the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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).
Raghu Vamshi et al.
Status: open (until 03 Jul 2023)
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RC1: 'Comment on essd-2023-161', Anonymous Referee #1, 01 Jun 2023
reply
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
Raghu Vamshi et al.
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 et al.
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