## 4 Discussion  
The SEM approach has been used to diagnose drivers of ET using high-frequency data collected using the EC method for forested systems. The results of this study in a BHF in the RSWMA revealed that the variability in ET is directly influenced by R\({}_{n}\) during spring, summer, and autumn, primarily vegetatively active seasons. However, during times of vegetative dormancy (i.e., in winter), the variability in ET is largely influenced by VPD and temperature, indirect controls of R\({}_{n}\). These results are consistent with the strong seasonal cycle for the variables that gradually increase from winter to summer and gradually diminish from summer to winter. This typical seasonal cycle also suggests that the influence of temperature and VPD on ET is indirectly driven by R\({}_{n}\). The greater control of temperature and VPD as drivers of ET during winter is suggestive of temperature-dependent ET, especially when the R\({}_{n}\)-dependent direct control on ET is minimal. Hence, the direct as well as indirect (through greater control of R\({}_{n}\) on temperature and VPD) control of R\({}_{n}\) on ET across all seasons reinforces the role of R\({}_{n}\) as a primary driver of ET in this forest. The results from the best-fit model in the AIC modeling support the inference from SEM that there is an interaction between R\({}_{n}\) and temperature. Furthermore, VPD showed independent influence as a predictor of ET compared to its interaction with R\({}_{n}\). This corroborates the increased influence of temperature on ET during vegetatively dormant seasons as opposed to the higher impact of R\({}_{n}\) on ET during vegetatively active seasons. Although independent, the positive impact of WS on ET has been previously established in many other studies [15; 24]; the interaction of WS with other variables like temperature and precipitation is likely to attenuate dryness (latent construct) and augment wetness, subsequently regulating the rate of ET in this system. The best model from AIC suggested that these interactions are highly plausible and significant in determining the dynamics of ET in this BHF. Consistent with the findings from the data collected in a BHF in the RSWMA from 2014 to 2021, which showed R\({}_{n}\) as the major direct and indirect driver of ET variability across different times of the year, research reports from most other forest types have characterized R\({}_{n}\) as a primary driver of ET. For example, [15] carried out an SEM analysis in various boreal, tundra, and permafrost ecosystems of high-latitude regions to demonstrate that R\({}_{n}\) is the major driver of ET variability, albeit having a smaller independent contribution due to its control on other variables. On the contrary, a similar path analysis conducted in a mid-latitude agricultural site in northern China reported that R\({}_{n}\) had the largest direct \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **K** & **AIC** & **\(\Delta\)AIC** & **AIC Weight** & **LL** \\ \hline ET \(-\) R\({}_{n}\)*Ta + VPD + WS*P & 9 & \(-\)104,579 & 0 & 0.98 & 52,298.53 \\ ET \(-\) R\({}_{n}\)*Ta + VPD + WS + P & 8 & \(-\)104,570 & 8.25 & 0.02 & 52,293.40 \\ ET \(-\) R\({}_{n}\)*Ta + VPD + WS & 7 & \(-\)104,383 & 195.37 & 0 & 52,198.84 \\ ET \(-\) R\({}_{n}\)*Ta + VPD & 6 & \(-\)104,344 & 234.54 & 0 & 52,178.26 \\ ET \(-\) R\({}_{n}\)*VPD + Ta + WS*P & 9 & \(-\)99,185 & 5393.08 & 0 & 49,601.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of AIC model selection with shortlisted candidate models with the number of parameters (K), Akaike’s Information Criteria (AIC), delta AIC, AIC weights, and log-likelihood (LL) values. independent contribution to ET [17]. [PERSON] [10] suggested that the increase in the amount of R\({}_{\rm n}\) received by a BHF in Missouri resulted in higher ET.