Optimizing water allocation in irrigation districts for enhanced sustainability and climate resilience

This figure illustrates, encompassing crop growth simulation, water production function fitting, water allocation optimization, and the analysis of future climate change and drought indices. The integrated optimization framework illustrates potential pathways for implementing optimal water management practices under both historical and future climate conditions, providing a comprehensive approach to improving water use efficiency and sustainability in irrigation districts. • An integrated framework was developed to optimize irrigation under climate change. • Irrigation optimization increased water productivity by up to 18.55% nationwide. • Total irrigation water use reduced by 8.6%, with GHG emissions cut by up to 45.3%. • Optimized allocation improved yields in two-thirds of future climate scenarios. • The approach supports food security and low-carbon agricultural transitions. Irrigation plays a crucial role in enhancing agricultural productivity. However, it simultaneously increases water consumption and greenhouse gas (GHG) emissions, a challenge that is further exacerbated by climate change. While many irrigation strategies have been proposed, their potential to balance crop yield growth, water productivity improvement and carbon emission reduction in large-scale irrigation districts remains unclear. This study aims to establish a decision-relevant, scalable basis for climate-resilient irrigation management by testing whether coordinated irrigation allocation across large irrigation districts can jointly sustain food production while improving water-use efficiency and reducing GHG emissions under climate change. An integrated optimization perspective is used to delineate the achievable potential and limits of irrigation allocation for aligning food security with water conservation and carbon-neutrality goals. In this study, the DSSAT crop model was used to simulate the biophysical response of main crops under irrigation and climate conditions, and water production function was extracted from random forest to realize rapid prediction. Then, the Pareto optimal irrigation strategy among grain yield, water productivity and greenhouse gas emission reduction was identified by NSGA-II, and it was calibrated and verified under the data of typical irrigation areas. Using an integrated framework, we show that irrigation optimization increases irrigation water productivity by 11.84%, while reducing water use by 8.6% and GHG emissions by 45.3%. Under climate change scenarios, optimized allocation decreases irrigation volume by 6.9–9.3%, with crop yields rising in two-thirds of scenarios, reaching gains of up to 31.3%. Nationwide, crop water productivity is projected to rise by 0.68%–18.55%, accompanied by a reduction in GHG emissions of 7.49%–32.90%. These results demonstrate that coordinated irrigation optimization can effectively decouple agricultural production from water consumption and carbon emissions, highlighting its potential as a robust and scalable strategy for safeguarding food security while advancing agricultural sustainability and carbon-neutrality objectives.