Zheng, Ziqin ， Dong, Zengchuan ， Wang, Wenzhuo ， Meng, Jinyu ， Ke, Hao ， Zhang, You
2026-02-01 ENVIRONMENTAL MODELLING & SOFTWARE 2026 197(卷), null(期), (null页)
The accelerated evolution of climate change and human activities as well as their increasingly complex interactions have led to a significant increase in runoff uncertainty and non-consistency. Understanding and assessing the impacts of both on runoff will be important for water resources planning and management. This study develops a general modelling framework for runoff attribution by proposing a dual-step refined timevarying attribution model based on Budyko framework, which combine revisions of traditional methods and improvements of the structure of the traditional attribution model. The proposed model is evaluated through application to the Lixia River Basin across multiple spatio-temporal scales. Results demonstrate that the model enhances the accuracy of runoff change separation by 11.42 %-33.46 % at annual scales and by 5.06 %-6.84 % at the multi-year average scales. This dual-step model contributes an accurate separation and generalizable modelling insights for assessment of hydrological responses to coupled climatic and anthropogenic drivers.