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We investigated the climatic and ecohydrological controls of the monthly methane emission fluxes from freshwater wetlands across the globe. Fluxes of methane, photosynthetically active radiation (PAR), soil temperature (TS), atmospheric pressure, latent heat flux (LE), wind speed (WS), friction velocity, vapor pressure deficit (VPD), soil water content (SWC), water table depth, and precipitation were obtained from 32 FLUXNET wetland sites. Multivariate pattern recognition techniques of principal component and factor analyses were utilized to classify and group climatic and ecological variables based on their similarity as drivers, examining their interrelation patterns across the different sites. Partial least squares regression models were developed to estimate the relative linkages of methane emission fluxes with the climatic and ecohydrological drivers. When the wetlands were flooded (i.e., positive water table depth relative to the ground), PAR, LE, VPD, and TS had the strongest controls on the methane emission fluxes. However, in the absence of flooding (i.e., negative water table depth), the methane emission fluxes were mainly controlled by SWC and WS. For the wetland sites with unavailable water table depth data, PAR, TS, and WS had the strongest controls on the methane emissions and subsequent transport. Our findings provided important knowledge and insights for predicting and managing methane emissions in freshwater wetlands at a global scale.
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This content will become publicly available on February 10, 2026
Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data
ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well (R2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.
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- Award ID(s):
- 2425290
- PAR ID:
- 10575198
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecohydrology
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1936-0584
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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