Predicting sediment and nutrient concentrations from high-frequency water-quality data

Abstract

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.

Fig 1. The study region.

Study sites (closed circles), rivers and catchment boundaries within north tropical Queensland, Australia (left panel), the Wet Tropics (MR; middle panel) and Mackay Whitsunday regions (PR and SC; right panel). Closed triangles show the major towns of Cairns, Townsville and Mackay.

Fig 2. Turbidity of water at each study site.

Laboratory-measured (open circles) and in situ sensor-measured turbidity (NTU). Mulgrave River (MR; purple points), Pioneer River (PR, blue points) and Sandy Creek (SC; light green points).

Fig 3. Conductivity of water at each study site.

Laboratory-measured (open circles) and in situ sensor-measured conductivity (μS/cm) at Mulgrave River (MR; purple points), Pioneer River (PR, blue points) and Sandy Creek (SC; light green points).

Fig 4. Height of water at each study site.

River level (m) measured on-site at the time of water sample collection (open circles) and by in situ sensors at Mulgrave River (MR; purple points), Pioneer River (PR, blue points) and Sandy Creek (SC; light green points).

Fig 5. Model development, selection and prediction for the final total suspended solids (TSS) and oxidized nitrogen (NOx) models.

LevelQ is a categorical variable with two levels based on first, second or third quartiles of the data (Q1, Q2 or Q3). Turbidity, conductivity and level covariates were all log₁₀-transformed prior to analysis.

Fig 6. Observed versus 5-fold cross-validated (cv) prediction values of total suspended solids (TSS) from the final TSS model.

TSS (mg/L; back-transformed with bias correction). Data from each site shown in purple (Mulgrave River, MR), blue (Pioneer River, PR) and light green (Sandy Creek, SC). Black lines show the 1:1 relationships between observations and predictions.

Fig 7. Observed versus 5-fold cross-validated (CV) prediction values of oxidized nitrogen (NOx) from the final NOx model.

NOx (mg/L; back-transformed with bias correction). Data from each site shown in purple (Mulgrave River, MR), blue (Pioneer River, PR) and light green (Sandy Creek, SC). Black lines show the 1:1 relationships between observations and predictions.

Fig 8. Observed total suspended solids (TSS, mg/L) versus predicted TSS (mg/L) from the leave-one-out cross validation (LOOCV).

Fig 9. Total suspended solids (TSS, mg/L) at each site predicted using the final TSS model and in situ sensor turbidity data (March 2017–2018).

Mulgrave River (MR, purple), Pioneer River (PR, blue) and Sandy Creek (SC, light green). Gray shading shows upper and lower boundaries of the 95% prediction interval, and the inner lines the predicted TSS concentrations through time. Gaps indicate periods of missing data in the sensor time series. Closed circles show the laboratory-measured TSS concentrations within the same period.

Fig 10. Observed oxidized nitrogen (NOx; mg/L) versus predicted NOx (mg/L) from the leave-one-out cross validation.

Fig 11. Oxidized nitrogen (NOx, mg/L; log₁₀ transformed) at each site predicted using the final NOx model and in situ sensor turbidity, conductivity and level data (March 2017–2018).

Mulgrave River (MR, purple), Pioneer River (PR, blue) and Sandy Creek (SC, light green). Gray shading shows upper and lower boundaries of the 95% prediction interval, and the inner lines the predicted TSS concentrations through time. Gaps indicate periods of missing data in the sensor time series. Closed circles show the laboratory-measured NOx concentrations within the same period.