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A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015
The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapot...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591528/ https://www.ncbi.nlm.nih.gov/pubmed/33110108 http://dx.doi.org/10.1038/s41597-020-00693-x |
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author | Niu, Zhongen He, Honglin Zhu, Gaofeng Ren, Xiaoli Zhang, Li Zhang, Kun |
author_facet | Niu, Zhongen He, Honglin Zhu, Gaofeng Ren, Xiaoli Zhang, Li Zhang, Kun |
author_sort | Niu, Zhongen |
collection | PubMed |
description | The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapotranspiration data) for China from 1981 to 2015 with spatial and temporal resolutions of 0.05° and 8 days, respectively. The T/ET dataset is based on a model-data fusion method that integrates the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration). The dataset is driven by satellite-based leaf area index (LAI) data from GLASS and GLOBMAP, and climate data from the Chinese Ecosystem Research Network (CERN). Observational annual T/ET were used to validate the model, with R(2) and RMSE values were 0.73 and 0.07 (12.41%), respectively. The dataset provides significant insight into T/ET and its changes over the Chinese terrestrial ecosystem and will be beneficial for understanding the hydrological cycle and energy budgets between the land and the atmosphere. |
format | Online Article Text |
id | pubmed-7591528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75915282020-10-29 A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 Niu, Zhongen He, Honglin Zhu, Gaofeng Ren, Xiaoli Zhang, Li Zhang, Kun Sci Data Data Descriptor The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapotranspiration data) for China from 1981 to 2015 with spatial and temporal resolutions of 0.05° and 8 days, respectively. The T/ET dataset is based on a model-data fusion method that integrates the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration). The dataset is driven by satellite-based leaf area index (LAI) data from GLASS and GLOBMAP, and climate data from the Chinese Ecosystem Research Network (CERN). Observational annual T/ET were used to validate the model, with R(2) and RMSE values were 0.73 and 0.07 (12.41%), respectively. The dataset provides significant insight into T/ET and its changes over the Chinese terrestrial ecosystem and will be beneficial for understanding the hydrological cycle and energy budgets between the land and the atmosphere. Nature Publishing Group UK 2020-10-27 /pmc/articles/PMC7591528/ /pubmed/33110108 http://dx.doi.org/10.1038/s41597-020-00693-x Text en © The Author(s) 2020 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 Niu, Zhongen He, Honglin Zhu, Gaofeng Ren, Xiaoli Zhang, Li Zhang, Kun A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title | A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title_full | A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title_fullStr | A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title_full_unstemmed | A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title_short | A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015 |
title_sort | spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in china from 1981–2015 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591528/ https://www.ncbi.nlm.nih.gov/pubmed/33110108 http://dx.doi.org/10.1038/s41597-020-00693-x |
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