Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data

Round 1

Reviewer 1 Report

The authors have done a good work by new method for calculating TWSC at the small basin scales. However, there is lack of novel component in the study. Many people have used these approach for ET and other water balance estimations at different spatial scales. Based on my assessment, the manuscript requires Major Revision and cannot be recommended for publication in its current form in “Remote sensing”. I believe that after duly addressing the comments authors can improve the quality of manuscript substantially to make it more insightful.

The overall presentation in Introduction section lacks synergy and exist in bits and pieces. Though authors have identified the research gaps, the literature survey part can be more streamlined especially while coming towards the problem statement.

 

In the Introduction, it is seen that authors have not adequately focused on the literature review for different hydrological models that have used satellite products for the estimation of TWSC, ET, runoff. As it can be seen, there are very few studies mention only. Authors have not talked about SWAT semi-distributed approach model; there are other wide variety of conceptual models and semi-distributed models such as VIC, SHM, IHACRES etc. VIC model is a globally applied hydrological model that accounts for sub-grid variability and is an important hydrological tool, which has been successfully at grid scale in tropical monsoon climatology (Srivastava et al., 2017). I would strongly recommend the authors to add some recent studies that have applied the VIC model in various river basins. Further, authors are suggested to mention about conceptual hydrological models too. Elaborate more on how this paper differs or affirms the findings and conclusion of those studies. These points need to be clearly addressed in the introduction section. Hence, I would strongly recommend adding these recent and important references to add more scientific weight in their Introduction in the lines mentioned above.

 

Srivastava, A., Sahoo, B., Raghuwanshi, N. S., & Singh, R. (2017). Evaluation of variable-infiltration capacity model and MODIS-terra satellite-derived grid-scale evapotranspiration estimates in a River Basin with Tropical Monsoon-Type climatology. Journal of Irrigation and Drainage Engineering, 143(8), 04017028. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199

 

The authors should elaborate more on the selection of the models selected in the study. The selection of all models over other existing models should be expounded. There needs to be a detailed comparison of various simulation models. The similarities, differences, shortfalls of other simulation models. While discussing these points authors can take into account some of the studies mention below:

 

Srivastava, A., Deb, P., & Kumari, N. (2020). Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem. Water Resources Management, 34(1), 327-341. https://doi.org/10.1007/s11269-019-02452-z

 

In the discussion section some of the recent key literature have not included that have studied the impact of ET and runoff by using different satellite products. Authors need to include the proper discussion by using the literature provided above.

Detailed water balance components should be provided in a tabular format.

 

Author Response

Dear editor and reviewers:

 

Thanks for your valuable comments about our manuscript. We have carefully considered your reviewer comments and made changes accordingly. All revisions are highlighted in red in the manuscript. Meanwhile, point-to-point responses to comments are also attached.

 

If any problem, please feel free to contact us.

 

Best regards,

-- Dr. Yulong Zhong

 

 

Response to Reviewer 1 Comments

Review

The overall presentation in Introduction section lacks synergy and exist in bits and pieces. Though authors have identified the research gaps, the literature survey part can be more streamlined especially while coming towards the problem statement.

Response: Thanks, we have revised the presentation in the Introduction to make it more streamlined.

 

In the Introduction, it is seen that authors have not adequately focused on the literature review for different hydrological models that have used satellite products for the estimation of TWSC, ET, runoff. As it can be seen, there are very few studies mention only. Authors have not talked about SWAT semi-distributed approach model; there are other wide variety of conceptual models and semi-distributed models such as VIC, SHM, IHACRES etc. VIC model is a globally applied hydrological model that accounts for sub-grid variability and is an important hydrological tool, which has been successfully at grid scale in tropical monsoon climatology (Srivastava et al., 2017). I would strongly recommend the authors to add some recent studies that have applied the VIC model in various river basins. Further, authors are suggested to mention about conceptual hydrological models too. Elaborate more on how this paper differs or affirms the findings and conclusion of those studies. These points need to be clearly addressed in the introduction section. Hence, I would strongly recommend adding these recent and important references to add more scientific weight in their Introduction in the lines mentioned above.

Response: Thanks for your insightful comments. We indeed have not adequately focused on the literature review for different hydrological models that have used satellite products for the estimation of TWSC, ET, and runoff. Many studies have estimated TWSC with different hydrological models. We are grateful to the reviewer for providing us with literature, and we have added some recent and important references, including those recommended by the reviewer in the introduction section. We add some words to elaborate on the difference between this study and the method of using hydrological models. “In recent years, applications of hydrological models have been greatly extended, which can be further used to estimate small-scale TWSC. In addition to runoff simulations, hydrological models are also used to simulate evapotranspiration (ET), soil moisture, and other water balance components. The hydrological models are an effective tool for linking the water balance of the basin with runoff and other hydrological processes. The hydrological models can be divided into empirical model, conceptual models and physically based models. Empirical models only obtain information from existing data without considering the characteristics and processes of the hydrological system, so these models are only effective within the boundary. The conceptual model uses a semi-empirical equation and large number of in-situ data is required for calibration. Calibration involves curve fitting, which makes interpretation difficult, so the effects of land-use change cannot be predicted with great confidence. While the physically based model can overcome many of the shortcomings of the other two models due to the use of parameters with a physical interpretation. It can provide a large amount of information even outside the boundaries and can be applied to a variety of situations [1]. Several studies using remote sensing and in-situ data based on water balance equations, combined with ET and runoff output from various hydrological models to estimate TWSC at grid scale [2-4]. However, as Srivastava et al. [2] mentioned, this method maybe suitable for the regions where in-situ data are lacking or unavailable. In the regions with relatively more in-situ data, e.g., this study, we can make full use of more in-situ data to further improve the estimate accuracy. In addition, as a relatively independent catchment unit, sub-basin can clearly reflect the hydrological processes within and be-tween units. Therefore, studying TWSC at the sub-basin scale can better reflect the TWSC variation pattern. Hence, in this study, we take the Ganjiang River Basin (GRB) as a case, divides the GRB into several sub-basins, and apply multi-source data, i.e., topography, hydrology, meteorology, and remote sensing as the data sources. Based on the water balance equation, ET and runoff are modeled by two state-of-the-art models, and we estimate TWSC at sub-basin scale (i.e., ~600 km²).”

 

The authors should elaborate more on the selection of the models selected in the study. The selection of all models over other existing models should be expounded. There needs to be a detailed comparison of various simulation models. The similarities, differences, shortfalls of other simulation models. While discussing these points authors can take into account some of the studies mention below:

Srivastava, A., Deb, P., & Kumari, N. (2020). Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem. Water Resources Management, 34(1), 327-341. https://doi.org/10.1007/s11269-019-02452-z.

Response: Well, thank you for your comments. We have supplemented the selection of models selected in this study in section 3.3. “Hydrological models are widely used to simulate the runoff process, many scholars employed some common models such as the Variable Infiltration Capacity (VIC) model [3-5], Soil and Water Assessment Tool (SWAT) model [6-8], Identification of Hydrographs and Components from Rainfall, Evaporation, and Stream (IHACRES) model [5-6] to simulate the runoff in the various river basin. However, the condition of each basin is different, especially the difference in climate, which makes the selection of hydrological models is critical to the accuracy of runoff simulation [6]. In terms of this issue, Huang et al. [9] evaluated the performance of 9 hydrological models for 12 large-scale river basins around the world. In their conclusions, the SWAT model performs well in simulating runoff in the Yangtze River Basin. Some other literature also shows that the SWAT model has shown good results in the simulation of runoff in China compared with other models e.g., the VIC model, Xinanjiang model [10-12]. As a physically based model, the SWAT model can simulate the short-term and long-term hydrological process, chemical process, erosion process, soil hydrological cycle, and crop production process of the basin rapidly [1, 13]. Compared with other models, the SWAT model can simulate the response of runoff to LUCC in most basins in China more obviously and accurately [10, 14]. The SWAT model considers the longitudinal movement of water flow in each small unit and the lateral ex-change of water flow between each unit and each other, so it is suitable for the simulation of lateral runoff at small scales. Therefore, the SWAT model was used to estimate the out-bound runoff at small scale basins in this study.” By discussing these points, we mentioned the research recommended by the reviewer.

 

In the discussion section some of the recent key literature have not included that have studied the impact of ET and runoff by using different satellite products. Authors need to include the proper discussion by using the literature provided above.

Response: we add some words to discuss the use of different satellite products to study the impact of ET and runoff in section 5.2 based on the literature provided by the reviewer. “In addition, the MODIS data was used to produce PML_V2 ET data. Zhang et al. [15] showed that when using the PML_V2 model to simulate ET, due to the forcing data of collection 6 MCD12 has spectral confusion between evergreen broadleaf forests (EBF) and evergreen needleleaf forests (ENF). It is difficult to distinguish several classes using MODIS coarse resolution data, which may lead to some potential uncertainty in ET estimation. Srivastava et al. [2] also showed that due to the MODIS algorithm does not consider the effects of cloud cover and leaf shadow effects, MODIS-ET is more accurate in the dry season than in the flood season. The impact of ET by using satellite products also needs to be considered in future research.”

 

Detailed water balance components should be provided in a tabular format.

Response: yes, we added Table 1 in Section 3.4 to describe the detailed water balance components.

Table 1. Detailed water balance components.

Component	Source	Forcing Data	Temporal Resolution	Spatial Resolution	Reference
Precipitation (P)	CMFD	GEWEX-SRB, GLDAS, TRMM, Meteorological data from the China Meteorological Administration (CMA)	monthly	0.1°×0.1°	He et al.2019 [29]
Evapotranspiration (ET)	PML_V2-simulated	CMFD, MCD15A3H.006, MCD43A3.006, MOD11A2.006, MCD12Q1	8-day	500m	Zhang et al.2019 [44]
Runoff (Q)	SWAT-simulated	CMFD, SRTMGL1, Land Use/Land Cover Data, Soil map data	monthly	virtual station	Luo et al.2020 [61]

 

References

1. Devia, G.K.; Ganasri, B.P.; Dwarakish, G.S. A Review on Hydrological Models. Aquatic Procedia. 2015, 4, 1001-7.
2. Srivastava, A.; Sahoo, B.; Raghuwanshi, N.S.; Singh, R. Evaluation of Variable-Infiltration Capacity Model and MODIS-Terra Satellite-Derived Grid-Scale Evapotranspiration Estimates in a River Basin with Tropical Monsoon-Type Climatology. J. Irrig. Drain. Eng. 2017, 143.
3. Sridhar, V.; Ali, S.A.; Lakshmi, V. Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE. J.Hydrol- Reg Stud. 2019, 24.
4. Xia, Y.; Cosgrove, B.A.; Mitchell, K.E.; Peters-Lidard, C.D.; Ek, M.B.; Brewer, M.; Mocko, D.; Kumar, S.V.; Wei, H.; Meng, J.; et al.. Basin-scale assessment of the land surface water budget in the National Centers for Environmental Prediction operational and research NLDAS-2 systems. J. Geophys. Res. Atmos. 2016, 121, 2750-79.
5. Srivastava, A.; Deb, P.; Kumari, N. Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem. Water Resour. Manag. 2020, 34, 327-41.
6. Esmali, A.; Golshan, M.; Kavian, A. Investigating the performance of SWAT and IHACRES in simulation streamflow under different climatic regions in Iran. Atmósfera. 2020.
7. Yen, H.; White, M.J.; Jeong, J.; Arabi, M.; Arnold, J.G. Evaluation of alternative surface runoff accounting procedures using SWAT model. Int J Agr Biol Eng. 2015, 8, 54-68.
8. Kumar, S.; Singh, A.; Shrestha, D.P. Modelling spatially distributed surface runoff generation using SWAT-VSA: a case study in a watershed of the north-west Himalayan landscape. Modeling Earth Systems and Environment. 2016, 2.
9. Huang, S.; Kumar, R.; Flörke, M.; Yang, T.; Hundecha, Y.; Kraft, P.; Gao, C.; Gelfan, A.; Liersch, S.; Lobanova, A.; et al.. Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide. Climatic Change. 2017, 141, 381-97.
10. Hu, H.C.; Wanga, G.X.; Bi, X.M.; Yang, F.M.; E, C.Y. Application of two hydrological models to Weihe River basin: a comparison of VIC - 3L and SWAT. In: Gong P, Liu YX, eds. Proceedings of SPIE, 2007, Vol. 6754.
11. Li, D.; Qu, S.; Shi, P.; Chen, X.; Xue, F.; Gou, J.; Zhang, W. Development and Integration of Sub-Daily Flood Modelling Capability within the SWAT Model and a Comparison with XAJ Model. Water. 2018, 10.
12. Shi, P.; Chen, C.; Srinivasan, R.; Zhang, X.; Cai, T.; Fang, X.; Qu, S.; Chen, X.; Li, Q. Evaluating the SWAT Model for Hydrological Modeling in the Xixian Watershed and a Comparison with the XAJ Model. Water Resour. Manag. 2011, 25, 2595-612.
13. Arnold, J.G.; Moriasi, D.N.; Gassman, P.W.; Abbaspour, K.C.; White, M.J.; Srinivasan, R.; Santhi, C.; Harmel, R.D.; van Griensven, A.; Van Liew, M.W.; et al.. Swat: model use, calibration, and validation. T. Asabe. 2012, 55, 1491-1508.
14. Luo, X.; Li, J.; Zhu, S.; Xu, Z.; Huo, Z. Estimating the Impacts of Urbanization in the Next 100 years on Spatial Hydrological Response. Water Resour. Manag. 2020, 34, 1673-1692.
15. Zhang, Q.; Yang, Y. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 2019, 222, 165-182.

 

Author Response File: Author Response.docx

Reviewer 2 Report

I found the manuscript titled "Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-source Data" is very interesting and suited to be published in the remote sensing journal.

The proposed estimation method presented in this manuscript is novel and can be improved in the future. Also, the discussion is useful and has many points that can be good starts toward improving this estimation method. Although I am in favor of accepting it to be published after correcting minor mistakes, I have a big concern about section 5.1. "Uncertainties".  the authors estimated the monthly uncertainties of TWSC by combining the individual uncertainties for precipitation, ET, and runoff, and they ignored the dependency among these factors. Statistically speaking, the variance of three dependent variables, say W in a relationship (X-Y-Z) is estimated by

Var(W) = Var(X-Y-Z) = Var(X)+Var(Y)+Var(Z)-2Cov(X,Y)-2Cov(X,Z) +2Cov(Y,Z)

Therefore, the standard deviation (uncertainty) of W is the square root of Var(W).

I want the authors to explain why the uncertainties were calculated in this way.

 

 Minor mistakes:

Line 10:  “However, how to monitor the TWSC at small basin...” should be “ However, monitoring the TWSC at small basin...”

Line 380: remove “And” at the beginning of the sentence” And we compared the Ground Water Level …”

 Line 411:  "with propagation of errors" add "the" to propagation.

Author Response

Dear editor and reviewers:

 

Thanks for your valuable comments about our manuscript. We have carefully considered your reviewer comments and made changes accordingly. All revisions are highlighted in red in the manuscript. Meanwhile, point-to-point responses to comments are also attached.

 

If any problem, please feel free to contact us.

 

Best regards,

-- Dr. Yulong Zhong

 

 

Review

I found the manuscript titled "Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-source Data" is very interesting and suited to be published in the remote sensing journal.

The proposed estimation method presented in this manuscript is novel and can be improved in the future. Also, the discussion is useful and has many points that can be good starts toward improving this estimation method. Although I am in favor of accepting it to be published after correcting minor mistakes, I have a big concern about section 5.1. "Uncertainties". the authors estimated the monthly uncertainties of TWSC by combining the individual uncertainties for precipitation, ET, and runoff, and they ignored the dependency among these factors. Statistically speaking, the variance of three dependent variables, say W in a relationship (X-Y-Z) is estimated by

Var(W) = Var(X-Y-Z) = Var(X)+Var(Y)+Var(Z)-2Cov(X,Y)-2Cov(X,Z) +2Cov(Y,Z)

Therefore, the standard deviation (uncertainty) of W is the square root of Var(W).

I want the authors to explain why the uncertainties were calculated in this way.

Response: well, thank you for your comments. Uncertainties of ET and runoff can come from multiple sources: model misrepresentation or parameterization, forcing data, and other variables [1, 2]. Due to the complexity of the model, it is difficult to estimate the uncertainty of ET and runoff caused by the uncertainty of the input precipitation forcing dataset. To quantify the uncertainty of the hydrological model output data, Koukoula et al. [3] evaluated water cycle components from 18 state-of-the-art water resources reanalysis (WRR) datasets derived from different hydrological models, meteorological forcing, and precipitation datasets. They found that the uncertainty in the estimation of the water cycle components from different products is mainly attributable to the differences in the schemes used by the different models, while different precipitation forcing datasets have less impacts on the precision of the WRR output data. Therefore, in this study, we assume that each variable is independent and estimate the uncertainties of TWSC. Therefore, in this study, we assume that each variable is independent and estimate the uncertainty of TWSC, and we have added this assumption to section 5.1.

In addition, we also try to estimate the spatial distribution of uncertainty for each sub-basin according to the following equation recommended by the reviewer.

Var(W) = Var(X-Y-Z) = Var(X)+Var(Y)+Var(Z)-2Cov(X,Y)-2Cov(X,Z) +2Cov(Y,Z)

And the spatial distribution of uncertainty is shown in the word document. However, the estimated uncertainty is relatively little, which would only reflect the inner precision between P, ET and Q estimates to some extent. If you think the following spatial distribution of uncertainty is more suitable for uncertainty estimates, we would further add it in the revised manuscript.

Minor mistakes:

Line 10: “However, how to monitor the TWSC at small basin...” should be “However, monitoring the TWSC at small basin...”

Response: Well, we modify it.

 

Line 380: remove “And” at the beginning of the sentence” And we compared the Ground Water Level …”

Response: Well, we modify it.

 

Line 411:  "with propagation of errors" add "the" to propagation.

Response: Well, we modify it.

References

1. Long, D.; Longuevergne, L.; Scanlon, B.R. Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour. Res. 2014, 50, 1131-51.
2. Han, Z.; Long, D.; Huang, Q.; Li, X.; Zhao, F.; Wang, J. Improving Reservoir Outflow Estimation for Ungauged Basins Using Satellite Observations and a Hydrological Model. Water Resour. Res. 2020, 56.
3. Koukoula, M.; Nikolopoulos, E.I.; Dokou, Z.; Anagnostou, E.N. Evaluation of Global Water Resources Reanalysis Products in the Upper Blue Nile River Basin. J. Hydrometeorol. 2020, 21, 935-52.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed the reviewer’s comments and in doing so, they have improved the quality of the manuscript. I have few minor corrections towards this research article which need to be considered before considering it for publication.

Authors need to provide the links and dates for all the data accessed in this article.

Line 300 – Need to mention a b and c in the Figure 3 caption.

Line 119 – The spatial resolution of CMFD – 0.1°x0.1°.

I highly recommend authors to show the FDC (flow distribution curve) after Figure 4 and 5, atleast for calibration and validation – to show the pattern of low, medium and high flows for observed and simulated runoff. In doing so, the authors are recommend to discuss about the same in discussion by adding and comparing the findings from this recent literature:

 Kumari, N., Srivastava, A., Sahoo, B., Raghuwanshi, N. S., & Bretreger, D. (2021). Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores. Natural Resources Research, 1-19.

Authors need to differentiate between the captions of Figures 7 and 8 to present the clarity.

Figure 9 – Add the y-axis title to the figure.

Author Response

Dear reviewer:

 

Thanks for your further comments about our manuscript. We have carefully considered your comments and made changes accordingly. All revisions are highlighted in red in the revised manuscript. Meanwhile, point-to-point responses to comments are also attached.

 

If any problem, please feel free to contact us.

 

Best regards,

-- Dr. Yulong Zhong, August 13, 2021

 

 

Reply to Comments

Review

Authors need to provide the links and dates for all the data accessed in this article.

Response: Thanks, we have added the Data Availability Statement section to provide the links and dates for all the data accessed in this article. “Data Availability Statement: The in-situ data used in this study are mainly provided by the institution that introduction in section 2.2.1. The China meteorological forcing dataset (CMFD) is available at (http://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/; last access: 13 August 2021). CMA Land Data Assimilation System Version 2.0 (CLDAS-V2.0) is available at (http://data.cma.cn/data/detail/dataCode/NAFP_CLDAS2.0_NRT/; last access: 12 August 2021). The GRACE Mascon product from the Center for Space Research is available at (www2.csr.utexas.edu/grace/; last access: 13 August 2021). The global 1 arc-second SRTM V3.0 dataset (SRTMGL1) is available at (https://lpdaac.usgs.gov/products/srtmgl1v003/; last access: 13 August 2021). The PML_V2 evapotranspiration is available at (https://code.earthengine.google.com/e0453cf3e7a6e62513da40989f29a029; last access: 13 August 2021 from GEE). The soil map is available at (http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/; last access: 13 August 2021). The dataset used to fill the temporal gaps in GRACE CSR Mascon product is available at (http://data.tpdc.ac.cn/en/data/71cf70ec-0858-499d-b7f2-63319e1087fc/; last access: 13 August 2021).”

 

Line 300 – Need to mention a b and c in the Figure 3 caption.

Response: Well, we modify it.

 

Line 119 – The spatial resolution of CMFD – 0.1°x0.1°

Response: Well, we modify it.

 

I highly recommend authors to show the FDC (flow distribution curve) after Figure 4 and 5, at least for calibration and validation – to show the pattern of low, medium and high flows for observed and simulated runoff. In doing so, the authors are recommend to discuss about the same in discussion by adding and comparing the findings from this recent literature:

Kumari, N., Srivastava, A., Sahoo, B., Raghuwanshi, N. S., & Bretreger, D. (2021). Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores. Natural Resources Research, 1-19.

Response: Thanks for your insightful comments. We have added the FDC (flow distribution curve) after Figure 4 and 5, for calibration and validation. “Figure 6 depicts the flow duration curves (FDCs) of SWAT model at monthly time scale. Variability in the runoff is displayed by the FDC at the Waizhou station for the calibration and validation periods. We can find that the SWAT model simulates the pattern of low, medium and high runoff satisfactorily at the monthly scale both in calibration and validation period.”(Figure 6 is shown in the word document ).

And we also discuss about the same in discussion based on the recent literature recommended by the reviewer. “Kumari et al. [1] also proved that when using the hydrological model to simulate runoff, the shape of a flow duration curve is mainly affected by reservoirs, land-use types and upstream abstractions of water. The GRB is widely distributed, and several artificial water conservancy facilities such as reservoirs and dams play an important role in the hydrological cycle. If these data are coupled into the model, the accuracy of runoff simulation will be further improved.”

 

 

Authors need to differentiate between the captions of Figures 7 and 8 to present the clarity.

Response: Well, we modify it.

 

Figure 9 – Add the y-axis title to the figure.

Response: Well, we modify it.

 

References

1. Kumari, N.; Srivastava, A.; Sahoo, B.; Raghuwanshi, N.S.; Bretreger, D., Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores. Nat Resour Res. 2021, 1-19.

Author Response File: Author Response.docx