The role of modeling in interpreting monitoring data and refining adaptive management plans in regulated rivers

Adaptive management is a widely used approach for supporting decision making in the face of uncertainty, but its success relies on the timely refinement of management strategies in response to monitoring outcomes. In this study, we demonstrate the role of modeling to inform implementation of an adaptive management plan for flow management in the urbanized San Gabriel River, California, USA. We applied modeling techniques to assess the impact of reduced discharge from water reclamation plants on the condition of riparian vegetation. Using stem water potential and canopy volume as indicators of plant stress, we examined their responses to changes in managed discharge over 5 years. Our results showed that stem water potential was more responsive based on environmental conditions than canopy volume, although no significant relationship was found between reduced discharge and overall plant stress. This lack of significant relationship is likely due to inherent variability within the system, including differences among riparian species and their proximity to the managed discharge and other stressors, which may obscure potential effects of flow reduction. By incorporating these findings into the adaptive management plan, we refined the monitoring strategy early on, optimizing resource reallocation and focusing on the most sensitive indicators of plant stress. Our study demonstrates the value of early implementation of modeling in adaptive management, allowing for more timely decision-making and improved management outcomes in flow-regulated systems.