A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments

• Published in: Environmental pollution (1987) Vol. 288; p. 117337
• Main Authors: Liu, Shuci, Ryu, Dongryeol, Webb, J. Angus, Lintern, Anna, Guo, Danlu, Waters, David, Western, Andrew W.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-11-2021
• Subjects: Catchment characteristics; Model averaging; Multi-model inference; Statistical model; Water quality; Multi-model inference; Statistical model; Model averaging; Water quality; Catchment characteristics
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2021.117337

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
[Display omitted]
•A multi-model approach reveals the key drivers for the spatial variation in water quality.•Catchment natural characteristics have the greatest impact on most constituents.•Land use has an important impact on dissolved nutrients.•Our results enable prediction of catchment water quality across space.
A multi-model approach indicated that catchment natural characteristics have the greatest impact on water quality overall, but land use has an important impact on dissolved nutrients.