Application of qualitative modelling to improve system understanding of the stressed Elbe estuary

Abstract

An estuary is a complex system that encompasses numerous, complex interactions between environmental factors and processes that are directly or indirectly influenced by human activities. A well-studied estuary is the Elbe estuary, which is under pressure from human activities. About 2300 publications focus on scientific aspects of its hydrology, morphodynamics, biology, chemistry or a combination of these, covering water, sediment and human interventions, among other topics. While it is important to understand the processes, selecting actions to improve the system should be based on a deep understanding of the estuary system as a whole and a confrontation with the complex interrelationship of the components that make up the estuary. This can be overwhelming, as most humans are able to understand only three or four indirectly related parameters simultaneously, whereas numerous variables are interlinked and affect each other in the environment. The resulting reluctance to address such an issue combined with a lack of common language of citizens, scientists and planning authorities can hamper public acceptance of management measures.

In this paper, we use the software iModeler to describe the Elbe estuary in its complexity as a stressed system and present results from the application of the model by a group of scientists from different backgrounds. This model is not intended to be an alternative to – for example – mathematical-hydrological modelling. It also does not claim to be factually correct, and it is certainly not complete. It should be seen as an exercise to deal with complex interactions in a simple way and to develop a deeper understanding of the system. Participants in the exercise defined 46 factors and 112 direct linkages. The model identified contaminant availability, turbidity and nutrient concentrations as the stressors with the greatest impact on the quality of the Elbe estuary. Dredging of shipping channels was the activity with the greatest negative impact, and extending nature protection areas would have the highest positive effect.

The results of the model, although subjective to some extent, were plausible when compared to the literature. The possibility of describing a more differentiated cause-effect relationship for some factors and their direct connection would have been beneficial. However, such collaborative qualitative modelling facilitates knowledge sharing, can reveal indirect effects and raises awareness of those factors that are strongly interwoven within system, and would have a large cumulative effect on the respective goal.