Application of the Principles of the Marine Ecosystems’ Adaptive Modeling to the Hydrochemical Observations in the Sevastopol Bay
I. E. Timchenko✉, E. M. Igumnova, S. V. Svishchev
Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russian Federation
✉ e-mail: timchenko.syst.analysis@mhi-ras.ru
Abstract
Introduction. Scenarios of development of the processes in the complex marine ecosystems are forecasted by the models developed for marine ecosystems. At that the method for formalizing the scheme of the cause-effect relations (impacts) is required. In other words, it is necessary to construct the equation system for the model variables connecting the functions representing the modeled processes.
Data and Methods. Proposed are the methods for modeling the processes in the marine ecosystems based on the system principles of the adaptive balance of causes and informational unity of the processes’ models and the corresponding observational data.
Results. It is shown that application of these principles permits to construct the adaptive models with negative feedbacks of the 1st and the 2nd orders between the model variables and the speed of their variation. These models provide automatic fitting of the model variables to each other and to the external effects; at the same time they preserve the matter balances in the substance transformation reactions in the marine environment. The simulation results reveal that the 2nd order adaptive models are more sensitive to the external effects influencing the ecosystem and adapt to them quicker. Application of the adaptive modeling principles is illustrated by the data of hydrochemical observations in the Sevastopol Bay. Two methods of reconstructing dynamics of the nitrite concentration are comparatively analyzed using the time series of the ammonium and nitrate observations.
Discussion and Conclusions. It is shown that the dynamic-stochastic equation provides much higher accuracy of reconstruction of the unobserved process of the nitrite concentration as compared to the method of the normalized relations of the mean values. Besides, the reconstruction accuracy increases with growth of length of the observation time series applied at constructing their covariance matrix.
Keywords
adaptive modeling, adaptive balance of causes, marine ecosystem, reconstruction of the unobserved processes, influence coefficients, covariance matrix, dynamic-stochastic equation, nitrification, Sevastopol Bay
Acknowledgements
The investigations are carried out within the framework of the state task on the theme No. 0827-214-0010 “Complex interdisciplinary investigations of the oceanic processes which condition functioning and evolution of the Black and Azov seas’ ecosystems based on the modern methods of marine environment state control and the hydraulic technologies”. The model is developed and the computing experiments are performed within the framework of the scientific project of the Russian Foundation for Basic Research and the Sevastopol Administration No. 18-47-920001 “Study of the principles for constructing adaptive models of the ecological-economic systems and digital informational technologies for managing the scenarios of sustainable development of natural and economical complexes in the Seavastopol region”.
Original russian text
Original Russian Text © I. E. Timchenko, E. M. Igumnova, S. V. Svishchev, 2019, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 35, Iss. 1, pp. 70–84 (2019)
For citation
Timchenko, I.E., Igumnova, E.M. and Svishchev, S.V., 2019.Application of the Principles of the Marine Ecosystems’ Adaptive Modeling to the Hydrochemical Observations in the Sevastopol Bay. Physical Oceanography, 26(1), pp. 63-76. doi:10.22449/1573-160X-2019-1-63-76
DOI
10.22449/1573-160X-2019-1-63-76
References
- Von Bertalanffy, L., 1972. The History and Status of General Systems Theory. The Academy of Management Journal, [e-journal] 15(4), pp. 407-426. https://doi.org/10.5465/255139
- Moiseev, N.N., Aleksandrov, V.V. and Tarko, A.M., 1985. Chelovek i Biosfera: Opyt Sistemnogo Analiza i Ehksperimenty s Modelyami [The Man and the Biosphere. Experience of System Analysis and Experiments with Models]. Moscow: Nauka, 271 p. (in Russian).
- Long, R.D., Charles, A. and Stephenson, R.L., 2015. Key Principles of Marine Ecosystem-Based Management. Marine Policy, [e-journal] 57, pp. 53-60. http://dx.doi.org/10.1016/j.marpol.2015.01.013
- Timchenko, I.E., Igumnova, E.M. and Timchenko, I.I., 2000. Sistemnyy Menedzhment i ABC- Tekhnologii Ustoychivogo Razvitiya [System Management and ABC-Technologies of Sustainable Development]. Sevastopol: ECOSI-Gidrofizika, 225 p. (in Russian).
- Costanza, R., Groot, R. de, Sutton, P., Ploeg, S. van der, Anderson, S.J., Kubiszewski, I., Farber, S. and Turner, R.K., 2014. Changes in the Global Value of Ecosystem Services. Global Environmental Change, [e-journal] 26, pp. 152-158. http://dx.doi.org/10.1016/j.gloenvcha.2014.04.002
- Kalman, R.E., 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, [e-journal] 82(1), pp. 35-45. http://dx.doi.org/10.1115/1.3662552
- Holling, C.S., ed., 1978. Adaptive Environmental Assessment and Management. Wiley IIASA. Chichester: John Wiley, 377 p. Available at: http://pure.iiasa.ac.at/id/eprint/823/1/XB- 78-103.pdf [Accessed: 30 December 2018].
- Timchenko, I.E., 1984. Stochastic Modeling of Ocean Dynamics. Chur: Harwood Academic Publishers, 320 p.
- Holland, J.H., 1992. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, Mass.: MIT Press, 211 p.
- Timchenko, I.E. and Igumnova, E.M., 2005. Integrated Management of Economic-Ecological Sea- Land Systems. Physical Oceanography, [e-journal] 15(4), pp. 247-263. https://doi.org/10.1007/s11110-005-0046-x
- Ivanov, V.A., Igumnova, E.M. and Timchenko, I.E., 2012. Coastal Zone Resources Management. Kiev: Academperiodika, 304 p.
- Timchenko, I.E., Igumnova, E.M. and Timchenko, I.I., 2016. Adaptive Balance Models for Environmental-Economic Systems. CreateSpace Independent Publishing Platform, 486 p.
- Platt, T., Mann, K.H. and Ulanowicz, R.E., eds., 1981. Mathematical Models in Biological Oceanography. Paris: The UNESCO Press, 157 p.
- Murray, J.D., 2008. Mathematical Biology II: Spatial Models and Biomedical Applications. New-York: Springer, 736 p.
- Fasham, M.J.R., Ducklow, H.W. and McKelvie, S.M., 1990. A Nitrogen-Based Model of Plankton Dynamics in the Oceanic Mixed Layer. Journal of Marine Research, [e-journal] 48(3), pp. 591-639. https://doi.org/10.1357/002224090784984678
- Forrester, J.W., 1991. System Dynamics and the Lessons of 35 Years. In: K. B. De Greene, ed., 1991. The Systemic Basis of Policy Making in the 1990s. Massachusetts: Sloan School of Management MIT, 35 p. Available at: http://static.clexchange.org/ftp/documents/system- dynamics/SD1991-04SDandLessonsof35Years.pdf [Accessed: 30 December 2018].
- Saaty, T.L., 2000. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. Pittsburgh: RWS Publications, 478 p.
- Svishchev, S.V., 2018. Adaptive Modeling of Nitrification in the Sevastopol Bay. Ekologicheskaya Bezopasnost' Pribrezhnoy i Shel'fovoy Zon Morya = Ecological Safety of Coastal and Shelf Zones of Sea, (2), pp. 60-65. doi:10.22449/2413-5577-2018-2-60-65