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Long-term trends and probability distributions of river water quality variables and their relationships with climate elasticity characteristics

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Abstract

The statistical characteristics of surface water quality play fundamental roles in climate change-related studies. Climatic drivers will affect surface water quality, and such potential effects will vary between different regions and climate types. Here, we studied the long-term trends and probability distributions of water quality variables (including water temperature, pH, turbidity, DO, C-,N-,P-variables, etc.) and their relationship with climate elasticity, a non-parametric estimator of the sensitivity of the response of water quality to climate drivers, based on three typical watersheds: the Yukon, the Mekong, and the Murray. Significant decreasing trends were observed in the Yukon and Murray watersheds for the majority of water quality variables, except turbidity and filtered nitrate plus nitrite, whereas increasing trends were exhibited by most water quality variables in the Mekong watershed. Compared with the Yukon and Murray watersheds, the probability distributions of most water quality variables and their corresponding percentage change-based elasticity estimator samples are characterized by a heavy-tailed distribution in the Mekong watershed. The precipitation elasticity results are statistically meaningful in the Mekong and Murray watersheds, whereas temperature elasticity is significant in the Yukon watershed. The revealed characteristics of long-term trends and probability distributions pattern of basic water quality variables are helpful for water quality modeling. The findings suggested that the increasing trends, heavy-tailed probability distribution patterns, and the response of water quality to precipitation and temperature, especially in densely populated developing areas, can be modulated by restoration efforts, which will reduce the potential impacts of climatic and non-climatic factors.

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Acknowledgements

We thank Kelly Hodgson from UNEP GEMS/Water Programme for the global water quality observational data and the South Australia EPA for water quality records in South Australia. UDel_AirT_Precip data are obtained from the website of NOAA/OAR/ESRL PSD, Boulder, Colorado, USA.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 51509061), additional support was provided by the Southern University of Science and Technology (Grant No. G01296001).

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Authors and Affiliations

  1. School of Environment, Harbin Institute of Technology, Harbin, 150090, China

    Afed U. Khan & Peng Wang

  2. Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Peshawar, 25000, Pakistan

    Afed U. Khan

  3. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Jiping Jiang

  4. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

    Bin Shi

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  1. Afed U. Khan
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Correspondence to Jiping Jiang.

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Khan, A.U., Wang, P., Jiang, J. et al. Long-term trends and probability distributions of river water quality variables and their relationships with climate elasticity characteristics. Environ Monit Assess 190, 648 (2018). https://doi.org/10.1007/s10661-018-7044-1

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