## 3 Results
### Climatic Effects on Carbon Fluxes and Balance  
The combined effects of Tair, Rn, GWT, and \(p\) explained 66%, 71%, and 29% of the variation in GPP, RE, and NEE, respectively (\(p<0.0001\), Table 2). Among the key climatic variables, the best fit models revealed Tair to be the main driver of GPP (F value = 119.29, \(p<0.0001\)), RE (255.13, \(p<0.0001\)), and NEE (42.31, \(p<0.0001\)) (Table 2). It was observed  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{3}{*}{**Site/Year**} & \multicolumn{6}{c}{**Annual Fluxes (g cm\({}^{-2}\)yr\({}^{-1}\))**} \\ \cline{2-10}  & \multicolumn{3}{c}{**GPP**} & \multicolumn{3}{c}{**RE**} & \multicolumn{3}{c}{**NEE**} \\ \cline{2-10}  & **Cumulative** & **Lower** & **Upper** & **Cumulative** & **Lower** & **Upper** & **Cumulative** & **Lower** & **Upper** \\  & **GPP** & **Limit** & **Limit** & **RE** & **Limit** & **Limit** & **NEE** & **Limit** & **Limit** \\ \hline US-NC4 & & & & & & & & & & \\
2009 & 1922 & 1910 & 1952 & 1554 & 1544 & 1566 & \(-368\) & \(-904\) & 168 \\
2010 & 2209 & 2155 & 2266 & 2295 & 2259 & 2332 & 87 & \(-491\) & 664 \\
2011 & 2210 & 2170 & 2259 & 2478 & 2458 & 2505 & 268 & \(-299\) & 837 \\
2012 & 2436 & 2044 & 2535 & 2544 & 2526 & 2562 & 108 & \(-150\) & 411 \\
2013 & 1566 & 1289 & 1633 & 1934 & 1827 & 2035 & 368 & \(-147\) & 885 \\
2014 & 1493 & 1466 & 1526 & 1595 & 1586 & 1613 & 103 & \(-375\) & 581 \\
2015 & 1723 & 1685 & 1759 & 1946 & 1923 & 1967 & 223 & \(-62\) & 510 \\
2016 & 1514 & 1488 & 1545 & 1614 & 1604 & 1624 & 100 & \(-297\) & 498 \\
2017 & 2082 & 2018 & 2127 & 2842 & 2819 & 2865 & 759 & \(-353\) & 1873 \\
2018 & 1818 & 1675 & 1957 & 1964 & 1854 & 2076 & 146 & 55 & 238 \\
2019 & 2113 & 1941 & 2297 & 2295 & 2090 & 2505 & 181 & 22 & 334 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Annual gross primary production (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE) from 2009 to 2019. The upper and lower limits were determined using the uncertainty analysis of [57] for NEE, while Monte Carlo (bootstrap) simulation was used for GPP and RE.  
Figure 3: Seasonal variation in (**a**) gross primary productivity (GPP), (**b**) ecosystem respiration (RE), and (**c**) net ecosystem exchange (NEE) from 2009–2019. Daily data were obtained from the eddy covariance flux tower (US-NC4) in Alligator River National Wildlife Refuge, Dare County, North Carolina, USA.  
that water level fluctuations better explained GPP and RE than the radiation component, whereas \(p\) explained the least in the GPP, RE, and NEE models (Table 2).  
Since the variation in Tair and Rn remained relatively constant across years, we focus on GWT variation in the succeeding sections, as it was the most varied among the climate variables. It was also the second best predictor variable for GPP and RE (Figure 2; Table 2). We evaluated how GWT was driving the carbon fluxes, especially RE, considering the prolonged inundation characteristic of the site, where tree mortality is accelerating.  
When GWT levels were below the soil surface (GWT = \(-\)0.01 cm to \(-\)14.26 cm), GPP and RE were enhanced by 35% and 28%, respectively (Figure 4). When GWT was taken as a sole predictor for GPP and RE alone, negative correlations were observed between GWT and GPP (\(R^{2}\) = 0.19) and GWT and RE (\(R^{2}\) = 0.20) during the growing season. However, the relationships between GWT with that of GPP and RE were weak during the non-growing season, with \(R^{2}\) = 0.05 and \(R^{2}\) = 0.02 for GPP and RE, respectively. (Figure 4). GWT had low explanatory power for the dynamics in NEE in both the growing and non-growing seasons.