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Meeting ecological water requirements (EWRs) is important for maintaining watershed system stability in many arid and semi-arid areas. Rainfall–runoff relationships under changing climate conditions could have many adverse effects on EWRs. The inherent uncertainties in water resource management and potential variations in EWRs should be considered to obtain suitable water allocation strategies under climate change. In this study, an integrated approach was proposed through incorporation of copula functions and a Markov Chain Monte Carlo (MCMC) simulation into a chance-constrained programming (CCP) model. The proposed approach had several advantages for water resource allocation under variable climatic conditions with respect to the following: (a) tackling correlated features of rainfall and watershed inflow under climate change based on copula–MCMC simulations, (b) obtaining runoff distributions using the copula sampling method under multiple climate change scenarios, (c) analyzing fluctuations in EWRs based on variable monthly flows and diverse runoff distributions, and (d) obtaining desired water allocation strategies through the developed CCP model with consideration of EWRs and water shortage risk. Application of the developed method to water resource management in the city of Dalian (China) indicated that the EWRs in the watersheds of Dalian would exhibit large variations under changing climatic conditions. Moreover, in comparison with the supply in 2025, an increase in water supply transferred from the Dahuofang Reservoir (Hun River) would be 6942–33,772, 6942–25,472, and 2849–14,259 Mt with risk tolerance levels of 20%, 50%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%–2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease. • A Copula-based programming model is developed for water allocation management. • Decision-makers' tolerance to water shortage risk is incorporated. • Variations of water demand along with socio-economic development are identified. [ABSTRACT FROM AUTHOR]