Predicting Sediment and Nutrient Concentrations in Rivers Using High Frequency Water Quality Surrogates

TL;DR

This study develops models using high-frequency sensor data to predict sediment and nutrient concentrations in rivers, highlighting turbidity's effectiveness for sediments but challenges in nutrient prediction due to complex dynamics.

Contribution

The paper introduces generalized least squares models that incorporate temporal autocorrelation to predict TSS and NOx from in situ sensor data in river systems.

Findings

Turbidity is a strong predictor of TSS in rivers.

Models accurately predict TSS but poorly predict NOx concentrations.

Nutrient dynamics are complex, requiring more sophisticated modeling approaches.

Abstract

A particular focus of water-quality monitoring is the concentrations of sediments and nutrients in rivers, 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 capture adequately 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 low-cost in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized least squares linear mixed effects models with a continuous first-order autoregressive correlation to data collected traditionally by manual sampling for subsequent analysis in the laboratory, then used these models to predict TSS or NOx from in situ sensor water-quality surrogate data, at two freshwater sites and one estuarine site. These models accounted for both temporal autocorrelation and unevenly time-spaced observations in the data. Turbidity proved a useful surrogate of TSS, with high predictive ability at both freshwater and estuarine sites. NOx models had much poorer fits, even when additional covariates of conductivity and river level were included along with turbidity. Furthermore, the relative influence of covariates in the NOx models was not consistent across sites. Our findings likely reflect the complexity of dissolved nutrient dynamics in rivers, which are influenced by multiple and interacting factors including physical, chemical and biological processes, and the need for greater and better incorporation of spatial and temporal components within models.