Development of operational data-assimilating water quality modelling system for South-East Tasmania

With the rapid advances in on-line observing system applications, the paradigm in environmental modelling is shifting from one-off models for specific purposes, to operational models, sequentially assimilating data streams from in situ and remote sensors. Such models can provide products and services to support a wide range of applications, from short-term forecasting to long-term scenarios, and are expected to deliver superior performance much more cost-effectively. In the marine field, this is most advanced for circulation models at large ocean scales. The potential benefit from these advances is even greater in the coastal zone, where human uses, impacts and ecosystem services are concentrated. However, there are substantial challenges to be overcome. Coastal applications typically require biogeochemical, ecological, and ultimately socioeconomic models. These additional models are more complex, with higher uncertainty, and require different approaches to data assimilation and uncertainty analysis. The uncertainties arise from a number of sources including poorly known parameters, structural errors and stochastic forcing. When model realisations are sufficiently fast, Monte Carlo techniques can be used to improve the model performance and assess its quality, otherwise alternative estimation techniques are required. This paper describes the development of an operational, data-assimilating coastal model for SE Tasmania, integrating across hydrodynamics, sediment dynamics and biogeochemistry. Inputs and outputs from the model are expected to be integrated into the regional information system (INFORMD), and to be used directly in multiple management applications, and as input into ecosystem models. A hydrodynamic model, nested inside an operational global model, will be assimilating data from the coastal sensor network and other sources, including remote sensing. The model is based on an operational modelling platform developed by CSIRO through the BlueLink project (ROAM), and will be used to implement and test data-assimilation techniques for coastal models under development in BlueLink. Operational sediment dynamic and biogeochemical models, will be coupled to the hydrodynamic model, either directly or through intermediate transport models. Data-assimilating techniques for these models currently are under development in Computational and Simulation Sciences theme, CSIRO. This paper outlines preliminary results from these developments. A number of candidate techniques including Kalman Filter, Particle Filter and MCMC are discussed. The utility of fast and cheap statistical surrogates of complex models (emulators) for sequential data assimilation is illustrated through the trial application of emulators to one-dimensional sediment/pollutant and 3-d sediment transport models.