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

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

North Temperate Lakes LTER Bythotrephes longimanus spiny water flea population monitoring in Wisconsin and Minnesota 2009 - 2014

Abstract
Three data tables are included describing population dynamics for Bythotrephes longimanus, spiny water flea, in Southern Wisconsin during invasion. General monitor took place in Lake Mendota, Lake Monona, Lake Waubesa, Lake Kegonsa, Stormy Lake, Gile Flowage, Lake Gogebic.Accompanying Bythotrephes morphological measurements from Lake Mendota monitoring efforts in 2011 and 2012. Included are individual measurements of body morphology and reproductive status for ~2,500 <em>Bythotrephes </em>collected from Lake Mendota in 2011 and 2012.Sediment cores from Lake Mendota were analyzed for spiny water flea evidence with age of sediment estimated.
Contact
Core Areas
Dataset ID
342
Date Range
-
Maintenance
complete
Methods
general monitoring for spiny water flea:
The dataset contains collected Bythotrephes longimanus monitoring efforts from 8 invaded lakes in Wisconsin that took place over the course of 2009 through 2014 using a zooplankton net. Monitoring efforts were conducted to 1) obtain more accurate estimates of Bythotrephes densities using a more appropriately sized net (50-cm diameter over 30-cm diameter) and 2) obtain detailed demographic measurements of Bythotrephes morphology and reproduction in each lake. Here only Bythotrephes densities are included.
The majority of samples occurred at a lakes deep hole with a 50-cm diameter and 150-micron mesh zooplankton net. Nets are lowered to 2 m off of the lake bottom before being towed to the surface. Samples are processed in their entirety
Exceptions to this are those at sites containing “LTER” (e.g., site IDs LTER-DH and LTER-MB) in their ID which were samples taken according to the Southern Lakes LTER zooplankton collection protocol with a 30-cm and 83-micron mesh. Other exceptions include sites outside the deep hole of the lake (site ID 5m = 5m lake depth north of the Center for Limnology on Lake Mendota; CFL = 15m lake depth north of the Center for Limnology; DH = deep hole but specific to Lake Mendota; MB = 15m lake depth southwest of Maple Bluffs in Madison on Lake Mendota; MO.5m = a 5m lake depth site in Lake Monona; MO.Y = 5m lake depth site at the mouth of the Yahara River on Lake Monona; TL = 15m lake depth west of Tenney Locks in Madison on Lake Mendota; WS = 15m site in northwestern basin of Lake Mendota, east of Picnic Point; WP = 5m site south of Warner Park on Lake Mendota). Several tows were taken using a 200m oblique (i.e., horizontal) net tow with the 50-cm diameter net (DH-ObliqueTow). Efforts in Southern Wisconsin were led by Jake Walsh while efforts in Northern Wisconsin were led by Carol Warden (site ID = CW), Pam Montz (site ID = PM), Sam Christel (site ID = SC), Sam Oliver (site ID = SM), as well as a researcher with initials (site ID) “EM”.
Version Number
8

North Temperate Lakes LTER Long-term winter chemical limnology and days since ice-on for primary study lakes 1983 - 2014

Abstract
This data set integrates long-term data sets on winter nutrient chemistry with ice phenology (number of days since ice-on), focusing on the subset of measurements taken during ice cover. Parameters characterizing limnology of 5 primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, are measured at one station in the deepest part of each lake at the surface, middle, and deep (~1 meter above bottom). These parameters include nitrate-N, ammonium-N, total dissolved phosphorus, dissolved inorganic carbon, water temperature, dissolved oxygen, and pH. Water temperature and dissolved oxygen values are the zonal averages from more complete depth profiles. Sampling Frequency: every 6 weeks during ice-covered season for the northern lakes. Number of sites: 5
Dataset ID
341
Date Range
-
Maintenance
completed
Methods
This is a compilation of three data sets

North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Nutrients, pH and Carbon 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-chemical-limnology-primary-study-lakes-nutrients-ph-and-carbon-19

North Temperate Lakes LTER: Physical Limnology of Primary Study Lakes 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-physical-limnology-primary-study-lakes-1981-current

North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-ice-duration-trout-lake-area-1981-current
Version Number
6

North Temperate Lakes LTER Estimated winter inputs of stream water and groundwater to primary study lakes 1983 - 2014

Abstract
This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day.Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate-Change_Effects_on_Streamflow_Lake_Water_Budgets_and_Stream_Temperature_Using_GSFLOW_and_SNTEMP_Trout_Lake_Watershed_WisconsinHunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.
Dataset ID
340
Data Sources
Date Range
-
LTER Keywords
Methods
This dataset is a compilations of:
USGS hydrologic model estimates (Hunt et al. in preparation)
and
North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-ice-duration-trout-lake-area-1981-current
Version Number
7

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
20

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

Satellite derived secchi disk depth and other lake and landscape characteristics in Wisconsin, USA, 1991 - 2012

Abstract
This data supports the following publication: Rose, K.C., S.R. Greb, M. Diebel, and M.G. Turner. Annual precipitation as a regulator of spatial and temporal drivers of lake water clarity. Ecological Applications. The data uses satellite remotely sensed estimates of Secchi disk depth (Landsat imagery), landscape features, and lake characteristics to understand how and why lakes vary and respond to different drivers through time and space. The data were produced by the authors and their collaborators, as acknowledged in the manuscript. The Secchi disk depth data span the time period 1991-2012.
Contact
Dataset ID
331
Date Range
-
Methods
The complete methods for this manuscript are described in the manuscript: Rose, K.C., S.R. Greb, M. Diebel, and M.G. Turner. Annual precipitation as a regulator of spatial and temporal drivers of lake water clarity. Ecological Applications.
NTL Keyword
Version Number
16

LTREB Terrestrial data 2012 - current

Abstract
Pitfall traps measure the activity density of ground-dwelling arthropod communities;Infall traps measure the deposition of flying insectsSticky Cards measure the amount of flying insects with distance from the shoreline.Emergence traps measure the quantity of emerging chironomids
Contact
Creator
Dataset ID
318
Date Range
-
LTER Keywords
DOI
10.6073/pasta/250c9264e33a177efe88b0d0e93ceab8
Metadata Provider
Methods
Pitfall trapsSETUP1. Locate hole and rock from pitfall in previous years near infall rebar2. If necessary remove dirt with trowel or widen hole with bread knife such that 12oz sample cup fits snugly inside with rim even with ground3. With fresh cup, fill with approximately two inches of 50percent ethylene glycol (as above)4. Add a drop of fragrance-free dish soap and swirl5. Place cup in hole6. Break two bamboo skewers in half7. Arrange skewers around cup to support deli lid 0.5 inch from the ground to keep out rainwater8. Place small rock on lid to secure (dont use soil, as it can make counting samples dirty work)FIELD COLLECTION1. Remove rock and lid2. Remove cup and pour through a sieve into another cup (keeping sample in the center of sieve)3. Invert sieve over plastic sample cup and spray into cup with 70percent ethanol squeeze bottle4. Add label and secure lid tightly5. Pour ethylene glycol back into pitfall cup and reset with lid and rock6. Add ethylene glycol until the pitfall container is 1/4 fullCOUTING AND PROCESSING1. Pour sample into clear plastic counting tray with grid2. Count taxa, referring to terrestrial arthropod guide Only Araneae, Opiliones, Trombidiidae, and Coleoptera are counted in pitfalls3. Record counts on pitfall data sheet4. Transfer sample to 20mL scintillation vial using plastic funnel and pipette with mesh tip5. Place the label into the vial6. Record sample information on lid with sharpie7. Archive sample
Version Number
21
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