Landscape and anthropogenic factors affecting spatial patterns of water quality trends in a large river basin, South Korea - Mainali, Janardan; Chang, Heejun (Journal of hydrology (Amsterdam), 2018)
•We analyzed water quality trends in the Han River basin for 20+ years.•Water quality generally improved in the basin with spatially varying trends.•Topography, land use, soil, and population explained the majority of the trends.•Eigenvector spatial filters removed residual spatial autocorrelation effectively.•100 m buffer scale analysis typically explained the variation of the trends most. Understanding changes in water quality over time and landscape and anthropogenic factors affecting them are of paramount importance to human and ecosystem health. We analyzed the seasonal trends of total nitrogen, total phosphorus, chemical oxygen demand, and total suspended solid (SS) in the Han River Basin (HRB) of South Korea using the Mann-Kendall test. We explored the effects of anthropogenic (land cover and population) and natural factors (topography and soil) on the trends by using Moran’s Eigenvector based spatial filtering regressions at four different spatial scales. Water quality of the HRB generally improved from the early 1990s to 2016 with decreasing summer nutrient and winter SS concentrations. Water quality trends were spatially autocorrelated with distinct spatial variations within the basin. Some stations close to the Seoul metropolitan area, however, still exhibited poor water quality conditions. Approximately 20–70 percent of spatial variation of different water quality trends were explained by some combination of current agricultural land cover, forest land cover, % area covered by water, change in those land covers and slope variations. The 100 m buffer and one-kilometer upstream scale analyses generally showed higher explanatory power than the sub-watershed scale analyses, while the effect of seasons differed for different parameters. The significant factors in each regression model typically differed among different scales but not among different seasons of the same scale. The spatial filtering approach removed the residual spatial autocorrelation and thus significantly increased the explanatory power of water quality trend models.