult to follow. They can develop this section as following (1) data used to simulate the SWOT mission; (2) HRR-VIC model and input data; (3) model calibration. Technical comments: Could the authors explain in more detail what did they modify in the VIC model to account for snowmelt (compared to the original version/explained why the original version cannot be applied in this case) instead of only giving a reference to Anderson (1973) and Barnhart et al. (2016)? Why is this modification important in this study? If it is important to note that the snow accumulation and melt is a relatively small components, then why do they modify the VIC model? Could the authors describe how to generate the Qgs data series in detail instead of giving the references? This is important to understand why model calibration with the SWOT-like product is similar to that with gauged data. Dang et al. (2020) used a VIC model and figured out reservoirs may influence on model calibration. The Ohio River Basin has been regulated by hydropower dams and locks. How do these artificial structures influence on the calibration exercise in this study? Wongchuig-Correa et al. (2020) used the SWOT-like observations and claimed that the main source of errors from climatic forcings and parameters of the model. How do climatic forcings influence on the calibration exercise in this study? Other VIC model parameters should be reported. For example, spatial resolution, spatial extent, soil data (other than the calibrated parameters), land use and land cover data, etc. How many layers in the VIC model? Why only the upper soil layer depth is used for model calibration? If the authors used the same parameter set for the whole basin (bi, Ds, Dsmax, usoilD), how the model performs at upstream locations compared to downstream locations? Whether a weighting factor should be considered? Is the code for calibration available together with the HRR-VIC model (I read Line 127 and see that only the modified version of VIC is available)? Could the authors share the code with the reader of the journal? Why don’t they conduct a model validation simulation and see the performance of the models calibrated with the three strategies? Minor comments: Line 32: satellite remote sensing measurements? The authors should redesign Figure 1. (1) Add an inset, showing the location of the river basin; (2) add a clearer boundary shapefile and labels; (3) improve image resolution; (4) add a coordinate grid (if possible). Similar comments are applied for Figure 2. Figures 3 and 4. The resolution is low. Please export this figure with a resolution of 300 DPI or more. Line 95: how is the GRWL database used in this study? Does the hydrological model provide simulated water widths, or the authors used the GRWL database to select the gauged location only? Please indicate this in the text. It would be easier to follow if the authors combine the equations (2), (3), and (4). Revise the manuscript for minor grammar mistakes. References Anderson, E. A. (1973). National Weather Service river forecast system: Snow accumulation and ablation model (Vol. 17). US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service. Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F., & Schneider, D. (2016). Snowmelt rate dictates streamflow. Geophysical Research Letters, 43(15), 8006-8016. Dang, T. D., Chowdhury, A. K., & Galelli, S. (2020). On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments. Hydrology and Earth System Sciences, 24(1), 397-416. Huang, Q., Long, D., Du, M., Han, Z., & Han, P. (2020). Daily continuous river discharge estimation for ungauged basins using a hydrologic model calibrated by satellite altimetry: Implications for the SWOT mission. Water Resources Research, 56(7), e2020WR027309.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The manuscript entitled “The applicability of SWOT’s Non-uniform space-time sampling in hydrologic model calibration” by Nickles et al. submitted to Remote Sensing. The authors addressed well all of my comments in the previous round. I only have a minor suggestion: please improve the resolution of Figures 4 and 5.

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