US Long-Term Ecological Research Network

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.
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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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North Temperate Lakes LTER General Lake Model Parameter Set for Lake Mendota, Summer 2016 Calibration

The General Lake Model (GLM), an open source, one-dimensional hydrodynamic model, was used to simulate various physical, chemical, and biological variables on Lake Mendota between 15 April 2016 and 11 November 2016. GLM (v.2.1.8) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires four major “scripts” to run the model. First, the glm2.nml file configures lake metadata, meteorological driver data, stream inflow and outflow driver data, and physical response variables. Second, the aed2.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, phosphorus, and nitrogen, among others. Third, aed2_phyto_pars.nml configures all parameters pertaining to phytoplankton dynamics. And fourth, aed2_zoop_pars.nml configures all parameters pertaining to zooplankton dynamics. This dataset contains parameter descriptions and values as they were used to simulate organic carbon and greenhouse gas production on Lake Mendota in summer 2016. Meteorological data and stream files used in this calibration are also included in this dataset. Additional methods and model descriptions can be found in J.A. hart’s Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017. Readers are referred to the GLM (Hipsey et al. 2014) and AED (Hipsey et al. 2013) science manuals for further details on model configuration.
Additional Information
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This study used the R packages “GLMr” ( and “glmtools” ( to interact with GLM-AED from the R statistical environment.

Hourly meteorological data were obtained for 2010-2016 from the North American Land Data Assimilation System (NLDAS) and included air temperature, relative humidity, short wave radiation, long wave radiation, wind speed, rain accumulation, and snow accumulation.

Average daily stream discharge and nitrogen and phosphorus loads were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS) water quality database. All streams included in this study are monitored by the USGS. This study used 2014 nutrient loading data over the simulation time period since 2016 nutrient data were not yet available. Only the Yahara River and Pheasant Branch Creek have data on daily nutrient loads; thus, Pheasant Branch nutrient concentrations were also applied to both Dorn Creek and Sixmile Creek, which contribute near equivalent volume to Lake Mendota. Since GLM-AED requires nutrient concentration rather than load, water column concentrations were back calculated using load and discharge.

Daily OC loads (POC and DOC, individually) were estimated for each individual inflow (n=4) from observed weekly measurements of OC concentration (doi: 10.6073/pasta/86094298f5a9085869518372934bf9d7)
using stream-specific linear models (Lathrop et al. 1998). The linear relationship between known log-scaled OC loads and log-scaled discharge for each individual stream was used to predict the OC load for each day in the model simulation time period. Again, water column OC concentrations were back calculated using the load and discharge.

A manual calibration routine was conducted to optimize the goodness-of-fit of various state variables in GLM-AED, especially those known to influence OC and GHG dynamics. We used visual comparison of predictions and observations, as well as a quantitative approach, to assess goodness-of-fit. We calibrated water balance and water temperature to effectively simulate lake level and stratification. We also calibrated DO, Secchi depth, TN, TP, DOC, and CH4. Finally, we calibrated phytoplankton dynamics according to four phytoplankton functional groups, recognizing that phytoplankton contribute substantially to the OC pool.

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Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016

This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake.

Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
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In two consecutive years, we measured lake-wide spatial patterning of cyanobacteria using the FLAMe platform (Crawford et al. 2015). To evaluate early warning indicators of a critical transition, in the first year we induced a cyanobacteria bloom through nutrient addition in an experimental lake while using a nearby unmanipulated lake as a reference ecosystem (Pace et al. 2017). During the second year, both lakes were left unmanipulated. Proposed detection methods for early warning indicators were compared between the manipulated and reference lakes to test for their ability to accurately detect statistical signals before the cyanobacteria bloom developed.
Peter and Paul Lakes are small, oligotrophic lakes (Peter: 2.5 ha, 6 m, 19.6 m and Paul: 1.7 ha, 3.9 m, 15 m, for surface area, mean, and max depth respectively) located in the Northern Highlands Lake District in the Upper Peninsula of Michigan, USA (89°32’ W, 46°13’ N). These lakes have similar physical and chemical properties and are connected via a culvert with Paul Lake being upstream. Both lakes stratify soon after ice-off and remain stratified usually into November (for extensive lake descriptions, see Carpenter and Kitchell, 1993).
In the first year, Peter Lake was fertilized daily starting on 1 June 2015 (DOY 152) with a nutrient addition of 20 mg N m-2 d-1 and 3 mg P m-2 d-1 (molar N:P of 15:1) through the addition of H3PO4 and NH4NO3 until 29 June (day of year, DOY 180). The decision to stop nutrient additions required meeting four predefined criteria based on temporal changes in phycocyanin and chlorophyll concentrations indicative of early warning behavior of a critical transition to a persistent cyanobacteria bloom state. (Pace et al. 2017). Nutrients uniformly mix within 1-2 days after fertilization based on prior studies (Cole and Pace 1998). No nutrient additions were made to Paul Lake. In the second year (2016), neither lake received nutrient additions.
We mapped the surface water characteristics of both experimental lakes to identify changes in the spatial dynamics of cyanobacteria. In 2015, mapping occurred weekly from 4 June to 15 August (11 sample weeks). In 2016, when neither lake was fertilized, the lakes were mapped three times in early to mid-summer. In both years, mapping occurred between the hours of 07:00 to 12:00 (before the daily nutrient addition). We rotated the order that we sampled the lakes to avoid potential biases due to differences in time of day. Each individual lake sampling event was completed in approximately one hour.
The FLAMe platform maps the spatial pattern of water characteristics. A boat-mounted sampling system continuously pumps surface water from the lake to a series of sensors while geo-referencing each measurement (complete description of the FLAMe platform in Crawford et al. 2015). For this study, the FLAMe was mounted on a small flat-bottomed boat propelled by an electric motor and was outfitted with a YSI EXO2™ multi-parameter sonde (YSI, Yellow Springs, OH, USA). We focused for this study on measures of phycocyanin (a pigment unique to cyanobacteria) and temperature. Phycocyanin florescence was measured using the optical EXO™ Total Algae PC Smart Sensor. The Total Algae PC Smart Sensor was calibrated with a rhodamine solution based on the manufacturer’s recommendations. Phycocyanin concentrations are reported as ug/L; however, these concentrations should be considered as relative because we did not calibrate the sensor to actual phycocyanin nor blue-green algae concentrations. Geographic positions were measured using a Garmin echoMAP™ 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Each sampling produced approximately 3500 measurements in the manipulated lake and 2000 in the reference lake. The measurements were distributed by following a gridded pattern across the entire lake surface to characterize spatial patterns over the extent of the lake.
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Lake Mendota Carbon and Greenhouse Gas Measurements at North Temperate Lakes LTER 2016

This original dataset contains carbon and greenhouse gas (GHG) data collected in Lake Mendota during the summer of 2016. Data were collected between 15 April 2016 and 14 November 2016 on both Lake Mendota and its surrounding streams—four major inflows and the primary outflow of Lake Mendota. The dataset is comprised of four linked tables, corresponding to carbon and GHG measurements on Lake Mendota (lake_weekly_carbon_ghg), weekly physico-chemical sonde casts on Lake Mendota (lake_weekly_ysi), ebullition rate estimates on Lake Mendota (lake_weekly_ebullition), and carbon and physico-chemical data from the four major inflows and primary outflow of Lake Mendota (stream_weekly_carbon_ysi). These data were used to explore the relationship between organic carbon dynamics and greenhouse gas production on a eutrophic lake. From these data, it is possible to estimate daily oxygen, methane, and carbon dioxide flux on Lake Mendota during the study time period. Additional methods and applications of this data can be found in J.A. Harts Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017.
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Carbon Sample Analysis: Weekly observational data were collected on Lake Mendota between 15 April 2016 and 14 November 2016. All lake samples collected from the deep hole were taken at five discrete depths (3, 10, 12, 14, and 20 m), intended to span the seasonal thermocline. All lakes samples collected in littoral zones (Point, Ubay, and Yahara) were taken at two discrete depths (0.1 and 2 m). Two liters of water were collected at each sampling location and depth using a Van Dorn sampler for measurement of particulate organic carbon (POC), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC). Between 1200 mL and 1800 mL of water was passed through a ProWeigh 47 mm filter (Environmental Express, Charleston, SC, USA) depending on how quickly water became impassable. POC was estimated by performing loss on ignition on the filter. The difference in mass before and after combustion at 500°C was multiplied by 0.484 to account for the OC fraction of organic matter (Thomas et al. 2005). Filtrate was analyzed for DOC and DIC on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer (Shimadzu Scientific Instruments, Kyoto, Japan), where organic carbon is measured by combustion and inorganic carbon after phosphoric acid digestion.
Dissolved Gas Analysis: Water samples for dissolved methane (CH4) and carbon dioxide (CO2) were collected at each depth in the lake using a Van Dorn sampler and stored in 30-mL serum vials. Serum vials were overfilled and capped in the field with a rubber septa and aluminum cap. Care was taken to ensure that no bubbles were present in the sample. Serum vials were then stored on ice until they could be placed in the refrigerator. Within 24 hours of collection, samples received a 3 mL N2 gas headspace, were shaken vigorously, and left to equilibrate at room temperature. Headspace CH4 and CO2 was analyzed on a Varian 3800 gas chromatograph, and headspace-water CH4 and CO2 partitioning was accounted for using Henrys Law.
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Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets

Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
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The Fast Limnology Automated Measurement (FLAMe) platform is a novel flow-through system designed to sample inland waters at both low- (0 to appr. 10 km hr-1) and high-speeds (10 to greater than 45 km hr-1) described in Crawford et al. (2015). The FLAMe consists of three components: an intake manifold that attaches to the stern of a boat; a sensor and control box that contains hoses, valves, a circulation pump and sensor cradles; and a battery bank to power the electrical components. The boat-mounted intake manifold serves multiple purposes. First, sensors are mounted inside the boat, protecting them from potential damage. Second, the intake system creates a constant, bubble-free water flow, thus preventing any issues for optical sensors due to cavitation. Finally, to analyze dissolved gases, a constant water source is needed on board. Water flow via both the slow- and high-speed intakes is regulated by the onboard impeller pump, allowing for seamless switching between slow- and high-speed operations. Any number of sensors could be integrated into the platform with simple modifications, and can be combined with common limnological instruments such as acoustic depth-finders. In our example applications we used a YSI EXO2 multiparameter sonde (EXO2; Yellow Springs, OH, USA), and a Satlantic SUNA V2 optical nitrate (NO3) sensor (Halifax, NS, Canada), both integrated into the control box plumbing with flow-through cells available from the manufacturer. Additionally, a Los Gatos Research ultraportable greenhouse gas analyzer (UGGA) (cavity enhanced absorption spectrometer; Mountain View, CA, USA) was used to measure dry mole fraction of carbon dioxide (CO2) and methane (CH4) dissolved in surface water by equilibrating water with a small headspace using a sprayer-type equilibration system that has previously been shown to have fast response times relative to other designs16 (Figure S1). Both the EXO2 and the UGGA are capable of logging data at 1 Hz. Because the SUNA was operated out of the water and on a boat during warm periods, data were collected less frequently (appr. 0.1 Hz) to minimize lamp-on time and avoid the lamp temperature cutoff of 35° C. The EXO2 sonde uses a combination of electrical and optical sensors for: specific conductivity, water temperature, pH, dissolved oxygen, turbidity, fluorescent dissolved organic matter (fDOM), chlorophyll-a fluorescenece, and phycocyanin fluorescence. The SUNA instrument measures NO3 using in situ ultraviolet spectroscopy between 190-370 nm, has a detection range of 0.3-3000 microM NO3, and a precision of 2 microM NO3. The UGGA has a reported precision of 1 ppb (by volume). In order to translate time-series data from the instruments into spatial data, we also logged latitude and longitude at 1 Hz with a global positioning system (GPS) with the Wide Area Augmentation System (WAAS) functionality enabled allowing for less than 3 m accuracy for 95percent of measured coordinates. Synchronized time-stamps from the EXO2, UGGA, SUNA, and GPS were used to combine data streams into a single spatially-referenced dataset.
We ran a simple set of experiments to determine the residence time of the system and the overall response time of the EXO2 and UGGA sensors integrated into the platform. After determining first-order response characteristics of each sensor, we applied an ordinary differential equation method to correct the raw data for significant changes in water input resulting in higher accuracy spatial data (see Crawford et al. 2015).
Sensor response experiments
We conducted a series of sensor response experiments on Lake Mendota on August 1, 2014. The goal was to understand the potential lags and minimum response times for the EXO2 and UGGA sensors integrated into the FLAMe platform. These data were then used to develop correction procedures for higher accuracy spatial datasets. To test sensor responses to step-changes in water chemistry, we mixed a 40 L tracer solution into a plastic carboy that was connected to the reservoir port on the FLAMe. The reservoir was mixed with 50 mL of rhodamine WT to test the phytoplankton fluorescence sensors, 6 mL of quinine sulfate solution in acid buffer (100 QSE) to test the fDOM sensor, 14 g of KCl to test the conductivity sensor, and appr. 2 kg of ice to reduce the temperature of the solution relative to lake water. The mixture volume was increased to 40 L using tap water. We did not modify the CO2 concentration or pH in the carboy as we found the municipal water source to have greater than ambient lake CO2 (4300 vs. 290 microatm, respectively) and lower pH (7.5 vs 8.3, respectively). At the beginning of the experiments, we allowed lake water to circulate through the system for appr. 10 minutes. We then switched to the tracer solution for a period of five minutes, followed by five minutes of lake water, then back to the tracer solution for an additional five minutes.
Using the step-change experiment data, we determined each sensors hydraulic time constant (Hr) and parameter time constant (taus). The sensor-specific Hr is a function of system water residence time and sensor position/shielding within the system. Taus is the time required for a 63 percent response to a step-change input. Hr was calculated based on the plateau experiments and was indicated by the first observation with a non-zero rate of change. The CO2 and CH4 sensors had a much greater Hr than the EXO2 sensors because water must travel further through the system before equilibrating with the gas solution being pumped to the UGGA. Using these Hr values, we offset response variables thus removing the hydraulic lag. This correction does not account for sensor-specific response patterns (tau s). The EXO2 sensors have manufacturer-reported taus values between 2-5 s, but these values are not appropriate to apply to the FLAMe system because they do not include system hydraulic lag and mixing. In order to match sensor readings with spatial information, we first applied Hr values from each sensor output according to equation 2. This step aligns the time at which each sensor begins responding to the changing water, and accounts for the physical distance the water must travel before being sensed
In order to match individual sensor response characteristics and to obtain more accurate spatial data, we then applied sensor-specific corrections using Equation 3 (Fofonoff et al., 1974).
We first smoothed the raw data using a running mean of 3 observations in order to reduce inherent noise of the 1 Hz data. We then calculated dX/dt using a 3-point moving window around Xc. Equation 3 should ideally lead to a step response to a step-change input. We note that this is the same strategy used to correct oceanographic conductivity and temperature instruments (see Fozdar et al., 1985). Overall, the taus-corrected data show good responses to step-change inputs and indicate that this is a useful technique for generating higher accuracy spatial data. We include three types of data for each variable including: raw (e.g., TempC), the hydraulic lag corrected (e.g., TempC_hydro) and the taus-corrected data (e.g., TempC_tau). Note that not all sensors were used in each survey and not all sensors have each type of correction. This data was from our preliminary FLAMe sampling campaigns and future studies will include additional sensor outputs and corrections.
We used the FLAMe throughout the summer of 2014 on four distinct aquatic ecosystems including: a small dystrophic lake, a stream/lake complex, a medium-sized eutrophic lake, and a managed reach of the Upper Mississippi River. Each of these applications demonstrates the spatial variability of surface water chemistry and the flexibility of FLAMe for limnological research.
Crawford JT, Loken LC, Casson NJ, Smith C, Stone AG, and Winslow LA (2015) High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science and Technology 49:442-450.
Fozdar FM, Parker GJ, and Imberger J (1985) Matching temperature and conductivity sensor response characteristics. Journal of Physical Oceanography 15:1557-1569.
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A Global database of methane concentrations and atmospheric fluxes for streams and rivers

This dataset, referred to as MethDB, is a collation of publicly available values of methane (CH4) concentrations and atmospheric fluxes for world streams and rivers, along with supporting information on location, geographic, physical, and chemical conditions of the study sites. The data set is composed of four linked tables, corresponding to the data sources (Papers_MethDB), the study sites (Sites_MethDB), concentrations (Concentrations_MethDB), and influx/efflux rates (Fluxes_MethDB). Information was extracted from journal articles, government reports, book chapters, and similar sources that were acquired before 15 September 2015. Concentrations and fluxes were converted to a standard unit (micromoles per liter for concentration and millimoles per square meter per day for flux) and both the author-reported and converted data are included in the database. MethDB was assembled as part of a larger synthesis effort on stream and river CH4 dynamics, and assembled data were used to identify large-scale patterns and potential drivers of fluvial CH4 and to generate an updated global-scale estimate of CH4 emissions from world rivers.
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CH4 data from streams and rivers are widely scattered, as values are often included as end-member in studies focused on other processes or types of ecosystems. Thus, while we sought to be as complete as possible in compiling existing data, some sources have undoubtedly been overlooked. Sources included journal articles, book chapters, dissertations, USGS open file reports, meeting proceedings, and unpublished results provided by individual investigators. Data incorporated into MethDB were strictly limited to surface waters of rivers and streams; values for groundwater, porewater, saturated soils, lakes, reservoirs, wetlands, estuaries, and floodplains were not included. Some papers were excluded because essential supporting information was missing (e.g., units), or extracting data from complex graphs was considered to be unwise. Data sources are listed in the Notes and Comments section below.
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Ebullitive methane emissions from oxygenated wetland streams at North Temperate Lakes LTER 2013

Stream and river carbon dioxide emissions are an important component of the global carbon cycle. Methane emissions from streams could also contribute to regional or global greenhouse gas cycling, but there are relatively few data regarding stream and river methane emissions. Furthermore, the available data do not typically include the ebullitive (bubble-mediated) pathway, instead focusing on emission of dissolved methane by diffusion or convection. Here, we show the importance of ebullitive methane emissions from small streams in the regional greenhouse gas balance of a lake and wetland-dominated landscape in temperate North America and identify the origin of the methane emitted from these well-oxygenated streams. Stream methane flux densities from this landscape tended to exceed those of nearby wetland diffusive fluxes as well as average global wetland ebullitive fluxes. Total stream ebullitive methane flux at the regional scale (103 Mg C yr-1; over 6400 km2) was of the same magnitude as diffusive methane flux previously documented at the same scale. Organic-rich stream sediments had the highest rates of bubble release and higher enrichment of methane in bubbles, but glacial sand sediments also exhibited high bubble emissions relative to other studied environments. Our results from a database of groundwater chemistry support the hypothesis that methane in bubbles is produced in anoxic near-stream sediment porewaters, and not in deeper, oxygenated groundwaters. Methane interacts with other key elemental cycles such as nitrogen, oxygen, and sulfur, which has implications for ecosystem changes such as drought and increased nutrient loading. Our results support the contention that streams, particularly those draining wetland landscapes of the northern hemisphere, are an important component of the global methane cycle.
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Rate of bubble releaseWe deployed 30 inverted funnel-style bubble traps (Molongoski and Klug, 1980; Baulch et al., 2011) on Allequash Creek on 31 May 2013 to measure volumetric bubble release rates. Fifteen traps were placed in two sandy sediment sections and 15 were placed in muck sediments in the wetland portion of the creek (number 8&ndash;22), which sits in-between the two sandy sections. Site 1 was the most downstream sampling site (water flows from East to West). Traps were sampled approximately every other day after 1 June 2013 until 31 October 2013 (we omitted the first samples collected 24 h following trap installation; total of 65 sample events per trap). Our sampling design allowed us to assess both the spatial and temporal variability in ebullition along Allequash Creek and how ebullition related to potential controlling factors such as sediment composition, atmospheric pressure, groundwater CH4, and organic matter content (discussed further below). To characterize our ebullition time series from Allequash Creek in the larger context of the NHLD, we installed an additional 12 traps on three additional creeks (Mann Creek, Stevenson Creek, and North Creek in the Trout Lake drainage; three per site in an even mix of sand and muck sediments) and the headwater spring ponds that drain into Allequash Creek on 23 June 2013 which we sampled approximately every week for the remainder of the study. Bubble traps had a bottom surface area of appr. 503 cm2 which narrowed at the top into a graduated (1 mL resolution) syringe and 3-way stopcock. Traps were attached to steel poles that were pounded into the substrate. Traps were almost completely submerged and contained no headspace at deployment. Water depth below traps averaged 55.7 cm, but we were unable to place traps in locations where water depth was shallower than 15 cm. Water velocity during baseflow at the traps averaged 0.06 m s -1 (range = 0.003&ndash;0.23 m s -1). We sampled traps by carefully approaching them either by boat (muck sites) or by wading (sandy sites) to avoid induced ebullition. Volume of accumulated gas in the trap was based on the graduated syringe, and volumes less than 1 mL were recorded as zero. Traps were reset between sampling events by refilling them completely with water to eliminate all headspace. To assess the hypothesis that declines in atmospheric pressure are related to increased bubble release (Mattson and Likens, 1990; Comas et al., 2011), we compared a 15 min resolution atmospheric pressure time series recorded using a Vaisala BAROCAP barometer deployed near trap number 7 with a subset of the bubble release time series.
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Greenhouse gas emissions from streams at North Temperate Lakes LTER 2012

Aquatic ecosystems can be important components of landscape carbon budgets. In lake-rich landscapes, streams may be important sources of greenhouse gases (CO2 and CH4) to the atmosphere in addition to lakes, but their source strength is poorly documented. The processes which control gas concentrations and emissions in these interconnected landscapes of lakes, streams and groundwater have not been adequately addressed. In this paper we use multiple datasets that vary in their spatial and temporal extent to investigate the carbon gas source strength of streams in a lake-rich landscape and to determine the roles of lakes and groundwater. We show that streams emit roughly the same mass of CO2 as regional lakes, and that stream CH4 emissions are an important component of the regional greenhouse gas balance.
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Sampling DesignSampling of gas partial pressures, and gas transfer velocities was performed weekly at five stream sites (Mann Creek, Allequash Lower Creek, Allequash Middle, Stevenson Creek, North Creek) that drain into Trout Lake (Figure 1), one of the regions larger lakes. Sampling began in May 2012 and continued through September 2012. These data were used to establish variability in gas transfer rates for the basin, and to investigate spatiotemporal patterns. To test for the effects of upstream lakes, sampling at 30 additional longitudinal transect sites along 6 streams (5 sites per stream; Figure 1) was conducted approximately every 3 weeks beginning in May 2012. Transect streams were chosen based on the presence or absence of lakes in the upstream watershed. Streams with lakes (Lost, White Sand, Aurora) were sampled starting at the approximate lake outlet (site selection based on aerial photographs), and along a 2000 m transect (0m, 250m, 500m, 1000m, 2000m). Upstream lake chemistry (epilimnion) was also sampled during late July or early August 2012 at the lake center to allow for a direct comparison with streams. Streams without upstream lakes (Stella, Mud, and North) were sampled at an arbitrary upstream location (0m) and followed the same sampling progression as streams with lakes. We analyzed stream chemistry and stream morphology data from a regional stream survey (Lottig and others 2011) which we use to scale fluxes to the NHLD (Figure 1). We also studied groundwater CO2 and CH4 patterns along a hillslope transect at Allequash Creek during 2001. In 2002, we monitored hourly CO2 and O2 dynamics at the four WEBB sites to assess the role of ecosystem metabolism.
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North Temperate Lakes LTER: Northern Highlands Stream Chemistry Survey 2006

We compared regional patterns in lake and stream biogeochemistry in the Northern Highlands Lake District (NHLD), Wisconsin, USA to ask how regional biogeochemistry differs as a function of the type of ecosystem considered (i.e., lakes versus streams); if lake-stream comparisons reveal regional patterns and processes that are not apparent from studies of a single ecosystem type; and if characteristics of streams and lakes scale similarly. Fifty-two streams were sampled using a stratified random design to determine regional distribution of 21 water chemistry variables during summer baseflow conditions.Sampling Frequency: once per site Number of sites: 52
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Site SelectionBecause lakes are a dominant feature of the region and stream characteristics could potentially differ based on their hydrologic connections to lakes, we classified streams into three categories as a function of their hydrologic connections to lakes. The first category was streams that had no lakes within the drainage network upstream of the sampling location. The second category was streams that originated from headwater lakes (i.e., no stream inlet but a stream outlet) and the headwater lake was the only lake in the drainage network above the sampling location. The final category had at least a single drainage lake (i.e., a lake with both stream inlet(s) and outlet) in the drainage network above the sampling location. We then used these categories to select sampling sites using a stratified random design for a variety of chemical and physical characteristics.All streams identified on 1:24,000 7.5 inch USGS topographical maps that crossed access points were selected as potential sampling locations and assigned to one of the three stream types. A stream could be classified by more than a single category depending on the sampling location within the drainage network. However, a single drainage network was never sampled more than once to ensure sample independence. Of the 500 possible sampling locations, 52 sites were selected and sampled.SamplingAll streams were sampled 7-10 channel widths upstream of an access point to minimize any influences caused by culverts and other features. Water samples were collected from the center of the channel using a peristaltic pump. Stream discharge was measured after Gore (2007) using cross sectional area and water velocity.Chemical AnalysesAll samples for both studies were collected and processed following the North Temperate Long Term Ecological Research (NTL-LTER) protocols ( Filtering was done in the field using an in-line 0.45 μm membrane filter. All samples were stored on ice and returned to the laboratory where they were preserved according to NTL-LTER protocols. Acid neutralizing capacity (ANC) was determined by Gran titration (APHA 2005). DOC was measured on a Shimadzu TOC-V carbon analyzer. Total nitrogen and phosphorus (unfiltered, TN and TP; filtered, TDN and TDP), nitrate+nitrite (NO3-N), and ammonium (NH4-N) were quantified with an Astoria-Pacific segmented flow auto-analyzer. Soluble reactive phosphorus (SRP) in streams was measured colormetrically on a Beckman DU-800 spectrophotometer (APHA 2005). Anions (Cl- and SO4 2-) were measured using a Dionix DX-500 ion chromatograph and cations (Ca, Mg, Na, K, Fe, K, and Mn) on a Perkin Elmer ICP mass spectrometerDissolved inorganic carbon (DIC) and pH were quantified differently in the lakes and stream data sets. For the lakes data, DIC was determined with a Shimadzu TOC-V carbon analyzer, whereas DIC for the streams dataset was determined by headspace equilibration of acidified water samples in the field and direct measurement of carbon dioxide (CO2) gas on a Shimadzu gas chromatograph (Cole et al. 1994). pH measurements for the lakes dataset were quantified on non-air equilibrated samples in the lab with a Accumet 950 pH meter while direct measurements were taken in the field for the streams dataset using a hand-held Orion model 266 pH meter that was allowed to equilibrated about 20 min in the center for the stream channel.Several variables presented in this study were determined from calculations based on measured values. In streams, dissolved organic nitrogen and phosphorus (DON and DOP, respectively) were determined by the difference between inorganic nutrients and total dissolved nutrients (e.g., DOP = TDP-SRP). We were unable to determine DON in lakes due to the lack of inorganic nitrogen data. It was assumed that DOP approximately equals TDP in lakes because dissolved inorganic phosphorus concentrations in the region are typically below detection limits in the epilimnion during the summer months and consequently not quantified (NTL-LTER unpublished data).
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