Biophysical controls on interannual variability in ecosystem‐scale CO₂ and CH₄ exchange in a California rice paddy

Abstract We present 6.5 years of eddy covariance measurements of fluxes of methane ( F CH4 ) and carbon dioxide ( F CO2 ) 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 F CO2 and F CH4 . Wavelet analysis indicated that photosynthesis (gross ecosystem production, GEP) induced the diel pattern in F CH4 , but soil temperature ( T s ) modulated its amplitude. Forward stepwise linear models and neural networking modeling were used to assess the variables regulating seasonal F CH4 . As expected due to their competence in modeling nonlinear relationships, neural network models explained considerably more of the variance in daily average F CH4 than linear models. During the growing season, GEP and water levels typically explained most of the variance in daily average F CH4 . However, T s explained much of the interannual variability in annual and growing season CH 4 sums. Higher T s also increased the annual and growing season ratio of F CH4 to GEP. The observation that the F CH4 to GEP ratio scales predictably with T s may help improve global estimates of F CH4 from rice agriculture. Additionally, T s 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.