Spatio-temporal dynamics of water quality and land use in the Lake Dianchi (China) system: A multi-source data-driven approach

Lake Dianchi, China. Urbanization and land development increasingly challenge water quality, yet their interactions remain unclear. This study examines these interactions in Lake Dianchi by developing an empirical water quality inversion model. Multi-year water quality data from monitoring stations were integrated with Landsat 8 L2 data to analyze seven key Water Quality Parameters (WQPs): TP, Hg, Cd, As, Zn, S, and NH₃-N. Land use was assessed using a multi-dimensional framework incorporating weighted overlays of multi-source data. The relationships between land use and water quality were analyzed using Spearman correlation, redundancy analysis (RDA), and the Optimal Parameter Geodetector (OPGD). From 2014–2022, pollutant concentrations declined, yet TP pollution remains severe, keeping the lake classified as Class V. Pollution was more concentrated in Caohai than Waihai, highlighting point-source pollution. Land use changes were dominated by “urban expansion” and the “returning farmland to forest” program. RDA results indicated that construction (positive) and farmland (negative) were the primary drivers of WPI. OPGD analysis showed that FACI∩CACI was the strongest explanatory factor for WPI. Over time, land use influence on WPI weakened due to urban saturation, environmental policies, and delayed water quality responses. These findings quantify land use-water quality interactions and provide insights for managing urban plateau lakes amid rapid development. • Investigated methods for long-term monitoring of lake water quality. • Demonstrated variations in pollution levels across different lake regions due to land use changes. • Provided critical insights into the relationship between land use and pollution sources through remote sensing. • Established a scientific foundation for sustainable lake management.