Putting space into modeling landscape and water quality relationships in the Han River basin, South Korea

• Published in: Computers, environment and urban systems Vol. 81; pp. 101461 - 12
• Main Authors: Mainali, Janardan, Chang, Heejun
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2020; Elsevier Science Ltd
• Subjects: Accumulation; Chemical oxygen demand; Dry season; Eigenvectors; Fluid filters; Hydrology; Land cover; Landscape; Modelling; Pattern analysis; Quality management; Regression analysis; Regression models; River basins; Sampling; Solid suspensions; Spatial filtering; Statistical analysis; Summer; Urban areas; Water purification; Water quality; Weighting; South Korea; Han River
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2020.101461

When examining the relationship between landscape characteristics and water quality, most previous studies did not pay enough attention to the spatial aspects of landscape characteristics and water quality sampling stations. We analyzed the spatial pattern of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and suspended solids (SS) in the Han River basin of South Korea to explore the role of different distance considerations and spatial statistical approaches to explaining the variation in water quality. Five-year (2012 through 2016) seasonal averages of those water quality attributes were used in the analysis as the response variables, while explanatory variables like land cover, elevation, slope, and hydrologic soil groups were subjected to different weighting treatments based on distance and flow accumulation. Moran's Eigenvector-based spatial filters were used to consider spatial relations among water quality sampling sites and were used in regression models. Distinct spatial patterns of seasonal water quality exist, with the highest concentrations of TN, TP, COD, and SS in downstream urban areas and the lowest concentrations in upstream forest areas. TN concentrations are higher in dry winter than the wet summer season, while SS concentrations are higher in wet summer than the dry season. Spatial models substantially improved the model fit compared to aspatial models. The flow accumulation-based models performed best when the spatial filters were not used, but all models performed similarly when spatial filters were used. The distance weighting approaches were instrumental in understanding watershed level processes affecting source, mobilization, and delivery of physicochemical parameters that flow into the river water. We conclude that a consideration of the spatial aspects of sampling sites is as important as accounting for different distances and hydrological processes in modeling water quality. •The spatial variations of TN, TP, COD, and SS are explained by a combination of topography, land cover and soil.•Distance-weighted models explained the spatial variations of water quality better than aspatial models.•The flow accumulation based models performed best when the spatial filters were not used.•The high percent forest cover with well-drained soils reduce the concentrations of TP in flow accumulation based models.•All distance-weighted models performed similarly when spatial filters were used.