Spatial management of riverine methane (CH4) emissions based on landscape drivers in the Yangtze River Basin.

Methane (CH4) is a significant greenhouse gas linked to key climate change drivers, garnering heightened global focus. However, existing CH4 risk management approaches lack spatial coordination for basin systems. Here, we combined hydrological simulation with machine learning to map the distribution of river CH4 emissions across 1932 sub-basins in the Yangtze River Basin and to identify their key driving factors. The spatial distribution of riverine CH4 fluxes exhibited significant spatial heterogeneity, with an average value of 0.90 ± 0.24 mg m-2 h-1. Human activities, particularly agrarianization and urbanization, are the primary driving factors, with their impacts surpassing those of geographical and hydrological factors. Spatial autocorrelation analysis revealed 464 sub-basins as critical control areas, where high CH4 fluxes formed statistically significant clusters. Based on the distinct landscape characteristics of these critical control areas, we propose a zonal management strategy: reducing cropland intensity in 440 human-intensive critical control areas in the middle and lower reaches while prioritizing ecological conservation in 24 upper-reach critical control areas where emissions are naturally driven. Scenario analysis indicates that targeting only 22.8% of sub-basins (the human-intensive critical control areas) could achieve a 4.27% basin-wide reduction in CH4 fluxes, offering a cost-effective trade-off between intervention area and mitigation efficacy. The findings demonstrate that integrating landscape-informed spatial planning into basin management provides a scalable approach for mitigating riverine CH4 emissions in human-dominated regions. By focusing efforts where human activity drives emissions, managers can achieve strong reductions without restricting natural zones, balancing environmental goals with development needs.