A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems.

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 R2 across all sites. The order of variable inclusion (and importance) was: VPD > gap-filled sensible heat flux (H_gf) > air temperature (Tair ) > friction velocity (u∗ ) > other variables. The model using VPD, H_gf, Tair , and u∗ showed the best performance during validation with independent data and had a mean testing R2  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 CO2 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.

Soil properties and sediment accretion modulate methane fluxes from restored wetlands.

Wetlands are the largest source of methane (CH4 ) globally, yet our understanding of how process-level controls scale to ecosystem fluxes remains limited. It is particularly uncertain how variable soil properties influence ecosystem CH4 emissions on annual time scales. We measured ecosystem carbon dioxide (CO2 ) and CH4 fluxes by eddy covariance from two wetlands recently restored on peat and alluvium soils within the Sacramento-San Joaquin Delta of California. Annual CH4 fluxes from the alluvium wetland were significantly lower than the peat site for multiple years following restoration, but these differences were not explained by variation in dominant climate drivers or productivity across wetlands. Soil iron (Fe) concentrations were significantly higher in alluvium soils, and alluvium CH4 fluxes were decoupled from plant processes compared with the peat site, as expected when Fe reduction inhibits CH4 production in the rhizosphere. Soil carbon content and CO2 uptake rates did not vary across wetlands and, thus, could also be ruled out as drivers of initial CH4 flux differences. Differences in wetland CH4 fluxes across soil types were transient; alluvium wetland fluxes were similar to peat wetland fluxes 3 years after restoration. Changing alluvium CH4 emissions with time could not be explained by an empirical model based on dominant CH4 flux biophysical drivers, suggesting that other factors, not measured by our eddy covariance towers, were responsible for these changes. Recently accreted alluvium soils were less acidic and contained more reduced Fe compared with the pre-restoration parent soils, suggesting that CH4 emissions increased as conditions became more favorable to methanogenesis within wetland sediments. This study suggests that alluvium soil properties, likely Fe content, are capable of inhibiting ecosystem-scale wetland CH4 flux, but these effects appear to be transient without continued input of alluvium to wetland sediments.

Evaluating the GHG mitigation-potential of alternate wetting and drying in rice through life cycle assessment.

Alternate wetting and drying (AWD), has gained increasing attention as a promising strategy for mitigating greenhouse gas emissions (GHG) in flooded rice systems. AWD involves periodic drainage of rice paddies in order to inhibit methane (CH4) emissions. To date, studies evaluating this practice have been limited in their scope and resolution. Our study evaluates the mitigation potential of AWD from a life cycle perspective using high-resolution CH4 modeling to more accurately estimate the mitigation potential of this practice. We simulated California rice production under continuous flooding and under five AWD schedules ranging in the severity and frequency of dry-downs. Production models were coupled with the Peatland Ecosystem Photosynthesis Respiration and Methane Transport (PEPRMT) model to simulate CH4 fluxes at daily intervals. We then evaluated the GHG mitigation potential of AWD using life cycle assessment models. Frequent or severe dry-downs reduced simulated grain yields, which negated some of the benefits of AWD when assessed on a yield-scaled basis. We also found AWD-induced mitigation of CH4 emissions modeled with PEPRMT to be roughly half the magnitude reported from up-scaling of chamber measurements, highlighting the importance of high resolution field data to better characterize GHGs in rice systems. Reduced yields and conservative CH4 mitigation in our model lessened the overall mitigation potential of AWD. When the entire rice life cycle was considered, mitigation of overall global warming potential (GWP) was further reduced by the presence of additional GHG sources, which comprised roughly half of life cycle GWP. Our simulations resulted in ≤12% reductions in GWP kg-1 across all AWD scenarios and saw an increase in GWP when yields were severely reduced. Our results highlight the importance of constraining uncertainties in CH4 emissions and considering a life cycle perspective expressed on a yield-scaled basis in characterizing the mitigation potential of AWD.

Catabolism of volatile organic compounds influences plant survival.

Plants emit a diverse array of phytogenic volatile organic compounds (VOCs). The production and emission of VOCs has been an important area of research for decades. However, recent research has revealed the importance of VOC catabolism by plants and VOC degradation in the atmosphere for plant growth and survival. Specifically, VOC catabolism and degradation have implications for plant C balance, tolerance to environmental stress, plant signaling, and plant-atmosphere interactions. Here we review recent advances in our understanding of VOC catabolism and degradation, propose experiments for investigating VOC catabolism, and suggest ways to incorporate catabolism into VOC emission models. Improving our knowledge of VOC catabolism and degradation is crucial for understanding plant metabolism and predicting plant survival in polluted environments.