Study of Artificial Reservoir's Bioproductivity Based on a Graph Model of Natural and Anthropogenic Factor Interaction

Introduction. Ignoring the systemic nature of a reservoir can lead to ineffective and damaging management decisions. However, the study of such objects often focuses on individual factors. The predictive potential of graph models is limited by a lack of expert information and outdated databases of indicators. This work aims to address these issues by evaluating the effectiveness of measures to improve the condition of the Tsimlyansk Reservoir. The solution is based on the author's graph model that takes into account the interaction of anthropogenic and biotic characteristics of the object.

Materials and Methods. The literature sources and information on hydrobiochemistry and species composition of fish were analyzed. A model was created that took into account 20 factors related to the state of the Tsimlyansk Reservoir. A hydrobiological analysis allowed us to create graph G(V, E, Y). V — set of vertices, v_k ∊ V, k = 1̅, ̅2̅0. E — set of oriented edges e_k = (v_i, v_j) in the form of ordered pairs of length 2, i ≠ j. Y — mapping, Y : V → V. A weight matrix was created based on an integral assessment of each factor by experts. The weighting coefficients (±0.5–±1) were calculated using information from hydrobiological and chemical databases.

Results. We investigated how the removal of zebra mussels would affect the facility during a single cleaning (scenario 1) and a three-year cleaning (scenario 2). We visualized the dynamics of pulses for the state of the water (v₁₅) and changes in the concentration of biological substances (v₁₈). In the first scenario, for the first factor, the maximum pulse (0.5) was fixed from the third year of exposure; the minimum (0) was during the first year. For the second factor, the pulse increased from a minimum (–0.5) to a maximum (0.25) over the third year. In the second scenario, both factors did not change in the first year. Then the pulse for v₁₅ increased (to 0.75), v₁₈ fell in the second year to –0.5, and then increased to –0.25.

Bream reproduction with v₅ feeding was evaluated for a year (scenario 3) and five years (scenario 4). The state of spawning fish v₁, replenishment of juveniles v₂, fishing v₇, and eutrophication v₁₄ were taken into account. v₂, v₇, and v₁₄ pulses remained zero for two years. Then v₂ and v₇ grew to one, and in the fourth year they fell to zero. The eutrophication pulse dropped to –1, and returned to zero by the end of the fourth year. With a five-year feeding, v₁ pulse dropped to –1 in the first year, v₁₄ — in the third, and its value did not change, and v₁ returned to 0 in the fifth year of modeling. The pulse for v₂ and v₇ grew from zero to one in three years.

Discussion. Annual cleaning of a reservoir from zebra mussel was more effective for improving the water condition and less effective for the concentration of nutrients. One-time feeding would increase the number of juveniles and fishing. Eutrophication would decrease, but there would be no sustainable results. Annual feeding would increase the number of juveniles, reduce eutrophication and lead to the development of fishing.

Conclusion. The proposed solution makes it possible to predict potential benefits or harm of anthropogenic activities on the reservoir. The model can be improved by fine-tuning the weighting coefficients, taking into account non-linear and threshold effects as well as other indicators.

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