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Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses
  • +26
  • Zhen Zhang,
  • Sheel Bansal,
  • Kuang-Yu Chang,
  • Etienne Fluet-Chouinard,
  • Kyle Delwiche,
  • Mathias Goeckede,
  • Adrian Gustafson,
  • Sara Helen Knox,
  • Antti Leppänen,
  • Licheng LIU,
  • Jinxun Liu,
  • Avni Malhotra,
  • Tiina Markkanen,
  • Gavin McNicol,
  • Joe R. Melton,
  • Paul A Miller,
  • Changhui Peng,
  • Maarit Raivonen,
  • William J. Riley,
  • Oliver Sonnentag,
  • Tuula Aalto,
  • Rodrigo Vargas,
  • Wenxin Zhang,
  • Qing Zhu,
  • Qiuan Zhu,
  • Qianlai Zhuang,
  • Lisamarie Windham-Myers,
  • Robert B. Jackson,
  • Benjamin Poulter
Zhen Zhang
University of Maryland

Corresponding Author:[email protected]

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Sheel Bansal
U.S. Geological Survey
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Kuang-Yu Chang
Lawrence Berkeley National Laboratory
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Etienne Fluet-Chouinard
ETH Zurich
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Kyle Delwiche
University of California, Berkeley
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Mathias Goeckede
Max Planck Institute for Biogeochemistry
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Adrian Gustafson
Lund University
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Sara Helen Knox
University of British Columbia
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Antti Leppänen
University of Helsinki
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Licheng LIU
University of Minnesota
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Jinxun Liu
U.S. Geological Survey
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Avni Malhotra
Stanford University
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Tiina Markkanen
University of Helsinki
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Gavin McNicol
Stanford University
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Joe R. Melton
Environment Canada
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Paul A Miller
Lund University
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Changhui Peng
University of Quebec at Montreal
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Maarit Raivonen
University of Helsinki
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William J. Riley
Lawrence Berkeley National Laboratory (DOE)
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Oliver Sonnentag
Université de Montréal
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Tuula Aalto
Finnish Meteorological Institute
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Rodrigo Vargas
University of Delaware
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Wenxin Zhang
Lund University
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Qing Zhu
Lawrence Berkeley National Lab
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Qiuan Zhu
Hohai University
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Qianlai Zhuang
Purdue University
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Lisamarie Windham-Myers
United States Geological Survey
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Robert B. Jackson
Stanford University
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Benjamin Poulter
NASA
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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.