{"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":"44 1","pages":""},"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\":\"44 1\",\"pages\":\"\"},\"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}
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.
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.