Multi-model coupled climate-land use-runoff feedback mechanism: analysis and prediction of spatial and temporal heterogeneity in the transboundary watershed of the Tumen River

The repercussions of climate change and land use change on water resources are becoming increasingly evident, particularly in the context of transboundary water resources research. This field necessitates the integration of various factors into research methodologies to achieve sustainable development objectives. The Tumen River Basin, a paradigmatic transboundary basin in Northeast Asia, has been confronted with the challenge of stabilizing water resources in view of the increased frequency of hydrological disasters in recent years. Therefore, in this study, a coupled model (M-S-C) combining the Mixed Cell Cellular Automata (MCCA), Soil and Water Assessment Tool (SWAT), and Coupled Model Intercomparison Project 6 (CMIP6) meteorological data was utilized to predict the annual runoff intervals from 2025 to 2070. Furthermore, the study sought to analyze the impacts of different factors on runoff in different countries, and to propose the concept of Contribution of Transboundary River Volume (CTRV). The findings indicate that the impact of climate is significantly more substantial than that of land use change within the study area. Forest land and cultivated land emerge as the predominant land types exerting influence on runoff. Geodetector q-statistics reveal interpretation rates of 58.21% and 48.85%, respectively. The runoff volume is estimated to range from 83.062 billion to 149.696 billion m3, with a decrease on the Chinese side and an increase on the North Korean side, as indicated by the CTRV slopes of − 0.023 and 0.005, respectively. The M-S-C coupled model and the CTRV concept offer novel insights for the monitoring of water resources in transboundary basins and the adaptive regulation of water-ecological coupling systems. These models provide significant guidance for the sustainable development of water resources in transboundary basins.