This chapter addresses the challenges of calibrating and validating multi-system models forUlva cultivation Ulva sp. cultivation, aiming to improve robustness across diverse environmental and operational conditions. A key limitation of previous models was their specificity to particular cultivation setups, restricting their applicability across broader parametric ranges. To overcome this, we integrate empirical data from three different reactor-scale cultivation systems, conducting both algorithmic and manual calibrations to optimize model performance. Calibration was performed using data from an indoor marine photobioreactor (MPBR) system and an outdoor brine-based system, while validation was conducted with independent datasets. Sensitivity analysis highlighted key parametersParameter sensitivity influencing model accuracy, particularly those related to light absorption, nitrogenLight and nitrogen effects dynamics, and temperature effects. Following calibration, the model was applied to simulate biomass production and nitrogen fluctuations across systems, including offshore environments where nitrogen levels were estimated through reverse modeling. Our findings demonstrate the model’s adaptability to multiple cultivation setups, underscoring the importance of extensive parameter tuning. The approach presented here enhances predictive accuracy for Ulva sp. biomass yield and nitrogen uptake, providing a framework for future multi-system modeling efforts in macroalgal cultivation.