Dynamics of the shelf ecosystem ecological risk: Model assessment

The article proposes an approach to simulating ecological risk dynamics in the shelf ecosystems based on synthesizing the probabilistic, dynamic simulation and the observation data. Objective of this work is to demonstrate the ecological risk dependence not only on the external factors, but also on the ecosystem intra-annual natural dynamics. Shelf ecosystem dynamic model and the ecological risk probabilistic model are introduced for this purpose. The article selects contrasting in productivity ecosystems of the Caspian and Arctic shelves as the simulation objects. The dynamic model makes it possible to compute annual variations in all its main components including the phytoplankton, zooplankton and nekton biomass, as well as in the nutrient concentrations (nitrogen and phosphorus compounds) and in the suspended and dissolved organic matter. The probabilistic risk model uses computation results according to the dynamic ecosystem model and the observation data. Computation is used in considering the technogenic stressors action in course of the oil fields development on the shelf. Testing the hypothesis on the ecosystem productivity influence on the final risk assessment is the purpose of selecting the contrasting ecosystems. Namely, it is a hypothesis on the ER reduced level in the low-productivity shelf ecosystems compared to those highly productive exposed to the technogenic stressors influence. The obtained results demonstrate significant dependence of the risk values not only on the stressor effects, but also on the intra-annual natural dynamics in the ecosystem state. Analysis of the risk computation results in regard to the highly productive Caspian ecosystem and the low-productivity Arctic shelf ecosystem demonstrates that risk assessment is not depending on the productivity level. •The model assessment of the intra-annual variation in ecological risk.•The model assessment the intra-annual variations in the allowable anthropogenic impact.•Synthesis of dynamic and probabilistic risk models and observation data.•The possibility to balance ecological and economic needs.•The ability to mitigate risks and cut costs.