Methods

Dissolved gas samples were collected at 0.5, 3, 5 and 7 m using the
headspace method. From January to March 2020, water at each discrete depth
was pumped directly into the bottom of a 1-L Nalgene bottle and flushed with
at least three times the volume before being capped with a rubber stopper.
60 mL of ambient air was added while 60 mL of sample water was removed from
the bottle and equilibrated by shaking for 90 seconds. From May 2020
onwards, water was pumped into a closed bottle system, and using syringe,
105mL of water was extracted and 35mL of ambient air was added. The
headspace was then equilibrated for 2 minutes by shaking and 10 mL of
equilibrated gas sample was then removed from the bottle and injected into a
5.9 mL Labco Exetainer vial that had been previously vacuumed. While in the
field, samples were stored in pouches within a survival suit to prevent
extreme temperature change. We analyzed the gas samples for CO2 and CH4 with
a gas chromatograph (GC-2014; Shimadzu Scientific Instruments) equipped with
a methanizer and flame ionization detector. Greenhouse gas concentrations
were calculated according to Henry’s law and corrected by measured ambient
air.<br/>

Methods

Detailed field and laboratory protocols can be found in the Cascade Methods Manual, found here: https://cascade.limnology.wisc.edu/public/public_files/methods/CascadeManual1998.pdf
POC, PON and DOC: 1. 100 - 300 ml (Typically ~200mL for PML, 150 metalimnion and 75 – 100 for the hypolimnion) of lake water from each depth was filtered through 153 um mesh to remove large zooplankton. Water was then filtered through a precombusted 25mm GF/F filter (0.7 um pore size) at less than 200 mm Hg pressure. Filters were placed in drying oven at 60 C to dry for at least 48 hours. 20mL of filtered water was stored in a scintillation vial and acidified with 200uL of 2N H2SO4 for DOC analysis. Blank samples for POC and DOC were prepared with deionized water to control for contamination. All samples were sent to the Cary Institute of Ecosystem Studies for analysis.

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.

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.

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.

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.

Methods

Sampling DesignSampling of gas partial pressures, and gas transfer velocities was performed weekly at five stream sites (Mann Creek, Allequash Lower Creek, Allequash Middle, Stevenson Creek, North Creek) that drain into Trout Lake (Figure 1), one of the regions larger lakes. Sampling began in May 2012 and continued through September 2012. These data were used to establish variability in gas transfer rates for the basin, and to investigate spatiotemporal patterns. To test for the effects of upstream lakes, sampling at 30 additional longitudinal transect sites along 6 streams (5 sites per stream; Figure 1) was conducted approximately every 3 weeks beginning in May 2012. Transect streams were chosen based on the presence or absence of lakes in the upstream watershed. Streams with lakes (Lost, White Sand, Aurora) were sampled starting at the approximate lake outlet (site selection based on aerial photographs), and along a 2000 m transect (0m, 250m, 500m, 1000m, 2000m). Upstream lake chemistry (epilimnion) was also sampled during late July or early August 2012 at the lake center to allow for a direct comparison with streams. Streams without upstream lakes (Stella, Mud, and North) were sampled at an arbitrary upstream location (0m) and followed the same sampling progression as streams with lakes. We analyzed stream chemistry and stream morphology data from a regional stream survey (Lottig and others 2011) which we use to scale fluxes to the NHLD (Figure 1). We also studied groundwater CO2 and CH4 patterns along a hillslope transect at Allequash Creek during 2001. In 2002, we monitored hourly CO2 and O2 dynamics at the four WEBB sites to assess the role of ecosystem metabolism.

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 &quot;no freeze&quot; 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 &ldquo;no freeze&rdquo; 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 &ldquo;no freeze&rdquo; 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.

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.

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_3-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)² *8100X24=1000*(.00019*(-.164* (sample hydrometer reading at 24 hours)+16.3)² *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.