ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), Australia.
Institute for Future Environments, Queensland University of Technology, Brisbane, Queensland, Australia.
PLoS One. 2019 Aug 30;14(8):e0215503. doi: 10.1371/journal.pone.0215503. eCollection 2019.
Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.
河流中的水质监测通常侧重于沉积物和养分的浓度，这些成分会使生物窒息并导致富营养化。然而，手动采样的物理和经济限制禁止以足够的频率收集数据，以充分捕捉随时间变化的浓度变化。在这里，我们开发了基于河流中浊度、电导率和水位原位传感器高频时间序列数据来预测总悬浮固体（TSS）和氧化氮（NOx）浓度的模型，这些模型流入大堡礁泻湖。我们拟合了具有连续一阶自回归相关结构的广义线性混合效应模型，用于在两个淡水点和一个河口点收集的水质数据，并使用拟合模型从原位传感器数据中预测 TSS 和 NOx。这些模型描述了数据的时间自相关，并处理了以不规则频率采集的观测值，这是水质监测数据的典型特征。浊度被证明是 TSS 的有用且可推广的替代物，在河口和淡水点具有很高的预测能力。浊度、电导率和水位是 NOx 的综合替代物。然而，NOx 与协变量之间的关系比 TSS 与浊度之间的关系更为复杂，因此，预测 NOx 的能力较低，并且在不同地点的可推广性也不如 TSS。此外，对于 TSS 和 NOx 模型，预测区间在事件期间往往会增加，这突出表明需要定期在水质报告中包含不确定性度量。我们的研究还表明，用于预测沉积物和养分的基于替代物的模型如果要使方差估计无偏且模型推断有意义，则需要更好地纳入时间成分。随着组织在整个流域范围内转向自动化水质监测，模型在不同地点（甚至可能在不同地区）的可转移性将变得越来越重要。
