Liu Shuci, Guo Danlu, Webb J Angus, Wilson Paul J, Western Andrew W
Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia.
Department of Environment, Land, Water & Planning, East Melbourne, Australia.
Environ Monit Assess. 2020 Sep 9;192(10):628. doi: 10.1007/s10661-020-08592-9.
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.
为了更精确地了解水质变化，地表水水质监测网络中越来越多地采用连续采样。然而，在识别水质随时间的变化方面，连续监测相对于定点采样能带来多大程度的改善仍不明确。本研究旨在：（1）评估利用高频和低频水质数据检测趋势的能力；（2）评估使用高频数据作为替代数据以帮助检测其他成分趋势的价值。利用统计回归模型识别时间趋势，进而评估澳大利亚维多利亚州采集的浊度和电导率（EC）数据的高频（15分钟）和低频（每月）数据的趋势检测能力。此外，我们开发了替代模型，根据径流、浊度和电导率模拟五种沉积物和营养成分。然后采用基于模拟的统计方法比较低频和高频水质记录之间的趋势检测能力。结果表明，高频采样在浊度、电导率以及模拟的沉积物和营养成分的趋势检测能力方面具有明显优势，尤其是在数据记录较短的时期。对于利用5年的数据检测1%的年趋势，与每月数据相比，高频数据在浊度和电导率趋势检测概率方面分别提高了97%和94%。我们研究结果凸显了通过更广泛应用高频采样来升级监测网络的益处。
