Sensor Instrumentation We used the Fast Limnology Automated Measurement
(FLAMe) platform. The FLAMe is a novel flow-through system designed to
sample inland waters at both low- (0 to ~ 10 km hr-1) and high-speeds (10 to
45 km hr-1) described in Crawford et al. (2015). The FLAMe consists of three
components: an intake manifold that attaches to the stern of a boat; a
sensor and control box that contains hoses, valves, a circulation pump and
sensor cradles; and a battery bank to power the electrical components. The
boat-mounted intake manifold serves multiple purposes. First, sensors are
mounted inside the boat, protecting them from potential damage. Second, the
intake system creates a constant, bubble-free water flow, thus preventing
any issues for optical sensors due to cavitation. Finally, to analyze
dissolved gases, a constant water source is needed on board. Water flow via
both the slow- and high-speed intakes is regulated by the onboard impeller
pump, allowing for seamless switching between slow- and high-speed
operations. Any number of sensors could be integrated into the platform with
simple modifications, and can be combined with common limnological
instruments such as acoustic depth-finders. In our example applications we
used a YSI EXO2 multiparameter sonde (EXO2; Yellow Springs, OH, USA), and a
Satlantic SUNA V2 optical nitrate (NO3) sensor (Halifax, NS, Canada), both
integrated into the control box plumbing with flow-through cells available
from the manufacturer. Both the EXO2 and the UGGA are capable of logging
data at 1 Hz. Because the SUNA was operated out of the water and on a boat
during warm periods, data were collected less frequently (~0.1 Hz) to
minimize lamp-on time and avoid the lamp temperature cutoff of 35_ C. The
EXO2 sonde uses a combination of electrical and optical sensors for:
specific conductivity, water temperature, pH, dissolved oxygen, turbidity,
fluorescent dissolved organic matter (fDOM), chlorophyll-a fluorescenece,
and phycocyanin fluorescence. The SUNA instrument measures NO3 using in situ
ultraviolet spectroscopy between 190-370 nm, has a detection range of
0.3-3000 _M NO3, and a precision of 2 _M NO3. The UGGA has a reported
precision of 1 ppb (by volume). In order to translate time-series data from
the instruments into spatial data, we also logged latitude and longitude at
1 Hz with a global positioning system (GPS) with the Wide Area Augmentation
System (WAAS) functionality enabled allowing for & lt; 3 m accuracy for
95% of measured coordinates. Synchronized time-stamps from the EXO2, SUNA,
and GPS were used to combine data streams into a single spatially-referenced
dataset. We ran a simple set of experiments to determine the residence time
of the system and the overall response time of the EXO2 and SUNA sensors
integrated into the platform. Methods for this procedure are outlined in
Crawford et al. (2015) and updated in Loken et al. (2019). After determining
first-order response characteristics of each sensor, we applied an ordinary
differential equation method to correct the raw data for significant changes
in water input resulting in higher accuracy spatial data (see Crawford et
al. 2015). Because we continually improved the FLAMe system and reduced the
sensor response times over the duration of this project, we assessed sensor
response dynamics each time we updated the FLAMe system. Modifications
included, changing of pumps, tubing, and sensor installation. In general, we
improved the response time of the system over the duration of the study.
Here we archive FLAMe sensor data for Lake Mendota. Each survey typically
began and ended at the NTL-LTER sampling buoy in the middle of the lake.
Each survey contains 3000 to 10,000 individual measurements. Data were
collected most frequently and consistently in 2016. Data have been provided
in three formats (raw, hydraulic-corrected, and tau-corrected). Note that
not all sensors were measured on each survey. Because the SUNA is the first
sensor in the flow-through system and has a very small flow-cell volume,
there is effectively zero lag time from sampling to measurement. Hence for
SUNA data, we provide only raw data. References Crawford JT, Loken LC,
Casson NJ, Smith C, Stone AG, and Winslow LA (2015) High-speed limnology:
Using advanced sensors to investigate spatial variability in biogeochemistry
and hydrology. Environmental Science and Technology 49:442-450. Loken LC,
Crawford JT, Schramm PJ, Stadler P, Desai AR, Stanley EH (2019) Large
spatial and temporal variability of carbon dioxide and methane in a
eutrophic lake. Journal of Geophysical Research - Biogeosciences
124:2248–2266
