## 3 Results
### Path Analysis  
While the factor analysis helped to identify variables with similar variability patterns, the correlations deduced from this analysis alone do not represent the relative importance of one variable in determining ET variability, independent of the effect of other interrelated variables. Hence, a combination of a path analysis with a factor analysis is helpful to demonstrate the direct dependencies of ET on one variable independent of other variables, whereby regression coefficients represent the strength of independent contributions. The direct and indirect dependencies and associations between different variables in terms of physical pathways derived from the path analysis are shown in Figure 5. Sensible heat flux and pressure were not included in the analysis due to poor model fit. Figure 4: Score plots of the first and second factor loadings by various timescales across different seasons. Variables include evapotranspiration (ET), net radiation (Rn), sensible heat flux (H), Vapor Pressure Deficit (VPD), temperature of air (Ta), pressure (Pa), windspeed (WS), and precipitation (P). Figure 5: _Cont_. The standardized solutions for the best-fit path model and structural model [\(\chi^{2}\) = 752 (df = 4, n = 42471), \(p\) < 0.0001], Comparative Fit Index ((CFI) = 0.98), and Tucker-Lewis Index ((TLI) = 0.96, RMSEA = 0.06, SRMR = 0.02) are shown in Figure 5a,b. While correlation and factor loading plots showed R\({}_{n}\) having the strongest correlation and shared variability pattern with ET, its direct independent influence on ET was lower. Similarly, R\({}_{n}\) not only had a comparable direct effect on ET but also a strong indirect effect via VPD and temperature. VPD showed a strong positive influence on ET, while in reverse, ET had a strong negative impact on VPD. VPD showed the largest impact on ET at the hourly timescale. Air temperature was strongly driven by R\({}_{n}\), which then influenced the VPD, ultimately affecting ET in the process. Windspeed had a negligible negative effect on ET, while precipitation (rain) had a feeble positive impact on ET, although windspeed showed a negative impact on temperature, thereby affecting ET indirectly as well. In the daily and weekly timescales, these regression coefficients generally increased for all the pathways. For example, R\({}_{n}\) emerged as a major direct influencer of ET, while the strengths of indirect linkages through VPD and temperature also increased at larger timescales. The direct effect of rain on ET increased positively, while that of windspeed decreased overall. The combined results of the path analysis with significant regression coefficients for different variables at hourly, daily, and weekly timescales are shown in Figure 6. R\({}_{n}\) stood out, with the highest median regression coefficient for all the timescales. VPD had the second-highest independent contribution to ET variability on the hourly scale, while rain and temperature showed the same at the daily and weekly timescales, respectively. The windspeed showed lesser influence at the hourly timescale, while this increased for the daily and weekly timescales. To test the significance of differences in the regression coefficients of different variables across these timescales, an ANOVA test was performed for all the categories. The results showed significant differences (\(p\) < 0.05) in regression coefficients on all timescales for all variables. This was followed by a Tukey HSD post hoc analysis to test the significance for pairwise differences in regression coefficients. Only the regression coefficients for R\({}_{n}\) showed significant differences from that of other variables. Thus, it was concluded that R\({}_{n}\) is the major direct driver of ET variability in this ecosystem across all timescales.