A data-based predictive model for spatiotemporal variability in stream water quality
Danlu Guo, Anna Lintern, J Angus Webb, Dongryeol Ryu, Ulrike Bende-Michl, Shuci Liu, Andrew William Western
HYDROLOGY AND EARTH SYSTEM SCIENCES | COPERNICUS GESELLSCHAFT MBH | Published : 2020
Our current capacity to model stream water quality is limited - particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx) and electrical conductivity (EC). The model stru..View full abstract
Awarded by Australian Research Council
This research has been supported by the Australian Research Council via a Linkage Project (grant no. LP140100495), with contributions from the following industrial collaborators: the Victorian Environment Protection Authority; the Victorian Department of Environment, Land, Water and Planning; the Australian Bureau of Meteorology; and the Queensland Department of Natural Resources, Mines and Energy.