Algal Bloom Research
To ensure that the project outcomes and products are trusted and useful, decision support tool end-users, tool developers and project team members will engage in a co-development process from project start to finish. Through facilitated dialogue and interaction at working group meetings, the team will collaboratively identify knowledge gaps, assess end-user needs and interests, and then match those with project team capabilities to iteratively integrate user and scientist perspectives in the co-development process.
The objective of this task is to compile existing datasets that are relevant to this study. This will include data from two on-going projects, the Surveying Estuarine Response to Freshwater Inflows (SERFIS) and the Phytoplankton Dynamics study. We are also synthesizing information on existing numerical models that simulate the effects of watershed runoff; lake hydrodynamics, biogeochemistry and operations; and canal transport on flows and nutrient and algal loads to the estuary.
The aim of this objective is to develop, verify and apply a hydrodynamic and water quality model for the St. Lucie Estuary. We are building a new model based on the open-source community Coupled Ocean-Atmosphere-Wave-Sediment Transport modeling system (COAWST). Since the coastal areas and estuaries of eastern Florida are impacted by extreme storms and these severe weather events can affect the water quality of the estuary, the proposed modeling framework will enable the inclusion of new physical or biogeochemical processes in the future (if needed).
The goal of this component of the study are threefold: a) Define the drivers of phytoplankton dynamics in the St. Lucie Estuary, b) Provide the phytoplankton data needed for the development of models that predict HAB events, and c) Identify management targets for the prevention and/or mitigation of HABs. The three sub-objectives associated with this component of the study are designed to address this variability to provide data for modeling the dynamics of phytoplankton community structure and biomass over a range of expected environmental conditions.
Under this task, we are first developing machine learning models to identify the dominant contributors to HABs by integrating data and existing numerical models. Secondly, we will develop a short-term (1- to 7-day) future HAB prediction model for the estuary. Overall, the outcomes of this task will integrate various data and models and develop an easy-to-use ML model for HAB prediction and management in a computationally efficient way. This will be integrated into the application that will be delivered to the end-users.
This project is co-developing (with the SFWMD and the USACE) a two-way web application system that delivers crucial information and tools generated as part of this project to end-users in a format that is most suitable to them. This web application would, for instance, allow collecting forecasted HABs data and showing a 2D map on a WebGIS-based platform. This type of visualization will allow rapid interpretation of short-term HAB forecast, HAB prediction under optimized management strategies, and HAB prediction under user-selected management strategies by a wide range of users with different technical backgrounds.