Due to the influence of climate change, population growth, politics, ecological feedback and related factors, the water use system of transboundary basins are characterized by a complicated Water-Energy-Food-Ecology nexus, which constitutes a complex nonlinear system with high uncertainty. Unfortunately, the traditional optimization of water resources is often constrained by such a complex nexus. Despite that integrated modeling approach is often used in the simulation of complex nexus, it remains unsupportive of big data needs, thereby making uncertainty reduction a difficult task. The Bayesian network (BN), which is considered a more robust tool for analyzing complex relationships, was applied in this study to characterize the Water-Energy-Food-Ecology nexus of Syr Darya river basin, a transboundary inland basin which contributed to the Aral Sea ecological crisis for unreasonable water use. The annual scale data was introduced into BN to model the impact of stochastic annual runoff and compare their differences using periods "before" and "after" the disintegration of the Soviet Union as benchmark. Results show that during the before period, the amount of water inflow into the Aral Sea was sensitive to increased irrigation for agricultural development, increased water storage of the upstream reservoir and stochastic runoff. This situation became reverse after the disintegration of the Soviet Union. The reverse situation resulted from unresolvable disputes between power generation in upstream and irrigation water in downstream countries. Comprehensive scenario analysis shows that it is effective to improve the proportion of food crops, the efficiency of water use for salt leaching and irrigation, and prevent drought damage. Based on the increased use of advanced drip irrigation technology from 50% to 80%, it is anticipated that the annual inflow into the Aral Sea will increase significantly, reaching 6.4 km ³ and 9.6 km ³, respectively; and is capable of ameliorating ecological crisis within the basin. Finally, this study shows that the BN is a cost-efficient approach for predicting systematic water usage as decision support in water management with less complexity, and it is effective in modeling casual relationships in the Water-Energy-Food-Ecology nexus.