Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales in Clark County, WA and the metropolitan area of Portland, OR

<section><para>The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R2 values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water chemistry and quality, a wider contributing area needs to be included in order to account for distant sources of pollutants.</para></section>

本研究针对美国俄勒冈州波特兰都会区（Portland Metro）及华盛顿州克拉克县（Clark County）区域内的土地覆被、地形地貌、建筑结构与河流水质之间的关联，采用普通最小二乘（ordinary least squares, OLS）回归与地理加权（geographically weighted, GWR）多元回归模型开展分析。本研究采用两种分析尺度——分区流域与缓冲区，分别从局地与全局视角探究河流水污染物的来源。以决定系数（R²）衡量的模型精度，会随分析尺度、季节及所采用的回归方法发生波动。多数雨季水质参数与城市土地覆被存在关联，而多数旱季水质参数则与高程、坡度等地形地貌特征相关。考虑空间自相关局地关联的地理加权（GWR）模型，其拟合效果优于普通最小二乘（OLS）回归模型。在多元回归模型中，除旱季pH值与河流水温参数外，分区流域的分析结果始终优于分区缓冲区的分析结果。这表明，尽管河岸带土地覆被确实会对水体化学性质与水质产生影响，但为了涵盖远距离污染物来源，研究需纳入范围更广的贡献区域。