Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero
{"title":"DayCent CUTE:DayCent的全球灵敏度、自动校准和不确定性分析工具","authors":"Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero","doi":"10.1016/j.envsoft.2023.105832","DOIUrl":null,"url":null,"abstract":"<div><p><span>Soil organic carbon<span> (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate </span></span>SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods.</p><p><span>A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R</span><sup>2</sup> values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"169 ","pages":"Article 105832"},"PeriodicalIF":4.8000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent\",\"authors\":\"Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero\",\"doi\":\"10.1016/j.envsoft.2023.105832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Soil organic carbon<span> (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate </span></span>SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods.</p><p><span>A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R</span><sup>2</sup> values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model.</p></div>\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"169 \",\"pages\":\"Article 105832\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364815223002189\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815223002189","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent
Soil organic carbon (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods.
A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R2 values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.