A Probabilistic Dynamic Reservoir Operation Framework (PDROF) for Adaptive Reservoir Operation Under Climate Variability: A Case Study of Kwan Phayao, Thailand

Reservoir operation under hydrological uncertainty has become increasingly challenging under changing climate conditions. This study proposes a Probabilistic Dynamic Reservoir Operation Framework (PDROF) that integrates stochastic inflow modeling, Monte Carlo simulation, and dynamic rule extraction for adaptive reservoir management. Historical inflow records were transformed into stochastic inflow ensembles and propagated through reservoir operation simulations to generate reservoir storage trajectories under varying hydrological conditions. From these trajectories, a representative operational rule, referred to as the Most Likely Line (MLL), was extracted to characterize the dominant storage behavior of the system. The results demonstrate that conventional deterministic rule curves are constrained by predefined hydrological classifications and limited flexibility under variable inflow conditions. In contrast, the proposed framework effectively captures seasonal variability and propagates hydrological uncertainty throughout the operational cycle. Long-term simulation over a 23-year period resulted in a total spill volume of 17.74 million cubic meters (MCM), with spill events occurring in only 7 months, indicating improved operational robustness and storage stability. A real flood event in 2024 further demonstrated reductions of 47.42 MCM in spill volume and 3.46 MCM in reservoir storage compared with conventional operation. These improvements are attributed to the anticipatory storage behavior of the MLL-based operational rule, which preserves flood-buffer capacity prior to peak inflow periods and reduces the likelihood of uncontrolled spill events. The proposed framework provides a practical transition from deterministic reservoir operation toward uncertainty-aware and adaptive water resources management. The methodology is scalable to data-scarce and climate-sensitive regions and can be further extended through real-time forecasting and multi-objective optimization in future studies.