🐳Biogeochemical Modeling
At Running Tide, we were working to develop macroalge growth and sinking interventions, and laying the groundwork for mCDR quantification and verification by answering a diverse set of scientific questions laid out in our Research Roadmap. A key part of this effort was building the modeling tools to assess growth rates and nutrient uptake in the nutrient-limited environment of the open ocean.
We explored different macroalgae models from the scientific literature and started to build our own statistical and process models for macroalgae growth using data collected at our research facility in St. Louis. Macroalgae nutrient and CO2 uptake in the open ocean is under-studied, and we would need to answer questions about nutrient competition for a complete accounting of the carbon impact of a macroalgae mCDR intervention.
The Ocean Modeling team applied macroalgae models to regions in the North Atlantic to select locations and dates for test deployments based on seasonally-dependent estimates of growth. We also studied regions for macroalgae cultivation globally and presented our preliminary findings at AGU Ocean Sciences Meeting in 2024.
Findings include:
Subarctic regions offer the most promising conditions for both Ulva and Sugar Kelp cultivation, due to abundant nutrients and suitable temperatures.
Ulva shows additional growth potential in warmer subtropical areas.
The optimal cultivation seasons differ between species:
Ulva thrives during summer and early fall
Sugar Kelp performs best when deployed in early spring
Growth is primarily controlled by water temperature and nutrient availability, varying by season and location.
Massive datasets, specific to macroalgae species and populations, were required to build accurate growth models. We started a multi-year (and perhaps it would have been multi-decade) program to collect macroalgae imagery at different stages of growth in different nutrient and wave energy environments called ORCA. ORCA imaging hardware built by our engineering team helped us correlate photos with biomass (and ultimately carbon content) while providing valuable growth data to build computational models.
Last updated
