Modeling the Dynamics of Harmful Phytoplankton Species Concentration in Taganrog Bay of the Azov Sea

Introduction. Researchers at the Southern Scientific Center of the Russian Academy of Sciences have repeatedly observed abundant microalgae blooms in Taganrog Bay, which negatively impact the health of people, animals, the environment, as well as the economy. Given the increasing depletion of biodiversity and deterioration of water quality in the Azov Sea due to reduced freshwater inflow, rising water temperatures, and increased nutrient runoff, the risk of toxic phytoplankton populations is increasing. This threatens the ecosystem, regional economy, and health of local residents. The rise in average annual temperatures and increased nitrogen and phosphorus runoff caused by urbanization and agriculture leads to accelerated growth of cyanobacteria, particularly during warmer months. This results in abundant blooms with the potential for toxicity, especially in Taganrog Bay. Models of phytoplankton population dynamics exist at the lag-phase and reactive norm levels. However, their adaptation to the conditions of the Azov Sea requires the consideration of the specific variations in depth, salinity, and seasonal characteristics. The aim of this study is to construct medium-term forecasts for the development of cyanobacteria using an integrated approach based on the application of mathematical modeling methods. Materials and Methods. Phytoplankton population dynamics were predicted using a three-dimensional, non-stationary mathematical model based on a system of non-stationary partial differential equations with nonlinear source functions. The input data for the model included values of water flow vector components from a hydrodynamic model, as well as measurements of salinity, temperature, nutrient concentrations, and phytoplankton populations from long-term observations. Difference approximation of the model was performed using explicit-implicit splitting schemes. A depth interpolation method was used to construct a three-dimensional computational domain. The described method was also applied to interpolate salinity and temperature values based on cartographic information. Results. A numerical experiment yielded three-dimensional distributions of cyanobacteria and green algae in Taganrog Bay during the growing season. The experiment considered the effects of salinity and temperature, as well as the nutrient limitation of phytoplankton populations. The interpolation method resulted in a depth map, salinity, and temperature distributions that were relatively smooth at the junction points, and served as input data for the model. Discussion. The mathematical model of the dynamics of phytoplankton populations presented in this paper takes into account the conditions for the development of blue-green and green algae and their environmental impact. It allows us to create a three-dimensional image of plankton distribution and determine the areas where blue-green and green algae are most abundant in Taganrog Bay and the Azov Sea. Conclusion. The described mathematical model and mathematical modeling methods have been integrated into a software package. This allows us to simulate and predict all changes that have occurred and will occur in the waters of Taganrog Bay. This, in turn, will undoubtedly contribute to informed decision-making in the future development of the Azov Sea.