| Sumario: | Rice cultivation faces multiple challenges from rising food demand as well as increasing water scarcity and greenhouse gas emissions, intensifying the tension of the food–water–climate nexus. Process-based modeling is pivotal for developing effective measures to balance these challenges. However, current models struggle to simulate their complex relationships under different water management schemes, primarily due to inadequate representation of critical physiological effects and a lack of efficient spatially explicit modeling strategies. Here, we propose an advancing framework that addresses these problems by integrating a process-based soil–crop model with vital physiological effects, a novel method for model upscaling, and the non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm at a parallel computing platform. Applying the framework accounted for 52 %, 60 %, 37 %, and 94 % of the experimentally observed variations in rice yield, irrigation water use, methane, and nitrous oxide emissions in response to irrigation schemes. Compared with the original model using traditional parameter upscaling methods, the advancing framework significantly reduced simulation errors by 35 %–85 %. Moreover, it well reproduced the multi-variable synergies and tradeoffs observed in China's rice fields and identified an additional 18 % areas feasible for irrigation optimization, along with an additional 11 % and 14 % reduction potentials of water use and methane emissions, without compromising production. Over 90 % of the potentials could be realized at the cost of 4 % less yield increase and 25 % higher nitrous oxide emissions under multiple objectives. Overall, this study provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale. The advancing framework also has implications for other process-based modeling improvement efforts.
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