Article(id=1223210588826882069, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222205, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666195200000, receivedDateStr=2022-10-20, revisedDate=1668700800000, revisedDateStr=2022-11-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565730529, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565730529, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565730529, creator=13701087609, updateTime=1769565730529, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=31, endPage=34, ext={EN=ArticleExt(id=1223210589166620713, articleId=1223210588826882069, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Evaluation Model of River Organic Carbon Flux Based on Functional Data Analysis and Its Application, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

Since total organic carbon (TOC) is not a routine hydrological monitoring data, the monitoring frequency is much lower than that of flow data and it often has missing values, which makes it difficult to evaluate TOC flux. This study proposes an improved computational model based on functional data analysis (FDA) for the evaluation of riverine TOC fluxes. First, the discrete data set is functionalized, and all the indicators are transformed into s function curves in the same evaluation time domain. And then the TOC flux of the evaluation section during the study period is calculated by Riemann integral. The results of the TOC evaluation at the Zhutuo station on the Yangtze River show that, The average value of the annual TOC flux at Zhutuo Station from 2018 to 2020 is 702 000 t; The intra-annual distribution of TOC fluxes is extremely uneven, with a high concentration in the flood season, especially in summer; The annual average value of TOC flux during the flood season (June to September) is 438 200 t, equivalent to 62.43% of the total; The annual average TOC flux in summer is 349 000 t, equivalent to 49.72% of the total; While the annual average value of TOC flux during the dry season (October to May) is 263 700 t, equivalent to 37.57% of the total; The annual average TOC flux in winter is 71 300 t, equivalent to 10.15% of the total; Compared with the traditional method, the improved TOC flux model is also able to deal with missing values and inconsistencies in multi-indicator monitoring sequences more effectively, and to accurately assess the course of TOC fluxes over different seasons and periods of abundance and depletion.

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由于总有机碳（TOC）不是常规水文监测数据，监测频率远低于流量资料，且常常具有缺失值，这对评估TOC通量和揭示碳在河流的输移过程带来了较大难度。针对不同监测频率下的河流TOC通量评价问题，基于函数型数据分析（FDA），提出了一种改进的计算模型。首先，对离散数据集进行函数化处理，将所有指标均转化为同一评价时域上的光滑连续的函数曲线；而后通过黎曼积分，计算评价断面在研究时段的TOC通量。对长江朱沱站的评价结果表明，朱沱站2018~2020年TOC年通量的均值为70.2×10⁴t；朱沱站TOC通量年内分配极不均匀，高度集中在汛期，特别是夏季。汛期（6~9月）的TOC通量年均值为43.82×10⁴t，相当于总量的62.43%；夏季TOC通量年均值为34.90×10⁴t，相当于总量的49.72%。而枯水期（10~5月）的TOC通量年均值仅为26.37×10⁴t，占全年总量的37.57%；冬季的TOC通量年均值近7.13×10⁴t，占全年总量的10.15%；与传统方法相比，改进后的TOC通量模型能更有效地处理缺失值和多指标监测序列不一致问题，并更准确地评估不同季节和丰枯水期的TOC通量变化过程。

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赵梓屹（1998-），女，硕士研究生，研究方向为生态水利，E-mail：bingtanghuluwa@163.com

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赵梓屹（1998-），女，硕士研究生，研究方向为生态水利，E-mail：bingtanghuluwa@163.com

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