SPATIO-TEMPORAL ASSIMILATION OF MODELLED CATCHMENT LOADS WITH MONITORING DATA IN THE GREAT BARRIER REEF

• 成果类型: Article
• 署名作者: Gladish, Daniel W.; Kuhnert, Petra M.; Pagendam, Daniel E.; Wikle, Christopher K.; Bartley, Rebecca; Searle, Ross D.; Ellis, Robin J.; Dougall, Cameron; Turner, Ryan D. R.; Lewis, Stephen E.; Bainbridge, Zoe T.; Brodie, Jon E.
• 署名单位: Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of Missouri System; University of Missouri Columbia; James Cook University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS950
• 发表日期: 2016
• 页码: 1590-1618
• 关键词: rating curves; suspended-sediment; nutrient loads; uncertainty; management; impacts; discharge

摘要

Soil erosion and sediment transport into waterways and the ocean can adversely affect water clarity, leading to the deterioration of marine ecosystems such as the iconic Great Barrier Reef (GBR) in Australia. Quantifying a sediment load and its associated uncertainty is an important task in delineating how changes in management practices can contribute to improvements in water quality, and therefore continued sustainability of the GBR. However, monitoring data are spatially (and often temporally) sparse, making load estimation complicated, particularly when there are lengthy periods between sampling or during peak flow periods of major events when samples cannot be safely taken. We develop a spatio-temporal statistical model that is mechanistically motivated by a process-based deterministic model called Dynamic SedNet. The model is developed within a Bayesian hierarchical modelling framework that uses dimension reduction to accommodate seasonal and spatial patterns to assimilate monitored sediment concentration and flow data with output from Dynamic SedNet. The approach is applied in the Upper Burdekin catchment in Queensland, Australia, where we obtain daily estimates of sediment concentrations, stream discharge volumes and sediment loads at 411 spatial locations across 20 years. Our approach provides a method for assimilating both monitoring data and modelled output, providing a statistically rigorous method for quantifying uncertainty through space and time that was previously unavailable through process-based models.