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Large observational bias on discharge in the Indus River since 1970s
The discharge of one of the world’s largest river - Indus River was reported to be increasing that was not supported by the Karakoram (KK) glacier expansion. A major hydrometric bias was ignored, which seemed similar to the montage that the Himalayan glaciers would disappear. This study proposed a f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251862/ https://www.ncbi.nlm.nih.gov/pubmed/30470822 http://dx.doi.org/10.1038/s41598-018-35600-3 |
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author | Liu, Jingshi Kang, Shichang Hewitt, Kenneth Hu, Linjin Xianyu, Li |
author_facet | Liu, Jingshi Kang, Shichang Hewitt, Kenneth Hu, Linjin Xianyu, Li |
author_sort | Liu, Jingshi |
collection | PubMed |
description | The discharge of one of the world’s largest river - Indus River was reported to be increasing that was not supported by the Karakoram (KK) glacier expansion. A major hydrometric bias was ignored, which seemed similar to the montage that the Himalayan glaciers would disappear. This study proposed a framework for quantifying the bias resulting from inaccurate data affecting hydrologic studies on the Indus. We constructed a statistical model by converting the rating curves of rivers into air temperature (T) – discharge (Q) curves from an adjacent catchment in China where flow measurement was carried out using a standard method. We found that most flow data for the Indus were much greater than the error limits of T-Q curves estimated by daily data, a greater bias occurred in recent decades when discharge increased, the higher the flow was, the larger the bias was. The estimated mean annual and maximum monthly bias was 22.5% and 210%, respectively. These biases indicated that discharge increase in the Indus probably resulted from the large errors of hydrometrics without a scientific basis. We suggested a montage bias was needed in the hydrologic science of KK’s rivers that may strongly affect water resource management. |
format | Online Article Text |
id | pubmed-6251862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62518622018-11-29 Large observational bias on discharge in the Indus River since 1970s Liu, Jingshi Kang, Shichang Hewitt, Kenneth Hu, Linjin Xianyu, Li Sci Rep Article The discharge of one of the world’s largest river - Indus River was reported to be increasing that was not supported by the Karakoram (KK) glacier expansion. A major hydrometric bias was ignored, which seemed similar to the montage that the Himalayan glaciers would disappear. This study proposed a framework for quantifying the bias resulting from inaccurate data affecting hydrologic studies on the Indus. We constructed a statistical model by converting the rating curves of rivers into air temperature (T) – discharge (Q) curves from an adjacent catchment in China where flow measurement was carried out using a standard method. We found that most flow data for the Indus were much greater than the error limits of T-Q curves estimated by daily data, a greater bias occurred in recent decades when discharge increased, the higher the flow was, the larger the bias was. The estimated mean annual and maximum monthly bias was 22.5% and 210%, respectively. These biases indicated that discharge increase in the Indus probably resulted from the large errors of hydrometrics without a scientific basis. We suggested a montage bias was needed in the hydrologic science of KK’s rivers that may strongly affect water resource management. Nature Publishing Group UK 2018-11-23 /pmc/articles/PMC6251862/ /pubmed/30470822 http://dx.doi.org/10.1038/s41598-018-35600-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liu, Jingshi Kang, Shichang Hewitt, Kenneth Hu, Linjin Xianyu, Li Large observational bias on discharge in the Indus River since 1970s |
title | Large observational bias on discharge in the Indus River since 1970s |
title_full | Large observational bias on discharge in the Indus River since 1970s |
title_fullStr | Large observational bias on discharge in the Indus River since 1970s |
title_full_unstemmed | Large observational bias on discharge in the Indus River since 1970s |
title_short | Large observational bias on discharge in the Indus River since 1970s |
title_sort | large observational bias on discharge in the indus river since 1970s |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251862/ https://www.ncbi.nlm.nih.gov/pubmed/30470822 http://dx.doi.org/10.1038/s41598-018-35600-3 |
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