| Autor: |
Vamshi, Raghu, McDonough, Kathleen, Csiszar, Susan A., Heisler, Ryan, Kapo, Katherine E., Ritter, Amy M., Ming Fan, Stanton, Kathleen |
| Předmět: |
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| Zdroj: |
Earth System Science Data Discussions; 5/5/2023, p1-29, 29p |
| Abstrakt: |
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). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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