Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

Abstract

Abstract Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

Keywords

• changing climate
• climate interactions
• explainable machine learning
• irrigation benefits
• maize gross primary productivity
• surface cooling