Search for: All records

The design of infrastructure used for deploying water quality sensors can potentially impact data quality. Despite this, sensor infrastructure design has not been well discussed in the peer‐reviewed literature. Here, we present side‐by‐side measurements from two contrasting designs; a “monopod” consisting of a strut driven into the streambed and a downrigger suspended from an “overhead cable.” We collected measurements over an approximately 6‐month period from two wadeable stream monitoring sites within the National Ecological Observatory Network. In general, we observed minimal differences between measurements, suggesting both designs to be viable options from a data quality perspective under normal operating conditions. However, the monopod design was more susceptible to coming out of the water during low stage and burial by sedimentation. While more expensive and logistically complex to install, the overhead cable design exhibited greater survivability, adjustability, and serviceability. We discuss additional design considerations and potential modifications that we hope will prove useful to other researchers in instrumenting their own sites.

Stream water chemistry is traditionally measured as variation over time at fixed sites, with sparse sites providing a crude understanding of spatial heterogeneity. An alternative Lagrangian reference frame measures changes with respect to both space and time as water travels through a network. Here, we collected sensor‐based measurements of water chemistry at high spatial resolution along nearly 500 km of the Upper Colorado River. Our objective was to understand sources of spatiotemporal heterogeneity across different solutes and determine whether longitudinal change manifests as smooth gradients as suggested by the River Continuum Concept (RCC) or as abrupt changes as suggested by the Serial Discontinuity Concept (SDC). Our results demonstrate that Lagrangian sampling integrates spatiotemporal variation, and profiles reflect processes that vary in both space and time and over different scales. Over each day of sampling, water temperature (T) and dissolved oxygen (DO) varied strongly in response to diel solar cycles, with most of the variation driven by sampling time rather than sampling location. Equilibration of T and DO with the atmosphere limited small scale spatial heterogeneity, with variation at the entire profile scale driven by regional climate gradients. As such, T and DO profiles more closely approximated the smooth gradients of the RCC (though including temporal sampling artefacts). Conversely, variation in specific conductance and nitrate (NO₃‐N) was largely driven by spatial patterns of lateral inflows such as tributaries and groundwater. This resulted in discrete shifts in the profiles at or downstream of discontinuities, appearing as the profiles expected with the SDC. The concatenation of spatiotemporal variation that produces observed Lagrangian profiles presents interpretive challenges but also augments our understanding of where, how, and critically why water chemistry changes in time and space as it moves through river networks.

Floods are dominant controls on export of solutes from catchments. In contrast, low‐flow periods such as droughts are potentially dominant control points for biogeochemical processing, enhancing spatiotemporal variation in solute concentrations, stream metabolism, and nutrient uptake. Using complementary time series (i.e., an Eulerian reference frame) and longitudinal profiling (i.e., a Lagrangian reference frame), we investigated hydrologic controls on temporal and spatial variation in solute flux and metabolism in the Lower Santa Fe River (FL, USA), where highly colored surface water mixes with exceptionally clear groundwater from springs. Gage measurements suggest groundwater inputs ranged from <1% (during extreme floods) to 86% (during extreme drought) of total discharge (Q). Mass transport of most solutes was dominated by high‐Qperiods. Most soluteC‐Qrelationships exhibited statistically significant slope breakpoints near the transition between surface and groundwater dominance. In particular, parameters controlling water column light attenuation were chemostatic above medianQbut markedly reduced at lowQ. As a result, river metabolism and assimilatory nitrate (NO₃⁻) uptake were consistently suppressed at highQand enhanced at lowQ, with greater variability in response to drivers other than water column light transmittance. Spatial variation in solute concentrations was also enhanced at lowQ, induced by discrete groundwater inflow and biogeochemical processing along the reach. Contrasting reference frames yielded corroborative evidence for transport dominance at highQ, which damps spatiotemporal heterogeneity. In contrast, low‐Qperiods enable localized mixing controls on solute concentrations and high rates of metabolism and nutrient processing that increase spatiotemporal variability.

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.