A Data-Constrained Framework for Marine Biogeochemistry Modeling with Applications to the Paranagu\'a Estuarine Complex

TL;DR

This paper presents a simple, computationally efficient biogeochemical model for the Paranaguá Estuarine Complex, along with a calibration framework using observational data, enabling reliable nutrient dynamics simulations in complex coastal systems.

Contribution

It introduces a novel, systematic calibration framework for marine biogeochemical models tailored to regional data and demonstrates its effectiveness in the Brazilian estuarine environment.

Findings

The simple model accurately reproduces observed nutrient dynamics after calibration.

The calibration framework effectively utilizes limited observational data.

The approach is adaptable for more complex modeling scenarios.

Abstract

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.