TY - JOUR
T1 - Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET-CH4 Sites Using Wavelet Analyses
AU - Zhang, Zhen
AU - Bansal, Sheel
AU - Chang, Kuang Yu
AU - Fluet-Chouinard, Etienne
AU - Delwiche, Kyle
AU - Goeckede, Mathias
AU - Gustafson, Adrian
AU - Knox, Sara
AU - Leppänen, Antti
AU - Liu, Licheng
AU - Liu, Jinxun
AU - Malhotra, Avni
AU - Markkanen, Tiina
AU - McNicol, Gavin
AU - Melton, Joe R.
AU - Miller, Paul A.
AU - Peng, Changhui
AU - Raivonen, Maarit
AU - Riley, William J.
AU - Sonnentag, Oliver
AU - Aalto, Tuula
AU - Vargas, Rodrigo
AU - Zhang, Wenxin
AU - Zhu, Qing
AU - Zhu, Qiuan
AU - Zhuang, Qianlai
AU - Windham-Myers, Lisamarie
AU - Jackson, Robert B.
AU - Poulter, Benjamin
N1 - Publisher Copyright:
© 2023. The Authors.
PY - 2023/11
Y1 - 2023/11
N2 - 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 was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) 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, especially at short time scales, in future wetland CH4 model developments.
AB - 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 was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) 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, especially at short time scales, in future wetland CH4 model developments.
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U2 - 10.1029/2022JG007259
DO - 10.1029/2022JG007259
M3 - Article
AN - SCOPUS:85176099332
SN - 2169-8953
VL - 128
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 11
M1 - e2022JG007259
ER -