Preprints
https://doi.org/10.5194/essd-2023-161
https://doi.org/10.5194/essd-2023-161
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Status: this preprint has been withdrawn by the authors.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).

This preprint has been withdrawn.

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

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-161', Anonymous Referee #1, 01 Jun 2023
    • AC2: 'Reply on RC1', Amy Ritter, 28 Jul 2023
    • AC3: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  • RC2: 'Comment on essd-2023-161', Anonymous Referee #2, 03 Jun 2023
    • AC4: 'Reply on RC2', Amy Ritter, 28 Jul 2023
  • AC1: 'Comment on essd-2023-161', Amy Ritter, 28 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-161', Anonymous Referee #1, 01 Jun 2023
    • AC2: 'Reply on RC1', Amy Ritter, 28 Jul 2023
    • AC3: 'Reply on RC1', Amy Ritter, 28 Jul 2023
  • RC2: 'Comment on essd-2023-161', Anonymous Referee #2, 03 Jun 2023
    • AC4: 'Reply on RC2', Amy Ritter, 28 Jul 2023
  • AC1: 'Comment on essd-2023-161', Amy Ritter, 28 Jul 2023
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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This preprint has been withdrawn.

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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