A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff

Abstract Attribution analysis of runoff variation holds significant importance for water resource conservation and management. This study established a methodological framework for runoff reconstruction, quantification, and analysis of contribution rates. The framework comprises four core components: (1) Multi-temporal scale analysis: implementing explicit aggregation rules based on variable physical meanings to aggregate hydro-meteorological variables across daily, weekly, two-week, monthly, two-month, seasonal, and annual scales; (2) Variable screening mechanism: determining optimal explanatory variables by calculating Spearman correlations between meteorological factors and runoff at each time scale combined with variance inflation factor (VIF) analysis to address multicollinearity; (3) Multi-model comparison: reconstructing runoff using Random Forest Regression Model (RFRM) and Soil and Water Assessment Tool (SWAT), with validation through metrics including Coefficient of Determination (R²) and relative bias (RBIAS); (4) Integrated attribution approach: combining remote sensing and statistical data to identify driving factors of anthropogenic impacts. Tested on river time-series data from the Lan River Basin, this framework quantified contribution percentages and provided methodological references for temporal scale/model selection and driver analysis in watershed hydrological studies.