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Peer-Review Record

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Remote Sens. 2021, 13(16), 3061; https://doi.org/10.3390/rs13163061
by Yuhang Zhang 1, Aizhong Ye 1,*, Phu Nguyen 2, Bita Analui 2, Soroosh Sorooshian 2,3 and Kuolin Hsu 2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Remote Sens. 2021, 13(16), 3061; https://doi.org/10.3390/rs13163061
Submission received: 1 July 2021 / Revised: 27 July 2021 / Accepted: 2 August 2021 / Published: 4 August 2021
(This article belongs to the Section Atmospheric Remote Sensing)

Round 1

Reviewer 1 Report

Authors should mention in the text how their methodology can be useful for many rainfall-related hydrologic topics. For example, we can refer to:

  1. Estimation of Areal Reduction Factor (ARF, Biondi et al., 2021 and references herein)
  2. Spatial classification of Rainfall events (Greco et al., 2020; Houze, 1981; Willems, 2001)

Moreover, I think that Relative Bias (RB) is correctly expressed as summation of terms (Si-Oi)/Oi, and not as a ratio between two summations, and (moreover) its range is [-oo; +oo] (and not [+oo, +oo]):

Consequently, authors should verify their calculations, and then eventually correct

References to be cited: 

  • Biondi, D.; Greco, A.; De Luca, D.L. Fixed-area vs storm-centered areal reduction factors: a Mediterranean case study. J. Hydrol. 2021, 595, 125654, https://doi.org/10.1016/j.jhydrol.2020.125654.
  • Houze, R. A. Jr. Structures of atmospheric precipitation systems – A global survey. Radio Sci. 1981, 16, 671-689. https://doi.org/10.1029/RS016i005p00671
  • Willems P. A spatial rainfall generator for small spatial scales. J. Hydrol. 2001, 252, 126-144. https://doi.org/10.1016/S0022-1694(01)00446-2

Author Response

Responses to comments of Reviewer #1:

Authors should mention in the text how their methodology can be useful for many rainfall-related hydrologic topics. For example, we can refer to:

  1. Estimation of Areal Reduction Factor (ARF, Biondi et al., 2021 and references herein)
  2. Spatial classification of Rainfall events (Greco et al., 2020; Houze, 1981; Willems, 2001)

Biondi, D.; Greco, A.; De Luca, D.L. Fixed-area vs storm-centered areal reduction factors: a Mediterranean case study. J. Hydrol. 2021, 595, 125654, https://doi.org/10.1016/j.jhydrol.2020.125654.

Houze, R. A. Jr. Structures of atmospheric precipitation systems – A global survey. Radio Sci. 1981, 16, 671-689. https://doi.org/10.1029/RS016i005p00671

Willems P. A spatial rainfall generator for small spatial scales. J. Hydrol. 2001, 252, 126-144. https://doi.org/10.1016/S0022-1694(01)00446-2

Response: Thank you for your kind comments and suggestions. We have added related literatures in our revised manuscript based on your suggestions.

 

This study not only deepens our understanding of satellite precipitation estimates products and their hydrological applications, but also contributes to the study of other rainfall-related topics, such as estimation of Areal Reduction Factor (ARF) [33,34] and spatial classification of Rainfall events [35-37].

 

Refence:

  1. Wright, D. B.; Smith, J. A.; Baeck, M. L. Critical examination of area reduction factors. J. Hydrol. Eng., 2014, 19(4), 769-776. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000855.
  2. Biondi, D.; Greco, A.; De Luca, D. L. Fixed-area vs storm-centered areal reduction factors: a Mediterranean case study. J. Hydrol. 2021, 595, 125654. https://doi.org/10.1016/j.jhydrol.2020.125654.
  3. 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.
  4. Houze, R. A. Jr. Structures of atmospheric precipitation systems – A global survey. Radio Sci. 1981, 16, 671-689. https://doi.org/10.1029/RS016i005p00671.
  5. Willems P. A spatial rainfall generator for small spatial scales. J. Hydrol. 2001, 252, 126-144. https://doi.org/10.1016/S0022-1694(01)00446-2.

Moreover, I think that Relative Bias (RB) is correctly expressed as summation of terms (Si-Oi)/Oi, and not as a ratio between two summations, and (moreover) its range is [-oo; +oo] (and not [+oo, +oo]):

Consequently, authors should verify their calculations, and then eventually correct

Response: Thank you for your kind comments. We are very sorry that the range [+oo; +oo] of RB was a mistake and we have corrected it in our revised manuscript.

However, we think that the equation of relative bias (RB) in our original manuscript is the commonly used one. See Eq.(2) in Xiang et al., 2021; Eq. (2) in Tang et al., 2018; Eq.(1) in Camici et al., 2020.

Reference:

Xiang, Y.; Chen, J.; Li, L.; Peng, T.; Yin, Z. Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling. Remote Sens. 2021, 13, 2831. https://doi.org/10.3390/rs13142831

Tang, G.; Behrangi, A.; Long, D.; Li, C.; Hong, Y. Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products. J. Hydrol., 2021, 559, 294-306. https://doi.org/10.1016/j.jhydrol.2018.02.057

Camici, S.; Massari, C.; Ciabatta, L.; Marchesini, I.; and Brocca, L. Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe, Hydrol. Earth Syst. Sci., 2020, 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

The research study by Zhang et. al. analyzes the performance of Satellite Precipitation Estimates (SPEs) based on a rainfall-runoff model based only on remote sensing information in the Yalong River basin. The research uses statistical metrics to determine the best performers SPEs at different scales. The research finds a threshold (i.e. 20,000 km2) in which for smaller catchment areas the streamflow simulation is not related to the input SPE. The research is thorough and the results of interest for the hydrologic modelling community.

Author Response

Responses to comments of Reviewer #2:

The research study by Zhang et. al. analyzes the performance of Satellite Precipitation Estimates (SPEs) based on a rainfall-runoff model based only on remote sensing information in the Yalong River basin. The research uses statistical metrics to determine the best performers SPEs at different scales. The research finds a threshold (i.e. 20,000 km2) in which for smaller catchment areas the streamflow simulation is not related to the input SPE. The research is thorough and the results of interest for the hydrologic modelling community.

Response: Thank you for your kind comments. We have further improved our English expressions to increase readability.

Reviewer 3 Report

  1. It is recommended to add to the manuscript a comment on the existence of flow regulation structures in the basin, for example dams or reservoirs, and how these could affect the results of the hydrological model.
  2. Mention the criterion followed in the study for the sub-basin subdivision threshold.

Author Response

Responses to comments of Reviewer #3:

  1. It is recommended to add to the manuscript a comment on the existence of flow regulation structures in the basin, for example dams or reservoirs, and how these could affect the results of the hydrological model.

Response: Thank you for your kind comments and suggestions. We have added some related information about flow structures in the YLJ basin (e.g., dams or reservoirs) and their functions in our revised manuscript based on your suggestions.

 

The downstream areas of the YLJ basin are heavily exploited for hydropower production through a complex system of reservoirs (e.g. Pingxiang Hydropower Station and Ertan Hydropower Station). These reservoirs are useful for flood control during the rainy season, guarantee human water use during the dry season, and provide abundant power re-sources through specific scheduling rules.

 

In order to ensure the effectiveness of the hydropower plants located in the lower YLJ basin, these reservoirs store more water through regular operations compared to natural conditions. Periodic reservoir operations lead to the observation of more undulating hydrologic curves than the smoother hydrologic curves we simulated (Figure 3a).

Figure 3. Model calibration and verification at four hydrological stations. (a) TZL. (b) YJ. (c) DF. (d) GZ. Pearson correlation coefficient (R); Nash-Sutcliffe efficiency (NSE); Kling-Gupta efficiency (KGE); Relative bias (RB).

 

  1. Mention the criterion followed in the study for the sub-basin subdivision threshold.

Response: Thank you for your kind comments and suggestions. We have made extensive explanation in our revised manuscript based on your suggestions. In fact, the threshold in this study is based on many years of modeling experience. This threshold was selected by considering the complexity of hydrological modeling, the running time step, the area of the Yalong River basin, the time scale of the hydrological simulation, and the resolution of the forcing data.

 

The minimum threshold for sub-basin is based on the complexity of hydrological modeling, the running time step, the area of the Yalong River basin, the time scale of the hydrological simulation, and the resolution of the forcing data.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

This manuscript can be published as it is

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