Characterizing performance of freshwater wetland methane models across
time scales at FLUXNET-CH4 sites using wavelet analyses
Abstract
Process-based land surface models are important tools for estimating
global wetland methane (CH4) emissions and projecting
their behavior across space and time. So far there are no performance
assessments of model responses to drivers at multiple time scales. In
this study, we apply wavelet analysis to identify the dominant time
scales contributing to model uncertainty in the frequency domain. We
evaluate seven wetland models at 23 eddy covariance tower sites. Our
study first characterizes site-level patterns of freshwater wetland
CH4 fluxes (FCH4) at different time
scales. A Monte Carlo approach has been developed to incorporate flux
observation error to avoid misidentification of the time scales that
dominate model error. Our results suggest that 1) significant
model-observation disagreements are mainly at short- to intermediate
time scales (< 15 days); 2) most of the models can capture the
CH4 variability at long time scales (> 32
days) for the boreal and Arctic tundra wetland sites but have limited
performance for temperate and tropical/subtropical sites; 3) model error
approximates pink noise patterns, indicating that biases at short time
scales (< 5 days) could contribute to persistent systematic
biases on longer time scales; and 4) differences in error pattern are
related to model structure (e.g. proxy of CH4
production). Our evaluation suggests the need to accurately replicate
FCH4 variability in future wetland CH4
model developments.