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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.

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.

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.

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

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.

N2O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N2O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N2O emissions, implying that EFs may be biased low. Here, we measured N2O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N2O fluxes from headwater streams often exceeded 29 nmol N2O-N m(-2) ⋅ s(-1) and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N2O emissions by 40%. We show that accounting for zero-order streams as potential N2O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5r underestimation explains the large differences observed between top-down and bottom-up emission estimates.

All-black thermopile pyranometers are commonly used to measure solar radiation. Ensuring that the sensors are stable and free of drift is critical to accurately measure small variations in global solar irradiance at the Earth’s surface (K↓), which is a potential driver of changes in surface temperature. We demonstrate that the decreased responsivities of Eppley PSP pyranometers of −1.5% y−1, or −0.38% (GJ m−2)−1, were accompanied by a change in its spectral response owing to a discoloration of the sensing element. These observations motivated further work to develop routines to detect probable pyranometer drift in historical time-series. The temporal trends in the following ratios were used to detect pyranometer sensor drift: photosynthetically active radiation (PAR) to K↓, K↓ to KEX (extraterrestrial radiation at the top of the atmosphere) and PAR to KEX. Data from 8 AmeriFlux sites spanning latitudes from ∼32 to 54°N were examined in this analysis. Probable drift in either a pyranometer or PAR sensor was identified at 5 of the 8 sites. The magnitude of the drift represented changes of 0.15–0.85% y−1, which is sufficient to obscure actual trends in K↓, although these should be considered conservative low end drift estimates, given that we were not making comparisons to co-located higher grade instruments. Deployment exposure errors caused by sensor shading were also discovered by comparing the daily correlations between (i) K↓ and KEX and (ii) PAR and KEX. Sensors drifting at rates similar to our defective PSP over a 5 year period would contribute to an underestimation of available energy of ∼70 W m−2, which is non-trivial in the context of assessing eddy covariance energy balance closure, employing Penman-Monteith or Bowen ratio methods or calculating albedo radiative forcings. Given that probable drift was identified at multiple AmeriFlux sites, we recommend enhancing network access to calibration services that are traceable to a high quality gold standard.

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4-Crop and CLM3.5-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5-CornSoy-simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long-wave radiation bias. CLM4-Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes.

Nitrous oxide (N2O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N2O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N2O mixing ratios from a very tall tower within the US Corn Belt-one of the most intensive agricultural regions of the world-combined with inverse modeling, shows large interannual variability in N2O emissions (316 Gg N2O-N⋅y-1 to 585 Gg N2O-N⋅y-1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N2O emissions that will exceed 600 Gg N2O-N⋅y-1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N2O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N2O emission mitigation efforts to achieve its goals.

Wetlands represent the dominant natural source of methane (CH(4)) to the atmosphere. Thus, substantial effort has been spent examining the CH(4) budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH(4) fluxes using a 3.5 year time series of simultaneous open- and closed path CH(4) flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH(4) flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH(4) fluxes were typically 6%–41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH(4) fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g − CH(4) m(−2) yr(−1)). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH(4) emissions for both EC systems were in excellent agreement (within 0.6 g − CH(4) m(−2) yr(−1)) and averaged 18 g − CH(4) m(−2) yr(−1). Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7–17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Solar induced fluorescence (SIF) has shown great promise for probing spatiotemporal variations in terrestrial gross primary production (GPP), the largest component flux of the global carbon cycle. However, scale mismatches between SIF and ground-based GPP have posed challenges towards fully exploiting these data. We used SIF obtained at high spatial sampling rates and resolution by NASA's Orbiting Carbon Observatory (OCO-2) satellite to elucidate GPP-SIF relationships across space and time in the US Corn Belt. Strong linear scaling functions (R2 ≥ 0.79) that were consistent across instantaneous to monthly timescales were obtained for corn ecosystems, and for a heterogeneous landscape based on tall tower observations. Although the slope of the corn function was ~56% higher than for the landscape, SIF was similar for corn (C4) and soybean (C3). Taken together, there is strong observational evidence showing robust linear GPP-SIF scaling that is sensitive to plant physiology but insensitive to the spatial or temporal scale.

INTRODUCTION Reliable estimation of gross primary production (GPP) from landscape to global scales is pivotal to a wide range of ecological research areas, such as carbon-climate feedbacks, and agricultural applications, such as crop yield and drought monitoring. However, measuring GPP at these scales remains a major challenge. Solar-induced chlorophyll fluorescence (SIF) is a signal emitted directly from the core of photosynthetic machinery. SIF integrates complex plant physiological functions in vivo to reflect photosynthetic dynamics in real time. The advent of satellite SIF observation promises a new era in global photosynthesis research. The Orbiting Carbon Observatory-2 (OCO-2) SIF product is a serendipitous but critically complementary by-product of OCO-2’s primary mission target—atmospheric column CO 2 ( X CO 2 ). OCO-2 SIF removes some important roadblocks that prevent wide and in-depth applications of satellite SIF data sets and offers new opportunities for studying the SIF-GPP relationship and vegetation functional gradients at different spatiotemporal scales. RATIONALE Compared with earlier satellite missions with SIF capability, the OCO-2 SIF product has substantially improved spatial resolution, data acquisition, and retrieval precision. These improvements allow satellite SIF data to be validated, for the first time, directly against ground and airborne measurements and also used to investigate the SIF-GPP relationship and terrestrial ecosystem functional dynamics with considerably better spatiotemporal credibility. RESULTS Coordinated airborne measurements of SIF with the Chlorophyll Fluorescence Imaging Spectrometer (CFIS) were used to validate OCO-2 retrievals. The validation shows close agreement between OCO-2 and CFIS SIF, with a regression slope of 1.02 and R 2 of 0.71. Landscape gradients in SIF emission, corresponding to differences in vegetation types, were clearly delineated by OCO-2, a capability that was lacking in previous satellite missions. The SIF-GPP relationships at eddy covariance flux sites in the vicinity of OCO-2 orbital tracks were found to be more consistent across biomes than previously suggested. Finally, empirical orthogonal function (EOF) analyses on OCO-2 SIF and available GPP products show highly consistent spatiotemporal correspondence in their leading EOF modes across the globe, suggesting that SIF and GPP are governed by similar dynamics and controlled by similar environmental and biological conditions. CONCLUSION OCO-2 represents a major advance in satellite SIF remote sensing. Our analyses suggest that SIF is a powerful proxy for GPP at multiple spatiotemporal scales and that high-quality satellite SIF is of central importance to studying terrestrial ecosystems and the carbon cycle. Although the possibility of a universal SIF-GPP relationship across different biome types cannot be dismissed, in-depth process-based studies are needed to unravel the true nature of covariations between SIF and GPP. Of critical importance in such efforts are the potential coordinated dynamics between the light-use efficiencies of CO 2 assimilation and fluorescence emission in response to changes in climate and vegetation characteristics. Eventual synergistic uses of SIF with atmospheric CO 2 enabled by OCO-2 will lead to more reliable estimates of terrestrial carbon sources and sinks—when, where, why, and how carbon is exchanged between land and atmosphere—as well as a deeper understanding of carbon-climate feedbacks. The marked ecological gradients depicted by OCO-2’s high-resolution SIF measurements along a transect of temperate deciduous forests, crops, and urban area from Indiana to suburban Chicago, Illinois.

Forests are expected to become more vulnerable to drought-induced tree mortality owing to rising temperatures and changing precipitation patterns that amplify drought lethality. There is a crucial knowledge gap regarding drought–pathogen interactions and their effects on tree mortality. The objectives of this research were to examine whether stand dynamics and 'background' mortality rates were affected by a severe drought in 2012; and to evaluate the importance of drought–pathogen interactions within the context of a mortality event that killed 10.0% and 26.5% of white (Quercus alba L.) and black (Q. velutina Lam.) oak stems, respectively, in a single year. We synthesized (i) forest inventory data (24 years), (ii) 11 years of ecosystem flux data with supporting biological data including predawn leaf water potential and annual forest inventories, (iii) tree-ring analyses of individual white oaks that were alive and ones that died in 2013, and (iv) documentation of a pathogen infection. This forest displayed stand dynamics consistent with expected patterns of decreasing tree density and increasing basal area. Continued basal area growth outpaced mortality implying a net accumulation of live biomass, which was supported by eddy covariance ecosystem carbon flux observations. Individual white and black oaks that died in 2013 displayed historically lower growth with the majority of dead trees exhibiting Biscogniauxia cankers. Our observations point to the importance of event-based oak mortality and that drought–Biscogniauxia interactions are important in shaping oak stand dynamics in this region. Although forest function has not been significantly impaired, these drought–pathogen interactions could amplify mortality under future climate conditions and thus warrant further investigation.

Silage effluent is a potent wastewater that can be produced when ensiling crops that have a high moisture content (MC). Silage effluent can cause fish–kills and eutrophication due to its high biochemical oxygen demand (BOD) and nutrient content, respectively. It has a high acidity (pH ≈ 3.5–5) making it corrosive to steel and damaging to concrete, which makes handling, storage and disposal a challenge. Although being recognized as a concentrated wastewater, most research has focused on preventing its production. Despite noted imprecision in effluent production models—and therefore limited ability to predict when effluent will flow—there has been little research aimed at identifying effective reactive management options, such as containment and natural treatment systems. Increasing climate variability and intensifying livestock agriculture are issues that will place a greater importance on developing comprehensive, multi–layered management strategies that include both preventative and reactive measures. This paper reviews important factors governing the production of effluent, approaches to minimize effluent flows as well as treatment and disposal options. The challenges of managing silage effluent are reviewed in the context of its chemical constituents. A multi-faceted approach should be utilized to minimize environmental risks associated with silage effluent. This includes: (i) managing crop moisture content prior to ensiling to reduce effluent production, (ii) ensuring the integrity of silos and effluent storages, and (iii) establishing infrastructure for effluent treatment and disposal. A more thorough investigation of constructed wetlands and vegetated infiltration areas for treating dilute silage effluent is needed. In particular, there should be efforts to improve natural treatment system design criteria by identifying pre–treatment processes and appropriate effluent loading rates. There is also a need for research aimed at understanding the effects of repeated land application of effluent on soil quality and crop yields, as spreading is a common disposal practice.

Objective—To evaluate use of a particulate bioscaffold consisting of the extracellular matrix (ECM) of the urinary bladder from pigs for treatment of acquired urinary incontinence in dogs resistant to medical treatment. Design—Case series. Animals—9 female dogs with acquired urinary incontinence. Procedure—In 6 dogs, 30 mg of particulate ECM in 1.0 mL of a carrier consisting of glycerin and saline (0.9% NaCl) solution was injected into each of 3 equally spaced sites around the circumference of the internal urethral sphincter via an endoscopic technique. In the remaining 3 dogs (control dogs), 1.0 mL of the carrier alone was injected in 3 equally spaced sites around the circumference of the internal urethral sphincter in a similar manner. Results—For dogs treated with the ECM, median duration of urinary continence following treatment was 168 days (range, 84 to 616 days), whereas for the control dogs, median duration of urinary continence following the procedure was 14 days (range, 7 to 31 days). Two of the 3 control dogs were treated with the ECM at the end of the study and were continent for 119 and 252 days. No adverse effects were observed in any dog. Conclusions and Clinical Relevance—Results suggest that endoscopically guided injection of particulate ECM into the internal urethral sphincter may be useful for the treatment of acquired urinary incontinence in female dogs. (J Am Vet Med Assoc 2005;226:1095–1097)

Constructed wetlands (CW) are a treatment option for agricultural wastewater. Their ability to adequately function in cold climates continues to be evaluated as they are biologically active systems that depend on microbial and plant activity. In order to assess their performance and to highlight regional specific design considerations, a review of CWs in Eastern Canada and the Northeastern USA was conducted. Here, we synthesize performance data from 21 studies, in which 25 full-scale wetlands were assessed. Where possible, data were separated seasonally to evaluate the climatic effects on treatment performance. The wastewater parameters considered were five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), E. coli, fecal coliforms, total Kjeldahl nitrogen (TKN), ammonia/ammonium (NH3/NH4+-N), nitrate-nitrogen (NO3−-N), and total phosphorus (TP). Average concentration reductions were: BOD5 81%, TSS 83%, TKN 75%, NH4+-N 76%, NO3−-N 42%, and TP 64%. Average log reductions for E. coli and fecal coliforms were 1.63 and 1.93, respectively. Average first order areal rate constants (ka, m·y−1) were: BOD5 6.0 m·y−1, TSS 7.7 m·y−1, E. coli 7.0 m·y−1, fecal coliforms 9.7 m·y−1, TKN 3.1 m·y−1, NH4+-N 3.3 m·y−1, NO3−-N 2.5 m·y−1, and TP 2.9 m·y−1. In general, CWs effectively treated a variety of agricultural wastewaters, regardless of season.