Toward measuring biogeochemistry within the stream‐groundwater interface at the network scale: An initial assessment of two spatial sampling strategies

Abstract

AbstractIt is important to understand how point measurements across spatially heterogeneous ecosystems are scaled to represent these systems. Stream biogeochemistry presents an illustrative example because water quality concerns within stream networks and recipient water bodies motivate heterogeneous watershed studies. Measurements of the stream water‐groundwater (SW‐GW) interface (i.e., the shallow stream subsurface) are well‐documented for point‐scale sampling density measurements (i.e., cm2–m2 features), but poorly characterized for network‐scale sampling density measurements (i.e., km2; stream reaches and networks). Sampling the SW‐GW interface is more time and labor intensive than surface water sampling, meaning sample point selection must be made with care for network‐scale analyses. In this study, we endeavor to determine which of two common spatial sampling schemes is appropriate for characterizing SW‐GW interface biogeochemistry across a third‐order stream network, focusing on dissolved organic carbon. The first scheme, called Local Sampling, focuses on characterizing small‐scale (< 10 m2) variability produced by the local physical and biogeochemical heterogeneity, with fewer points across the stream network. The second scheme, called Longitudinal Sampling, has approximately the same number of measurements distributed over many more points across the stream network with less local variability characterization. This comparison reveals that selection of a Local Sampling versus a Longitudinal Sampling scheme influences the biogeochemical pattern interpretation at the stream network scale. Additionally, this study found that increasing observation efforts at the local scale added limited information for reach‐ to network‐scale biogeochemical patterns, suggesting that emphasis should be placed on characterizing variability across broader spatial scales with the Longitudinal Sampling approach.