Key controls of catchment attribute on spatial differences and export regimes in riverine water quality: a study across the Australian continent using a Bayesian approach

Investigations of concentration (C) and discharge (Q) relationships (C–Q relationships) at the catchment scale are commonly used to characterize export regimes of instream particulates and solutes. C–Q relationships also provide insights on spatial and temporal variability in pollutant export, allowing identification of the sources and transfer pathways of pollutants. Previous studies have shown that several key catchment attributes control the export of sediment and dissolved nutrients within catchments. These catchment attributes include land use, topography, geology and soils. However, only few studies have investigated the relative importance of multiple catchment attributes over large spatial scales (e.g., at the continental scale) and between different climate zones. This is mostly due to either a limited number of catchments that have been monitored or a strong focus on temperate catchments. Therefore, our current understanding of key controls on spatial variability and export regimes across different climates is still limited. In this study, we investigated spatial differences and the C–Q relationships of six commonly monitored constituents (i.e., total suspended solid – TSS, total nitrogen – TN, sum of nitrate and nitrite – NO_x, total phosphorus – TP, soluble reactive phosphorus – SRP and electrical conductivity – EC) from 507 catchments across the Australian continent. These catchments represent five main climate zones in Australia (i.e., arid, Mediterranean, temperate, subtropical and tropical). We used a hierarchical Bayesian multi-model averaging approach to 1) identify key catchment attributes (e.g., land use, topography, geology and hydrology) driving the spatial variability of mean concentration and export regimes (C–Q relationship) for individual constituents; 2) understand the role of climatic gradients in determining the magnitude and direction of the key controls, and 3) use the key controls identified to predict the mean concentration and C–Q relationship in multiple catchments across Australia.

The proposed Bayesian modelling framework provided a higher predictive capability for mean concentrations (Nash-Sutcliffe efficiency (NSE) ranging from 0.58 for SRP to 0.86 for EC), compared to log(C) – log(Q) slopes (NSE ranging from 0.25 for NO_x to 0.39 for TP). For mean concentrations, land use (e.g., agriculture and urban) has a significantly positive effect on nutrients (i.e., TN, NO_x, TP and SRP), particularly in the Mediterranean, subtropical and tropical regions, indicating that land use is a key driver for these constituents. For log(C) – log(Q) slopes, catchment topographical characteristics (e.g., slope and maximum flow pathway) have relatively high impacts on TSS, TP and EC, indicating export of sediments and solutes in catchments largely controlled by mobilization (sediment) and surface-subsurface flow interaction (solutes). Findings from our study provide a data-driven understanding of key controls on riverine water quality across multiple climate types and can inform future water quality management strategies.