基于同位素的线性混合模型与贝叶斯混合模型在确定水分回收率方面的比较

IF 2.7 3区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Yanqiong Xiao, Liwei Wang, Shengjie Wang, Kei Yoshimura, Yudong Shi, Xiaofei Li, Athanassios A. Argiriou, Mingjun Zhang
{"title":"基于同位素的线性混合模型与贝叶斯混合模型在确定水分回收率方面的比较","authors":"Yanqiong Xiao, Liwei Wang, Shengjie Wang, Kei Yoshimura, Yudong Shi, Xiaofei Li, Athanassios A. Argiriou, Mingjun Zhang","doi":"10.1007/s40333-024-0016-0","DOIUrl":null,"url":null,"abstract":"<p>Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation, i.e., the moisture recycling ratio, but various isotope-based models usually lead to different results, which affects the accuracy of local moisture recycling. In this study, a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio. Among the three vapor sources including advection, transpiration, and surface evaporation, the advection vapor usually played a dominant role, and the contribution of surface evaporation was less than that of transpiration. When the abnormal values were ignored, the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9% for transpiration, 0.2% for surface evaporation, and −1.1% for advection, respectively, and the medians were 0.5%, 0.2%, and −0.8%, respectively. The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied, and the contribution of advection was relatively larger. The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios. Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input, and it was important to accurately estimate the isotopes in precipitation vapor. Generally, the Bayesian mixing model should be recommended instead of a linear model. The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.</p>","PeriodicalId":49169,"journal":{"name":"Journal of Arid Land","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of isotope-based linear and Bayesian mixing models in determining moisture recycling ratio\",\"authors\":\"Yanqiong Xiao, Liwei Wang, Shengjie Wang, Kei Yoshimura, Yudong Shi, Xiaofei Li, Athanassios A. Argiriou, Mingjun Zhang\",\"doi\":\"10.1007/s40333-024-0016-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation, i.e., the moisture recycling ratio, but various isotope-based models usually lead to different results, which affects the accuracy of local moisture recycling. In this study, a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio. Among the three vapor sources including advection, transpiration, and surface evaporation, the advection vapor usually played a dominant role, and the contribution of surface evaporation was less than that of transpiration. When the abnormal values were ignored, the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9% for transpiration, 0.2% for surface evaporation, and −1.1% for advection, respectively, and the medians were 0.5%, 0.2%, and −0.8%, respectively. The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied, and the contribution of advection was relatively larger. The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios. Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input, and it was important to accurately estimate the isotopes in precipitation vapor. Generally, the Bayesian mixing model should be recommended instead of a linear model. The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.</p>\",\"PeriodicalId\":49169,\"journal\":{\"name\":\"Journal of Arid Land\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Arid Land\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s40333-024-0016-0\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Arid Land","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s40333-024-0016-0","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

稳定水同位素是量化水汽循环对局地降水贡献的天然示踪剂,即水汽循环比,但各种基于同位素的模型通常会导致不同的结果,从而影响局地水汽循环的准确性。本研究选取了中国四个典型地区的 18 个站点,比较了基于同位素的线性混合模式和贝叶斯混合模式的性能,并确定了局地水汽循环比。在平流、蒸腾和地表蒸发三种水汽源中,平流水汽通常起主导作用,地表蒸发的贡献小于蒸腾。在忽略异常值的情况下,基于同位素的线性混合模式与贝叶斯混合模式的算术平均值分别为蒸腾作用 0.9%、地表蒸发作用 0.2%、平流作用 -1.1%,中值分别为 0.5%、0.2%、-0.8%。采用贝叶斯混合模式时,大多数情况下蒸腾的重要性略低,而平流的贡献相对较大。由于线性模型有时会导致负贡献比,因此贝叶斯混合模型在确定有效解决方案方面表现更好。用两种同位素方案进行的灵敏度测试表明,贝叶斯模型对同位素输入变化的灵敏度相对较低,因此准确估计降水水汽中的同位素非常重要。一般来说,应推荐使用贝叶斯混合模型,而不是线性模型。研究结果有助于了解基于同位素的线性混合模式和贝叶斯混合模式在不同气候背景下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of isotope-based linear and Bayesian mixing models in determining moisture recycling ratio

Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation, i.e., the moisture recycling ratio, but various isotope-based models usually lead to different results, which affects the accuracy of local moisture recycling. In this study, a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio. Among the three vapor sources including advection, transpiration, and surface evaporation, the advection vapor usually played a dominant role, and the contribution of surface evaporation was less than that of transpiration. When the abnormal values were ignored, the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9% for transpiration, 0.2% for surface evaporation, and −1.1% for advection, respectively, and the medians were 0.5%, 0.2%, and −0.8%, respectively. The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied, and the contribution of advection was relatively larger. The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios. Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input, and it was important to accurately estimate the isotopes in precipitation vapor. Generally, the Bayesian mixing model should be recommended instead of a linear model. The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Arid Land
Journal of Arid Land ENVIRONMENTAL SCIENCES-
CiteScore
4.70
自引率
6.70%
发文量
768
审稿时长
3.2 months
期刊介绍: The Journal of Arid Land is an international peer-reviewed journal co-sponsored by Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Science Press. It aims to meet the needs of researchers, students and practitioners in sustainable development and eco-environmental management, focusing on the arid and semi-arid lands in Central Asia and the world at large. The Journal covers such topics as the dynamics of natural resources (including water, soil and land, organism and climate), the security and sustainable development of natural resources, and the environment and the ecology in arid and semi-arid lands, especially in Central Asia. Coverage also includes interactions between the atmosphere, hydrosphere, biosphere, and lithosphere, and the relationship between these natural processes and human activities. Also discussed are patterns of geography, ecology and environment; ecological improvement and environmental protection; and regional responses and feedback mechanisms to global change. The Journal of Arid Land also presents reviews, brief communications, trends and book reviews of work on these topics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信