Scale effects and threshold management in the influence of landscape patterns on non-point source pollution

• We construct different buffer zones based on the landscape resistance costs. • The effects of landscape proximity on water quality vary with seasons. • Dominant landscape factors affecting water quality differ across spatial scales. • The land-use intensify index is a key landscape metric of the high-proximity zone. • The land-use intensify in this watershed should be maintained below 183.22. The migration of nonpoint source (NPS) pollution is influenced by the resistance cost distance between landscape units and rivers. Understanding the relationship between landscape proximity and riverine pollutants is crucial for optimizing landscape patterns to mitigate NPS pollution. However, given that water quality responses to landscape patterns may depend on the proximity of landscape units to rivers and exhibit nonlinear tendencies, these relationships remain poorly understood. This study applied redundancy analysis and nonlinear segmented regression analysis to evaluate the effects of landscape patterns on NPS pollution migration in a headwater watershed in eastern China, comprising 29 sub-watersheds with diverse landscape characteristics. The results revealed that landscape patterns in the high-proximity zone were most effective in explaining riverine pollutant variations during the wet season, while those in the extremely low-proximity zone were more influential during the dry season. Therefore, landscape pattern regulation should adopt a multiscale perspective. The key landscape indicators affecting water quality differed across proximity zones. In the high-proximity zone, the land-use intensity index (LI), percentage of residential area (Res), and aggregation index of residential areas (AI_res) were crucial. In the extremely low-proximity zone, LI and the aggregation index of forestland (AI_for) played dominant roles. To improve water quality, landscape planning should consider maintaining LI < 183.22 and AI_res < 92.66 % in the high-proximity zone, and AI_for < 95.68 % in the extremely low-proximity zone. This study highlights that optimizing landscape patterns through a multiscale approach and the consideration of landscape thresholds could enhance the effectiveness of NPS pollution control and ultimately improve water quality in headwater watersheds.