Trophic predictability analysis: Employing constancy and contingency – A case study of Ilam reservoir

Abstract

This study explores the concept of “trophic state predictability” (TP) in aquatic ecosystems using the Ilam reservoir as a case study. Trophic predictability integrates constancy and contingency to assess how well historical data reflects possible trophic states. The CE-QUAL-W2 model has been employed to project phosphorus levels and analyze spatial and temporal variations in trophic states. Results showed that Station 2 has predictability of 96% in the hypolimnion and 50% in the epilimnion, Station 3 shows low hypolimnion predictability (P = 0.4) but 33% constancy and 32% contingency in the epilimnion, and Station 1 demonstrates predictability of 88% in the hypolimnion (84% from constancy) and 64% in the epilimnion (40% from constancy). The limitations of TP in the face of shifting baselines (SB) have been investigated which refers to the precipitation of the current state of the environment as the baseline. Various scenarios highlight the complexities of using TP and incorporating the stakeholder perception were defined. Results showed that Station 2 has high TP, suggesting potential for undetected baseline shifts (Scenario A). Station 3 exhibits lower TP, indicating environmental variability or complex dynamics (Scenarios B or C). Station 1 has moderate TP, suggesting potential historical shifts or internal dynamics (Scenario B or C). All stations showed a high eutrophication potential, with Station 1 experiencing the most severe conditions. This research provides insights into the predictability and variability of trophic states, emphasizing the importance of the interplay between constancy, contingency, and stakeholder perception. Also, it highlights the significance of TP for understanding future trophic states, while acknowledging the limitations imposed by shifting baselines. By considering both past changes and potential future disruptions, TP can be a valuable tool for water resource managers. Continuously monitoring aquatic ecosystems and incorporating additional data sources are crucial for ensuring their long-term health.