Open source data enhances water quality assessment: Examining coupled hydrologic and biogeochemical processes at aquatic sites across the National Ecological Observatory Network

Abstract:

Examining catchment-scale interactions between hydrologic and biogeochemical processes in waterways provides a more comprehensive view into water quality than examining the processes separately. At the National Ecological Observatory Network (NEON), our goal is to provide high quality standardized data to the scientific community. As such, we are developing tools that allow the scientific community to assess water quality using open source data on a continental-scale. By integrating instrumental and observational hydrology data with Bayesian modelling techniques, we are developing stage-discharge rating curves that provide 1-min resolution continuous discharge data for NEON’s 27 stream and river sites. Continuous discharge outputs, when coupled with NEON aquatic biogeochemistry data products, would allow users to assess fluxes of constituents. This would enhance the understanding of how waterways transport, process, and export compounds central to water quality assessment, including, but not limited to, nutrients, chlorophyll, suspended sediments, and greenhouse gasses. At NEON aquatic sites, automated sensors measure chemical and physical properties of surface water at high temporal resolutions. These data can be coupled with observational biogeochemistry samples from surface water, groundwater, sediment, and aquatic organisms to provide a comprehensive catchment-scale view into water quality in waterways across the United States. Here, we present an analysis based on data published by NEON in our first year of full operations. We show that, when used together to conduct analyses, NEON’s open source hydrologic and biogeochemical data represent tools for water quality assessment that can be used by researchers, agencies, and educators alike. Finally, we discuss the steps NEON is making to advance water quality science by focusing on data quality, providing a platform for developing synthesis products, and stimulating collaborative efforts using NEON infrastructure and data resources.