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Agricultural water scarcity is a global problem and effective management of limited water resources for irrigation to meet socioeconomic demands for sustainable development is a huge challenge. A stochastic multi-objective non-linear programming (SMONLP) model is developed for the identification of sound irrigation water allocation schemes. The SMONLP model improves upon previous methods by tackling contradictions of society-economy-resources as well as reflecting uncertainty expressed as probability distributions in an agricultural irrigation system. The SMONLP model permits in-depth analyses of various water allocation policies that are associated with different levels of water supply and climate change. The developed SMONLP model is applied to optimal irrigation allocation in a semi-arid river basin in China. Results reveal that the model coordinates the regulation of interactions of society-economy-resources by balancing the targets of water productivity, allocation equity, profit, economic benefit risk, blue water utilization, and leakage loss. Moreover, surface water availability associated with different violation risk probabilities can lead to the changes in comprehensive benefit of society-economy-resources and irrigation shortages. Nearly each of the 17 irrigation regions suffers from water deficit, because water is insufficient to satisfy the requirement of crops, however, the degree of water shortage is gradually weakened when flow level ranges from low to high. The coordination degree is also used to evaluate the sustainability of water allocation and the results of comparison show that the irrigation water allocation under RCP 4.5 presents lower coordination of society-economy-resources which are mainly attributed to the aggravated contradiction between water supply and demand. A real world study demonstrates the practicability of the developed model, allowing the river basin authorities to determine irrigation water allocation strategies in a changing environment, thus promoting sustainable development of agricultural irrigation systems. (C) 2019 Elsevier Ltd. All rights reserved.