Model-Data Assimilation Framework For Harmful Algal Bloom (Cyanohab) Prediction In Inland Waters On A Continental Scale

Surface water quality in Australia is declining and recurring harmful algal blooms by toxic cyanobacteria species (CyanoHAB) are widespread. CyanoHABs impact ecosystem services, harming the health of water ecosystems and limiting recreational and cultural water uses. With current approaches, predicting and managing HABs require intensive local monitoring and data analysis for each waterbody. With thousands of reservoirs, wetlands and coastal lagoons scattered around Australia, only a few can be managed in this way. To address this, here we propose a model-data assimilation framework for algal bloom prediction at the continental scale, combining Earth Observation, in-lake monitoring, and coupled modelling frameworks to allow early detection and forecasting of CyanoHABs. The development of a national framework for prediction of harmful algal blooms will transform our ability to manage aquatic ecosystem health in data sparse environments. This project combines recent developments in inland water remote sensing of algal pigments, bio-optical (remote sensing) studies, and water quality models for algal blooms in lakes and reservoirs across Australia. Our initial study has focused on developing model components using Lake Burley Griffin, ACT, Australia as a case study.