Spatio-Temporal Modeling of Pollutant Loads in Great Barrier Reef Catchments

• Activity Number: 469
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Daniel W. Gladish*+ and Petra M. Kuhnert and Daniel E. Pagendam and Christopher K. Wikle and Erin E. Peterson
• Companies: CSIRO Computational Informatics and CSIRO Computational Informatics and CSIRO Computational Informatics and University of Missouri and CSIRO Computational Informatics
• Keywords: Hierarchical modeling ; Physical-statistical ; Spatio-temporal ; Data assimilation ; Pollutant loads ; Hydrology

Abstract

Physical processes can typically be quite complex, exhibiting dependencies in space and time. Difficulties arise in modeling such processes due to uncertainties associated with observations, high dimensionality of the data, and the general dynamics of the system. One such process is pollutant load runoff from catchments into the Great Barrier Reef (GBR) lagoon. Due to the potential ecological impact pollutant runoff has on the GBR, it is critical to develop statistical models that accurately quantify sediment loads. However, observational data are sparse due to difficulties in monitoring catchments. Further complications arise with large changes in magnitude of water flow. We use the Bayesian hierarchical modeling framework utilizing the two-tiered dimension reduction approach in space and time as a basis for our modeling procedure in assimilating data and sediment concentration, erosion, and flow processes from catchments into the GBR lagoon. The result is a model that provides a level of confidence in the estimation of pollutant loads and exceedance probabilities that highlight problem areas in the catchment where loads are persistently high.