Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions

Abstract. Rice cultivation faces multiple challenges of rising food demand while increasing water scarcity and greenhouse gas emissions, intensifying the tension of the food-water-climate nexus. Process-based modeling of the nexus is pivotal for developing effective measures to address 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 the absence 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 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, and methane and nitrous oxide emissions in response to irrigation schemes. Compared with the origin model using traditional parameter upscaling methods, the advancing framework significantly reduced simulation errors by 35 %−85 %. Moreover, it well reproduced the multivariable synergies and tradeoffs observed in China’s rice fields and identified 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. The advancing framework also has implications for other process-based modelling improvements efforts.