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A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records

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Abstract

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

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Acknowledgements

The authors would like to acknowledge the efforts of the Victorian DELWP who provided both low- and high-frequency water quality monitoring data used in this study.

Funding

This study was financially supported by the Victorian Department of Environment, Land, Water and Planning (DELWP).

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  1. Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia

    Shuci Liu, Danlu Guo, J. Angus Webb & Andrew W. Western

  2. Department of Environment, Land, Water & Planning, East Melbourne, Australia

    Paul J. Wilson

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Liu, S., Guo, D., Webb, J.A. et al. A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records. Environ Monit Assess 192, 628 (2020). https://doi.org/10.1007/s10661-020-08592-9

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