Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling

Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. We selected ten watersheds in the USA and analyzed if water quality variables with higher Moran's I values undergo greater increases in the coefficient of determination (R-2) and greater decreases in residual SAC (rSAC). We compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R-2 and decreases in rSAC after performing spatial regressions. In this study, we found a generally linear relationship between the spatial model outcomes (R-2 and rSAC) and the degree of SAC in each water quality variable. We suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed.