US Long-Term Ecological Research Network

North Temperate Lakes LTER Regional Survey Water Chemistry 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. The regional lakes survey in 2015 followed the standard LTER protocol for standard water chemistry and biology. Samples were taken as close to solar noon as possible. Seven lakes had replicates performed, which were chosen at random.
Contact
Dataset ID
380
Date Range
-
Maintenance
ongoing
Methods
Inorganic and organic carbon
Inorganic carbon is analyzed by phosphoric acid addition on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Organic carbon is analyzed by combustion, on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Version Number
2

Molecular composition of dissolved organic matter in NTL-LTER lakes detected by Fourier-transform ion cyclotron resonance mass spectrometry

Abstract
The composition of dissolved organic matter (DOM) varies widely in the environment due to distinct sources of the material and subsequent processing. DOM composition drives its reactivity in terms of many processes including photochemical reactions, microbial metabolism, and carbon cycling within water bodies. This study uses ultra-high resolution mass spectrometry via a Fourier-transform ion cyclotron resonance mass spectrometer (FT-ICR MS) to evaluate DOM composition at the molecular level to determine differences in DOM composition among the NTL-LTER lakes. Whole water samples were collected from the surface of each lake near the shore on August 18th and 19th in 2016 in. Ultraviolet-visible spectra were recorded as light absorbance can also give information about DOM composition. Additionally, concentrations of anions, cations, and pH were measured waters because these can all alter DOM reactivity in the environment. Both water chemistry and DOM composition vary widely among the lakes with the bogs displaying the most terrestrial-like signature in DOM and the oligotrophic lakes show more microbial-like or environmentally processed DOM.
Core Areas
Dataset ID
378
Date Range
-
Maintenance
comleted
Methods
Molecular Composition

Water was acidified to pH = 2 with concentrated hydrochloric acid and organic matter was extracted from the water using Agilent PPL cartridges. Extracts were diluted 100x in 50:50 acetonitrile to ultra-pure water and directly injected into a Bruker SolarX 12T Fourier-transform ion cyclotron resonance mass spectrometer. Ionization was achieved with electrospray ionization by an Advian NanoMate delivery system in both positive and negative mode.

Version Number
2

Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011

Abstract
High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.
Dataset ID
360
Date Range
-
Methods
Data were collected at 5 minute intervals using in-situ automated sensors (sondes). All measurements and samples were collected from a stationary raft over the deepest part of the lake.
Sondes were suspended from floats with probes at a depth of 0.75m below the surface. Sonde sensors were cleaned daily in the field and calibrated monthly following manufacturer guidelines. Peter and Paul lakes were each monitored with two YSI multiparameter sondes (model 6600 V2-4) fitted with optical DO (model 6150), pH (model 6561), optical Chl-a (model 6025), and conductivity-temperature (model 6560) probes. Sensor measurements were made at 0.75 m every 5 min and were calibrated weekly. PAR was measured and the UNDERC meteorology station maintained by the University of Notre Dame or by the North Temperate Lakes Weather Station at Woodruff Airport.
Outliers were replaced by NA. Occasional gaps in the record due to instrument cleaning are NA.
Version Number
1

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

Abstract
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.
Contact
Dataset ID
343
Date Range
-
Maintenance
complete
Methods
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.
Version Number
15

Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets

Abstract
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).
Dataset ID
337
Date Range
-
Maintenance
completed
Methods
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.
References
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.
Version Number
14

Saint Louis River Estuary Water Chemistry, Wisconsin, Minnesota, USA 2012 - 2013

Abstract
These data pertain to water and sediments collected from the Saint Louis River Estuary (SLRE) and its nearby water sources by Luke Loken and collaborators for his Masters thesis and additional publications. In brief, we sampled SLRE surface waters and sediments for a variety of physical, chemical, and biological attributes. Ten estuary stations were sampled approximately monthly from April 2012 through September 2013. On four of the sampling campaigns, water was collected from an additional 20 sites. Sites were selected to represent a gradient from the Saint Louis River to Lake Superior and included several tributaries that drain directly into the estuary. This design aimed to understand the spatial and temporal mixing pattern of the estuary as it receives water from several rivers, 2 waste water treatment plant, and Lake Superior. We sampled the estuary to assess the magnitude and timing of source water contributions to the estuary and establish a baseline of chemical and physical measurements to aid in future limnological research. Additionally, we performed nitrogen and carbon cycling rate experiments to determine the estuary-wide influence on nitrate, ammonium, and dissolved organic carbon. This included 8 sediment denitrification, 1 nitrification, and 2 breakdown dissolved organic carbon (BDOD) surveys. This work was funded by the Minnesota and Wisconsin Sea Grant and in coordination with the establishment of the Lake Superior National Estuary Research Reserve (LSNERR).
Contact
Dataset ID
322
Date Range
-
DOI
10.6073/pasta/08fdc0fb8528e37dd7ef6d6ad2b77f99
Maintenance
completed
Metadata Provider
Methods
We collected water samples from 10 estuary stations to represent a gradient from river to lake on 13 dates between April 2012 and September 2013. Stations 1-5 represented upper estuary sites, while stations 6-10 were lower. Stations were situated near the thalweg, but were shifted laterally to avoid traffic within the shipping channel. Sampling occurred approximately monthly during the open water season when sites were accessed by boat, and once during winter ice cover when a subset of sites were visited on foot. In addition to the core 10 stations, we sampled an additional 20 sites, four times over the two-year study during a high flow and baseflow period. These sites include 7 end members (Saint Louis River, Nemadji River, Bluff Creek, Kinsbury Creek, Pokegama River, and Lake Superior) and an additional 15 in-estuary sites (i.e., stations 16-30). Additional sites were occasionally visited and geographic locations to all stations are provided in SLRESitesTable.Physical LimnologyWe used a YSI EXO2 or 6-Series sonde (Yellow Springs, OH) to measure temperature, specific conductivity, dissolved oxygen, pH, turbidity, and algae fluorescence. Briefly, the sonde was lowered to appr. 0.5 m depth and allowed to stabilize. The sonde was calibrated in the lab that morning according to Lake Superior National Estuary Research Reserve (LSNERR) protocols.Light extinction was determined by lowering a photosynthetically active radiation (PAR) sensor (Licor model 192 or 193) attached to a light meter (Licor model 250A) through the water column. The sensor was allowed to stabilize at 0.25 m depth intervals. We linearly regressed the natural log of the measured light intensity against depth. The slope of this regression is the negative light extinction coefficient (k). Briefly k values closer to zero indicate clearer waters that transit more light.Water ChemistrySurface water from each station was collected into an HDPE carboy and processed in the lab within 10 h of collection. We processed samples in the lab (instead of on the boat) to expedite sample collection so that all stations could be visited within a single day (or within 2 days for spatial intensive surveys). Integrated water samples were taken from 0-2 m using a peristaltic pump or an integrated water sampler and stored in a cooler to maintain ambient temperature. Samples for dissolved solute analysis were filtered through a 0.45 microm Geotech capsule filter. All samples were refrigerated, frozen, or acidified (dependent on the analysis in question) within 12 h of collection. See meta data for SLREWaterChemTable for specifics regarding lab responsible for analyses.Samples for major cations (Calcium (Ca), Iron (Fe), Potassium (K), Sodium (Na), Magnesium (Mg), and Manganese (Mn)) were filtered upon collection into 60 mL acid-washed HDPE bottles, acidified to 1 percent ultrapure hydrochloric acid (HCl) and stored at room temperature until analysis (within 6 months). Cations were analyzed simultaneously on an optical inductively-coupled plasma emission on a Perkin-Elmer model 4300 DV ICP spectrophotometer according to methods outlined at the North Temperate Lakes- Long Term Ecological Research site.Samples for major anions (Chloride (Cl) and sulfate (SO4)) were filtered into a new 20 mL HDPE scintillation vials and stored at 4degree C until analysis (within 3 months). Anion samples were analyzed simultaneously by Ion Chromatography, using a hydroxide eluent determined by a Dionex model ICS 2100 using an electro-chemical suppressor.Samples for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were analyzed on a Shimadzu TOC analyzer. DOC and DIC samples were filtered into acid-washed 24 mL glass vials and capped with septa, leaving no headspace. DOC samples were acidified with 100 microL of 2 M HCL upon collection. Both DOC and DIC were stored at 4 degreeC, and then analyzed within three weeks at the University of Minnesota-Twin Cities. Both DOC and DIC were collected in duplicate and reported as means.Samples for UV absorbance were filtered into ashed 40 mL glass amber vials and stored at 4degree C until analysis (within 2 months). We measured UV absorbance at 254 nm (Abs254) using a spectrophotometer (Cary 50 UV-Vis Spectrophotometer, Varian, Palo Alto, CA). Specific UV absorbance at 254 nm (SUVA254) was then calculated by dividing Abs254 by the DOC concentration of the water sample.Nitrate plus nitrite nitrogen (referred to as NO3-N), ammonium plus ammonia nitrogen (referred to as NH4-N), and soluble reactive phosphorus (SRP) were analyzed colormetrically. Samples were filtered into new 20 mL plastic scintillation vials and frozen within 8 h of collection. Samples were thawed within 4 months and were analyzed in parallel by automated colorimetric spectrophotometry, using an Astoria-Pacific Astoria II segmented flow autoanalyzer. NO3-N was determined using the automated cadmium reduction method with absorption monitored at lambda=520 nm. NH4-N was determined using the Berthelot Reaction, producing a blue colored indophenol compound, where the absorption was monitored at lambda=660 nm. SRP was determined by forming a phosphoantimonymoledbeun complex and was measured as lambda=880nm.Samples for total and dissolved nitrogen and phosphorus analysis were collected together and in-line filtered (dissolved nitrogen and phosphorus only) into 60 ml LDPE bottles and acidified to a 1 percent HCl. Once acidified, the samples were stored at room temperature until analysis, which occurred within one year. The samples were first prepared for analysis by adding a NaOH–Persulfate digestion reagent and heated for 1 h at 120 degreeC and 18-20 pounds per square inch (psi) in an autoclave. The samples were analyzed for total nitrogen and total phosphorus simultaneously by automated colorimetric spectrophotometry, using a segmented flow autoanalyzer. Total nitrogen is determined by utilizing the automated cadmium reduction method where the absorption is monitored at 520 nm; total phosphorus is determined using ascorbic acid-molybdate method where the absorption is monitored at 880 nm. Both are described in LTER standard methods.We determined dual isotopic natural abundance of nitrate (NO3) and water (H2O) from a subset of collected water samples. Samples for delta18O-NO3 and delta15N-NO3 were filtered into acid-washed 60 mL HDPE bottles and frozen within 8 h of collection. Nitrate isotope samples were analyzed using the denitrifier method at the Colorado Plateau Stable Isotope Laboratory. delta18O-NO3 and delta15N-NO3 isotopes were reported as the per mil (per-mille) deviation from Vienna Standard Mean Ocean Water (VSMOW) and air standards, respectively. Samples for isotopes of water (delta18O-H2O and delta2H-H2O) were collected without headspace in glass vials and measured using isotope ratio infrared spectroscopy at the University of Minnesota – Biometeorology lab. Six replicates were run per sample, and delta18O-H2O and delta2H-H2O were determined relative to VSMOW.Chlorophyll ALaboratory analysis of chlorophyll A (ChlA) uses the Turner Designs model 10-AU fluorometer, following improvements described in Welschmeyer (1994). In this method, ChlA in 90percent acetone is separated from other pigments by the use of specialized optical filters. ChlA samples were preserved within 24 h of water sampling, by collecting filtrand on a 0.2 microm cellulose nitrate filter, placing the filter in a 15 mL falcon tube, and freezing it. Between 200 and 1000 mL of sample was based through the filter until the filter was moderately stained and filtering speed slowed. Within three weeks of collection, filters were thawed, and 12.0 mL of acetone was added to tube, which was allowed to steep for 18-24 h in the dark at 4 degreeC. After steeping, samples were centrifuged at high speed in Sorvall GLC-2B centrifuge for 20 min and warmed to room temperature. Sample fluorescence was then measured on a calibrated Turner Designs model 10-AU fluorometer (excitation 436 nm, emission 680 nm). Sample fluorescence was then converted to a water column concentration by multiplying by the extract volume (i.e., 12 mL) and divided by the volume of water that passed through the filter (i.e., 200-1000 mL).ParticulatesSimilar to ChlA, particulate carbon, nitrogen, and phosphorus samples were collected by passing 200-1000 mL of water through a pre-combusted 0.7 glass fiber filter (GFF) and analyzing the filtrand. Filters were frozen immediately after filtration, and then dried at 60 degreeC for at least 48 hours. Particulate carbon and nitrogen was measured using a Thermo Fisher Flash 2000 elemental analyzer. Particulate phosphorus was determined from a separate filter. Filters were digested in 5 mL potassium persulfate and phosphorus was analyzed spectrophotometrically using the ascorbic acid-molybdate method (Menzel and Corwin 1965).NitrificationWater column nitrification rates were determined on 30 July 2013 for a subset of the water chemistry sampling stations (n = 15) that represented the full spatial extent and previously observed NH4-N range of the estuary. Water from each station was transferred to 333 mL polycarbonate bottles within 10 h of collection and spiked with 15NH4Cl to achieve a concentration of 0.03 micromol 15NH4 L-1. Samples were incubated at ambient temperature (20 degreeC ) in a dark cooler for 20 h. Pre- and post-incubation samples were filtered through 0.45 microm filters and analyzed for NO3-N, NH4-N and delta15N-NO3. Nitrification rates were determined based on changes in NO3-N, NH4-N, and delta15N-NO3 according to methods outlined in Small et al. (2013). Analysis for each station was performed in duplicate and reported as the mean.SedimentsSediments were collected on 8 of the water chemistry survey dates from stations 2-9 to determine spatial and temporal patterns of denitrification and sediment organic content. We also collected a single sediment sample from additional lower (n = 17) and upper (n = 6) stations on 19 June 2012 and 24 June 2013, respectively, to increase the spatial extent of our survey. In total, 56 and 42 individual sediment collections were made in 2012 and 2013, respectively. Sediments were collected from the upper 5-20 cm of the benthic zone using an Ekman dredge. At least 500 mL of benthic material was transferred to 1-L widemouth Nalgene containers and used in denitrification rate experiments. Fifteen mL of the uppermost sediment layer was transferred into sterile 100 mL disposable plastic screw-top containers to be analyzed for sediment composition content. Sediments were stored in a cooler while on the boat and transferred to 4 degreeC within 6 h.To assess the effects of sediment composition on denitrification, dry:wet ratios, bulk density, particle size distributions, loss-on-ignition (LOI), percent carbon, and percent nitrogen were determined from the 15 mL sediment subsamples. Sediments were weighed before and after drying at 60 degreeC for at least 48 h to determine dry:wet ratios and bulk density. Sediment particle size composition was determined optically using a Coulter LS-10 particle size analyzer and sizes were binned into percent clay (0-2.0 microm), silt (2.0-63.0 microm) and sand (63-2000 microm) (Scheldrick and Wang 1993). LOI was determined by the loss in mass of 2.0plus/-0.2 g dried homogenized sediment combusted at 550 degree Celsius for 4 h. Sediments were ground and analyzed for percent carbon and nitrogen using a Thermo Fisher Flash 2000 elemental analyzer.Sediment denitrificationWe determined actual (DeN) and potential (DEA) sediment denitrification rates in the laboratory using the acetylene block technique modified from Groffman et al. (1999) within 48 h of collection. We incubated 40±2 g of wet sediment saturated with 40±0.1 mL of estuary water in 125 mL glass Wheaton bottles at 20 degreeC. DEA incubations were spiked with glucose and NO3-N to a final concentration of 40 mg C L-1 and 100 mg N L-1, respectively; DeN incubations were given no amendments. All incubations were augmented with 10 mg L-1 chloramphenicol to inhibit microbial proliferation (Smith and Tiedje 1979). Samples were capped with rubber septa, flushed with helium (He) for 5 min to remove oxygen (O2), and injected with 10 mL acetylene. We allowed the acetylene 30 min to fully diffuse into the sediment slurry before taking the initial headspace sample (T0). Samples were placed on a shaker table in the dark for 2.6 h then sampled the final headspace (T1). The change in headspace N2O partial pressures (pN2Ofinal - pN2Oinitial) was used to determine the denitrification rate using the Bunsen correction and the ideal gas law. For both T0 and T1 samples, 10 mL of headspace was withdrawn from incubation bottles and injected into a He-flushed 12 mL gas-tight glass vials (Exetainers) sealed with rubber septa. We determined pN2O and pO2 in parallel on a gas chromatograph equipped with an electron capture detector (ECD) and thermal conductivity detector (TCD) using methods outlined in Spokas et al. (2005). Gas samples with O2 concentrations greater than 5percent were removed from analysis due to potential gas leakage. Denitrification rates were standardized to sediment dry mass. Samples collected on or before 6 June 2013 were incubated in triplicate; samples collected after were incubated in duplicate.Denitrification controls were further investigated by amending sediments with combinations of NO3-N and two types of organic carbon: glucose and natural organic matter (NOM; supplied by the International Humic Substance Society). On two dates in 2013, we incubated sediments from five of our core stations that spanned a gradient of sediment organic content with the following amendments: NO3-N only, NO3-N and glucose (DEA), NO3-N and NOM, glucose only, NOM only, and no amendments (DeN). The two carbon treatments were intended to test for possible effects of carbon quality, with NOM representing a recalcitrant, humic-rich carbon source similar to allochthonous materials in the SLRE to contrast the labile glucose treatment. Both carbon sources were amended to 40 mg C L-1, and NO3-N was amended to 100 mg N L-1. Sediments were incubated in parallel (see above).Breakdown Dissolved Organic Carbon (bDOC)Breakdown of DOC (bDOC) was determined from core stations (1-10) from water collected on 23 April and 30 July 2012. Briefly, 250 mL of estuary water was filtered through a 0.45 microM Geotech flow-through filter using a peristaltic pump into sealable glass jars. 25 mL of 2.0 microm filtered water from a common estuary source was added to the filtered jars. DOC samples were collected after 0, 1, 2, 4 ,8, 16, and 32 d and analyzed for DOC (see above). A linear model was fit between time since inoculation and DOC concentration to determine the breakdown of DOC from water column microbes.ReferencesMeyers PA, Teranes JL. 2001. Sediment organic matter. Pages 239-269, In: Track Enviornmental Change Using Lake Sediments Vol 2 Phys Geochemical Methods. Dordrecht: Kluwer Academic Publishers.Groffman, Peter M, Holland EA, Myrold DD, Robertson GP, Zou X. 1999. Denitirification. Pages 272-288 in Standand Soil Methods Long-Term Ecological Research, Oxford University, New York.Menzel DW, Corwin N. 1965. The measurement of total phosphorus in seawater based on the liberation of organically bound fractions by persulfate oxidation. Limnol and Oceanogr 10: 280–282.Scheldrick HB, Wang C. 1993. Particle size distribution. Pages 499-512 In: Soil Sampling and Methods of Analysis. Boca Raton: CRC Press LLC.Small GE, Bullerjahn GS, Sterner RW, Beall BFN, Brovold S, Finlay JC, McKay RML, Mukherjee M. 2013. Rates and controls of nitrification in a large oligotrophic lake. Limnol Oceanogr. 58:276–86.Smith MS, Tiedje JM (1979) Phases of denitrification following oxygen depletion in soil. Soil Biol Biochem 11:261-267Spokas K, Wang D, Venterea R. 2005. Greenhouse gas production and emission from a forest nursery soil following fumigation with chloropicrin and methyl isothiocyanate. Soil Biol Biochem. 37:475–85.Welschmeyer, N.A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol Oceanogr 39:1985-1992. 
Version Number
17

North Temperate Lakes LTER High Frequency Water Temperature Data, Dissolved Oxygen, Chlorophyll, pH - Crystal Lake 2011 - 2014

Abstract
Data from the instrumented buoy on Crystal Lake include micrometeorological parameters, relative humidity, air temperature, wind velocity, wind driection (2 m height),and water temperatures, pH, chlorophyll, and dissolved oxygen measured by a sonde that is moving through the water column.
Dataset ID
303
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Data from the instrumented buoy on Crystal Lake include micrometeorological parameters, relative humidity, air temperature, wind velocity, wind driection (2 m height),and water temperatures, pH, chlorophyll, and dissolved oxygen measured by a sonde that is moving through the water column.
Version Number
20

River Nutrient Uptake and Transport at North Temperate Lakes LTER (2005-2011)

Abstract
These data were collected by Stephen Michael Powers and collaborators for his Ph.d. research, documented in his dissertation: River Nutrient Uptake and Transport Across Extremes in Channel Form and Drainage Characteristics. A major goal of this research was to better understand how ecosystem form and landscape setting dictate aquatic biogeochemical functioning and elemental transport through rivers. To achieve this goal, major and minor ions were measured in both northern and southern Wisconsin streams located in a variety of land use settings. In total, 27 different streams were sampled at 104 different stations (multiple stations per system) from both groundwater and surface water sources. Organic and inorganic carbon and nitrogen pools were also measured in northern and southern Wisconsin streams. The streams that were sampled in northern Wisconsin flow through wetland ecosystems. In sampling such streams, the goal was to better understand how wetland ecosystems influence river nutrient deliveries. There is a large amount of stream chemistry data for Big Spring Creek, WI; where the influence of a small reservoir on solute transportation and transformation was studied in an agricultural watershed. All stream chemistry data is incorporated in a single data file, Water Chemistry 2005-2011. While the data is not included in the dissertation, a sediment core study was also done in the small reservoir and channel of Big Spring (BS) Creek, WI. The results of this study are featured in three data tables: BS Creek Sediment Core Analysis, BS Creek Sediment Core Chemistry, and BS Creek Longitudinal Profile. Finally, two data tables list the geospatial information of sampling sites for stream chemistry and sediment coring in Big Spring Creek. Documentation: Powers, S.M., 2012. River nutrient uptake and transport across extremes in channel form and drainage characteristics. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 140.
Dataset ID
281
Date Range
-
Metadata Provider
Methods
I. Stream chemistry sample collection methods: core-sediment core was taken from the benthic zone of the streamgeopump-geopump used to pump stream water into collection bottlegrab-collection bottle filled with stream water by hand and filtered in the fieldgrabfilter- stream water collected by hand and filtered in field. Unfiltered and filtered samples placed in separate collection bottles.isco- sample collected by use of an ISCO automated samplerpoint- sampled collected by method outlined in patent US8337121sedimentgrab- sediment sample taken in field by hand and placed in collection bottlesyringe- sample collected from stream by syringe and placed in collection bottlesyringe_filter- sample collected from stream by syringe filter. Unfiltered and filtered samples placed in separate collection bottles. II. Stream chemistry analytical methods: All water samples were kept on ice and in the dark following collection, then were either acidified (TN/TP, TDN/TDP) or frozen until analysis (all other analytes).no32_2- This is NO<sub>3-</sub>N which is operationally defined as nitrate nitrogen + nitrite nitrogen. Determined by flow injection analysis on Astoria Pacific Instruments Autoanalyzer (APIA).nh4_n, tn1, tp1, tdn, tdp- All analytes measured by flow injection analysis on Astoria Pacific Instruments Autoanalyzer (APIA).srp- measured colorometrically using the molybdate blue method [APHA 1995] and a Beckman spectrophotometer.doc- measured using a Shimadzu carbon analyzer.doc_qual- the goal in doing this analysis is to determine the source of dissolved organic carbon (doc) measured in a particular riverine ecosystem. This was achieved by UV absorbance which provides an estimate of the aromaticity of the doc in a sample, and by extension, the potential source of the doc.cl, no2, no3, br, and so4- all measured by ion chromatography. See http://www.nemi.gov; method number 4110C. Detection limits for method number 4110C: cl-20&micro;g/l, no2-15&micro;g/l, no3-17&micro;g/l, br-75&micro;g/l, and so4-75&micro;g/l.ysi_cond, do, ph_field, wtemp- all measured by use of a standard YSI meter.tss- measured by standard methods. A thoroughly mixed sample is filtered and dried at 103-105 degreesCelcius. The obtained residue represents the amount of solids suspended in the sample solution. See http://www.nemi.giv; method number D5907.tot_om- measured by standard methods. The residue obtained from the tss procedure is ignited at 550 degreesCelcius and weighed, the difference in weight representing total volatile solids. Total volatile solids represents the portion of the residue that is composed of organic molecules. See http://www.nemi.gov; method number 160.4.turbid- measured by use of a nephelometer. III. Big Spring Sediment Coring Methods A. Field Methods- collecting sediment coresSediment core samples taken with WDNR piston core samplerB. Sediment Analysis- HydrometerDocumentation: Robertson, G.P., Coleman, D.C., Bledsoe, C.S. and Sollins, P., 1999. Standard Soil Methods for Long-Term Ecological Research. Oxford University Press, New York, 462 pp.Hydrometer Analysis- procedure used to determine percent clay:<p style="margin-left:.25in;">1. Dry the sample in a pre-weighed aluminum pan for at least 24 hr at 105 C. Make sure sample is completely dry before weighing.<p style="margin-left:.25in;">2. Weigh the dried sample, then ash for at least 8 hr at 550 C. Make sure to break up any large clumps before ashing.<p style="margin-left:.25in;">3. Weigh the ashed sample, then crush any aggregates with a pestal. Mix sample thoroughly.<p style="margin-left:.25in;">4. Transfer 40g, plus or minus one gram, of the sample into a 500mL wide mouth bottle<p style="margin-left:.25in;">5. Add 10g of sodium hexametaphosphate to the bottle.<p style="margin-left:.25in;">6. Add approx 200mL of deionized water to bottle. Shake vigorously with hand.<p style="margin-left:.25in;">7. Stir samples on shaker table for at least 8 hr at speed 40. Putting them in a box and fastening with bungee cords works best.<p style="margin-left:.25in;">8. Transfer sample to 1L cylinder, making sure to get all of sample out of bottle. Fill cylinder with deionized water up to the 1L mark.<p style="margin-left:.25in;">9. Prepare a blank cylinder by adding 10g of sodium hexametaphosphate and filling to 1L.<p style="margin-left:.25in;">10. Allow all cylinders to equilibrate to room temperature ( approx 30 min).<p style="margin-left:.25in;">11. Starting with the blank cylinder, put stopper into cylinder and shake end-over-end for approx 5 min. Rinse stopper. Repeat this step for all cylinders, rinsing stopper between cylinders.<p style="margin-left:.25in;">12. Record the time that you stopped shaking each cylinder.<p style="margin-left:.25in;">13. At 1.5 hr from time of shaking, record temperature and hydrometer level of the blank cylinder. Then record the 1.5 hr hydrometer level for each successive cylinder.<p style="margin-left:.25in;">14. At 24 hr from time of shaking, record temperature and hydrometer level of the blank cylinder. Then record the 24 hr hydrometer level for each successive cylinder. Sieve Analysis- procedure used to determine quantity of sand and silt<p style="margin-left:.25in;">1. After hydrometer analysis, pour the entire sample into the .063mm sieve. Rinse the sample thoroughly until all the clay is out. Try to break up any clay clumps you see.<p style="margin-left:.25in;">2. Transfer the sample to a pre-weighed and labeled aluminum pan. You will probably need to backwash the sieve to get the entire sample out. You can use a syringe to pull water from the pan if it gets too full. Dry the sample for 48 hours at 50-60C.<p style="margin-left:.25in;">3. Before transferring the dried sample to the sieves, make sure you pre-weigh the sieves and put their weight on the data sheet. You will need to do this before every sample as you might not get all the sample out of the sieves from the previous sample. Stack the sieves in the following order, top to bottom : 4mm, 2mm, 1mm, 0.5mm, 0.25mm, 0.125mm, 0.063mm, and pan. Pour the sample into the top sieve. Place the lid on, located on sieve shaker, and put the stack of sieves into the sieve shaker. Fasten the tie downs. Set shaker for 3 minutes. <p style="margin-left:.25in;">4. Remove stack of sieves from shaker. It&rsquo;s ok to leave the pan behind temporarily as it might be tight. Weigh each sieve and record the weight in the data sheet. If you see any clay clumps, break them up with your fingers and re-shake the stack a little, using hands is okay.<p style="margin-left:.25in;">5. Dump the sample out in the trash and clean the sieve with the brush. At the end of the day it might be necessary to backwash the sieves with water and dry overnight in the oven. <p style="margin-left:.25in;"> Calculations:1. percent clay was determined by the hydrometer analysis- P1.5, P24, X1.5, X24, and m are the variables that were calculated to determine percent clay by the hydrometer analysis.P1.5= ((sample hydrometer reading at 1.5 hours- blank hydrometer reading at 1.5 hours)/ (sample weight)) multiplied by 100.P24= ((sample hydrometer reading at 24 hours- blank hydrometer reading at 24 hours)/ (sample weight)) multiplied by 100X1.5= 1000*(.00019*(-.164* (sample hydrometer reading at 1.5 hours)+16.3)<sup>2</sup> *8100X24=1000*(.00019*(-.164* (sample hydrometer reading at 24 hours)+16.3)<sup>2</sup> *8100m= (P1.5-P24)/(ln(X1.5/X24))percent clay = m * ln(2/X24)) + P24clay (grams) = total weight * ( percent clay/ 100)2. percent Sand and percent Silt were determined based on the results of the sieve analysis which determined the grams of sand and silt.percent sand= total weight * (percent sand/ 100)percent silt= total weight * (percent silt/ 100)3. Othersorganic matter (grams) was calculated in this analysis as dry weight (grams) &ndash; ashed weight (grams)percwnt organic matter was calculated as ((organic matter (grams))/(total dry weight (grams)) multiplied by 100 C. Sediment Chemical Analysis1. SRP/ NaOH-PChemical analysis was done according to the protocol outlined in Pionke and Kunishi (1992). Each sample was first centrifuged and separated into aqueous and sediment fractions. The sediment fraction was then dried. The aqueous fraction was analyzed for soluble reactive phosphorus (srp) by automated colorimetry Nemi Method Number 365.4; see http://www.nemi.gov. NaOH P was then determined by NaOH extractions as described in Pionke and Kunishi (1992). Documentation: Pionke HB, Kunishi HM (1992) Phosphorus status and content of suspended sediment in a Pennsylvania watershed. Soil Sci 153:452&ndash;462.2. NH4 / KCl-NH4 The exact procedure that was used to analyze samples for ammonium is unknown. However, it is known that a KCl extraction was used. The KCl-NH4 was calculated as the concentration of ammonium in milliGramsPerLiter divided by the sediment weight in grams. 3. NO3 / KCl-NO3The exact procedure that was used to analyze samples for nitrate is also unknown. Again, it is known that a KCL extraction was used. The KCl-NO3 was calculated as the concentration of nitrate in milliGramsPerLiter divided by the sediment weight in grams.Note: The same sediment sample was used to measure ammonium and nitrate IV. Big Spring Creek Longitudinal Profile A standard longitudinal stream profile was conducted at Big Spring Creek, WI (wbic=176400) on unknown date(s). It is speculated that the profile was done during the summer of 2005, during which the rest of the data for Big Spring Creek was collected. Measurements for the profile began at the Big Spring Dam site (43.67035,-89.64225), a dam which was subsequently removed. The first (x_dist, y_dist) of (2.296, 5.57) corresponds to the location where the stream crosses Golden Court Road, whereas the second coordinate pair of (-2.615, -36.303) corresponds to the point below the previous Big Spring Creek Dam site. The third (x_dist, y_dist) of (-9.472, 7.681) corresponds to the top of the dam gates and is assigned a distance=0 as it is the starting point.
Version Number
23

Fluxes project at North Temperate Lakes LTER: Random lake survey 2004

Abstract
The overarching goal of this project is to understand carbon and nutrient cycles for a landscape on which terrestrial and freshwater systems are intimately connected in multiple and reciprocal ways. In the Northern Highlands region of Wisconsin, they are studying a spatially complex landscape in which water features make up almost half of the land area, with wetlands (27% of land surface) and lakes (13%) both prevalent throughout the region, interspersed in upland forests.Weather and limnological data from a set of 170 lakes in the NHLD samples summer 2004. The sampled lakes were from a random stratified subsample (N=300 of 7588 total) of all the lakes in the NHLD.
Contact
Core Areas
Dataset ID
277
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Hanson PC, Carpenter S, Cardille JA, Coe MT, Winslow LA. 2007. Small lakes dominate a random sample of regional lake characteristics. Freshwater Biology. 52:814-22Lakes were selected from unique Water Body Identification Codes (WBICs). Linear features and water bodies identified as impoundments or stream openings were identified from maps digitised by the Departments of Natural Resources of Michigan and Wisconsin (1 : 24 000 USGS 7.5&rsquo; topographic quadrangles) and were excluded. More than 7500 lakes ranging in size from about 0.01 to over 2800 ha remained in the data set. We used a stratified random survey, an approach consistent with the Environmental Monitoring and Assessment Program (EMAP) guidelines (Larsen et al., 1994) of the U.S. Environmental Protection Agency, to select and sample 300 lakes from the data set as follows. All lakes were ordered by area and divided into 20 bins of equal population. From each bin, 15 lakes were chosen at random. Because of logistical issues in travelling to many lakes scattered over a wide geographical region, we clustered lakes into 31 geographically small regions of about 150 km2 each. The order of regions sampled was randomised to reduce correlation of geographic region with time. For any one sampling date we visited only one region, although not all lakes in a region could be visited on a single trip. After all 31 regions were visited, the regions were again selected at random, and lakes previously not visited were sampled. There were 45 sampling days spread between May 20 and August 19. Some lakes that were chosen for sampling could not be visited. Difficulty portaging the sampling gear to a lake or failure to gain access to a lake through private property were reasons for abandoning a sampling effort.Lakes were sampled at their approximate geographic centre. Lake depth and water clarity were measured with a Secchi disk. Our measurement of lake depth was neither a measurement of the maximum nor the mean depth. Because the measurement was made in the middle of the lake and most lakes in the region tend to be bowl shaped, our measurement was probably between mean and maximum depth. Dissolved oxygen (DO) and thermal profiles were obtained from a YSI Model 58 (YSI, Inc., Yellow Springs, OH, U.S.A.) metre (DO air calibrated; temperature calibrated in the laboratory), and the approximate middle of the epilimnion was estimated from the profile. Thermal stratification was calculated from the thermal profile according to the methods listed on the Internet at the North Temperate Lakes Long Term Ecological Research (NTL-LTER) program Web site (http://lter.limnology.wisc.edu). Water samples for later analyses (Table 1, chemical variables) were obtained from the middle of the epilimnion, using a peristaltic pump. For samples that required filtration [dissolved inorganic carbon (DIC), DOC, cations and anions], a 0.45 μm filter was attached in-line. All samples were refrigerated upon returning to the vehicle, and samples for total nitrogen (TN) and total phosphorus (TP) were preserved by acidification. Acid neutralizing capacity (ANC) and pH were determined the day of sampling by Gran alkalinity titration (for ANC) and measurement by pH probe (Accumet 950; Fisher Scientific, Hanover Park, IL U.S.A.). pH was not air equilibrated. DIC and DOC were measured with a carbon analyzer (TOC-V; Shimadzu Scientific Instruments, Columbia, MD, U.S.A.). TN and TP were measured with a segmented flow auto-analyzer (Astoria-Pacific, Inc., Clackamas, OR, U.S.A.). Anions were measured using an ion chromatograph (DX500; Dionex Corporation, Sunnyvale, CA, U.S.A.), and cations using mass spectrometry (ICP-MS; PerkinElmer Life and Analytical Sciences, Shelton, CT, U.S.A.). Details of chemical analyses are available on the Internet at the NTL-LTER Web site listed above.To correct for bias introduced by not sampling all 300 lakes, we replaced missing data using multiple imputation (Levy, 1999). Multiple imputation is a technique for estimating the uncertainty of imputed variables. For each variable for each lake not sampled in a given bin, we chose at random (with replacement) a value from lakes sampled in that bin. We repeated the imputation 1000 times to provide a distribution of estimates for each variable in the lakes not sampled. The distribution mean for each variable in each lake was used in the calculation of the median for the regional lake population. We chose to present the median for the 300 lakes because distributions tended to be highly skewed. For comparison purposes, we also calculated the median from sampled lakes only (i.e. excluding imputed data). The mean cumulative distributions for some variables, including 95% confidence intervals, were plotted from the 1000 cumulative distributions generated by multiple imputation.We fit a Pareto distribution to the regional lake area data set to compare the size distribution of NHLD lakes with those of other regions. We used the maximum likelihood estimator for parameter estimates (Bernardo &amp; Smith, 2000). Of particular interest is the parameter (β) that describes the logarithmic decline in number of lakes with lake area, because this parameter has been used previously (Downing et al., 2006, Table 1) to compare lake area distributions among regions and to estimate the global abundance of lakes.Where indicated, results have been area weighted to reflect the influence of lake size. For correlations, data were transformed (log10) to normalise distributions and linearise relationships. Shoreline development factor (SDF), an index of the irregular shape of lakes, was calculated for each lake according to Kalff (2002). The minimum SDF, 1, indicates a lake is a perfect circle.
NTL Keyword
Version Number
25

Trout Lake USGS Water, Energy, and Biogeochemical Budgets (WEBB) Stream Data 1975-current

Abstract
This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.
Creator
Dataset ID
276
Date Range
-
Maintenance
Completed.
Metadata Provider
Methods
DL is used to represent “detection limit” where known.NOTE (1): Each method listed below corresponds with a USGS Parameter Code, which is listed after the variable name. NOTE (2): If the NEMI method # is known, it is also specified at the end of each method description.NOTE (3): Some of the variables are calculated using algorithms within QWDATA. If this is the case see Appendix D of the NWIS User’s Manual for additional information. However, appendix D does not list the algorithm used by the USGS. If a variable is calculated with an algorithm the term: algor, will be listed after the variable name.anc: 99431, Alkalinity is determined in the field by using the gran function plot methods, see TWRI Book 9 Chapter A6.1. anc_1: 90410 and 00410, Alkalinity is determined by titrating the water sample with a standard solution of a strong acid. The end point of the titration is selected as pH 4.5. See USGS TWRI 5-A1/1989, p 57, NEMI method #: I-2030-89.2. c13_c12_ratio: 82081, Exact method unknown. The following method is suspected: Automated dual inlet isotope ratio analysis with sample preparation by precipitation with ammoniacal strontium chloride solution, filtration, purification, acidified of strontium carbonate; sample size is greater than 25 micromoles of carbon; one-sigma uncertainty is approximately ± 0.1 ‰. See USGS Determination of the delta13 C of Dissolved Inorganic Carbon in Water, RSIL Lab Code 1710. Chapter 18 of Section C, Stable Isotope-Ratio Methods Book 10, Methods of the Reston Stable Isotope Laboratory.3. ca, mg, mn, na, and sr all share the same method. The USGS parameter codes are listed first, then the method description with NEMI method #, and finally DL’s:ca- 00915, mg- 00925, mn- 01056, na- 00930, sr- 01080All metals are determined simultaneously on a single sample by a direct reading emission spectrometric method using an inductively coupled argon plasma as an excitation source. Samples are pumped into a crossflow pneumatic nebulizer, and introduced into the plasma through a spray chamber and torch assembly. Each analysis is determined on the basis of the average of three replicate integrations, each of which is background corrected by a spectrum shifting technique except for lithium (670.7 nm) and sodium (589.0 nm). A series of five mixed-element standards and a blank are used for calibration. Method requires an autosampler and emission spectrometry system. See USGS OF 93-125, p 101, NEMI Method #: I-1472-87.DL’s: ca- .02 mg/l, mg-.01 mg/l, mn-1.0 ug/l, na- .2 mg/l, sr- .5 ug/l4. cl, f, and so4 all share the same method. The USGS parameter codes are listed first, then the method description with NEMI method #, and finally DL’s:cl- 00940, f-00950, so4-00945All three anions (chloride, flouride, and sulfate) are separated chromatographically following a single sample injection on an ion exchange column. Ions are separated on the basis of their affinity for the exchange sites of the resin. The separated anions in their acid form are measured using an electrical conductivity cell. Anions are identified on the basis of their retention times compared with known standards. 19 The peak height or area is measured and compared with an analytical curve generated from known standards to quantify the results. See USGS OF 93-125, p 19, NEMI method #: I-2057.DL’s: cl-.2 mg/l, f-.1 mg/l, so4-.2 mg/lco2: 00405, algor, see NWIS User's Manual, QW System, Appendix D, Page 285.co3: 00445, algor.color: 00080, The color of the water is compared to that of the colored glass disks that have been calibrated to correspond to the platinum-cobalt scale of Hazen (1892), See USGS TWRI 5-A1 or1989, P.191, NEMI Method #: I-1250. DL: 1 Pt-Co colorconductance_field: 00094 and 00095, specific conductance is determined in the field using a standard YSI multimeter, See USGS TWRI 9, 6.3.3.A, P. 13, NEMI method #: NFM 6.3.3.A-SW.conductance_lab: 90095, specific conductance is determined by using a wheat and one bridge in which a variable resistance is adjusted so that it is equal to the resistance of the unknown solution between platinized electrodes of a standardized conductivity cell, sample at 25 degrees celcius, See USGS TWRI 5-A1/1989, p 461, NEMI method #: I-1780-85.dic: 00691, This test method can be used to make independent measurements of IC and TC and can also determine TOC as the difference of TC and IC. The basic steps of the procedure are as follows:(1) Removal of IC, if desired, by vacuum degassing;(2) Conversion of remaining inorganic carbon to CO<sub>2</sub> by action of acid in both channels and oxidation of total carbon to CO<sub>2</sub> by action of ultraviolet (UV) radiation in the TC channel. For further information, See ASTM Standards, NEMI method #: D6317. DL: n/adkn: 00623 and 99894, Organic nitrogen compounds are reduced to the ammonium ion by digestion with sulfuric acid in the presence of mercuric sulfate, which acts as a catalyst, and potassium sulfate. The ammonium ion produced by this digestion, as well as the ammonium ion originally present, is determined by reaction with sodium salicylate, sodium nitroprusside, and sodium hypochlorite in an alkaline medium. The resulting color is directly proportional to the concentration of ammonia present, see USGS TWRI 5-A1/1989, p 327, NEMI method #: 351.2. DL: .10 mg/Ldo: 0300, Dissolved oxygen is measured in the field with a standard YSI multimeter, NEMI Method #: NFM 6.2.1-Lum. DL: 1 mg/L.doc: 00681, The sample is acidified, purged to remove carbonates and bicarbonates, and the organic carbon is oxidized to carbon dioxide with persulfate, in the presence of an ultraviolet light. The carbon dioxide is measured by nondispersive infrared spectrometry, see USGS OF 92-480, NEMI Method #: O-1122-92. DL: .10 mg/L.don: 00607, algor, see NWIS User's Manual, QW System, Appendix D, page 291.dp: 00666 and 99893, All forms of phosphorus, including organic phosphorus, are converted to orthophosphate ions using reagents and reaction parameters identical to those used in the block digester procedure for determination of organic nitrogen plus ammonia, that is, sulfuric acid, potassium sulfate, and mercury (II) at a temperature of 370 deg, see USGS OF Report 92-146, or USGS TWRI 5-A1/1979, p 453, NEMI method #: I-2610-91. DL= .012 mg/L.fe: 01046, Iron is determined by atomic absorption spectrometry by direct aspiration of the sample solution into an air-acetylene flame, see USGS TWRI 5-A1/1985, NEMI method #: I-1381. DL= 10µg/L.h_ion: 00191, algor.h20_hardness: 00900, algor.h20_hardness_2: 00902, algor.hco3: 00440, algor.k: 00935, Potassium is determined by atomic absorption spectrometry by direct aspiration of the sample solution into an air-acetylene flame , see USGS TWRI 5-A1/1989, p 393, NEMI method #: I-1630-85. DL= .01 mg/L.n_mixed: 00600, algor.n_mixed_1: 00602, algor.n_mixed_2: 71887, algor.nh3_nh4: 00608, Ammonia reacts with salicylate and hypochlorite ions in the presence of ferricyanide ions to form the salicylic acid analog of indophenol blue (Reardon and others, 1966; Patton and Crouch, 1977; Harfmann and Crouch, 1989). The resulting color is directly proportional to the concentration of ammonia present, See USGS OF 93-125, p 125/1986 (mg/l as N), NEMI Method #: I-2525. DL= .01 mg/L.nh3_nh4_1: 71846, algor.nh3_nh4_2: 00610, same method as 00608, except see USGS TWRI 5-A1/1989, p 321. DL = .01 mg/L.nh3_nh4_3: 71845, algor.no2: 00613, Nitrite ion reacts with sulfanilamide under acidic conditions to form a diazo compound which then couples with N-1-naphthylethylenediamine dihydrochloride to form a red compound, the absorbance of which is measured colorimetrically, see USGS TWRI 5-A1/1989, p 343, NEMI method #: I-2540-90. DL= .01 mg/L.no2_2: 71856, algor.no3: 00618, Nitrate is determined sequentially with six other anions by ion-exchange chromatography, see USGS TWRI 5-A1/1989, P. 339, NEMI method #: I-2057. DL= .05 mg/L.no3_2: 71851, algor.no32: 00630, An acidified sodium chloride extraction procedure is used to extract nitrate and nitrite from samples of bottom material for this determination(Jackson, 1958). Nitrate is reduced to nitrite by cadmium metal. Imidazole is used to buffer the analytical stream. The sample stream then is treated with sulfanilamide to yield a diazo compound, which couples with N-lnaphthylethylenediamine dihydrochloride to form an azo dye, the absorbance of which is measured colorimetrically. Procedure is used to extract nitrate and nitrite from bottom material for this determination (Jackson, 1958), see USGS TWRI 5-A1/1989, p 351. DL= .1 mg/Lno32_2: 00631, same as description for no32, except see USGS OF 93-125, p 157. DL= .1 mg/L.o18_o16_ratio: 82085, Sample preparation by equilibration with carbon dioxide and automated analysis; sample size is 0.1 to 2.0 milliliters of water. For 2-mL samples, the 2-sigma uncertainties of oxygen isotopic measurement results are 0.2 ‰. This means that if the same sample were resubmitted for isotopic analysis, the newly measured value would lie within the uncertainty bounds 95 percent of the time. Water is extracted from soils and plants by distillation with toluene; recommended sample size is 1-5 ml water per analysis, see USGS Determination of the Determination of the delta (18 O or 16O) of Water, RSIL Lab Code 489.o2sat: Dissolved oxygen is measured in the field with a standard YSI multimeter, which also measures % oxygen saturation, NEMI Method #: NFM 6.2.1-Lum.ph_field: 00400, pH determined in situ, using a standard YSI multimeter, see USGS Techniques of Water-Resources Investigations, book 9, Chaps. A1-A9, Chap. A6.4 "pH," NEMI method # NFM 6.4.3.A-SW. DL= .01 pH.ph_lab: 00403, involves use of laboratory pH meter, see USGS TWRI 5-A1/1989, p 363, NEMI method #: I-1586.po4: 00660, algor, see NWIS User's Manual, QW System, Appendix D, Page 286.po4_2: 00671, see USGS TWRI 5-A1/1989, NEMI method #: I-2602. DL= .01 mg/L.s: 63719, cannot determine exact method used. USGS method code: 7704-34-9 is typically used to measure sulfur as a percentage, with an DL =.01 µg/L. It is known that the units for sulfur measurements in this data set are micrograms per liter.sar: 00931, algor, see NWIS User's Manual, QW System, Appendix D, Page 288.si: 00955, Silica reacts with molybdate reagent in acid media to form a yellow silicomolybdate complex. This complex is reduced by ascorbic acid to form the molybdate blue color. The silicomolybdate complex may form either as an alpha or beta polymorph or as a mixture of both. Because the two polymorphic forms have absorbance maxima at different wavelengths, the pH of the mixture is kept below 2.5, a condition that favors formation of the beta polymorph (Govett, 1961; Mullen and Riley, 1955; Strickland, 1952), see USGS TWRI 5-A1/1989, p 417, NEMI method #: I-2700-85. DL= .10 mg/L.spc: 00932, algor, see NWIS User's Manual, QW System, Appendix D, Page 289.tds: 70300 and 70301, A well-mixed sample is filtered through a standard glass fiber filter. The filtrate is evaporated and dried to constant weight at 180 deg C, see " Filterable Residue by Drying Oven," NEMI method #: 160.1, DL= 10 mg/l. Note: despite DL values occur in the data set that are less than 10 mg/l.tds_1: 70301, algor, see NWIS User's Manual, QW System, Appendix D, Page 289.tds_2: 70303, algor, see NWIS User's Manual, QW System, Appendix D, Page 290.tkn: 00625 and 99892, Block digester procedure for determination of organic nitrogen plus ammonia, that is, sulfuric acid, potassium sulfate, and Mercury (II) at a temperature of 370°C. See the USGS Open File Report 92-146 for further details. DL: .10 mg/L.toc: 00680, The sample is acidified, purged to remove carbonates and bicarbonates, and the organic carbon is oxidized to carbon dioxide with persulfate, in the presence of an ultraviolet light. The carbon dioxide is measured by nondispersive infrared spectrometry, see USGS TWRI 5-A3/1987, p 15, NEMI Method #: O-1122-92. DL=.10 mg/L.ton: 00605, algor, See NWIS User's Manual, QW System, Appendix D, page 286.tp: 00665 and 99891, This method may be used to analyze most water, wastewater, brines, and water-suspended sediment containing from 0.01 to 1.0 mg/L of phosphorus. Samples containing greater concentrations need to be diluted, see USGS TWRI 5-A1/1989, p 367, NEMI method #: I-4607. tp_2: 71886, algor.tpc: 00694, The basic steps of this test method are:1) Conversion of remaining IC to CO2 by action of acid, 2) Removal of IC, if desired, by vacuum degassing, 3) Split of flow into two streams to provide for separate IC and TC measurements, 4) Oxidation of TC to CO2 by action of acid-persulfate aided by ultraviolet (UV) radiation in the TC channel, 5) Detection of CO2 by passing each liquid stream over membranes that allow the specific passage of CO2 to high-purity water where change in conductivity is measured, and 6) Conversion of the conductivity detector signal to a display of carbon concentration in parts per million (ppm = mg/L) or parts per billion (ppb = ug/L). The IC channel reading is subtracted from the TC channel reading to give a TOC reading, see ASTM Standards, NEMI Method #: D5997. DL= .06 µg/L.tpn: 49570, A weighed amount of dried particulate (from water) or sediment is combusted at a high temperature using an elemental analyzer. The combustion products are passed over a copper reduction tube to covert nitrogen oxides to molecular nitrogen. Carbon dioxide, nitrogen, and water vapor are mixed at a known volume, temperature, and pressure. The concentrations of nitrogen and carbon are determined using a series of thermal conductivity detectors/traps, measuring in turn by difference hydrogen (as water vapor), carbon (as carbon dioxide), and nitrogen (as molecular nitrogen). Procedures also are provided to differentiate between organic and inorganic carbon, if desired, see USEPA Method 440, NEMI method #: 440. DL= .01 mg/L.
Short Name
TL-USGS-WEBB Data
Version Number
15
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