Spatial scale effects of integrated landscape indicators on river water quality in Chaohu Lake basin, China

Climate change and rapid urbanization have changed the characteristics of basin landscapes. Non-point-source (NPS) pollution affects river water quality. Exploring the impact of landscapes on river water quality is crucial for the control of water pollution in a basin. Current researchers focus on the impact of landscape pattern change on NPS pollution in the basin, but few consider climate, terrain, soil, and other geographical factors. In this study, we selected a subtropical agricultural basin in China named Chaohu Lake basin as the study area, added precipitation, soil erosion resistance, and slope to the original landscape pattern indicators. We quantified the spatial scale effect and seasonal dependence of integrated landscape indicators on water quality and comprehensively analyzed the optimal spatial scale and key landscape indicators. According to the nonlinear relationship between the key landscape indicators and river nutrients, we also determined the Type-1 threshold values of key landscape indicators for water quality protection in the basin. The results showed that the rivers in Chaohu Lake basin were mainly polluted by nitrogen and phosphorus. The strength of interpretation of the integrated landscape indicators of river water quality increased with riparian zone width. We determined the subbasin scale to be the optimal spatial scale. The key landscape indicators affecting water quality in the wet season at the optimal scale were precipitation and aggregation index of construction land (AI_bul), whereas those in the dry season were AI_bul and COHESION. The interpretation of the key landscape indicators in the wet season was slightly higher than that in the dry season. The above conclusions provide a scientific reference for NPS pollution control and water quality protection in subtropical agricultural basins.