## 2 Materials and Methods
### Data Collection
#### 2.2.3 Carbon and Water Flux Processing  
Two different methods of carbon and water flux processing were done in this study: one using EddyPro [37; 38] and REddyProc [22] and another using Fluxpart software [22]. For the EddyPro/REddyProc EC method, the 20 Hz high-frequency data was processed into 30 min average fluxes via the EddyPro software (v7.0.4, Li-Cor Inc., Lincoln, NE, USA). Fluxes were corrected for density, sensor separation [37; 39] and coordinate rotation (double rotation [40]). This flux data was further subject to filtering to ensure sufficient samples per 30 min (n > 0.85 \(\times\) 6000) were available. In addition, a suite of statistical filtering was completed that first determined an acceptable growing season range. Measurements which exceeded the mean of the dataset plus the \(\pm\)3.5 times the standard deviation of the entire dataset were excluded. Additionally, the growing season datasets were grouped by each 30 min diurnal timestamp (00:30-24:00), where each half hour was filtered if it was out of the range of the mean plus the \(\pm\)3.5 times the standard deviation for that 30 min group. Furthermore, a moving window (\(\pm\)2.5 h and 5 h, depending on record set greater than 80%) was applied where values larger than 3.5 times the standard deviation were removed. A final inspection was completed with a manual inspection where data was filtered based on physically realistic site values. Finally, a flux footprint analysis was performed in 30 min intervals, which ensured that all fluxes originated from within the crop of interest [41] where canopy height was adjusted across the field season to correspond with the in situ measurements, followed by a frictional velocity (u*) threshold of u* = 0.1 m s\({}^{-1}\) filter, which was used to remove periods with low turbulence [42]. Net ecosystem exchange (NEE) was gap-filled and partitioned into gross primary productivity (GPP) and respiration (Re) following the methods of (REddyProc [23]). ET was calculated from a closed energy balance [43] and gap-filled using a site-specific PET [44] to ET relationship. Fluxpart [22] is a program that implements flux variance similarity (FVS) partitioning theory [45; 46]. ET is calculated directly from water component,  
\[\text{ET}=\{\text{w}^{\prime}\text{q}^{\prime}\} \tag{1}\]  
where w represents vertical wind velocity, q represents water vapor concentration, \(\{\}\) indicate temporal means over a 30-min interval, and the prime (\({}^{\prime}\)) represents deviations away from the 30-min mean. Similarly, NEE is calculated directly from carbon dioxide component fluxes according to,  
\[\text{NEE}=\{\text{w}^{\prime}\text{c}^{\prime}\} \tag{2}\]  
where c represents carbon dioxide concentration. Briefly, FVS partitions ET and NEE into component water (transpiration (T) and evaporation (E)) and carbon (GPP and Re) fluxes based on deviations from Monin-Obukhov similarity theory [46; 47]. Monin-Obukhov similarity theory stipulates that water vapor and carbon dioxide concentrations exhibit perfect correlation when measured from the same spot within a homogenous atmospheric layer. Disturbances to the perfect correlation can be attributed to the presence of multiple sources and sinks from these fluxes. The FVS technique relies on the degree of disturbance to infer the relative amounts of stomatal (T and GPP) and non-stomatal (E and Re) fluxes present [22; 46]. The contribution of stomatal components to water and carbon fluxes is calculated through a series of algebraic equations and assumptions outlined in [22], based on work by [46].