Network analysis reveals multiscale controls on streamwater chemistry

Significance Headwater streams are important sources of water for downstream ecosystems and human communities. These streams comprise the vast majority of stream and river kilometers in watersheds and affect regional water quality. However, the actual spatial variation of water quality in headwater streams is often unknown. Our study uses an unusually high-resolution spatial dataset from a headwater stream network and employs a statistical tool to objectively describe spatial patterns of streamwater chemistry within a stream network. This approach provides insights on how flowing water interacts with vegetation, soil, and geologic materials in the surrounding landscape. Application of this method may help to identify factors impairing water quality and to inform strategies for protecting aquatic ecosystems. By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.