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A new vector-based global river network dataset accounting for variable drainage density
Spatial variability of river network drainage density (D(d)) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of D(d) worldwide. It...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838288/ https://www.ncbi.nlm.nih.gov/pubmed/33500418 http://dx.doi.org/10.1038/s41597-021-00819-9 |
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author | Lin, Peirong Pan, Ming Wood, Eric F. Yamazaki, Dai Allen, George H. |
author_facet | Lin, Peirong Pan, Ming Wood, Eric F. Yamazaki, Dai Allen, George H. |
author_sort | Lin, Peirong |
collection | PubMed |
description | Spatial variability of river network drainage density (D(d)) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of D(d) worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate D(d) based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and D(d) are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved D(d) patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions. |
format | Online Article Text |
id | pubmed-7838288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78382882021-01-29 A new vector-based global river network dataset accounting for variable drainage density Lin, Peirong Pan, Ming Wood, Eric F. Yamazaki, Dai Allen, George H. Sci Data Data Descriptor Spatial variability of river network drainage density (D(d)) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of D(d) worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate D(d) based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and D(d) are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved D(d) patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions. Nature Publishing Group UK 2021-01-26 /pmc/articles/PMC7838288/ /pubmed/33500418 http://dx.doi.org/10.1038/s41597-021-00819-9 Text en © The Author(s) 2021 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 Lin, Peirong Pan, Ming Wood, Eric F. Yamazaki, Dai Allen, George H. A new vector-based global river network dataset accounting for variable drainage density |
title | A new vector-based global river network dataset accounting for variable drainage density |
title_full | A new vector-based global river network dataset accounting for variable drainage density |
title_fullStr | A new vector-based global river network dataset accounting for variable drainage density |
title_full_unstemmed | A new vector-based global river network dataset accounting for variable drainage density |
title_short | A new vector-based global river network dataset accounting for variable drainage density |
title_sort | new vector-based global river network dataset accounting for variable drainage density |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838288/ https://www.ncbi.nlm.nih.gov/pubmed/33500418 http://dx.doi.org/10.1038/s41597-021-00819-9 |
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