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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. 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 (ANN) 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 structure from some with large areas of open water and little vegetation to very densely vegetated wetlands, 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 R2across all sites. The order of variable inclusion (and importance) was: vapor pressure deficit (VPD) > gap-filled sensible heat flux (H_gf) > air temperature (Tair) > friction velocity (u∗) > other variables. Overall, 36 ANNs were analyzed. The model using VPD, H_gf, Tair, and u∗(F11), showed an average testing R2value across all sites of 0.853. In comparison with the model that included all 10 variables (F36), F11 generally performed better during validation with independent data. In comparison to other methods described in the literature, the ANN method generated more consistent T/ET partitioning results especially for more complex 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.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET). ISSN:0168-1923 ISSN:1873-2240

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions, including their seasonality, due to quasi-continuous and high temporal resolution of flux measurements, coincident measurements of carbon, water, and energy fluxes, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we 1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4- community-product/). FLUXNET-CH4 includes half-hourly and daily gap-filled and non gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we 2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally, because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands and because freshwater wetlands are a substantial source of total atmospheric CH4 emissions; and 3) provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions, but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20° S to 20° N) the spring onset of elevated CH4 emissions starts three days earlier, and the CH4 emission season lasts 4 days longer, for each degree C increase in mean annual air temperature. On average, the onset of increasing CH4 emissions lags soil warming by one month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling, and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). The FLUXNET-CH4 dataset provides an open-access resource for CH4 flux synthesis, has a range of applications, and is unique in that it includes coupled measurements of important CH4 drivers such as GPP and temperature. Although FLUXNET-CH4 could certainly be improved by adding more sites in tropical ecosystems and by increasing the number of site-years at existing sites, it is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4408468. Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/, and a complete list of the 79 individual site data DOIs is provided in Table 2 in the Data Availability section of this document., Earth System Science Data Discussions, ISSN:1866-3591

While growth history of vegetation within upland systems is well studied, plant phenology within coastal tidal systems is less understood. Landscape-scale, satellite-derived indicators of plant greenness may not adequately represent seasonality of vegetation biomass and productivity within tidal wetlands due to limitations of cloud cover, satellite temporal frequency, and attenuation of plant signals by tidal flooding. However, understanding plant phenology is necessary to gain insight into aboveground biomass, photosynthetic activity, and carbon sequestration. In this study, we use a modeling approach to estimate plant greenness throughout a year in tidal wetlands located within the San Francisco Bay Area, USA. We used variables such as EVI history, temperature, and elevation to predict plant greenness on a 14-day timestep. We found this approach accurately estimated plant greenness, with larger error observed within more dynamic restored wetlands, particularly at early post-restoration stages. We also found modeled EVI can be used as an input variable into greenhouse gas models, allowing for an estimate of carbon sequestration and gross primary production. Our strategy can be further developed in future research by assessing restoration and management effects on wetland phenological dynamics and through incorporating the entire Sentinel-2 time series once it becomes available within Google Earth Engine.

Water is a limited and valuable resource in California. A large proportion of the fresh water for southern California is supplied by the Sacramento and San Joaquin rivers. With recent efforts to restore large areas of land in the Sacramento–San Joaquin Delta region from farmland to managed wetlands, it is important to investigate the effect of wetland restoration on the local water cycle. We measured evapotranspiration using the eddy covariance method over six different sites in the Sacramento–San Joaquin Delta from 2013 to 2016. The three restored wetlands sites differed in time since restoration and wetland structure, i.e. the area of open water compared to closed vegetation. The three agricultural sites were a flooded rice field, an alfalfa field, and a grazed cattle pasture. In most years, annual evapotranspiration was significantly lower at the drained agricultural sites, with 652 ± 131 mm (2 year average ± standard deviation, SD) for the pasture and 901 ± 24 mm (3 year average ± SD) for the alfalfa field, compared to the two open water wetlands, with 1091 ± 144 mm (4 year average ± SD) for the Mayberry wetland and 1140 ± 67 mm (3 year average ± SD) at the East End wetland. Annual evapotranspiration at the flooded rice site (975 ± 50 mm, 4 year average ± SD) or the closed vegetation wetland (West Pond wetland, 996 ± 63 mm, 4 year average ± SD) was not significantly different from the drained alfalfa site across individual years. Our analysis showed that the structural difference between the wetlands, specifically the fraction of open water compared to closed vegetation, has a large impact on evapotranspiration dynamics. Through analysis of normalized equilibrium evaporation and nighttime evapotranspiration measurements we deduced that evapotranspiration at the wetland with a low fraction of open water surfaces was almost entirely dominated by plant transpiration with very little contribution from evaporation, despite the fact that the site was flooded and water was readily available. Both evaporation and transpiration contributed substantially to evapotranspiration at the two wetlands with larger fraction of open water surfaces. At the closed canopy site evaporation from subcanopy water seemed to be inhibited through two mechanisms: first, the closed canopy prevented heating of the water column and led to significantly cooler water temperatures, which reduced surface vapor pressure. Second, the closed canopy decoupled the water surface from the atmosphere and inhibited turbulent transport of water vapor away from the water surface. Our results provide valuable insights into the water use in California wetlands and can inform decisions on how to maximize water conservation during wetland restoration.

EPA reports a steady decline of US anthropogenic NO x emissions in 2005–2019 summers, while NO2 vertical column densities (VCDs) from the OMI satellite over large spatial domains have flattened since 2009. To better understand the contributing factors to a flattening of the OMI NO2 trends, we investigate the role of soil and lightning NOx emissions on this apparent disagreement. We improve soil NO x emissions estimates using a new observation-based temperature response, which increases the linear correlation coefficient between GEOS-Chem simulated and OMI NO2 VCDs by 0.05–0.2 over the Central US. Multivariate trend analysis reveals that soil and lightning NO x combined emissions trends change from −3.95% a−1 during 2005–2009 to 0.60% a−1 from 2009 to 2019, thereby rendering the abrupt slowdown of total NO x emissions reduction. Non-linear inter-annual variations explain 6.6% of the variance of total NO x emissions. As background emissions become relatively larger with uncertain inter-annual variations, the NO2 VCDs alone at the national scale, especially in the regions with vast rural areas, will be insufficient to discern the trend of anthropogenic emissions.

Wetlands have the ability to accumulate large amounts of carbon (C), and therefore wetland restoration has been proposed as a means of sequestering atmospheric carbon dioxide (CO2) to help mitigate climate change. There is a growing interest in using the C services of wetlands to help reduce habitat loss and finance restoration projects. However, including wetlands in C markets worldwide requires a better mechanistic understanding of CO2 and methane exchange and instruments and models that can accurately and inexpensively monitor and predict these fluxes across global wetlands. Remote sensing technology, including near-surface and satellite instruments/approaches, is an effective tool for modeling C fluxes including gross primary productivity (GPP) from the site to global scale. In this study, we evaluate the potential of using digital cameras as a simple, cost-effective means of estimating GPP in restored wetlands, and assess the suitability of using Landsat data to model GPP in these environments for regional upscaling. Our research focused on restored temperate freshwater marshes due to their high C sequestration potential. As observed in other ecosystems, daily GPP was strongly correlated with site greenness derived from camera imagery (GCCcam). Based on this, we show the potential of using GCCcam and eddy covariance data to adapt and parameterize a light use efficiency (LUE) model to predict daily GPP. The LUE model combining GCCcam and meteorological data was able to explain up to 91% of the variation in daily GPP at the restored marshes, and predict annual GPP budgets within 0% to 20% of observed budgets. However, model performance decreased with increasing site complexity, highlighting the need to explicitly consider spatial heterogeneity in LUE models. We also tested a similar model using Landsat-derived indices, and found that although model performance was high at a homogeneous wetland dominated by emergent vegetation, data-model agreement decreased at a site comprised of a mixture of open water and vegetation, reflecting limitations of Landsat data. Nonetheless, we show that digital camera and Landsat imagery can be used to model photosynthesis in restored wetlands, providing low-cost methods for monitoring C capture that can be used in C market-funded wetland conservation and restoration.

Wetlands and flooded peatlands can sequester large amounts of carbon (C) and have high greenhouse gas mitigation potential. There is growing interest in financing wetland restoration using C markets; however, this requires careful accounting of both CO2 and CH4 exchange at the ecosystem scale. Here we present a new model, the PEPRMT model (Peatland Ecosystem Photosynthesis Respiration and Methane Transport), which consists of a hierarchy of biogeochemical models designed to estimate CO2 and CH4 exchange in restored managed wetlands. Empirical models using temperature and/or photosynthesis to predict respiration and CH4 production were contrasted with a more process-based model that simulated substrate-limited respiration and CH4 production using multiple carbon pools. Models were parameterized by using a model-data fusion approach with multiple years of eddy covariance data collected in a recently restored wetland and a mature restored wetland. A third recently restored wetland site was used for model validation. During model validation, the process-based model explained 70% of the variance in net ecosystem exchange of CO2 (NEE) and 50% of the variance in CH4 exchange. Not accounting for high respiration following restoration led to empirical models overestimating annual NEE by 33–51%. By employing a model-data fusion approach we provide rigorous estimates of uncertainty in model predictions, accounting for uncertainty in data, model parameters, and model structure. The PEPRMT model is a valuable tool for understanding carbon cycling in restored wetlands and for application in carbon market-funded wetland restoration, thereby advancing opportunity to counteract the vast degradation of wetlands and flooded peatlands.

Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, such as carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening – up to 73 days, start of senescence – up to 79 days, and end of senescence – up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatially-explicit analyses to inform assessments of restoration and management outcomes.

We present 6.5 years of eddy covariance measurements of fluxes of methane (FCH4) and carbon dioxide (FCO2) from a flooded rice paddy in Northern California, USA. A pronounced warming trend throughout the study associated with drought and record high temperatures strongly influenced carbon (C) budgets and provided insights into biophysical controls of FCO2 and FCH4. Wavelet analysis indicated that photosynthesis (gross ecosystem production, GEP) induced the diel pattern in FCH4, but soil temperature (Ts) modulated its amplitude. Forward stepwise linear models and neural networking modeling were used to assess the variables regulating seasonal FCH4. As expected due to their competence in modeling nonlinear relationships, neural network models explained considerably more of the variance in daily average FCH4 than linear models. During the growing season, GEP and water levels typically explained most of the variance in daily average FCH4. However, Ts explained much of the interannual variability in annual and growing season CH4 sums. Higher Ts also increased the annual and growing season ratio of FCH4 to GEP. The observation that the FCH4 to GEP ratio scales predictably with Ts may help improve global estimates of FCH4 from rice agriculture. Additionally, Ts strongly influenced ecosystem respiration, resulting in large interannual variability in the net C budget at the paddy, emphasizing the need for long-term measurements particularly under changing climatic conditions.

Methane (CH4) exchange in wetlands is complex, involving nonlinear asynchronous processes across diverse time scales. These processes and time scales are poorly characterized at the whole-ecosystem level, yet are crucial for accurate representation of CH4 exchange in process models. We used a combination of wavelet analysis and information theory to analyze interactions between whole-ecosystem CH4 flux and biophysical drivers in two restored wetlands of Northern California from hourly to seasonal time scales, explicitly questioning assumptions of linear, synchronous, single-scale analysis. Although seasonal variability in CH4 exchange was dominantly and synchronously controlled by soil temperature, water table fluctuations, and plant activity were important synchronous and asynchronous controls at shorter time scales that propagated to the seasonal scale. Intermittent, subsurface water table decline promoted short-term pulses of methane emission but ultimately decreased seasonal CH4 emission through subsequent inhibition after rewetting. Methane efflux also shared information with evapotranspiration from hourly to multiday scales and the strength and timing of hourly and diel interactions suggested the strong importance of internal gas transport in regulating short-term emission. Traditional linear correlation analysis was generally capable of capturing the major diel and seasonal relationships, but mesoscale, asynchronous interactions and nonlinear, cross-scale effects were unresolved yet important for a deeper understanding of methane flux dynamics. We encourage wider use of these methods to aid interpretation and modeling of long-term continuous measurements of trace gas and energy exchange.

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.

© 2018 John Wiley & Sons Ltd 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 CH4emissions on annual time scales. We measured ecosystem carbon dioxide (CO2) and CH4fluxes by eddy covariance from two wetlands recently restored on peat and alluvium soils within the Sacramento–San Joaquin Delta of California. Annual CH4fluxes 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 CH4fluxes were decoupled from plant processes compared with the peat site, as expected when Fe reduction inhibits CH4production in the rhizosphere. Soil carbon content and CO2uptake rates did not vary across wetlands and, thus, could also be ruled out as drivers of initial CH4flux differences. Differences in wetland CH4fluxes across soil types were transient; alluvium wetland fluxes were similar to peat wetland fluxes 3 years after restoration. Changing alluvium CH4emissions with time could not be explained by an empirical model based on dominant CH4flux 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 CH4emissions 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 CH4flux, but these effects appear to be transient without continued input of alluvium to wetland sediments.

Event-driven and diel dynamics of soil respiration (Rs) strongly influence terrestrial carbon (C) emissions and are difficult to predict. Wetting events may cause a large pulse or strong inhibition of Rs. Complex diel dynamics include hysteresis in the relationship between Rs and soil temperature. The mechanistic basis for these dynamics is not well understood, resulting in large discrepancies between predicted and observed Rs. We present a unifying approach for interpreting these phenomena in a hot arid agricultural environment. We performed a whole ecosystem wetting experiment with continuous measurement of Rs to study pulse responses to wetting in a heterotrophic system. We also investigated Rs during cultivation of Sorghum bicolor to evaluate the role of photosynthetic C in the regulation of diel variation in Rs. Finally, we adapted a Rs model with sensitivity to soil O2 and water content by incorporating two soil C pools differing in lability. We observed a large wetting-induced pulse of Rs from the fallow field and were able to accurately simulate the pulse via release of labile soil C. During the exponential phase of plant growth, Rs was inhibited in response to wetting, which was accurately simulated through depletion of soil O2. Without plants, hysteresis was not observed; however, with growing plants, an increasingly significant counterclockwise hysteresis developed. Hysteresis was simulated via a dynamic photosynthetic C pool and was not likely controlled by physical processes. These results help characterize the complex regulation of Rs and improve understanding of these phenomena under warmer and more variable conditions.

Fertilized soils have large potential for production of soil nitrogen oxide (NOx=NO+NO2), however these emissions are difficult to predict in high-temperature environments. Understanding these emissions may improve air quality modelling as NOx contributes to formation of tropospheric ozone (O3), a powerful air pollutant. Here we identify the environmental and management factors that regulate soil NOx emissions in a high-temperature agricultural region of California. We also investigate whether soil NOx emissions are capable of influencing regional air quality. We report some of the highest soil NOx emissions ever observed. Emissions vary nonlinearly with fertilization, temperature and soil moisture. We find that a regional air chemistry model often underestimates soil NOx emissions and NOx at the surface and in the troposphere. Adjusting the model to match NOx observations leads to elevated tropospheric O3. Our results suggest management can greatly reduce soil NOx emissions, thereby improving air quality., Soil NOx emissions can significantly impact air quality in agricultural regions, particularly high temperature fertilized systems. Here, the authors investigate NOx emissions in one such system in California and suggest that the NOx emissions are the highest ever observed, with implications for air quality.