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Climate warming is expected to increase global methane (CH 4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH 4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH 4 remain uncertain. Here, we use established drivers to hindcast changes in CH 4 fluxes (FCH 4 ) since the early 1980s. We trained a machine learning (ML) model on CH 4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH 4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH 4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH 4 , with increases in FCH 4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH 4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH 4 (defined according to the 95th percentile of the daily FCH 4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH 4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH 4 emission seasons and boosting extreme emissions., (© 2024 Battelle Memorial Institute and The Authors. Global Change Biology published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year -1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations., (© 2023 John Wiley & Sons Ltd.)

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity 1,2 . Nevertheless, the global extent of natural wetland loss remains remarkably uncertain 3 . Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9-3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16-23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration 4 ., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions., (© 2021 John Wiley & Sons Ltd.)

Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO2) and methane (CH4) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO2 and CH4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO2 and CH4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (Reco) modules explained, on average, 73% of the variation in CO2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH4 module also explained the majority of variance in CH4 emissions in validation sites (R2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on Reco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH4 emissions, and (d) CH4 responses to non‐periodic changes in salinity. Plain Language Summary: Tidal wetlands play a crucial role in storing carbon, but there is uncertainty in how they absorb and release carbon dioxide and methane, two of the most important greenhouse gases. Existing models for tidal wetlands have yet to be thoroughly tested using high‐frequency data across diverse sites. The new model presented here was parameterized and validated using 13 years of data from five different tidal wetland sites with representation from the three marine coasts of North America. The model explained on average more than half of the variation in carbon dioxide and methane exchange with low model error across diverse tidal wetlands used in model validation. This is strong performance given the high variation in greenhouse gas fluxes in tidal wetlands, which are known to be biogeochemically complex environments. Our model helps in assessing the greenhouse gas budgets from diverse tidal wetlands and the ability of wetlands to combat climate change. An analysis of model results highlights the need for more research on how tidal pumping and pulse changes in soil and water chemistry, not just at the surface but also across soil layers, influence carbon dioxide and methane production and exchange. Key Points: We present a process‐based modeling approach to estimate greenhouse gas flux in tidal wetlands with representation from the three marine coasts of North AmericaModel validation showed that primary productivity, respiration and methane modules explained >50% of the variation in CO2 and CH4 exchangeThe PEPRMT‐Tidal‐CMEM model is a key advance toward constraining estimates of greenhouse gas emissions across diverse tidal wetlands [ABSTRACT FROM AUTHOR]

Cold‐season methane (CH4) emissions may be poorly constrained in wetland models. We examined cold‐season CH4 emissions simulated by 16 models participating in the Global Carbon Project model intercomparison and analyzed temporal and spatial patterns in simulation results using prescribed inundation data for 2000–2020. Estimated annual CH4 emissions from northern (>60°N) wetlands averaged 10.0 ± 5.5 Tg CH4 yr−1. While summer CH4 emissions were well simulated compared to in‐situ flux measurement observations, the models underestimated CH4 during September to May relative to annual total (27 ± 9%, compared to 45% in observations) and substantially in the months with subzero air temperatures (5 ± 5%, compared to 27% in observations). Because of winter warming, nevertheless, the contribution of cold‐season emissions was simulated to increase at 0.4 ± 0.8% decade−1. Different parameterizations of processes, for example, freezing–thawing and snow insulation, caused conspicuous variability among models, implying the necessity of model refinement. Plain Language Summary: Wetlands in the northern high latitudes are a major source of methane (CH4) to the atmosphere, mainly during the warm season. Previously, models have assumed that cold‐season CH4 emissions are low, but recent observations suggest high‐latitude wetlands can be substantial sources even in winter. We compared CH4 emissions simulated by 16 state‐of‐the‐art wetland models, participating in a model intercomparison project with a focus on the cold‐season in northern wetlands. The model simulations indicated that nearly one third of annual emissions were simulated to occur from September to May, and CH4 emissions to the atmosphere were not negligible even under freezing air temperatures, although the results differed greatly among the models. However, field studies suggest cold‐season emissions account for an even larger fraction of annual emissions. These results highlight the contribution of cold‐season emissions to the annual CH4 budget, which future climatic warming is expected to affect severely, and they also show that simulations of cold‐season CH4 emissions from wetlands need to be improved. Key Points: Cold‐season methane (CH4) emissions simulated by 16 Global Carbon Project‐CH4 wetland models were analyzedMost models underestimate the cold‐season emissions in comparison with observational dataFurther model improvement by including cold‐season processes is required to reduce the model bias and uncertainty [ABSTRACT FROM AUTHOR]