US Long-Term Ecological Research Network

Chloride Concentrations, Conductivity, and Water Temperature Data from Upper Yahara River Watershed Tributaries in Dane County, WI: December 2019 – April 2021

Conductivity and chloride were measured for 2 years in nine tributaries of Lake
Mendota and Lake Monona in Dane County, WI. HOBO Conductivity loggers continuously
measured absolute conductivity and water temperature every 30 minutes. Breaks in
data collection were due to a calibration period or if the loggers were out of the
water. Grab samples for chloride concentration occurred weekly or biweekly.
Conductivity and water temperature were measured with a field meter at each sampling
excursion. This data was needed for a master’s research thesis with the goal of
characterizing the spatial distribution and loading of chloride in the Upper Yahara
River Watershed.<br/>
Core Areas
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Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>
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Spatial heterogeneity of within-stream methane concentrations North Temperate Lakes LTER, 2014

Streams, rivers, and other freshwater features may be significant
sources of CH4 to the atmosphere. However, high spatial and temporal
variabilities hinder our ability to understand the underlying
processes of CH4 production and delivery to streams and also challenge
the use of scaling approaches across large areas. We studied a stream
having high geomorphic variability to assess the underlying scale of
CH4 spatial variability and to examine whether the physical structure
of a stream can explain the variation in surface CH4. A combination of
high-resolution CH4 mapping, a survey of groundwater CH4
concentrations, quantitative analysis of methanogen DNA, and sediment
CH4 production potentials illustrates the spatial and geomorphic
controls on CH4 emissions to the atmosphere.
Core Areas
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LTER Keywords
We determined stream CH4 concentrations at a large spatial extent (10
km) and fine grain (35,000 total measurements) by using a
biogeochemical mapping platform on a small boat [Crawford et al.,
2015]. Water was pumped on board where gases were stripped from the
water by using a sprayer-type equilibrator and analyzed with a Los
Gatos Research ultraportable greenhouse gas analyzer (using a
cavity-enhanced absorption technique). Survey speeds were very slow
(less than 3 kph) to enable the detection of small-scale changes in
CH4 concentrations over short distances. CH4 concentrations were
corrected for hydraulic and equilibrator lags by using first-order
step-change experiments detailed in Crawford et al. [2015] following
the outline provided in Fozdar et al. [1985]. Lag-corrected CH4 values
were georeferenced by using concurrent Global Positioning System
readings with the Wide Area Augmentation System capability enabled.
The highresolution transect was sampled on 24 and 25 July 2014 (two
morning to afternoon segments were combined into one data set). Both
days were similar in terms of weather and in-stream conditions.
Maximum daily air temperatures were 22.4degC and 20.9degC. Mean daily
air temperatures were 18.5deg and 19.1degC. At the middle site, daily
mean discharges were 3.3 and 3.7 × 10 2 m3 (Julys mean Q is 3.7 × 10 2
m3). Mean water temperatures were 18.1degC and 17.8degC (Julys mean
temperature is 19.6).
Using the high-resolution spatial CH4 data sets, we assessed the
degree of spatial autocorrelation by using semivariograms (spherical
model, using the function autofitVariogram in the R package automap).
We focused on the semivariogram range parameter which describes the
average scale of autocorrelation (i.e., the average patch size). We
also assessed the structure of spatial autocorrelation by using the
global Morans I statistic. The Morans I statistic evaluates whether a
series of geospatial observations are randomly distributed in space
(the null model), clustered, or dispersed. Statistically significant
positive values indicate spatial clustering, whereas negative values
indicate dispersed patterns. We used the Anselin Local Morans I
statistic for spatial cluster analysis of high-resolution CH4 data
[Anselin, 1995]. Statistically significant values of Local Morans I
identify regions (clusters) of high or low values relative to the
global data set, in addition to outliers (e.g., low outliers
surrounded by high values). The analysis was executed by using the
Spatial Statistics toolbox in ArcMap 10.2.
Groundwater Methane Sources
We analyzed groundwater CH4 from a series of wells near the middle and
lower sites and from wells at the head of the drainage near the spring
ponds (upper site) during the time frame of this study by using a
headspace equilibration method [Striegl et al., 2001]. Wells were
developed by using a peristaltic pump, and a minimum of two well
volumes were purged before sample collection. The goal was to evaluate
additional (external) lateral and vertical sources of CH4 beyond the
hyporheic zone. Despite a relatively homogenous sand aquifer,
groundwater flow paths and residence times are complex in this
catchment [Pint et al., 2003; Walker et al., 2003]. A combination of
flow paths including deep groundwater derived from meteoric recharge,
deep groundwater derived from lakes, and meteoric riparian water all
contribute to surface flow in the catchment. These water sources and
flow paths have been studied for over a decade as part of the USGS
WEBB program [Pint et al., 2003; Walker et al., 2003]. Differences in
substrate, organic matter availability, oxygen conditions, and other
metrics of redox state were previously shown to relate to the
concentrations of dissolved gases in wells at the middle site based on
historical data [Crawford et al., 2014b]. Here we expand the survey to
correspond to the timing of surface water mapping and to determine
whether patterns previously observed at the middle site held for the
catchment in general.
Sediment Methanogen Distribution and Abundance
CH4 production potential within stream sediments was first determined
by extracting DNA from stream sediment cores and quantifying the
abundance of methanogenic Archaea. We collected 14 cores approximately
22 cm long in sand and organic-rich wetland locations near the middle
site (locations correspond to odd numbered transect locations in
Figure 1; also corresponding to CH4 bubble trap locations described in
Crawford et al. [2014b]). Sediment cores were collected by using a 2.5
cm diameter, 30 cm length, stainless steel corer with an internal
polycarbonate tube attached to a one-way flow valve and a PVC
extension. Intact cores were transported to the laboratory within 2 h
and immediately frozen. Sediment cores were split into 2 cm segments
followed by DNA extraction by using a PowerSoil DNA isolation kit
(MoBio Laboratories Inc., Carlsbad, CA). We used quantitative
polymerase chain reaction (qPCR) targeting the gene encoding the alpha
subunit of methyl coenzyme-M reductase (mcrA) to quantify both
longitudinal and vertical distributions of methanogens. The mcrA gene
encodes a component of the terminal enzyme complex in the methane
generation pathway and is thought to be unique to methanogens and well
conserved [Thauer, 1998]. Many previous studies have used mcrA as a
genetic marker to determine methanogen abundance and community
composition [Luton et al., 2002; Earl et al., 2003; Freitag et al.,
2010; West et al., 2012]. Each extracted sample containing mcrA was
amplified in a 20 uL qPCR reaction in an ep gradient s realplex2
master cycler (Eppendorf), using SYBR Green as the reporter dye. Each
reaction contained 1 uL of 1/10 diluted sample DNA template, 1× iQ
SYBR Green Supermix (Biorad), and 0.25 uM of each primer targeting
mcrA: mcrAqF (50-AYGGTATGGARCAGTACGA-30) and mcrAqR
(50-TGVAGRTCGTABCCGWAGAA-30) [West et al., 2012]. Thermocycling
conditions for the mcrA qPCR were as follows: an initial denaturation
at 94degC for 1 min, followed by 40 cycles of 94degC denaturation for
40 s, 54degC annealing for 30 s, 72degC elongation for 30 s, and a
fluorescent detection at 85degC for 20 s. Melting curves were run to
ensure absence of nonspecific amplification. Amplification,
fluorescence data collection, and initial data analysis were all
performed by using the Eppendorf realplex2 software (Eppendorf,
Hauppauge, NY, USA).
Despite collecting greater than 20 cores, we were not able to perform
cluster analysis similar to that for CH4 concentrations on the genetic
data because the sample size was too low. Instead, we elected to
compare organicrich versus sand sediment mcrA gene abundance by using
a t test. To determine if methanogen abundance was correlated with CH4
production, we fit a linear model (log transformed) of mcrA abundance
versus average CH4 ebullition documented in the same year [see
Crawford et al., 2014b] with the R statistical programming language [R
Core Team, 2014]. We contend that the comparison between microbial
communities and integrated CH4 bubble flux over time is a stronger
comparison than that of point measurements of CH4 concentration.
Sediment Methane Production Potential
We collected surface sediments from Allequash Creek and placed them in
sealed jars the morning of the start of laboratory experiments. These
sediments were presumed to be mostly anoxic per the oxygen profile
study by Crawford et al. [2014b]. In the lab, about 75 mL of water
saturated surface sediments was transferred into a 150 mL glass
container, flushed with N2, sealed with a gas-tight lid equipped with
a butyl rubber septum for headspace gas sampling, and placed on a
shaker table in the dark at room temperature (about 22degC). Gas
samples were collected after 24 h for CH4 determination by using a
Shimadzu GC-2014 gas chromatograph. Headspace volume of each sample
was determined after gas sampling, then sediments were transferred to
a preweighted aluminum pan for drying (72 h at 50degC) and ashing (4 h
at 500degC). CH4 production potential was determined as headspace CH4
accumulation per gram of dry sediment and per gram of ash-free dry
mass (AFDM) per hour. Production rates were based on two-point
measurements of CH4 concentration and are thus presumed to be linear
over time. Because gas production rates could not be transformed to
meet assumptions of normality, significant differences among
treatments were assessed by using a Kruskal-Wallis test followed by
Wilcoxon rank tests for pairwise comparisons with Bonferroni-adjusted
P value using R.
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Historical Plat Maps of Dane County Digitized and Converted to GIS (1962-2005)

We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.
Dataset ID
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Metadata Provider
Overview: Hard copy historical plat maps of Dane County in four year intervals from 1962to 2005 were digitized and converted to a GIS format using a process known as rectification, wherebycontrol points are set such that a point placed on the scanned image takes on the coordinates of thepoint chosen from the earliest GIS dataset, which for Dane County is from 2005. After a number ofcontrol points are set, the map is assigned the coordinates of the 2005 GIS dataset. In this way,the scanned plat map is now an image file with a distinct spatial location. Since the scanned platmaps do not have any attributes associated with the parcels, the third step is to assign attributesby working backwards from the 2005 GIS dataset. This process begins by making a copy of the 2005 GISdataset, then overlaying this new layer with the rectified scanned image. A subdivision choice isidentified where the parcel lines on the GIS layer are not in agreement with the scanned plat maps.The last step is to modify the copy of the 2005 GIS layer so that it matches the underlying plat map- in effect creating a historical GIS layer corresponding to the year of the plat map. When thelines that delineate a parcel appear in the GIS file but not the plat map, the multiple smallparcels in the 2005 GIS layer are merged together to represent the pre-subdivision parcel. Thisprocess is repeated for each historical year that plat maps are available. In the end, each timeperiod-1974 through 2000 in 4 year intervals-has a GIS file with all of the spatial attributes ofthe parcels.Land Atlas - Plat Books: The Land Atlas plat books were obtained for Dane County from theMadison Public Library, Stoughton Public Library and Robinson Map Library. With these materials onloan the pages were scanned at 150ppi in grayscale format; this process took place at the RobinsonMap Library. Once scanned, these images were georeferenced based on the 2000 digital parcel map.This process of rectification was done in Russell Labs using ESRI ArcMap 9.3. Control points such asroad intersections, were chosen to accurately georeference the 1997 scanned parcel map (1973 wasdone in this way as well). This process was done using a specific ArcGIS tool(View/Toolbars/Georeferencing). For the other years the scanned images were georeferenced based offthe four corners of the 1997 georeferenced scanned images. Georeferencing off the 1997 rectifiedimage allows for easier and quicker rectification but also facilitated detection of differencesbetween the scanned plats. The scanned image of the land ownership could be turned on and off foreasy comparison to the previous time set; these differences are the changes which were made on thedigital ownership map. We scanned and digitized the following years:Scanned plats: 1958, 1962, 1968, 1973, 1978, 1981, 1985, 1989, 1993, 1997, 2001, 2005Digitized plats: 1962, 1968, 1973, 1978, 1981, 1985, 1989, 1993, 1997, 2001, 2005Prepping the parcel Map: Digital parcel shapefiles for the years 2000 and 2005 wereprovided by the Dane County Land Information Office( and were used as the starting reference.These datasets needed to be prepared for use. Many single parcels were represented by multiplecontiguous polygons. These were dissolved. (Multi-part, or non-contiguous polygons were notdissolved.) Here is the process to dissolve by NAME_CONT (contact name): Many polygons do not have acontact name. The majority of Madison and other towns do not have NAME_CONT, but most large parcelsdo. In order not to dissolve all of the parcels for which NAME_CONT is blank we did the following:Open the digital parcel shapefile and go to Selection/Select by Attributes. In this window choose thecorrect layer, chose method create new selection , scroll and double click NAME_CONT, then in thebottom both make sure it says [ &quot;NAME_CONT&quot; &lt;&gt;; ] (without brackets). This will select allpolygons which do not have an empty Name Contact attribute (empty value). From those polygonsselected they were aggregated based on the Name Contact field (parcels with the same NameContact were combined), where borders were contiguous. To do this the dissolve tool in DataManagement Tool/Generalization/Dissolve was used. Dissolve on field NAME_CONT and enter everyother field into the statistical fields menu. This was done without the multipart feature optionchecked, resulting in parcels only being combined when they share border. Keep these dissolvedpolygons highlighted. Once the dissolve process is complete use select by attributes tool again butthis time choose method of Add to Current Selection and say [ &quot;NAME_CONT&quot; = ]. This will provide adigital layer of polygons aggregated by name as well as nameless polygons to be manuallymanipulated.Parcel Map Manipulation: The goal from here was to, as accurately as possible, recreate adigital replica of the scanned parcel map, and aggregate up parcels with the same owner. This goalof replication is in regards to the linework as opposed to the owner name or any other informationin order to accurately capture the correct area as parcel size changed. This process of movingboundaries was independent of merging parcels. If individual scanned parcel boundaries are differentfrom the overlayed digital parcel shapefile, then the digital parcel linework must be changed. Asthis project utilizes both parcel shape and area, the parcels must be accurate. When mergingparcels, parcels with the same owner name, same owner connected on the plat map with an arrow, sameowner but separated by a road, or same owner and share a same point (two lots share a single pointat the corner) were merged to create a multi-part feature. Parcels with the same owner separated byanother parcel of a different owner with no points touching where not merged. This process ofreverse digitization was done using ArcMap. The already dissolved shapefile was copied to create onefile that was a historical record and one file to be edited to become the previous year (the nextyear back in time). With the digital parcel shapefile loaded, the rectified scanned plat maps werethen added. Once open, turn on the Editor Toolbar and Start Editing . The tools to use are thesketch tool and the merge tool. Quick keys where used (editor tool bar\customize) to speed thisprocess. To edit, zoom to a comfortable level (1:12,000) and slowly move across the townships in apattern which allows no areas to be missed (easiest to go township by township). When polygonsneeded to be reconstructed (the process of redrawing the parcel boundary linework), this was doneusing the sketch tool with either the create new polygon option or cut polygon option in theeditor toolbar. Using the sketch tool, with area highlighted, you can redraw the boundaries bycutting the polygons. Areas can be merged then recut to depict the underlying parcel map. If, forexample, a new development has gone in, many small parcels can be merged together to create a bigparcel, and then that large parcel can be broken into the parcels that were originally combined toform the subdivision. We can do this because the names in the attribute are not being preserved.This is a key note: THE OWNER NAME IS NOT A VARIABLE WE ARE CREATING, PRESERVING, OR OTHERWISEREPRESENTING. Once you merge the parcels, they will only maintain one of the names (and which nameis maintained is pretty much random). After the entire county is complete, go through again to checkthe new parcel shapefile, there will be mistakes. Snake through, going across the bottom one row ofsquares at a time. Examples of mistakes include primarily multi-part features that were exploded tochange one part, where the other parts would need to be re-merged. Another common correction arosebecause we typically worked on one township at a time, whereas ownership often crossed townships, soduring this second pass, we corrected cross-township ownership at the edges of the two scannedparcel maps. Finally, some roads which had been built into parcels (driveways) needed to be removedand these were not always caught during the first pass. Once the second run through is complete copythis shapefile so that it also has a back up.
<p>Our purpose was to forecast detailed empirical distributions of the spatial pattern of land-use and ecosystem change and to test hypotheses about how economic variables affect land development in the Yahara watershed.</p>
Quality Assurance
<p>Accuracy was double check by visually comparing against corresponding plat book twice.</p>
Short Name
Historical Plat Maps of Dane County
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