Assessing the impacts of climatic and anthropogenic factors on water level variation in the Taihu Plain based on non-stationary statistical models-Reference-Cited by-同舟云学术

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Assessing the impacts of climatic and anthropogenic factors on water level variation in the Taihu Plain based on non-stationary statistical models

Published:2020-04-22 Issue:18 Volume:27 Page:22829-22842
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Wang Yuefeng,Xu Youpeng,Song Song,Wang Jie,Zhang Junyi

Funder

the National Key Technology Support Program

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-020-08889-9.pdf

Reference59 articles.

1. Allaire M, Vogel R, Kroll C (2015) The hydromorphology of an urbanizing watershed using multivariate elasticity. Adv Water Resour 86:147–154. https://doi.org/10.1016/j.advwatres.2015.09.022

2. Assani A, Landry R, Biron S, Frenette J (2014) Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010. Hydrol Process 28:4011–4022. https://doi.org/10.1002/hyp.9941

3. Bazrafshan J, Hejabi S (2018) A non-stationary reconnaissance drought index (NRDI) for drought monitoring in a changing climate. Water Resour Manag 32(8):2611–2624. https://doi.org/10.1007/s11269-018-1947-z

4. Chen Z, Wang Z (1999) Yangtze Delta, China: Taihu lake-level variation since the 1950s, response to sea-level rise and human impact. Environ Geol 37(4):333–339. https://doi.org/10.1007/s002540050

5. Cheng X, Evans E, Wu H, Thorne C, Han S, Simm J, Hall J (2013) A framework for long-term scenario analysis in the Taihu Basin, China. J Flood Risk Manag 6(1):3–13. https://doi.org/10.1111/jfr3.12024

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