US Long-Term Ecological Research Network

North Temperate Lakes LTER Regional Survey water temperature DO 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.
Water temperature and dissolved oxygen profiles were taken on sampling days.
Contact
Dataset ID
382
Date Range
-
Maintenance
ongoing
Methods
water temperature and dissolved oxygen were measured at 1 meter intervals with a opto sonde
Version Number
1

Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011

Abstract
High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.
Dataset ID
360
Date Range
-
Methods
Data were collected at 5 minute intervals using in-situ automated sensors (sondes). All measurements and samples were collected from a stationary raft over the deepest part of the lake.
Sondes were suspended from floats with probes at a depth of 0.75m below the surface. Sonde sensors were cleaned daily in the field and calibrated monthly following manufacturer guidelines. Peter and Paul lakes were each monitored with two YSI multiparameter sondes (model 6600 V2-4) fitted with optical DO (model 6150), pH (model 6561), optical Chl-a (model 6025), and conductivity-temperature (model 6560) probes. Sensor measurements were made at 0.75 m every 5 min and were calibrated weekly. PAR was measured and the UNDERC meteorology station maintained by the University of Notre Dame or by the North Temperate Lakes Weather Station at Woodruff Airport.
Outliers were replaced by NA. Occasional gaps in the record due to instrument cleaning are NA.
Version Number
1

Cascade Project at North Temperate Lakes LTER Core Data Physical and Chemical Limnology 1984 - 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.
Dataset ID
352
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

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

Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment

Abstract
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Contact
Dataset ID
326
Date Range
-
Maintenance
completed
Methods
NLA data were obtained from US Environmental Protection Agency National Aquatic Resource Surveys website (https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys). We incorporated the NLAs definition of human-made lakes, lakes that did not exist prior to European settlement and resulted from impoundment, as reservoirs in our analysis. From this database, we identified geographically co-located lake and reservoir pairs. Pairs were defined as lakes and reservoirs within a 50 km radius of one another. We developed pairings using the near proximity analysis tool for Geographic Information Systems (ArcGIS 10.1). If more than one lake was found within 50 km of a reservoir, the closest lake was chosen for the analysis. We identified 66 lake-reservoir pairs and for each lake or reservoir, we consolidated its catchment and water body attributes from the NLA data onto our data spreadsheet.
Laboratory and field methods for the NLA data are reported by the US Environmental Protection Agency (https://www.epa.gov/national-aquatic-resource-surveys/national-lakes-assessment-2007-results). We used the NLA data directly to collect basic geographic and morphometric parameters (elevation, catchment area, lake area, lake perimeter, maximum depth) and physical parameters (Secchi disk depth, turbidity, chlorophyll, surface water temperature, bottom water temperature). Mean residence times were provided by Renée Brooks (pers. communication) and were estimated using stable isotopes of hydrogen and oxygen as described in Brooks et al. (2014). If more than one parameter was collected for a site, then the average among the values was used in the analysis. From these parameters, we also calculated the ratio of catchment area to surface area (CA:SA) and depth-corrected difference in temperature between the surface and bottom waters.
Version Number
11

WSC - Temperature and relative humidity data from 150 locations in and around Madison, Wisconsin from 2012 -2017

Abstract
To study the urban heat island and other local climatic processes in Madison, Wisconsin, in March 2012, 135 HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields (Onset Computing) were attached to streetlight and utility poles in and around Madison, Wisconsin. Additional locations were added in 2012 and 2013 for a total of 150 locations. The sensors were installed at a height of 3.5 meters, and they automatically record instantaneous temperature and relative humidity every 15 minutes. This dataset includes all temperature/humidity measurements and a separate file with the coordinates of each measurement location.
Dataset ID
324
Date Range
-
Metadata Provider
Methods
Temperature and relative humidity were recorded using Onset HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields, which were attached to streetlight and utility poles in and around Madison, Wisconsin. The sensors were installed at a height of 3.5 meters, were oriented north, and automatically recorded instantaneous temperature and relative humidity every 15 minutes beginning in March 2012.
Version Number
20

WSC - Hourly meteorological data for Wibu field site, 2012-2013

Abstract
Hourly measurements of incoming shortwave radiation, air temperature, relative humidity, precipitation, and wind speed for the Wibu field site. This is a site-specific synthesis of local monitoring equipment and the Arlington Automated Weather Observing Network station operated by UW Extension
Core Areas
Dataset ID
315
Data Sources
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Except for the dates/times below, data is from the Arlington Automated Weather Observing Network station operated by UW Extension.- Precipitation measured on-site from 5/17/2013 18:00 to 11/30/2013 23:00 using an Onset HOBO RG-3 tipping bucket rain gauge mounted on a post off the south edge of the field at site WIBU-9, elevation 1.5 m.- Temperature & relative humidity measured on-site from 5/4/2012 13:00 to 12/31/2013 23:00 using an Onset HOBO Pro v2 temp/RH sensor mounted at an elevation of 3.5 m on the north side of a telephone pole on the west edge of the field.
Version Number
15

North Temperate Lakes LTER: High Frequency Meteorological and Dissolved Oxygen Data - Sparkling Lake UCSD buoy 2013

Abstract
During the summer of 2013 an additional buoy with wind, pressure, temperature and precipitation sensors was located on Sparkling Lake.
Contact
Dataset ID
304
Date Range
-
Maintenance
completed
Metadata Provider
Methods
wind, pressure, temperature and precipitation sensors on buoy.
Version Number
16

North Temperate Lakes LTER High Frequency Water Temperature Data, Dissolved Oxygen, Chlorophyll, pH - Crystal Lake 2011 - 2014

Abstract
Data from the instrumented buoy on Crystal Lake include micrometeorological parameters, relative humidity, air temperature, wind velocity, wind driection (2 m height),and water temperatures, pH, chlorophyll, and dissolved oxygen measured by a sonde that is moving through the water column.
Dataset ID
303
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Data from the instrumented buoy on Crystal Lake include micrometeorological parameters, relative humidity, air temperature, wind velocity, wind driection (2 m height),and water temperatures, pH, chlorophyll, and dissolved oxygen measured by a sonde that is moving through the water column.
Version Number
20

Additional Daily Meteorological Data for Madison Wisconsin (1884-2010)

Abstract
These data are in addition to "Madison Wisconsin Daily Meteorological Data 1869-current." Additional variables added include: daily cloud cover, wind, solar radiation, vapor pressure, dew point temperature, total atmospheric pressure, and average relative humidity for Madison, Wisconsin. In addition, the adjustment factors which were applied on a given date to calculate the adjusted parameters in "Madison Wisconsin Daily Meteorological Data 1869-current" are also included in these data. Raw data, in English units, were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field, center of field, 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-2005 were obtained from Sullivan at http://www.weather.gov/climate/index.php?wfo=mkx%20 ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Relative humidity data was obtained from 1986 to 1995 from CD's at the State Climatologist's Office. Since Robertson (1989) adjusted all historical data to that collected prior to 1989; no adjustments were applied to the recent data except for wind and estimated vapor pressure. Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yi-Fang "Yvonne" Hsieh, see methods. Estimated vapor pressure after April 2002 was updated by Yvonne Hsieh, see methods.
Dataset ID
282
Date Range
-
Metadata Provider
Methods
Raw data (in English units) were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field (Center of Field), 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-05 were obtained from Sullivan at <a href="http://www.weather.gov/climate/index.php?wfo=mkx%20">http://www.weather.gov/climate/index.php?wfo=mkx</a> ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Since Robertson (1989) adjusted all historical data to that collected from 1884-1989; no adjustments were applied to the recent data except for (1) wind and (2) estimated vapor pressure:(1) Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yvonne Hsieh.In 1996, a discontinuity in the wind record was caused by change in observational techniques and sensor locations (Mckee et al. 2000). To address the non-climatic changes in wind speed, data from MSN were carefully compared with those collected from the tower of the Atmospheric and Oceanic Science Building at the University of Wisconsin-Madison, see http://ginsea.aos.wisc.edu/labs/mendota/index.htm. Hourly data from both sites (UMSN,hourly and UAOS,hourly) during 2003&ndash;2010 were used to form a 4&times;12 (four components of wind direction &times; 12 months) matrix (K4,12) of wind correction factors, yielding UAOS,daily= Ki,j&times;UMSN,daily. The comparison results indicated that the MSN weather station reported a higher magnitude in winds out of the east by 5% and lower magnitude in winds out of the west and south by 30% and 10%. The adjusted wind data (=Ki,j&times;UMSN,daily) were therefore employed and used in the model simulation. After adjustments, there was a decrease in wind velocities starting shortly before 1996. Overall the adjusted wind data had a decline in wind velocities of 16% from 1988&ndash;93 to 1994&ndash;2009) compared to a 7% decline at a nearby weather station with no known observational changes (St. Charles, Illinois; 150 km southeast of Lake Mendota). (2) Estimated vapor pressure was updated (after April 2002) by using the equation from DYRESM for estimation of vapor pressure (a function of both air temperature and dew point temperature); where a=7.5, b=237.3, and c=.7858.
Version Number
23
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