We present 6.5 years of eddy covariance measurements of fluxes of methane (FCH4) and carbon dioxide (FCO2) from a flooded rice paddy in Northern California, USA. A pronounced warming trend throughout the study associated with drought and record high temperatures strongly influenced carbon (C) budgets and provided insights into biophysical controls of FCO2 and FCH4. Wavelet analysis indicated that photosynthesis (gross ecosystem production, GEP) induced the diel pattern in FCH4, but soil temperature (Ts) modulated its amplitude. Forward stepwise linear models and neural networking modeling were used to assess the variables regulating seasonal FCH4. As expected due to their competence in modeling nonlinear relationships, neural network models explained considerably more of the variance in daily average FCH4 than linear models. During the growing season, GEP and water levels typically explained most of the variance in daily average FCH4. However, Ts explained much of the interannual variability in annual and growing season CH4 sums. Higher Ts also increased the annual and growing season ratio of FCH4 to GEP. The observation that the FCH4 to GEP ratio scales predictably with Ts may help improve global estimates of FCH4 from rice agriculture. Additionally, Ts strongly influenced ecosystem respiration, resulting in large interannual variability in the net C budget at the paddy, emphasizing the need for long-term measurements particularly under changing climatic conditions.
