Incorporating a generalised additive model of river nutrient concentrations into a mechanistic receiving water model

eReefs is a large, collaborative project that is building catchment and marine models for Australia's Great Barrier Reef Lagoon (GBRL), a world-heritage environmental asset. The eReefs package includes three-dimensional mechanistic biogeochemical, sediment and hydrodynamic models for the entire GBRL on 4 km and 1 km grid scales, along with a relocatable coastal and estuary model (RECOM) that can be nested within the larger-scale models. Source Catchment models developed by the Government of Queensland for each GBRL catchment will be used to run scenarios to predict the effects of management and land use changes on nitrogen, phosphorus and sediment loads reaching each river. For day-to-day near-real-time and forecast-mode running of the marine models, however, another approach is needed to provide the river loads of sediments, dissolved and particulate loads required as boundary conditions. Generalised Additive Models (GAMs) have been shown (e. g. Kuhnert et al., 2012) to be powerful tools for the prediction of suspended sediment and particulate nutrient loads in tropical rivers. Here, we extend previous work to build GAMs that are able to predict concentrations of suspended sediments, dissolved and particulate nutrients in the Fitzroy River (Queensland) on a daily time-step. In developing the GAMs, we tested a number of routinely and frequently measured meteorological and hydrological variables for potential predictive power. The new terms considered included water temperature (which may alter biogeochemical processing rates), air temperature (a more reliably measured proxy for water temperature), electrical conductivity (which may reflect the influence of particular subcatchment sources), barometric pressure (an indicator of local storm activity), wind stress (which may affect resuspension and mixing in the river and its weirs) and flow from river tributaries (a direct measure of the influence of particular subcatchments). The models generated were tested with regard to the validity of key statistical assumptions, and were then validated against a subset of observational data that had been held back from the original calibration. The strongest models included flow in the Fitzroy River, flow in one or more tributaries, and a discounted flow term that reflected flow in the preceding days and weeks. Models that did not include tributary flow were able to predict concentrations of particulate, but not dissolved materials. Neither meteorological terms nor electrical conductivity proved to be useful predictors, while water temperature was of marginal value. The final GAM provide more accurate predictions on a daily time-step than previously available methods, for both dissolved and particulate materials, and is being used to provide time-series input (e. g. Figure 1) to mechanistic marine models

Prediction of sediment, particulate nutrient and dissolved nutrient concentrations in a dry tropical river to provide input to a mechanistic coastal water quality model

A Generalised Additive Modelling (GAM) approach is applied to prediction of both particulate and dissolved nutrient concentrations in a wet-tropical river (the Fitzroy River, Queensland, Australia). In addition to covariant terms considered in previous work (i.e. flow, discounted flow and a rising-falling limb term), we considered several new potential covariates: meteorological and hydrological variables that are routinely monitored, available in near-real time, and were considered to have potential predictive power. Of the additional terms considered, only flows from three tributaries of the Fitzroy River (namely, the Nogoa, Comet and Isaac Rivers) were found to significantly improve the model. Inclusion of one or more of these additional flow terms greatly improved results for dissolved nitrogen and dissolved phosphorus concentrations, which were not otherwise amenable to prediction. In particular, the Nogoa sub-catchment, dominated by pasture for cattle, was found to be important in determining dissolved inorganic nitrogen and phosphorus concentrations reaching the river mouth. This insight may direct further research, including future refinement of processed-based catchment models. The GAMs described here are used to provide near real-time river boundary conditions for a complex coupled hydrodynamic and biogeochemical model of the Great Barrier Reef Lagoon, and can be coupled with a forecasting hydrological model to allow integrated forecasting simulations of the catchment to coast system