Abstract Eutrophication became a dominant process in the Mondego estuarine system in the 1980s, presumably as a result of excessive nutrient release into coastal waters. The main symptoms were the occurrence of seasonal blooms of Enteromorpha spp., green macroalgae, and a drastic reduction of the Zostera noltii meadows. Previous results suggest that this process will determine changes in species composition at other trophic levels. This paper aims at integrating the available information to provide a theoretical interpretation of the recent physicochemical and biological changes in the Mondego estuarine ecosystem, which will be further used as basic framework for the development of a structurally dynamic model. Exergy-based indices, the Exergy Index and Specific Exergy, were applied as ecological indicators (orientors) to describe the state of the ecosystem, taking into account different scenarios along a spatial gradient of eutrophication symptoms. This allowed elucidating the present conditions along the spatial gradient as representing various stages in the temporal evolution of the system, within the framework of bifurcation, Chaos, and Catastrophe theories. Eutrophication appeared as the major driving force behind the gradual shift in primary producers from a community dominated by rooted macrophytes ( Z. noltii) to a community dominated by green macroalgae. Through time, concomitant changes at other trophic levels will most probably give origin to a new trophic structure. Moreover, river management emerged as a key question in establishing scenarios in order to determine secondary effects in eutrophied systems. Results suggest that a more conservative river management may be used as a powerful tool to remedy affected areas, including the implementation of ecological engineering principles in different possible management practices. The recent biological changes in the Mondego estuarine ecosystem were found to comply with the framework of the theories considered, while both Exergy-based indices were able to capture the state of the system and distinguish between different scenarios.