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The oak (Quercus) species of eastern North America are declining in abundance, threatening the many socioecological benefits they provide. We discuss the mechanisms responsible for their loss, many of which are rooted in the prevailing view that oaks are drought tolerant. We then synthesize previously published data to comprehensively review the drought response strategies of eastern US oaks, concluding that whether or not eastern oaks are drought tolerant depends firmly on the metric of success. Although the anisohydric strategy of oaks sometimes confers a gas exchange and growth advantage, it exposes oaks to damaging hydraulic failure, such that oaks are just as or more likely to perish during drought than neighboring species. Consequently, drought frequency is not a strong predictor of historic patterns of oak abundance, although long-term climate and fire frequency are strongly correlated with declines in oak dominance. The oaks’ ability to survive drought may become increasingly difficult in a drier future.

BACKGROUND Hypoglycemia can be a common occurrence in hospitalized patients, both those with and without diabetes. Hypoglycemia poses significant risks to hospitalized patients, including increased mortality. OBJECTIVES This was a retrospective pre-post study of hypoglycemic patients in an academic medical center of an intervention to improve timely staff nurse adherence to a hypoglycemia protocol. The number of mild and severe hypoglycemia events pre- and postintervention, timeliness of adherence to the hypoglycemia protocol, the number of treatment interventions, and time to return patients to euglycemia were analyzed. METHODS Data from hospitalizations of patients who experienced hypoglycemia (

International audience; Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the `potential' to substantially reduce conceptual and modelling uncertainties.

International audience; The coordination of plant leaf water potential (psi(L)) regulation and xylem vulnerability to embolism is fundamental for understanding the tradeoffs between carbon uptake and risk of hydraulic damage. There is a general consensus that trees with vulnerable xylem more conservatively regulate psi(L) than plants with resistant xylem. We evaluated if this paradigm applied to three important eastern US temperate tree species, Quercus alba L., Acer saccharum Marsh. and Liriodendron tulipifera L., by synthesizing 1600 psi(L) observations, 122 xylem embolism curves and xylem anatomical measurements across 10 forests spanning pronounced hydroclimatological gradients and ages. We found that, unexpectedly, the species with the most vulnerable xylem (Q. alba) regulated psi(L) less strictly than the other species. This relationship was found across all sites, such that coordination among traits was largely unaffected by climate and stand age. Quercus species are perceived to be among the most drought tolerant temperate US forest species; however, our results suggest their relatively loose psi(L) regulation in response to hydrologic stress occurs with a substantial hydraulic cost that may expose them to novel risks in a more drought-prone future.

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure–volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring below-ground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj

At the leaf level, stomata control the exchange of water and carbon across the air-leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, it has not been evaluated how well the optimization models perform at larger scales. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves or the entire canopy. As a result, they are unable to evaluate the impact of vertical gradients within the canopy, or directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth System model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (c. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1 : 1 comparison (slope increases from c. 0.34 to 0.91 for the gymnosperm forest, and from c. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

International audience; It is well-known that soil carbon composition and turnover rate vary with soil depth, and the responses of soil carbon to global change in deeper soil layers may differ from those near the surface. Therefore, vertically resolved soil carbon models are needed for accurately predicting future soil carbon under global warming. In this study, we developed a vertically resolved soil carbon model by including vertical transport of soil carbon and using Michaelis-Menten kinetics for soil carbon decomposition by microbes. The model was calibrated against six sites with the observed profiles of both soil carbon concentration and 14C measurements, and against 91 forest sites with soil carbon concentrations across a wide range of climate and soil conditions for four forest types in Europe and China. Results of independent model validation at another 93 sites showed that the calibrated model explained 40%–94% of the observed variance of soil carbon concentrations at different depths. Model sensitivity analysis showed that microbial activity and root carbon inputs are more important than soil carbon diffusion in simulating soil carbon profile. Results from our study highlight the need for detailed measurements of soil microbial activities and root carbon input at different soil depths in the field.

The methane (CH(4)) budget and its source partitioning are poorly constrained in the Midwestern United States. We used tall tower (185 m) aerodynamic flux measurements and atmospheric scale factor Bayesian inversions to constrain the monthly budget and to partition the total budget into natural (e.g., wetlands) and anthropogenic (e.g., livestock, waste, and natural gas) sources for the period June 2016 to September 2017. Aerodynamic flux observations indicated that the landscape was a CH(4) source with a mean annual CH(4) flux of +13.7 ± 0.34 nmol m(−2) s(−1) and was rarely a net sink. The scale factor Bayesian inversion analyses revealed a mean annual source of +12.3 ± 2.1 nmol m(−2) s(−1). Flux partitioning revealed that the anthropogenic source (7.8 ± 1.6 Tg CH(4) yr(−1)) was 1.5 times greater than the bottom-up gridded United States Environmental Protection Agency inventory, in which livestock and oil/gas sources were underestimated by 1.8-fold and 1.3-fold, respectively. Wetland emissions (4.0 ± 1.2 Tg CH(4) yr(−1)) were the second largest source, accounting for 34% of the total budget. The temporal variability of total CH(4) emissions was dominated by wetlands with peak emissions occurring in August. In contrast, emissions from oil/gas and other anthropogenic sources showed relatively weak seasonality.

The coordination of plant leaf water potential (ΨL) regulation and xylem vulnerability to embolism is fundamental for understanding the tradeoffs between carbon uptake and risk of hydraulic damage. A legacy of observations in drylands suggests plants with vulnerable xylem more carefully regulate ΨL than plants with resistant xylem. We synthesized over 1600 ΨL observations, 122 xylem embolism curves, and xylem anatomical measurements of Quercus alba L., Liriodendron tulipifera L., and Acer saccharum Marsh. across ten contrasting forests to evaluate if the paradigm linking conservative ΨL regulation to vulnerable xylem applies to temperate deciduous trees. Additionally, we explored generalizable patterns of hydraulic trait acclimation in relation to forest age and climate. Contrary to the dryland paradigm, we found that the tree species with the most vulnerable xylem (e.g., Q. alba) regulated ΨL less strictly (anisohydric behavior) than the species with xylem more resistant to embolism (e.g., A. saccharum and L. tulipifera). This relationship was found across all sites, suggesting coordination among traits was largely unaffected spatio-temporal factors. Our findings indicate drought-response traits of temperate deciduous forest species are coordinated in fundamentally different ways than vegetation in arid climates.

The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future.

Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.

Water and salt stress often occur simultaneously in heavily irrigated arid agricultural areas, yet they are usually studied in isolation. To understand the physiological bases of water use efficiency (WUE) of field-grown maize (Zea mays) at multi-scales under combined water and salt stress, we investigated the joint effects of water and salt stress on physiology, growth, yield, and WUE of two genotypes (XY335 and ZD958). We measured leaf stomatal conductance (gs), net photosynthesis rate (A) and hydraulic traits, whole-plant growth and water use (ET), and final biomass and grain yield. Leaf osmotic adjustment was a key trait of the physiological differences between XY335 and ZD958 under water and salt stress. Although the responses of the two genotypes were different, mild water and salt stress improved intrinsic water use efficiency (iWUE = A/gs) by (i) decreasing gs via increasing osmotic adjustment and hydraulic resistance, and (ii) declining A via increasing stomatal limitations rather than reducing photosynthetic capacity. Joint water and salt stress had a synergistic effect on reproductive growth and grain formation of maize. Mild water and salt stress reduced ET, stabilized grain yield, and improved grain WUE via declining gs, maintaining photosynthetic capacity, and improving harvest index. Collectively, our study provides a novel insight into the physiological mechanisms of WUE and demonstrates an approach for the efficient management of water and salt by using a growth stage-based deficit irrigation strategy or/and selecting genotypes with strong osmotic adjustment capacity and high harvest index.

Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux (H). We evaluated aerodynamic resistance and surface roughness lengths for momentum (z0m) and heat (z0h) using the kB−1 parameter (ln(z0m/z0h)). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer (Rah). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with Rah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in Rah were not linked to green-up. Our findings highlight that decreases in kB−1 are an important control over Rah, explaining > 50% of seasonal variation in Rah across most sites. Decreases in kB−1 during green-up are likely caused by increasing z0h in response to higher leaf area index. Accounting for seasonal variation in kB−1 is key for predicting H as well; assuming kB−1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.

Sun-induced chlorophyll fluorescence (SIF) from spaceborne sensors is a promising tool for global carbon cycle monitoring, but its application is constrained by insufficient understanding of the drivers underlying diurnal SIF dynamics. SIF measurements from ground-based towers can reveal diurnal SIF dynamics across biomes and environmental conditions; however, meaningful interpretation of diurnal variations requires disentangling impacts from canopy structure, plant physiology, instrument configuration and retrieval methods, which often interact with and confound each other. This study aims to unpack these drivers using 1) concurrent ground and airborne canopy-scale and leaf-scale measurements at a corn field, 2) a mechanistic SIF model that explicitly considers the dynamics of photochemistry (via the fraction of open photosystem II reaction centers, qL) and photoprotection (via nonphotochemical quenching, NPQ) as well as their interactive dependence on the sub-canopy light environment, and 3) cross-comparison of SIF instrument configurations and retrieval methods. We found that crop row orientations and sun angles can introduce a distinctive midday dip in SIF in absence of stress, due to a midday drop of absorbed photosynthetically active radiation (APAR) when crop rows are north-south oriented. Canopy structure caused distinctive responses in both qL and NPQ at different positions within the vertical canopy that collectively influenced fluorescence quantum yield (ΦF) at the leaf scale. Once integrated at the canopy scale, diurnal dynamics of both APAR and canopy escape probability (e) are critical for accurately shaping diurnal SIF variations. While leaf-level qL and NPQ exhibited strong diurnal dynamics, their influence was attenuated at the canopy scale due to opposing effects on SIF at different canopy layers. Furthermore, different system configurations (i.e., bi-hemispherical vs. hemispherical-conical) and retrieval methods can bias the SIF magnitude and distort its diurnal shapes, therefore confounding the interpretation of inherent strength and dynamics of SIF emission. Our findings demonstrate the importance of crop row structures, interactive variations in canopy structure and plant physiology, instrument configuration, and retrieval method in shaping the measured dynamics of diurnal SIF. This study highlights the necessity to account for these factors to accurately interpret satellite SIF, and informs future synthesis work with different SIF instrumentation and retrieval methods across sites.

Remotely sensed microwave observations of vegetation optical depth (VOD) have been widely used for examining vegetation responses to climate. Nevertheless, the relative impacts of phenological changes in leaf biomass and water stress on VOD have not been explicitly disentangled. In particular, determining whether leaf water potential (ψL) affects VOD may allow these data sets as a constraint for plant hydraulic models. Here we test the sensitivity of VOD to variations in ψL and present a conceptual framework that relates VOD to ψL and total biomass including leaves, whose dynamics are measured through leaf area index, and woody components. We used measurements of ψL from three sites across the US—a mixed deciduous forests in Indiana and Missouri and a pinon-juniper woodland in New Mexico—to validate the conceptual model. The temporal dynamics of X-band VOD were similar to those of the VOD signal estimated from the new conceptual model with observed ψL (R2 = 0.6–0.8). At the global scale, accounting for a combination of biomass and estimated ψL (based on satellite surface soil moisture data) increased correlations with VOD by ~ 15% and 30% compared to biomass and water potential, respectively. In wetter regions with denser and taller canopy heights, VOD has a higher correlation with leaf area index than with water stress and vice versa in drier regions. Our results demonstrate that variations in both phenology and ψL must be considered to accurately interpret the dynamics of VOD observations for ecological applications.

Agriculture and waste are thought to account for half or more of the U.S. anthropogenic methane source. However, current bottom-up inventories contain inherent uncertainties from extrapolating limited in situ measurements to larger scales. Here, we employ new airborne methane measurements over the U.S. Corn Belt and Upper Midwest, among the most intensive agricultural regions in the world, to quantify emissions from an array of key agriculture and waste point sources. Nine of the largest concentrated animal feeding operations in the region and two sugar processing plants were measured, with multiple revisits during summer (August 2017), winter (January 2018), and spring (May-June 2018). We compare the top-down fluxes with state-of-science bottom-up estimates informed by U.S. Environmental Protection Agency methodology and site-level animal population and management practices. Top-down point source emissions are consistent with bottom-up estimates for beef concentrated animal feeding operations but moderately lower for dairies (by 37% on average) and significantly lower for sugar plants (by 80% on average). Swine facility results are more variable. The assumed bottom-up seasonality for manure methane emissions is not apparent in the aircraft measurements, which may be due to on-site management factors that are difficult to capture accurately in national-scale inventories. If not properly accounted for, such seasonal disparities could lead to source misattribution in top-down assessments of methane fluxes.

Forests provide many important service functions, including habitat for wildlife, timber production and watershed water regulation. Short-term drought can make forests more susceptible to wildfire and insect attack, while long-term drought can directly increase forest mortality. Evapotranspiration (ET) is a key parameter that links the hydrological and ecological processes. Studying the impact of drought on ET, especially at stand-scale, can provide useful information for forest management. In this study, we applied a multi-scale data fusion ET modeling method using remotely sensed data to estimate daily 30 m ET over a natural forest in central US from 2010 to 2012, with 2012 as an extreme drought year. The estimated ET agrees well with the observed ET. Drought impact on ET is further analyzed and demonstrates the value of remotely sensed ET in studying drought impact on forest water use.

Greenhouse gas (GHG) emissions, especially methane (CH4 ), from manure storage facilities can be substantial. Methane production requires adapted microbial communities ("inoculum") to be present in the manure. Complete removal of liquid dairy manure (thus removing all inoculum) from storage tanks in the spring has been shown to significantly reduce CH4 emissions over the following warm season. This study examined whether the same mitigation effect would occur after fall removal of liquid dairy manure. Emissions of CH4 , nitrous oxide (N2 O), ammonia (NH3 ), and CO2 were measured from six 11.88-m3 tanks equipped with flow-through chambers. There were three inoculated controls (20% inoculum) and three uninoculated treatments, where inoculum was completely removed in the fall/winter (0% inoculum). Direct N2 O and NH3 (indirect N2 O) were minor contributors to the total GHG budget, contributing

Stored liquid dairy manures are methane (CH) emission hotspots because of the large amount of slurry volatile solids (VS) converted into CH by methanogens under anaerobic conditions. Our research has indicated that a reduction of total solids (TS) of slurries before storage can reduce CH emissions. In the current study, methanogen abundance was characterized in tanks with different CH emissions. Using mesoscale slurry storage facilities equipped for continuous gaseous emission monitoring, we stored dairy slurries having TS from 9.5 to 0.3% for up to 6 mo. Samples were taken after Day 30 and Day 120 of the storage (20 May-16 Nov. 2010) from the upper and bottom layers of the slurries. Methanogenic communities were studied by targeting the gene encoding the α subunit methyl-coenzyme M reductase (), which catalyzes the final step of methanogenesis. Interestingly, mean abundances of methanogens increased by ∼8 and 23% at the top and bottom sections, respectively, as slurry TS decreased from 9.5 to 0.3%. Cumulative CH emissions, however, decreased by ∼70% as slurry TS decreased from 9.5 to 0.3%. Nevertheless, compared with Day 30 of storage, mean abundances of methanogens were relatively higher at Day 120 (up to 19%), consistent with an increase in the cumulative CH emissions. Polymerase chain reaction denaturing gel electrophoresis analysis indicated a low methanogen diversity, with most bands sequenced closely related to the genus (>95% amino acid sequence similarity), the hydrogenotrophic methanogens. Results suggest that available carbon substrate and not methanogen abundance may be limiting cumulative CH emissions at reduced TS levels of dairy slurries.

Nitrous oxide (N2O) emissions within the US Corn Belt have been previously estimated to be 200–900% larger than predictions from emission inventories, implying that one or more source categories in bottom-up approaches are underestimated. Here we interpret hourly N2O concentrations measured during 2010 and 2011 at a tall tower using a time-inverted transport model and a scale factor Bayesian inverse method to simultaneously constrain direct and indirect agricultural emissions. The optimization revealed that both agricultural source categories were underestimated by the Intergovernmental Panel on Climate Change (IPCC) inventory approach. However, the magnitude of the discrepancies differed substantially, ranging from 42 to 58% and from 200 to 525% for direct and indirect components, respectively. Optimized agricultural N2O budgets for the Corn Belt were 319 ± 184 (total), 188 ± 66 (direct), and 131 ± 118 Gg N yr−1 (indirect) in 2010, versus 471 ± 326, 198 ± 80, and 273 ± 246 Gg N yr−1 in 2011. We attribute the interannual differences to varying moisture conditions, with increased precipitation in 2011 amplifying emissions. We found that indirect emissions represented 41–58% of the total agricultural budget, a considerably larger portion than the 25–30% predicted in bottom-up inventories, further highlighting the need for improved constraints on this source category. These findings further support the hypothesis that indirect emissions are presently underestimated in bottom-up inventories. Based on our results, we suggest an indirect emission factor for runoff and leaching ranging from 0.014 to 0.035 for the Corn Belt, which represents an upward adjustment of 1.9–4.6 times relative to the IPCC and is in agreement with recent bottom-up field studies.

Increasing atmospheric humidity and convective precipitation over land provide evidence of intensification of the hydrologic cycle – an expected response to surface warming. The extent to which terrestrial ecosystems modulate these hydrologic factors is important to understand feedbacks in the climate system. We measured the oxygen and hydrogen isotope composition of water vapor at a very tall tower (185 m) in the upper Midwest, United States, to diagnose the sources, transport, and fractionation of water vapor in the planetary boundary layer (PBL) over a 3-year period (2010 to 2012). These measurements represent the first set of annual water vapor isotope observations for this region. Several simple isotope models and cross-wavelet analyses were used to assess the importance of the Rayleigh distillation process, evaporation, and PBL entrainment processes on the isotope composition of water vapor. The vapor isotope composition at this tall tower site showed a large seasonal amplitude (mean monthly δ18Ov ranged from −40.2 to −15.9 ‰ and δ2Hv ranged from −278.7 to −113.0 ‰) and followed the familiar Rayleigh distillation relation with water vapor mixing ratio when considering the entire hourly data set. However, this relation was strongly modulated by evaporation and PBL entrainment processes at timescales ranging from hours to several days. The wavelet coherence spectra indicate that the oxygen isotope ratio and the deuterium excess (dv) of water vapor are sensitive to synoptic and PBL processes. According to the phase of the coherence analyses, we show that evaporation often leads changes in dv, confirming that it is a potential tracer of regional evaporation. Isotope mixing models indicate that on average about 31 % of the growing season PBL water vapor is derived from regional evaporation. However, isoforcing calculations and mixing model analyses for high PBL water vapor mixing ratio events ( > 25 mmol mol−1) indicate that regional evaporation can account for 40 to 60 % of the PBL water vapor. These estimates are in relatively good agreement with that derived from numerical weather model simulations. This relatively large fraction of evaporation-derived water vapor implies that evaporation has an important impact on the precipitation recycling ratio within the region. Based on multiple constraints, we estimate that the summer season recycling fraction is about 30 %, indicating a potentially important link with convective precipitation.

Tropical peatlands are a major, but understudied, biophysical feedback factor on the atmospheric greenhouse effect. The largest expanses of tropical peatlands are located in lowland areas of Southeast Asia and the Amazon basin. The Loreto Region of Amazonian Peru contains ~63,000 km2 of peatlands. However, little is known about the biogeochemistry of these peatlands, and in particular, the cycling of carbon dioxide (CO2) and methane (CH4), and their responses to hydrometeorological forcings. To address these knowledge gaps, we established an eddy covariance (EC) flux tower in a natural palm (Mauritia flexuosa L.f.) swamp peatland near Iquitos, Peru. Here, we report ecosystem-scale CO2 and CH4 flux observations for this Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on CO2 and CH4 fluxes. High air temperature and vapor pressure deficit (VPD) exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration (RE) was either inhibited or enhanced depending on water table position. The cumulative net ecosystem CO2 exchange indicated that the peatland was a significant CO2 sink ranging from −465 (−279 to −651) g C m−2 y−1 in 2018 to −462 (−277 to −647) g C m−2 y−1 in 2019. The forest was a CH4 source of 22 (20 to 24) g C m−2 y−1, similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Thus, the annual carbon budget of this Amazonian palm swamp peatland appears to be a major carbon sink under current hydrometeorological conditions.

Mid-Missouri experienced up to 2 min 40 s of totality at around solar noon during the total eclipse of 2017. We conducted the Mid-Missouri Eclipse Meteorology Experiment to examine land-atmosphere interactions during the eclipse. Here, research examining the eclipse responses in three contrasting ecosystems (forest, prairie, and soybeans) is described. There was variable cloudiness around first and fourth contacts (i.e., the start and end of partial solar obscuration) at the forest and prairie; however, solar irradiance (K(↓)) signals during the eclipse were relatively clean. Unfortunately, the eclipse forcing at the soybean field was contaminated by convective activity, which decreased K(↓) beginning about an hour before first contact and exposed the field to cold outflow ~30 min before second contact. Turbulence was suppressed during the eclipse at all sites; however, there was also an amplified signal at the soybean field during the passage of a gust front. The standard deviations of the horizontal and vertical wind velocities and friction velocities decreased by ~75% at the forest (aerodynamically rough), and ~60% at the prairie (aerodynamically smooth). The eddy fluxes of energy were highly coherent with the solar forcing with the latent and sensible heat fluxes approaching 0 W/m(2) and changing in direction, respectively. For the prairie site, we estimated a canopy-scale time constant for the surface conductance light response of 10 min. Although the eclipse imparted large forcings on surface energy balances, the air temperature response was relatively muted (1.5–2.5 °C decrease) due to the absence of topographic effects and the relatively moist land and atmosphere.

Recent progress in observing sun-induced Chl fluorescence (SIF) provides an unprecedented opportunity to advance photosynthesis research in natural environments. However, we still lack an analytical framework to guide SIF studies and integration with the well-developed active fluorescence approaches. Here, we derive a set of coupled fundamental equations to describe the dynamics of SIF and its relationship with C3 and C4 photosynthesis. These equations show that, although SIF is dynamically as complex as photosynthesis, the measured SIF simplifies photosynthetic modeling from the perspective of light reactions by integrating over the dynamic complexities of photosynthesis. Specifically, the measured SIF contains direct information about the actual electron transport from photosystem II to photosystem I, giving a quantifiable link between light and dark reactions. With much-reduced requirements on inputs and parameters, the light-reactions-centric, SIF-based biophysical model complements the traditional, dark-reactions-centric biochemical model of photosynthesis. The SIF-photosynthesis relationship, however, is nonlinear. This is because photosynthesis saturates at high light whereas SIF has a stronger tendency to keep increasing, as fluorescence quantum yield has a relatively muted sensitivity to light levels. Successful applications of the SIF-based model of photosynthesis will depend on a predictive understanding of several previously underexplored physiological and biophysical processes. Advances can be facilitated by coordinated efforts in plant physiology, remote sensing, and eddy covariance flux observations.

Accurate simulations of soil respiration and carbon dioxide (CO2) efflux are critical to project global biogeochemical cycles and the magnitude of carbon (C) feedbacks to climate change in Earth system models (ESMs). Currently, soil respiration is not represented well in ESMs, and few studies have attempted to address this deficiency. In this study, we evaluated the simulation of soil respiration in the Energy Exascale Earth System Model (E3SM) using long-term observations from the Missouri Ozark AmeriFlux (MOFLUX) forest site in the central U.S. Simulations using the default model parameters significantly underestimated annual soil respiration and gross primary production, while underestimating soil water potential during growing seasons and overestimating it during non-growing seasons. A site-specific soil water retention curve significantly improved modelled soil water potential, gross primary production and soil respiration. However, the model continued to underestimate soil respiration during peak growing seasons, and overestimate soil respiration during non-peak growing seasons. One potential reason may be that the current model does not adequately represent the seasonal cycle of microbial organisms and soil macroinvertebrates, which have high biomass and activity during peak growing seasons and tend to be dormant during non-growing seasons. Our results confirm that modelling soil respiration can be significantly improved by better model representations of the soil water retention curve.

A 2-yr study compared the performance of seasonally and continuously loaded constructed wetlands treating dairy farm wastewater. One wetland was loaded during the growing season (GS) periods only, while the other was continuously loaded. Weekly samples were analyzed for 5-d biochemical oxygen demand (BOD), total suspended solids (TSS), total Kjeldahl N (TKN), total ammoniacal N (TAN), total P (TP), and . Annual average daily mass removal rates (kg ha) were similar for both wetlands in both years; however, seasonal differences were observed. With the exception of BOD in Year 2, average daily GS areal mass removal rates were higher for the seasonal wetland. However, GS mass exports from the seasonal wetland were higher by 28 to 94%, with the exception of BOD in Year 1. Annual mass reductions (MRs; %) for nutrients were higher for the continuous wetland in both years. Annual MRs were similar for in both years and for TSS in Year 2. Annual mass exports from the seasonal wetland were higher for nutrients and by 14 to 77% in both years. Pollutant MRs generally decreased during the nongrowing season (NGS) for the continuous wetland; however, in Year 2 when lower loading rates were used, the wetland still removed 84 to 99% of the pollutant masses. The continuous wetland also performed better during periods of high flow that occurred during the GS. Although there were minimal differences in annual treatment performance, continuously loaded systems require less additional infrastructure and should require less maintenance and may, therefore, be more attractive for agricultural applications.