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Supercomputer Simulation of Hydrobiological Processes of Coastal Systems

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Mathematical Models and Computer Simulations Aims and scope

Abstract

This paper discusses supercomputer modeling of the dynamics of plankton populations, including phyto- and zooplankton, in coastal systems. The mathematical model of the dynamics of plankton populations includes a system of convection-diffusion-reaction equations with nonlinear terms, which makes it possible to study the mechanism of external hormonal regulation based on the scenario approach. The proposed mathematical 3D model is linearized, discretized, and based on coordinate splitting, a chain consisting of two-dimensional and one-dimensional problems is obtained. For the numerical implementation of the proposed mathematical model of hydrobiology of the coastal system in the form of a software module (SM), a multiprocessor computer system (MCS) designed for massively parallel computing is used; its use makes it possible to significantly reduce the running time of the SM. To improve the accuracy of calculations, a procedure is used to refine the solution on a sequence of condensing uniform rectangular grids. The effect of the mechanism of ectocrine regulation and the mode of intake of biogenic substances on the production and destruction processes of plankton is studied. The mathematical model includes a nonlinear dependence used to describe the growth rate of algae cells on the concentration of the metabolite, which made it possible to describe the ability of algae excretion products to control their growth even under conditions of the massive intake of pollutants. The approach used corresponds to modern ideas about the functioning of hydrobiocenosis. Based on the developed software toolkit, focused on the supercomputer, not only direct trophic interactions but also the actions of the products of the vital activity of individuals, which are mediated–chemical interactions, are studied.

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Notes

  1. Hydrobionts are organisms adapted to living in the aquatic environment.

  2. Benthos is the common name for organisms living in the bottom areas of water bodies.

  3. A metabolite is a waste product of an organism.

REFERENCES

  1. M. A. Novikov and M. N. Kharlamova, “Transabiotic factors in water environment (a review),” Zh. Obshch. Biol. 61 (1), 22–46 (2000).

    Google Scholar 

  2. W. R. DeMott and F. Moxter, “Foraging on cyanobacteria by copepods: Responses to chemical defenses and resources abundance,” Ecology 72 (5), 1820–1834 (1991). https://doi.org/10.2307/1940981

    Article  Google Scholar 

  3. E. G. Jørgensen, “Growth-inhibing substances formed by algae,” Physiol. Plant. 9 (4), 712–726 (1956). https://doi.org/10.1111/j.1399-3054.1956.tb07833.x

    Article  Google Scholar 

  4. W. Wang, “Chromate ion as a reference toxicant for aquatic phytotoxicity tests,” Environ. Toxicol. Chem. 6 (12), 953–960 (1987). https://doi.org/10.1002/etc.5620061207

    Article  Google Scholar 

  5. J. Findenegg, “Factors controlling primary productivity, especially with regard to water replenishment, stratification, and mixing,” in Primary Productivity in Aquatic Environments, Proc. I.B.P. PF Symposium (Pallanza, I-taly, 1965), Ed. by C. R. Goldman (Univ. California Press, Berkeley, 1966), pp. 105–119. https://doi.org/10.1525/9780520318182-009.

  6. L. M. Zimina and T. G. Sazykina, “Excretion of exometabolites by microalgae as a mechanism of population density regulation,” Gidrobiol. Zh. 23 (4), 50–55 (1987).

    Google Scholar 

  7. Yu. A. Dombrovskii and G. S. Markman, Spatial and Temporal Ordering in Ecological and Biochemical Systems (Izd. Rostov. Univ., Rostov-on-Don, 1983) [in Russian].

    Google Scholar 

  8. A. A. Samarskii and P. N. Vabishchevich, “Finite-difference approximations to the transport equation. II,” Differ. Equations 36 (7), 1069–1077 (2000). https://doi.org/10.1007/BF02754509

    Article  MathSciNet  MATH  Google Scholar 

  9. A. A. Sukhinov, A. E. Chistyakov, and E. V. Alekseenko, “Numerical realization of the three-dimensional model of hydrodynamics for shallow water basins on a high-performance system,” Math. Models Comput. Simul. 3 (5), 562–574 (2011). https://doi.org/10.1134/S2070048211050115

    Article  MathSciNet  MATH  Google Scholar 

  10. G. G. Matishov, V. G. Il’ichev, V. L. Semin, and V. V. Kulygin, “Adaptation of populations to temperature conditions: Results of computer simulation,” Dokl. Biol. Sci. 420 (1), 183–186 (2008). https://doi.org/10.1134/S0012496608030125

    Article  MATH  Google Scholar 

  11. Yu. V. Tyutyunov, A. D. Zagrebneva, and A. I. Azovsky, “Spatiotemporal pattern formation in a prey-predator system: The case study of short-term interactions between diatom microalgae and microcrustaceans,” Mathematics 8 (7), 1065–1080 (2020). https://doi.org/10.3390/math8071065

    Article  Google Scholar 

  12. A. Yu. Perevaryukha, “Noninterpreted behavior of population dynamics models and boundaries of parametric intervals,” Ekol. Sist. Prib., No. 6, 15–23 (2021).

  13. O. L. Zhdanova and A. I. Abakumov, “Modeling of the phytoplankton dynamics considering the mechanisms of ectocrine regulation,” Mat. Biol. Bioinf. 10 (1), 178–192 (2015). https://doi.org/10.17537/2015.10.178

    Article  Google Scholar 

  14. K. Fennel, “The generation of phytoplankton patchiness by mesoscale current patterns,” Ocean Dyn. 52 (2), 58–70 (2001). https://doi.org/10.1007/s10236-001-0007-y

    Article  Google Scholar 

  15. E. Alekseenko, B. Roux, D. Fougere, and P. G. Chen, “The effect of wind induced bottom shear stress and salinity on Zostera noltii replanting in a Mediterranean coastal lagoon,” Estuarine, Coastal Shelf Sci. 187, 293–305 (2017). https://doi.org/10.1016/j.ecss.2017.01.010

    Article  Google Scholar 

  16. A. I. Sukhinov, A. E. Chistyakov, V. V. Sidoryakina, and E. A. Protsenko, “Economic explicit-implicit schemes for solving multidimensional diffusion–convection problems,” J. Appl. Mech. Tech. Phys. 61 (7), 1257–1267 (2020). https://doi.org/10.1134/S0021894420070159

    Article  MathSciNet  MATH  Google Scholar 

  17. A. I. Sukhinov, A. E. Chistyakov, and E. A. Protsenko, “Difference scheme for solving problems of hydrodynamics for large grid Péclet numbers,” Komp. Issled. Model. (Comput. Res. Model.) 11 (5), 833–848 (2019). https://doi.org/10.20537/2076-7633-2019-11-5-833-848

  18. A. A. Samarskii and P. N. Vabishhevich, Numerical Methods for Solving Convection–Diffusion Problems (URSS, Moscow, 2009) [in Russian].

    Google Scholar 

  19. A. I. Sukhinov, A. E. Chistyakov, and Yu. V. Belova, “The difference scheme for the two-dimensional convection-diffusion problem for large Peclet numbers,” MATEC Web Conf. 226, 04030 (2018). https://doi.org/10.1051/matecconf/201822604030.

  20. A. I. Sukhinov, A. E. Chistyakov, G. A. Ugol’nitskii, A. B. Usov, A. V. Nikitina, M. V. Puchkin, and I. S. Semenov, “Game-theoretic regulations for control mechanisms of sustainable development for shallow water ecosystems,” Autom. Remote Control 78 (6), 1059–1071 (2017). https://doi.org/10.1134/S0005117917060078

    Article  MathSciNet  MATH  Google Scholar 

  21. A. Nikitina, Yu. Belova, and A. Atayan, “Mathematical modeling of the distribution of nutrients and the dynamics of phytoplankton populations in the Azov Sea, taking into account the influence of salinity and temperature,” AIP Conf. Proc. 2188, 050027 (2019). https://doi.org/10.1063/1.5138454

    Article  Google Scholar 

  22. Ecological Atlas of the Sea of Azov, Atlas Information System (Yuzhn. Nauchn. Tsentr Ross. Acad. Nauk, Rostov-on-Don, 2018). http://atlas.iaz.ssc-ras.ru/sitemap-ecoatlas.html.

  23. Ecological Atlas. Black and Azov Seas, PAO NK Rosneft, OOO Arkticheskii Nauchnyi Tsentr, and Fond NIR (Fond NIR, Moscow, 2019) [in Russian].

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Funding

This study was supported by the Russian Science Foundation (grant no. 21-71-20050). The calculations were carried out using a K60 hybrid supercomputer installed at the Supercomputer Center for shared use of the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences.

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Correspondence to A. I. Sukhinov, A. V. Nikitina, A. M. Atayan, V. N. Litvinov, Yu. V. Belova or A. E. Chistyakov.

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Sukhinov, A.I., Nikitina, A.V., Atayan, A.M. et al. Supercomputer Simulation of Hydrobiological Processes of Coastal Systems. Math Models Comput Simul 14, 677–690 (2022). https://doi.org/10.1134/S2070048222040123

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