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HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling
Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consiste...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952866/ https://www.ncbi.nlm.nih.gov/pubmed/29762550 http://dx.doi.org/10.1038/sdata.2018.91 |
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author | Ross, C. Wade Prihodko, Lara Anchang, Julius Kumar, Sanath Ji, Wenjie Hanan, Niall P. |
author_facet | Ross, C. Wade Prihodko, Lara Anchang, Julius Kumar, Sanath Ji, Wenjie Hanan, Niall P. |
author_sort | Ross, C. Wade |
collection | PubMed |
description | Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions. |
format | Online Article Text |
id | pubmed-5952866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-59528662018-05-30 HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling Ross, C. Wade Prihodko, Lara Anchang, Julius Kumar, Sanath Ji, Wenjie Hanan, Niall P. Sci Data Data Descriptor Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions. Nature Publishing Group 2018-05-15 /pmc/articles/PMC5952866/ /pubmed/29762550 http://dx.doi.org/10.1038/sdata.2018.91 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Ross, C. Wade Prihodko, Lara Anchang, Julius Kumar, Sanath Ji, Wenjie Hanan, Niall P. HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title | HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title_full | HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title_fullStr | HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title_full_unstemmed | HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title_short | HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
title_sort | hysogs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952866/ https://www.ncbi.nlm.nih.gov/pubmed/29762550 http://dx.doi.org/10.1038/sdata.2018.91 |
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