ABSTRACT:

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM

This protocol will detail the process for calibrating and launching STIC (Stream Temperature Intermittency & Relative Conductivity) sensors.

The "living" version of this SOP can be found on Google Docs: https://docs.google.com/document/d/1gTZ5MecE8Xjp6ymhH4rB92V_i1lH93Jv/edit

From this SOP, the following data types will be created: Time series of temperature and conductivity. [AIMS rTypes: STIC]

ABSTRACT:

This study was conducted in the South Fork of King’s Creek at Konza Prairie Biological Station, one of the most well-studied prairie streams in the world. At the USGS gage located on the mainstem (06879560; est. 1979), Kings Creek is a 5th order intermittent stream draining 1059-ha of tallgrass prairie in the Kansas Flint Hills.

This synoptic survey was designed in support of the sampling goals of the Aquatic Intermittency effects on Microbiomes in Streams (AIMS) Project. During June, July, and August 2021, a field team co-collected datasets characterizing the hydrology and biogeochemistry across 50 locations within a sub-drainage of the South Fork of Kings’ Creek. The 50 sites were selected to balance multiple competing priorities: (i) strategically targeting existing monitoring infrastructure with long-term data (n=14); (ii) including sites near several known springs and tributary junctions (n=9); and (iii) including a range of drainage area and topographic wetness index (TWI) values (n=27), both of which have been correlated with flow permanence elsewhere. Briefly, the sites selected based on drainage area and TWI were chosen by binning drainage area into 10 bins and then binning TWI into quintiles within each drainage area bin (Supplemental Info 1). We then randomly selected a point in each bin after accounting for points selected based on existing infrastructure, springs, and tributaries;, and enforcing a minimum spacing of 100 m between locations. We then made minor adjustments to points to account for field conditions, for instance adjusting locations with respect to a road crossing. For a detailed description of the site selection process, please see (Swenson et al., 2023).

These are the dissolved gas datasets, including greenhouse gas (GHG) and membrane inlet mass spectrometer (MIMS) for the Konza Approach 3 data (June 2021) and the mini-synoptic stretch data (July and August 2021). GHG samples were analyzed on an Agilent 7890B Gas Chromatograph (GC) equipped with a thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD) and calculated using standard curves. Detection limits were <5% for all gasses. Concentrations for CO2 (200ppm, 2000 ppm, 10000 ppm), N2O (0,1 ppm, 1.01 ppm, 4 ppm), and CH4 (1 ppm, 10 ppm, 100 ppm) were used for our calibration curves. A bubble-free technique with ZnCI2 added for preservation was used for the MIMS samples for high-precision measurements of N2, Ar, and O2 concentrations and N2:Ar and O2:Ar ratios. MIMS data was processed via the R package, ‘MIMSY’, while all GHG data was processed via modified EPA NEON GHG code. All gas data is in uM. For a detailed description of the sample processing, please see Zarek SOP (MIMS) and Wilhelm SOP (GHG).

AIMS OSF site: https://osf.io/e7s9j/