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Sensors

On top of tower at 110 ft., 20-30 ft above top of canopy:

- Li-7500 open-path gas analyzer (C and H20 flux)

- Gill Windmaster sonic anemometer (3D wind)

- Kipp and Zonen CNR4 net radiometer

- Kipp and Zonen albedometer

- Apogee aspirated shield houses a CS 215 temperature sensor

On ground near tower base:

- 4x CS 655 soil moisture reflectometers and Teros 21 matric potential sensors at 10, 20, 30, and 50 cm

- 2x heat flux stations using 2 Huskeflux heat flux plates and two thermistors at each station

- A vertical profile of shielded air temperature sensors every 3 m from 3 to 30 m

- A vertical profile of snow temperature sensors every 10 cm from 10 to 140 cm above the soil surface.

These sensors are measuring unshielded air temperature when not covered with snow.

- Tree diameter with 6 digital recording dendrometers

Using the AMERIFLUX full dataset output AMERIFLUX defined variable names, we first added more columns to the set by partitioning TIMESTAMP_START into Year, Month, Day (of the month), and Hour columns. For data taken at the half-hour, it was reflected in the Hour column by adding 0.5 to the current hour. We set negative SW_IN and SW_OUT to NA. AT_DIFF is the difference in air temperature at 3 meters and 30 meters.

Gap Filling Methods

For the gap-filling procedure, each variable (LE, H, and FC) was assigned two models, a comprehensive model, and a limited model. The comprehensive model used more variables for prediction than the limited model. This allows for the use of the comprehensive model for predictions, but when some of those variables are not available, to rely on the limited model to make the prediction. For LE, the limited model used: USTAR, G, AT_3, AT_30, AT_DIFF, MONTH, DAY, and HOUR. Note, AT_3 and AT_30 are the air temperatures at 3 and 30 meters respectively. For the LE comprehensive model, the variables used were: AT_DIFF, SW_IN, SW_OUT, LW_IN, LW_OUT, G, VPD, USTAR, MONTH, DAY, HOUR, YEAR. For H, the limited model used: USTAR, AT_3, AT_30, MONTH DAY, HOUR, YEAR, G, and AT_DIFF. The comprehensive model for H used: USTAR, SW_IN, SW_OUT, LW_IN, LW_OUT, G, AT_DIFF, AT_3, AT_30. The limited model for FC used: USTAR, VPD, G, AT_DIFF, YEAR, MONTH, HOUR, and DAY. The comprehensive model for FC used: USTAR, WD, WS, VPD, SWC_1, SWC_2, NETRAD, SW_IN, SW_OUT, LW_IN, LW_OUT, G, AT_3, AT_30, AT_DIFF, YEAR, MONTH, DAY, and HOUR. Using the listed variables, we randomly split the data set into training (70%) and testing (30%) sets when all the variables' values were available. Then each model was made using the 'randomForest' package in R with the 'randomForest' function. Using the models, we compared their predictions to the testing sets using the Nash-Sutcliffe equilibrium (NSE) value. The predictions were made with the 'predict' function. Finally, the final data set was created using all the predictions from each model. When the comprehensive model had a prediction, it was used over the limited model because the comprehensive models were better predictors according to the NSE. The gap-filled values are labeled in the columns LE_METH, H_METH, and FC_METH indicating which method was used (comprehensive, limited, or measured).