Deriving phase-contingent dynamic drought-limited water levels: An adaptive framework for managing megadrought evolution

The Xiaolangdi Reservoir in the Yellow River Basin, China. Static drought-limited water levels (DLWLs) often fail to address the non-stationary nature of prolonged megadroughts. To address this gap, we derived dynamic, stage-dependent DLWLs that evolve alongside the drought lifecycle. Instead of treating drought as a uniform event, our method splits the process into four distinct evolutionary phases—Gradual Emergence, Escalation, Persistence, and Recovery—based on observable shifts in meteorological signals. We employed a supervised Random Forest (RF) model, trained on a physically constrained Comprehensive Drought Index (CDI), to grade severity. Crucially, we contrasted this approach against an unsupervised K-means clustering baseline to test whether statistical patterns alone could capture the necessary physical drought signals. Our analysis shows that the supervised model far exceeds the unsupervised baseline in reliability (AUC=0.98; RMSE=0.62). This result is significant because it proves that operational tools must be anchored in physical benchmarks—specifically those capturing soil moisture memory—rather than relying on latent statistical clusters. The primary contribution here is a structured set of dynamic DLWL t curves. We found that the operational implication of a specific drought severity is contingent upon the timing: a "severe" signal demands preemptive hedging during the emergence phase but survival-based conservation during persistence. By aligning reservoir decisions with these specific evolutionary stages, this approach offers a practical pathway to improve resilience in semi-arid basins. ● Phase-contingent DLWLs adapt reservoir operations to megadrought evolution. ● Supervised classification outperforms unsupervised clustering for drought diagnosis. ● Operational logic shifts from preemptive hedging to terminal survival storage. ● Soil moisture acts as the primary determinant of hydrological drought severity.