GCN250, new global gridded curve numbers for hydrologic modeling and design

The USDA curve-number (CN) method is fundamental for rainfall-runoff modeling. A global CN database is not currently available for geospatial hydrologic analysis at a resolution higher than 0.1°. We developed a globally consistent, gridded dataset defining CNs at the 250 m spatial resolution from ne...

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Autores principales: Jaafar, Hadi H., Ahmad, Farah A., El Beyrouthy, Naji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690949/
https://www.ncbi.nlm.nih.gov/pubmed/31406223
http://dx.doi.org/10.1038/s41597-019-0155-x
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author Jaafar, Hadi H.
Ahmad, Farah A.
El Beyrouthy, Naji
author_facet Jaafar, Hadi H.
Ahmad, Farah A.
El Beyrouthy, Naji
author_sort Jaafar, Hadi H.
collection PubMed
description The USDA curve-number (CN) method is fundamental for rainfall-runoff modeling. A global CN database is not currently available for geospatial hydrologic analysis at a resolution higher than 0.1°. We developed a globally consistent, gridded dataset defining CNs at the 250 m spatial resolution from new global land cover (300 m) and soils data (250 m). The resulting data product – GCN250 – represents runoff for a combination of the European space agency global land cover dataset for 2015 (ESA CCI-LC) resampled to 250 m and geo-registered with the hydrologic soil group global data product (HYSOGs250m) released in 2018. Our analysis indicated that medium to high runoff potential currently dominates the globe, with curve numbers ranging between 75 and 85. Global curve numbers were 62, 78, and 90 for dry, average, and wet antecedent runoff conditions, respectively. Australia has the highest runoff potential, while Europe has the lowest. Runoff ratios compare well with GLDAS. The potential application of this data includes hydrologic design, land management applications, flood risk assessment, and groundwater recharge modeling.
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spelling pubmed-66909492019-08-19 GCN250, new global gridded curve numbers for hydrologic modeling and design Jaafar, Hadi H. Ahmad, Farah A. El Beyrouthy, Naji Sci Data Data Descriptor The USDA curve-number (CN) method is fundamental for rainfall-runoff modeling. A global CN database is not currently available for geospatial hydrologic analysis at a resolution higher than 0.1°. We developed a globally consistent, gridded dataset defining CNs at the 250 m spatial resolution from new global land cover (300 m) and soils data (250 m). The resulting data product – GCN250 – represents runoff for a combination of the European space agency global land cover dataset for 2015 (ESA CCI-LC) resampled to 250 m and geo-registered with the hydrologic soil group global data product (HYSOGs250m) released in 2018. Our analysis indicated that medium to high runoff potential currently dominates the globe, with curve numbers ranging between 75 and 85. Global curve numbers were 62, 78, and 90 for dry, average, and wet antecedent runoff conditions, respectively. Australia has the highest runoff potential, while Europe has the lowest. Runoff ratios compare well with GLDAS. The potential application of this data includes hydrologic design, land management applications, flood risk assessment, and groundwater recharge modeling. Nature Publishing Group UK 2019-08-12 /pmc/articles/PMC6690949/ /pubmed/31406223 http://dx.doi.org/10.1038/s41597-019-0155-x Text en © The Author(s) 2019 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 associated with this article.
spellingShingle Data Descriptor
Jaafar, Hadi H.
Ahmad, Farah A.
El Beyrouthy, Naji
GCN250, new global gridded curve numbers for hydrologic modeling and design
title GCN250, new global gridded curve numbers for hydrologic modeling and design
title_full GCN250, new global gridded curve numbers for hydrologic modeling and design
title_fullStr GCN250, new global gridded curve numbers for hydrologic modeling and design
title_full_unstemmed GCN250, new global gridded curve numbers for hydrologic modeling and design
title_short GCN250, new global gridded curve numbers for hydrologic modeling and design
title_sort gcn250, new global gridded curve numbers for hydrologic modeling and design
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690949/
https://www.ncbi.nlm.nih.gov/pubmed/31406223
http://dx.doi.org/10.1038/s41597-019-0155-x
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