A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records
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A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records Shuci Liu & Danlu Guo & J. Angus Webb J. Wilson & Andrew W. Western
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Paul
Received: 19 December 2019 / Accepted: 3 September 2020 # Springer Nature Switzerland AG 2020
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 Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10661-020-08592-9) contains supplementary material, which is available to authorized users. S. Liu (*) : D. Guo : J. A. Webb : A. W. Western Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia e-mail: [email protected] P. J. Wilson Department of Environment, Land, Water & Planning, East Melbourne, Australia
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. Keywords Water quality . Trend detection power . Surrogate models . Turbidity . Electrical conductivity
Introduction Stream water quality is highly variable over time at timescales ranging from sub-event fluctuations to changes occurring over longer time periods (Godsey et al. 2010). Understanding those environmental changes is important for planning catchment management activities and reporting the condition of streams (Altenburger et al. 2015; Parr et al. 2003). Historically, the standard approach to monitoring water quality has been to collect manual spot samples at relatively low frequencies (e.g. ranging from weekly to monthly) or even sparse and irregular samples(Cassidy and Jordan 2011; Zhan
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