A Bayesian network-based bi-level multi-objective programming model for uncertainty agricultural water management and allocation optimization

Water resource management in irrigation districts is of pivotal importance for safeguarding agricultural production, promoting regional economic development, and maintaining social stability. The development of a scientific and reasonable water allocation model has emerged as a pivotal research area in the context of sustainable development of irrigation districts, particularly in the context of climate change. This study explores the use of a combination of methodologies for agricultural water management under uncertainty conditions, including Bayesian network, interval parameter programming, and a bi-level multi-objective programming model approach. The Bayesian network has been demonstrated to quantify the nonlinear effects of precipitation, temperature, evaporation, and other factors on water diversion. The bi-level multi-objective programming model is designed to balance economic efficiency and social equity between the macro and micro decision-making levels. A case study conducted in Jiaokou Irrigation District, Shaanxi Province of China, demonstrated the adaptability of the model under different scenarios. The findings indicate that the aggregate benefits of the irrigation district under the normal scenario can amount to 3.30 × 10⁹ yuan. The fluctuation in the mean value of water diversion is less than about 15 %. The Gini coefficient is maintained within the range of 0.02–0.29. This study rationally and dynamically allocates water resources through multi-model coupling, achieving scientific management of agricultural water resources. It offers novel concepts and specialized technical assistance for the harmonized implementation of the Sustainable Development Goals at the irrigation district level, with the objective of addressing the progressively intricate challenges posed by water scarcity and uncertainty.