Understanding processes governing water quality in catchments using principal component scores

The analysis of spatial–temporal patterns of scores, including their association with supplementary data, can refine a principal component analysis of water quality data. We hypothesized that this type of analysis could considerably improve the understanding of processes governing water quality at catchment scales. To test this, water quality data from the 180 km2 Ammer catchment in south-western Germany was investigated using principal component analysis. We analyzed data for (a) surface water from the Ammer River and its tributaries, (b) spring water from the main aquifers and (c) deep groundwater from wells. Using the analysis of scores, we found that the quality of both surface and groundwater primarily reflected the input of solutes determined by land use and geology. For water quality in the Ammer catchment, the conservative mixing of water of different origins and ages was more important than reactive transport processes along the flow paths. These results demonstrate the potential of our analysis of principal component scores to identify dominant processes at catchment scales.