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https://hdl.handle.net/20.500.14094/90008283
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2024-04-20
12:29 集計
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90008283 (fulltext)
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メタデータID
90008283
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open access
出版タイプ
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タイトル
Unbiased Estimates and Confidence Intervals for Riverine Loads
著者
著者ID
A1197
研究者ID
1000000263400
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=07794444fbf802f3520e17560c007669
著者名
Tada, Akio
多田, 明夫
タダ, アキオ
所属機関名
農学研究科
著者ID
A0425
研究者ID
1000080171809
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=ed6688b2c03d0d28520e17560c007669
著者名
Tanakamaru, Haruya
田中丸, 治哉
タナカマル, ハルヤ
所属機関名
農学研究科
収録物名
Water Resources Research
巻(号)
57(3)
ページ
e2020WR028170
出版者
American Geophysical Union (AGU)
刊行日
2021-03
公開日
2021-10-01
抄録
Estimating the uncertainty in annual riverine constituent loads, which is the mass that passes through a river cross-section into a receiving water body, using infrequent water quality (WQ) observations is a difficult and unsolved task. Therefore, we propose an unbiased point estimation method and interval estimation method for river loads based on the rating curve (RC) method using importance sampling and the bootstrap method, respectively. In this paper, we first statistically explain the unbiasedness of load estimates using the proposed method. Second, the effectiveness of point and interval estimates by the proposed method is demonstrated for river loads from a small catchment and from large watersheds based on discharge and WQ data of solutes, nutrients, and suspended sediments with 10-min to daily intervals. The results show that the proposed method provides unbiased estimates and appropriate coverage of confidence intervals regardless of the RC model used. The results also reveal that the dominant cause of bias in load estimates based on ordinary RC methods, such as the Loadest model, is not due to the poor simulation of observed loading rates by the RCs or because of the nonnormality of the regression residuals but rather improper sampling strategies. The proposed method is currently not feasible for WQ monitoring sites in large rivers due to the unmanageability of missing observations or censored data and an inefficient sampling strategy, although the requirement of unbiased estimation explained here can aid in scheduling high-flow sampling for monitoring sites.
キーワード
confidence interval
importance sampling
load estimation
rating curve
river load
unbiased estimation
カテゴリ
農学研究科
学術雑誌論文
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© 2021. American Geophysical Union. All Rights Reserved.
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資源タイプ
journal article
言語
English (英語)
ISSN
0043-1397
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eISSN
1944-7973
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NCID
AA0088653X
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関連情報
DOI
https://doi.org/10.1029/2020WR028170
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