Global river flow data developed from surface runoff based on the Curve Number method

Vamshi, Raghu; McDonough, Kathleen; Csiszar, Susan A.; Heisler, Ryan; Kapo, Katherine E.; Ritter, Amy M.; Fan, Ming; Stanton, Kathleen

doi:https://doi.org/10.5194/essd-2023-161

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https://doi.org/10.5194/essd-2023-161

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05 May 2023

| 05 May 2023

Status: this preprint has been withdrawn by the authors.

Global river flow data developed from surface runoff based on the Curve Number method

Raghu Vamshi, Kathleen McDonough, Susan A. Csiszar, Ryan Heisler, Katherine E. Kapo, Amy M. Ritter, Ming Fan, and Kathleen Stanton

Abstract. The availability of detailed surface runoff and river flow data across large geographic areas is needed for several scientific applications, such as refined freshwater environmental risk assessments. Some limiting factors in developing detailed river flow datasets over large spatial scales have been paucity of detailed input spatial data and challenges in processing of these data. The well-established USDA Curve Number (CN) method was applied for spatially distributed hydrologic processing to estimate surface runoff. Publicly available global datasets for hydrologic soil groups, land cover, and precipitation were spatially processed by applying the CN equations to create a global mean annual surface runoff grid of 50 meters. Runoff was spatially combined with global hydrology of catchments and rivers from publicly available datasets to estimate daily mean annual flow (MAF) across the globe. Estimated daily MAF were compared with measured gauge flow at rivers in several countries which showed good correlation (R² of 0.76–0.98). These flow estimates can be used for diverse applications at local watersheds to larger regions across the globe. The two spatial data products of this project representing MAF at the global scale are publicly available for download at https://doi.org/10.6084/m9.figshare.22694146 (Heisler, et al., 2023).

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Received: 26 Apr 2023 – Discussion started: 05 May 2023

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Raghu Vamshi, Kathleen McDonough, Susan A. Csiszar, Ryan Heisler, Katherine E. Kapo, Amy M. Ritter, Ming Fan, and Kathleen Stanton

Interactive discussion

Status: closed

RC1:
'Comment on essd-2023-161', Anonymous Referee #1, 01 Jun 2023

Dear editor,

Thank you very much for inviting me to review this manuscript. In this paper, the authors produce a dataset on global annual mean surface runoff and another dataset on global annual mean river flow rate. The topic is interesting and important, and the method used in this study have been explained detailedly. Nonetheless, in my opinion, the manuscript should be further improved before it can be finally accepted, and I am a bit worry about the novelty of this study.
1）As resolutions of all input data (land cover, soil group & precipitation) for driving the CN approach is larger than 250 m, why the runoff is produced at 50 m? Why not produce runoff data at 250m, 100 m, or 10 m? In addition, for many large rivers, the width of the river channel can be hundreds or thousands of meters. A high resolution, like 50 m, is necessary only when you are going to calculate the water flow in small rivers. However, in your approach, the small rivers in each of the small level-12 catchment have been aggregated. For a global river flow database, is it necessary to produce river discharge data at a resolution of 50 m?
To my understanding, the only outstanding point of the runoff and river flow datasets produced in this study, compared with previous datasets, is the high spatial resolution (50 m). However, as I have expressed above, a spatial resolution of 50 m is not necessary for larger rivers. It can be very useful to calculate the river flows in small rivers. However, the authors have aggregated all small rivers in each small catchment.
2） I would suggest the authors to further evaluate the interannual variation of the simulated river flows in this study using the observations. For such a dataset, it will be great if the scheme used in this study can capture the change trend of river flow. Otherwise, the datasets produced in this study seems to be not that useful.
3） Is it possible for the authors to compare the accuracy of river flows simulated in this study with existing global datasets of river flow? Are the results of this study more accurate than existing datasets?
4） I did not find any discussion on the uncertainties in the datasets produced in this study. The authors have calculated the surface runoff and river flow using the simple empirical CN equations, and the CN equations only consider land cover and soil type. However, many other factors, such as topography, dam/reservoir, irrigation, underground drainage and temperature, can also strongly affect river flow. I would suggest the authors to add some discussion on these factors.
5） In Figure 1, the authors have provided a nice flowchart to show the approach for calculating surface runoff. I would suggest the authors to add a detailed flowchart to show the approach for calculating the river flow based on the surface runoff, HydroBASINS and HydroRIVERS.
Please further polish the English of this paper.

Minor comments:
L15: ** to create a global gridded dataset of annual mean surface runoff at a spatial resolution of 50 meters.

L160: how the monthly precipitation data is converted into daily precipitation rates? Did you assume the precipitation is evenly distributed in each month?

L225-280: Based on the description in sections 2.5, 2.6, surface runoff has been calculated for each day using Eq. 2. Why not produce the river flow rate for each day or each month?

Fig. 2: the unit of surface runoff in panel (a) should be mm d^-1, and the unit of water flow in panel (b) should be changed to m³ s^-1

Please check the whole manuscript to make sure the format of units is consistent through the whole paper.

Citation: https://doi.org/10.5194/essd-2023-161-RC1
- AC2: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  
  Response: We would like to thank the reviewer for the very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version. Additionally, we have revised the introduction to elaborate on the focus of the key application and novelty of this dataset, and is discussed further below.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC2
- AC3: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  
  Response: We would like to thank the reviewer for their very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version. Additionally, we have revised the introduction to elaborate on the focus of the key application and novelty of this dataset, and is discussed further in our responses in the attached .pdf.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC3
RC2:
'Comment on essd-2023-161', Anonymous Referee #2, 03 Jun 2023
OVERVIEW
The paper describes the development of global dataset of mean annual flow obtained by using the Curve Number method.

GENERAL COMMENTS
The paper is fairly well written and well structured. The topic is of interest for the readership of ESSD as a 50-m resolution dataset of mean annual flow (MAF) could have high impact in the hydrological community.
I read the paper with interest but I have contrasting feeling. The dataset, mainly due to its very high spatial resolution, might be relevant, but the method used for its development has several issues that must be carefully addressed for having the paper published. I believe that the actual spatial resolution is much coarser and that the usability of the datasets in several areas is limited.
To clarify, I have listed below the major issues with the indication of their relevance.

MAJOR: The main problem is related to the spatial resolution, or better sampling, of the developed dataset. The dataset is distributed at 50 m resolution but the forcing, i.e., precipitation, and mainly the method is not appropriate at such resolution. The method does not consider human impact on streamflow, such as reservoirs, wetlands, water diversions for agricultural, civil and industrial water uses. Other missing processes are related to high altitude regions, such as snow melting, glacier processes. If we want to deliver a MAF dataset at 50 m resolution, these processes must be included. Otherwise, in many areas, the MAF dataset here developed has very little reliability and applicability. This must be discussed, and better it should be fixed.

MAJOR: A second major problem is related to the choices made for the application of the method. The method is applied by using monthly precipitation data disaggregated (simply the monthly values divided by the number of days) at daily resolution and it is not appropriate. Several global scale datasets of precipitation are currently available. The choice of the 1 km climatological dataset is questionable. CN values for intermediate antecedent moisture conditions seem to be used, and it has no sense in many areas of the world. It is not clear how the time of concentration at basin scale is used, and similarly the travel time for the river. Actually, I believe that the way the method was used in the paper to develop the dataset is not appropriate. It must be clarified and discussed in details.

MODERATE: The developed dataset is compared with data over US, and GRDC data globally. Currently, 30-year reanalysis datasets developed everywhere in the world are available. Such datasets should be used for the assessment of the developed dataset.

MAJOR: The high values of R-square obtained in the paper are mostly related to the difference in river discharge from basins of different size. Larger basins have larger river discharge than smaller ones. A more robust assessment should be carried out by computing the mean annual flow normalized by the basin area. Moreover, section 3.3 should be strongly summarized. The assessment in terms of RMSE has little assessment for basins of different size. Normalized scores should be used (as RSR, but its formula should be given). The gauges used for the comparison should be shown in a map. This section should be significantly revised.

A number of specific comments should be also addressed, but I believe the text should be significantly revised and technical corrections are not relevant at this stage.

SPECIFIC COMMENTS (L: line or lines)
L69: River discharge is generated by surface, subsurface and groundwater runoff, all components should be considered to obtain a physically reasonable approach.
L211-221: The comparison with GCN250 dataset is not clear. The comparison of maps with the computation of BIAS or RMSE should be carried out. It would be clearer.
In Figure 2a it is hardly possible to distinguish the different classes. To be improved.
L384: typo.
L428: What is the number of adjusted gauges? It should be reported to GRDC to correct the database.
L523: typo.

RECOMMENDATION
On this basis, I suggest a major revision to carefully address the raised comments.
Citation: https://doi.org/10.5194/essd-2023-161-RC2
- AC4: 'Reply on RC2', Amy Ritter, 28 Jul 2023
  
  We would like to thank the reviewer for their very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC4
AC1: 'Comment on essd-2023-161', Amy Ritter, 28 Jul 2023

Response to both Reviewers
We thank both reviewers for their helpful and insightful comments which greatly helped us improve this manuscript. We believe that the manuscript and analysis has been substantially improved and we are pleased to submit alongside our responses a revised manuscript which addresses the comments and suggestions provided by the reviewers.
Within the revised the manuscript, we have clarified several aspects, included additional discussions on the primary purpose of the resulting flow dataset, and outlined its uncertainties and limitations. Additionally, we have provided additional analyses of the results as suggested by the reviewers.
As the reviewers’ comments have provided us with key guidance to improve the paper, we have added an acknowledgement of the two anonymous reviewers at the end of the manuscript.
Our responses to Reviewers 1 and 2 are posted separately in the discussion below as replies to each Reviewers’ set of comments. We have also attached to this AC a .pdf of the combined responses to both Reviewers.
Sincerely,
Manuscript authors

Citation: https://doi.org/10.5194/essd-2023-161-AC1

Interactive discussion

Status: closed

RC1:
'Comment on essd-2023-161', Anonymous Referee #1, 01 Jun 2023

Dear editor,

Thank you very much for inviting me to review this manuscript. In this paper, the authors produce a dataset on global annual mean surface runoff and another dataset on global annual mean river flow rate. The topic is interesting and important, and the method used in this study have been explained detailedly. Nonetheless, in my opinion, the manuscript should be further improved before it can be finally accepted, and I am a bit worry about the novelty of this study.
1）As resolutions of all input data (land cover, soil group & precipitation) for driving the CN approach is larger than 250 m, why the runoff is produced at 50 m? Why not produce runoff data at 250m, 100 m, or 10 m? In addition, for many large rivers, the width of the river channel can be hundreds or thousands of meters. A high resolution, like 50 m, is necessary only when you are going to calculate the water flow in small rivers. However, in your approach, the small rivers in each of the small level-12 catchment have been aggregated. For a global river flow database, is it necessary to produce river discharge data at a resolution of 50 m?
To my understanding, the only outstanding point of the runoff and river flow datasets produced in this study, compared with previous datasets, is the high spatial resolution (50 m). However, as I have expressed above, a spatial resolution of 50 m is not necessary for larger rivers. It can be very useful to calculate the river flows in small rivers. However, the authors have aggregated all small rivers in each small catchment.
2） I would suggest the authors to further evaluate the interannual variation of the simulated river flows in this study using the observations. For such a dataset, it will be great if the scheme used in this study can capture the change trend of river flow. Otherwise, the datasets produced in this study seems to be not that useful.
3） Is it possible for the authors to compare the accuracy of river flows simulated in this study with existing global datasets of river flow? Are the results of this study more accurate than existing datasets?
4） I did not find any discussion on the uncertainties in the datasets produced in this study. The authors have calculated the surface runoff and river flow using the simple empirical CN equations, and the CN equations only consider land cover and soil type. However, many other factors, such as topography, dam/reservoir, irrigation, underground drainage and temperature, can also strongly affect river flow. I would suggest the authors to add some discussion on these factors.
5） In Figure 1, the authors have provided a nice flowchart to show the approach for calculating surface runoff. I would suggest the authors to add a detailed flowchart to show the approach for calculating the river flow based on the surface runoff, HydroBASINS and HydroRIVERS.
Please further polish the English of this paper.

Minor comments:
L15: ** to create a global gridded dataset of annual mean surface runoff at a spatial resolution of 50 meters.

L160: how the monthly precipitation data is converted into daily precipitation rates? Did you assume the precipitation is evenly distributed in each month?

L225-280: Based on the description in sections 2.5, 2.6, surface runoff has been calculated for each day using Eq. 2. Why not produce the river flow rate for each day or each month?

Fig. 2: the unit of surface runoff in panel (a) should be mm d^-1, and the unit of water flow in panel (b) should be changed to m³ s^-1

Please check the whole manuscript to make sure the format of units is consistent through the whole paper.

Citation: https://doi.org/10.5194/essd-2023-161-RC1
- AC2: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  
  Response: We would like to thank the reviewer for the very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version. Additionally, we have revised the introduction to elaborate on the focus of the key application and novelty of this dataset, and is discussed further below.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC2
- AC3: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  
  Response: We would like to thank the reviewer for their very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version. Additionally, we have revised the introduction to elaborate on the focus of the key application and novelty of this dataset, and is discussed further in our responses in the attached .pdf.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC3
RC2:
'Comment on essd-2023-161', Anonymous Referee #2, 03 Jun 2023
OVERVIEW
The paper describes the development of global dataset of mean annual flow obtained by using the Curve Number method.

GENERAL COMMENTS
The paper is fairly well written and well structured. The topic is of interest for the readership of ESSD as a 50-m resolution dataset of mean annual flow (MAF) could have high impact in the hydrological community.
I read the paper with interest but I have contrasting feeling. The dataset, mainly due to its very high spatial resolution, might be relevant, but the method used for its development has several issues that must be carefully addressed for having the paper published. I believe that the actual spatial resolution is much coarser and that the usability of the datasets in several areas is limited.
To clarify, I have listed below the major issues with the indication of their relevance.

MAJOR: The main problem is related to the spatial resolution, or better sampling, of the developed dataset. The dataset is distributed at 50 m resolution but the forcing, i.e., precipitation, and mainly the method is not appropriate at such resolution. The method does not consider human impact on streamflow, such as reservoirs, wetlands, water diversions for agricultural, civil and industrial water uses. Other missing processes are related to high altitude regions, such as snow melting, glacier processes. If we want to deliver a MAF dataset at 50 m resolution, these processes must be included. Otherwise, in many areas, the MAF dataset here developed has very little reliability and applicability. This must be discussed, and better it should be fixed.

MAJOR: A second major problem is related to the choices made for the application of the method. The method is applied by using monthly precipitation data disaggregated (simply the monthly values divided by the number of days) at daily resolution and it is not appropriate. Several global scale datasets of precipitation are currently available. The choice of the 1 km climatological dataset is questionable. CN values for intermediate antecedent moisture conditions seem to be used, and it has no sense in many areas of the world. It is not clear how the time of concentration at basin scale is used, and similarly the travel time for the river. Actually, I believe that the way the method was used in the paper to develop the dataset is not appropriate. It must be clarified and discussed in details.

MODERATE: The developed dataset is compared with data over US, and GRDC data globally. Currently, 30-year reanalysis datasets developed everywhere in the world are available. Such datasets should be used for the assessment of the developed dataset.

MAJOR: The high values of R-square obtained in the paper are mostly related to the difference in river discharge from basins of different size. Larger basins have larger river discharge than smaller ones. A more robust assessment should be carried out by computing the mean annual flow normalized by the basin area. Moreover, section 3.3 should be strongly summarized. The assessment in terms of RMSE has little assessment for basins of different size. Normalized scores should be used (as RSR, but its formula should be given). The gauges used for the comparison should be shown in a map. This section should be significantly revised.

A number of specific comments should be also addressed, but I believe the text should be significantly revised and technical corrections are not relevant at this stage.

SPECIFIC COMMENTS (L: line or lines)
L69: River discharge is generated by surface, subsurface and groundwater runoff, all components should be considered to obtain a physically reasonable approach.
L211-221: The comparison with GCN250 dataset is not clear. The comparison of maps with the computation of BIAS or RMSE should be carried out. It would be clearer.
In Figure 2a it is hardly possible to distinguish the different classes. To be improved.
L384: typo.
L428: What is the number of adjusted gauges? It should be reported to GRDC to correct the database.
L523: typo.

RECOMMENDATION
On this basis, I suggest a major revision to carefully address the raised comments.
Citation: https://doi.org/10.5194/essd-2023-161-RC2
- AC4: 'Reply on RC2', Amy Ritter, 28 Jul 2023
  
  We would like to thank the reviewer for their very helpful input provided that helped to improve the manuscript and clarify some key points in the previous manuscript version.
  As we have provided extensive responses to your comments, we have uploaded these as .pdf for ease of use.
  
  Citation: https://doi.org/10.5194/essd-2023-161-AC4
AC1: 'Comment on essd-2023-161', Amy Ritter, 28 Jul 2023

Response to both Reviewers
We thank both reviewers for their helpful and insightful comments which greatly helped us improve this manuscript. We believe that the manuscript and analysis has been substantially improved and we are pleased to submit alongside our responses a revised manuscript which addresses the comments and suggestions provided by the reviewers.
Within the revised the manuscript, we have clarified several aspects, included additional discussions on the primary purpose of the resulting flow dataset, and outlined its uncertainties and limitations. Additionally, we have provided additional analyses of the results as suggested by the reviewers.
As the reviewers’ comments have provided us with key guidance to improve the paper, we have added an acknowledgement of the two anonymous reviewers at the end of the manuscript.
Our responses to Reviewers 1 and 2 are posted separately in the discussion below as replies to each Reviewers’ set of comments. We have also attached to this AC a .pdf of the combined responses to both Reviewers.
Sincerely,
Manuscript authors

Citation: https://doi.org/10.5194/essd-2023-161-AC1

Raghu Vamshi, Kathleen McDonough, Susan A. Csiszar, Ryan Heisler, Katherine E. Kapo, Amy M. Ritter, Ming Fan, and Kathleen Stanton

Data sets

Global HydroRIVERS River Network MAF R. Heisler, S. Csiszar, K. McDonough, A. Ritter, B. Kent, R. Vamshi, M. Fan, and K. Kapo https://doi.org/10.6084/m9.figshare.22694146

Raghu Vamshi, Kathleen McDonough, Susan A. Csiszar, Ryan Heisler, Katherine E. Kapo, Amy M. Ritter, Ming Fan, and Kathleen Stanton

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Cumulative views and downloads (calculated since 05 May 2023)

Month	HTML	PDF	XML	Total
May 2023	304	81	8	393
Jun 2023	124	35	6	165
Jul 2023	75	35	9	119
Aug 2023	41	20	3	64
Sep 2023	33	20	2	55
Oct 2023	28	26	6	60
Nov 2023	13	23	0	36
Dec 2023	22	11	0	33
Jan 2024	34	20	2	56
Feb 2024	18	19	3	40
Mar 2024	26	22	3	51
Apr 2024	26	13	6	45
May 2024	27	21	7	55
Jun 2024	23	10	1	34
Jul 2024	21	9	6	36
Aug 2024	25	19	2	46
Sep 2024	22	10	0	32
Oct 2024	12	11	0	23
Nov 2024	11	13	1	25
Dec 2024	3	7	0	10

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Short summary

The objective of this research was to create a mean annual river flow dataset at a global scale from computing surface runoff based on the USDA Curve Number method in combination with publicly available global datasets which were spatially processed. To validate the estimated flows, calculated mean annual flows were compared with measured flows at gauges for 11 countries which provided positive correlations. This dataset can be used for countries without publicly available river flow data.


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