Abstract 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.

中文翻译：

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将空间用于建模韩国汉河流域的景观和水质关系

摘要 在考察景观特征与水质的关系时，以往的研究大多没有对景观特征和水质采样站的空间方面给予足够的重视。我们分析了韩国汉江流域总氮（TN）、总磷（TP）、化学需氧量（COD）和悬浮物（SS）的空间格局，探讨不同距离考虑和空间统计的作用解释水质变化的方法。这些水质属性的五年（2012年至2016年）季节性平均值作为响应变量用于分析，而土地覆盖、海拔、坡度和水文土壤组等解释变量根据距离和距离进行不同的加权处理。流量积累。Moran 的基于特征向量的空间过滤器用于考虑水质采样点之间的空间关系，并用于回归模型。季节性水质存在明显的空间格局，TN、TP、COD和SS浓度在下游城市地区最高，上游森林地区浓度最低。TN 浓度在干燥的冬季高于潮湿的夏季，而 SS 的浓度在潮湿的夏季高于干燥的季节。与非空间模型相比，空间模型大大改进了模型拟合。当不使用空间过滤器时，基于流量累积的模型表现最好，但当使用空间过滤器时，所有模型的表现都相似。距离加权方法有助于理解影响源、动员、和输送流入河水中的理化参数。我们得出的结论是，在模拟水质时，考虑采样点的空间方面与考虑不同的距离和水文过程同样重要。