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 world's 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.
Because of the possibility of getting the right answers for the wrong reasons, the predictive performance of a complex systems model is not by itself a reliable indicator of hypothesis quality for ...the purposes of scientific learning about processes. The predictive performance of a structurally adequate model should be an emergent property of its functional performance. In this context, any Pareto trade‐off between measures of predictive performance versus functional performance indicates process‐level error in the model; this trade‐off, if it exists, indicates that the model's predictions are right for the wrong functional reasons. This paper demonstrates a novel concept based on information theory that is capable of attributing observed errors to specific processes. To demonstrate that the concept and method hold true for models and observations of real systems, we employ a minimal single‐parameter‐variation sensitivity analysis using a sophisticated ecohydrology model, MLCan, for a well‐monitored field site (Bondville IL Ameriflux Soybean). We identify both functional and predictive error in MLCan, and also evidence of the hypothesized tradeoffs between the two. This trade‐off indicates structural error within MLCan. For example, the sensible heat flux process can be calibrated to achieve good predictive performance at the cost of poor functional performance. In contrast, we find little structural error for processes driven by solar radiation, which appear “right for the right reasons.” This method could be applied broadly to pinpoint process error and structural error in a wide range of system models, beyond the ecohydrological scope demonstrated here.
Key Points
Information theory can attribute model error to structural and process‐level sources
Pareto‐style trade‐offs exist between functional and predictive performance
The MLCan ecohydrology model demonstrates both well‐posed and ill‐posed trade‐offs between different processes
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.
This study evaluates the large-scale seasonal phenology and physiology of vegetation over northern high latitude forests (40°–55°N) during spring and fall by using remote sensing of solar-induced ...chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and observation-based estimate of gross primary productivity (GPP) from 2009 to 2011. Based on GPP phenology estimation in GPP, the growing season determined by SIF time-series is shorter in length than the growing season length determined solely using NDVI. This is mainly due to the extended period of high NDVI values, as compared to SIF, by about 46days (±11days), indicating a large-scale seasonal decoupling of physiological activity and changes in greenness in the fall. In addition to phenological timing, mean seasonal NDVI and SIF have different responses to temperature changes throughout the growing season. We observed that both NDVI and SIF linearly increased with temperature increases throughout the spring. However, in the fall, although NDVI linearly responded to temperature increases, SIF and GPP did not linearly increase with temperature increases, implying a seasonal hysteresis of SIF and GPP in response to temperature changes across boreal ecosystems throughout their growing season. Seasonal hysteresis of vegetation at large-scales is consistent with the known phenomena that light limits boreal forest ecosystem productivity in the fall. Our results suggest that continuing measurements from satellite remote sensing of both SIF and NDVI can help to understand the differences between, and information carried by, seasonal variations vegetation structure and greenness and physiology at large-scales across the critical boreal regions.
•We assess the differences of seasonal cycle between SIF, NDVI, and GPP at large-scale.•Satellite SIF well captures the seasonal hysteresis of plant function.•Satellite SIF can be used as a direct proxy of vegetation physiology at large-scale.
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.
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 Vcmax, 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 Vcmax is weak at the selected site.
Key Points
We examined how environmental conditions affect the relationship between remotely sensed solar‐induced fluorescence and photosynthesis
The relationship appeared robust even under high light and stress conditions although some nonlinearity was noticed
Solar‐induced fluorescence provided very weak constrains on Vcmax estimates
The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence ...(SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m2/sr/μm for typical SIF retrievals, which can be reduced to <0.2 W/m2/sr/μm when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsat imagery (NDVI, EVI, Tskin), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction.
•CFIS, a new airborne instrument for retrievals of far red chlorophyll fluorescence was built.•A new algorithm for CFIS-based fluorescence retrievals is described and tested on real data.•We mapped fluorescence across agricultural fields in Mead at 30 m resolution.
The use of Penman–Monteith (PM) equation in thermal remote sensing based surface energy balance modeling is not prevalent due to the unavailability of any direct method to integrate thermal data into ...the PM equation and due to the lack of physical models expressing the surface (or stomatal) and boundary layer conductances (gS and gB) as a function of surface temperature. Here we demonstrate a new method that physically integrates the radiometric surface temperature (TS) into the PM equation for estimating the terrestrial surface energy balance fluxes (sensible heat, H and latent heat, λE). The method combines satellite TS data with standard energy balance closure models in order to derive a hybrid closure that does not require the specification of surface to atmosphere conductance terms. We call this the Surface Temperature Initiated Closure (STIC), which is formed by the simultaneous solution of four state equations. Taking advantage of the psychrometric relationship between temperature and vapor pressure, the present method also estimates the near surface moisture availability (M) from TS, air temperature (TA) and relative humidity (RH), thereby being capable of decomposing λE into evaporation (λEE) and transpiration (λET). STIC is driven with TS, TA, RH, net radiation (RN), and ground heat flux (G). TS measurements from both MODIS Terra (MOD11A2) and Aqua (MYD11A2) were used in conjunction with FLUXNET RN, G, TA, RH, λE and H measurements corresponding to the MODIS equatorial crossing time. The performance of STIC has been evaluated in comparison to the eddy covariance measurements of λE and H at 30 sites that cover a broad range of biomes and climates. We found a RMSE of 37.79 (11%) (with MODIS Terra TS) and 44.27Wm−2 (15%) (with MODIS Aqua TS) in λE estimates, while the RMSE was 37.74 (9%) (with Terra) and 44.72Wm−2 (8%) (with Aqua) in H. STIC could efficiently capture the λE dynamics during the dry down period in the semi-arid landscapes where λE is strongly governed by the subsurface soil moisture and where the majority of other λE models generally show poor results. Sensitivity analysis revealed a high sensitivity of both the fluxes to the uncertainties in TS. A realistic response and modest relationship was also found when partitioned λE components (λEE and λET) were compared to the observed soil moisture and rainfall. This is the first study to report the physical integration of TS into the PM equation and finding analytical solution of the physical (gB) and physiological conductances (gS). The performance of STIC over diverse biomes and climates points to its potential to benefit future NASA and NOAA missions having thermal sensors, such as HyspIRI, GeoSTAR and GOES-R for mapping multi-scale λE and drought.
•Use of land surface temperature (TS) in Penman-Monteith (PM) equation is uncommon.•Physical models are not available to express the conductances as a function of TS.•Our method integrates TS into PM equation to estimate conductances and heat fluxes.•Evaluation of fluxes revealed promising results over multiple biomes and climates.•Evaporative fluxes behaved realistically with observed soil moisture and rainfall.
Abstract
Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land ...surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (
H
) and precipitation (
P
) during summer months in temperate regions, and between
H
and
P
during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to
P
peaks at aridity (
P
/potential evapotranspiration ET
p
) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as
P
/ET
p
approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather.
Surface and subsurface moisture dynamics are strongly influenced by the ability of vegetation to take up and redistribute soil moisture using hydraulic redistribution (HR). These dynamics in turn ...affect soil biogeochemical cycling through controls on decomposition and mineralization rates and ion transport. The goal of this study is to explore this coupling between HR and biogeochemistry using a numerical model. We examine decomposition and mineralization of organic matter and analyze whether differences in decomposition rates induced by HR influence the long‐term storage of carbon in the soil and the movement of nitrate (
NO 3−) and ammonium (
NH 4+) in the rhizosphere. These dynamics are studied in a framework that incorporates the interaction between multiple plant species. The net effect of HR on decomposition is controlled by a trade‐off between the resultant moisture and temperature states. This trade‐off is conditioned by the availability of fine roots near the surface, and it impacts the long‐term storage and vertical distribution of carbon in the soil. HR also impacts the transport and uptake of ions from the soil. It reduces the leaching of nitrate considerably, and, therefore facilitates the uptake of nitrate by vegetation roots. Furthermore, the magnitude and patterns of the feedbacks induced by HR are also influenced by the presence of different plant species that coexist. These results suggest that the alteration of soil moisture by plants through associated processes such as HR can have considerable impact on the below‐ground biogeochemical cycling of carbon and nitrogen.
Key Points
HR impacts long term accumulation and distribution of organic C and mineral N
HR reduces the leaching of nitrate and enhances plant uptake.
The presence of different vegetation species influences the implications of HR
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