The impact of extreme climate events on carbon and water fluxes in grassland ecosystems on the Qinghai-Xizang plateau

The Qinghai-Xizang Plateau (QXP) plays a crucial role in regulating terrestrial carbon and water exchanges. However, the impacts of increasingly frequent extreme climate events on ecosystem carbon and water fluxes remain insufficiently understood. Here, machine learning models using multi-source datasets were applied to simulate gross primary productivity (GPP) and evapotranspiration (ET) across QXP grassland ecosystems from 1958 to 2024 and to quantify their responses to extreme climate events. Results showed that: (1) the random forest model outperformed other models and revealed significant increasing trends in both GPP and ET; (2) carbon and water fluxes were generally positively correlated with warm extremes, negatively correlated with cold extremes, and showed predominantly negative responses to precipitation-related extremes; and (3) cold compound events (cold/dry and cold/wet) posed the greatest risk to carbon-water cycling, whereas hot compound events exerted weaker impacts on GPP but increased water consumption. Overall, QXP grassland carbon and water fluxes were most vulnerable to cold compound events. These findings improve understanding of alpine grassland responses to climate extremes and provide a scientific basis for ecosystem management under future climate change.