The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods

Round 1

Reviewer 1 Report

The study aims to assess the response of the HGS (HydroGeo-Sphere) model based on different spatial interpolation methods. These interpolation methods, including Thiessen polygons (TP), Co-Kriging (CK) interpolation, and simulated annealing (SA) methods, are used to obtain the precipitation distribution for the model input. The accuracy of the simulation results relies on the comparison between modeled and measured data. The study showed that the simulated streamflow patterns using SA fit the observations better than those using the TP and CK method for a large precipitation event. The objectives are achieved based on the manuscript. However, the presentation needs to be more precise for the model input and be clear for result discussion. Therefore, I would propose a major revision of the manuscript.

 The following is a list of my comments:

1. Lines 102-103. Why select the three interpolation methods. Why not others?
2. Line 181. How to simulate the Meishan and Nianyushan reservoirs? What are the conditions for the transient conditions? Are there observations from the reservoirs?
3. Line 187. Please provide the theoretical concept used in the model. How is the water interaction between surface water and saturated/unsaturated zones?
4. Line 190. What is the role of the groundwater(saturated zone)? The depth of the soil is 2m. How can the water flow dynamics be included in the shallow aquifer system?
5. Line 223. There must be calibration and validation procedures for the model. Please show the plan and the target site-specific observations. The authors might use the river discharge to calibrate the model. How about the groundwater?
6. Line 315. The step shows that the groundwater is not simulated. The groundwater levels are the input to constrain the bottom boundary. Please clarify them. If a transient state is simulated, what will be the groundwater levels for the input?
7. Lines 347-355. The plot for the groundwater level distribution might be necessary for the model.
8. Line 359. The spatial data analyses are important for the Kriging and SA interpolations. What are the experimental variograms for the data? A table for the variogram parameters is useful. Is the geostatistical structure change with time?
9. Figure 6. There is no geostatistical structure shown in the interpolation results. The results show the values at cells only. The parameters for Kriging and SA might be wrong. How is the interpolation result for the TP?
10. Figures 7 and 8. The results in flood and dry seasons show different behavior for a specific interpolation method. For example, in Figure 7, the model underestimates the flood season for TP but obtains an overestimation in the dry season. The OK and SA are differnt pattrens. Did the authors make adjustments for the modeling parameters when using different interpolated precipitation? If this is the case, the comparison of TP, OK, and SA might not be fair.
11. Is that OK or CK for the kriging method? OK represents the kriging in the Figures.
12. Some references are not shown correctly.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

First of all, English should be revised in depth before publication.

Then, Authors should also consider (or at least mention in the introduction) the possibility of using also the Inverse Weighted Distance (IDW) method as interpolation techniques.

In fact, IDW is one of the most used deterministic interpolation method to reproduce rainfall fields. Several studies compared different interpolation techniques concerning rainfall data, and asserted that IDW method is comparable or even more suitable than other techniques (including geostatistics approaches) for finer temporal resolutions, such as a daily resolution or less (Kurtzman et al., 2009; Chen et al., 2017). Moreover, having to perform a large number of interpolations, the use of IDW requires shorter computation times than others (Caruso et al., 1998; Greco et al., 2020)

References to be cited:

• Kurtzman, D.; Navon, S.; Morin, E. Improving interpolation of daily precipitation for hydrologic modeling: spatial patterns of preferred interpolators. Process. 2009, 23, 3281–3291. https://doi.org/10.1002/hyp.7442
• Chen, T.; Ren, L.; Yuan, F.; Yang, X.; Jiang, S.; Tang, T.; Liu, Y.; Zhao, C.; Zhang, L. Comparison of spatial interpolation schemes for rainfall data and application in hydrological modelling. Water. 2017, 9, 342. https://doi.org/10.3390/w9050342
• Caruso, C.; Quarta, F. Interpolation methods comparison. Math. Appl. 1998, 35, 109–126. https://doi.org/10.1016/S0898-1221(98)00101-1
• Greco, A.; De Luca, D.L.; Avolio, E. Heavy Precipitation Systems in Calabria Region (Southern Italy): High-Resolution Observed Rainfall and Large-Scale Atmospheric Pattern Analysis. Water 2020, 12, 1468. https://doi.org/10.3390/w12051468

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

No additional comment.

Author Response

Dear Reviewer: 

    maybe something missed in this part, we are pleased to receive any more comments for improving this manuscript 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.