US Long-Term Ecological Research Network

WSC - The Influence of Legacy P on Lake Water Quality

Abstract
Using a suite of numerical models, we investigated the influence of legacy P on water quality in the Yahara Watershed of southern Wisconsin, USA. The suite included Agro-IBIS, a terrestrial ecosystem model; THMB, a hydrologic and nutrient routing model; and the Yahara Water Quality Model which estimates water quality indicators in the Yahara chain of lakes. Using five alternative scenarios of antecedent P storage (legacy P) in soils and channels under historical climate conditions, we simulated outcomes of P yield from the landscape, lake P loading, and three lake water quality indicators. Data and code may also be found in https://github.com/SRCarpen/Yahara2070LakeModel_Fits2data.git
Core Areas
Dataset ID
384
Date Range
-
Methods
We developed a watershed modeling framework that can simulate an array of ecosystem services, including land-to-lake flows of water, sediment, and nutrients, and surface water quality (Figure 1). The framework includes process-based representation of natural and managed ecosystems (Agro- IBIS), hydrologic routing of water, sediment, and nutrients through the surface hydrologic network (THMB), and prediction of Secchi disk depth (a measure of lake transparency), summertime lake total phosphorus (TP) concentration, and the probability of hypereutrophy in each lake (Yahara Water Quality Model).
for detailed description of modeling approach please see:
Motew, M., Chen, X., Booth, E.G. et al. The Influence of Legacy P on Lake Water Quality in a Midwestern Agricultural Watershed. Ecosystems 20, 1468–1482 (2017). https://doi.org/10.1007/s10021-017-0125-0
Version Number
1

North Temperate Lakes LTER Regional Survey Water Chemistry 2015 - current

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

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

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

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

Version Number
2

Cascade Project at North Temperate Lakes LTER Phosphorus, Chlorophyll, DOC, Color, and pH for Twenty UNDERC Lakes 1995 - 2003

Abstract
Data on total phosphorous, chlorophyll a, dissolved organic carbon, water color, and pH for a set of lakes located at the University of Notre Dame Environmental Research Center (UNDERC). Surface water samples were collected monthly from May through August either from shore with a telescoping pole or from a boat. Twenty lakes were sampled from 1995-2000. Fifteen of these lakes were sampled from 2001-2003.
Contact
Dataset ID
361
Date Range
-
Methods
Methods are described in Pace and Cole 2002 (https://doi.org/10.4319/lo.2002.47.2.0333). Surface water samples for the analysis of pH, dissolved organic carbon (DOC), chlorophyll a , total phosphorus color were collected by dipping a sample bottle. The total phosphorus (TP) samples were stored in a separate acid-washed bottle. Samples were collected monthly from May through August from a set of 20 lakes for the years 1995-2000. A subset of fifteen lakes were sampled in the same way from 2001-2003. Samples were stored in a cooler and returned the lab for processing within a few hours.
Version Number
3

Cascade Project at North Temperate Lakes LTER Core Data Nutrients 1991 - 2016

Abstract
Physical and chemical variables are measured at one central station near the deepest point of each lake. In most cases these measurements are made in the morning (0800 to 0900). Vertical profiles are taken at varied depth intervals. Chemical measurements are sometimes made in a pooled mixed layer sample (PML); sometimes in the epilimnion, metalimnion, and hypolimnion; and sometimes in vertical profiles. In the latter case, depths for sampling usually correspond to the surface plus depths of 50percent, 25percent, 10percent, 5percent and 1percent of surface irradiance. The 1991-1999 chemistry data was obtained from the Lachat auto-analyzer. Like the process data, there are up to seven samples per sampling date due to Van Dorn collections across a depth interval according to percent irradiance. Voichick and LeBouton (1994) describe the autoanalyzer procedures in detail. Nutrient samples were sent to the Cary Institute of Ecosystem Studies for analysis beginning in 2000. The Kjeldahl method for measuring nitrogen is not used at IES, and so measurements reported from 2000 onwards are Total Nitrogen.
Core Areas
Dataset ID
351
Date Range
-
Methods
Methods for 1984-1990 were described by Carpenter and Kitchell (1993) and methods for 1991-1997 were described by Carpenter et al. (2001).
Version Number
14

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

LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States

Abstract
Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix iws) and HUC 4 (variable prefix hu4) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. (2015).
Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.
Dataset ID
333
Date Range
-
Methods
See Oliver et al. (submitted) and Soranno et al. (2015) for details on sources of data, methods of collection, and derivation of parameters
Oliver S.K., Collins S.M., Soranno P.A., Wagner T., Stanley E.H., Jones J.R., Stow C.A., Lottig N.R. Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990. Submitted to Global Change Biology.
Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4 .
NTL Themes
Version Number
15

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

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

A Global database of methane concentrations and atmospheric fluxes for streams and rivers

Abstract
This dataset, referred to as MethDB, is a collation of publicly available values of methane (CH4) concentrations and atmospheric fluxes for world streams and rivers, along with supporting information on location, geographic, physical, and chemical conditions of the study sites. The data set is composed of four linked tables, corresponding to the data sources (Papers_MethDB), the study sites (Sites_MethDB), concentrations (Concentrations_MethDB), and influx/efflux rates (Fluxes_MethDB). Information was extracted from journal articles, government reports, book chapters, and similar sources that were acquired before 15 September 2015. Concentrations and fluxes were converted to a standard unit (micromoles per liter for concentration and millimoles per square meter per day for flux) and both the author-reported and converted data are included in the database. MethDB was assembled as part of a larger synthesis effort on stream and river CH4 dynamics, and assembled data were used to identify large-scale patterns and potential drivers of fluvial CH4 and to generate an updated global-scale estimate of CH4 emissions from world rivers.
Dataset ID
321
Date Range
-
DOI
10.6073/pasta/21f5bd6642e9689baf90262f3c85ac4a
Metadata Provider
Methods
CH4 data from streams and rivers are widely scattered, as values are often included as end-member in studies focused on other processes or types of ecosystems. Thus, while we sought to be as complete as possible in compiling existing data, some sources have undoubtedly been overlooked. Sources included journal articles, book chapters, dissertations, USGS open file reports, meeting proceedings, and unpublished results provided by individual investigators. Data incorporated into MethDB were strictly limited to surface waters of rivers and streams; values for groundwater, porewater, saturated soils, lakes, reservoirs, wetlands, estuaries, and floodplains were not included. Some papers were excluded because essential supporting information was missing (e.g., units), or extracting data from complex graphs was considered to be unwise. Data sources are listed in the Notes and Comments section below.
Version Number
5375866

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

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