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Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Author

Listed:
  • Xiaobin Zhang

    (Zhejiang Academy of Agricultural Sciences
    Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China)

  • Ligang Ma

    (Xinjiang University)

  • Yihang Zhu

    (Zhejiang Academy of Agricultural Sciences)

  • Weidong Lou

    (Zhejiang Academy of Agricultural Sciences)

  • Baoliang Xie

    (Zhejiang Academy of Agricultural Sciences)

  • Li Sheng

    (Zhejiang Academy of Agricultural Sciences
    Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China)

  • Hao Hu

    (Zhejiang Academy of Agricultural Sciences
    Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China)

  • Kefeng Zheng

    (Zhejiang Academy of Agricultural Sciences
    Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China)

  • Qing Gu

    (Zhejiang Academy of Agricultural Sciences
    Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China)

Abstract

Better characterizing the spatio-temporal pattern of water quality would increase the ability to effectively manage water resources. This study applied the concept of temporal stability analysis (TSA) to explore the temporal characteristics of spatial variability in surface water quality. Measurement data from 41 monitoring stations in Qiantang River, China during 2017–2019 were used for analysis. The data included four water quality indicators: dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and ammonia nitrogen (NH3–N). A Spearman’s rank correlation for each pair of monitoring times was performed to characterize the spatial pattern of water quality. A temporal analysis of relative differences was applied to examine the temporal stability of the sampling sites. The rank correlation analysis suggests that the spatial pattern of water quality was maintained for a specific period of time and the TP concentration was most temporally stable compared with the other three indicators across the study area. The standard deviation of the relative difference (SDRD) and index of temporal stability (ITS) were found to be better for identifying the stable sites compared to the mean absolute bias error (MABE) and root mean square error (RMSE) in this study. A correlation analysis between the temporal stability indices and potential influencing factors showed that land use proportions (forest, built-up land, and agricultural land), and socio-economic indicators (gross domestic product [GDP] and population density) were closely associated with the temporal stability of water quality. The results showed evidence that the TSA method was feasible and effective in identifying the temporal stability of surface water quality and optimizing the water quality monitoring program. This study’s method and findings can help improve surface water quality monitoring strategies and water resource management.

Suggested Citation

  • Xiaobin Zhang & Ligang Ma & Yihang Zhu & Weidong Lou & Baoliang Xie & Li Sheng & Hao Hu & Kefeng Zheng & Qing Gu, 2022. "Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1413-1429, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03090-8
    DOI: 10.1007/s11269-022-03090-8
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    References listed on IDEAS

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    1. Qianyu Gao & Guofang Li & Jin Bao & Jian Wang, 2021. "Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4435-4456, October.
    2. Shahin Zandmoghaddam & Ali Nazemi & Elmira Hassanzadeh & Shadi Hatami, 2019. "Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3579-3594, August.
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    Cited by:

    1. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    2. Vanita Jain & Aarushi Dhingra & Eeshita Gupta & Ish Takkar & Rachna Jain & Sardar M. N. Islam, 2023. "Influence of Land Surface Temperature and Rainfall on Surface Water Change: An Innovative Machine Learning Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3013-3035, June.

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