Patricia Y. Oikawa

Unifying soil respiration pulses, inhibition, and temperature hysteresis through dynamics of labile soil carbon and O2

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.

Identifying scale‐emergent, nonlinear, asynchronous processes of wetland methane exchange

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.

Biophysical controls on interannual variability in ecosystem-scale CO2 and CH4 exchange in a California rice paddy

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.

Unusually high soil nitrogen oxide emissions influence air quality in a high-temperature agricultural region

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.

Offsetting high water demands with high productivity: Sorghum as a biofuel crop in a high irradiance arid ecosystem

High irradiance arid environments are promising, yet understudied, areas for biofuel production. We investigated the productivity and environmental trade‐offs of growing sorghum (Sorghum bicolor) as a biofuel feedstock in the low deserts of California (CA). Using a 5.3 ha experimental field in the Imperial Valley, CA, we measured aboveground biomass production and net ecosystem exchange of CO2 and H2O via eddy covariance over three growing periods between February and November 2012. Environmental conditions were extreme, with high irradiance, vapor pressure deficit (VPD), and air temperature throughout the growing season. Air temperature peaked in August with a maximum of 45.7 °C. Sorghum produced an annual aboveground biomass yield of 43.7 Mg per hectare. Net ecosystem exchange (NEE) was highest during the summer growth period and reached a maximum of −68 μmol CO2 m−2 s−1. Water use efficiency, or biomass water ratio (BWR), was high (4.0 g dry biomass kg−1 H2O) despite high seasonal evapotranspiration (1094 kg H2O m−2). The BWR of sorghum surpassed that of many C4 biofuel candidate crops in the United States, as well as that of alfalfa which is currently widely grown in the Imperial Valley. Sorghum also outperformed many US biofuel crops in terms of radiation use efficiency (RUE), achieving 1.5 g dry biomass MJ−1. We found no evidence of saturation of NEE at high levels of photosynthetically active radiation (PAR) (up to 2250 μmol m−2 s−1). In addition, we found no evidence that NEE was inhibited by either high VPD or air temperature during peak photosynthetic phases. The combination of high productivity, high BWR, and high RUE suggests that sorghum is well adapted to this extreme environment. The biomass production rates and efficiency metrics spanning three growing periods provide fundamental data for future Life Cycle Assessments (LCA), which are needed to assess the sustainability of this sorghum biofuel feedstock system.

Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands

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.

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.