A Data-Constrained Framework for Marine Biogeochemistry Modeling with Applications to the Paranaguá Estuarine Complex
This work addresses the problem of computationally demanding and empirically calibrated models for marine biogeochemistry, particularly in Brazilian estuaries, but it is incremental as it builds on existing modeling approaches with a focus on simplicity and calibration.
The thesis tackled the challenge of developing and calibrating marine biogeochemical models for specific environments like estuaries by creating a simple, computationally inexpensive model for the Paranaguá Estuarine Complex and proposing a systematic calibration framework using tracer datasets and optimization techniques, resulting in the model successfully reproducing observed nutrient dynamics when calibrated.
Marine biogeochemical models are widely used to study nutrient dynamics, water quality, and climate-related processes in coastal and estuarine systems. However, developing models that reliably represent specific environments remains computationally demanding, which makes their application to complex systems such as river plumes and estuarine environments challenging. In addition, these models contain several parameters that must be calibrated for the region of interest, a process that is often performed empirically using limited observational data. This thesis advances the development and calibration of marine biogeochemical models in the Brazilian context through three main contributions. First, we develop a conceptual model describing nutrient-phytoplankton dynamics in the Paranagua Estuarine Complex (PEC) in southern Brazil. The model is intentionally simple and computationally inexpensive, allowing simulations to be performed on standard personal computers. Second, we propose a systematic calibration framework based on tracer datasets and derivative-free optimization techniques. Finally, we demonstrate the practical application of this approach by calibrating the PEC model using in situ observations. Results show that, despite its simplicity, the model can reproduce observed nutrient dynamics when properly calibrated. The proposed framework is general and can be extended to multi-parameter calibration, seasonal parameter variation, and the coupling of biogeochemical models with higher-fidelity hydrodynamic models.