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.