LAUSR

Non-point source pollution (NSP) and runoff intensities and distribution are primarily affected by landscape structure and composition. Multiple causalities hinder our ability to determine significant variables that influence NSP. Therefore, we developed an approach that integrates the Soil and Water Assessment Tool (SWAT), random forest regression model, redundancy analysis, and correlation coefficient to assess the role of landscape structure on runoff and NSP in the Dongsheng basin. We used R to calculate landscape metrics and the SWAT to simulate NSP loads from 1990 to 2019. redundancy analysis (RDA), random forest, and Pearson correlation were used to analyze the relationships among landscape metrics and NSP variables. The largest patch index (LPI) shows a significant negative correlation with NSP, with an R2 of −0.58 for TP and TN and −0.62 for sediment load. The findings indicate that landscapes with larger patch sizes, a high number of patches, and aggregation of patches largely influence pollution distribution. Overall, the results suggest that the role of landscape patterns on NSP outweighs that of runoff. Moreover, the findings infer that the aggregation and connectivity of forest patches contribute to the decline in NSP load and vice versa for cropland cover. Thus, for sustainable watershed management, it is crucial to encourage unfragmented landscapes, especially pollutant-intercepting landcovers such as forests.