A global distributed basin morphometric dataset

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in a...

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Autores principales: Shen, Xinyi, Anagnostou, Emmanouil N., Mei, Yiwen, Hong, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5216664/
https://www.ncbi.nlm.nih.gov/pubmed/28055032
http://dx.doi.org/10.1038/sdata.2016.124
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author Shen, Xinyi
Anagnostou, Emmanouil N.
Mei, Yiwen
Hong, Yang
author_facet Shen, Xinyi
Anagnostou, Emmanouil N.
Mei, Yiwen
Hong, Yang
author_sort Shen, Xinyi
collection PubMed
description Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack’s law.
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spelling pubmed-52166642017-01-09 A global distributed basin morphometric dataset Shen, Xinyi Anagnostou, Emmanouil N. Mei, Yiwen Hong, Yang Sci Data Data Descriptor Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack’s law. Nature Publishing Group 2017-01-05 /pmc/articles/PMC5216664/ /pubmed/28055032 http://dx.doi.org/10.1038/sdata.2016.124 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.
spellingShingle Data Descriptor
Shen, Xinyi
Anagnostou, Emmanouil N.
Mei, Yiwen
Hong, Yang
A global distributed basin morphometric dataset
title A global distributed basin morphometric dataset
title_full A global distributed basin morphometric dataset
title_fullStr A global distributed basin morphometric dataset
title_full_unstemmed A global distributed basin morphometric dataset
title_short A global distributed basin morphometric dataset
title_sort global distributed basin morphometric dataset
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5216664/
https://www.ncbi.nlm.nih.gov/pubmed/28055032
http://dx.doi.org/10.1038/sdata.2016.124
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