US Long-Term Ecological Research Network

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

Abstract
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
completed
Contact
Dataset ID
348
Date Range
-
Methods
This study used the R packages “GLMr” (https://github.com/GLEON/GLMr) and “glmtools” (https://github.com/USGS-R/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.

Version Number
13

North Temperate Lakes LTER Processed eddy covariance time series fluxes from tower located on roof of the CFL building oriented toward Lake Mendota 2012 - current

Abstract
We calculated eddy covariance based fluxes of CO2, H2O, heat, and momentum to study lake-atmosphere exchanges since 2012. These data were collected by Ankur Desai from 2012 to present using a CSAT-3 sonic anemometer and LI-7500 gas analyzer located on the roof of the CFL building. A footprint model (Kljun) was used to screen for lake only data.
Contact
Creator
Dataset ID
347
Data Sources
Date Range
-
Methods
Sonic anemometer: Campbell Scientific, Inc. CSAT-3
Gas analyzer: Licor, Inc. LI-7500
We merged data from the CFL Lake Mendota David buoy for air temperature and water temperature (1st level), and also the AOSS rooftop RIG tower for incoming solar radiation. These data were used in the analysis presented in Reed et al (2017) based on gap-filling conducted with REddyProc.
Methodology: Reed, D.R., Dugan, H., Flannery, A., and Desai, A.R., 2017. The carbon sink and source see-saw of a eutrophic deep lake Limnology and Oceanography Letters, #LOL2-17-0040, submitted.
Version Number
5

Lake Mendota Carbon and Greenhouse Gas Measurements at North Temperate Lakes LTER 2016

Abstract
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.
Core Areas
Dataset ID
339
Date Range
-
Methods
lake_weekly_carbon_ghg.csv
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.
Version Number
19

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

Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output

Abstract
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information. Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156.
Core Areas
Dataset ID
286
Date Range
-
Metadata Provider
Methods
The spatially explicit Lakes, Uplands, Wetlands Integrator (LUWI) model of the NHLD was used to explore the interactions among climate, watershed connections, hydrology and carbon cycling. See Cardille et al. 2007 and Cardille et al. 2009 for details on the LUWI model. See Van de Bogert (2011) for a discussion of how these model outputs are used in conjunction with LUWI to predict the effects on lake carbon cycling under the current and "no freeze" climate scenarios.The climate data used in this modeling effort, precipitation and temperature, were obtained from Minoqua, Wisconsin, USA from 1948-2000. In order to test the effect of a climate without freezing temperatures on lake water and carbon cycling the current climate was modeled in addition to a “no freeze” scenario where a minimum air temperature of 0 degrees Celsius was imposed on the model. Note that Van de Bogert (2011) only focuses on the current and “no freeze” climate scenarios, but these data are representative of four climate scenarios: the current climate (base_minoqua_precip), the scenario where the current climate is artificially floored to zero degrees Celsius (no_below_zero), and the scenarios where the current climate is increased and decreased by 10 degrees Celsius (minus_10_degrees and plus_10_degrees).Furthermore, the temperature and precipitation data that was used for the current climate model runs was broken up into aggregates.The aggregates are the length of the 1948-2000 Minoqua temperature and precipitation data that was used in model runs. A total of seven different aggregates were used for model runs under each of the four climate scenarios. The aggregates include temperature and precipitation data from Minoqua, WI, USA for 1. the complete record from 1948-2000 (1948_2000) 2. the driest year which was 1976 (1976_driest) 3. The wettest year which was 1953 (1953_wettest) 4. the five driest years on record from 1948-2000 (5_driest) 5. the five wettest years on record from 1948-2000 (5_wettest) 6. the five coldest years on record for December, January, and February from 1948-2000 (5_coldest_djf) 7. the five warmest years on record for December, January, and February from 1948-2000 (5_warmest_djf).The volume and timing of precipitation to the region were unchanged between scenarios.Evaporation rates were derived from values obtained from the NTL-LTER study site, Sparkling Lake (46.01, -89.70). Refer to Van de Bogert (2011) for a more complete discussion of model inputs and a discussion of the results of the model output. Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156.Cardille, J.A., Carpenter, S.R., Coe, M.T., Foley, J.A., Hanson, P.C., Turner, M.G., Vano, J.A., 2007. Carbon and water cycling in lake-rich landscapes: Landscape connections, lake hydrology, and biogeochemistry. Journal of Geophysical Research-Biogeosciences 112.Cardille, J.A., Carpenter, S.R., Foley, J.A., Hanson, P.C., Turner, M.G., Vano, J.A., 2009. Climate change and lakes: Estimating sensitivities of water and carbon budgets. Journal of Geophysical Research-Biogeosciences 114.
Version Number
20

Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007

Abstract
Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape."; The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation :Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Core Areas
Dataset ID
285
Date Range
-
Metadata Provider
Methods
Data were collected from two lakes, Sparkling Lake (46.008, -89.701) and Peter Lake (46.253, -89.504), both located in the northern highlands Lake District of Wisconsin and the Upper Peninsula of Michigan over a 10 day period on each lake in July and August of 2007. Refer to Van de Bogert et al. 2011 for limnological characteristics of the study lakes.Measurements of dissolved oxygen and temperature were made every 10 minutes using multiple sondes dispersed horizontally throughout the mixed-layer in the two lakes (n=35 sondes for Sparkling Lake and n=27 sondes for Peter Lake). Dissolved oxygen measurements were corrected for drift.Conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were also measured by a subset of sensors in each lake. Of the 35 sondes in Sparkling Lake, 31 were from YSI Incorporated: 15 of model 600XLM, 14 of model 6920, and 2 of model 6600). The remaining sondes placed in Sparkling Lake were 4 D-Opto sensors, Zebra-Tech, LTD. In Peter Lake, 14 YSI model 6920 and 13 YSI model 600XLM sondes were used.Sampling locations were stratified randomly so that a variety of water depths were represented, however, a higher density of sensors were placed in the littoral rather than pelagic zone. See Van de Bogert et al. 2012 for the thermal (stratification) profile of Sparkling Lake and Peter Lake during the period of observation, and for details on how locations were classified as littoral or pelagic. In Sparkling Lake, 11 sensors were placed within the shallowest zone, 12 in the off-shore littoral, and 6 in each of the remaining two zones, for a total of 23 littoral and 12 pelagic sensors. Similarly, 15 sensors were placed in the two littoral zones, and 12 sensors in the pelagic zone.Sensors were randomly assigned locations within each of the zones using rasterized bathymetric maps of the lakes and a random number generator in Matlab. Within each lake, one pelagic sensor was placed at the deep hole which is used for routine-long term sampling.Note that in Sparkling Lake this corresponds to the location of the long-term monitoring buoy. After locations were determined, sensors were randomly assigned to each location with the exception of the four D-Opto sensor is Sparkling Lake, which are a part of larger monitoring buoys used in the NTL-LTER program. One of these was located near the deep hole of the lake while the other three were assigned to random locations along the north shore, south shore and pelagic regions of the lake. Documentation: Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Version Number
17

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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