Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.

The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has be...

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Autores principales:	Wei-Ping Chan, Hsiao-Wei Yuan, Cho-Ying Huang, Chung-Ho Wang, Shou-De Lin, Yi-Chen Lo, Bo-Wen Huang, Kent A Hatch, Hau-Jie Shiu, Cheng-Feng You, Yuan-Mou Chang, Sheng-Feng Shen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9f7d85f559b04cf0a76c00b113a3098b
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spelling	oai:doaj.org-article:9f7d85f559b04cf0a76c00b113a3098b2021-11-18T07:04:54ZRegional scale high resolution δ18O prediction in precipitation using MODIS EVI.1932-620310.1371/journal.pone.0045496https://doaj.org/article/9f7d85f559b04cf0a76c00b113a3098b2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23029053/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ(18)O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ(18)O are highly correlated and thus the EVI is a good predictor of precipitated δ(18)O. We then test the predictability of our EVI-δ(18)O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ(18)O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ(18)O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape.Wei-Ping ChanHsiao-Wei YuanCho-Ying HuangChung-Ho WangShou-De LinYi-Chen LoBo-Wen HuangKent A HatchHau-Jie ShiuCheng-Feng YouYuan-Mou ChangSheng-Feng ShenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e45496 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wei-Ping Chan Hsiao-Wei Yuan Cho-Ying Huang Chung-Ho Wang Shou-De Lin Yi-Chen Lo Bo-Wen Huang Kent A Hatch Hau-Jie Shiu Cheng-Feng You Yuan-Mou Chang Sheng-Feng Shen Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
description	The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ(18)O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ(18)O are highly correlated and thus the EVI is a good predictor of precipitated δ(18)O. We then test the predictability of our EVI-δ(18)O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ(18)O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ(18)O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape.
format	article
author	Wei-Ping Chan Hsiao-Wei Yuan Cho-Ying Huang Chung-Ho Wang Shou-De Lin Yi-Chen Lo Bo-Wen Huang Kent A Hatch Hau-Jie Shiu Cheng-Feng You Yuan-Mou Chang Sheng-Feng Shen
author_facet	Wei-Ping Chan Hsiao-Wei Yuan Cho-Ying Huang Chung-Ho Wang Shou-De Lin Yi-Chen Lo Bo-Wen Huang Kent A Hatch Hau-Jie Shiu Cheng-Feng You Yuan-Mou Chang Sheng-Feng Shen
author_sort	Wei-Ping Chan
title	Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
title_short	Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
title_full	Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
title_fullStr	Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
title_full_unstemmed	Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.
title_sort	regional scale high resolution δ18o prediction in precipitation using modis evi.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/9f7d85f559b04cf0a76c00b113a3098b
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Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.

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