Methods

With advice from Ed Hopkins, the Assistant State Climatologist, pdf’s of the original paper copies of the daily precipitation submissions of both stations were downloaded. Almost universally, precipitation over weekends and holidays was not recorded at the stations such that totals were recorded the following workweek day. In the early years, while the records for each single day were not always accurately recorded, the cumulative weekend/holiday totals were recorded such that one could get a full monthly total that was generally accurate. However, starting in 1996, because the cumulative weekend/holiday precipitation didn’t allow to know daily totals, those records were not submitted electronically to NOAA’s final database so they were recorded as missing data in NOAA’s electronic dataset. However, from the hand-written pdf’s it was possible to compile the monthly totals. In the process quite a few months were checked and a few errors in the NOAA totals fixed.<br/>

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.

Methods

Data contained within is derived from data publicly available through the North Temperature Lakes Long Term Ecological Research (NTL-LTER) website at the following url: https://lter.limnology.wisc.edu/. All data is for the upper meter of the water column, collected in the deepest point of the lake. For DOC concentration data and ancillary data such as pH, iron, and total N, please see the NTL-LTER website.
The file dat.w.blank.fin 7_29_16.csv contains the absorbance scans that were used to calculate the spectral metrics (NTL LTER 2012). Samples for absorbance scans were collected approximately quarterly. Absorbance scans were run on a spectrophotometer over the wavelengths from 200-800 nm. In this file, the column value is the raw value for absorbance read directly off the instrument for the corresponding wavelength. The column blank.value is the value of the DI blank taken nearest in time for the corresponding wavelength and cuvette width. The column cor.value is the blank.value column subtracted from the value column. The cor.value column was used in all subsequent analyses and calculations.
The file metrics for_analysis.csv contains the spectral metrics calculated from the absorbance scans as well as select meteorological metrics. ab.254 is raw absorbance measured by the instrument (after correcting for DI blanks), corrected for a 1 m pathlength. lin.slope.275.295 is the slope for the log transformed absorbance scan over the wavelengths 275-295 nm, determined by linear regression. nlin.slope.275.295 is the spectral slope (S275-295) over the wavelengths 275-295 nm, calculated using non-linear regression in R version 3.2.3 (R Core Team 2015), fitting the following equation:
alambda = alambdarefe-S(lambda-lambdaref)
In this equation a is the Naperian absorption coefficient (see below), lambda is the wavelength, lambdaref is the reference wavelength, and S is the spectral slope (nm-1) (Twardowski et al. 2004; Helms et al. 2008). The initial estimate supplied to the non-linear regression procedure was supplied by the value for lin.slope.275.295. lin.slope.350.400 and nlin.slope.350.400 were calculated in a similar fashion, but over the wavelengths 350-400 nm. This calculation yields the spectral slope over the wavelengths 350-400 nm (S350-400). slope.ratio is the slope ratio (SR), the ratio of S275-295 to S350-400 (Helms et al. 2008). ab.254.1cm is similar to ab.254, but is corrected for a 1 cm cuvette width. a254.nap is the Naperian absorption coefficient and is calculated from the equation:
a = 2.303A/l
In this equation, a is the Naperian absorption coefficient, A is raw absorbance measured by the spectrophotomer, and l is the path length (Green and Blough 1994). SUVA was calculated by dividing raw absorbance at 254 nm by the DOC concentration of the DOC sample collected nearest in time to the absorbance sample. SUVA is reported as Ltimesmg C-1timesm-1 (Weishaar et al. 2003).
wk.prcp, mth.prcp, and ninety prcp are precipitation totals for the 7, 30, and 90 days up to and including the sampling date (mm). These data come from the Minocqua, WI station in the Global Historical Climatology Network (GHCN) dataset (available at https://www.ncdc.noaa.gov/data-access/quick-links#ghcn) (Menne et al. 2010).
wk.tmp, mth.tmp, and ninety.tmp are mean values for the mid-daily temperature in the 7, 30, and 90 days up to and including the sampling date (°C). These are from the same data source as the precipitation data.
The l.lev column contains lake level from the NTL LTER website in meters above sea level. wk.insol, mth.insol, and ninety.insol are mean solar insolation incident on a horizontal surface in the 7, 30, and 90 days up to and including the sampling date (kWh/m2/day) (these data were obtained from the NASA Langley Research Center Atmospheric Sciences Data Center NASA/GEWEX SRB Project).
Monthly Palmer Drought Severity Index (PDSI) data corresponding to the study period can be accessed at: ftp://ftp.ncdc.noaa.gov/pub/data/cirs/climdiv/ using StateCode=47 and Division=2. These data were retrieved from the US National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI). These data were used to relate trends and synchrony in spectral metrics to moisture conditions.
The DOC column contains DOC concentration (mg L-1) for the corresponding sample date, obtained from the NTL LTER website.

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 &amp; 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.

Methods

wind, pressure, temperature and precipitation sensors on buoy.

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.

Methods

See abstract for methods.

Methods

The instrumented raft on Sparkling Lake is equipped with a D-Opto dissolved oxygen sensor, a thermistor chain, and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, evaporation rates, and lake metabolism. Estimating the flux of solutes to and from lakes requires accurate water budgets. Evaporation rates are a critical component of the water budget of lakes. Data from the instrumented raft on Sparkling Lake includes micrometeorological parameters from which evaporation can be calculated. Raft measurements of relative humidity and air temperature (2 m height), wind velocity (2m) , and water temperatures (from thermistors placed throughout the water column at intervals varying from 0.5 to 3m) are combined with measurements of total long-wave and short-wave radiation data from a nearby shore station to determine evaporation by the energy budget technique. Comparable evaporation estimates from mass transfer techniques are calibrated against energy budget estimates to produce a lake-specific mass transfer coefficient for use in estimating evaporation rates. After correcting for flux to or from the atmosphere and vertical mixing within the water column, high frequency measurements of dissolved gases such as carbon dioxide and oxygen can be used to estimate gross primary productivity, respiration, and net ecosystem productivity, the basic components of whole lake metabolism. Other parameters measured include precipitation, wind direction (beginning in 2008), and barometric pressure (beginning in 2008). Sampling Frequency: one minute; averaged to hourly and daily values as well as higher resolution values such as 2 min and 10 min.Dissolved oxygen sensors: 2004-2006: Greenspan Technology series 1200; 2007-2016: Zebra-Tech Ltd. D-Opto; 2018+: OTT HydrolabCO2 sensors: 2018+: ProOceanos MiniCO2 for dissolved CO2; Eosense Inc. eosGP for atmospheric CO2