DOI: 10.5061/dryad.3tx95x6j5
title: Barro Colorado Island - eddy covariance flux data (2012-2017)

description: This dataset contains CO2/H2O eddy covariance fluxes and other
 microclimatic observations observed on Barro Colorado Island (Panama) from
 2012 to 2017. Data were acquired and processed using standard routines
 (see Methods). Data are organized in a spreadsheet with each variable in a
 column.
description: Turbulent fluxes and meteorological variables were measured from
 a 41 m Eddy Covariance (EC) flux tower located on the central plateau of
 the island at about 140 m ASL from July 2012 to August 2017. The eddy
 covariance system included a sonic anemometer (CSAT3, Campbell Scientific)
 and an open-path infrared
 CO<sub>2</sub>/H<sub>2</sub>O gas analyzer
 (LI-7500A, LiCOR Bioscience). Hi-frequency (10Hz) measurements were
 acquired by a datalogger (CR1000, Campbell Scientific) and stored on a
 local PC. Additional environmental variables used in this study were
 recorded at 5-min intervals and include rainfall (Tipping Bucket Rain
 Gauges TB4, Hydrological Services), temperature and relative humidity
 (HC2S3, Rotronic), solar radiation (CMP11, Kipp&Zonen), upwelling
 and downwelling shortwave and longwave radiations (CNR1,
 Kipp&Zonen), direct and diffuse PAR (BF5, Delta-T Devices), and
 canopy temperature obtained from five infrared thermometers (SI-131,
 Apogee Instruments) pointing toward the crowns of the four canopy trees
 closest to the tower. Three time domain reflectometers (CS616, Campbell
 Scientific) were installed vertically into the soil around the tower and
 calibrated with gravimetric soil moisture samples taken in different
 moisture regimes in close proximity to the probes. Data
 were processed with a custom program using a standard routine described in
 (Detto et al. 2010). Fluxes were computed on a 30-min moving averaging
 window with a 5-min time step. This redundancy allowed better
 identification of gaps, especially gaps due to frequent rains,
 non-stationary and not ideal turbulent conditions, and increased temporal
 resolution. QA/QC criteria follow guidelines for removing erroneous values
 (Mauder et al. 2013) and exclude periods of non-ideal turbulent
 conditions, during and immediately after rain, outliers of several scalar
 statistics, poor energy budget closure and light response, non-stationary
 conditions, and known sensor malfunctioning. GPP was
 derived from net ecosystem exchange (NEE) daytime values by adding the
 corresponding mean daily ecosystem respiration (RECO). Two approaches were
 implemented. In the first, RECO equaled the intercept of the light
 response curve (Lasslop et al. 2010). In the second, RECO equaled mean
 nighttime fluxes. Both methods were performed on a ±15-day moving window
 using a relatively restricted filter (friction velocity &gt;0.4 m
 s<sup>-1</sup>) to minimize flux underestimation. Because of
 relatively small variation in air temperature, lack of temperature
 dependence of soil respiration (Rubio & Detto 2017), and not
 detectable temperature dependence of nighttime fluxes, the temperature was
 not included as a covariate for estimating RECO. The two methods provided
 similar results, but the first method yielded higher RECO values. However,
 when compared to soil chamber measurements available for the same period
 in the tower's footprint (Rubio & Detto 2017), these fluxes
 appeared underestimated, considering that RECO includes also above ground
 respiration which can contribute up to 40-50% of total respiration (Malhi
 et al. 2011). The energy balance closure was comparable to closures in
 most other Fluxnet sites (Wilson et al. 2002) with an imbalance of about
 25%, and no appreciable differences between wet and dry seasons (Fig.
 S10). In order to compute daily and monthly
 time-integrated values of GPP, evapotranspiration (ET), and sensible heat
 (H), gaps in the time series were filled using an Artificial Neural
 Network (Papale & Valentini 2003) with hydro-meteorological inputs
 as predictors (soil moisture, various radiation components, temperature,
 VPD, and air pressure). The data were randomly divided into a training set
 (70%), a validation set (15%), and a test set (15%). A two-layer
 feed-forward network with 10 sigmoid hidden neurons and linear output
 neurons was trained using the Levenberg-Marquardt algorithm until the mean
 square error (MSE) of the validation set stop improving (Hagan &
 Menhaj 1994). Performance, in terms of MSE, was evaluated using the test
 set at the end of the training. This procedure was repeated 100 times to
 produce 100 estimates of GPP and ET. Training multiple times generates
 different results due to different initial conditions and random sampling
 of the training, validation and test sets. The ensemble was obtained as a
 weighted average from the 100 Artificial Neural Network predictions using
 the reciprocal of MSE of the test set as weights.
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 Properties of Biologically Active Scalar Concentration Fluctuations in the
 Atmospheric Surface Layer over a Managed Peatland. Boundary-Layer
 Meteorol., 136, 407–430. Hagan, M.T. & Menhaj,
 M.. (1994). Training feedforward networks with the Marquardt algorithm.
 IEEE Trans. Neural Networks, 5, 989–993. Lasslop, G.,
 Reichstein, M., Detto, M., Richardson, A.D. & Baldocchi, D.D.
 (2010). Comment on Vickers et al.: Self-correlation between assimilation
 and respiration resulting from flux partitioning of eddy-covariance
 CO2fluxes. Agric. For. Meteorol., 150, 312–314. Malhi,
 Y., Doughty, C. & Galbraith, D. (2011). The allocation of
 ecosystem net primary productivity in tropical forests. Philos. Trans. R.
 Soc. B Biol. Sci., 366, 3225–3245. Mauder, M., Cuntz,
 M., Drüe, C., Graf, A., Rebmann, C., Schmid, H.P., et al. (2013). A
 strategy for quality and uncertainty assessment of long-term
 eddy-covariance measurements. Agric. For. Meteorol., 169,
 122–135. Papale, D. & Valentini, R. (2003). A
 new assessment of European forests carbon exchanges by eddy fluxes and
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 forest. Ecol. Evol., 7. Wilson, K., Meyers, T.,
 Goldstein, A., Baldocchi, D., Falge, E., Tenhunen, J., et al. (2002).
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