Interannual Variability in Greenhouse Gas Emissions Challenges Post‐Restoration Net Sink Predictions in California Delta Wetlands - Kuno Kasak; Arman Ahmadi; Iryna Dronova; Ariane Arias‐Ortiz; Tianxin Wang; Alex C. Valach; Daphne Szutu; Joseph Verfaillie; Dennis D. Baldocchi (Global Change Biology, 2026)
Globally, wetlands can sequester and store large amounts of soil carbon over the long term due to high primary productivity and slow decomposition. Yet centuries of drainage for agriculture and development have turned many of these carbon sinks into greenhouse gas (GHG) sources. Restoring degraded wetlands, particularly in peat‐rich landscapes, is increasingly promoted as a nature‐based solution for climate change mitigation. However, the trajectory and timing of recovery remain uncertain, especially given the complex interplay among vegetation dynamics, hydrology, and GHG fluxes. In this study, we analyzed 44 site‐years of continuous eddy covariance measurements of carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes from restored wetlands in California's Sacramento‐San Joaquin Delta. Our findings reveal substantial interannual variability in GHG exchange across sites, driven by differences in restoration design, water management, and vegetation establishment. While rapid vegetation growth, especially dense stands of macrophytes, can enhance CO 2 uptake, it often elevates CH 4 emissions and complicates predictions of when wetlands become net GHG sinks. Crucially, wetlands with delayed vegetation establishment due to high or inconsistent water levels (e.g., significant drawdown) remained persistent GHG sources, even years after restoration. Conversely, sites with tailored planting or natural and rapid recolonization exhibited earlier transitions to net sink status, including earlier shifts towards net negative radiative forcing since the restoration. The study highlights the importance of adaptive, site‐specific restoration strategies and long‐term monitoring to capture switchover dynamics from sources to sinks. As global investment in wetland restoration grows, our findings underscore the need to balance climate mitigation goals with ecological realities and the self‐designing processes of vegetation succession.

Assessing nitrous oxide emissions from grass-clover ley within a crop rotation using measurements and modeling - Yuqiao Wang; Alex Valach; Marcio dos Reis Martins; Christof Ammann (European Journal of Agronomy, 2025)

Validation of a short-range dispersion and deposition model using field-scale ammonia and methane release experiments - Christoph Häni; Albrecht Neftel; Christophe Flechard; Christof Ammann; Alex Valach; Thomas Kupper (Agricultural and Forest Meteorology, 2024)

High methane emissions as trade-off for phosphorus removal in surface flow treatment wetlands - Ülo Mander; Martin Maddison; Alex C. Valach; Kaido Soosaar; Keit Kill; Kuno Kasak (Aquatic Botany, 2024)

A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems - Elke Eichelmann; Mauricio C. Mantoani; Samuel D. Chamberlain; Kyle S. Hemes; Patricia Y. Oikawa; Daphne Szutu; Alex Valach; Joseph Verfaillie; Dennis D. Baldocchi (Global Change Biology, 2022)
Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation ( E ) and transpiration ( T ) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local‐to‐regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E . Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento‐San Joaquin Delta, California, USA, as well as leaf‐level T data for validation. The four wetlands vary in their vegetation make‐up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R 2 across all sites. The order of variable inclusion (and importance) was: VPD > gap‐filled sensible heat flux ( H _gf) > air temperature ( T air ) > friction velocity ( u ∗ ) > other variables. The model using VPD, H _gf, T air , and u ∗ showed the best performance during validation with independent data and had a mean testing R 2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T /ET partitioning results which were more consistent with CO 2 exchange data especially for more heterogeneous sites with large E contributions. Our method improves the understanding of T /ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T /ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales - Sara H. Knox; Sheel Bansal; Gavin McNicol; Karina Schafer; Cove Sturtevant; Masahito Ueyama; Alex C. Valach; Dennis Baldocchi; Kyle Delwiche; Ankur R. Desai; Eugenie Euskirchen; Jinxun Liu; et al. (Global Change Biology, 2021)
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.

Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half - Jiangong Liu; Yulun Zhou; Alex Valach; Robert Shortt; Kuno Kasak; Camilo Rey‐Sanchez; Kyle S. Hemes; Dennis Baldocchi; Derrick Y. F. Lai (Global Change Biology, 2020)
The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO 2 ) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH 4 ) emissions can potentially offset the carbon burial rates in low‐salinity coastal wetlands, there is hitherto a paucity of direct and year‐round measurements of ecosystem‐scale CH 4 flux (F CH4 ) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem‐scale F CH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F CH4 reached a peak of over 0.1 g CH 4 ‐C m −2 day −1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime F CH4 was negligible. In this mangrove, the mean annual CH 4 emission was 11.7 ± 0.4 g CH 4 ‐C m –2 year −1 while the annual net ecosystem CO 2 exchange ranged between −891 and −690 g CO 2 ‐C m −2 year −1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F CH4 could offset the negative radiative forcing caused by CO 2 uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained‐flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F CH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem‐scale F CH4 in a mangrove wetland with long‐term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH 4 emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.