Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface
Kun Mei, Haonan Shi, Yingying Wu, Randy A. Dahlgren, Xiaoliang Ji, Minmin Yang, Yueru Guan
Abstract
• Optimal spatial scales for assessing water quality were evaluated for a mixed area. • Landscape patterns impact water quality more significantly in the rainy season. • Dynamic subwatershed exhibit the highest explanatory power for water quality. • Largest patch indices (LPI) for agriculture and waterbody are key factors. • Controlling LPI of agriculture < 10 % and LPI of waterbody > 1.5 % mitigates pollution. Urban river water pollution is a serious environmental challenge confronting urban areas worldwide. The scientific quantification of the impact of landscape patterns on water quality provides essential support for pollution control and watershed landscape optimization. In this study, we onducted an in-depth and meticulous exploration of the relationship between the landscape patterns and water quality in the Wen-Rui Tang River watershed at four distinct spatial scales (single subwatershed, dynamic subwatershed, riparian buffer, and reach buffer) and two time scales (dry season and rainy season). Regression models were used to investigate the quantitative impact of landscape patterns on water quality, redundancy analysis was employed to assess the overall explanatory power, and non-parametric point-of-change analysis was applied to evaluate variations in water quality along landscape gradients and identify critical landscape threshold ranges. The results indicate that the model performance of disolved oxygen at the reach buffer scale, pH and total nitrogen at the riparian buffer scale, and ammonia nitrogen, nitrate nitrogen, and total phosphorus at the dynamic subwatershed scale were better than at other scales. Overall, the dynamic subwatershed scale showed the strongest explanatory power for water quality indicators, with 91.2 % for the rainy season and 83.2 % for the dry season. The largest patch index of agricultural land (A_LPI) and water bodies (W_LPI) were identified as the most critical landscape indices influencing water quality at the dynamic sub-watershed scale, with threshold values for these indices established as A_LPI < 10 % and W_LPI > 1.5 %. These findings offer a comprehensive understanding of the spatiotemporal impacts of landscape patterns on water quality and offer valuable insights for watershed management and ecological planning.