US Long-Term Ecological Research Network

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

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

North Temperate Lakes LTER Soil Temperature - Woodruff Airport 2006 - current

Abstract
Soil temperature data are being gathered at a site at the Noble F. Lee municipal airport located at Woodruff, WI. Soil temperature is measured at depths of 0.05m, 0.1m and 0.5m at 1-minute intervals. High resolution data are collected (typically at 10 minute intervals) along with 1-hour and 24-hour averages. Daily minimum and maximum soil temperatures and the times these occur are reported for these same depths. Data are automatically updated into the database every six hours. Prior to August 2006, only hourly averaged data are available. Starting in 2008, soil temperatures are only available from 0.5m depth. Sampling frequency: varies for instantaneous samples; averaged to hourly and daily values from one minute samples. Number of sites: 1.
Dataset ID
133
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
See abstract for methods.
Short Name
NTLSOIL1
Version Number
32

North Temperate Lakes LTER: Patterns of Soil Phosphorus - Y Plot Analysis 2001

Abstract
In natural soils, patterns of variance are generated by driving forces such as parent materials, climate, hydrology, relief, disturbance and biological activity. These drivers, operating at particular scales and interacting with other drivers across scales, create a complex pattern of soil variability. Human activity may change the natural patterns of variance by changing the scale at which the governing processes are operating or the governing processes that are dominant at a given scale. In the case of soils and phosphorus (P) concentrations, this may involve changing dominant forces from plant-soil interactions and parent material to fertilizer inputs. Here, we examine the hypothesis that human activity changes natural patterns of variance in soil P concentrations across several spatial scales. We measured soil P concentrations and variability at 3 distinct levels of analysis - among sites, within a field, and within a 10-m diameter plot - and across 4 management regimes - remnant prairie, lawns, cash grain farms, and dairies. Variance changed across scale in any one management regime and across management regimes at the same scale. Rescaling the pattern of P accumulation and variability has implications for managing P runoff from uplands. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 30
Core Areas
Dataset ID
103
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
Each core was analyzed for extractable phosphorusconcentration (Bray-1 method) at the University ofWisconsin Soil and Plant Analysis Lab. Bray-1, ameasure of extractable P, is a commonly usedmeasure of plant availability of phosphorus inagricultural systems. Although these extractionswere generally developed to estimate plant availableP and not to reflect P storage in the soil or P runoff, arelationship between extractable soil P concentrationsand dissolved P runoff has been noted in somesystems (Sharpley et al. 1993; Sharpley 1995).Bennett EM, Carpenter S, Clayton MK. 2004. Soil phosphorus variability: scale-dependency in an urbanizing agricultural landscape. Landscape Ecology. 20:389-400
Short Name
SOILPY
Version Number
4

North Temperate Lakes LTER: Patterns of Soil Phosphorus Across an Urbanizing Agricultural Landscape 2000 - 2001

Abstract
Understanding the magnitude and location of soil phosphorus (P) accumulation in watersheds is a critical step toward managing runoff of this pollutant to aquatic ecosystems. Here, we examined the usefulness of urban-rural gradients (URGs), an emerging paradigm in urban ecology, for predicting soil P concentrations across a rapidly urbanizing agricultural watershed in southern Wisconsin. We compared several measures of an urban-rural gradient to predictors of soil P such as soil type, slope, topography, land use, land cover, and fertilizer and manure use. Most of the factors that were expected to drive differences in soil P concentrations were not found to be good predictors of soil P; while there were several significant relationships, most explained only a small proportion of the variation. There was a significant relationship between soil P concentration and each of the urban-rural gradients, but these relationships explained only a small amount of the variation in soil P concentrations. Soil P concentration, unlike some other ecosystem properties, is not well predicted by urban-rural gradients Additional Chemical Analyses: These additional analyses were done to provide comparisons to Bray-1 P. Specifically, we wanted to know whether, in Dane County, there was a consistent relationship between total P and Bray-1 P. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 334; 20 of these sites with additional chem analyses
Core Areas
Dataset ID
105
Date Range
-
Maintenance
completed
Metadata Provider
Methods
A combination map, consisting of the information in both the population density map and the modified-distance map, was also created (Figure 2c). This map is simply based on a grid cell by grid cell multiplication of the reclassified values from the population density and the modified distance map. In this paper, I will refer to this map as the combination map.Data points for measuring soil P and associated factors were stratified by zone and randomly located within each zone according to the combination map&mdash;with approximately 70 data points per zone. Location and address of each point were determined using the Madison and Dane County parcel GIS layers. Permission was requested from landowners to take a soil sample, and the precise location of the sample on the property was determined using standard randomizing techniques. If permission was denied (2 cases out of 330) or if there was no one present at the location, a coin toss was used to determine movement one parcel to the right or to the left along the same road. Landowners were also asked about their fertilizer use practices, manure use, dog ownership, and the date the house was built, if known.Approximately 400 soil samples were taken in the top soil horizon to a depth of 13.5 cm with a standard soil corer (diameter = approximately 1.6 cm). This depth was always within the surface horizon and any grass thatch was removed from lawn samples. Other data collected include percent slope, convex or concave nature of the slope, land use, land-cover type, and percent vegetative cover. The visually perceived zone was also recorded. The visually perceived zone was determined by visual inspection using a predetermined set of definitions of each zone. For example, urban sites were those with the highest housing density or some industrial use; suburban sites were those of moderate housing density and residential character; suburban fringe were newer residential developments of low housing density and larger houses; agricultural fringe were older residential developments of low density; and agricultural were those areas that were actively farmed. A handheld global positioning device was used to determine the precise (&plusmn; 1 m) location of the soil sample.Soil samples were stored for no more than 3 weeks at room temperature before analysis. They were then dried for 15&ndash;24 hours at 50&ndash;55degreeC and sieved (1.8-mm mesh). Soil samples were then analyzed for Bray-1 P at the University of Wisconsin Soil and Plant Analysis Lab. Bray-1, a measure of extractable P, is a commonly used measure of phosphorus available to plants in agricultural systems. While relationships between extractable soil P and dissolved P in runoff have been noted in some systems (Sharpley and others 1993, Sharpley 1995), these extractions were generally developed to estimate plant available P, not to reflect P storage in the soil or P runoff. Therefore, we tested a subset (60) of our samples for total P, a better measure of P storage in soils. A regression of our samples indicates a reasonably close relationship between Bray-1 P and total P in our study area soils (Figure 3), indicating that our Bray-1 P results are probably a satisfactory estimate of both extractable P and the sorbed P that tends to accumulate in agricultural soils.
Short Name
SOILPVC
Version Number
4
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