Spatial and non-spatial factors: When do they affect landscape indicators of watershed loading?

The percentage of a watershed occupied by agricultural areas is widely used to predict nutrient loadings and in-stream water chemistry because water quality is often linked to non-point sources in a watershed. Measures of the spatial location of source areas have generally not been incorporated into such landscape indicators although empirical evidence and watershed loading models suggest that spatially explicit information is useful for predicting loadings. I created a heuristic grid-based surface-flow model to address the discrepancies between spatially explicit and non-spatial approaches to understanding watershed loading. The mean and variance in loading were compared among thousands of simulated watersheds with varying percentages of randomly located source and sinks. The variability in loading among replicate landscapes was greatest for those landscapes with similar to 65% source areas. This variance peak suggests that considering the spatial arrangement of cover types is most important for watersheds with intermediate relative abundances of sources and sinks as the wide variety of different spatial configurations can lead to either very high or very low loading. Increasing the output from source pixels (relative to the amount absorbed by sink pixels) among different landscapes moved the peak in variance to landscapes with lower percentages of sources. A final scenario examined both broad- and fine-scale heterogeneity in source output to disentangle the relative contributions of spatial configuration, percentage of source covers, and heterogeneity of sources in governing variability in loading. In landscapes with high percentages of source pixels, fine-scale heterogeneity in source output was responsible for a greater portion of the total variability in loading among different watersheds than was spatial arrangement. These results provide several testable hypotheses for when spatial and non-spatial approaches might be most useful in relating land cover to water chemistry and suggest improvements for the spatial sensitivity analyses of eco-hydrologic watershed models.