Adaptive water management is a promising management paradigm for rivers that addresses the uncertainty of decision consequences. However, its implementation into current practice is still a challenge. An optimization assessment can be framed within the adaptive management cycle allowing the definition of environmental flows (e-flows) in a suitable format for decision making. In this study, we demonstrate its suitability to mediate the incorporation of e-flows into diversion management planning, fostering the realization of an adaptive management approach. We used the case study of the Pas River, Northern Spain, as the setting for the optimization of surface water diversion. We considered e-flow requirements for three key river biological groups to reflect conditions that promote ecological conservation. By drawing from hydrological scenarios (i.e., dry, normal, and wet), our assessment showed that the overall target water demand can be met, whereas the daily volume of water available for diversion was not constant throughout the year. These results suggest that current the decision making needs to consider the seasonal time frame as the reference temporal scale for objectives adjustment and monitoring. The approach can be transferred to other study areas and can inform decision makers that aim to engage with all the stages of the adaptive water management cycle.
Adaptive Water Management in the Face of Uncertainty: Integrating Machine Learning, Groundwater Modeling and Robust Decision Making
