Darren T. Drewry

Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States

To meet emerging bioenergy demands, significant areas of the large-scale agricultural landscape of the Midwestern United States could be converted to second generation bioenergy crops such as miscanthus and switchgrass. The high biomass productivity of bioenergy crops in a longer growing season linked tightly to water use highlight the potential for significant impact on the hydrologic cycle in the region. This issue is further exacerbated by the uncertainty in the response of the vegetation under elevated CO2 and temperature. We use a mechanistic multilayer canopy-root-soil model to (i) capture the eco-physiological acclimations of bioenergy crops under climate change, and (ii) predict how hydrologic fluxes are likely to be altered from their current magnitudes. Observed data and Monte Carlo simulations of weather for recent past and future scenarios are used to characterize the variability range of the predictions. Under present weather conditions, miscanthus and switchgrass utilized more water than maize for total seasonal evapotranspiration by approximately 58% and 36%, respectively. Projected higher concentrations of atmospheric CO2 (550 ppm) is likely to decrease water used for evapotranspiration of miscanthus, switchgrass, and maize by 12%, 10%, and 11%, respectively. However, when climate change with projected increases in air temperature and reduced summer rainfall are also considered, there is a net increase in evapotranspiration for all crops, leading to significant reduction in soil-moisture storage and specific surface runoff. These results highlight the critical role of the warming climate in potentially altering the water cycle in the region under extensive conversion of existing maize cropping to support bioenergy demand.

Simultaneous improvement in productivity, water use, and albedo through crop structural modification

Spanning 15% of the global ice-free terrestrial surface, agricultural lands provide an immense and near-term opportunity to address climate change, food, and water security challenges. Through the computationally informed breeding of canopy structural traits away from those of modern cultivars, we show that solutions exist that increase productivity and water use efficiency, while increasing land-surface reflectivity to offset greenhouse gas warming. Plants have evolved to maximize capture of radiation in the upper leaves, thus shading competitors. While important for survival in the wild, this is suboptimal in monoculture crop fields for maximizing productivity and other biogeophysical services. Crop progenitors evolved over the last 25 million years in an atmosphere with less than half the [CO2] projected for 2050. By altering leaf photosynthetic rates, rising [CO2] and temperature may also alter the optimal canopy form. Here using soybean, the worlds most important protein crop, as an example we show by applying optimization routines to a micrometeorological leaf canopy model linked to a steady-state model of photosynthesis, that significant gains in production, water use, and reflectivity are possible with no additional demand on resources. By modifying total canopy leaf area, its vertical profile and angular distribution, and shortwave radiation reflectivity, all traits available in most major crop germplasm collections, increases in productivity (7%) are possible with no change in water use or albedo. Alternatively, improvements in water use (13%) or albedo (34%) can likewise be made with no loss of productivity, under Corn Belt climate conditions.

OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence

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 CO2 (XCO2). 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 R2 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 CO2 assimilation and fluorescence emission in response to changes in climate and vegetation characteristics. Eventual synergistic uses of SIF with atmospheric CO2 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. Quantifying gross primary production (GPP) remains a major challenge in global carbon cycle research. Spaceborne monitoring of solar-induced chlorophyll fluorescence (SIF), an integrative photosynthetic signal of molecular origin, can assist in terrestrial GPP monitoring. However, the extent to which SIF tracks spatiotemporal variations in GPP remains unresolved. Orbiting Carbon Observatory-2 (OCO-2)’s SIF data acquisition and fine spatial resolution permit direct validation against ground and airborne observations. Empirical orthogonal function analysis shows consistent spatiotemporal correspondence between OCO-2 SIF and GPP globally. A linear SIF-GPP relationship is also obtained at eddy-flux sites covering diverse biomes, setting the stage for future investigations of the robustness of such a relationship across more biomes. Our findings support the central importance of high-quality satellite SIF for studying terrestrial carbon cycle dynamics.

Effect of environmental conditions on the relationship between solar induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site.

Reintroducing radiometric surface temperature into the Penman‐Monteith formulation

Here we demonstrate a novel method to physically integrate radiometric surface temperature (TR) into the Penman-Monteith (PM) formulation for estimating the terrestrial sensible and latent heat fluxes (H and λE) in the framework of a modified Surface Temperature Initiated Closure (STIC). It combines TR data with standard energy balance closure models for deriving a hybrid scheme that does not require parameterization of the surface (or stomatal) and aerodynamic conductances (gS and gB). STIC is formed by the simultaneous solution of four state equations and it uses TR as an additional data source for retrieving the “near surface” moisture availability (M) and the Priestley-Taylor coefficient (α). The performance of STIC is tested using high-temporal resolution TR observations collected from different international surface energy flux experiments in conjunction with corresponding net radiation (RN), ground heat flux (G), air temperature (TA), and relative humidity (RH) measurements. A comparison of the STIC outputs with the eddy covariance measurements of λE and H revealed RMSDs of 7–16% and 40–74% in half-hourly λE and H estimates. These statistics were 5–13% and 10–44% in daily λE and H. The errors and uncertainties in both surface fluxes are comparable to the models that typically use land surface parameterizations for determining the unobserved components (gS and gB) of the surface energy balance models. However, the scheme is simpler, has the capabilities for generating spatially explicit surface energy fluxes and independent of submodels for boundary layer developments.

Chlorophyll can be reduced in crop canopies with little penalty to photosynthesis

An empirically parameterized model of canopy photosynthesis in soybeans reveals that leaf chlorophyll can be reduced with significant nitrogen savings and only minor reductions in daily carbon gain. The hypothesis that reducing chlorophyll content (Chl) can increase canopy photosynthesis in soybeans was tested using an advanced model of canopy photosynthesis. The relationship among leaf Chl, leaf optical properties, and photosynthetic biochemical capacity was measured in 67 soybean (Glycine max) accessions showing large variation in leaf Chl. These relationships were integrated into a biophysical model of canopy-scale photosynthesis to simulate the intercanopy light environment and carbon assimilation capacity of canopies with wild type, a Chl-deficient mutant (Y11y11), and 67 other mutants spanning the extremes of Chl to quantify the impact of variation in leaf-level Chl on canopy-scale photosynthetic assimilation and identify possible opportunities for improving canopy photosynthesis through Chl reduction. These simulations demonstrate that canopy photosynthesis should not increase with Chl reduction due to increases in leaf reflectance and nonoptimal distribution of canopy nitrogen. However, similar rates of canopy photosynthesis can be maintained with a 9% savings in leaf nitrogen resulting from decreased Chl. Additionally, analysis of these simulations indicate that the inability of Chl reductions to increase photosynthesis arises primarily from the connection between Chl and leaf reflectance and secondarily from the mismatch between the vertical distribution of leaf nitrogen and the light absorption profile. These simulations suggest that future work should explore the possibility of using reduced Chl to improve canopy performance by adapting the distribution of the “saved” nitrogen within the canopy to take greater advantage of the more deeply penetrating light.

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (gA) and due to inequalities between radiometric (TR) and aerodynamic temperatures (T0). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates TR observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T0 and gA determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10–52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12– 25%) in all ecosystems. Uncertainty in TR versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r 5 0.33–0.43), evaporative index (r 5 0.77–0.90), and climatological dryness (r 5 0.60–0.77) explained a strong association between ecohydrological extremes and TR in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components. Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.