Abstract
The continuous decline of the Dead Sea water level represents one of the most critical environmental challenges in the Middle East, primarily caused by reduced inflow from the Jordan River basin and increased regional water consumption. Predicting the long-term evolution of the Dead Sea is therefore essential for evaluating the potential effectiveness of large-scale restoration initiatives such as the Red Sea-Dead Sea (RSDS) conveyance project. In this study, a stochastic simulation framework is proposed to model and predict the long-term dynamics of the Dead Sea water level under multiple inflow scenarios. The framework integrates probabilistic increment generation, recursive hydrological updating, and stability-aware monitoring within a unified computational procedure. Annual water-level increments are generated using statistically fitted probability distributions derived from historical observations, enabling the model to capture the inherent variability and uncertainty of natural hydrological processes. The system state is iteratively updated across the simulation horizon, while a stability index is used to evaluate convergence behavior and detect potential equilibrium conditions. Five representative RSDS scenarios are investigated, including baseline recovery, moderate inflow, accelerated recharge, threshold-stabilized inflow, and high-variability environmental conditions. Simulation results indicate that under the baseline scenario the Dead Sea level increases gradually from approximately −434 m to about −60 m within 500 simulation steps, whereas accelerated recharge conditions can raise the level beyond 200 m due to higher early-stage inflow rates. The threshold-stabilized scenario reveals regime transitions in increment dynamics that lead to a near-equilibrium state, while the high-variability scenario exhibits strong stochastic fluctuations with annual increments ranging between 0.4 and 2.2 m. Overall, the results demonstrate that the proposed framework effectively captures diverse hydrological behaviors and provides a flexible and computationally efficient tool for long-term forecasting and policy evaluation of Dead Sea restoration strategies.